Electric energy meter fault monitoring method and system based on signal decomposition

Abstract

The invention discloses an electric energy meter fault monitoring method and system based on signal decomposition. A power supply point where an electric energy meter is located is positioned; acquiring a power signal detected by the electric energy meter, and performing windowing wavelet transform on the power signal to obtain a frequency domain function of the power signal; separating a fundamental wave signal and a harmonic wave signal from the frequency domain function of the power signal; obtaining a time domain function of the fundamental wave signal and the harmonic wave signal, and calculating an electric power parameter of the time domain function; correcting the error of the mutual inductor according to the power data of the primary winding and the secondary winding in the electric energy meter and the hardware parameters of the mutual inductor; and judging a wiring fault and a mutual inductor coil fault by using a fundamental wave signal in a time domain and a no-load ratio difference between the voltage and the current mutual inductor. Automatic separation of fundamental power and harmonic power can be realized, working parameters of an electric energy meter filter can be optimized, line loss in an electric energy measurement process can be reduced, electric energy detection efficiency can be improved, wiring faults of the electric energy meter can be identified, accurate metering of the electric energy meter can be ensured, and overall energy efficiency of an electric power metering system can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric energy meter monitoring, and in particular relates to an electric energy meter fault monitoring method and system based on signal decomposition. Background Art

[0002] An electric energy meter is a device used to measure electrical energy parameters in power supply or transmission lines. Commonly used high-voltage electric energy meters consist of a measuring chip, current transformer, voltage transformer, power supply, MCU, and terminal blocks. During operation, electric energy meters can experience various anomalies, such as wiring faults, transformer failures, and meter reading failures, which can cause the meter to inaccurately measure power data. The transformer portion of an electric energy meter consists of a primary winding circuit and a secondary winding circuit. During measurement, problems such as transformer damage or measurement fluctuations exceeding the specified range can cause transformer errors or secondary circuit contact failures.

[0003] For example, patent application publication number CN118011307A assesses the health of an electricity meter by calculating its aging and environmental impact index. First, the aging is calculated based on the meter's number of days in use and the number of repairs. Then, the environmental impact index is calculated based on the ambient temperature and humidity of the meter on different monitoring days. By assigning weights to the aging and environmental impact indexes, a display abnormality impact factor is derived, further assessing the meter's failure risk. Another example is patent application publication number CN116699236A, which combines a metering unit, a monitoring unit, a main control unit, and a display to monitor electricity meter failures. The metering unit measures voltage and current signals, generates a reference voltage, and transmits it to the monitoring unit. The monitoring unit compares the reference voltage with the meter's operating status. If an abnormality is detected, the main control unit displays the fault information on a display. Another example is patent application publication number CN115986930A, which includes a data collection master station, a data collection terminal, and multiple electricity meters. Each energy meter is equipped with a carrier module, which communicates with the data collection terminal through the carrier module, reporting the meter's archive information and operating status in real time. The data collection terminal forwards this information to the main data collection station, which determines its monitoring results based on the substation archive and energy meter data.

[0004] The deficiencies of the existing technology are mainly reflected in the following aspects: First, the commonly used fault detection method usually relies on an auxiliary electric energy meter for secondary test synchronization. This method can only detect whether the electric energy meter has a fault, but cannot repair the reading of the electric energy meter, resulting in it being difficult to restore the normal function of the electric energy meter when a fault occurs. Secondly, the electric energy meter is usually manually wired, which is prone to problems such as missed connections, wrong connections, or loose terminal connections. These problems will cause the measurement circuit of the electric energy meter to deviate from the original circuit, thereby causing detection reading deviations or line inconsistencies. This makes it impossible for the electric energy meter to accurately reflect the actual detection status, further increasing the difficulty of troubleshooting and locating problems. For abnormal wiring problems, traditional methods often take a lot of time to troubleshoot and are inefficient. Therefore, the existing technology still has a lot of room for improvement in electric energy meter fault detection and repair. Summary of the Invention

[0005] In order to solve the deficiencies in the prior art, the present invention provides an electric energy meter fault monitoring method and system based on signal decomposition, which locates the power supply point where the electric energy meter is located; obtains the power signal detected by the electric energy meter, performs a windowed wavelet transform on the power signal, and obtains the frequency domain function of the power signal; separates the fundamental signal and the harmonic signal in the frequency domain function of the power signal; obtains the time domain function of the fundamental signal and the harmonic signal, and calculates the power parameters of the time domain function; corrects the mutual inductor error based on the power data of the primary winding and the secondary winding of the electric energy meter and the hardware parameters of the mutual inductor; and uses the fundamental signal in the time domain and the load-free ratio difference of the voltage and current mutual inductors to judge the wiring fault and the mutual inductor coil fault. The present invention can realize the automatic separation of fundamental power and harmonic power, optimize the working parameters of the electric energy meter filter, reduce the line loss in the electric energy measurement process, improve the efficiency of electric energy detection, and simultaneously identify the wiring fault of the electric energy meter, ensure the accurate measurement of the electric energy meter, and improve the overall energy efficiency of the electric energy metering system.

[0006] The present invention adopts the following technical solutions.

[0007] The present invention proposes a method for monitoring electric energy meter faults based on signal decomposition, comprising:

[0008] S1: Locate the power supply point where the energy meter is located, establish an energy management center in the cloud, and generate an address database using the location of each power supply point as an index. Different power supply points with the same transmission and distribution specifications are considered to be the same source power supply points and stored in the same address database.

