Line zero line fault monitoring and diagnosing system

Abstract

The invention relates to the technical field of power fault monitoring, and particularly discloses a line zero line fault monitoring and diagnosis system which comprises a plurality of current sensors, an edge computing gateway and a fault analysis platform which are in network connection in sequence, and the plurality of current sensors are respectively installed on a main line and branch lines of a zero line; the current sensor is used for sampling according to a received synchronizing signal and sending sampled current data to the edge computing gateway, and the edge computing gateway is used for sending a clock synchronizing signal to the current sensor, receiving and storing the sampled current data and analyzing the current data to obtain a data analysis result; a data analysis result is sent to the fault analysis platform; and the fault analysis platform calculates a fault type according to the data analysis result and displays the fault type. The invention aims to solve the problems of low transmission speed, short transmission distance and over-high cost of the existing fault diagnosis method.

Description

Technical Field

[0001] The invention relates to the technical field of power fault monitoring, and in particular to a line neutral line fault monitoring and diagnosis system. Background Art

[0002] In the distribution substation area, the zero line of the 400V line is often energized or the zero line of the entire area is energized. For this kind of fault, the method of segmented line removal is often used to find the branch where the zero line is energized. By disconnecting the sub-control switches of each branch in the main control cabinet, find out which branch the zero line is energized in, and disconnect the customer's main switch in the user's meter collection box under this branch. That is, disconnect one household and then test it again with a test pen. This method is time-consuming, laborious, and inefficient, and requires frequent power outages and power supplies, which seriously affects the user's electricity use. When the zero line or phase line power failure occurs, it will cause phase deviation, which is very likely to cause a phase voltage to be too high and burn electrical equipment. Therefore, such accidents must be handled quickly and in a timely manner to minimize the impact of the fault.

[0003] To solve the above problems, the existing patent number "CN202010500735.6" discloses a low-voltage substation neutral line live fault diagnosis device and diagnosis and positioning method, which proposes to diagnose the fault current characteristics. By setting up a sensor module, a signal preprocessing circuit, a data acquisition and processing module, a wireless transmission module and a human-computer interaction module, the current on the three-phase and neutral lines can be measured synchronously. Through the preprocessing circuit, the waveform of the effective frequency band is extracted, and the data is uploaded to the human-computer interaction module of the host computer through the Zigbee wireless transmission module for display, which is used for analysis and diagnosis by testers. Since Zigbee wireless transmission is mainly used for low-power, low-data-rate Internet of Things (IoT) applications. Its standard transmission speed is generally between 20-250 kilobits per second (kbps); there are problems of slow transmission speed and short transmission distance; therefore, the number of sensors required for the same line length increases, resulting in increased costs. Summary of the invention

[0004] In view of the deficiencies in the prior art, the technical problem solved by the present invention is to provide a line neutral line fault monitoring and diagnosis system to solve the problems of slow transmission speed, short transmission distance and high cost of the existing fault diagnosis method.

[0005] In order to solve the above problems, the technical solution adopted by the present invention is: a line neutral line fault monitoring and diagnosis system, comprising a current sensor, an edge computing gateway and a fault analysis platform connected in sequence to a network, wherein a plurality of current sensors are provided, and the plurality of current sensors are respectively installed on the main line and the branch line of the neutral line; the current sensor is used to sample according to the received synchronization signal, and send the sampled current data to the edge computing gateway, the edge computing gateway is used to send a clock synchronization signal to the current sensor, and receive the sampled current data and store it, and analyze the current data to obtain a data analysis result, and send the data analysis result to the fault analysis platform; the fault analysis platform calculates the fault type according to the data analysis result and displays the fault type; the data analysis result is a frequency array and corresponding wavelet transform coefficients obtained by the edge computing gateway performing continuous wavelet transform on the current data.

