Distributed Wireless Control System and Method for Transformer Winding Deformation Detection

Abstract

The present invention discloses a distributed wireless control system and method for transformer winding deformation detection, including a plurality of transformers, a first deformation detection device and a second deformation detection device electrically connected to the windings of the transformers, an edge computing server, and a cloud platform; the first deformation detection device is used to generate a first fault signal when detecting a winding deformation fault; the edge computing server is used to analyze and obtain a first fault credibility level according to the type of the faulty winding and the change amount of the short-circuit impedance; the cloud platform is used to obtain a second fault credibility according to the historical data of the transformer winding, and also determine a secondary fault judgment strategy according to the first fault credibility level and the second fault credibility. The present invention sets two different types of winding deformation detection devices to work together, and executes a secondary judgment strategy based on the judgment of the fault credibility level and the fault credibility, reducing the problem of false alarms of deformation faults in actual work and improving the accuracy and reliability of deformation fault detection.

Description

Technical Field

[0001] The present invention belongs to the technical field of transformer winding deformation detection, and particularly relates to a distributed wireless control system and method for transformer winding deformation detection. Background Art

[0002] A transformer is an important power equipment in power transmission and distribution work. Transformer failures often lead to power supply safety problems, so various devices need to be set up to detect transformer failures. Transformer winding deformation failure is a common type of transformer failure.

[0003] In the prior art, various methods such as short-circuit impedance method, pulse signal method, frequency response method, vibration measurement method, and optical fiber sensing method are used to detect transformer winding deformation failures. However, in actual applications, there are often problems of false alarms of deformation failures. The reason is that the working environment of the transformer is complex and changeable, and it is often difficult to set accurate fault thresholds based on a single deformation detection method. When the deformation amount of the transformer winding is near the fault threshold, it is often impossible to accurately detect, resulting in false alarms of deformation failures. Usually, fault signals will cause the transformer to shut down and require staff to go to deal with it, so it consumes a lot of manpower and material resources. Therefore, there is an urgent need for a technical solution that can accurately and reliably detect winding deformation failures to solve the above technical problems.

[0004] The prior art invention patent application CN118408509A discloses a transformer winding deformation detection system and method based on distributed sensing, including a plurality of transformers to be detected, a plurality of winding deformation detection devices, and a monitoring cloud platform; the plurality of winding deformation detection devices include at least one standard detection device and a plurality of distributed wireless detection devices; the windings of the first transformer to be detected are connected with the standard detection device and the distributed wireless detection device; the windings of the second transformer to be detected are connected with the distributed wireless detection device. This invention patent application obtains more real and reliable detection data by comparing different deformation detection devices, improving the accuracy of transformer winding deformation judgment. However, this invention patent application fails to solve technical problems such as false alarms of deformation failures. Summary of the Invention

[0005] The present invention provides a distributed wireless control system, system, and readable storage medium for transformer winding deformation detection to solve the technical problem of false alarms of deformation failures.

[0006] In a first aspect, the present invention provides a distributed wireless control system for detecting transformer winding deformation, including a plurality of transformers, a first deformation detection device, a second deformation detection device, an edge computing server, and a cloud platform, which are electrically connected to the windings of the transformers; the first deformation detection device and the second deformation detection device are both communicatively connected to the edge computing server through a wireless transmission module; the edge computing server is communicatively connected to the cloud platform; the first deformation detection device is configured to generate a first fault signal when a winding deformation fault is detected;

[0007] The edge computing server is configured to receive the first fault signal and analyze the first fault credibility level based on the type of the faulty winding and the change amount of the short-circuit impedance;

[0008] The cloud platform is configured to obtain a second fault credibility based on the historical data of the transformer winding, and the second fault credibility includes a longitudinal fault credibility and a transverse fault credibility; the cloud platform is further configured to determine a secondary fault judgment strategy based on the first fault credibility level and the second fault credibility.

[0009] Further, the edge computing server includes a fault credibility level calculation module, and the fault credibility level calculation module is configured to query the voltage level and power level of the faulty winding according to the working state information of the faulty winding; judge whether the faulty winding is a high-voltage winding or a low-voltage winding according to the voltage level and power level; obtain the change amount of the short-circuit impedance of the faulty winding in the first fault signal, if the faulty winding is a high-voltage winding, mark the change amount of the short-circuit impedance as the first change amount, and if the faulty winding is a low-voltage winding, mark the change amount of the short-circuit impedance as the second change amount; judge the first fault credibility level according to the first change amount and / or the second deformation amount.