[0009] S2: Obtain the power signal detected by the electric energy meter. Based on the AC waveform of the same power supply point in the cloud address database, select the wavelet function with the corresponding frequency as the base frequency, perform a windowed wavelet transform on the power signal, and obtain the frequency domain function of the power signal.

[0010] S3, using the error range of the AC power frequency of the power supply system to obtain a frequency domain interval; based on the frequency domain interval, separating the fundamental signal and the harmonic signal from the frequency domain function of the power signal; performing an inverse transform on the fundamental signal and the harmonic signal to obtain a time domain function of the fundamental signal and the harmonic signal, and calculating the power parameters of the time domain function;

[0011] S4, connecting the voltage and current transformers to different ports of the metering chip, respectively, and using the voltage and current transformers to measure the power data of the primary winding and secondary winding of the electric energy meter; calculating the error of the transformer based on the power data and the hardware parameters of the transformer, and correcting the error;

[0012] S5, calculates the correlation between the fundamental wave signal in the time domain and the historical fundamental wave signal to determine whether the wiring is wrong; obtains the ratio difference of the voltage and current transformer from the metering chip, and determines whether the electric energy meter is abnormal by comparing the ratio difference; compares the no-load ratio difference of the voltage and current transformer with the power supply test voltage in the electric energy meter to determine whether the coil is faulty.

[0013] Furthermore, S1 includes:

[0014] S11, using a positioning device to locate the power supply point where the electric energy meter is located, the power supply point including the meter box, the power supply point, and the generator set; power supply points with the same power transmission and distribution specifications are recorded as common power supply points; the common power transmission and distribution specifications refer to nodes with the same voltage, phase, and frequency requirements;

[0015] S12, establish an energy management center in the cloud, store the location of the same-source power supply point, the fundamental wave signal of voltage and current, the harmonic signal of voltage and current, and the measurement error in the same address library, and use the location of all the same-source power supply points as the index.

[0016] Furthermore, S2 includes:

[0017] S21, detecting power signals of the electric energy meter, including voltage signals and current signals, and obtaining time domain functions of the voltage signals and current signals;

[0018] Locate the energy meter, use the location as an index, search the cloud address database for the same source power supply point, obtain the AC waveform of the same source power supply point, and select a wavelet function with a frequency that is an integer multiple of the AC frequency and the same waveform as the fundamental frequency of the voltage and current signals;

[0019] S22, performing a windowed wavelet transform on the power signal to obtain a frequency domain function of the power signal.

[0020] Furthermore, in S22, a windowed wavelet transform is performed on the power signal with a variable window to determine a frequency domain function:

[0021]

[0022] Among them, f(a,b) is the frequency domain function, a is the frequency scale, b is the frequency domain offset, t0 is the window length, t represents time, δ(t) is the fundamental frequency wavelet function, U(t) is the time domain function of the power signal, and T is the sampling time of the power signal.

[0023] Furthermore, S3 includes:

[0024] S31, obtaining the AC power frequency of the power supply system, including the frequency domain scale and frequency domain offset of the AC power, and presetting a frequency domain interval centered on the standard frequency domain scale and frequency domain offset;

[0025] The power signal is filtered according to the error interval, and the component in the frequency domain function that is within the frequency domain interval of the frequency domain scale and the frequency domain offset is taken as the fundamental component, otherwise it is taken as the harmonic component, so that the frequency domain function of the power signal is f(a,b)=f1(a,b)+f2(a,b), where f1(a,b) and f2(a,b) represent the frequency domain functions of the fundamental component and the harmonic component, respectively;

[0026] S32 performs inverse transformation on f1(a, b) and f2(a, b) to obtain the time domain functions f1(t) and f2(t) of the fundamental signal and harmonic signal, respectively. Detect f1(t) and f2(t) to obtain the power parameters of the fundamental signal and harmonic signal, including voltage, current, and power, and upload the obtained data to the cloud.

[0027] Furthermore, S4 includes:

[0028] S41: Connect the primary winding and secondary winding of the electric energy meter to the high-voltage power supply line and the power consumption line, respectively, with the primary winding and the secondary winding sharing a set of voltage transformers and current transformers; utilize a bridge shunt circuit to enable the secondary winding to receive signals from the voltage and current transformers, and transmit the signals in the secondary winding to the electric energy meter; measure power data from the primary winding and the secondary winding in the electric energy meter, and remove harmonic signals from the power data;

[0029] S42, calculate the voltage transformer error based on the power data and the transformer hardware parameters:

[0030]

[0031] Where fU and δU represent the ratio error and angle error of the voltage transformer, respectively; Ym is the excitation admittance of the voltage transformer; X1 is the primary reactance of the voltage transformer; Xk is the short-circuit reactance of the voltage transformer; r1 is the primary winding resistance; rk is the short-circuit resistance of the voltage transformer; θ is the deflection angle between the main magnetic flux and the excitation current of the voltage transformer; ω is the power angle between the secondary winding loop voltage and current; and Y2 is the short-circuit excitation admittance of the voltage transformer.

