Patent Description:
Electrical distribution systems are found in many settings. For example, residential buildings, commercial buildings, industrial settings, automobiles, airplanes, ships, trains, etc. all typically have some type of electrical distribution system. Various faults in an electrical distribution system may lead to the occurrence of arcing, which carries a risk of fire and/or heat damage to the system.

Many automobiles currently use <NUM> Volt distribution systems. At this voltage, the risk of arcing is low. As such, many current automobiles do not include arc detection and/or arc protection devices. However, modern automobiles are increasing the voltage in the distribution system. For example, some modern automobiles are being designed with <NUM> Volt distribution systems. Furthermore, electric automobiles and hybrid automobiles often have electric distribution systems having voltages far greater than the common <NUM> Volt automobile distribution system. At these higher voltages, the potential for arcing and its associated risks is increased. Thus, there is a need for arc detection in automobiles.

In accordance with its abstract, <CIT> relates to: 'An arc detecting device includes: a detector for acquiring time series data concerning a characteristic quantity, such as a voltage or a current in a circuit, targeted for detecting an arc; a basic data generator for generating basic data made of a plurality of frequency components, through frequency analysis from the acquired time series data; a data processor for statistically processing the generated basic data, thereby converting the basic data to an evaluation value highly correlative to an occurrence of the arc; and an arc judging unit for judging the occurrence of the arc, if the evaluation value exceeds a predetermined arc judgment threshold value. In this manner, the arc detecting device that is capable of effectively detecting the arc in a direct-current power supply circuit is provided.

<CIT> relates to an insulation degradation diagnostic device comprises a current transformer, a first amplifier, a first high-pass filter, a low-pass filter, a second amplifier, a second high-pass filter and a discharge judgment section.

In accordance with its abstract <CIT> relates to: 'A system, method and medium of controlling a semiconductor manufacturing tool using a feedback control mechanism. The feedback control mechanism includes features for receiving data points relating to an output of the tool. The data points include a current data point and at least one previous data point. The feedback control mechanism also includes features for determining whether the current data point is an erroneous outlier by comparing the current data point to a statistical representation of the at least one previous data point, and based on whether the at least one previous data point is an outlier. The feedback control mechanism further includes features for disregarding the current data point in calculating a feedback value of the feedback control mechanism if the current data point is determined as an erroneous outlier.

The present invention provides an apparatus according to claim <NUM>. Preferred embodiments are defined in dependent claims. Current arc detectors and methods of detecting current arcs are provided. The embodiments detailed herein are described in the context of an automobile electrical distribution system. However, this is done for convenience and clarity of presentation and not to be limiting. The current variance arc detectors detailed herein, and the associated methods can be provided to detect arcs in electrical distribution systems implemented in other areas, outside automobiles, such as, for example, avionics, aerospace, residential, commercial, or other settings where arc detection in electrical distribution systems is desired.

In general, an electrical distribution system can develop either a serial or a parallel arc. With serial arcing, the main current continues to flow through the supplied electric load and therefore does not increase beyond expected nominal current values, which makes detection of a serial arcing fault difficult to achieve. With parallel arcing, a new current path in parallel to the connected load forms. Depending on the impedance of the new current path, the arcing current can reach short circuit current values and be detected by conventional circuit protection systems. However, if the measured current does not reach defined threshold values, parallel arcing can remain unnoticed. Accordingly, many conventional devices are unable to detect arcing in high impedance systems as the arcing current is below a threshold value and/or cannot be distinguished from the normal operating condition currents.

Arcing is typically accompanied by the generation of disturbances and electromagnetic radiation with a broad frequency spectrum. Arc signatures related to these disturbances are used to detect arcs. However, complicating arc detection in automotive systems is the fact that operating conditions of many electric loads in an automotive power distribution system lead to diverse time-current curves. Many loads are switched on and off with different transient behaviors. Pulse-modulated controlled loads or switching converters create numerous disturbances in the distribution system. All these factors complicate arc detection as clear and consistent arc signatures are difficult to determine.

Another complication versus conventional arc detection systems is that arcing within a direct current (DC) circuit cannot be detected based on the periodicity of the voltage and current as is often done for arc detection within alternating current (AC) circuits.