[0010] Further, the cloud platform includes a fault credibility analysis module, and the fault credibility analysis module includes a longitudinal credibility analysis module and a transverse credibility analysis module;

[0011] The longitudinal credibility analysis module is used to query the fault confirmation signal from the database, and the fault confirmation signal includes a timestamp; obtain the fault time series signal according to the timestamp signal; obtain the first time period between each adjacent confirmed fault; obtain N short-circuit impedance value sequences within the N first time periods closest to the current moment before the first fault signal; respectively compare the size relationship between the N first time periods and the fault-free time period before the current moment of the first fault signal, and take the first time period with the smallest absolute value difference from the fault-free time period among the N first time periods as the second time period; intercept data with the same maximum time length within the second time period and the fault-free time period, and obtain the first trend curve and the second trend curve through curve fitting; perform correlation analysis on the first trend curve and the second trend curve, and calculate the correlation degree as the longitudinal fault credibility.

[0012] Further, the transverse credibility analysis module is used to trace the other two-phase windings of the transformer corresponding to the first fault signal and the control historical data corresponding to the fault-free time period; judge whether there is a fault confirmation signal in the control historical data; query other transformers of the same model and obtain the short-circuit impedance historical data; fit the control historical data into a third trend curve through curve fitting, and fit the data of the winding corresponding to the first fault signal within the fault-free time period into a fourth trend curve through curve fitting; perform correlation analysis on the third trend curve and the fourth trend curve, and calculate the correlation degree as the transverse fault credibility.

[0013] In a second aspect, the present invention provides a distributed wireless control method for transformer winding deformation detection, including:

[0014] Step S1: Online monitor the deformation amount of several transformer windings based on the short-circuit impedance method, and generate a first fault signal when an abnormality occurs; the first fault signal includes a fault identifier, fault winding working state information, and the change amount of the short-circuit impedance of the fault winding; the execution subject of the online monitoring is the first deformation detection device; the first deformation detection device is an online detection device for transformer winding deformation amount based on the short-circuit impedance method.

[0015] Step S2: The edge computing server receives the first fault signal and analyzes to obtain the first fault credibility level according to the type of the fault winding and the change amount of the short-circuit impedance.

[0016] Step S3: Analyze the historical data of the transformer winding based on the cloud platform to obtain the second fault credibility, including:

[0017] Step S31: Analyze the historical data of the transformer winding to obtain the longitudinal fault credibility.

[0018] Step S32: Analyze the historical data of the transformer winding to obtain the lateral fault credibility;

[0019] Step S4: Determine the secondary fault judgment strategy according to the first fault credibility level and the second fault credibility;

[0020] Step S5: The second deformation detection device performs deformation detection on the faulty winding corresponding to the first fault signal based on the frequency response method. If the detection is passed, the first fault signal is eliminated; otherwise, a fault confirmation signal is generated.

[0021] Further, the step S2 includes:

[0022] Step S21: The edge computing server queries the voltage level and power level of the faulty winding according to the working state information of the faulty winding;

[0023] Step S22: The edge computing server determines whether the faulty winding is a high-voltage winding or a low-voltage winding according to the voltage level and power level;

[0024] Step S23: Obtain the short-circuit impedance change amount of the faulty winding in the first fault signal. If the faulty winding is a high-voltage winding, mark the short-circuit impedance change amount as the first change amount; if the faulty winding is a low-voltage winding, mark the short-circuit impedance change amount as the second change amount;

[0025] Step S24: Judge the first fault credibility level according to the first change amount and / or the second deformation amount.

