Patent Description:
An electric meter is a device that monitors the amount of electric power drawn from an electric distribution system at a customer site. One type of electric meter that is used to monitor a customer's electric power consumption from an electric distribution system is an electric smart meter (also referred to herein as a smart meter). Smart meters monitor electric power usage, electric current, and/or voltage at customer sites that are connected to the electric distribution system. Smart meters can also monitor the amount of electric power and/or electric current provided from customer sites to the electric distribution system. Smart meters automatically transmit electric power usage and voltage data from customer sites to the electric utility that provides electric power to the customer sites. Smart meters may be part of an advanced metering infrastructure (AMI) or an automatic meter reading (AMR) system. <CIT> discloses a computer system method for automatic correction of a topology of an intelligent network that uses the information contained in voltage measurements provided by smart meters, in order to determine which counter is attached to which transformer. <CIT> suggests techniques for phase identification using feature-based clustering approaches. "<NPL>) discloses an algorithm and promising testing results within a practical BC Hydro system. The algorithm's potential use for phase detection by collectively leveraging smart meter and feeder meter data is explored.

Electric utilities face challenges in identifying and maintaining accurate information regarding the connections of the components in their electric distribution systems. An electric distribution system is also referred to herein as an electric grid. An electric utility ideally has accurate connection information that indicates how the customer sites that receive electric power (i.e., electricity) from the utility are interconnected downstream of each power distribution substation. The connection information should accurately indicate the customer sites that are connected via utility distribution lines to each distribution transformer that is connected downstream of each power distribution substation.

A common issue faced by electric utilities is maintaining accurate connection information about their electric distribution systems. The connections between utility customer sites and distribution transformers may change during maintenance or repair of the electric distribution system. For example, emergency repairs made to an electric distribution system during restoration of electric service after an outage may change connections between customers and distribution transformers. New meter to transformer connection errors may be unintentionally introduced into an electric distribution system as compared to a utility's connection information after emergency rebuilding of the electric distribution system during storms, incorrect construction which does not match the job as designed, data errors introduced during geographical information system (GIS) mapping, etc..

The information that an electric utility has about connections between customer sites and distribution transformers may become less accurate over time when compared to the connections between customer sites and distribution transformers that exist in the electric distribution system. It can be difficult to track all of the changes to the electric grid that occur over time to maintain accurate connection information. An electric utility may not be able to trace each underground utility line to identify which distribution transformer each customer's electric meter is connected to. Visual inspection of each customer's electric meter is typically expensive, slow, and labor intensive.

An electric utility may collect utility data for a set of electric meters (e.g., smart meters) that is associated with each distribution transformer in an electric grid. The utility data may include, for example, information collected on at least an hourly basis for each electric meter connected to a distribution transformer. The utility data may include an hourly average voltage for each electric meter and hourly power consumption data for each electric meter.

The theft of power from an electric grid that distributes electricity to customers is often a major source of lost revenue for electric utilities. Because electricity may be stolen at several endpoints of a large electric grid, it can be difficult to identify each source of power theft. As an example, some utility customers have been known to connect illegal bypass lines around their electric meters in order to steal electricity from an electric grid. The bypass line is intended to prevent the electric meter from sensing at least a portion of the customer's electricity usage. Thus, a bypass line may prevent an electric meter from accurately monitoring the electricity consumption at a customer site.

The voltage sensed by an electric meter at a customer site changes in response to changes in the electric power drawn by the customer site from the electric grid. For example, if a customer's electricity consumption from an electric grid increases, the electric meter at that customer site senses a lower voltage. If a customer's electricity consumption decreases, the electric meter senses a greater voltage. If a bypass line is illegally connected around an electric meter, the electric meter may not sense all of the electric power that is being drawn by the load at the customer site. However, the electric meter will sense changes in the voltage that are caused by changes in the electric power being drawn by the load at the customer site, even if a bypass line is connected around the electric meter. If power theft is occurring at a customer site, the electric meter at that customer site may generate a significantly different voltage profile than the voltage profiles generated by the electric meters at other customer sites in the same geographic area and/or that are connected to the same distribution transformer, due to the impedance in the distribution lines between the electric meters.

Thus, the voltage profile sensed by an electric meter at a customer site may be indicative of power theft from the electric grid. As an example, if a customer site is drawing more power from an electric grid during some hours of each day than other customer sites that are connected to the same transformer in the electric grid and/or are in the same geographic area, an electric meter at the customer site will indicate that the voltage is decreasing more during these hours than the voltages sensed by the electric meters at the other customer sites.

According to some embodiments disclosed herein, voltage data from smart meters are monitored to determine the possible occurrence of power theft at one or more customer sites. If anomalies are identified in voltage data from a customer's smart meter compared to other customer's smart meters that are connected to the same distribution transformer or are in the same geographic area, the customer's smart meter may be flagged for further investigation. One possible cause for anomalies in voltage data is that the utility's connection information may be inaccurate for the reasons discussed above, and the smart meter sensing the anomalies in the voltage data is connected to a different distribution transformer than the utility's connection information currently indicates. Another possibility is that the anomalies in the voltage data are caused by theft of power from the electric grid at the customer site.

<FIG> is a diagram illustrating a system that uses smart meters to measure voltages, electric current, and electricity usage at utility customer sites, according to an embodiment. In the embodiment of Figure (<FIG>, utility customer sites 101A, 101B, 101C, and 101D (collectively referred to herein as utility customer sites <NUM>) associated with smart meters 102A, 102B, 102C, and 102D (collectively referred to herein as smart meters <NUM>), respectively, are connected through a network <NUM> to a computer system <NUM>. Each of the smart meters <NUM> is connected to receive electric power from an electric grid and is connected to provide the electric power to an associated one of the customer sites <NUM>. The electric grid is not shown in <FIG>. Four utility customer sites <NUM> and four associated smart meters <NUM> are shown in <FIG> as examples. Although it should be understood that an electric grid is typically connected to provide electric power to hundreds, thousands, or even millions of customer sites. Each of the customer sites <NUM> has an electric meter, such as a smart meter, that monitors electricity usage and voltage.

Each of the smart meters <NUM> periodically sends measurements of voltages (i.e., voltage data), electric current, and/or electricity consumption through network <NUM> to computer system <NUM> for processing. Each of the smart meters <NUM> measures the voltage at its connection to the electric grid at the associated customer site <NUM>. As an example, each of the smart meters <NUM> may send the voltage data to computer system <NUM> each hour of each day. Communications of data from the smart meters <NUM> to the computer system <NUM> may be through wireless connections, wires, power lines, or any combination thereof in network <NUM>.

Computer system <NUM> includes one or more input/output (I/O) interfaces <NUM>, one or more processor devices <NUM>, one or more storage devices <NUM>, analytic software <NUM>, and one or more databases <NUM>. The components <NUM>-<NUM> of the computer system <NUM> can communicate through connections <NUM>. Computer system <NUM> may, for example, be located at a facility that is operated by the electric utility. Processors <NUM> may include any processing circuit or device, such as, for example, a CPU (central processing unit), microprocessor, graphics processing unit (GPU), field programmable gate array, and/or other forms of processor circuitry. Storage <NUM> may include memory devices such as solid state devices, disc storage devices, magnetic tape, etc. The storage devices <NUM> may be organized into any suitable configuration, for example, a storage area network. The input/output interfaces <NUM> may include, for example, devices for inputting data to the processors <NUM> (e.g., a mouse and a keyboard), and a mechanism for providing results from the processors <NUM> (e.g., printers and computer monitors).

