Spectrum reduction of AC current and voltage in telecommunication circuit breakers

Abstract

A circuit interruption device provides a controller and method thereof, the controller being programmed to receive a plurality of data sequences, each data sequence comprising a set of data points corresponding to a waveform of a line current flowing through the circuit interruption device. The controller performs spectrum reduction on the plurality of data sequences using a finite series transform to reduce bandwidth requirements, and transmits spectrum reduced versions of the plurality of data sequences to an external system using the reduced bandwidth. The data can then be processed and converted into metadata for monitoring and tracking purposes.

Description

Technical Field

[0001] The present disclosure relates to electronic circuit breakers, and more particularly to communicating large amounts of information regarding operational aspects of electronic circuit breakers in real time by using spectrum reduction to reduce required bandwidth. Background Art

[0002] Modern electronic circuit breakers and other circuit interrupting devices have controllers and other electronic components that provide a variety of functions not available in older purely thermal-mechanical circuit breakers. The controller provides the electronic circuit breaker with the ability to detect and differentiate between, for example, ground faults (a conductive path formed between an ungrounded current-carrying conductor and the earth) and arc faults (an intermittent fault that can be caused by insulation wear or damage), loose connections, conductor breaks, etc. The circuit breaker can detect such faults by using the controller to constantly obtain samples of the line current and / or voltage and monitoring the samples to determine whether the current / voltage exceeds a predefined threshold.

[0003] The electronic circuit breakers described above can be installed in both residential and commercial settings. Each circuit breaker protects a specific circuit in a house or building from overcurrent and short-circuit current. The circuits protected by circuit breakers are often referred to as branch circuits. Circuit breakers are most often installed in slots in one or more distribution panels, one circuit breaker per slot. One or more distribution panels serve as load centers to distribute power from the AC mains throughout a house or building. Load centers are most often located in low-traffic areas such as a garage or basement in a house or utility closets in a building.

[0004] While many advances have been made in the field of electronic circuit breakers, it is readily recognized that there is a need for continued improvement. Summary of the invention

[0005] The present disclosure relates to a circuit interruption device having a controller and a method thereof, the controller being programmed to receive a plurality of data sequences, each data sequence comprising a set of data points corresponding to a waveform of a line current flowing through the circuit interruption device. The controller performs spectral reduction on the plurality of data sequences using a finite series transform to reduce bandwidth requirements, and transmits spectrally reduced versions of the plurality of data sequences to an external system using the reduced bandwidth. The data may then be processed and converted into metadata for monitoring and tracking purposes.

[0006] In general, in one aspect, the present disclosure relates to a method of communicating high-resolution information from a circuit interruption device to an external system. The method includes, among other things, receiving a plurality of data sequences at a controller of the circuit interruption device, each data sequence comprising a set of data points corresponding to a waveform of a line current flowing through the circuit interruption device. The method also includes performing spectral reduction on the plurality of data sequences at the controller using a finite series transform, and transmitting spectrally reduced versions of the plurality of data sequences to the external system.

[0007] According to one or more embodiments, spectral reduction is performed on multiple data sequences by: performing a first transformation on each of the multiple data sequences at a controller using a finite series transform to generate a plurality of waveform vectors, each waveform vector corresponding to a corresponding data sequence and containing a set of first spectral coefficients, each coefficient in the set of first spectral coefficients corresponding to a data point in the corresponding data sequence, and reducing each of the multiple waveform vectors at the controller to generate a plurality of spectrally significant waveform vectors, each spectrally significant waveform vector corresponding to a corresponding waveform vector and containing only spectrally significant first spectral coefficients from the corresponding waveform vector.

[0008] According to one or more embodiments, spectral reduction is performed on multiple data sequences by: performing a second transformation on each of a plurality of spectrally significant waveform vectors using a finite series transformation at a controller to generate a plurality of grouped vectors, each grouped vector containing a set of second spectral coefficients, each coefficient in the set of second spectral coefficients corresponding to a spectrally significant first spectral coefficient of a different spectrally significant waveform vector from the plurality of spectrally significant waveform vectors, and reducing each of the plurality of grouped vectors at the controller to generate a plurality of spectrally significant grouped vectors, each spectrally significant grouped vector corresponding to a corresponding grouped vector and containing only spectrally significant second spectral coefficients from the corresponding grouped vector.

[0009] According to one or more embodiments, reducing each of the plurality of waveform vectors at a controller to generate a plurality of spectrally significant waveform vectors includes determining an optimal number of spectrally significant first spectral coefficients from a set of first spectral coefficients in each waveform vector. According to one or more embodiments, reducing each of the plurality of grouped vectors at a controller to generate a plurality of spectrally significant grouped vectors includes determining an optimal number of spectrally significant second spectral coefficients from a set of second spectral coefficients in the grouped vectors.

[0010] According to one or more embodiments, determining at a controller an optimal number of spectrally significant waveform vectors includes determining whether a spectral ratio of a Euclidean distance for an optimal number of spectrally significant first spectral coefficients to a Euclidean distance for a set of first spectral coefficients in each waveform vector meets a quality of service (QoS) requirement. According to one or more embodiments, determining an optimal number of spectrally significant second spectral coefficients includes determining whether a spectral ratio of a Euclidean distance for an optimal number of spectrally significant second spectral coefficients to a Euclidean distance for a set of second spectral coefficients in each waveform vector meets a quality of service (QoS) requirement, a spectral ratio.

[0011] According to one or more embodiments, a plurality of spectrally significant grouping vectors are inserted into a data packet, the finite series transform is a discrete cosine transform, and / or a plurality of data sequences are transmitted over a wireless connection.

[0012] In general, in another aspect, the present disclosure is directed to a circuit interrupting device that includes, among other things, a controller programmed to detect a fault condition in a branch circuit connected to the circuit interrupting device, and a trip unit coupled to communicate with the controller and operable to interrupt current flowing through the circuit interrupting device in response to receiving a trip signal from the controller. The controller is further programmed to receive a plurality of data sequences, each data sequence comprising a set of data points corresponding to a waveform of a line current flowing through the circuit interrupting device, perform spectral reduction on the plurality of data sequences using a finite series transform, and transmit spectrally reduced versions of the plurality of data sequences to an external system.

[0013] According to one or more embodiments, the controller is also programmed to: perform a first transformation on each of a plurality of data sequences at the controller using a finite series transformation to generate a plurality of waveform vectors, each waveform vector corresponding to a corresponding data sequence and containing a set of first spectral coefficients, each coefficient in the set of first spectral coefficients corresponding to a data point in the corresponding data sequence, and reduce each of the plurality of waveform vectors at the controller to generate a plurality of spectrally significant waveform vectors, each spectrally significant waveform vector corresponding to a corresponding waveform vector and containing only spectrally significant first spectral coefficients from the corresponding waveform vector.