Thus, there is a need for current arc detection devices and methods suitable for modern automotive electrical distribution systems and other such electrical distribution systems.

<FIG> illustrates an electrical distribution system <NUM> in accordance with an exemplary embodiment. The electrical distribution system <NUM> includes a battery <NUM> coupled to several loads <NUM>. In general, the electrical distribution system <NUM> could be implemented in an automotive vehicle. The battery <NUM> may be a single battery or several batteries. For example, battery <NUM> could be a bank of batteries coupled in series, parallel, or some combination of series and parallel connection. The battery <NUM> may be any of a variety of types of batteries. For example, battery <NUM> could be a lead-acid based battery, a nickel-cadmium (NiCad) based battery, a nickel-metal hydride based battery, a lithium-ion (Li-ion) based battery, a Li-ion polymer based battery, a zinc-air based battery, or a molten-salt battery. Examples are not limited in this context.

The loads <NUM> could be any of a variety of loads arranged to draw power from the battery <NUM>. That is, loads <NUM> could be DC loads in an electrical distribution system, such an automotive distribution system. For example, loads <NUM> could be any number or combination of motors, relays, pumps, safety devices, entertainment devices, or the like. Examples are not limited in this context.

The current variance arc fault detector <NUM> is coupled between the battery <NUM> and the loads <NUM>. In general, the current variance arc fault detector <NUM> is arranged to detect current arc faults between the battery <NUM> and one (or more) of the loads <NUM>. Additionally, the current variance arc fault detector <NUM> is arranged to detect current arc faults between one (or more) of the loads <NUM> and ground. As used herein, a current arc fault is an electrical breakdown of the resistance of air resulting in an electric arc. Arc faults can occur where there is sufficient voltage in the electrical distribution system <NUM> and a path to a lower voltage or ground. Examples of such arc faults are serial arc faults, parallel arc faults and ground arc faults.

The current variance arc fault detector <NUM> is configured to detect, based on a variance of periodic current measurements, an arc fault in the electrical distribution system <NUM>. The current variance arc fault detector <NUM> can repeatedly measure a current flow between battery <NUM> and loads <NUM>, derive a variance between the values of the repeatedly measured current flow values, and determine whether a current arc fault is present between the battery <NUM> and one (or more) of the loads <NUM> or between one (or more) of the loads <NUM> and ground based on the derived variance.

In some examples, current variance arc fault detector <NUM> could be arranged to repeatedly measure current between the battery and several of the loads <NUM> to determine the presence of an arc fault on any one of the load branches within the electrical distribution system <NUM>.

<FIG> illustrates an electrical distribution system <NUM> in accordance with an exemplary embodiment. The electrical distribution system <NUM> includes a source <NUM> coupled to several loads <NUM>. In this example, the source <NUM> is an alternating current (AC) power source. The source <NUM> could be a single-phase source, or a multiple (e.g., <NUM>, or the like) phase source. In general, the electrical distribution system <NUM> could be implemented in any of a variety of settings, such as, for example, industrial, commercial, residential, transportations, or the like.

The loads <NUM> could be any of a variety of loads arranged to draw power from the source <NUM>. In general, loads <NUM> could be AC loads in an electrical distribution system. For example, loads <NUM> could be any number or combination of motors, relays, pumps, safety devices, entertainment devices, or the like. Examples are not limited in this context.

The current variance arc fault detector <NUM> is coupled between the source <NUM> and the loads <NUM>. In general, the current variance arc fault detector <NUM> is arranged to detect current arc faults between the source <NUM> and one (or more) of the loads <NUM>. As an example, arc faults within distribution system <NUM> could occur between phases of source <NUM>, between a phase of source <NUM> and ground, or between a phase of source <NUM> and neutral. Furthermore, it is noted, arc faults could occur between loads <NUM> or between one (or more) loads <NUM> and ground. For example, an arc fault could develop within a panel in which loads <NUM> are coupled to source <NUM>. The current variance arc fault detector <NUM> may be arranged to detect any such arc faults.