[0032] S43, calculates the error of the current transformer based on the power data and the hardware parameters of the current transformer:

[0033]

[0034] Where fI and δI represent the ratio difference and angle difference of the current transformer respectively, I m is the excitation current, I1 is the primary winding current, σ is the deflection angle between the voltage and the induced electromotive force in the secondary winding circuit, and θ is the deflection angle between the main magnetic flux of the voltage transformer and the excitation current;

[0035] S44, correcting the errors of the voltage and current transformers according to the calculated ratio difference and angle difference.

[0036] Furthermore, S5 includes:

[0037] S51, obtains the historical fundamental wave signal from the address database in the cloud, uses the Pearson correlation algorithm to calculate the correlation between the fundamental wave signal under the current power supply parameters and the historical fundamental wave signal in the time domain, and displays an electric energy meter wiring error when the correlation is lower than the threshold;

[0038] S52, judging the ratio difference of the voltage and current transformers, the errors are consistent in a normal state, otherwise it is judged to be an abnormal state; the test voltage and test current are input to the electric energy meter by the power supply in the electric energy meter, and the excitation reactance, no-load ratio difference and no-load angle difference of the current and voltage transformers under actual load are calculated according to the interpolation method. When the no-load ratio differences of the voltage and current transformers are inconsistent, it is judged that the transformer coil is faulty.

[0039] The present invention also proposes an electric energy meter fault monitoring system based on signal decomposition, comprising a directional storage module, a signal decomposition module, a mutual inductance error module, a fault comparison module and an excitation calibration module, characterized in that:

[0040] The directional storage module is used to set a positioning device in the electric energy meter. According to the location of the electric energy meter, the historical voltage and current test results of the current power supply point are retrieved from the cloud address library. After the test is completed, the test data is uploaded to the cloud. The cloud address library is updated with the power supply point where the electric energy meter address is located as the index. The fundamental wave, harmonics and mutual inductor error in this test are also recorded.

[0041] The signal decomposition module is used to obtain the current signal between the wiring ports of the electric energy meter, decompose the current signal using a variable window windowing transform, decompose the current signal into time domain signals within each preset sub-band, decompose the time domain current signal into a fundamental signal and harmonic signals according to the frequency range of the AC circuit, calculate the voltage, current and power of the fundamental signal and harmonic signals respectively, and store the calculation results as power data;

[0042] The mutual inductance error module is used to connect the primary winding of the energy meter to the high-voltage power supply line and the secondary winding to the power line. It also connects the voltage transformer and current transformer through the shunt circuit to different ports of the energy meter chip. Based on the power data of the harmonic signal and the hardware parameters of the transformer, it calculates the errors of the voltage and current transformers, filters out the errors from the measured results, and compares the errors with the historical harmonic signals and errors in the cloud address library to obtain the historical hardware parameters of the transformer. If the hardware parameter drop rate exceeds the preset range, it is judged that the transformer hardware is damaged.

[0043] The fault comparison module is used to perform correlation fitting on the power data of the fundamental and harmonic signals with the error data in the cloud address library. The fitting results are analyzed. If the correlation between the fundamental signal and the historical signal frequency or phase is lower than the threshold, it is judged as a power meter wiring fault. The excitation errors of the voltage transformer and current transformer ports are compared. If they are inconsistent, it is judged as a transformer coil fault.

[0044] The excitation calibration module is used to cut off the power supply to the winding circuit when a transformer fault is detected. The power supply in the electricity meter inputs the test voltage and test current to the electricity meter, calculates the excitation reactance of the voltage transformer under actual load based on the interpolation method, and calibrates the error of the voltage transformer based on the excitation reactance between the secondary windings.

[0045] Furthermore, the directional storage module includes: a virtual positioning unit and a data indexing unit;

[0046] The virtual positioning unit is used to locate the meter box, power supply point and generator set where the energy meter is located through satellite or radio positioning equipment. When the power transmission and distribution specifications of the power supply points where the energy meter is located are the same, they are regarded as the same location;

[0047] The data indexing unit is used to establish an electric energy management center in the cloud, receive the location and measurement data sent by the electric energy meters at various locations, and build a cloud address library.

[0048] Furthermore, the signal decomposition module includes: a terminal connection unit, a windowing transformation unit and a harmonic separation unit;

[0049] The terminal connection unit is composed of a terminal and a switch, and is used to connect the detection terminal of the electric energy meter to the circuit port of the device to be detected;

[0050] The windowing transformation unit is used to select a wavelet function according to the detection records of the same address in the cloud address library, and perform a windowed wavelet transform on the detected voltage signal;

[0051] The harmonic separation unit is used to perform frequency domain cutting on the frequency domain function within the range of the alternating current frequency, obtain fundamental wave signals and harmonic wave signals after inverse transformation, and obtain power parameters from the signal waveforms.

[0052] Furthermore, the mutual inductance error module includes: a winding shunt unit, a fundamental wave simulation unit and an error filtering unit;

[0053] The winding shunt unit is used to process the current of the electric energy meter and the voltage transformer through the shunt circuit and measure them separately;

[0054] The fundamental wave simulation unit is used to calculate the mutual inductor error based on the power data of the harmonics and the fundamental wave;

[0055] The error filtering unit is used to filter out harmonic signals and data caused by mutual inductance errors from the fundamental wave signal of the mutual inductor;

[0056] The fault comparison module includes: a wiring comparison unit and a mutual inductance fault unit;

[0057] The wiring comparison unit is used to calculate the correlation between the fundamental wave signal error and the cloud, and when the correlation is lower than a threshold, a wiring error is reported;

[0058] The mutual inductance fault unit is used to obtain the excitation errors of the voltage transformer and the current transformer from the ports of the metering chip respectively, and to report a coil error when the errors are inconsistent.