The voltage, current, and/or power usage data provided from smart meters <NUM> through network <NUM> may be stored in storage devices <NUM> in computer system <NUM>. The analytic software <NUM> can access the voltage, current, and/or power usage data stored in storage devices <NUM> using database <NUM>. The analytic software <NUM> can analyze the voltage, current, and/or the power usage data and generate the results disclosed herein. Analytic software <NUM> can be stored in memory. Analytic software <NUM> can be run on one or more processors <NUM>. One or more users may interface with the analytic software <NUM> using one or more I/O interfaces <NUM>.

<FIG> are flow charts illustrating examples of operations that can be used to identify outliers in voltage data received from smart meters that are connected to an electric grid, according to an embodiment. The operations of <FIG> may, for example, be performed as one check of multiple checks that are performed to identify outliers in voltage data received from smart meters. Alternatively, the operations of <FIG> may be performed alone without additional checks.

In the embodiment of <FIG>, each of the smart meters <NUM> is connected to a distribution transformer in the electric grid through one or more utility distribution lines, such as a lateral and/or a feeder. The operations of <FIG> may, for example, be performed by the analytic software <NUM> in computer system <NUM> using I/O interfaces <NUM>, processors <NUM>, storage devices <NUM>, and database <NUM> shown in <FIG>.

Initially, time series voltage data (also referred to herein as voltage data) generated by smart meters connected to an electric grid is collected from storage <NUM> using database <NUM> and provided to the analytic software <NUM>. Also, geographical information system (GIS) data that maps electric meter to distribution transformer connections and distribution lines in the electric grid is collected. In operation <NUM>, a distribution transformer (also referred to herein merely as a transformer) connected to the electric grid is selected. The transformer selected in operation <NUM> is one of the transformers that the GIS data indicates is connected to a set of the smart meters from which the voltage data was collected. The transformer may be selected automatically by the analytic software <NUM> or in response to user input. Additional iterations of the operations of <FIG> may be performed for the smart meters that are connected to the other transformers to be evaluated in the electric grid.

In operation <NUM>, a smart meter is selected that is connected to the transformer selected in operation <NUM>. Operation <NUM> may determine which smart meters are connected to the transformer selected in operation <NUM> using the GIS data that indicates the meter to transformer connections. A smart meter is connected to a transformer if the smart meter receives power from the electric grid through the transformer, even if the smart meter is not directly connected to the transformer. The power provided from the electric grid through the transformer to the smart meter is received at a load at the utility customer site. Each distribution transformer in an electric grid is typically connected to and transmits power to multiple smart meters at multiple customer sites. As an example that is not intended to be limiting, each distribution transformer may be connected to <NUM>-<NUM> smart meters.

As discussed above, an electric utility's GIS data about its electric grid may contain some inaccurate information about which smart meters are connected to which distribution transformers. The operations of <FIG> can help identify the existence of inaccurate connection information between smart meters and transformers in the GIS data.

Operations <NUM>-<NUM> in <FIG> and operations <NUM>-<NUM> in <FIG> are performed using the voltage data collected from the smart meters. The time series voltage data from each of the smart meters is plotted as a voltage waveform that varies over time. The slope of each voltage waveform generated using the time series voltage data from a smart meter is then determined at predefined time intervals (e.g., hourly). The slopes of the voltage waveforms generated using the time series voltage data from the smart meters are also referred to herein as voltage slopes.

In operation <NUM>, each predefined time interval is identified in which the polarity of the voltage slope for the smart meter selected in operation <NUM> is the opposite of the polarity of the voltage slopes of the majority of the other smart meters connected to the transformer selected in operation <NUM>. Operation <NUM> is repeated in each time interval, for example, every hour, every half hour, every <NUM> minutes, etc. using the voltage data over a predefined time period (e.g., days, a week, a month, or several months).

In operation <NUM>, a flag is generated for the smart meter selected in operation <NUM> each time the magnitude of its voltage slope at one of the predefined time intervals identified in operation <NUM> is an outlier compared to the magnitudes of the voltage slopes of the majority of the other smart meters connected to the transformer. A flag is only generated for the smart meter in operation <NUM> if the condition analyzed in operation <NUM> was met at the respective predefined time interval.

In some situations, too many flags may be generated if only the polarity of the voltages slopes of the voltage waveforms are compared to each other, and operation <NUM> is omitted. For example, if the voltage waveform for one smart meter goes slightly positive, while the voltage waveforms for the other smart meters go slightly negative, flags would be generated if only the polarities of the voltage slopes were compared. By also comparing the magnitudes (i.e., absolute values) of the voltage slopes in operation <NUM>, flags are not generated in these situations. As a more specific example, the voltage slopes of three smart meters may be -. <NUM>, and the voltage slope of a fourth smart meter may be. In this example, no flags are generated in operation <NUM>, because the magnitude of the voltage slope of the fourth smart meter is not an outlier. Thus, operation <NUM> can avoid the problem of false flags being generated when the polarity of the voltage slope of the smart meter selected in operation <NUM> is the opposite of the polarity of the voltage slopes of the majority of the other smart meters, but the magnitude of the voltage slope of the smart meter selected in operation <NUM> is not an outlier compared to the magnitudes of the voltage slopes of the majority of the other smart meters.

The magnitude of the voltage slope for a smart meter may be defined as an outlier in operation <NUM> if it is greater than or less than the magnitudes of the voltage slopes of the majority of the other smart meters by a predefined threshold. Dixon's Q test is an example of an equation that can be used to identify the magnitude of the voltage slope for the smart meter as an outlier in operation <NUM>. Dixon's Q test performs a test for an outlier based on the chi squared distribution of the squared differences between data and the sample mean. To apply Dixon's Q test, a data set is arranged in order of increasing values, and a questionable point Q is calculated as <MAT>. In Dixon's Q test, Gap is defined as the absolute difference between the outlier in question and the closest number in the data set to the outlier in question, and Range is the range of values of the data set. If Q > QTable, where QTable is a reference value that corresponds to the sample size and the confidence level, then the questionable point Q is identified as an outlier in operation <NUM>. Only one point can be rejected from a data set using a Dixon's Q test. The flags generated in operation <NUM> are counted over the predefined time period to generate a total number of flags. The analytic software <NUM> may, for example, store the flags generated for the smart meter in operation <NUM> in database <NUM>.

<FIG> is a graph of hourly voltage data from four smart meters over four days, according to an exemplary embodiment. The voltage data from each of the four smart meters is plotted as a voltage waveform. Each of the <NUM> voltage waveforms shown in <FIG> plots voltage data from a different one of the four smart meters that are all connected to and receive power from an electric grid. In the embodiment of <FIG>, the four smart meters are analyzed together, because the GIS data indicates that these four smart meters are all connected to the electric grid through the same distribution transformer. The solid line waveform <NUM> represents the voltage data from one of the smart meters. The three dotted line waveforms represent the voltage data from the other three smart meters. As can be seen in <FIG>, the solid line voltage waveform <NUM> deviates from the three dotted line voltage waveforms over several hours of the four day period shown in <FIG>.

The graph of <FIG> illustrates examples of how operations <NUM>-<NUM> can be applied to voltage data from four smart meters. For example, if the operations of <FIG> are applied to the voltage data graphed in <FIG>, a flag is generated at each of times T1, T2, and T3 in operation <NUM>. At each of the times T1-T3 in <FIG>, the polarity of the voltage slope of the solid line waveform <NUM> is the opposite of the polarity of the voltage slopes of the <NUM> dotted line waveforms. Also, at each of times T1-T3, the magnitude of the voltage slope of the solid line waveform <NUM> is an outlier compared to the magnitudes of the voltage slopes of the <NUM> dotted line waveforms.

