Distributed intelligent remote terminal units

Multiple autonomous intelligent Remote Terminal Units (RTUs) are positioned on distribution lines in an electrical power grid. The intelligent RTUs perform analytics on power flowing through the distribution lines to provide real-time analysis of the power. The intelligent RTUs acquire sensor data from the distribution lines and locally perform the analytics on the sensor data to create processed data signals that are transmitted to a control center server to facilitate power distribution monitoring of the electrical power grid.

BACKGROUND OF THE INVENTION

1. Technical Field

The present disclosure relates in general to the field of electricity distribution grids, and particularly to monitoring electricity distribution grids. Still more particularly, the present disclosure relates to intelligent remote terminal units used to monitor electricity distribution grids.

2. Description of the Related Art

Existing electricity distribution grids may be monitored using Remote Terminal Units (RTUs) with a Supervisory Control and Data Acquisition (SCADA) control system. Such standard RTUs are controlled by the SCADA control system, and provide limited sampling and processing of line sensor data, are difficult and expensive to scale, have high latency, have very limited time measurement capabilities (thus making synchronization technically difficult), and do not support fast reporting.

SUMMARY OF THE INVENTION

Multiple autonomous intelligent Remote Terminal Units (RTUs) are positioned on distribution lines in an electrical power grid. The intelligent RTUs perform advanced analytics on power flowing through the distribution lines, thus providing accurate real-time analysis of the power.

The above, as well as additional purposes, features, and advantages of the present invention will become apparent in the following detailed written description.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

With reference now toFIG. 1, there is depicted a block diagram of an exemplary computer102, in which the present invention may be utilized. Note that some or all of the exemplary architecture shown for computer102may be utilized by software deploying server150, control center server216(shown inFIG. 2), and/or substation server410(shown inFIG. 4).

Computer102includes a processor unit104that is coupled to a system bus106. A video adapter108, which drives/supports a display110, is also coupled to system bus106. System bus106is coupled via a bus bridge112to an Input/Output (I/O) bus114. An I/O interface116is coupled to I/O bus114. I/O interface116affords communication with various I/O devices, including a keyboard118, a mouse120, a Compact Disk-Read Only Memory (CD-ROM) drive122, a floppy disk drive124, and a transmitter126. Transmitter126may be a wire-based or wireless-based transmitter, configured to transmit a signal over a wire or a wireless signal (e.g., a radio wave). The format of the ports connected to I/O interface116may be any known to those skilled in the art of computer architecture, including but not limited to Universal Serial Bus (USB) ports.

Computer102is able to communicate with a software deploying server150via a network128using a network interface130, which is coupled to system bus106. Network128may be an external network such as the Internet, or an internal network such as an Ethernet or a Virtual Private Network (VPN). Note the software deploying server150may utilize a same or substantially similar architecture as computer102.

Application programs144include a browser146. Browser146includes program modules and instructions enabling a World Wide Web (WWW) client (i.e., computer102) to send and receive network messages to the Internet using HyperText Transfer Protocol (HTTP) messaging, thus enabling communication with software deploying server150.

Application programs144in computer102's system memory (as well as software deploying server150's system memory) also include a Remote Terminal Unit Processing Logic (RTUPL)148. RTUPL148includes code for implementing the processes described inFIGS. 2-9B. In one embodiment, computer102is able to download RTUPL148from software deploying server150, including in an “on demand” basis, as described in greater detail below inFIGS. 8A-9B. Note further that, in a preferred embodiment of the present invention, software deploying server150performs all of the functions associated with the present invention (including execution of RTUPL148), thus freeing computer102from having to use its own internal computing resources to execute RTUPL148.

With reference now toFIG. 2, an exemplary electric power grid202, having novel features described by the present invention, is presented. Electric power is initially generated by a power generator204, which may be powered by water (hydroelectric), fossil fuel (e.g., coal powered), nuclear material (i.e., nuclear power), etc. The electrical power is then transmitted along transmission lines206(typically high voltage lines) to a distribution substation208, which may step down the voltage before passing the power on to distribution lines210. The distribution lines210may be sub-trunk lines within the distribution substation208, sub-trunk lines coming out of (from) the distribution substation208, and/or drop lines coming directly from a final step-down transformer (not shown), from which the power ultimately reaches a customer212.