[0026] Further, the step S24 includes:

[0027] Step S241: If the first change amount is greater than or equal to the first threshold value, or the second change amount is greater than or equal to the second threshold value, then the first fault credibility level is high;

[0028] Step S242: If the first change amount is less than the third threshold value and greater than or equal to the fifth threshold value, or the second change amount is less than the fourth threshold value and greater than or equal to the sixth threshold value, then the first fault credibility level is low, and go to step S3;

[0029] Step S243: If the first change amount is greater than or equal to the third threshold value and less than the first threshold value, or the second change amount is greater than or equal to the fourth threshold value and less than the second threshold value, then the first fault credibility level is medium, and go to step S3; where the fifth threshold value is the fault threshold value of the high-voltage winding, the sixth threshold value is the fault threshold value of the low-voltage winding, and the fifth threshold value < the sixth threshold value < the third threshold value < the fourth threshold value < the first threshold value < the second threshold value;

[0030] Step S244: If the first change is less than the fifth threshold, or the second change is less than the sixth threshold, the counter is incremented and timing starts, and the value of the counter within a preset time period is counted. If it exceeds the preset value, the first fault credibility level is low, and the process goes to step S3.

[0031] Furthermore, the step S31 includes:

[0032] Step S311, querying a fault confirmation signal from a database, wherein the fault confirmation signal includes a timestamp;

[0033] Step S312, obtaining a fault time series signal according to the timestamp signal;

[0034] Step S313, obtaining a first time period between each adjacent confirmed fault;

[0035] Step S314, obtaining a sequence of N short-circuit impedance values ​​in the N first time periods closest to the current moment of the first fault signal, where N is a preset positive integer;

[0036] Step S315, respectively compare the magnitude relationships between the N first time periods and the fault-free time period before the current moment of the first fault signal, and take the first time period with the smallest absolute value difference with the fault-free time period among the N first time periods as the second time period;

[0037] Step S316, intercepting data of the same time length as the fault-free time period at the maximum, and obtaining a first trend curve and a second trend curve by curve fitting;

[0038] Step S317: perform correlation analysis on the first trend curve and the second trend curve, and calculate the correlation degree as the longitudinal fault credibility.

[0039] Furthermore, the step S32 includes:

[0040] Step S321, tracing back the comparison historical data of the other two-phase windings of the transformer corresponding to the first fault signal and the fault-free time period;

[0041] Step S322, judging whether there is a fault confirmation signal in the comparison historical data, if there is a fault confirmation signal in the other two phases, proceeding to step S41, otherwise proceeding to step S42;

[0042] Step S323, query other transformers of the same model to obtain short-circuit impedance historical data, and return to step S31 until comparison historical data without a fault confirmation signal is found;

[0043] Step S324: Fit the control historical data into a third trend curve by curve fitting, and fit the data of the winding corresponding to the first fault signal during the fault-free time period into a fourth trend curve by curve fitting;

[0044] Step S325: Conduct a correlation analysis on the third trend curve and the fourth trend curve, and calculate the correlation degree, which is the lateral fault credibility.

[0045] Further, the step S4 includes:

[0046] Step S41: If the first fault credibility level is low, and when the longitudinal fault credibility is less than a preset value or the lateral fault credibility is less than a preset value, enter step S5;

[0047] Step S42: If the first fault credibility level is medium, and when the longitudinal fault credibility is less than a preset value and the lateral fault credibility is less than a preset value, enter step S5;

[0048] Step S43: If the first fault credibility level is high, directly generate a fault confirmation signal.

[0049] The present invention has the following beneficial effects compared with the prior art:

[0050] The present invention provides two different types of winding deformation detection devices, wherein the first deformation detection device is an on-line winding deformation detection device based on the short-circuit impedance method; the second deformation detection device is an off-line winding deformation detection device and / or an on-line detection device based on the frequency response method; the two types of detection devices complement each other's strengths and weaknesses, and can more accurately detect winding deformation faults;

[0051] For the fault signal generated by the first deformation detection device, the present invention conducts a judgment of the credibility level and a judgment of the credibility. In the judgment of the credibility level, the difference characteristics between the high-voltage winding and the low-voltage winding are considered, which is more objective and accurate; in the judgment of the credibility, two dimensions of longitudinal and lateral are comprehensively considered, and the credibility level of the fault signal can be comprehensively judged, and based on this, it is decided whether to activate the second deformation detection device for a secondary judgment of the deformation fault, which can greatly improve the detection efficiency.