<FIG> is a graph of hourly voltage data from five smart meters over eight hours, according to another exemplary embodiment. In <FIG>, hourly voltage data from each of the smart meters is plotted as a voltage waveform. Each of the <NUM> voltage waveforms shown in <FIG> plots the hourly voltage data from a different one of the five smart meters that are all connected to and receive power from an electric grid. In the embodiment of <FIG>, the five smart meters are analyzed together, because the GIS data indicates that these smart meters are all connected to the electric grid through the same distribution transformer. The solid line waveform represents the voltage data from one of the smart meters that is referred to herein and in <FIG> as meter <NUM>. The four dotted line waveforms represent the voltage data from the other four smart meters that are referred to herein and in <FIG> as meters <NUM>-<NUM>.

The computer system determines the voltage slopes of the five voltage waveforms shown in <FIG> for the five smart meters at each hour. Eight hours of voltage data for the smart meters are shown in <FIG> merely as an example. It should be understood that the computer system can determine voltage data for many more smart meters over longer periods of time, such as days, weeks, months, or years. Table <NUM> below contains the voltages and the voltage slopes of the exemplary voltage data from the five smart meters at hours <NUM> and <NUM>, which are shown in <FIG>.

As shown in Table <NUM>, meters <NUM>-<NUM> have positive voltage slopes between hours <NUM> and <NUM>, and meter <NUM> has a negative voltage slope between hours <NUM> and <NUM>. In addition, the magnitudes of the voltage slopes of meters <NUM>-<NUM> are in the range of <NUM>-<NUM> between hours <NUM> and <NUM>, and the magnitude of the voltage slope of meter <NUM> is <NUM> between hours <NUM> and <NUM>. Based on the values in Table <NUM>, meter <NUM> may be flagged as an outlier in operation <NUM> when compared to meters <NUM>-<NUM> for the <NUM> hour time interval between hour <NUM> and hour <NUM>.

In decision operation <NUM>, a determination is made as to whether there are any more smart meters to analyze that are connected to the same transformer selected in operation <NUM> and that have not been analyzed in previous iterations of operations <NUM>-<NUM>. If there are more smart meters connected to the transformer that have not been analyzed yet, one of the smart meters that has not been analyzed in a previous iteration of operations <NUM>-<NUM> is selected in operation <NUM> using the GIS data.

Then, operations <NUM>-<NUM> are repeated using the voltage data from the smart meter selected in operation <NUM>. Operations <NUM>-<NUM> are then repeated for each of the other smart meters connected to the transformer selected in operation <NUM>. Thus, an iteration of operations <NUM>-<NUM> is performed for each of the smart meters connected to the transformer. Each iteration of operations <NUM>-<NUM> generates a set of flags for one of the smart meters that indicate if the voltage data from that smart meter is an outlier compared to the voltage data from the majority of other smart meters connected to the transformer.

If all of the smart meters connected to the transformer selected in operation <NUM> have been analyzed at the current iteration of operation <NUM>, then the process of <FIG> proceeds to operation <NUM>, which is shown in detail in <FIG>. Referring now to <FIG>, in operation <NUM>, a list of the one or more smart meters having the greatest number of flags from the iterations of operation <NUM> is created. The list generated in operation <NUM> includes the smart meter having the greatest number of flags generated in the previous iterations of operation <NUM>. The list generated in operation <NUM> may, for example, rank the smart meters from highest to lowest number of flags.

The smart meter having voltage data that generates the most flags in one of the previous iterations of operation <NUM> may, for example, be identified as a suspected misallocated smart meter in operation <NUM>. In operation <NUM>, the geographical information system (GIS) data is then used to determine the nearest distribution transformers to the distribution transformer selected in operation <NUM>. As a specific example that is not intended to be limiting, the <NUM> nearest transformers to the transformer selected in operation <NUM> may be identified in operation <NUM>. The nearest transformers may be determined in operation <NUM> using physical distances in the electric grid that are indicated by the GIS data.

<FIG> is a diagram that shows examples of connections between a utility distribution substation and <NUM> distribution transformers in an electric grid, according to an embodiment. In the embodiment of <FIG>, utility distribution substation <NUM> provides electric power through electricity distribution lines <NUM> and distribution transformers 402A-402F to customer sites via smart meters <NUM>-<NUM>, respectively. Transformers 402A-402F are connected to and provide electric power to smart meters 403A-403C, 404A-404C, 405A-405C, 406A-406B, 407A-407B, and 408A-408B, respectively. Although <NUM> or <NUM> smart meters are shown in <FIG> as being connected to each of the transformers 402A-402F, each transformer <NUM> in the electric grid may be connected to additional smart meters.

<FIG> may not indicate accurate relative physical distances between the utility distribution station, the transformers, and the smart meters. However, the diagram of <FIG> may be used to determine which of the distribution transformers in the electric grid are the nearest transformers to the transformer that is connected to a suspected misallocated smart meter in operation <NUM>. As an example, if smart meter 404B is the suspected misallocated smart meter, the <NUM> nearest transformers to transformer 402B are identified as transformers 402A and 402C-402F in operation <NUM> using the diagram of <FIG>.

In operation <NUM>, one of the nearest transformers that was identified in operation <NUM> is selected. Subsequently, in operations <NUM>-<NUM>, the voltage data from the suspected misallocated smart meter is compared to the voltage data from the smart meters that are connected to the transformer selected in operation <NUM>. As an example, the voltage data from smart meter 404B may be compared to the voltage data from smart meters 403A-403C that are connected to transformer 402A in operations <NUM>-<NUM>.

In operation <NUM>, each predefined time interval is identified in which the polarity of the voltage slope for the suspected misallocated smart meter identified in operation <NUM> is the opposite of the polarity of the voltage slopes of the majority of the smart meters connected to the transformer selected in operation <NUM>. Operation <NUM> is repeated in each time interval, for example, every hour, every half hour, etc. using the voltage data over a predefined time period (e.g., over a week, a month, etc.).

In operation <NUM>, a flag is generated for the suspected misallocated smart meter each time the magnitude of its voltage slope at one of the predefined time intervals identified in operation <NUM> is an outlier compared to the magnitudes of the voltage slopes of the majority of the smart meters connected to the transformer selected in operation <NUM>. A flag is only generated for the suspected misallocated smart meter in operation <NUM> if the condition analyzed in operation <NUM> was met at the respective predefined time interval. The flags generated in operation <NUM> are counted over the predefined time period to generate a total number of flags. The magnitude of the voltage slope of the suspected misallocated smart meter may be identified as an outlier in operation <NUM> if the magnitude is greater than or less than the magnitudes of the voltage slopes of the majority of the smart meters connected to the transformer selected in operation <NUM> by a threshold. Dixon's Q test described above with respect to operation <NUM> is also an example of an equation that can be used to identify the magnitude of the voltage slope of the suspected misallocated smart meter as an outlier in operation <NUM>. The examples disclosed in <FIG> are also examples of how flags may be generated in operation <NUM>.

In decision operation <NUM>, a determination is made as to whether any of the nearest transformers that were identified in operation <NUM> have not been previously selected in operation <NUM> or in a previous iteration of operation <NUM>. If any of the transformers identified in operation <NUM> have not been previously selected, the next one of the nearest transformers identified in operation <NUM> is selected in operation <NUM>.

Then, operations <NUM>-<NUM> are repeated by comparing the voltage data from the suspected misallocated smart meter to the voltage data from the smart meters that are connected to the transformer selected in operation <NUM>. Additional iterations of operations <NUM>-<NUM> are subsequently performed until the voltage data from the smart meters connected to each of the nearest transformers identified in operation <NUM> has been analyzed. In each of the iterations of operations <NUM>-<NUM>, a set of flags may be generated by comparing the voltage data from the suspected misallocated smart meter to the voltage data from the smart meters connected to one of the nearest transformers identified in operation <NUM>.