The current variance arc fault detector <NUM> is configured to detect, based on a variance of periodic current measurements, an arc fault in the electrical distribution system <NUM>. The current variance arc fault detector <NUM> can repeatedly measure a current flow between source <NUM> and loads <NUM>, derive a variance between the values of the repeatedly measured current flow values, and determine whether a current arc fault is present between the source <NUM> and one of the loads <NUM> or between one of the loads <NUM> and ground based on the derived variance.

In some examples, current variance arc fault detector <NUM> could be arranged to repeatedly measure current between the source <NUM> and several of the loads <NUM> to determine the presence of an arc fault on any one of the load branches within the electrical distribution system <NUM>.

<FIG> depict example embodiments of current variance arc fault detectors <NUM> and <NUM>, respectively. Current variance arc fault detectors <NUM> and <NUM> could be implemented in the electrical distribution system <NUM> and/or <NUM>, for example, as current variance arc fault detector <NUM>, <NUM>, etc. It is noted, however, that current variance arc fault detectors <NUM> and <NUM> are described with reference to the electrical distribution system <NUM> of <FIG> for purposes of convenience and not limitation. Of note, the current variance arc fault detectors <NUM> and <NUM> could be implemented in an electrical distribution system different from the electrical distribution system <NUM> depicted in <FIG>, such as, for example, the distribution system <NUM> of <FIG>. As such, reference to a battery, DC source, or other type of feature from distribution system <NUM> is done for example only and not to be limiting.

Turning more particularly to <FIG>, the current variance arc fault detector <NUM> is depicted. Current variance arc fault detector <NUM> includes terminals <NUM> and <NUM>, which can couple to a source and load, respectively, in an electrical distribution system. For example, terminal <NUM> could couple to battery <NUM> while terminal <NUM> can couple to one (or more) of loads <NUM> from the electrical distribution system <NUM> of <FIG>. Current variance arc fault detector <NUM> includes an ammeter <NUM> coupled in series between terminals <NUM> and <NUM>. Ammeter <NUM> is arranged to measure a current flowing between terminals <NUM> and <NUM>. Thus, for example, ammeter <NUM> can measure a current flowing between a source and a load in an electrical distribution system, such as, current flowing between battery <NUM> and one (or more) of loads <NUM>. In some examples, ammeter <NUM> could be a hall effect sensor, a shunt, or a rogowski coil. However, other current sensor devices and/or circuits arranged to measure a current could be implemented without departing from the scope of the claimed subject matter.

Current variance arc detector <NUM> further includes processor <NUM> and a memory <NUM>. Processor <NUM> can be any of a variety of processors, such as, for example, a microprocessor, a general-purpose processor, an application specific integrated circuit, or a field programmable gate array. Memory <NUM> can be any of a variety of computer-readable mediums arranged to store, in a non-transitory manner, instructions <NUM>, machine learning model <NUM>, and a variance threshold <NUM>.

Instructions <NUM> can comprise instructions, executable by processor <NUM>, which when executed by processor <NUM> cause current variance arc fault detector <NUM> to implement any of a variety of actions as described herein. Instructions <NUM> can be firmware for current variance arc fault detector <NUM> arranged to enable current variance arc fault detector <NUM> to detect an arc fault as detailed herein.

Machine learning model <NUM> can be a machine-leaning model, executable by processor <NUM>, to cause current variance arc fault detector <NUM> to detect an arc fault as detailed herein. For example, machine learning model <NUM> could be a neural network, a fuzzy logic model, convolutional network, or other such model trained to detect arc faults as detailed herein.

During operation, processor <NUM>, in executing instructions <NUM>, can cause ammeter <NUM> to repeatedly measure current flowing between terminals <NUM> and <NUM>. In some examples, processor <NUM>, in executing instructions <NUM>, can cause ammeter <NUM> to periodically measure current flowing between terminals <NUM> and <NUM>. As a specific example, processor <NUM>, in executing instructions <NUM>, can cause ammeter <NUM> to measure current flowing between terminals <NUM> and <NUM> at a sampling rate between <NUM> and <NUM> mega-samples per second (MS/s). In some implementations, processor <NUM>, in executing instructions <NUM>, can cause ammeter <NUM> to measure current flowing between terminals <NUM> and <NUM> at a sampling rate of <NUM>/s. Processor <NUM>, in executing instructions <NUM>, can store, in memory <NUM>, the repeatedly (or periodically) measured current values as sampled current values <NUM>.