[0014] According to one or more embodiments, the controller is also programmed to: perform a second transformation using a finite series transformation on each of a plurality of spectrally significant waveform vectors at the controller to generate a plurality of grouped vectors, each grouped vector containing a set of second spectral coefficients, each coefficient in the set of second spectral coefficients corresponding to a spectrally significant first spectral coefficient of a different spectrally significant waveform vector from the plurality of spectrally significant waveform vectors, and reduce each of the plurality of grouped vectors at the controller to generate a plurality of spectrally significant grouped vectors, each spectrally significant grouped vector corresponding to a corresponding grouped vector and containing only spectrally significant second spectral coefficients from the corresponding grouped vector.

[0015] According to one or more embodiments, the controller is further programmed to reduce each of the plurality of waveform vectors by determining an optimal number of spectrally significant first spectral coefficients from the set of first spectral coefficients in each waveform vector to produce a plurality of spectrally significant waveform vectors. According to one or more embodiments, the controller is further programmed to reduce each of the plurality of grouped vectors by determining an optimal number of spectrally significant second spectral coefficients from the set of second spectral coefficients in the grouped vector to produce a plurality of spectrally significant grouped vectors.

[0016] According to one or more embodiments, the controller is further programmed to determine an optimal number of spectrally significant waveform vectors by determining whether a spectral ratio of a Euclidean distance for an optimal number of spectrally significant first spectral coefficients to a Euclidean distance for a set of first spectral coefficients in each waveform vector satisfies a quality of service (QoS) requirement. According to one or more embodiments, the controller is further programmed to determine an optimal number of spectrally significant second spectral coefficients by determining whether a spectral ratio of a Euclidean distance for an optimal number of spectrally significant second spectral coefficients to a Euclidean distance for a set of second spectral coefficients in each waveform vector satisfies a quality of service (QoS) requirement.

[0017] According to one or more embodiments, the controller is further programmed to insert a plurality of spectrally significant grouping vectors into the data packet, and / or the finite series transform is a discrete cosine transform.

[0018] In general, in yet another aspect, the present disclosure relates to a non-transitory computer-readable medium containing program logic that, when executed by operation of one or more computer processors, performs operations including: receiving a plurality of data sequences at a controller of a circuit interruption device, each data sequence comprising a set of data points corresponding to a waveform of a line current flowing through the circuit interruption device, performing spectral reduction on the plurality of data sequences using a finite series transform at the controller, and transmitting spectrally reduced versions of the plurality of data sequences to an external system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] A more detailed description of the disclosure briefly summarized above can be obtained by reference to various embodiments, some of which are shown in the accompanying drawings. Although the drawings illustrate selected embodiments of the disclosure, these drawings should not be considered limiting of its scope, as the disclosure may admit to other equally effective embodiments.

[0020] Figure 1 is a schematic diagram illustrating an exemplary load center utilizing a circuit breaker according to an embodiment of the present disclosure;

[0021] Figure 2 is a block diagram illustrating an exemplary circuit breaker that may perform spectrum reduction according to an embodiment of the present disclosure;

[0022] Figure 3 is a flow chart illustrating an exemplary spectrum reduction process for a circuit breaker according to an embodiment of the present disclosure;

[0023] Figure 4 is a diagram illustrating an embodiment of the present disclosure Figure 3 A flowchart of a portion of an exemplary spectrum reduction process;

[0024] Figure 5 is a diagram illustrating an embodiment of the present disclosure Figure 3 A flowchart of another portion of an exemplary spectrum reduction process;

[0025] Figure 6 is a block diagram illustrating an exemplary data packet for transmitting data that has been reduced according to an embodiment of the present disclosure;

[0026] Figure 7 is a flow chart illustrating an exemplary spectrum reconstruction process for a circuit breaker according to an embodiment of the present disclosure;

[0027] Figure 8 is a sequence diagram illustrating exemplary communications between a circuit breaker, an external system, and an end user according to an embodiment of the present disclosure; and

[0028] Figures 9A-9B is an exemplary waveform diagram illustrating the efficacy of a spectrum reduction process according to an embodiment of the present disclosure.

[0029] Wherever possible, identical reference numerals are used to designate identical elements that are common to the figures. However, elements disclosed in one embodiment may be beneficially utilized on other embodiments without specific recitation. DETAILED DESCRIPTION

[0030] This description and the accompanying drawings illustrate exemplary embodiments of the present disclosure and should not be considered as limiting, and the claims define the scope of the present disclosure, including equivalents. Without departing from the scope of this description and the claims, various mechanical, compositional, structural, electrical and operational changes, including equivalents, may be made. In some cases, well-known structures and techniques are not shown or described in detail, so as not to confuse the present disclosure. In addition, as long as feasible, the elements described in detail with reference to an embodiment and their associated aspects may be included in other embodiments in which they are not specifically shown or described. For example, if an element is described in detail with reference to an embodiment and the element is not described with reference to a second embodiment, the element can still be claimed to be included in the second embodiment.

[0031] It is noted that, as used in this specification and the appended claims, the singular forms "a," "an," and "the," as well as any singular use of any word, include plural referents unless expressly and unambiguously limited to one referent. As used herein, the term "include" and its grammatical variations are intended to be non-limiting, such that recitation of items in a list is not to the exclusion of other like items that can be substituted or added to the listed items.

[0032] At a high level, embodiments of the present disclosure provide systems and methods for implementing communication circuit breakers and other circuit interruption or circuit protection devices that can perform spectral reduction on data acquired by the circuit breaker. The circuit breaker acquires data by sampling the line current passing through the circuit breaker at a high sampling rate. The circuit breaker then performs spectral reduction by applying a finite series transform that converts the data from the time domain to the frequency domain. In some embodiments, the finite series transform used is a discrete cosine transform (DCT), which produces a set of frequency domain coefficients for a set of time domain data. Of course, other finite series transforms that operate similarly to the DCT can be used. The DCT has the characteristic that the coefficients containing the most "spectral energy" are grouped near the low-frequency end of the spectrum. These DCT coefficients are considered to be "spectrally significant" because they greatly affect the accurate reconstruction of the data. The circuit breaker retains these spectrally significant DCT coefficients based on quality of service (QoS) requirements and discards the remaining coefficients.

[0033] A second application of DCT is then performed on the spectrally significant coefficients to produce a second set of DCT coefficients, wherein the spectrally significant coefficients are again clustered near the front end of the spectrum. The circuit breaker again retains only the spectrally significant coefficients from the second set of DCT coefficients based on the QoS requirements. The retained spectrally significant coefficients are then packaged and transmitted to an external system. At the external system, the spectrum reduction process is reversely performed using an inverse finite series transform to reconstruct the waveform within the QoS requirements. In this way, the circuit breaker can communicate high-resolution information about the line current, such as metering and usage information, by transmitting only a small number of spectrally significant DCT coefficients. The above arrangement uses a minimum amount of power and bandwidth (i.e., transmission capacity) to achieve the transmission of a large amount of data to an external system in real time. The above use of a minimum amount of bandwidth (i.e., transmission capacity) is particularly advantageous in the case where the circuit breaker transmits data via Wi-Fi, Bluetooth or other bandwidth-limited connections. Several such circuit breakers can then be installed at the load center to provide home and building owners with real-time data for energy usage tracking and management purposes.