[0059] Furthermore, the excitation calibration module includes: an access control unit and a reactance calibration unit;

[0060] The access control unit is used to control the current supply of the primary winding circuit and the secondary winding circuit, and adjust the access status of the power supply and circuit in the electric energy meter;

[0061] The reactance calibration unit is used to calculate the excitation reactance of the mutual inductor according to the test result of the internal power supply, and calibrate the indication of the electric energy meter.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] 1. The present invention uses a variable window windowing transform to decompose the current signal, decompose the current signal into time domain signals in each sub-band, decompose the current signal into a fundamental signal and a harmonic signal, and calculate the voltage, current and power of the fundamental signal and the harmonic signal respectively, thereby realizing the automatic separation of the fundamental power and the harmonic power during the metering process, thereby optimizing the operating parameters of the electric energy meter filter, reducing the line loss during the electric energy measurement process, and improving the efficiency of electric energy detection.

[0064] 2. The present invention sets a positioning device in the electric energy meter, establishes a measurement database with the coordinate range as the index, analyzes the electric energy parameters of the fundamental wave signal, compares the electric energy parameters with the average fundamental wave signal within the coordinate range, determines whether the wiring is consistent with the original wiring, identifies the wiring fault of the electric energy meter, ensures the accurate measurement of the electric energy meter, and reduces non-technical electric energy measurement losses.

[0065] 3. The present invention connects the primary winding of the electric energy meter to the high-voltage power supply line and the secondary winding to the power consumption line, calculates the errors of the voltage and current transformers according to the parameters of the harmonic signals, filters out the errors from the measured results, and outputs the error amount, thereby determining the transformer fault, shutting down the primary winding circuit, calibrating the transformer error, avoiding the interference of the mutual inductance error on the measurement, ensuring the reliability of the electric energy data, and improving the overall energy efficiency of the power metering system. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a schematic diagram of the steps of a method for monitoring electric energy meter faults based on signal decomposition of the present invention;

[0067] Figure 2 It is a structural schematic diagram of an electric energy meter fault monitoring system based on signal decomposition of the present invention. DETAILED DESCRIPTION

[0068] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0069] like Figure 1 As shown, the present invention provides an electric energy meter fault monitoring method based on signal decomposition, comprising the following steps:

[0070] Step S1. Locate the power supply point where the energy meter is located, establish an energy management center in the cloud, and generate an address database using the location of each power supply point as an index. If the power transmission and distribution specifications of different power supply points are the same, these power supply points are considered to be the same source power supply points and stored in the same address database;

[0071] Step S1 includes:

[0072] Step S11. Using satellite or radio positioning equipment, locate the power supply point where the energy meter is located at the decimeter or centimeter level. The power supply point includes: the meter box, power supply station, and generator set. Power supply points with the same power transmission and distribution specifications are recorded as common power supply points. The common power transmission and distribution specifications refer to nodes with the same voltage, phase, and frequency requirements.

[0073] Step S12. Establish an energy management center in the cloud. The energy management center has data storage, user interaction, information transmission and reception, and data calculation functions. The location of the same-source power supply point, the fundamental wave signal of voltage and current, the harmonic signal of voltage and current, and the measurement error are all stored in the same address library, and the location of all the same-source power supply points is used as an index.

[0074] Step S2. Obtain the power signal detected by the energy meter. Based on the AC waveform of the same power supply point in the cloud address database, select the wavelet function of the corresponding frequency as the fundamental frequency, perform a windowed wavelet transform on the power signal, and obtain the frequency domain function of the power signal.

[0075] Step S2 includes:

[0076] Step S21. Detecting the power signal of the electric energy meter, including the voltage signal and the current signal, and obtaining the time domain function of the voltage signal and the time domain function of the current signal;

[0077] Locate the energy meter, use the location as an index, search the cloud address database for the same power supply point, obtain the AC waveform of the same power supply point, and select a wavelet function with an integer multiple of the AC frequency and the same waveform as the fundamental frequency of the voltage or current signal;

[0078] Step S22: Perform windowed wavelet transform on the power signal with a variable window to determine the frequency domain function:

[0079]

[0080] Among them, f(a,b) is the frequency domain function, a is the frequency scale, b is the frequency domain offset, t0 is the window length, t represents time, δ(t) is the fundamental frequency wavelet function, U(t) is the time domain function of the power signal, and T is the sampling time of the power signal.