[0052] The present invention can reduce the problem of false alarms of deformation faults in actual work, and improve the accuracy and reliability of deformation fault detection. Description of the Drawings

[0053] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0054] Figure 1 Schematic diagram of a distributed wireless control system for transformer winding deformation detection provided by an embodiment of the present invention;

[0055] Figure 2 Structural diagram of a FIR filter system for transformer winding deformation detection provided by an embodiment of the present invention;

[0056] Figure 3 Flowchart of a distributed wireless control method for transformer winding deformation detection provided by an embodiment of the present invention. Detailed implementation manners

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0058] The embodiments of the present invention provide a distributed wireless control system for transformer winding deformation detection. Specifically, please refer to Figure 1 , Figure 1 Schematic diagram of a distributed wireless control system for transformer winding deformation detection provided by an embodiment of the present invention. The system includes:

[0059] A number of transformers, a first deformation detection device and a second deformation detection device electrically connected to the windings of the transformers, an edge computing server, and a cloud platform;

[0060] Both the first deformation detection device and the second deformation detection device are communicatively connected to the edge computing server through a wireless transmission module;

[0061] The edge computing server is communicatively connected to the cloud platform;

[0062] The first deformation detection device is used to generate a first fault signal when a winding deformation fault is detected;

[0063] The edge computing server is used to receive the first fault signal and analyze and obtain a first fault credibility level based on the type of the faulty winding and the change amount of the short-circuit impedance,

[0064] The cloud platform is used to obtain a second fault credibility based on the historical data of the transformer winding, and the second fault credibility includes a longitudinal fault credibility and a transverse fault credibility;

[0065] The cloud platform is further used to determine a secondary fault judgment strategy according to the first fault credibility level and the second fault credibility.

[0066] Wherein, the first deformation detection device is an on-line winding deformation detection device based on the short-circuit impedance method;

[0067] The second deformation detection device is an off-line winding deformation detection device and / or an on-line detection device based on the frequency response method.

[0068] Preferably, the edge computing server includes a fault credibility level calculation module, and the fault credibility level calculation module is used to query the voltage level and power level of the faulty winding according to the working state information of the faulty winding;

[0069] Judge whether the faulty winding is a high-voltage winding or a low-voltage winding according to the voltage level and the power level;

[0070] Obtain the change amount of the short-circuit impedance of the faulty winding in the first fault signal. If the faulty winding is a high-voltage winding, mark the change amount of the short-circuit impedance as the first change amount. If the faulty winding is a low-voltage winding, mark the change amount of the short-circuit impedance as the second change amount;

[0071] Judge the first fault credibility level according to the first change amount and / or the second deformation amount.

[0072] Preferably, the cloud platform includes a fault credibility analysis module, and the fault credibility analysis module includes a longitudinal credibility analysis module and a transverse credibility analysis module;

[0073] The longitudinal credibility analysis module is used to query a fault confirmation signal from the database, and the fault confirmation signal includes a timestamp; obtain a fault time series signal according to the timestamp signal;

[0074] Obtain the first time period between each adjacent confirmed fault; obtain N short-circuit impedance value sequences within the N first time periods closest to the current moment of the first fault signal;

[0075] Compare the size relationship between the N first time periods and the fault-free time period before the current moment of the first fault signal respectively, and take the first time period with the smallest absolute value difference from the fault-free time period among the N first time periods as the second time period;

[0076] Intercepting data of the same time length as that of the fault-free time period at most, and obtaining a first trend curve and a second trend curve by curve fitting;

[0077] A correlation analysis is performed on the first trend curve and the second trend curve, and the calculated correlation is the longitudinal fault credibility.

[0078] Preferably, the lateral credibility analysis module is used to trace back the comparison historical data of the other two-phase windings of the transformer corresponding to the first fault signal and the fault-free time period;

[0079] Determining whether there is a fault confirmation signal in the comparison historical data;

[0080] Query other transformers of the same model to obtain historical short-circuit impedance data;

[0081] The comparison historical data is fitted into a third trend curve by curve fitting, and the data of the winding corresponding to the first fault signal in the fault-free time period is fitted into a fourth trend curve by curve fitting;

[0082] A correlation analysis is performed on the third trend curve and the fourth trend curve, and the calculated correlation is the lateral fault credibility.