Remote Terminal Units (RTUs)214, which may be placed in any type of distribution lines210described above, take quantitative and qualitative readings of sensor data describing the power. Some or all of the RTUs214may then process the sensor data, and forward the processed data to a server (e.g., substation server410shown below inFIG. 4) for further processing. Additionally, some or all of the RTUs214may be configured to more fully processing the sensor data (in a manner described below), and to transmitt the processed data directly to a control center server216, which utilizes the processed data in a manner described below. Thus, some or all of the RTUs214, known as standard RTUs, are configured to perform rudimentary signal processing (e.g., converting an analog reading of voltage or current into a digital signal), while other RTUs214, known as intelligent RTUs, are configured to perform more complex processing (e.g., streaming waveforms, calculating power factors, etc.). Note that the transmission of processed and/or semi-processed data may be transmitted via any medium selected by the user, including, but not limited to, transmission along the transmission lines206and/or distribution lines210themselves, a separate data line (not shown), wireless transmission media (e.g., radio waves—not shown), etc. Note also that if the RTUs214are standard RTUs, then the local substation server410will be required to perform all local processing.

With reference now toFIG. 3, an exemplary intelligent RTU302(“smart RTU”), as contemplated by the present invention, is presented. Intelligent RTU302includes a sensor304, which monitors amperage, voltage, power, phase, and/or other characteristics of electrical power read from a power line306(e.g., distribution lines210). The intelligent RTU302incorporates a signal processor308, which is able to take readings from sensor304in order to discern advanced analytics of voltage and/or current and/or power using a discrete Fourier transform, an even-odd extraction, root mean square (RMS) of the current or voltage, total harmonic distortion (THD) of the voltage, an RMS/THD relation, a voltage crest factor, a current k-factor, triplens of the current, power factor, real power via dot product, arc detector, and digital filter, a Global Positioning System (GPS) time, etc. Exemplary formulas for calculating these values are shown in Table I:

All calculations described above can be calculated by the signal processor308in the intelligent RTU302, or by a dedicated processing logic devoted only to performing such calculations. Some or all of these calculations can also be performed by substation server, shown and described in detail below inFIGS. 4-5.

Continuing withFIG. 3, once the signal processor308processes the sensor data from one or more sensors304associated with the intelligent RTU302, the processed data is then sent to a transmitter310(e.g., transmitter126shown inFIG. 1), which transmits the processed data, via a transmission medium312(e.g., transmission lines206, a wireless signal, etc.), to the control center server216.

With reference now toFIG. 4, note that standard RTUs402and/or smart RTUs404(e.g., as described above for intelligent RTU302inFIG. 3) are positioned to take readings from power lines, including the distribution lines210described above. If a standard RTU402is utilized, then the standard RTUs402perform only rudimentary signal processing. Alternatively, RTUs402may be replaced by only sensors (not shown, but similar to sensor304described inFIG. 3), which send raw signal data to a primary processing logic406in a substation server410for processing. The processed data (from the smart RTUs404and/or the primary processing logic206) is then sent to a secondary processing logic408, which is logic that computes, for all three phases of power, arc signal, total apparent power, phase impedance, and fault distance (using formulas shown below in Table II), as well as phasors (represented in polar form as magnitude/angle pairs) showing relationships between phases in multi-phase power.

As shown inFIG. 4, this processed data can then be sent from the secondary processing logic408to the remotely located control center server216.

Note also that a Global Positioning System (GPS) timer412may be associated with one or more of the smart RTUs404. GPS timer412takes advantage of the fact that every GPS satellite has on-board atomic clocks, which generate GPS time (which does not match time based on the rotation of the earth). This GPS time provides a level of accuracy needed to generate timing signals and timing tags used to coordinate activities among RTUs. That is, assume that multiple intelligent RTUs directly interact to perform advanced analytics on sensor data from the multiple intelligent RTUs (e.g., using an analytics engine502describe inFIG. 5). GPS timer412supplies the requisite level of time accuracy needed for this coordinated advanced analytics process.