[0081] Step S3. Using the error range of the AC power frequency of the power supply system to obtain a frequency domain interval; based on the frequency domain interval, separating the fundamental signal and the harmonic signal in the frequency domain function of the power signal; performing an inverse transform on the fundamental signal and the harmonic signal to obtain the time domain function of the fundamental signal and the harmonic signal, and calculating the power parameters of the time domain function;

[0082] Step S3 includes:

[0083] Step S31. Acquire the AC power frequency of the power supply system, including the frequency domain scale and frequency domain offset of the AC power, and preset a frequency domain interval centered on the standard frequency domain scale and frequency domain offset;

[0084] The power signal is filtered according to the error interval, and the component in the frequency domain function that is within the frequency domain interval of the frequency domain scale and the frequency domain offset is taken as the fundamental component, otherwise it is taken as the harmonic component, so that the frequency domain function of the power signal is f(a,b)=f1(a,b)+f2(a,b), where f1(a,b) and f2(a,b) represent the frequency domain functions of the fundamental component and the harmonic component, respectively;

[0085] Step S32. Perform inverse transformation on f1(a, b) and f2(a, b) respectively to obtain the time domain functions f1(t) and f2(t) of the fundamental signal and the harmonic signal, perform detection on f1(t) and f2(t) to obtain the power parameters of the fundamental signal and the harmonic signal, including voltage, current and power, and upload the obtained data to the cloud.

[0086] Step S4. Connect the voltage and current transformers to different ports of the metering chip, respectively, and use the voltage and current transformers to measure the power data of the primary and secondary windings of the electric energy meter; calculate the error of the transformer based on the power data and the hardware parameters of the transformer, and correct the error;

[0087] Step S4 includes:

[0088] Step S41. Connect the primary and secondary windings of the energy meter to the high-voltage power supply line and the power consumption line, respectively. The primary and secondary windings share a set of voltage transformers and current transformers. Utilize a bridge shunt circuit to allow the secondary winding to receive signals from the voltage and current transformers and transmit the signals from the secondary winding to the energy meter. Measure power data from the primary and secondary windings in the energy meter and remove harmonic signals from the power data.

[0089] Step S42. Calculate the voltage transformer error based on the power data and the transformer hardware parameters:

[0090]

[0091] Among them, fU and δU represent the ratio difference and angle difference of the voltage transformer respectively, Ym is the excitation admittance of the voltage transformer, X1 is the primary side reactance of the voltage transformer, and X k is the short-circuit reactance of the voltage transformer, r1 is the primary winding resistance, r k is the short-circuit resistance of the voltage transformer, θ is the deflection angle between the main magnetic flux and the excitation current of the voltage transformer, ω is the power angle between the secondary winding circuit voltage and current, and Y2 is the short-circuit excitation admittance of the voltage transformer;

[0092] Step S43: Calculate the error of the current transformer:

[0093]

[0094] Where fI and δI represent the ratio difference and angle difference of the current transformer respectively, I m is the excitation current, I1 is the primary winding current, σ is the deflection angle between the voltage and the induced electromotive force in the secondary winding circuit, and θ is the deflection angle between the main magnetic flux of the voltage transformer and the excitation current;

[0095] Step S44: Correct the errors of the voltage and current transformers according to the calculated ratio difference and angle difference.

[0096] Specifically, the correction method is an existing technology and will not be described in detail here.

[0097] Step S5. Calculate the correlation between the fundamental wave signal in the time domain and the historical fundamental wave signal to determine whether the wiring is wrong; obtain the ratio difference of the voltage and current transformer from the metering chip, and determine whether the electric energy meter is abnormal by comparing the ratio difference; compare the no-load ratio difference of the voltage and current transformer with the power supply test voltage in the electric energy meter and the no-load ratio difference of the current transformer to determine whether the coil is faulty.

[0098] Step S5 includes:

[0099] Step S51. Obtain historical fundamental wave signals from the cloud address database, and use the Pearson correlation algorithm to calculate the correlation between the fundamental wave signal under the current power supply parameters and the historical fundamental wave signals in the time domain. If the correlation is lower than the threshold, a power meter wiring error is displayed;

[0100] Step S52. Determine the ratio difference of the voltage and current transformers. Under normal conditions, the errors are consistent. Otherwise, it is determined to be an abnormal state. The winding circuit is powered off. The power supply in the electric energy meter inputs the test voltage and test current to the electric energy meter. The excitation reactance, no-load ratio difference and no-load angle difference of the current and voltage transformers under actual load are calculated according to the interpolation method. When the no-load ratio differences of the voltage and current transformers are inconsistent, it is determined that the transformer coil is faulty.

[0101] For example, an electric energy meter is used to detect the line voltage, and the obtained voltage signal f(t) = 220·sin(50.2·t) is decomposed into a fundamental signal and a harmonic signal using frequency domain transformation to obtain a fundamental signal f1(t) = 220sin(50·t) and a harmonic signal f2(t) = -440·cos(50.1·t)sin(0.1·t). The corresponding harmonic signal is filtered out from the voltage signal, and the correlation between the fundamental signal and the historical fundamental signal is compared. The comparison result is 0.1, which is within the correlation range and does not trigger a wiring error.

[0102] See also Figure 2 The present invention also provides a technical solution: an electric energy meter fault monitoring system based on signal decomposition, comprising: a directional storage module, a signal decomposition module, a mutual inductance error module, a fault comparison module and an excitation calibration module:

[0103] The directional storage module is used to set a positioning device in the electric energy meter, retrieve the historical voltage and current detection results of the current power supply point from the cloud address library according to the location of the electric energy meter, upload the detection data to the cloud after the detection is completed, update the cloud address library with the power supply point where the electric energy meter address is located as the index, and record the fundamental wave, harmonics and mutual inductor error in this test;

[0104] The directional storage module includes: a virtual positioning unit and a data index unit;

[0105] The virtual positioning unit is used to locate the meter box, power supply point and generator set where the energy meter is located at the decimeter or centimeter level through satellite or radio positioning equipment. When the power transmission and distribution specifications of the power supply points where the energy meter is located are the same, they are regarded as the same location;

[0106] The data indexing unit is used to establish an electric energy management center in the cloud, receive the location and measurement data sent by the electric energy meters at various locations, and build a cloud address library.