[0034] Reference now Figure 1, a schematic diagram of an exemplary load center 100 according to some embodiments of the present disclosure is shown. The load center 100 can be a residential load center designed with current capacity suitable for use in homes and other residential environments, or it can be a commercial load center designed with higher current capacity suitable for use in office buildings and other commercial environments. In either case, the load center 100 is equipped with networking capabilities that allow the load center to connect to a network 104 via a network connection 102. The network 104 in turn provides the load center 100 with access to one or more local network services 106 and cloud or Internet-based services 108 (e.g., data storage, data processing, data analysis, etc.). Examples of suitable load centers that can be used as load center 100 include the EcoStruxure TM Series of intelligent load centers.

[0035] A plurality of telecommunication circuit breakers 110 (only three are shown for economy) are installed in the load center 100 to provide overcurrent and short circuit protection. These electronic circuit breakers 110 have a standard form factor that allows them to be installed in a typical circuit breaker slot of a typical distribution board (not explicitly shown). The circuit breakers 110 can be of any available size, including molded-case circuit breakers (MCCBs), miniature circuit breakers (MCBs), and other sizes, and can include any available fault detection functions, such as arc fault detection, ground fault detection, combined arc-ground fault detection, and other functions.

[0036] According to an embodiment of the present disclosure, one or more (or all) of the circuit breakers 110 are electronic circuit breakers that can communicate information about various operational aspects of the circuit breakers to an external system. The operational information may include, for example, whether the circuit breaker 110 is on / off, tripped, reset, locked, etc. Each of the one or more circuit breakers 110 may also monitor the current on the protected line to find the presence of a broadband signal that may indicate an arc fault or other fault. The circuit breaker 110 may then provide the monitoring results to an external system, such as an aggregator 112 within the load center 100. Similarly, the circuit breaker 110 may provide data for deriving current usage (i.e., metering) information and usage type (i.e., context) information about the electrical load monitored by the circuit breaker. The circuit breaker 110 may obtain data for the above information by performing high frequency or high resolution sampling of the current and / or voltage waveforms on the protected line.

[0037] In most embodiments, the circuit breaker 110 provides the above data to the aggregator 112 in the form of data packets. The aggregator 112 collects the data packets from the circuit breaker 110 and forwards them to the network 104. Any aggregator device that can receive and forward data packets can be used as the aggregator 112, including gateways, hubs, routers, edge computing devices, etc. The aggregator 112 can be a discrete component installed in the load center 100 or it can be an integrated function built into one of the switchboards (not explicitly shown) of the load center 100. Various load processing models and machine learning (ML)-based algorithms can be provided in the aggregator 112 for performing load identification, decomposition (i.e., separation), and other functions, especially when the aggregator 112 is an edge computing device. The data can also be further reduced at the edge computing device based on group packaging of data from several circuit breakers 110. Although one aggregator 112 is depicted, there can be multiple aggregators in the load center 100, each of which is assigned to collect and forward data packets for a subset of the circuit breakers 110.

[0038] In some embodiments, the circuit breakers 110 transmit data to the aggregator 112 via a wireless connection 114. Each circuit breaker 110 includes the ability to connect to the aggregator 112 using Bluetooth, Wi-Fi, or other wireless technology. Of course, it is also possible for the circuit breakers 110 to transmit data via a wired connection (such as a twisted pair connection), and / or to forgo the aggregator 112 altogether and send the data directly to the network 104. However, in some embodiments, the use of the aggregator 112 and / or the wireless connection 114 provides greater flexibility in configuring the load center 100 for a particular application or purpose.

[0039] Regardless of which connection technology is used, the circuit breaker 110 is programmed to reduce the amount of data before transmission to the aggregator 112. For example, given the need to adequately sample broadband signals on current and / or voltage waveforms, a sampling rate of 20ksps to 40ksps (thousand samples per second) is typically required. Similar high sampling rates may be required to accurately capture load transients and obtain high-definition shapes of waveforms during steady-state operation. If sampled at 20ksps or higher, the AC current itself can also provide a wealth of information for load detection and decomposition purposes. Similarly, if sampled at 20ksps or higher, the AC voltage can provide accurate estimates of active and reactive power. However, transmitting such high-resolution samples can require up to 3.375GB / day or more of bandwidth (20ksps x 2 bytes / sample x 3600 seconds / hour x 24 hours / day = 3.375GB / day) and associated power per circuit breaker.

[0040] Thus, the circuit breaker 110 includes the ability to reduce the required bandwidth by performing the aforementioned spectrum reduction on the high resolution samples to reduce the amount of data transmitted to the aggregator 112. As will be explained, the efficient spectrum reduction process used in the circuit breaker 110 can reduce the amount of data transmitted from approximately 3.375 GB / day of data per circuit breaker 110 to in some cases hundreds or tens of MB / day or less, depending on the QoS requirements. This reduction in the amount of data transmitted to the aggregator 112 also significantly reduces the amount of power required to transmit the data, whether wirelessly or conducted.

[0041] The reduced amount of data additionally reduces the amount of processing required at the aggregator 112. Specifically, significantly less memory and processing time are required at the aggregator 112 to process the data, thereby freeing up aggregator resources for other purposes, such as data analysis. This opens up other options for the type of equipment that can be used as the aggregator 112. For example, edge appliances and edge computing devices can be used to both receive and analyze data, rather than sending the analysis to one of the cloud services 108. If a cloud service 108 is required, then a similar reduction can be achieved in the required cloud storage and processing services. This reduction can significantly reduce transaction costs and other costs associated with cloud-based data storage and other data storage.

[0042] The reduction in transmitted data also allows each circuit breaker 110 to package samples into smaller data packets for real-time transmission to the aggregator 112. Smaller data packets may be particularly useful as electronic devices are increasingly interconnected in the Internet of Things (IoT). In this IoT environment, circuit breakers and circuit interruption devices are a logical choice to provide users with current metering, non-intrusive load identification, and other context-specific usage information. The smaller data packet format may also aid in the expansion of the IoT environment by allowing faster processing for load identification and decomposition purposes. Metadata (e.g., load information, processing models, operating status, etc.) may also be transmitted in smaller data packets.

[0043] In short, the use of communicating circuit breakers 110 in a load center 100 can provide home and building owners with extensive information about their energy usage in real time. Home / building owners can track and manage overall energy consumption as well as consumption on a per room / area basis and potentially on an individual appliance / equipment basis. Due to the reduction in required bandwidth, alarms and notifications about circuit breaker trips and potential fault conditions can be received in real time from the load center 100. Other advantages and benefits of the load center 100 will be apparent to those skilled in the art.