[0006] Furthermore, the fault analysis platform includes a database and an analysis module, wherein the database is used to store a high-resistance fault array, a low-resistance fault array, a high-resistance fault wavelet transform coefficient, and a low-resistance fault wavelet transform coefficient; the analysis module is used to autocorrelate the frequency array with the high-resistance fault array and the low-resistance fault array, respectively, to obtain a frequency high-resistance autocorrelation coefficient array and a frequency low-resistance autocorrelation coefficient array; the analysis module is also used to autocorrelate the wavelet transform coefficient with the high-resistance fault wavelet transform coefficient and the low-resistance fault wavelet transform coefficient, respectively, to obtain a high-resistance transform coefficient autocorrelation coefficient array and a low-resistance transform coefficient low-resistance autocorrelation coefficient array. Relation number array; and perform an AND operation on the frequency high-resistance autocorrelation coefficient array and the high-resistance transformation coefficient autocorrelation coefficient array to obtain a high-resistance AND array, and perform an OR operation on multiple high-resistance AND arrays to obtain a high-resistance result. If the high-resistance result is 1, it indicates a high-resistance fault, and if the high-resistance result is 0, it indicates that it does not belong to a high-resistance fault; and perform an AND operation on the frequency low-resistance autocorrelation coefficient array and the low-resistance transformation coefficient autocorrelation coefficient array to obtain a low-resistance AND array, and perform an OR operation on multiple low-resistance AND arrays to obtain a low-resistance result. If the low-resistance result is 1, it indicates a low-resistance fault, and if the low-resistance result is 0, it indicates that it does not belong to a low-resistance fault.

[0007] Furthermore, the sampling duration of the current sensor is 15-30 ms, and the sampling rate is 10-20 ksps.

[0008] Furthermore, the edge computing gateway communicates with the fault analysis platform through a 4G or 5G network, and the current sensor communicates with the edge computing gateway through wireless LoRa transmission.

[0009] The beneficial effects of this solution are: the autocorrelation and cross-correlation method of multiple sensors is adopted, which concentrates the advantages of cross-correlation and autocorrelation. Compared with simply adopting one method, it can avoid the overall error of a certain method and improve the accuracy of judgment; the data transmission distance is long, the system cost is low, the fault information is reported in time, and the fault handling efficiency is high. By setting the positions of multiple sensors, according to the location of the sensor with abnormal data, it is helpful to quickly locate the fault location, so that the repair service changes from passive to active, effectively shortening the customer's power outage time, ensuring the user's reliable electricity demand, and improving user satisfaction. The present invention provides an effective method and means for monitoring live faults in the neutral line of a 400V line. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This is a schematic diagram of the system operation.

[0011] Figure 2 This is a schematic diagram of the system diagnosis process. DETAILED DESCRIPTION

[0012] The following is further described in detail through specific implementation methods: As attached Figure 1 , Figure 2 As shown: a line neutral line fault monitoring and diagnosis system, including a current sensor, an edge computing gateway and a fault analysis platform connected in sequence to a network, wherein multiple current sensors are provided, and the multiple current sensors are respectively installed on the main line and branch line of the neutral line; the current sensor is used to sample according to the received synchronization signal, and send the sampled current data to the edge computing gateway, the edge computing gateway is used to send a clock synchronization signal to the current sensor, and receive the sampled current data and store it, and analyze the current data to obtain data analysis results, and send the data analysis results to the fault analysis platform; the fault analysis platform calculates the fault type according to the data analysis result, displays the fault type and issues an alarm.

[0013] The edge computing gateway communicates with the fault analysis platform through the 4G network, and the current sensor communicates with the edge computing gateway through wireless LoRa transmission; The data analysis result is the frequency array and the corresponding wavelet transform coefficients obtained by the edge computing gateway performing continuous wavelet transform on the current data; the sampling time of the current sensor is 15-30ms, the sampling rate is 10-20ksps, and the sampling data volume is 80-120 bytes; in this embodiment, the sampling time of the current sensor is preferably 20ms, the sampling rate is 20ksps, and the sampling data volume is 100 bytes.