Referring now toFIG. 5, additional detail for processing logic in the substation server410is presented. The substation server410includes logic for performing advanced substation analytics, which in one embodiment, are the same advanced analytics that are performed by the signal processor308in the intelligent RTU302, which utilizes one or more of the analytics engine502, interface to substation control devices504, event correlation engine506, message generation logic508, event extraction logic510, calculations logic512, thresholds and alarms logic514, distributed grid extraction process logic516, circular buffer data table520, circular buffer data table522, RTU event message queue524, scan table526, and Distributed Network Protocol—Three (DNP3) scanner528.

As shown inFIG. 5, sensor data is received by line RTUs538(which may be intelligent RTUs, and thus already have some or all of the internal logic described above for substation server410). Alternatively, RTUs538may merely be standard sensors that are incapable of the advanced analytics described above. Similarly, sensor data (either processed or unprocessed) may be received from inside a distribution substation (e.g., distribution substation208shown inFIG. 2), using substation Intelligent Electronic Devices (IEDs—“smart RTUs”) and sensors536. This sensor data is sent to a substation RTU532for further processing, before being sent to a local historian Remote Terminal (RT) service530, which provides sensor data (processed if from an intelligent or standard RTU, or unprocessed if from a naked sensor) to a distributed historian540. The substation RTU532, like a sensor data transport534is able to send sensor data (raw, semi-processed, or processed) to the DNP3 scanner528, which scans for data from the RTU sensors. Note that DNP3 scanner528utilizes the DNP3 protocol, which is a layer 2 protocol that is able to check for data errors (prevalent in power transmission of “dirty” power) through a heavy use of Cyclic Redundancy Checks (CRCs) embedded in data packets from the RTU sensors.

DNP3 scanner528stores receives information from scan tables526, which help the DNP3 scanner528identify which RTUs are sending data to the DNP3 scanner528. The DNP3 scanner528sends the sensor data (raw, semi-processed, processed) to a circular buffer data table520(for analog data, such as an analog wave signal from a sensor or RTU) and/or a circular buffer data table522(for digital data, such as digitized information describing a status of a sensor or RTU). This information is sent to a grid state extraction process logic518, which passes the data on to a distributed grid state storage516.

Sensor data from the circular buffer data table520, circular buffer data table522, and/or an RTU event message queue524is sent to the analytics engine502. The analytics engine is able to perform the advances analytics described above in Tables I and II. For example, event extraction logic510is able to identify a particular event from sensor data received from an RTU. Such a particular event may be a power signal being “dirty” from line induction, etc. This “dirty” power is recognized by the event extraction logic510, and sent to an event correlation engine506, which is able to correlate this particular event with other events from (the same or other RTUs) to recognize a wide-spread problem. Similarly, calculations logic512is able to perform the calculations described above in Tables I and II, in order to perform the advanced analytics discussed above. Note again that these advanced analytics can be performed within the intelligent RTUs themselves (e.g., intelligent RTU302shown inFIG. 3and smart RTUs404Shown inFIG. 4), thus making these RTUs autonomously able to provide rapid, high-rate (e.g., take 256 samples per cycle) sensor evaluations and advanced analytics. By making these intelligent RTUs perform such advanced analytics, timing problems, staleness (of data) problems, etc., which would be prevalent in a centralized analytical engine system, are eliminated or reduced.

Continuing withFIG. 5, note that thresholds and alarms logic514can also generate alarms (based on simple threshold monitoring of data signals from the RTUs), which can result in a message generation logic508producing and transmitting an alarm message to a control center event correlation (e.g., a control center server216, such as shown inFIG. 2).

Note also that the event correlation engine506can transmit processed data (from or processed by the RTUs) to an interface504to substation control devices. Interface504thus provides processed analytical data to control devices such as switching gear, power controls, meters, etc. (not shown), based on the advanced analytics performed on the sensor data.