[0044] Figure 2A block diagram of an exemplary communicating circuit breaker 110 according to some embodiments of the present disclosure is shown. The circuit breaker 110 includes a plurality of functional components or modules, some of which are represented here by separate boxes. Within the scope of the present disclosure, each box can of course be divided into several constituent boxes, or two or more boxes can be combined into a single box. As can be seen, the circuit breaker 110 includes a controller 200, a ground fault sensing circuit 202, an arc fault sensing signal 204, a current sensing circuit 206, and a trip unit 208. The controller 200 itself includes at least one analog-to-digital converter (ADC) 210, a ground fault detection module 212, an arc fault detection module 214, and a wireless interface module 216. In some embodiments, the ADC 210 may also be a separate component from the controller 200. Other functional components and modules not explicitly shown may also be included within the scope of the present disclosure. Likewise, one or more of the explicitly shown functional components and modules may be removed without departing from the present disclosure.

[0045] The operation of the above components and modules is well known in the art, so only a brief description of their specific functions is provided below. In general, the controller 200 receives analog signals from the ground fault sensing circuit 202, the arc fault sensing circuit 204, the current sensing circuit 206, and other sensing circuits. These analog signals, which may include current signals and voltage signals, are sampled by the ADC 210 at a specific frequency, such as 20ksps to 40ksps, or other sufficiently high sampling rates, for load detection and decomposition, and / or for waveform shape detection. The ADC 210 converts each sample into a numerical value proportional to the amplitude of the sample and outputs this value as data. The data is then processed by various modules in the controller 200 in a known manner to detect the occurrence of ground faults, arc faults, short circuits, etc. Upon detecting a fault condition, the controller 200 sends a trip signal to the trip unit 208, which causes the trip unit to immediately cut off further current flowing through the circuit breaker. Any suitable controller may be used as controller 200, including one of the PIC series of microcontrollers from Microchip Technology, an ARM (Advanced RISC Machine) based microcontroller, as well as a digital signal processor (DSP), an ASIC, and the like.

[0046] The controller 200 also transmits the data to the aggregator 112 via the wireless interface 216 for forwarding to the network 104 (see Figure 1). However, prior to transmission, the data is reduced by a spectrum reduction module 218 in the controller 200 that reduces the amount of data transmitted. As previously mentioned, if the data is transmitted in an unreduced form, a sampling rate of 20ksps can generate approximately 3.375GB / day or more of data for each circuit breaker 110. Depending on the QoS requirements, the spectrum reduction module 218 can reduce the amount of data transmitted to a few hundred MB / day for each circuit breaker 110 by performing spectrum reduction on the data. As also previously mentioned, the spectrum reduction module 218 uses a discrete cosine transform (DCT) in some embodiments to achieve spectral reduction of the data. Other suitable finite series transforms, including any discrete Fourier related transforms, may also be used within the scope of the present disclosure.

[0047] The DCT is a generally well-known transform that expresses a sequence of time domain values, such as amplitude samples at points along a waveform, in terms of a sum of frequency domain cosine functions. The following equation (1) shows an example of a 1-D (1-dimensional) DCT, where x(i) is a data point in a sequence of N samples to be transformed, y[k] is the DCT coefficient representing the N samples in the frequency domain, and C k is an arbitrary constant, where for k≠0, C0=1 / √2 and C k =1. See Taylor, F. and The Athena Group, Inc., Mellot J., Hands-On Digital Signal Processing, Gainesville, FL: McGraw Hill, 1998; and Jain, AK, Fundamentals of Digital Image Processing, Englewood Cliffs, NJ: Prentice-Hall, 1989. A characteristic of DCT coefficients is that, for a given current or voltage waveform, most of the useful information in the waveform (i.e., "spectral energy") is concentrated in a small subset of coefficients near the front (i.e., low frequencies) of the spectrum. This allows the remaining (i.e., high frequency) least influential coefficients to be discarded with minimal impact on the ability to accurately reconstruct the waveform or its shape within a given QoS requirement. In the following, the term "waveform" may refer to either a current waveform or a voltage waveform.

[0048]

[0049] Figure 33 is a flow chart illustrating a method 300 that a controller (e.g., controller 200) may use in some embodiments to perform spectral reduction on high-resolution samples of a waveform passing through a circuit breaker (e.g., circuit breaker 110). Generally speaking, spectral reduction is performed by applying a DCT to a series of K sequential waveforms to be transmitted in a data packet, each waveform having N samples. The N samples of each waveform constitute a data sequence for the waveform. For a 60 Hz line current and a 20 ksps sampling rate, the number of samples for each waveform is approximately 336. The value of K is typically the same across all data packets and may vary, for example, in the range of approximately 60 to 240 waveforms (approximately 1 to 4 seconds of sampling). For example, in the example above, each data packet has 240 waveforms, each waveform having 336 data points, indicating that each packet has a total of [336 x 240] (i.e., [N x K]) data points to be transmitted in a reduced form.

[0050] A DCT is applied to a group of [NxK] data points in two stages, a first DCT stage and a second DCT stage performed in a direction orthogonal to the first DCT stage. The first DCT stage is performed on the data points themselves using equation (1) above and results in a [NxK] matrix of first (1-D) coefficients. As discussed further below, most of the spectral energy in this matrix of first (1-D) coefficients is concentrated in a small subset of spectrally significant [LxK] coefficients. A second DCT is then performed on a subset of the spectrally significant first (1-D) coefficients, also using equation (1), producing a [LxK] matrix of second (2-D) coefficients. Also as discussed below, most of the spectral energy in this matrix of second (2-D) coefficients can be found in a small subset of spectrally significant [LxM] coefficients. Method 300 then packages the subset of spectrally significant second (2-D) coefficients into data packets and transmits the data packets to an external system (e.g., aggregator 112).

[0051] More succinctly, given a set of K waveforms, each having N time domain samples, method 300 reduces N->L and K->M, where L≤N and M≤K, and where -> denotes transforming the time domain samples into frequency domain coefficients and reducing the number of frequency domain coefficients.

[0052] refer to Figure 3, the method 300 generally begins at an analog-to-digital block 302, where an analog-to-digital conversion is performed (e.g., by the ADC 210), which converts high-resolution samples of the K waveforms into data, one data point per sample. The analog-to-digital block 302 then groups the data points into data vectors (i.e., a one-dimensional matrix) on a per-waveform basis for each of the K waveforms, with each data vector having N data points. Depending on the fundamental frequency of the line voltage (50 Hz to 60 Hz + / - 10%) and the ADC sampling rate, the value of N can vary between approximately 300 and 440 data points per data vector. The result from the analog-to-digital block 302 is a set of K data vectors, each having [Nx1] data points.

[0053] The method 300 then proceeds to encoding block 304, where the set of K data vectors from the analog-to-digital block 302 is encoded (e.g., by the spectral reduction module 218). The encoding process at encoding block 304 involves applying equation (1) to the set of K data vectors on a per data vector basis to produce a set of K waveform vectors, each waveform vector having [Nx1] 1-D coefficients corresponding to the [Nx1] data points in the corresponding data vector. For each of these waveform vectors, most of the spectral energy is contained in a small subset of [Lx1] spectrally significant coefficients near the front of the spectrum, where L≤N. The encoding process then derives an optimal value for L, which can be applied across each waveform vector, as described later. Figure 4 In some embodiments, the optimal value of L may be in the range of from 16 to 32, reflecting a potential reduction factor of 5 to 12 from the original N data points per waveform (i.e., from 336 data points down to 32 spectrally significant coefficients). The output from encoding block 304 is a spectrally significant waveform vector having [Lx1] spectrally significant 1-D coefficients for each of the K data vectors.