If in decision operation <NUM>, a determination is made that all of the transformers identified in operation <NUM> have been selected in operation <NUM> or in previous iterations of operation <NUM>, then operation <NUM> is performed. If the flags generated in one iteration of operations <NUM>-<NUM> are less than the flags generated for the suspected misallocated smart meter in operations <NUM>-<NUM>, then in operation <NUM> the transformer that generated the least number of flags in operations <NUM>-<NUM> is identified for possible further analysis.

One possibility is that the suspected misallocated smart meter is connected to the transformer that generated the least number of flags in an iteration of operations <NUM>-<NUM> instead of the transformer selected in operation <NUM>. The original transformer selected in operation <NUM> is the transformer that the utility's GIS data indicates is connected to the suspected misallocated smart meter. In or after operation <NUM>, the suspected misallocated smart meter may, for example, be assigned to the transformer that generated the least number of flags in an iteration of operations <NUM>-<NUM>. As a specific example that is not intended to be limiting, if <NUM> flags are generated in an iteration of operations <NUM>-<NUM> using smart meters connected to the original transformer, and only <NUM> flags are generated in an iteration of operations <NUM>-<NUM> using smart meters connected to one of the nearest transformers, then the suspected misallocated smart meter is assigned to that one of the nearest transformers.

Another possibility is that power theft may be occurring at the utility customer site having the suspected misallocated smart meter (e.g., using a bypass line around that smart meter). After operation <NUM>, additional checks may be performed to determine if the suspected misallocated smart meter is connected to a different transformer than the original transformer selected in operation <NUM> or if power theft is occurring. A second check that may be performed to determine if the suspected misallocated smart meter is connected to a different transformer is disclosed herein with respect to <FIG>.

<FIG> illustrate flow charts showing examples of operations that can be performed to identify anomalies in voltage data received from smart meters that are connected to an electric grid, according to an embodiment. The operations of <FIG> may, for example, be performed as a second check to confirm the anomalies in the smart meter voltage data that were identified using the operations of <FIG>. Thus, in some exemplary embodiments, operations of <FIG> are performed as a first check to identify anomalies in voltage data received from smart meters, and operations of <FIG> are performed as a second check to confirm any anomalies identified in the first check. Thus, in some embodiments, a computer system, such as computer system <NUM>, may perform the operations of <FIG> and the operations of <FIG>.

There may be some situations in which a smart meter's voltage data more closely matches the voltage data of smart meters connected to a different transformer than the transformer identified in the first check of <FIG>. Performing the operations of <FIG> as a second check may improve the accuracy of the process of identifying which smart meters are connected to different transformers or associated with power theft. The second check of <FIG> may be used to analyze more voltage data and/or allow for a faster analysis than the process of <FIG>.

Alternatively, the operations of <FIG> may be performed as a first check or alone without additional checks. In these embodiments, a computer system may perform some or all of the operations of <FIG> without the operations of <FIG>. The operations of <FIG> may, for example, be performed by the analytic software <NUM> in computer system <NUM> using I/O interfaces <NUM>, processors <NUM>, storage devices <NUM>, and database <NUM> shown in <FIG>.

In operation <NUM>, a machine learning algorithm is performed to reduce the dimensionality of the top principal components of the voltage data from the smart meters connected to a transformer. The transformer selected for analysis in operation <NUM> may be any distribution transformer in the electric grid. As an example, operations <NUM>-<NUM> of <FIG> may be performed using voltage data from the smart meters connected to the transformer selected in operation <NUM>.

In an exemplary embodiment of <FIG>, the existing smart meter to transformer connections are determined from the GIS data, and historic time series voltage data is collected for the smart meters spanning a selected period of time (e.g., days, weeks, or months). In order to diversify the voltage profiles, one transformer per feeder may be used to create a test sheet. The test sheet allows for greater exaggerations of the potential misallocation of smart meters. The slopes and magnitudes of the voltage data for each smart meter connected to a transformer are then calculated.

In an exemplary embodiment, the slopes and magnitudes of the voltage data for each smart meter are normalized and run through a principal component analysis (PCA). The PCA extracts the top principal components that uniquely identify each time series voltage waveform generated based on the smart meter voltage data. The top principal components typically have many more than <NUM> dimensions. After the top principal components are determined in this exemplary embodiment, they are run through a t-distribution stochastic nearest neighbor embedding (t-SNE) algorithm in operation <NUM> in order to reduce the dimensions of the top principal components down to two or three dimensions. T-SNE is a machine learning algorithm using a nonlinear dimensionality reduction technique that is suited for embedding high dimensional data for visualization in a low dimensional space of two or three dimensions. T-SNE can model a high-dimensional object by a two or three dimensional point in a graph in such a way that similar objects are modeled by nearby points in the graph and dissimilar objects are modeled by distant points in the graph with a high probability. The t-SNE algorithm can be used to determine a low dimensional point in a graph for each smart meter based on the top principal components of the voltage data from the respective smart meter that are extracted using PCA. The low dimensional points may be, for example, two dimensional (2D) points in a 2D graph or three dimensional points (3D) in a 3D graph. Each of the points generated by the t-SNE algorithm is a data point that represents data from one smart meter. The t-SNE algorithm may perform several iterations on the voltage data. As an example, the t-SNE algorithm may reduce the dimensionality of the hourly voltage data from the smart meters to three dimensional (3D) points on a 3D graph having <NUM> coordinates after about <NUM> iterations.

Thus, for each smart meter connected to the transformer, a low dimensional point is generated in a graph for the top principal components of the voltage data from that smart meter. The points are then analyzed to identify outliers. In operation <NUM>, the centroid of the points corresponding to the top principal components of the smart meter voltage data is calculated. After the centroid is calculated in operation <NUM>, the distance between the centroid and each of the points is calculated in operation <NUM>. As an example, the Mahalonobis distance technique may be used to calculate the distance between the centroid and each of these points. The distances calculated in operation <NUM> are then associated with their respective smart meters.

In operation <NUM>, determine if any of the smart meters have anomalies relative to the other smart meters based on the distances of the points to the centroid to identify a suspected misallocated smart meter among the smart meters connected to the transformer. As an example, the smart meters may be ranked according to the distances of the points to the centroid calculated in operation <NUM> to determine if any of the smart meters is an outlier relative to the other smart meters connected to the transformer. The smart meter corresponding to the point having the greatest distance from the centroid may represent an anomaly, for example, if the distance of its corresponding point in the graph is greater than a threshold. The smart meter identified as representing an anomaly may, for example, be connected to a different transformer than the transformer selected in operation <NUM>. As another example, the smart meter identified as representing an anomaly may be associated with power theft at the utility customer site. The smart meter determined to have an anomaly in operation <NUM> may be identified as a suspected misallocated smart meter.

In operation <NUM>, the machine learning algorithm is performed to reduce the dimensionality of the top principal components of the voltage data from the smart meters connected to one or more transformers that are nearby the transformer selected in operation <NUM>. As an example, operation <NUM> can be performed using the principal components of the voltage data from the smart meters connected to the transformer having the least flags identified in operation <NUM>. As another example, operation <NUM> can be performed using the principal components of the voltage data from the smart meters connected to <NUM>, <NUM>, <NUM>, <NUM>, or more of the nearest transformers that are identified using the GIS data (e.g., as identified in operation <NUM>).