Note that, in a preferred embodiment, the data from the RTUs is sent to the DNP3 scanner using Internet Protocol (IP), thus requiring the DNP3 scanner528to have a unique IP address. Similarly, the RTU event message queue524uses Transmission Control Protocol/Internet Protocol (TCP/IP), thus making transmission possible over the World Wide Web.

With reference now toFIG. 6, a high-level flow-chart of steps taken to utilize intelligent RTUs, which perform advanced analytics of electrical power being transmitted, to monitor and control such transmission, is presented. After initiator block602, a determination is made as to whether an RTU that is monitoring a power line is dumb or smart (query block606). If the RTU is a standard RTU (or else is just a sensor), then primary processing, of the sensor data, is performed by (block608) and transmitted from (block610) a primary processing logic (e.g., primary processing logic406shown inFIG. 4). If the RTU is smart, then the primary processing has already been performed (e.g., calculations shown above in Table I), and thus only secondary processing is needed (e.g., calculations shown in Table II), as describe in block612. The finally processed signal, having advanced analytics performed thereon, is then sent to a remotely located control center (block614), and the process ends (terminator block616).

The advanced analytics (described in block612) may include discerning components of voltage or current using a discrete Fourier transform, an even-odd extraction, root mean square (RMS) of the current or voltage, total harmonic distortion (THD) of the voltage, an RMS/THD relation, a voltage crest factor, a current k-factor, triplens of the current, power factor, real power via dot product, arc detector, and digital filter, a Global Positioning System (GPS) time, etc. In addition, such advanced analytics can also accomplish a real-time waveform streaming and display. For example, assume, as shown inFIG. 7, that a Graphical User Interface (GUI)702, displayed on a control center server216(shown inFIG. 2) is receiving streaming waveforms from multiple (intelligent) RTUs704a-n(where “n” is an integer). These streaming waveforms can be generated by taking an analog signal from sensors in the RTUs704a-n, and digitizing these waveforms using Analog to Digital Converters (ADCs—not shown) in the RTUs704a-n. These digital packets are then streamed in real-time to the GUI702, resulting in corresponding real-time graphs706a-n. A supervisor, watching the GUI702on the control center server216, is thus able to determine if power on a particular line is normal (e.g., has a normal sine wave as shown in real-time graph706a), is frozen at a high-voltage (real-time graph706b), is “dead” (real-time graph706c), or is demonstrating an unexpected waveform due to clipping (e.g., the chopped wave shown in real-time graph706n). Note that some or all of the real-time graphs706a-nwould not be possible if advanced analytics were performed at the control center server216, since too much time would be required to receive sensor data from the various RTUs, particularly if such data was taken at a high rate (e.g., 256 times a second).

It should be understood that at least some aspects of the present invention may alternatively be implemented in a computer-readable medium that contains a program product. Programs defining functions of the present invention can be delivered to a data storage system or a computer system via a variety of tangible signal-bearing media, which include, without limitation, non-writable storage media (e.g., CD-ROM), writable storage media (e.g., hard disk drive, read/write CD ROM, optical media), as well as non-tangible communication media, such as computer and telephone networks including Ethernet, the Internet, wireless networks, and like network systems. It should be understood, therefore, that such signal-bearing media when carrying or encoding computer readable instructions that direct method functions in the present invention, represent alternative embodiments of the present invention. Further, it is understood that the present invention may be implemented by a system having means in the form of hardware, software, or a combination of software and hardware as described herein or their equivalent.

Software Deployment

As described above, in one embodiment, the processes described by the present invention, including the functions of RTUPL148, are performed by service provider server150. Alternatively, RTUPL148and the method described herein, and in particular as shown and described inFIGS. 2-7, can be deployed as a process software from service provider server150to computer102. Still more particularly, process software for the method so described may be deployed to service provider server150by another service provider server (not shown).

Referring then toFIGS. 8A-8B, step800begins the deployment of the process software. The first thing is to determine if there are any programs that will reside on a server or servers when the process software is executed (query block802). If this is the case, then the servers that will contain the executables are identified (block804). The process software for the server or servers is transferred directly to the servers' storage via File Transfer Protocol (FTP) or some other protocol or by copying though the use of a shared file system (block806). The process software is then installed on the servers (block808).