Processor <NUM>, in executing instructions <NUM>, can identify and remove outliers from the sampled current values. With some implementations, processor <NUM>, in executing instructions <NUM>, identifies and removes outliers from a portion of the sampled current values, such as, a range of recent current values (e.g., <NUM> to <NUM>,<NUM>, or the like). In a specific example, processor <NUM>, in executing instructions <NUM>, can process the most recent <NUM>,<NUM> samples from sampled current values and can identify and remove outliers from the <NUM>,<NUM> most recent samples. In some embodiments, outliers are identified and removed based on a confidence interval of twice the standard deviation within the data set (e.g., most recent <NUM>,<NUM> sampled current values <NUM>, or the like). The processor <NUM>, in executing instructions <NUM>, can store, in memory <NUM>, the subset of current values with outliers removed as processed current values <NUM>.

In some examples, processor <NUM>, in executing instructions <NUM>, can prefilter sampled current values <NUM>. Processor <NUM>, in executing instructions <NUM> could apply filtering, such as, high pass filtering, to sampled current values <NUM> before and/or in conjunction with identifying and removing outliers as described above. With a specific example, processor <NUM>, in executing instructions <NUM> could apply a high pass filter with a cut-off frequency of between <NUM> kilo Hertz (kHz) and <NUM> to sampled current values <NUM>.

Processor <NUM>, in executing instructions <NUM>, can derive, calculate, or determine, a variance of the processed current values <NUM> and can store, in memory <NUM>, the derived variance as variance <NUM>. Processor <NUM>, in executing instructions <NUM>, can derive the variance as the sum of the squared distances between each value and the mean of the values, or: <MAT> where X is the values of processed current values <NUM>, µ is the mean of the processed current values <NUM>, and N is the number of values in the set of processed current values <NUM>. Processor <NUM>, in executing instructions <NUM>, can determine whether the variance <NUM> exceeds variance threshold <NUM>. Processor <NUM>, in executing instructions <NUM>, increment a variance interval <NUM> based on a determination that the variance <NUM> exceeds the variance threshold <NUM>. In some examples, variance interval <NUM> is incremented <NUM> each cycle that processor <NUM> determines the variance <NUM> exceeds the variance threshold <NUM>. Processor <NUM>, in executing instructions <NUM>, can reset variance interval <NUM> to zero based on a determination that that variance <NUM> does not exceed the variance threshold <NUM>. With some examples, the variance threshold <NUM> can be a maximum level of the variance observed during normal operation of the monitored load branch where arcing is not present. Said differently, the variance threshold <NUM> may be determined in advance and programmed based on the type and/or specific circuit to be monitored for arcing.

Processor <NUM>, in executing machine learning model <NUM>, can determine whether an arc fault exists in the electrical distribution line corresponding to terminals <NUM> and <NUM> based on the variance interval <NUM>. For example, processor <NUM>, in executing machine learning model <NUM>, can detect an arc fault between battery <NUM> and one of loads <NUM> (or between load(s) <NUM> and ground) based on the determined variance interval <NUM>. This is described in greater detail below, for example, with reference to <FIG> and <FIG>.

Turning more particularly to <FIG>, the current variance arc fault detector <NUM> is depicted. Current variance arc fault detector <NUM> includes terminals <NUM> and <NUM>, which can couple to a source and load, respectively, in an electrical distribution system. For example, terminal <NUM> could couple to battery <NUM> while terminal <NUM> can couple to one (or more) of loads <NUM> from the electrical distribution system <NUM> of <FIG>. Current variance arc fault detector <NUM> includes an ammeter <NUM> coupled in series between terminals <NUM> and <NUM>. Ammeter <NUM> is arranged to measure a current flowing between terminals <NUM> and <NUM>. Thus, ammeter <NUM> can measure a current flowing between a source and a load in an electrical distribution system, such as, current flowing between battery <NUM> and one (or more) of loads <NUM>. In some examples, ammeter <NUM> could be a hall effect sensor, a shunt, or a rogowski coil.