Operation <NUM> may, for example, include normalizing and then running the slopes and magnitudes of the voltage data for each smart meter through a principal component analysis (PCA). The top principal components of the voltage data may then, for example, be run through a t-SNE algorithm in order to reduce the dimensions of the top principal components down to two or three dimensions. The top principal components in two or three dimensions are then used to generate a 2D or 3D point in a graph for each of the smart meters.

In operation <NUM>, cluster analysis is performed to determine if the point for the suspected misallocated smart meter more closely matches the cluster of points for the smart meters connected to one of the nearby transformers than the cluster of points for the smart meters connected to the transformer selected in operation <NUM>. As an example, a k nearest neighbors (KNN) algorithm may be used in operation <NUM> to perform unsupervised clustering of the dimension characteristics indicated by the 2D or 3D points in the graph generated in operations <NUM> and <NUM>. The unsupervised clustering may determine if the point for the suspected misallocated smart meter more closely matches the cluster of points corresponding to one of the nearby transformers evaluated in operation <NUM> than the cluster of points corresponding to the transformer selected in operation <NUM>.

If the computer system determines in operation <NUM> that the point for the suspected misallocated smart meter more closely matches the cluster of points for the smart meters connected to one of the nearby transformers than the cluster of points for the smart meters connected to the transformer selected in operation <NUM>, the suspected misallocated smart meter is flagged as possibly being connected to that one of the nearby transformers in operation <NUM>. The voltage profile of the suspected misallocated smart meter may then be compared with the voltage profiles of the smart meters connected to the possible matching nearby transformer for an additional check. Then, locations shown in the GIS data may be field verified to confirm the results. Additional iterations of the operations <NUM>-<NUM> of <FIG> can be performed using voltage data from the smart meters connected to the other transformers to be evaluated in the electric grid.

<FIG> is a three dimensional (3D) graph illustrating exemplary 3D points that represent the top principal components of the voltage data from the smart meters connected to <NUM> transformers, according to an embodiment. The graph of <FIG> has three dimensions that are identified as TSNE1, TSNE <NUM>, and TSNE3. The <NUM> transformers are identified as TX1, TX2, TX3, TX4, and TX5 in <FIG>. The 3D points shown in <FIG> are generated using the top principal components of the voltage data from the smart meters connected to the transformer TX1 selected in operation <NUM> and from the smart meters connected to <NUM> nearby transformers TX2-TX5 identified in operation <NUM>. In <FIG>, 3D point <NUM> represents the top principal components of the voltage data from the suspected misallocated smart meter identified in operation <NUM>. The 3D points of the top principal components of the voltage data from the smart meters connected to the <NUM> transformers TX1, TX2, TX3, TX4, and TX5 are indicated as circles, triangles, diamonds, squares, and stars, respectively, in <FIG>.

In operation <NUM>, cluster analysis may be performed on the data shown in <FIG> to determine which (if any) of the clusters of 3D points from the <NUM> transformers TX1-TX5 point <NUM> correlates with. As shown in <FIG>, point <NUM> is an outlier compared to the points from the smart meters connected to transformers TX1-TX4. The nearest neighbors to point <NUM> are the points from the smart meters connected to transformer TX5. In the example of <FIG>, the operations of <FIG> may indicate that the suspected misallocated smart meter is more closely correlated with data from the smart meters connected to transformer TX5 than to data from the smart meters connected to the transformer TX1 selected in operation <NUM>. The suspected misallocated smart meter may then be reassigned to transformer TX5 in the example of <FIG>.

In some embodiments, a visual inspection and a distance inspection may be performed on the smart meter voltage data as a third check after the operations of <FIG>. If a smart meter has been identified as a suspected misallocated smart meter in both check <NUM> (<FIG>) and check <NUM> (<FIG>), the third check may be performed to confirm the results. This third check is an optional check that may be included or omitted. In the third check, a software tool may subset the original transformer selected in operations <NUM> and <NUM> and find the one of the smart meters connected to the original transformer having a voltage profile that is nearest to the voltage profile of the suspected misallocated smart meter. The software tool may also subset the predicted transformer identified in operation <NUM> and/or operation <NUM> and find the one of the smart meters connected to the predicted transformer having a voltage profile that is nearest to the voltage profile of the suspected misallocated smart meter.

If the voltage profile of the suspected misallocated smart meter matches one of the smart meters connected to the predicted transformer more than any of the smart meters connected to the original transformer, then the predicted transformer may be confirmed to be connected to the suspected misallocated smart meter in the third check. This process of inspecting and matching the voltage profiles may be performed visually or automatically by the software tool. The software tool may display the physical distances between the smart meters as well as the voltage waveforms of all three smart meters in order to visually perform the visual and distance inspections. The three smart meters include the suspected misallocated smart meter, the smart meter connected to the original transformer having a voltage profile that is nearest to the suspected misallocated smart meter, and the smart meter connected to the predicted transformer having a voltage profile that is nearest to the suspected misallocated smart meter. The software tool used in the third check may, for example, be the analytic software <NUM> in computer system <NUM> that uses I/O interfaces <NUM>, processors <NUM>, storage devices <NUM>, and database <NUM> shown in <FIG>.

If the computer system determines in operation <NUM> that the point for the suspected misallocated smart meter more closely matches the cluster of points for the smart meters connected to the transformer selected in operation <NUM> than the cluster of points for the smart meters connected to one of the nearby transformers, the computer system proceeds to operation <NUM>, which is shown in <FIG>. In operation <NUM>, the computer system determines whether the voltage data from a smart meter indicates that the customer site has a solar photovoltaic (PV) system that is sending power to the electric distribution system. The smart meter analyzed in operations <NUM>-<NUM> may be the same smart meter analyzed in operations <NUM>-<NUM> or a different smart meter. The computer system may, for example, perform an iteration of operations <NUM>-<NUM> for each of the smart meters connected to a transformer or in a local area or for each of a subset of the smart meters connected to a transformer or in a local area.

A solar PV system typically increases the voltage measured by the smart meter at the customer site during times of solar power generation. A solar PV system typically has an inverter that converts direct current (DC) power generated from PV fuel cells to alternating current (AC). The solar PV system can then send power back to the electric distribution system, for example, at a nominal voltage of <NUM> volts. Typically, a smart meter measures a voltage anomaly in response to receiving power generated by a solar PV system.

The voltage data from the smart meter can be analyzed to determine if the voltage repeatedly increases more than the voltages from other smart meters in the same area during mid-day hours. As an example, if there are <NUM> smart meters connected to a transformer, <NUM> of the <NUM> smart meters measure voltages of <NUM>-<NUM> volts at solar noon, and the seventh smart meter measures a voltage of <NUM>-<NUM> volts at solar noon, then the computer system can conclude that the seventh smart meter is at a customer site that has a solar PV system.

An example of a voltage profile from a smart meter that indicates a solar PV system at a customer site is sending power to the electric distribution system during daylight hours is shown in <FIG>. As shown in <FIG>, the voltage measured by a smart meter at a customer site having a solar PV system (shown by solid line <NUM>) increases substantially more during the mid-day hours of each day around solar noon than the voltages from other smart meters in the same local area. Dotted lines <NUM>-<NUM> are examples of voltage data from two of the other smart meters in the same local area that measure smaller voltages during the mid-day hours.

If the computer system determines that the voltage data from the smart meter indicates that the customer site has a solar PV system in operation <NUM>, then the smart meter is flagged in operation <NUM> as being at a customer site having a solar PV system. If the computer system determines that the voltage data from the smart meter indicates that the customer site does not have a solar PV system in operation <NUM>, then the computer system proceeds to operation <NUM>.