Next, a determination is made on whether the process software is to be deployed by having users access the process software on a server or servers (query block810). If the users are to access the process software on servers, then the server addresses that will store the process software are identified (block812).

A determination is made if a proxy server is to be built (query block814) to store the process software. A proxy server is a server that sits between a client application, such as a Web browser, and a real server. It intercepts all requests to the real server to see if it can fulfill the requests itself. If not, it forwards the request to the real server. The two primary benefits of a proxy server are to improve performance and to filter requests. If a proxy server is required, then the proxy server is installed (block816). The process software is sent to the servers either via a protocol such as FTP or it is copied directly from the source files to the server files via file sharing (block818). Another embodiment would be to send a transaction to the servers that contained the process software and have the server process the transaction, then receive and copy the process software to the server's file system. Once the process software is stored at the servers, the users, via their client computers, then access the process software on the servers and copy to their client computers file systems (block820). Another embodiment is to have the servers automatically copy the process software to each client and then run the installation program for the process software at each client computer. The user executes the program that installs the process software on his client computer (block822) then exits the process (terminator block824).

In query step826, a determination is made whether the process software is to be deployed by sending the process software to users via e-mail. The set of users where the process software will be deployed are identified together with the addresses of the user client computers (block828). The process software is sent via e-mail to each of the users' client computers (block830). The users then receive the e-mail (block832) and then detach the process software from the e-mail to a directory on their client computers (block834). The user executes the program that installs the process software on his client computer (block822) then exits the process (terminator block824).

Lastly a determination is made as to whether the process software will be sent directly to user directories on their client computers (query block836). If so, the user directories are identified (block838). The process software is transferred directly to the user's client computer directory (block840). This can be done in several ways such as but not limited to sharing of the file system directories and then copying from the sender's file system to the recipient user's file system or alternatively using a transfer protocol such as File Transfer Protocol (FTP). The users access the directories on their client file systems in preparation for installing the process software (block842). The user executes the program that installs the process software on his client computer (block822) and then exits the process (terminator block824).

VPN Deployment

The present software can be deployed to third parties as part of a service wherein a third party VPN service is offered as a secure deployment vehicle or wherein a VPN is build on-demand as required for a specific deployment.

A virtual private network (VPN) is any combination of technologies that can be used to secure a connection through an otherwise unsecured or untrusted network. VPNs improve security and reduce operational costs. The VPN makes use of a public network, usually the Internet, to connect remote sites or users together. Instead of using a dedicated, real-world connection such as leased line, the VPN uses “virtual” connections routed through the Internet from the company's private network to the remote site or employee. Access to the software via a VPN can be provided as a service by specifically constructing the VPN for purposes of delivery or execution of the process software (i.e. the software resides elsewhere) wherein the lifetime of the VPN is limited to a given period of time or a given number of deployments based on an amount paid.

The process software may be deployed, accessed and executed through either a remote-access or a site-to-site VPN. When using the remote-access VPNs the process software is deployed, accessed and executed via the secure, encrypted connections between a company's private network and remote users through a third-party service provider. The enterprise service provider (ESP) sets a network access server (NAS) and provides the remote users with desktop client software for their computers. The telecommuters can then dial a toll-free number or attach directly via a cable or DSL modem to reach the NAS and use their VPN client software to access the corporate network and to access, download and execute the process software.

When using the site-to-site VPN, the process software is deployed, accessed and executed through the use of dedicated equipment and large-scale encryption that are used to connect a company's multiple fixed sites over a public network such as the Internet.

The process software is transported over the VPN via tunneling which is the process of placing an entire packet within another packet and sending it over a network. The protocol of the outer packet is understood by the network and both points, called tunnel interfaces, where the packet enters and exits the network.

Software Integration

The process software which consists of code for implementing the process described herein may be integrated into a client, server and network environment by providing for the process software to coexist with applications, operating systems and network operating systems software and then installing the process software on the clients and servers in the environment where the process software will function.

The first step is to identify any software on the clients and servers, including the network operating system where the process software will be deployed, that are required by the process software or that work in conjunction with the process software. This includes the network operating system that is software that enhances a basic operating system by adding networking features.