Current variance arc fault detector <NUM> additionally includes an accumulator <NUM>, an outlier removal circuit <NUM>, a variance derivation circuit <NUM>, a variance interval circuit <NUM> and a filter <NUM>. Furthermore, current variance arc fault detector <NUM> could include a memory (e.g., registers, flash, random access memory, or the like) arranged to store values as described herein. However, memory is not depicted within this figure for purposes of convenience.

Accumulator <NUM> is arranged to accumulate sampled current values <NUM>. Sampled current values <NUM> can be current values measured by ammeter <NUM> on a repeating basis. The repeating basis can be periodic. For example, accumulator <NUM> can be arranged to store measured current values at a sampling rate of between <NUM> and <NUM> mega-samples per second (MS/s). In some implementations, accumulator <NUM> can be arranged to store measured current values at a sampling rate of <NUM>/s. Accumulator <NUM> can store the measured current values as sampled current values <NUM>. With some examples, accumulator <NUM> can be arranged to store a specified number of measured current values, such as, for example, <NUM> to <NUM>,<NUM>. In a specific example, accumulator <NUM> can store <NUM>,<NUM> measured current values as sampled current values <NUM>. Accumulator <NUM> could be arranged with registers or a buffer to store measured current values. For example, accumulator <NUM> could include a first in first out (FIFO) buffer arranged to store the most recent (e.g., <NUM>,<NUM>, or the like) current values measured by ammeter <NUM>.

Current variance arc fault detector <NUM> could optionally include a high-pass filter <NUM> arranged between ammeter <NUM> and accumulator <NUM>. High-pass filter <NUM> could be, for example, a butterworth filter (or the like) arranged to with a cut-off frequency of between <NUM> and <NUM>. High-pass filter <NUM> could be implemented to filter raw current values measured by ammeter <NUM> and only pass current values above the cut-off frequency to accumulator <NUM>.

Outlier removal circuit <NUM> is arranged to identify and remove outliers from sampled current values <NUM>. In some embodiments, outlier removal circuit <NUM> removes outliers based on a confidence interval of twice the standard deviation within the data set (e.g., sampled current values <NUM>) and stores (e.g., in memory, or the like) the sampled current values with outliers removed as processed current values <NUM>. In some examples, outlier removal circuit <NUM> can be a logical circuit comprised of gates, transistors, and/or registers arranged to average sampled current values and remove ones of the sampled current values outside a defined limit of the average. As another example, outlier removal circuit can <NUM> be an FPGA arranged to generate processed current values <NUM> from sampled current values <NUM> as discussed above.

Variance derivation circuit <NUM> can derive, calculate, or determine, a variance of the processed current values <NUM> and can store the derived variance as variance <NUM>. Variance derivation circuit <NUM> can derive the variance as the sum of the squared distances between each value and the mean of the values, or: <MAT>
where X is the values of processed current values <NUM>, µ is the mean of the processed current values <NUM>, and N is the number of values in the set of processed current values <NUM>. In some examples, variance derivation circuit <NUM> can be a logical circuit comprised of gates, transistors, and/or registers arranged to determine a variance between the processed current values and store (e.g., in memory, or the like) the derived variance as variance <NUM>. As another example, variance deviation circuit can <NUM> be an FPGA arranged to generate variance <NUM> from processed current values <NUM> as discussed above.

Variance interval circuit <NUM> can increment a variance interval <NUM> based on a determination that the variance <NUM> exceeds a variance threshold <NUM>. In some examples, variance interval <NUM> is incremented <NUM> each cycle that variance interval circuit <NUM> determines variance <NUM> exceeds variance threshold <NUM>. Additionally, variance interval circuit <NUM> can reset variance interval <NUM> to zero based on a determination that variance <NUM> does not exceeds variance threshold <NUM>. With some examples, the variance threshold <NUM> can be a maximum level of the variance observed during normal operation of the monitored load branch where arcing is not present. Said differently, the variance threshold <NUM> may be determined in advance and programmed based on the type and/or specific circuit to be monitored for arcing.

Filter <NUM> can output arc detection signal <NUM> based on an input of variance interval <NUM>. In some examples, filter <NUM> can be a machine learning model (e.g., neural network, convolutional network, fuzzy logic model, or the like) arranged to operate as a low pass filter. As another example, filter <NUM> can be a low pass filter tuned to output arc detection signal <NUM> from variance interval <NUM>.