[0083] Preferably, the second deformation detection device includes a monitoring chip and adopts an embedded algorithm, including a digital filtering algorithm, a sinusoidal signal amplitude and phase solution algorithm, and a correlation coefficient algorithm;

[0084] Preferably, according to the sampling law, the highest frequency of the input analog signal should be less than half of the sampling frequency. In order to prevent the high-frequency components in the analog input signal (for example, local discharge signals, space electromagnetic wave signals, etc. mixed therein) from affecting the sampling of low-frequency components, a low-pass filter should be used to filter out high-frequency interference. In the online transfer function detection, in order to eliminate the interference of the power frequency and its harmonic signals, a high-pass filter should be used to filter out low-frequency interference.

[0085] Therefore, the dedicated chip should have a digital filter function, which should be placed after the A / D conversion of each channel signal acquisition and before other processing. The digital filter can use a finite impulse response FIR filter, solidify its impulse response function h(n) in the dedicated chip, and process the data through the following convolution operation:

[0086] ,

[0087] In the formula, , Input binary signal for convolution operation, , , , is an element of the response function matrix 、 is the response signal;

[0088] The above process can be completed by displacement, multiplication, and accumulation during data acquisition.

[0089] Preferably, the correlation coefficient solving algorithm includes:

[0090] There are two transfer function magnitude sequences of length N 、 , k = 0, l,..., N - 1, and the correlation coefficient can be calculated according to the following formula.

[0091] Calculate the standard deviation of the two sequences:

[0092] ,

[0093] ,

[0094] In the formula, 、 are the two transfer function magnitude sequences respectively, is the standard deviation of the transfer function magnitude sequence , is the standard deviation of the transfer function magnitude sequence , is the length of the transfer function magnitude;

[0095] Calculate the covariance of the two sequences:

[0096] ,

[0097] In the formula, is the covariance of the two transfer function magnitude sequences;

[0098] Calculate the normalized covariance of the two sequences:

[0099] ,

[0100] In the formula, is the normalized covariance of the two transfer function magnitude sequences;

[0101] Calculate the correlation coefficient:

[0102] ,

[0103] In the formula, is the correlation coefficient;

[0104] Preferably, in all links of generating, transmitting, and processing the monitoring signal, some interference signals will inevitably be introduced. To extract the useful signal reflecting the winding deformation from them, filtering must be carried out. The present invention selects a FIR digital filter and designs a FIR filter that meets the requirements of transformer winding deformation fault detection based on FPGA.

[0105] The order of the digital filter is 256. The specific implementation method is to divide the designed 256-order FIR filter into 64 4-order sub-filters. Each filter is implemented by a 4-input LUT. Since the filter coefficients have even symmetry, the required number of LUTs can be halved. The designed 256-order FIR filter only requires 32 4-input LUTs. The working process of designing the digital filter is as follows:

[0106] (1) Read the 256 12-bit signed sampling data from the ADC at the front end of the filter into the shift register one by one and perform shift buffering;

[0107] (2) According to the symmetry of the filter coefficients, pre-add the data multiplied by the same coefficients in the input sampling data to form 128 new input data;

[0108] (3) Perform serial-to-parallel conversion on the pre-added data, form the address by the lowest bit of the input address, group every four bits as a group, and send them to 32 lookup table modules respectively;

[0109] (4) Form the address by the second lowest bit of the input address, address 32 lookup tables respectively by grouping every four bits as a group, add the outputs of each lookup table, the sum is the second partial product, add it to the value stored in the register, then shift it one bit to the right, and store it in the register;

[0110] (5) Repeat steps (3) and (4) until the address formed by the 11th bit of the input data is addressed completely, obtain the eleventh partial product, add it to the value in the register, shift the obtained sum one bit to the right and store it in the register, and finally subtract the value addressed by the 12th bit (sign bit) of the sampling value to obtain the output result of the FIR filter.

[0111] According to the above working process, the FIR digital filter system can be divided into an input delay module, a pre-addition module, a serial-to-parallel conversion module, a lookup table module, and a shift accumulation module. For the specific structure diagram, please refer to Figure 2 , Figure 2 which is the structure diagram of a FIR filter system for transformer winding deformation detection provided by an embodiment of the present invention.