[0014] The fault analysis platform includes a database, an analysis module, a large-screen display module and an alarm module. The large-screen display module is used to display the fault analysis results, and the alarm module is used to issue an alarm according to the fault analysis results; the database is used to store high-resistance fault arrays, low-resistance fault arrays, high-resistance fault wavelet transform coefficients and low-resistance fault wavelet transform coefficients; the analysis module is used to autocorrelate the frequency array with the high-resistance fault array and the low-resistance fault array, respectively, to obtain a frequency high-resistance autocorrelation coefficient array and a frequency low-resistance autocorrelation coefficient array; the analysis module is also used to autocorrelate the wavelet transform coefficients with the high-resistance fault wavelet transform coefficients and the low-resistance fault wavelet transform coefficients, respectively, to obtain a high-resistance The conversion coefficient autocorrelation coefficient array and the low-resistance conversion coefficient autocorrelation coefficient array are performed; and the frequency high-resistance autocorrelation coefficient array and the high-resistance conversion coefficient autocorrelation coefficient array are performed AND operation to obtain a high-resistance AND array, and multiple high-resistance AND arrays are performed OR operation to obtain a high-resistance result. If the high-resistance result is 1, it indicates a high-resistance fault, and if the high-resistance result is 0, it indicates that it does not belong to a high-resistance fault; and the frequency low-resistance autocorrelation coefficient array and the low-resistance conversion coefficient autocorrelation coefficient array are performed AND operation to obtain a low-resistance AND array, and multiple low-resistance AND arrays are performed OR operation to obtain a low-resistance result. If the low-resistance result is 1, it indicates a low-resistance fault, and if the low-resistance result is 0, it indicates that it does not belong to a low-resistance fault.

[0015] Taking the setting of 5 current sensors as an example, the process of fault diagnosis is specifically described. The 5 current sensors are numbered as current sensor 1#, current sensor 2#, current sensor 3#, current sensor 4#, and current sensor 5# respectively; the edge computing gateway data analysis operation process is: calculate the continuous wavelet transform of current sensor 1#, current sensor 2#, current sensor 3#, current sensor 4#, and current sensor 5# to form five wavelet transform frequency arrays Freq1[n1], Freq2[n2], Freq3[n3], Freq4[n4], Freq5[n5] and corresponding wavelet transform coefficients WL1[m1], WL2[m2], WL3[m3], WL4[m4], WL5[m5], and transmit them to the fault analysis platform.

[0016] The analysis module of the fault analysis platform compares the frequencies of the frequency arrays Freq1[n1], Freq2[n2], Freq3[n3], Freq4[n4], and Freq5[n5] with the frequencies of the high-resistance fault arrays FreqHL1[n1], FreqHL2[n2], FreqHL3[n3], FreqHL4[n4], and FreqHL5[n5] and the low-resistance fault arrays FreqLL1[n1], FreqLL2[n2], and FreqLL4[n4] stored in the database. eqLL3[n3], FreqLL4[n4], and FreqLL5[n5] are respectively autocorrelated to obtain the frequency high-impedance autocorrelation coefficient arrays coeHL1[n1], coeHL2[n2], coeHL3[n3], coeHL4[n4], and coeHL5[n5] and the frequency low-impedance autocorrelation coefficient arrays coeLL1[n1], coeLL2[n2], coeLL3[n3], coeLL4[n4], and coeLL5[n5].

[0017] The analysis module compares the wavelet transform coefficients WL1[m1], WL2[m2], WL3[m3], WL4[m4], WL5[m5] with the high-resistance fault wavelet transform coefficients WLHL1[m1], WLHL2[m2], WLHL3[m3], WLHL4[m4], WLHL5[m5] and the low-resistance fault wavelet transform coefficients WLLL1[m1], WLLL2[m2], WLLL3[m3], WLLL4[m4], WLLL5[m5] stored in the database. WLLL5[m5] are autocorrelated respectively to obtain the high-resistance transformation coefficient autocorrelation coefficient arrays coeWHL1[n1], coeWHL2[n2], coeWHL3[n3], coeWHL4[n4], coeWHL5[n5] and the low-resistance transformation coefficient low-resistance autocorrelation coefficient arrays coeWLL1[n1], coeWLL2[n2], coeWLL3[n3], coeWLL4[n4], coeWLL5[n5].

[0018] The analysis module performs an AND operation on the frequency high-impedance autocorrelation coefficient array and the high-impedance transformation coefficient autocorrelation coefficient array, that is, coeWHL1[n1], coeWHL2[n2], coeWHL3[n3], coeWHL4[n4], coeWHL5[n5] and coeHL1[n1], coeHL2[n2], coeHL3[n3], coeHL4[n4], coeHL5[n5] are ANDed; the steps are: coeWHL 1[n1]&&coeHL1[n1], coeWHL2[n2]&&coeHL2[n2], coeWHL3[n3]&&coeHL3[n3], coeWHL4[n4]&&coeHL4[n4], coeWHL5[n5]&&coeHL5[n5] obtain 5 high-resistance and arrays HLand1, HLand2, HLand3, HLand4, HLand5, and then calculate HLand1 || HLand2 || HLand3 || HLand4 || HLand5. If the high-resistance result is 1, it means that the fault is a high-resistance fault. If the high-resistance result is 0, it means that it is not a high-resistance fault.