In operation <NUM>, the computer system determines if the power usage data from the smart meter indicates that the customer site has an electric vehicle that charges from the electric distribution system. An electric vehicle (e.g., an electric automobile) typically generates a unique pattern of drawing power to charge its battery, compared to other types of devices used in residences. An electric vehicle (EV) typically ramps up its usage of power very quickly after the EV is plugged into an outlet and connected to the electric distribution system. Fast chargers for electric vehicles (EVs) ramp up their power draw even more quickly. Also, EVs generally draw substantially more power when they are charging compared to other types of devices used in residences. As the battery in an EV is close to being in a fully charged state, the power drawn by the EV drops off quickly.

Most drivers of electric vehicles (EVs) charge their EVs soon after they return home from driving their EVs. For example, many EV drivers charge their EVs after work hours. As another example, some EV drivers charge their EVs late at night or in the early morning hours to take advantage of lower electricity rates. The pattern of charging an EV at a customer site is often repeated several days over a week, multiple weeks, or a month, but the charging pattern may not occur everyday. The computer system can analyze power usage data from the smart meter to determine if the power usage repeatedly increases substantially more than the power usage measured by other smart meters in the same area during certain times of the day over a multiple day period (e.g., weeks or months). The power usage data from the smart meter can also indicate what type of EV is causing the increased power usage, because different types of EVs generate different power usage profiles based on their charging patterns. The power usage data from the smart meter can even indicate the distance the driver of the EV drove before re-charging the EV, e.g., based on the duration of time to re-charge the EV.

<FIG> is a graph that illustrates power usage measured by a smart meter over one day at a customer site having an electric vehicle. As shown in <FIG>, the smart meter at a customer site having an EV measures a rapid increase in power usage (line <NUM>) that peaks near <NUM> kilowatt hours (kWh) after the EV is plugged in, remains around <NUM>-<NUM> kWh for a few hours while the EV is charging, then decreases to <NUM> kWh for about an hour, and then to near <NUM> kWh. <FIG> is a graph that illustrates power usage from a smart meter at a customer site having an electric vehicle over several days. As shown in <FIG>, the smart meter at the customer site having an EV measures rapid increases (peaks <NUM>-<NUM>) in power usage that peak around <NUM> kilowatt hours (kWh) on two days and rapid increases (peaks <NUM>-<NUM>) that peak around <NUM>-<NUM> kWh on two other days that occur while an EV is charging. As specific examples that are not intended to be limiting, each of these peaks may last about <NUM> hours, and an EV charger can use from <NUM>-<NUM> Amperes of electric current. <FIG> shows power usage over about <NUM> days. In contrast, smart meters at customer sites not having EVs typically measure much smaller increases in power usage that may peak, for example, around <NUM>-<NUM> kWh on some days. Peak <NUM> in <FIG> is an example of a power usage peak that can be from non-EV power usage. The computer system in operation <NUM> can determine if the smart meter is at a customer site having an EV based on the power usage rapidly increasing to substantially more kWh than other smart meters in the area and then rapidly decreasing. The computer system in operation <NUM> can determine if this pattern of rapid spikes in power usage repeats over several days during a measured time period (e.g., multiple weeks or months) to confirm that the smart meter is at a customer site having one or more EVs.

If the computer system determines that the power usage data from the smart meter indicates that the customer site has an EV in operation <NUM>, then the smart meter is flagged in operation <NUM> as being at a customer site having an EV. If the computer system determines that the power usage data from the smart meter indicates that the customer site does not have an EV in operation <NUM>, then the computer system proceeds to operation <NUM>.

In operation <NUM>, the computer system determines if the data from the smart meter indicates that the customer site has a grow house. A grow house may, for example, be a building where a large number of plants (e.g., dozens, hundreds, or thousands) are being cultivated indoors under electric lighting. The plants may be, for example, marijuana plants or plants that are used to produce other types of controlled substances.

The power usage profile from a smart meter at a customer site having a grow house may, for example, look similar to a square wave. Often, the lights used for indoor cultivation draw a large amount of power load continuously for several hours per day (e.g., <NUM>-<NUM> hours), usually during night and early morning hours. In contrast, a typical air conditioning (AC) unit does not have a continuous, large draw of power for this long. If the amount of power usage associated with indoor cultivation is extreme in terms of the amount and duration of power usage (e.g., greater than <NUM> kWh), it can be easily separated from that of an AC or large motor. This large usage of power correlates with a voltage drop at the customer site that lasts for long periods of time (e.g., an average <NUM> hours per day).

The computer system in operation <NUM> can determine if the smart meter is at a grow house based on an increase in the power usage measured by the smart meter that is large and continuous (e.g., relative to the power measured by other smart meters in the area) and that occurs over several hours each day or on most days, particularly during the night or early morning hours. The amount of power drawn by a grow house may also indicate other information, such as what type of lights the grow house is using, how close the operators of the grow house are to harvesting, and/or how many plants are being grown at the grow house using known data regarding wattage and lumens of various types of lights and watts of lighting needed per plant over the plant's life cycle.

<FIG> is a graph that illustrates an example of power usage measured by a smart meter at a customer site having a grow house, according to an embodiment. In the example of <FIG>, power usage at a grow house is shown over <NUM> days. In <FIG>, power usage increases to around <NUM> kWh for about <NUM>-<NUM> hours each day when indoor lights for growing plants are turned on, and then decreases to about <NUM>-<NUM> kWh when these indoor lights are turned off. The large power usage may, for example, occur during the late night or early morning hours. <FIG> shows approximately <NUM> kWh maximum usage occurring each day merely as one example of a grow house. As other examples, the power usage at other grow houses may peak somewhere in the range of <NUM>-<NUM> kWh (or more) and remain within this range for <NUM>-<NUM> hours per day after falling back to a more typical power usage for the remainder of each day (e.g., <NUM>-<NUM> kWh).

As discussed above, the voltage profile sensed by an electric meter at a customer site may be indicative of power theft from the electric distribution system. If power theft is occurring at a customer site, the smart meter at that customer site may generate a different voltage profile than the voltage profiles generated by the smart meters at other customer sites in the same geographic area and/or that are connected to the same distribution transformer. For example, the voltage measured by a smart meter at a grow house may drop by <NUM>-<NUM> volts during several hours of the night and early morning. In contrast, the voltage measured by other smart meters connected to the same transformer or in the same area may drop by only a few volts or may be increasing during the same nighttime and early morning hours. If the voltage measured by a smart meter drops significantly more than the voltage measured by other smart meters in the same area or connected to the same transformer over several hours each day, then the computer system in operation <NUM> can determine that the smart meter is at a grow house.

If the computer system determines that data from the smart meter indicates that the customer site has a grow house in operation <NUM>, the smart meter is flagged as being at a grow house. Then, in operation <NUM>, the computer system determines if data from the smart meter indicates that power theft is occurring at the customer site. A specific example of how the computer system can determine if power theft is occurring at a customer site is described below.

If power theft is occurring at a customer site, the voltage drop between the smart meter at that customer site and the neatest distribution transformer typically does not correlate with the electric current or power usage measured by the smart meter at that customer site. The computer system can receive values indicating the instantaneous electric current measured by each smart meter connected to each distribution transformer. A smart meter can directly provide a measurement of electric current (in Amperes) to the computer system or can provide a measurement of power usage and voltage from which electric current can be calculated. The computer system can add together the electric current values received from all of the smart meters connected to a distribution transformer to determine the total electric current that is being drawn from that transformer to the smart meters.