<FIG> depicts a logic flow <NUM>, which can be implemented by the current variance arc fault detectors described herein. For example, logic flow <NUM> can be implemented by current variance arc fault detector <NUM>, current variance arc fault detector <NUM>, current variance arc fault detector <NUM> and/or current variance arc fault detector <NUM>. Additionally, logic flow <NUM> could be implemented by a current variance arc fault detector different than those described herein without departing from the sprit and scope of the disclosure.

Logic flow <NUM> is described with reference to current variance arc fault detector <NUM> and additionally with reference to <FIG> depict waveforms, or time curves, of several signals which may be measured and/or determined according to the present disclosure. For example, <FIG> depicts a time curve <NUM> of measured load current, <FIG> depicts a time curve <NUM> of derived variance, <FIG> depicts a time curve <NUM> of a determined variance interval, and <FIG> depicts a time curve <NUM> of an arc detection signal.

Logic flow <NUM> may begin with block <NUM>. At block <NUM> "periodically measure current values between a source and a load" current values between a source and a load are periodically measured. For example, processor <NUM>, in executing instructions <NUM>, can cause ammeter <NUM> to periodically measure current values between a source (e.g., battery <NUM>) and a load (e.g., load <NUM>). These periodically measured current values can be stored as sampled current values <NUM>. <FIG> depicts an example time curve <NUM> illustrating periodically measured current values <NUM> between the source and the load.

Continuing to block <NUM> "derive a variance of the periodically measured current values" a variance of the periodically measured current values can be derived. For example, processor <NUM>, in executing instructions <NUM>, can derive a variance of the sampled current values, <NUM>, or the values measured at block <NUM>. <FIG> depicts an example time curve <NUM> illustrating the derived variance <NUM> of the periodically measured current values <NUM> between the source and the load.

Continuing to decision block <NUM> "is the variance greater than a threshold variance?" it can be determined whether the variance is greater than a threshold variance. For example, processor <NUM>, in executing instructions <NUM>, can determine whether the variance derived at block <NUM> is greater than a threshold variance value. Logic flow <NUM> can continue from decision block <NUM> to either block <NUM> or block <NUM>. At block <NUM> "increment a variance interval" processor <NUM>, in executing instructions <NUM>, can increment a variance interval based on a determination that the derived variance is greater than the threshold variance. Conversely, at block <NUM> "reset a variance interval" processor <NUM>, in executing instructions <NUM>, can reset the variance interval based on a determination that the derived variance is not greater than the variance threshold. <FIG> depicts an example time curve <NUM> illustrating a variance interval signal <NUM> corresponding to an incremented variance interval <NUM>, which is reset where the derived variance is not greater than a threshold. As depicted, the variance interval signal <NUM> has the appearance of a "saw tooth" signal where the "teeth" of the signal may be larger in the presence of arcing.

Logic flow <NUM> continues from both block <NUM> and block <NUM> to block <NUM> "detect an arc between the source and the load based on the variance interval" an arc fault can be detected based on the variance interval. Processor <NUM>, in executing machine learning model <NUM>, can detect an arc fault based on the variance interval. <FIG> depicts an example time curve <NUM> illustrating an arc fault signal <NUM> generated from the variance interval <NUM>. Said differently, the time curve <NUM> can correspond to a signal generated by inputting the variance interval <NUM> of the time curve <NUM> into a machine learning model (e.g., a neural network, or the like) to detect the presence of arcing based on the derived variance and an incremented variance interval as detailed herein.

Logic flow <NUM> may further include blocks to extinguish the detected arc. For example, logic flow <NUM> could include a block to cause a switch or arc protection device to open thereby shunting the flow of current and extinguishing the detected arc.