[0107] The signal decomposition module is used to obtain the current signal between the connection ports of the electric energy meter, decompose the current signal using a windowing transformation with a variable window, decompose the current signal into time domain signals within each preset sub-band, decompose the time domain current signal into a fundamental signal and a harmonic signal according to the frequency range of the AC circuit, calculate the voltage, current and power of the fundamental signal and the harmonic signal respectively, and store the calculation results as power data;

[0108] The signal decomposition module includes: a terminal connection unit, a windowing transformation unit and a harmonic separation unit;

[0109] The terminal connection unit is composed of a terminal and a switch, and is used to connect the detection terminal of the electric energy meter to the circuit port of the device to be detected;

[0110] The windowing transformation unit is used to select a wavelet function according to the detection records of the same address in the cloud address library, and perform a windowed wavelet transform on the detected voltage signal;

[0111] The harmonic separation unit is used to perform frequency domain cutting on the frequency domain function within the range of the alternating current frequency, obtain fundamental wave signals and harmonic wave signals after inverse transformation, and obtain power parameters from the signal waveforms.

[0112] The mutual inductance error module is used to connect the primary winding of the electric energy meter to the high-voltage power supply line, the secondary winding to the power line, and pass the voltage transformer and current transformer through different ports of the electric energy meter metering chip through a shunt circuit. According to the power data of the harmonic signal and the hardware parameters of the transformer, the errors of the voltage and current transformers are calculated, the errors are filtered out from the measured results, and the errors are compared with the historical harmonic signals and errors in the cloud address library to obtain the historical hardware parameters of the transformer. When the hardware parameter drop rate exceeds a preset range, it is determined that the transformer hardware is damaged;

[0113] The mutual inductance error module includes: a winding shunt unit, a fundamental wave simulation unit and an error filtering unit;

[0114] The winding shunt unit is used to process the current of the electric energy meter and the voltage transformer through the shunt circuit and measure them separately;

[0115] The fundamental wave simulation unit is used to calculate the mutual inductor error based on the power data of the harmonics and the fundamental wave;

[0116] The error filtering unit is used to filter out harmonic signals and data caused by mutual inductance errors from the fundamental wave signal of the mutual inductor.

[0117] The fault comparison module is used to perform correlation fitting on the power data of the fundamental signal and the harmonic signal with the error data in the cloud address library, analyze the fitting results, and if the correlation between the fundamental signal and the frequency or phase of the historical signal is lower than a threshold, it is determined that there is a fault in the wiring of the electric energy meter. The excitation errors of the voltage transformer and the current transformer ports are compared, and if they are inconsistent, it is determined that there is a transformer coil fault;

[0118] The fault comparison module includes: a wiring comparison unit and a mutual inductance fault unit;

[0119] The wiring comparison unit is used to calculate the correlation between the fundamental wave signal error and the cloud, and when the correlation is lower than a threshold, a wiring error is reported;

[0120] The mutual inductance fault unit is used to obtain the excitation errors of the voltage transformer and the current transformer from the ports of the metering chip respectively, and to report a coil abnormality error when the errors are inconsistent.

[0121] The excitation calibration module is used to cut off the power supply to the winding circuit when a transformer fault is detected, input a test voltage and a test current to the electric energy meter from the power supply in the electric energy meter, calculate the excitation reactance of the voltage transformer under actual load according to the interpolation method, and calibrate the error of the voltage transformer according to the excitation reactance between the secondary windings.

[0122] The excitation calibration module includes: an access control unit and a reactance calibration unit;

[0123] The access control unit is used to control the current supply of the primary winding circuit and the secondary winding circuit, and adjust the access status of the power supply and circuit in the electric energy meter;

[0124] The reactance calibration unit is used to calculate the excitation reactance of the mutual inductor according to the test result of the internal power supply, and calibrate the indication of the electric energy meter.

[0125] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0126] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for monitoring electric energy meter faults based on signal decomposition, characterized in that: include: S1: Locate the power supply point where the electricity meter is located, establish an energy management center in the cloud, and generate an address database using the location of each power supply point as an index; Different power supply points with the same transmission and distribution specifications are considered as the same source power supply points and stored in the same address database; S2: Obtain the power signal detected by the electric energy meter. Based on the AC waveform of the same power supply point in the cloud address database, select the wavelet function with the corresponding frequency as the base frequency, perform a windowed wavelet transform on the power signal, and obtain the frequency domain function of the power signal. S3, obtaining a frequency domain interval using the error range of the AC power frequency of the power supply system; According to the frequency domain interval, the fundamental signal and the harmonic signal are separated from the frequency domain function of the power signal; the fundamental signal and the harmonic signal are inversely transformed to obtain the time domain function of the fundamental signal and the harmonic signal, and the power parameters of the time domain function are calculated; S4, connecting the voltage and current transformers to different ports of the metering chip, respectively, and using the voltage and current transformers to measure the power data of the primary winding and secondary winding of the electric energy meter; calculating the error of the transformer based on the power data and the hardware parameters of the transformer, and correcting the error; S5, calculates the correlation between the fundamental wave signal in the time domain and the historical fundamental wave signal to determine whether the wiring is wrong; obtains the ratio difference of the voltage and current transformer from the metering chip, and determines whether the electric energy meter is abnormal by comparing the ratio difference; compares the no-load ratio difference of the voltage and current transformer with the power supply test voltage in the electric energy meter to determine whether the coil is faulty.