[0054] Each spectrally significant waveform vector from encoding block 304 is then queued in order (e.g., via a first-in, first-out (FIFO) buffer) for K data vectors at block 306. The value of K can be selected as desired and can depend on a given QoS level, which takes into account, among other things, load transition frequencies, such as device on / off and connect / disconnect, etc. As previously mentioned, the value of K can vary between approximately 60 and 240 waveforms (representing 1 to 4 seconds of sampling). At block 306, the output from the queue is a set of K spectrally significant waveform vectors, each vector having [Lx1] spectrally significant 1-D coefficients for a total of [LxK] spectrally significant 1-D coefficients.

[0055] The method 300 then proceeds to the packing block 308, where the [LxK] spectrally significant 1-D coefficients from the queue at block 306 are packed (e.g., by the spectral reduction module 218). The packing process at the packing block 308 involves applying equation (1) to the [LxK] spectrally significant 1-D coefficients to produce a set of L grouping vectors, each grouping vector having [1xK] 2-D coefficients. Note that the grouping vectors operate in a direction orthogonal to the spectrally significant waveform vectors, so that for each grouping vector, each 2-D coefficient in the grouping vector comes from a different spectrally significant waveform vector. And, as previously mentioned, for each grouping vector, most of the spectral energy is contained in a small subset of [1xM] spectrally significant coefficients near the front of the spectrum, where M≤K. The packing process then determines an optimal value of M that can be applied to each grouping vector, as described later. Figure 5 In some embodiments, depending on the QoS requirements, the optimal value of M may be in the range from 1 to 128. The output from the packing block 308 is a set of L spectrally significant grouping vectors, each grouping vector having [1xM] spectrally significant 2-D coefficients for a total of [LxM] spectrally significant 2-D coefficients.

[0056] The packing process then arranges the [LxM] spectrally significant 2-D coefficients in a data packet P. This involves inserting the [LxM] spectrally significant 2-D coefficients and the L, M, and K values ​​into a specific packet format along with other required information. The resulting data packet P[LxM] is thereafter provided to a transmission block 310 for transmission to an external system (e.g., aggregator 112) via a network interface (e.g., network interface 216).

[0057] Figure 4 is a more detailed illustration of some embodiments of the Figure 3 Flowchart of the encoding block 304 of FIG. As can be seen, the encoding block 304 begins with a data sub-block 400 which receives, for each sampled waveform, a time domain data sequence x[n] spanning a period of the waveform. The data sub-block 400 then outputs a data vector of length N for each data sequence x[n] received.

[0058] Next, DCT sub-block 402 receives the data vector for each data sequence x[n] from data sub-block 400 and derives 1-D coefficients y[k] for the data points in the data vector. This is the first of the two DCT stages mentioned above. DCT sub-block 402 can derive the 1-D coefficients y[k] by applying equation (1) to the data points to generate a waveform vector. For a data sequence x[n] containing N data points, DCT sub-block 402 generates an N-length waveform vector having [Nx1] 1-D coefficients y[k], each spectral coefficient y[k] corresponding to a data point in the data sequence.

[0059] Thereafter, the waveform reduction sub-block 404 receives each N-length waveform vector from the DCT sub-block 402 to determine the optimal number L of spectrally significant coefficients in the vector. As previously mentioned, a characteristic of the DCT is that, for a given waveform, most of the "spectral energy" of the waveform is concentrated in a small subset of coefficients near the low end of the spectrum. Based on a given QoS requirement, the remaining coefficients can be discarded as not spectrally significant. In some embodiments, the optimal value of L is determined by calculating the Euclidean distance d of all 1-D coefficients y[k] in each waveform vector, as described in equation (2) below. For context, the Euclidean distance d is proportional to the spectral energy contained in the 1-D coefficient y[k]. The Euclidean distance d' is also calculated for the L spectrally significant coefficients y[k] contained in each waveform vector, as described in equation (3). The optimal value of L can then be determined as the value of the spectral ratio p that results in meeting a given QoS requirement, as described in equation (4). Based on a specific QoS requirement, the spectral ratio p can be in a range between, for example, 0.90 and 0.99. The output from the waveform reduction sub-block 404 is a spectrally significant waveform vector of length L having [L x 1] spectrally significant 1-D coefficients y[k].

[0060]

[0061]

[0062]

[0063] Graph 406 graphically depicts an exemplary waveform 408 for which a data sequence x[n] is received in data subbox 400. In the graph, the horizontal axis shows the data point number and the vertical axis shows the value of each data point. The waveform 408 in this example has a frequency of 60 Hz and uses a sampling rate of 20 ksps to acquire data points, resulting in approximately 336 N data points for the data sequence x[n].

[0064] Graph 410 graphically depicts an exemplary set of L spectrally significant coefficients in a spectrally significant waveform vector. In this graph, the horizontal axis indicates the coefficient number of the L spectrally significant coefficients, and the vertical axis shows the intensity of each coefficient, one of which is labeled 412. As can be seen, in this example, based on a spectral ratio p of 0.99, the optimal value of L is 32. For a different QoS corresponding to, for example, a spectral ratio p of 0.90, the optimal value of L can be reduced to 16 spectrally significant coefficients, as indicated by box 414.

[0065] Figure 5 In some embodiments, Figure 3 Flowchart of the packing block 308 of the embodiment. As shown in this example, the packing block 308 includes a waveform sub-block 500 for receiving a spectrally significant waveform vector of length L. For a given data packet, there are K such spectrally significant waveform vectors, each having [Lx1] spectrally significant 1-D coefficients, for a total of [LxK] spectrally significant 1-D coefficients. For the purpose of efficient processing, the [LxK] spectrally significant 1-D coefficients may be received in matrix form or on a vector-by-vector basis. In the latter case, each of the K spectrally significant waveform vectors is queued in turn until the queue contains the equivalent of a [LxK] matrix of spectrally significant 1-D coefficients.

[0066] Next, the DCT sub-block 502 receives the [LxK] matrix of spectrally significant 1-D coefficients and derives the 2-D coefficients Y for the 1-D coefficients in the [LxK] matrix i [j] This is the second DCT stage mentioned above. DCT sub-block 502 can derive the 2-D coefficients Y i [j], for example, by applying equation (1) in a known manner to the 1-D coefficients y[k] (i.e., Y i [j] = DCT(y[k])). In some embodiments, equation (1) is applied in a direction orthogonal to the K spectrally significant waveform vectors to produce a set of L grouped vectors, each with [1xK] 2-D coefficients Y i [j]. The result is that, for each grouping vector, each 2-D coefficient Y in the grouping vector i [j] corresponds to a different one of the K spectrally significant waveform vectors. The output of the DCT sub-block 502 is a set of L grouped vectors, for a total of [LxK] 2-D coefficients Y i [j], each vector has [1xK] 2-D coefficients Y i [j].