<FIG> depicts an example machine learning model <NUM> according to the present disclosure. The machine learning model <NUM> can be implemented as the machine learning model <NUM>, the filter <NUM>, or the like. That is, the machine learning model <NUM> can be arranged to detect or output a signal to detect an arc fault based on a variance interval (e.g., variance interval <NUM>, variance interval <NUM>, or the like. ) The machine learning model <NUM> depicted in this figure is a recurrent neural network having a single hidden layer. It is noted, that the machine learning model <NUM> could be a different type of neural network or could have a different arrangement, such as, different number of hidden layer or different number of nodes, etc. In general, the machine learning model <NUM> takes as input a variance interval <NUM> and outputs a fault signal <NUM>. The input <NUM> is processed at nodes <NUM> connected via connections <NUM>. At each connection <NUM>, the input to the connection is scaled by a weight (W) and then processed by an activation function at the node. A number of activation functions can be utilized, and an exhaustive list is not provided here. Furthermore, recurrence of prior inputs is accounted for by the loops <NUM>. Accordingly, inputs <NUM> are input to the machine learning model <NUM>. For example, let X represent a time series of variance intervals X for times T. The variance intervals <NUM> Xt can be fed into the machine learning model <NUM>, which is scaled by connections <NUM>-<NUM> and <NUM>-<NUM> and then processed by nodes <NUM>-<NUM> and <NUM>-<NUM>. Nodes <NUM>-<NUM> and <NUM>-<NUM> also receive as input the output from each respective node at the prior time (e.g., output of the node resulting from input Xt-<NUM>). The output from nodes <NUM>-<NUM> and <NUM>-<NUM> are scaled by connections <NUM>-<NUM> and <NUM>-<NUM> and then processed by node <NUM>-<NUM>. Th output from node <NUM>-<NUM> is the fault signal <NUM>.

The weights W at which each connection <NUM> scales is learned during a training phase of the machine learning model <NUM>. The machine learning model <NUM> may be trained, for example, using conventional machine learning training techniques from a data set including variance interval signals corresponding to known arc faults, or the like. Said differently, the machine learning model <NUM> can be iterated until the weights in the nodes converge upon an acceptable solution to detect arc flashing as detailed herein. As such, the machine learning model <NUM> may be "trained" to detect (or output a fault signal <NUM>) indicating the presence of an arc fault based on variance interval <NUM> signals during operation of a current variance arc fault detector as detailed herein.

<FIG> illustrates an embodiment of a storage medium <NUM>. The storage medium <NUM> may comprise an article of manufacture. In some examples, the storage medium <NUM> may include any non-transitory computer readable medium or machine readable medium, such as an optical, magnetic or semiconductor storage. The storage medium <NUM> may store various types of processor executable instructions or machine learning models <NUM>. For example, storage medium <NUM> can be coupled to processor(s) described herein (e.g., processor <NUM>, etc.) while such processor(s) can be arranged to execute instructions and/or machine learning models. As an example, the storage medium <NUM> may store various types of computer executable instructions to implement logic flow <NUM>. As another example, the storage medium <NUM> may store a description or values representative of the machine learning model <NUM>. As another example, the storage medium <NUM> may store a description or values representative of the filter <NUM> (e.g., connection weights, nodes, activations functions, etc.) As another example, the storage medium <NUM> may store a description or values representative of the machine learning model <NUM>. Additionally, the storage medium <NUM> may store computer executable instructions to execute the machine learning models or filter.

Examples of a computer readable or machine-readable storage medium may include any tangible media capable of storing electronic data, including volatile memory or non-volatile memory, removable or non-removable memory, erasable or non-erasable memory, writeable or re-writeable memory, and so forth. Examples of computer executable instructions may include any suitable type of code, such as source code, compiled code, interpreted code, executable code, static code, dynamic code, object-oriented code, visual code, and the like. The examples are not limited in this context.

Claim 1:
An apparatus comprising:
a processor (<NUM>); and
memory (<NUM>) comprising instructions, which when executed by the processor (<NUM>) cause the processor to:
receive, from a current sensor, a plurality of sensed current values;
wherein the plurality of sensed current values is received at a sampling rate determined by the processor (<NUM>);
further characterized by causing the processor to:
remove at least one outlier from a range of recent current values of the plurality of sensed current values, the range being selected by the processor (<NUM>), and the removal being based on a standard deviation across the range of recent current values; derive a variance between the plurality of sensed current values;
determine whether the variance is greater than a threshold value and, if yes, increment a variance interval,
or, if no, reset a variance interval;
and
detect an arc between based on the variance interval.