2. The method for monitoring electric energy meter faults based on signal decomposition according to claim 1, characterized in that: S1 includes: S11, using a positioning device to locate the power supply point where the electric energy meter is located, the power supply point including the meter box, the power supply point, and the generator set; power supply points with the same power transmission and distribution specifications are recorded as common power supply points; the common power transmission and distribution specifications refer to nodes with the same voltage, phase, and frequency requirements; S12, establish an energy management center in the cloud, store the location of the same-source power supply point, the fundamental wave signal of voltage and current, the harmonic signal of voltage and current, and the measurement error in the same address library, and use the location of all the same-source power supply points as the index.

3. The method for monitoring electric energy meter faults based on signal decomposition according to claim 1, characterized in that: S2 includes: S21, detecting power signals of the electric energy meter, including voltage signals and current signals, and obtaining time domain functions of the voltage signals and current signals; Locate the energy meter, use the location as an index, search the cloud address database for the same source power supply point, obtain the AC waveform of the same source power supply point, and select a wavelet function with a frequency that is an integer multiple of the AC frequency and the same waveform as the fundamental frequency of the voltage and current signals; S22, performing a windowed wavelet transform on the power signal to obtain a frequency domain function of the power signal.

4. The method for monitoring electric energy meter faults based on signal decomposition according to claim 3, characterized in that: In S22, a windowed wavelet transform is performed on the power signal using a variable window to determine the frequency domain function: Among them, f(a,b) is the frequency domain function, a is the frequency scale, b is the frequency domain offset, t0 is the window length, t represents time, δ(t) is the fundamental frequency wavelet function, U(t) is the time domain function of the power signal, and T is the sampling time of the power signal.

5. The method for monitoring electric energy meter faults based on signal decomposition according to claim 1, characterized in that: S3 includes: S31, obtaining the AC power frequency of the power supply system, including the frequency domain scale and frequency domain offset of the AC power, and presetting a frequency domain interval centered on the standard frequency domain scale and frequency domain offset; The power signal is filtered according to the error interval, and the component in the frequency domain function that is within the frequency domain interval of the frequency domain scale and the frequency domain offset is taken as the fundamental component, otherwise it is taken as the harmonic component, so that the frequency domain function of the power signal is f(a,b)=f1(a,b)+f2(a,b), where f1(a,b) and f2(a,b) represent the frequency domain functions of the fundamental component and the harmonic component, respectively; S32 performs inverse transformation on f1(a, b) and f2(a, b) to obtain the time domain functions f1(t) and f2(t) of the fundamental signal and harmonic signal, respectively. Detect f1(t) and f2(t) to obtain the power parameters of the fundamental signal and harmonic signal, including voltage, current, and power, and upload the obtained data to the cloud.

6. The method for monitoring electric energy meter faults based on signal decomposition according to claim 1 or 5, characterized in that: S4 includes: S41: Connect the primary winding and secondary winding of the electric energy meter to the high-voltage power supply line and the power consumption line, respectively, with the primary winding and the secondary winding sharing a set of voltage transformers and current transformers; utilize a bridge shunt circuit to enable the secondary winding to receive signals from the voltage and current transformers, and transmit the signals in the secondary winding to the electric energy meter; measure power data from the primary winding and the secondary winding in the electric energy meter, and remove harmonic signals from the power data; S42, calculate the voltage transformer error based on the power data and the transformer hardware parameters: Where fU and δU represent the ratio error and angle error of the voltage transformer, respectively; Ym is the excitation admittance of the voltage transformer; X1 is the primary reactance of the voltage transformer; Xk is the short-circuit reactance of the voltage transformer; r1 is the primary winding resistance; rk is the short-circuit resistance of the voltage transformer; θ is the deflection angle between the main magnetic flux and the excitation current of the voltage transformer; ω is the power angle between the secondary winding loop voltage and current; and Y2 is the short-circuit excitation admittance of the voltage transformer. S43, calculates the error of the current transformer based on the power data and the hardware parameters of the current transformer: Where fI and δI represent the ratio difference and angle difference of the current transformer respectively, I m is the excitation current, I1 is the primary winding current, σ is the deflection angle between the voltage and the induced electromotive force in the secondary winding circuit, and θ is the deflection angle between the main magnetic flux of the voltage transformer and the excitation current; S44, correcting the errors of the voltage and current transformers according to the calculated ratio difference and angle difference.