[0067] Thereafter, the group reduction sub-block 504 receives [LxK] 2-D coefficients Y i[j] To determine the optimal number M of spectrally significant 2-D coefficients. In some embodiments, this can be accomplished by using equations (2)-(4) above (or variations thereof) to calculate the total number of 2-D coefficients Y in each grouping vector i [j], then for each grouping vector within the M spectrally significant 2-D coefficients Y i [j] calculates the Euclidean distance. The optimal value of M can then be determined as the value that results in a spectral ratio p that meets a given QoS requirement. In some embodiments, the spectral ratio p can again be in the range between 0.90 and 0.99, depending on the QoS requirements. The result is a set of L spectrally significant grouping vectors, for a total of [LxM] spectrally significant 2-D coefficients Y i [j], each vector has [1xM] spectrally significant 2-D coefficients Y i [j]. [LxM] spectrally significant 2-D coefficients Y i [j] can then be used to reconstruct the original [NxK] data points within a certain QoS level. Thus, by discarding some high frequency content, the number of DCT coefficients required to adequately reconstruct the waveform within a given QoS level can be significantly reduced (i.e., primarily the quantization noise is filtered out).

[0068] The format sub-block 506 receives the [LxM] spectrally significant 2-D coefficients Y from the group reduction sub-block 504. i [j] and put them into data packets P[LxM] according to a specific packet format for transmission. Any suitable packet format known to those skilled in the art may be used for data packets without departing from the scope of the present disclosure. An exemplary data packet P[LxM] is described later in this document. Figure 8 discuss.

[0069] Then the spectrally significant 2-D coefficients Y from the format sub-block 506 are i The data packet P[LxM] of [j] is provided to the overhead sub-block 508 to create packet header information and other information required to reconstruct the original [NxK] data points. Such information may include or be created using the values ​​of K, L, and M discussed above and other data for the data packet P[LxM].

[0070] Graph 510 graphically depicts an exemplary set of K spectrally significant waveform vectors indicated at 512 that may be received at coefficient sub-block 500. In the graph, the horizontal axis shows the coefficient number of the spectrally significant 1-D coefficients, the vertical axis shows the intensity of each coefficient, and the depth axis shows the waveform number for each of the K waveform vectors. For this example, based on a given QoS requirement, there are 240 waveform vectors that are reduced to form each data packet, each waveform vector having 32 spectrally significant 1-D coefficients.

[0071] Graph 514 graphically depicts an exemplary data packet P[LxM] of spectrally significant 2-D coefficients that may be generated at format subblock 506, indicated at 516. In this graph, the horizontal axis shows the coefficient number for the L spectrally significant 2-D coefficients, the vertical axis shows the intensity of each coefficient, and the depth axis shows the coefficient number for each of the M spectrally significant 2-D coefficients. For this example, based on a given QoS requirement, an optimal value for L has been determined to be 32, and an optimal value for M has been determined to be 36. Depending on the QoS requirements, a potential reduction factor of from 5 to 20 times the original K waveform vectors (e.g., from 240 waveform vectors to 36 waveform vectors) may be achieved. The data packet P[LxM] may then be transmitted to an external system using significantly less bandwidth and power than transmitting the data in an unreduced form.

[0072] Table 1 below shows exemplary transmission data volumes for several (13) data packets of an exemplary circuit breaker according to some embodiments. In the table, the P[LxM] column shows the size of each data packet based on the optimal number and data type (single = 4 bytes) of L and M spectral significant coefficients, while the Energy (%) column shows the percentage of the spectral energy content retained by each data packet as a percentage of the original unreduced spectral energy content. For a total sampling interval of 50.5 seconds, each data packet spans a sampling interval of 4 seconds (K = 240 waveforms), except for the last row of data packets, which lasts 2.5 seconds. The optimal value of L is determined to be 32 across data packets, and the optimal value of M varies depending on the waveform samples of each data packet. The total amount of data transferred in 50.5 seconds is 76,625 bytes, which translates to a required bandwidth of 126.1MB / day (76,625 bytes ÷ 50.5 seconds x 3600 seconds / hour x 24 hours / day), which is well below the 3.375GB / day of bandwidth that would be required if the data were transferred without reduction. Note that several factors, including the frequency of transitions (fast or slow transients), can increase or decrease the number of coefficients required for each packet, so the size of the packets transmitted by the circuit breaker can vary.

[0073] Grouping P[L x M] M K(4s) Energy (%), L = 32 byte 1 32x41 single 41 240 84.56％ 5248 2 32x102 single 102 240 90.57％ 13056 3 32x40 single 40 240 99.82％ 5120 4 32x24 single 24 240 99.69％ 3072 5 32x36 single 36 240 99.57％ 4608 6 32x54 single 54 240 99.53％ 6912 7 32x47 single 47 240 99.45％ 6016 8 32x40 single 40 240 99.35％ 5120 9 32x54 single 54 240 99.27％ 6912 10 32x39 single 39 240 99.22％ 4992 11 32x47 single 47 240 99.19％ 6016 12 32x56 single 56 240 99.10％ 7168 13 32x33 single 33 154 99.05％ 4224

[0074] Table 1: Amount of data transmitted per packet

[0075] For reference, an exemplary [LxM] matrix of spectrally significant coefficients is shown below in Table 2, where L extends downward and M extends horizontally. Such a [LxM] matrix may then be included in one of the data packets from Table 1 for transmission by a circuit breaker (or other circuit protection device).

[0076] L x M 1 2 3 4 5 6 … 1 0.328147 0.46899 0.014071 0.189514 -0.50218 -0.285 -0.04128 2 123.3972 9.073149 8.348643 17.23315 2.54667 -9.99246 5.772872 3 -41.1827 -9.63262 -0.82118 6.678512 -1.07536 -5.81981 3.202426 4 -98.8184 -12.86 -9.20525 -16.3541 -3.2456 9.460416 -5.22338 5 -4.09619 -0.47298 -0.04318 -0.30297 0.308367 -0.00681 -0.04213 6 -69.8283 -16.6537 -9.80787 -14.7546 -3.36007 7.763303 -3.96868 7 -40.8684 14.70151 3.888582 -10.8922 1.995416 11.65408 -5.83052 … 51.20016 20.90072 9.773629 11.78581 4.130432 -6.12535 2.356154

[0077] Table 2: Matrix of spectral significance coefficients

[0078] Figure 6 Illustrated is an exemplary data packet 600, which contains data that has been reduced according to the spectrum reduction disclosed herein. It can be seen that the data packet 600 has several data fragments, including a 4-byte packet number 602 and data representing certain information required for reconstructing the data carried by the packet. The information includes the values ​​of L 604, M 606 and K 608, and each fragment requires 2 bytes. It is noted that the ordering of the information shown here is only for illustrative purposes, and different arrangements of information can be used within the scope of the present disclosure. In some embodiments, the data packet 600 can also include a certain number of empty or invalid (null) fragments 610 for filling purposes to ensure that the total length of the data packet meets the predefined packet length requirements. In these embodiments, filling is required because the [LxM] matrix 612 of the spectrum significant coefficients (4 bytes for each coefficient) included in the packet can vary in the number of coefficients, depending on the conversion frequency and other factors in the waveform.