[0019] The frequency low-resistance autocorrelation coefficient array and the low-resistance transformation coefficient autocorrelation coefficient array are ANDed, that is, WLLL1[m1], WLLL2[m2], WLLL3[m3], WLLL4[m4], WLLL5[m5] are ANDed with coeLL1[n1], coeLL2[n2], coeLL3[n3], coeLL4[n4], coeLL5[n5], the steps are: coeWLL1[n1] &&coeLL1[n1], coeLHL2[n2]&&coeLL2[n2], coeLHL3[n3]&&coeLL3[n3], coeLHL4[n4]&&coeLL4[n4], coeLHL5[n5]&&coeLL5[n5] obtain the low resistance and arrays LLand1, LLand2, LLand3, LLand4, LLand5, and then calculate LLand1 || LLand2 || LLand3 || LLand4 || LLand5. If the low resistance result is 1, it means that the fault is a low resistance fault. If the low resistance result is 0, it means that it is not a low resistance fault.

[0020] The above is only an embodiment of the present invention, and the common knowledge such as the known specific structure and characteristics in the scheme is not described in detail here. It should be pointed out that for those skilled in the art, several deformations and improvements can be made without departing from the structure of the present invention, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A line neutral fault monitoring and diagnosis system, characterized in that: It includes a current sensor, an edge computing gateway and a fault analysis platform connected in sequence to a network, wherein a plurality of current sensors are provided, and the plurality of current sensors are respectively installed on the trunk line and the branch line of the neutral line; the current sensor is used to sample according to the received synchronization signal, and send the sampled current data to the edge computing gateway, the edge computing gateway is used to send a clock synchronization signal to the current sensor, and receive and store the sampled current data, and analyze the current data to obtain a data analysis result, and send the data analysis result to the fault analysis platform; The fault analysis platform calculates the fault type based on the data analysis results and displays the fault type; the data analysis results are the frequency array and the corresponding wavelet transform coefficients obtained by the edge computing gateway performing continuous wavelet transform on the current data.

2. A line neutral fault monitoring and diagnosis system according to claim 1, characterized in that: The fault analysis platform includes a database and an analysis module. The database is used to store a high-resistance fault array, a low-resistance fault array, a high-resistance fault wavelet transform coefficient, and a low-resistance fault wavelet transform coefficient. The analysis module is used to autocorrelate the frequency array with the high-resistance fault array and the low-resistance fault array, respectively, to obtain a frequency high-resistance autocorrelation coefficient array and a frequency low-resistance autocorrelation coefficient array; the analysis module is also used to autocorrelate the wavelet transform coefficient with the high-resistance fault wavelet transform coefficient and the low-resistance fault wavelet transform coefficient, respectively, to obtain a high-resistance transform coefficient autocorrelation coefficient array and a low-resistance transform coefficient low-resistance autocorrelation coefficient array. An array of numbers; and an AND operation is performed on the frequency high-resistance autocorrelation coefficient array and the high-resistance transformation coefficient autocorrelation coefficient array to obtain a high-resistance AND array, and multiple high-resistance AND arrays are ORed to obtain a high-resistance result. If the high-resistance result is 1, it indicates a high-resistance fault, and if the high-resistance result is 0, it indicates that it does not belong to a high-resistance fault; and an AND operation is performed on the frequency low-resistance autocorrelation coefficient array and the low-resistance transformation coefficient autocorrelation coefficient array to obtain a low-resistance AND array, and multiple low-resistance AND arrays are ORed to obtain a low-resistance result. If the low-resistance result is 1, it indicates a low-resistance fault, and if the low-resistance result is 0, it indicates that it does not belong to a low-resistance fault.

3. A line neutral fault monitoring and diagnosis system according to claim 1, characterized in that: The sampling time of the current sensor is 15-30 ms, and the sampling rate is 10-20 ksps.

4. A line neutral fault monitoring and diagnosis system according to claim 1, characterized in that: The edge computing gateway communicates with the fault analysis platform via a 4G or 5G network, and the current sensor communicates with the edge computing gateway via wireless LoRa transmission.

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