An electric utility typically has resistance data about its electric distribution lines, such as the lengths of the distribution lines and the resistance of the distribution lines per unit of length. An electric utility also typically has data about the distribution transformers in its electric distribution system, including the resistance in the wire windings, magnetic core losses, and eddy current loss. The computer system can calculate the parasitic losses of a distribution transformer from the resistance in the wire windings, magnetic core losses, and eddy current loss. The computer system can use the parasitic losses for a transformer and the total electric current drawn from the transformer to calculate the voltage at the transformer. The computer system can use the electric current measured by each smart meter and the resistance data to calculate the voltage drop through each section of the distribution lines between the transformer and each of the smart meters. Typically, the voltage drop between the transformer and a smart meter as calculated by the computer system using the electric current measured by the smart meter matches the voltage drop between the transformer and the voltage measured by that smart meter.

If the voltage drop between the transformer and the voltage measured by any of the smart meters does not match the respective voltage drop calculated by the computer system using the measured electric current, the computer system can then calculate the amount of electric current that would cause the voltage drop between the transformer and the voltage measured by that smart meter using the resistance data. The computer system then compares the electric current measured by the smart meter to the electric current calculated by the computer system using the voltage measured by the smart meter. If the electric current measured by the smart meter is substantially less than the electric current calculated by the computer system using the voltage measured by the smart meter, then the computer system flags the smart meter as being associated with power theft at that customer site in operation <NUM>. As an example that is not intended to be limiting, the difference between the electric current measured by a smart meter and the electric current calculated by the computer system may be over <NUM> Amps at a customer site where power theft is occurring.

If the computer system determines that the data from the smart meter does not indicate that the customer site has a grow house in operation <NUM>, then the computer system proceeds to operation <NUM>. In operation <NUM>, the computer system determines if the data from the smart meter indicates that there is a large on-site battery at the customer site charging from the electric distribution system. A large on-site battery may draw a significant amount of power while it is charging (e.g., for several hours) and may have a unique charging pattern. If the computer system determines in operation <NUM> that the data from the smart meter indicates that there is a large on-site battery at the customer site, then the smart meter is flagged in operation <NUM> as being at a customer site having a large on-site battery.

If the computer system determines in operation <NUM> that the data from the smart meter is not consistent with a large on-site battery at the customer site, then the computer system flags the smart meter for possible power quality issues in operation <NUM>. Low power quality may be caused by numerous issues, such as overheated transformers or overloaded neutrals. The process of <FIG> can allow a utility to proactively identify power quality issues before they cause an outage, a fire, or property damage.

The following examples, excluding examples <NUM>-<NUM> and <NUM>-<NUM>, pertain to further embodiments. Example <NUM> is a computer system comprising at least one processor device, wherein the computer system is configured to: generate voltage waveforms based on time series voltage data received from first smart meters connected to a first transformer in an electric grid; identify as a first identified interval each time interval during which a polarity of a slope of the voltage waveform of a first one of the first smart meters is opposite to a polarity of slopes of the voltage waveforms of a majority of the first smart meters; and generate a flag for the first one of the first smart meters each time a magnitude of the slope of the voltage waveform of the first one of the first smart meters at one of the first identified intervals is an outlier compared to magnitudes of the slopes of the voltage waveforms of the majority of the first smart meters.

In Example <NUM>, the computer system of Example <NUM> can optionally be further configured to: identify as a second identified interval each time interval during which a polarity of a slope of the voltage waveform of a second one of the first smart meters is opposite to the polarity of the slopes of the voltage waveforms of the majority of the first smart meters; and generate a flag for the second one of the first smart meters each time a magnitude of the slope of the voltage waveform of the second one of the first smart meters at one of the second identified intervals is an outlier by a threshold percentage compared to magnitudes of the slopes of the voltage waveforms of the majority of the first smart meters.

In Example <NUM>, the computer system of any one of Examples <NUM>-<NUM> can optionally be further configured to: identify one of the first smart meters that is associated with a greatest number of the flags as a suspected misallocated smart meter; identify a second transformer that is nearby the first transformer in the electric grid; and generate voltage waveforms based on time series voltage data received from second smart meters connected to the second transformer.

In Example <NUM>, the computer system of Example <NUM> can optionally be further configured to: identify as a second identified interval each time interval during which a polarity of a slope of the voltage waveform of the suspected misallocated smart meter is opposite to a polarity of slopes of voltage waveforms of a majority of the second smart meters; and generate a flag for the suspected misallocated smart meter each time a magnitude of the slope of the voltage waveform of the suspected misallocated smart meter at one of the second identified intervals is an outlier compared to magnitudes of the slopes of the voltage waveforms of the majority of the second smart meters.

In Example <NUM>, the computer system of Example <NUM> can optionally be further configured to: identify a third transformer that is nearby the first transformer in the electric grid; and generate voltage waveforms based on time series voltage data received from third smart meters connected to the third transformer.

In Example <NUM>, the computer system of Example <NUM> can optionally be further configured to: identify as a third identified interval each time interval during which a polarity of a slope of the voltage waveform of the suspected misallocated smart meter is opposite to a polarity of slopes of voltage waveforms of a majority of the third smart meters; and generate a flag for the suspected misallocated smart meter each time a magnitude of the slope of the voltage waveform of the suspected misallocated smart meter is an outlier at one of the third identified intervals compared to magnitudes of the slopes of the voltage waveforms of the majority of the third smart meters.

In Example <NUM>, the computer system of any one of Examples <NUM>-<NUM> can optionally be further configured to: perform a machine learning algorithm to reduce dimensionality of top principal components of the time series voltage data from the first smart meters to generate first points in a graph; calculate a centroid of the first points; calculate a distance of each of the first points to the centroid; and determine if any of the first smart meters have anomalies relative to the other ones of the first smart meters based on the distances of the first points to the centroid to identify a suspected misallocated smart meter from among the first smart meters.

In Example <NUM>, the computer system of Example <NUM> can optionally be further configured to: perform the machine learning algorithm to reduce dimensionality of top principal components of additional time series voltage data from second smart meters connected to a second transformer that is nearby the first transformer in the electric grid to generate second points in the graph.

In Example <NUM>, the computer system of Example <NUM> can optionally be further configured to: perform cluster analysis to determine if one of the first points that corresponds to the suspected misallocated smart meter more closely matches the second points for the second smart meters than the first points for the first smart meters.

Example <NUM> is a non-transitory computer readable storage medium storing instructions executable on a processor in a computer system, the executable instructions comprising: instructions executable to perform a machine learning algorithm to reduce dimensionality of top principal components of first time series voltage data received from first smart meters connected to a first transformer in an electric distribution system to generate first points in a graph, wherein each of the first points corresponds to one of the first smart meters; instructions executable to calculate a centroid of the first points; instructions executable to calculate a distance of each of the first points to the centroid; and instructions executable to determine if any of the first smart meters have anomalies relative to the other ones of the first smart meters based on the distances of the first points to the centroid to identify a suspected misallocated smart meter from among the first smart meters.

In Example <NUM>, the non-transitory computer readable storage medium of Example <NUM> can optionally further comprise: instructions executable to perform the machine learning algorithm to reduce dimensionality of top principal components of second time series voltage data received from second smart meters connected to a second transformer that is nearby the first transformer in the electric distribution system to generate second points in the graph, wherein each of the second points corresponds to one of the second smart meters.

In Example <NUM>, the non-transitory computer readable storage medium of any one of Examples <NUM>-<NUM> can optionally further include wherein the machine learning algorithm is a t-distribution stochastic nearest neighbor embedding algorithm, and/or wherein the first and second points are three dimensional points.