[0079] Now turn to Figure 7 , shows an exemplary method 700, which in some embodiments can be used by an external system (e.g., aggregator 112, local service 106, cloud service 108) to receive and reconstruct a data sequence x[n] from data packets P[LxM]. In general, data packets P[LxM] can be obtained by executing in reverse order Figure 3 The reconstruction method 700 is performed by the spectrum reduction method 300 described in the previous section, including the use of inverse DCT. Therefore, the reconstruction method 700 begins by receiving a data packet P[LxM] at a receiving block 702. At this block 702, the packet header and other packet-related overhead can be removed to obtain [LxM] spectrally significant 2-D coefficients Y i[j]. The unpacking block 704 processes the [LxM] matrix by performing the steps described above with respect to the packing block 308 but in reverse order to recover the [LxK] spectrally significant 1-D coefficients y[k]. Thereafter, the decoding block 706 processes the [LxK] matrix by performing the steps described above with respect to the encoding block 304 but in reverse order to recover each N-length data sequence x[n]. Specifically, each [1xM] spectrally significant data packet vector is zero-padded to a size of K, and each [Lx1] spectrally significant waveform vector is zero-padded to a size of N, and then a corresponding inverse discrete cosine transform is performed to restore the complete signal to its original waveform shape and size based on the requested specified QoS with very little loss. In some embodiments, the [LxM] matrix obtained by the receiving block 702 can also be stored in a storage system or database at the storage block 704 for further processing.

[0080] Figure 8 A sequence diagram 800 is illustrated, which shows an exemplary communication of data that has been reduced according to spectrum reduction disclosed herein. The communication in this example occurs between a circuit breaker 802 or other circuit protection device, an external system 804 (e.g., an aggregator, an edge computing device, a cloud storage, a server, etc.), and a user application 806 (e.g., a building management system, etc.).

[0081] The sequence diagram 800 generally begins with the circuit breaker 802 turning on at 808 and attempting to establish a connection with the external system 804 at 810. At 812, the external system 804 receives a request for metadata (such as metering or usage context data) from the user application 806. The external system 804 then sends a request for data to the circuit breaker 802 at 814 and specifies a certain QoS level that may be based on the type of information requested. At 816, either as part of a regular sampling routine or specifically in response to the request, the circuit breaker 802 obtains a collection of waveform samples and converts the samples into data. The circuit breaker 802 then encodes and packages the data into data packets at 818 using the spectral reduction discussed herein, and transmits the data to the external system 804 at 820. At 822, the external system 804 processes and stores the data, and then converts the data into metadata at 824. The external system 804 thereafter sends the metadata to the user application 806 at 826. Concurrently, the user application 806 presents a graphical user interface (or other application) at 828 in preparation for receiving metadata, which is received at 830 and used to update and refresh the graphical user interface (or other application).

[0082] At 832, the circuit breaker 802 acquires additional waveform samples and converts them into data, which is then encoded and packaged into data packets at 834. At 836, the circuit breaker 802 transmits the data packets to the external system 804, which processes and stores the data at 838 and converts the data into metadata at 840. The external system 804 then sends the metadata to the user application 806 at 842, where the metadata is used to update and refresh the graphical user interface (or other application). The above process continues at 844, where the circuit breaker 802 again acquires additional waveform samples and converts them into data, which is then encoded and packaged into data packets at 834, and so on, as described above.

[0083] Fig. 9A A graph 900 is shown that demonstrates the utility of the spectral reduction discussed herein applied to a line current signal 902. The original signal is indicated at 902 and the spectrally reduced signal (or a reconstructed version thereof) is indicated at 904. As can be seen, the spectrally reduced signal 904 has a shape that is almost identical to that of the original signal 902. Fig. 9B The accuracy of the reconstruction can be seen more clearly in Fig. 9B Shows Fig. 9A 904. In this figure, the respective waveforms of the original signal 902 and the spectrally reduced signal 904 can be seen. As shown, the waveform of the spectrally reduced signal 904 closely follows the waveform of the original signal 902, except for the peaks and valleys along the waveform, which correspond to the discarded high frequency coefficients.

[0084] In the above, reference is made to various embodiments. However, the scope of the present disclosure is not limited to the specifically described embodiments. Instead, any combination of the described features and elements, whether or not related to different embodiments, is contemplated to implement and practice the intended embodiments. In addition, although the embodiments may achieve advantages over other possible solutions or prior art, whether a particular advantage is achieved by a given embodiment does not limit the scope of the present disclosure. Therefore, unless expressly stated in (one or more) claims, the foregoing aspects, features, embodiments and advantages are merely illustrative and are not considered to be elements or limitations of the appended claims.

[0085] The various embodiments disclosed herein may be implemented as systems, methods, or computer program products. Thus, various aspects may take the form of a complete hardware embodiment, a complete software embodiment (including firmware, resident software, microcode, etc.), or an embodiment of a combination of software and hardware aspects, which may generally be referred to as a "circuit," "module," or "system." In addition, various aspects may take the form of a computer program product embodied in one or more computer-readable media having a computer-readable program code embodied therein.

[0086] Any combination of one or more computer-readable media may be utilized. The computer-readable medium may be a non-transitory computer-readable medium. The non-transitory computer-readable medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of non-transitory computer-readable media may include the following: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. The program code embodied on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

[0087] The computer program code for performing the operation of various aspects of the present disclosure can be written in any combination of one or more programming languages. Moreover, such computer program code can be executed using a single computer system or multiple computer systems that communicate with each other (for example, using a dedicated area network (PAN), a local area network (LAN), a wide area network (WAN), the Internet, etc.). Although the aforementioned various features are described with reference to flow charts and / or block diagrams, it will be understood by those of ordinary skill in the art that each box of the flow chart diagram and / or the block diagram, and the combination of the boxes in the flow chart diagram and / or the block diagram can be implemented by computer logic (for example, computer program instructions, hardware logic, a combination of the two, etc.). In general, computer program instructions can be provided to (one or more) processors of general-purpose computers, special-purpose computers, or other programmable data processing devices. Moreover, using (one or more) processors to execute such computer program instructions generates a machine that can perform (one or more) functions or (one or more) actions specified in one or more flow chart diagrams and / or block diagram boxes.

[0088] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functions and / or operations of the possible implementations of various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a fragment or part of a code, which includes one or more executable instructions for implementing (one or more) specified logical functions. It should also be noted that in some alternative embodiments, the functions marked in the box may not appear in the order marked in the figure. For example, depending on the functions involved, two boxes displayed in succession can actually be executed substantially concurrently, or sometimes these boxes can be executed in the opposite order. It will also be noted that each box illustrated in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs a specified function or action, or by a combination of dedicated hardware and computer instructions.