Next, the software applications and version numbers will be identified and compared to the list of software applications and version numbers that have been tested to work with the process software. Those software applications that are missing or that do not match the correct version will be upgraded with the correct version numbers. Program instructions that pass parameters from the process software to the software applications will be checked to ensure the parameter lists match the parameter lists required by the process software. Conversely parameters passed by the software applications to the process software will be checked to ensure the parameters match the parameters required by the process software. The client and server operating systems including the network operating systems will be identified and compared to the list of operating systems, version numbers and network software that have been tested to work with the process software. Those operating systems, version numbers and network software that do not match the list of tested operating systems and version numbers will be upgraded on the clients and servers to the required level.

After ensuring that the software, where the process software is to be deployed, is at the correct version level that has been tested to work with the process software, the integration is completed by installing the process software on the clients and servers.

On Demand

The process software is shared, simultaneously serving multiple customers in a flexible, automated fashion. It is standardized, requiring little customization and it is scalable, providing capacity on demand in a pay-as-you-go model.

The process software can be stored on a shared file system accessible from one or more servers. The process software is executed via transactions that contain data and server processing requests that use CPU units on the accessed server. CPU units are units of time such as minutes, seconds, hours on the central processor of the server. Additionally the accessed server may make requests of other servers that require CPU units. CPU units describe an example that represents but one measurement of use. Other measurements of use include but are not limited to network bandwidth, memory utilization, storage utilization, packet transfers, complete transactions etc.

When multiple customers use the same process software application, their transactions are differentiated by the parameters included in the transactions that identify the unique customer and the type of service for that customer. All of the CPU units and other measurements of use that are used for the services for each customer are recorded. When the number of transactions to any one server reaches a number that begins to affect the performance of that server, other servers are accessed to increase the capacity and to share the workload. Likewise when other measurements of use such as network bandwidth, memory utilization, storage utilization, etc. approach a capacity so as to affect performance, additional network bandwidth, memory utilization, storage etc. are added to share the workload.

The measurements of use for each service and customer are sent to a collecting server that sums the measurements of use for each customer for each service that was processed anywhere in the network of servers that provide the shared execution of the process software. The summed measurements of use are periodically multiplied by unit costs and the resulting total process software application service costs are alternatively sent to the customer and/or indicated on a web site accessed by the customer which then remits payment to the service provider.

In another embodiment, the service provider requests payment directly from a customer account at a banking or financial institution.

In another embodiment, if the service provider is also a customer of the customer that uses the process software application, the payment owed to the service provider is reconciled to the payment owed by the service provider to minimize the transfer of payments.

With reference now toFIGS. 9A-9B, initiator block902begins the On Demand process. A transaction is created than contains the unique customer identification, the requested service type and any service parameters that further, specify the type of service (block904). The transaction is then sent to the main server (block906). In an On Demand environment the main server can initially be the only server, then as capacity is consumed other servers are added to the On Demand environment.

The server central processing unit (CPU) capacities in the On Demand environment are queried (block908). The CPU requirement of the transaction is estimated, then the server's available CPU capacity in the On Demand environment are compared to the transaction CPU requirement to see if there is sufficient CPU available capacity in any server to process the transaction (query block910). If there is not sufficient server CPU available capacity, then additional server CPU capacity is allocated to process the transaction (block912). If there was already sufficient available CPU capacity then the transaction is sent to a selected server (block914).

Before executing the transaction, a check is made of the remaining On Demand environment to determine if the environment has sufficient available capacity for processing the transaction. This environment capacity consists of such things as but not limited to network bandwidth, processor memory, storage etc. (block916). If there is not sufficient available capacity, then capacity will be added to the On Demand environment (block918). Next the required software to process the transaction is accessed, loaded into memory, then the transaction is executed (block920).

The usage measurements are recorded (block922). The utilization measurements consist of the portions of those functions in the On Demand environment that are used to process the transaction. The usage of such functions as, but not limited to, network bandwidth, processor memory, storage and CPU cycles are what is recorded. The usage measurements are summed, multiplied by unit costs and then recorded as a charge to the requesting customer (block924).