[0112] An embodiment of the present invention also provides a distributed wireless control method for transformer winding deformation detection. For details, please refer to Figure 3 , Figure 3A flowchart of a distributed wireless control method for transformer winding deformation detection provided by an embodiment of the present invention. The method includes the following steps:

[0113] Step S1: Online monitor the deformation amounts of several transformer windings based on the short-circuit impedance method, and generate a first fault signal when an abnormality occurs;

[0114] The first fault signal includes a fault identifier, working state information of the faulty winding, and the change amount of the short-circuit impedance of the faulty winding;

[0115] The execution entity of the online monitoring is the first deformation detection device;

[0116] The first deformation detection device is an online detection device for the deformation amount of the winding based on the short-circuit impedance method;

[0117] Step S2: The edge computing server receives the first fault signal, and analyzes and obtains the first fault credibility level according to the type of the faulty winding and the change amount of the short-circuit impedance;

[0118] Preferably, step S2 includes:

[0119] Step S21: The edge computing server queries the voltage level and power level of the faulty winding according to the working state information of the faulty winding;

[0120] Step S22: The edge computing server determines whether the faulty winding is a high-voltage winding or a low-voltage winding according to the voltage level and power level;

[0121] Step S23: Obtain the change amount of the short-circuit impedance of the faulty winding in the first fault signal. If the faulty winding is a high-voltage winding, mark the change amount of the short-circuit impedance as the first change amount; if the faulty winding is a low-voltage winding, mark the change amount of the short-circuit impedance as the second change amount;

[0122] Step S24: Determine the first fault credibility level according to the first change amount and / or the second deformation amount.

[0123] In actual experiments of the present invention, it is found that for the same degree of deformation, the change amount of the short-circuit reactance caused by the deformation of the low-voltage winding is greater than that caused by the deformation of the high-voltage winding; that is, the low-voltage is more sensitive and the detection is more accurate, and in practice, the deformation often occurs in the low-voltage winding. Therefore, in this application, the fault credibility levels of the high-voltage winding and the low-voltage winding are considered separately;

[0124] Preferably, step S24 includes:

[0125] Step S241: If the first change amount is greater than or equal to the first threshold, or the second change amount is greater than or equal to the second threshold, then the first fault credibility level is high;

[0126] Step S242: If the first change amount is less than the third threshold and greater than or equal to the fifth threshold, or the second change amount is less than the fourth threshold and greater than or equal to the sixth threshold, then the first fault credibility level is low, and proceed to step S3;

[0127] Step S243: If the first change amount is greater than or equal to the third threshold and less than the first threshold, or the second change amount is greater than or equal to the fourth threshold and less than the second threshold, then the first fault credibility level is medium, and proceed to step S3; where the fifth threshold is the fault threshold of the high-voltage winding, the sixth threshold is the fault threshold of the low-voltage winding, and the fifth threshold < the sixth threshold < the third threshold < the fourth threshold < the first threshold < the second threshold;

[0128] Step S244: If the first change amount is less than the fifth threshold, or the second change amount is less than the sixth threshold, then increment the counter and start timing. Statistically analyze the value of the counter within a preset time period. If it exceeds the preset value, then the first fault credibility level is low, and proceed to step S3.

[0129] In step S244, if a fault signal is received, but the change amount of the short-circuit impedance is very small and less than the fault threshold, it indicates that it must be a false alarm. This may be affected by accidental errors, resulting in a certain lag between the fault signal and the measured value of the short-circuit impedance. If this situation occurs repeatedly within a short period of time, it means that the change amount of the measured value of the short-circuit impedance is very small, but it fluctuates in the vicinity of the fault threshold. Therefore, it is necessary to further judge the fault credibility.

[0130] Step S3: Analyze the historical data of the transformer winding based on the cloud platform to obtain the second fault credibility, where the second fault credibility includes the longitudinal fault credibility and the transverse fault credibility;

[0131] Step S31: Analyze the historical data of the transformer winding to obtain the longitudinal fault credibility;

[0132] Preferably, step S31 includes:

[0133] Step S311: Query the fault confirmation signal from the database, where the fault confirmation signal includes a timestamp;

[0134] Step S312: Obtain the fault time series signal according to the timestamp signal;

[0135] Step S313: Obtain the first time period between each adjacent confirmed fault;

[0136] Step S314: Obtain the sequence of N short-circuit impedance values within the N first time periods closest to the current moment of the first fault signal, where N is a preset positive integer;

[0137] Step S315, respectively compare the magnitude relationships between the N first time periods and the fault-free time period before the current moment of the first fault signal, and take the first time period with the smallest absolute value difference with the fault-free time period among the N first time periods as the second time period;

[0138] Step S316, intercepting data of the same time length as the fault-free time period at the maximum, and obtaining a first trend curve and a second trend curve by curve fitting;

[0139] Step S317: perform correlation analysis on the first trend curve and the second trend curve, and calculate the correlation degree as the longitudinal fault credibility.