[0089] It should be understood that the above description is intended to be illustrative rather than restrictive. After reading and understanding the above description, many other implementation examples are apparent. Although the present disclosure describes specific examples, it should be appreciated that the system and method of the present disclosure are not limited to the examples described herein, but can be modified to practice within the scope of the attached claims. Thus, the description and the drawings are to be considered illustrative rather than restrictive. Therefore, the scope of the present disclosure should be determined with reference to the attached claims and the full scope of equivalents given by these claims.

Claims

1. A method of communicating high resolution information from a circuit interruption device to an external system, comprising: receiving, at a controller of the circuit interrupting device, a plurality of data sequences, each data sequence comprising a set of data points corresponding to a waveform of a line current flowing through the circuit interrupting device; performing spectral reduction on the plurality of data sequences using a finite series transform at the controller; as well as transmitting spectrally reduced versions of the plurality of data sequences to the external system; The performing of spectrum reduction on the plurality of data sequences comprises: performing a first transform using the finite series transform at the controller on each of the plurality of data sequences to generate a plurality of waveform vectors, each waveform vector corresponding to a respective data sequence and comprising a set of first spectral coefficients, each coefficient in the set of first spectral coefficients corresponding to a data point in the respective data sequence; reducing, at the controller, each of the plurality of waveform vectors to produce a plurality of spectrally significant waveform vectors, each spectrally significant waveform vector corresponding to a corresponding waveform vector and containing only spectrally significant first spectral coefficients from the corresponding waveform vector; performing, at the controller, a second transform using the finite series transform on each of the plurality of spectrally significant waveform vectors to generate a plurality of grouped vectors, each grouped vector comprising a set of second spectral coefficients, each coefficient in the set of the second spectral coefficients corresponding to a spectrally significant first spectral coefficient from a different spectrally significant waveform vector from the plurality of spectrally significant waveform vectors; and Each of the plurality of grouping vectors is reduced at the controller to produce a plurality of spectrally significant grouping vectors, each spectrally significant grouping vector corresponding to a corresponding grouping vector and containing only spectrally significant second spectral coefficients from the corresponding grouping vector.

2. The method of claim 1 , wherein reducing each of the plurality of waveform vectors at the controller to produce a plurality of spectrally significant waveform vectors comprises determining an optimal number of spectrally significant first spectral coefficients from the set of the first spectral coefficients in each waveform vector.

3. A method as claimed in claim 1, wherein reducing each of the multiple grouping vectors at the controller to produce a plurality of spectrally significant grouping vectors includes determining an optimal number of spectrally significant second spectral coefficients from a set of the second spectral coefficients in the grouping vector.

4. The method of claim 2, wherein determining at the controller an optimal number of spectrally significant waveform vectors comprises determining whether a spectral ratio of the Euclidean distance for the optimal number of spectrally significant first spectral coefficients to the Euclidean distance for a set of the first spectral coefficients in each waveform vector meets quality of service requirements.

5. A method as claimed in claim 3, wherein determining the optimal number of spectrally significant second spectral coefficients includes determining whether a spectral ratio of the Euclidean distance for the optimal number of spectrally significant second spectral coefficients and the Euclidean distance for the set of second spectral coefficients in each waveform vector meets quality of service requirements.

6. The method of claim 1, further comprising inserting the plurality of spectrally significant grouping vectors into a data packet.

7. The method of claim 1, wherein the finite series transform is a discrete cosine transform.

8. The method of claim 1, wherein transmitting the plurality of data sequences comprises transmitting over a wireless connection.

9. A circuit interruption device comprising: a controller programmed to detect a fault condition in a branch circuit connected to said circuit interrupting device; as well as a trip unit coupled in communication with the controller and operable to interrupt current flow through the circuit interruption device in response to receiving a trip signal from the controller; The controller is also programmed to: receiving a plurality of data sequences, each data sequence comprising a set of data points corresponding to a waveform of a line current flowing through the circuit interruption device; performing spectral reduction on the plurality of data sequences using a finite series transform; transmitting spectrally reduced versions of the plurality of data sequences to an external system; performing a first transform at the controller on each of the plurality of data sequences using a finite series transform to generate a plurality of waveform vectors, each waveform vector corresponding to a corresponding data sequence and comprising a set of first spectral coefficients, each coefficient in the set of first spectral coefficients corresponding to a data point in the corresponding data sequence; reducing, at the controller, each of the plurality of waveform vectors to produce a plurality of spectrally significant waveform vectors, each spectrally significant waveform vector corresponding to a corresponding waveform vector and containing only spectrally significant first spectral coefficients from the corresponding waveform vector; performing a second transform at the controller using a finite series transform on each of the plurality of spectrally significant waveform vectors to generate a plurality of grouped vectors, each grouped vector comprising a set of second spectral coefficients, each coefficient in the set of second spectral coefficients corresponding to a spectrally significant first spectral coefficient from a different spectrally significant waveform vector from the plurality of spectrally significant waveform vectors; and Each waveform vector of the plurality of waveform vectors is reduced at the controller to produce a plurality of spectrally significant waveform vectors, each spectrally significant waveform vector corresponding to a corresponding waveform vector and containing only spectrally significant second spectral coefficients from the corresponding waveform vector.

10. A circuit interrupting device as described in claim 9, wherein the controller is further programmed to reduce each waveform vector of the plurality of waveform vectors to produce a plurality of spectrally significant waveform vectors by determining an optimal number of spectrally significant first spectral coefficients from the set of the first spectral coefficients in each waveform vector.

11. A circuit interruption device as described in claim 9, wherein the controller is further programmed to reduce each of the multiple grouping vectors to produce multiple spectrally significant grouping vectors by determining an optimal number of spectrally significant second spectral coefficients from the set of second spectral coefficients in the grouping vector.

12. A circuit interruption device as described in claim 10, wherein the controller is further programmed to determine the optimal number of spectrally significant waveform vectors by determining whether a spectral ratio of the Euclidean distance for the optimal number of spectrally significant first spectral coefficients and the Euclidean distance for the set of first spectral coefficients in each waveform vector meets the quality of service requirements.

13. A circuit interruption device as described in claim 11, wherein the controller is further programmed to determine the optimal number of spectrally significant second spectral coefficients by determining whether a spectral ratio of the Euclidean distance for the optimal number of spectrally significant second spectral coefficients and the Euclidean distance for the set of second spectral coefficients in each waveform vector meets quality of service requirements.

14. The circuit interruption device of claim 9 wherein said controller is further programmed to insert said plurality of spectrally significant grouping vectors into a data packet.

15. The circuit interrupting device of claim 9 wherein said finite series transform is a discrete cosine transform.

16. A non-transitory computer readable medium containing program logic that, when executed by the operation of one or more computer processors, performs the operations of the method of any one of claims 1-8.

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