In Example <NUM>, the non-transitory computer readable storage medium of any one of Examples <NUM>-<NUM> can optionally further comprise: instructions executable to perform cluster analysis to determine if one of the first points that corresponds to the suspected misallocated smart meter more closely matches a cluster of the second points for the second smart meters than a cluster of the first points for the first smart meters.

In Example <NUM>, the non-transitory computer readable storage medium of Example <NUM> can optionally further include wherein the cluster analysis includes a k nearest neighbors algorithm that performs unsupervised clustering of dimension characteristics indicated by the first and second points.

In Example <NUM>, the non-transitory computer readable storage medium of any one of Examples <NUM>-<NUM> can optionally further comprise: instructions executable to extract the top principal components that identify time series voltage waveforms generated based on the first time series voltage data for the first smart meters using a principal component analysis.

Example <NUM> is a method for identifying outliers in time series voltage data received from smart meters in an electric distribution system using at least one processor circuit in a computer system, the method comprising: generating a voltage waveform for each of first smart meters connected to a first transformer in the electric distribution system based on first time series voltage data received from the first smart meters; identifying as a first identified interval each predefined time interval during which a polarity of a slope of the voltage waveform of a first one of the first smart meters is opposite to a polarity of slopes of the voltage waveforms of a majority of the first smart meters; and generating a flag for the first one of the first smart meters each time a magnitude of the slope of the voltage waveform of the first one of the first smart meters at one of the first identified intervals is an outlier compared to magnitudes of the slopes of the voltage waveforms of the majority of the first smart meters.

In Example <NUM>, the method of Example <NUM> can optionally further comprise: identifying one of the first smart meters that is associated with a greatest number of the flags as a suspected misallocated smart meter; identifying a second transformer that is nearby the first transformer in the electric distribution system; and generating a voltage waveform for each of second smart meters connected to the second transformer based on second time series voltage data received from the second smart meters.

In Example <NUM>, the method of Example <NUM> can optionally further comprise: identifying as a second identified interval each predefined time interval during which a polarity of a slope of the voltage waveform of the suspected misallocated smart meter is opposite to a polarity of slopes of voltage waveforms of a majority of the second smart meters; and generating a flag for the suspected misallocated smart meter each time a magnitude of the slope of the voltage waveform of the suspected misallocated smart meter at one of the second identified intervals is an outlier compared to magnitudes of the slopes of the voltage waveforms of the majority of the second smart meters.

In Example <NUM>, the method of Example <NUM> can optionally further comprise: identifying a third transformer that is nearby the first transformer in the electric distribution system; and generating a voltage waveform for each of third smart meters connected to the third transformer based on third time series voltage data received from the third smart meters.

In Example <NUM>, the method of Example <NUM> can optionally further comprises: identifying as a third identified interval each predefined time interval during which a polarity of a slope of the voltage waveform of the suspected misallocated smart meter is opposite to a polarity of slopes of voltage waveforms of a majority of the third smart meters; and generating a flag for the suspected misallocated smart meter each time a magnitude of the slope of the voltage waveform of the suspected misallocated smart meter at one of the third identified intervals is an outlier compared to magnitudes of the slopes of the voltage waveforms of the majority of the third smart meters.

Example <NUM> is a non-transitory computer readable storage medium including instructions that, when executed by a processor circuit, cause the processor circuit to implement any of Examples <NUM>-<NUM> or <NUM>-<NUM>.

Example <NUM> is a computer system to implement any of Examples <NUM>-<NUM>.

Example <NUM> is a method to implement any of Examples <NUM>-<NUM>.

Example <NUM> is a computer system comprising at least one processor device, wherein the computer system is configured to: determine if voltage data received from a smart meter connected to an electric distribution system indicates that the smart meter is located at a customer site having a solar photovoltaic system that is sending power to the electric distribution system; determine if data indicating power usage received from the smart meter indicates that the smart meter is located at a customer site having an electric vehicle that charges from the electric distribution system; determine if the voltage data or the data indicating power usage received from the smart meter indicates that the smart meter is located at a grow house that uses substantially more power than other smart meters in a local area; and determine if the voltage data and the data indicating power usage received from the smart meter indicates that the power usage of the smart meter accounts for voltage drops measured by the smart meter to identify power theft.

In Example <NUM>, the computer system of Example <NUM> can optionally include wherein the computer system is further configured to determine if voltage data received from the smart meter indicates that the smart meter is located at a customer site having a solar photovoltaic system by analyzing the voltage data from the smart meter to determine if voltage measured by the smart meter repeatedly increases more than voltages from at least some of the other smart meters in the local area during mid-day hours of multiple days of a multiple day period.

In Example <NUM>, the computer system of any one of Examples <NUM>-<NUM> can optionally include wherein the computer system is further configured to determine if the data indicating power usage received from the smart meter indicates that the smart meter is located at a customer site having an electric vehicle by determining if the power usage increases rapidly, draws substantially more power than most of the other smart meters in the local area, and then decreases rapidly during multiple days of a multiple day period.

In Example <NUM>, the computer system of any one of Examples <NUM>-<NUM> can optionally include wherein the computer system is further configured to determine if the voltage data or the data indicating power usage received from the smart meter indicates that the smart meter is located at a grow house by determining if the power usage is substantially and continuously larger than power usage indicated by the other smart meters in the local area for several hours of multiple days of a multiple day period.

In Example <NUM>, the computer system of any one of Examples <NUM>-<NUM> can optionally include wherein the computer system is further configured to determine if the voltage data or the data indicating power usage from the smart meter indicates that the smart meter is located at a customer site having a large on-site battery that is charging from the electric distribution system, and wherein the processor device flags the smart meter as being associated with possible power quality issues if the voltage data or the data indicating power usage from the smart meter indicates that the smart meter is not located at a customer site having a large on-site battery.

Example <NUM> is a non-transitory computer readable storage medium including instructions that, when executed by one or more processor circuits, cause the one or more processor circuits to implement any of Examples <NUM>-<NUM>.

Embodiments of the present invention may be implemented using hardware, software, a non-transitory computer-readable medium containing program instructions, or a combination thereof. Software implemented by embodiments of the present invention or results of the present invention can be stored in some form of a non-transitory computer-readable medium such as semiconductor memory devices, hard drive, CD-ROM, DVD, or other non-transitory media for subsequent purposes such as being executed or processed by a processor, being displayed to a user, etc. Also, software implemented according to the present invention or results of the present invention may be transmitted in a signal over a network. Results of the present invention can be used for various purposes such as being executed or processed by a processor, being displayed to a user, transmitted in a signal over a network, etc. It is intended that the scope of the present invention be limited not with this detailed description, but rather by the claims appended hereto.

Claim 1:
A computer system (<NUM>) comprising at least one processor device (<NUM>), wherein the computer system (<NUM>) is configured to:
generate (<NUM>) voltage waveforms based on time series voltage data received from first smart meters (102A-D, 403A-408B) that are indicated as being connected to a first transformer (402A-F) in an electric grid;
characterized in that
the computer system (<NUM>) is further configured to:
identify (<NUM>) as a first identified interval each time interval during which a polarity of a slope of the voltage waveform of a first one of the first smart meters (102A-D, 403A-408B) is opposite to a polarity of slopes of the voltage waveforms of a majority of the first smart meters (102A-D, 403A-408B); and
generate (<NUM>) a flag for the first one of the first smart meters (102A-D, 403A-408B) each time a magnitude of the slope of the voltage waveform of the first one of the first smart meters (102A-D, 403A-408B) at one of the first identified intervals is an outlier compared to magnitudes of the slopes of the voltage waveforms of the majority of the first smart meters (102A-D, 403A-408B).