7. The method for monitoring electric energy meter faults based on signal decomposition according to claim 1, characterized in that: S5 includes: S51, obtains the historical fundamental wave signal from the address database in the cloud, uses the Pearson correlation algorithm to calculate the correlation between the fundamental wave signal under the current power supply parameters and the historical fundamental wave signal in the time domain, and displays an electric energy meter wiring error when the correlation is lower than the threshold; S52, judging the ratio difference of the voltage and current transformers, the errors are consistent in a normal state, otherwise it is judged to be an abnormal state; the test voltage and test current are input to the electric energy meter by the power supply in the electric energy meter, and the excitation reactance, no-load ratio difference and no-load angle difference of the current and voltage transformers under actual load are calculated according to the interpolation method. When the no-load ratio differences of the voltage and current transformers are inconsistent, it is judged that the transformer coil is faulty.

8. A power meter fault monitoring system based on signal decomposition, comprising a directional storage module, a signal decomposition module, a mutual inductance error module, a fault comparison module, and an excitation calibration module, characterized in that: The directional storage module is used to set a positioning device in the electric energy meter. According to the location of the electric energy meter, the historical voltage and current test results of the current power supply point are retrieved from the cloud address library. After the test is completed, the test data is uploaded to the cloud. The cloud address library is updated with the power supply point where the electric energy meter address is located as the index. The fundamental wave, harmonics and mutual inductor error in this test are also recorded. The signal decomposition module is used to obtain the current signal between the wiring ports of the electric energy meter, decompose the current signal using a variable window windowing transform, decompose the current signal into time domain signals within each preset sub-band, decompose the time domain current signal into a fundamental signal and harmonic signals according to the frequency range of the AC circuit, calculate the voltage, current and power of the fundamental signal and harmonic signals respectively, and store the calculation results as power data; The mutual inductance error module is used to connect the primary winding of the energy meter to the high-voltage power supply line and the secondary winding to the power line. It also connects the voltage transformer and current transformer through the shunt circuit to different ports of the energy meter chip. Based on the power data of the harmonic signal and the hardware parameters of the transformer, it calculates the errors of the voltage and current transformers, filters out the errors from the measured results, and compares the errors with the historical harmonic signals and errors in the cloud address library to obtain the historical hardware parameters of the transformer. If the hardware parameter drop rate exceeds the preset range, it is judged that the transformer hardware is damaged. The fault comparison module is used to perform correlation fitting on the power data of the fundamental and harmonic signals with the error data in the cloud address library. The fitting results are analyzed. If the correlation between the fundamental signal and the historical signal frequency or phase is lower than the threshold, it is judged as a power meter wiring fault. The excitation errors of the voltage transformer and current transformer ports are compared. If they are inconsistent, it is judged as a transformer coil fault. The excitation calibration module is used to cut off the power supply to the winding circuit when a transformer fault is detected. The power supply in the electricity meter inputs the test voltage and test current to the electricity meter, calculates the excitation reactance of the voltage transformer under actual load based on the interpolation method, and calibrates the error of the voltage transformer based on the excitation reactance between the secondary windings.

9. The electric energy meter fault monitoring system based on signal decomposition according to claim 8, characterized in that: The directional storage module includes: a virtual positioning unit and a data index unit; The virtual positioning unit is used to locate the meter box, power supply point and generator set where the energy meter is located through satellite or radio positioning equipment. When the power transmission and distribution specifications of the power supply points where the energy meter is located are the same, they are regarded as the same location; The data indexing unit is used to establish an electric energy management center in the cloud, receive the location and measurement data sent by the electric energy meters at various locations, and build a cloud address library.

10. The electric energy meter fault monitoring system based on signal decomposition according to claim 8, characterized in that: The signal decomposition module includes: a terminal connection unit, a windowing transformation unit and a harmonic separation unit; The terminal connection unit is composed of a terminal and a switch, and is used to connect the detection terminal of the electric energy meter to the circuit port of the device to be detected; The windowing transformation unit is used to select a wavelet function according to the detection records of the same address in the cloud address library, and perform a windowed wavelet transform on the detected voltage signal; The harmonic separation unit is used to perform frequency domain cutting on the frequency domain function within the range of the alternating current frequency, obtain fundamental wave signals and harmonic wave signals after inverse transformation, and obtain power parameters from the signal waveforms.

11. The electric energy meter fault monitoring system based on signal decomposition according to claim 8, characterized in that: The mutual inductance error module includes: a winding shunt unit, a fundamental wave simulation unit and an error filtering unit; The winding shunt unit is used to process the current of the electric energy meter and the voltage transformer through the shunt circuit and measure them separately; The fundamental wave simulation unit is used to calculate the mutual inductor error based on the power data of the harmonics and the fundamental wave; The error filtering unit is used to filter out harmonic signals and data caused by mutual inductance errors from the fundamental wave signal of the mutual inductor; The fault comparison module includes: a wiring comparison unit and a mutual inductance fault unit; The wiring comparison unit is used to calculate the correlation between the fundamental wave signal error and the cloud, and when the correlation is lower than a threshold, a wiring error is reported; The mutual inductance fault unit is used to obtain the excitation errors of the voltage transformer and the current transformer from the ports of the metering chip respectively, and to report a coil error when the errors are inconsistent.

12. The electric energy meter fault monitoring system based on signal decomposition according to claim 8, characterized in that: The excitation calibration module includes: an access control unit and a reactance calibration unit; The access control unit is used to control the current supply of the primary winding circuit and the secondary winding circuit, and adjust the access status of the power supply and circuit in the electric energy meter; The reactance calibration unit is used to calculate the excitation reactance of the mutual inductor according to the test result of the internal power supply, and calibrate the indication of the electric energy meter.

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