If the customer has requested that the On Demand costs be posted to a web site (query block926), then they are posted (block928). If the customer has requested that the On Demand costs be sent via e-mail to a customer address (query block930), then these costs are sent to the customer (block932). If the customer has requested that the On Demand costs be paid directly from a customer account (query block934), then payment is received directly from the customer account (block936). The On Demand process is then exited at terminator block938.

As described herein, the present invention distributes the signal and data processing for electric grid line sensors across a hierarchical, heterogeneous set of RTU's and substation servers, allowing standard RTU's to provide what data they can while simultaneously allowing advanced “smart” RTU's to provide higher speed sampling and advanced calculations at higher reporting rates. The invention provides for servers located inside electric substations to act as distributed communications masters to scan local subsets of RTU's (organized by distribution feeder circuits) and to deliver the RTU data directly to substation server applications that combine the line sensor RTU calculations and data with data originating in the substation, perform even more advanced calculations and then hand off the results with minimum latency to localized analytics rules engines. The local (substation) rules engines can then perform analyses that can be acted upon directly at the substation level, and/or can be passed on to the utility control center for further processing, action, and/or logging. Line sensor RTU's may be scanned by more than one substation server and no centralized scan control is necessary.

The present invention offers multiple novel and unexpected improvements over the prior art, including: the ability to support mixed smart and standard RTU's, sample rates, reporting rates, and varied subsets of RTU calculation capabilities in one system; the ability to combine signal sampling, signal processing, parameter calculations, and event processing into a single scalable, hierarchical architecture that supports large numbers of analytics on multiple synchronized time scales with geospatial distribution and intelligence distribution, as opposed to using a centralized architecture that scans slowly and without good time synchronization; the ability to deliver line sensor data and results to substations with minimal latency, thus enabling substation control functions not possible in traditional SCADA/DMS systems (example: modifying circuit breaker recloser cycles in real time, meaning milliseconds); the ability to provide increased analytics reliability through distributed architecture (failure of one server or communication path does not take down all analytics, as is the case in a centralized system approach; the ability to support advanced autonomous substation operations, such as automated load rollover and high impedance fault mitigation; and the ability to stream raw waveform data over TCP/IP networks in real time to a client program (remote virtual oscilloscope/vectorscope) and to capture waveform snapshots and store them in standard file formats.

As described above, these advantages are accomplished by an architecture that connects line sensor RTU's directly to (possibly multiple) substation servers that perform RTU scanning, thus achieving independence from a central Supervisory Control and Data Acquisition (SCADA) systems and/or Distributed Management Systems (DMS); an architecture that provides distributed processing of line sensor data, where some processing is performed in the RTU and some at the substations (and even some at the control center); software to perform basic RTU calculations (such as RMS current and voltage, real and reactive power, and THD), and advanced calculations such as Teager-Kaiser energy operator (used to calculate the energy in a signal), k-factor (weighting of harmonic load currents in a distribution line according to the harmonic load currents' effects on transformer heating), impedance phasors, voltage and current phasors, and inter-phasor angles, and synchrophasors; an architecture that provides for use of a mixed set of variable capability RTU's; a signal processing architecture for smart RTU's that supports extensive high speed calculations on high resolution line sensor sampled data, as well as software for waveform streaming and display; a signal processing architecture the provides partitioning of calculations across a combination of RTU and substation analytics server; a server/RTU architecture that allows computations to be updated or changed without the need to physically re-visit the RTU's or the substation servers; and the use of GPS timing to accurately time stamp data, enabling advanced grid analysis tools, such as synchrophasors.

While the present invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention. For example, while the present description has been directed to a preferred embodiment in which custom software applications are developed, the invention disclosed herein is equally applicable to the development and modification of application software. Furthermore, as used in the specification and the appended claims, the term “computer” or “system” or “computer system” or “computing device” includes any data processing system including, but not limited to, personal computers, servers, workstations, network computers, main frame computers, routers, switches, Personal Digital Assistants (PDA's), telephones, and any other system that is configured to process, transmit receive, capture and/or store data.