[0140] Step S32, analyzing the historical data of the transformer winding to obtain the lateral fault credibility;

[0141] Preferably, the step S32 includes:

[0142] Step S321, tracing back the comparison historical data of the other two-phase windings of the transformer corresponding to the first fault signal and the fault-free time period;

[0143] Step S322, judging whether there is a fault confirmation signal in the comparison historical data, if there is a fault confirmation signal in the other two phases, proceeding to step S41, otherwise proceeding to step S42;

[0144] Step S323, query other transformers of the same model to obtain short-circuit impedance historical data, and return to step S31 until comparison historical data without a fault confirmation signal is found;

[0145] Step S324, fitting the reference historical data into a third trend curve by curve fitting, and fitting the data of the winding corresponding to the first fault signal in the fault-free time period into a fourth trend curve by curve fitting;

[0146] Step S325 , performing correlation analysis on the third trend curve and the fourth trend curve, and calculating the correlation degree as the lateral fault credibility.

[0147] Step S4, determining a secondary fault judgment strategy according to the first fault credibility level and the second fault credibility;

[0148] Preferably, the step S4 comprises:

[0149] Step S41: if the first fault credibility level is low, and the longitudinal fault credibility is less than a preset value or the transverse fault credibility is less than a preset value, proceed to step S5;

[0150] Step S42: If the first fault credibility level is medium, and when the longitudinal fault credibility is less than the preset value and the lateral fault credibility is less than the preset value, proceed to Step S5;

[0151] Step S43: If the first fault credibility level is high, directly generate a fault confirmation signal.

[0152] Step S5: The second deformation detection device performs deformation detection on the faulty winding corresponding to the first fault signal based on the frequency response method. If the detection is passed, the first fault signal is eliminated; otherwise, a fault confirmation signal is generated.

[0153] In this specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple. For the relevant parts, reference can be made to the description in the method section.

[0154] Those skilled in the art can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described according to their functions in the above description. Whether these functions are executed in the form of hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0155] Finally, it should also be noted that in this article, relationships such as first and second are only used 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 term "comprising", "including" or any other variant is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or device.

Claims

1. A distributed wireless control system for transformer winding deformation detection, comprising a plurality of transformers, a first deformation detection device, a second deformation detection device, an edge computing server, and a cloud platform, where the first and second deformation detection devices are electrically connected to the windings of the transformers; The first deformation detection device and the second deformation detection device are both communicatively connected to the edge computing server through a wireless transmission module; the edge computing server is communicatively connected to the cloud platform; it is characterized in that The first deformation detection device is configured to generate a first fault signal when a winding deformation fault is detected. The edge computing server is configured to receive the first fault signal and analyze and obtain a first fault credibility level based on the type of the faulty winding and the change amount of the short-circuit impedance. The cloud platform is configured to obtain a second fault credibility based on the historical data of the transformer winding, where the second fault credibility includes a longitudinal fault credibility and a transverse fault credibility; the cloud platform is further configured to determine a secondary fault judgment strategy based on the first fault credibility level and the second fault credibility. The cloud platform includes a fault credibility analysis module, and the fault credibility analysis module includes a longitudinal credibility analysis module and a transverse credibility analysis module. The longitudinal credibility analysis module is configured to query a fault confirmation signal from a database, where the fault confirmation signal includes a timestamp; obtain a fault time series signal based on the timestamp signal; obtain a first time period between each adjacent confirmed fault; obtain N short-circuit impedance value sequences within N of the first time periods closest to the current moment before the current moment of the first fault signal; respectively compare the sizes of the N first time periods and the fault-free time period before the current moment of the first fault signal, and take the first time period with the smallest absolute value difference from the fault-free time period among the N first time periods as the second time period; intercept data of the same time length at most within the second time period and the fault-free time period, and obtain a first trend curve and a second trend curve by curve fitting; perform a correlation analysis on the first trend curve and the second trend curve, and calculate the correlation degree as the longitudinal fault credibility. The transverse credibility analysis module is configured to trace the other two-phase windings of the transformer corresponding to the first fault signal and the control historical data corresponding to the fault-free time period; determine whether there is a fault confirmation signal in the control historical data; query other transformers of the same model and obtain short-circuit impedance historical data; fit the control historical data into a third trend curve by curve fitting, and fit the data of the winding corresponding to the first fault signal within the fault-free time period into a fourth trend curve by curve fitting; perform a correlation analysis on the third trend curve and the fourth trend curve, and calculate the correlation degree as the transverse fault credibility.

2. The distributed wireless control system for transformer winding deformation detection according to claim 1, wherein, The edge computing server includes a fault credibility level calculation module, and the fault credibility level calculation module is configured to query the voltage level and power level of the faulty winding according to the working state information of the faulty winding. Judge whether the faulty winding is the high-voltage winding or the low-voltage winding according to the voltage level and power level; obtain the short-circuit impedance change amount of the faulty winding in the first fault signal, mark the short-circuit impedance change amount as the first change amount if the faulty winding is the high-voltage winding, and mark the short-circuit impedance change amount as the second change amount if the faulty winding is the low-voltage winding; judge the first fault credibility level according to the first change amount and / or the second deformation amount.

3. A distributed wireless control method for transformer winding deformation detection, which is applied to the distributed wireless control system for transformer winding deformation detection described in any one of claims 1-2, and is characterized in that, The method includes: Step S1: Online monitor the deformation amount of several transformer windings based on the short-circuit impedance method, and generate a first fault signal when an abnormality occurs; the first fault signal includes a fault identifier, the working state information of the faulty winding, and the short-circuit impedance change amount of the faulty winding; the execution entity of the online monitoring is the first deformation detection device; the first deformation detection device is an online detection device for the deformation amount of the winding based on the short-circuit impedance method; Step S2: The edge computing server receives the first fault signal and analyzes to obtain the first fault credibility level according to the type of the faulty winding and the short-circuit impedance change amount; Step S3: Analyze the historical data of the transformer winding based on the cloud platform to obtain the second fault credibility, including: Step S31: Analyze the historical data of the transformer winding to obtain the longitudinal fault credibility; Step S32: Analyze the historical data of the transformer winding to obtain the transverse fault credibility; Step S4: Determine the fault secondary judgment strategy according to the first fault credibility level and the second fault credibility; Step S5: The second deformation detection device performs deformation detection on the faulty winding corresponding to the first fault signal based on the frequency response method. If the detection is passed, the first fault signal is eliminated; otherwise, a fault confirmation signal is generated.

4. The distributed wireless control method for transformer winding deformation detection according to claim 3, wherein The said Step S2 includes: Step S21: The edge computing server queries the voltage level and power level of the faulty winding according to the working state information of the faulty winding; Step S22: The edge computing server judges whether the faulty winding is the high-voltage winding or the low-voltage winding according to the voltage level and power level; Step S23: Obtain the short-circuit impedance change amount of the faulty winding in the first fault signal, mark the short-circuit impedance change amount as the first change amount if the faulty winding is the high-voltage winding, and mark the short-circuit impedance change amount as the second change amount if the faulty winding is the low-voltage winding; Step S24: Judge the first fault credibility level according to the first change amount and / or the second deformation amount.

5. The distributed wireless control method for transformer winding deformation detection according to claim 4, wherein The said Step S24 includes: Step S241: If the first change amount is greater than or equal to the first threshold, or the second change amount is greater than or equal to the second threshold, then the first fault credibility level is high; Step S242: If the first change amount is less than the third threshold and greater than or equal to the fifth threshold, or the second change amount is less than the fourth threshold and greater than or equal to the sixth threshold, then the first fault credibility level is low, and enter Step S3; Step S243: If the first change amount is greater than or equal to the third threshold and less than the first threshold, or the second change amount is greater than or equal to the fourth threshold and less than the second threshold, then the first fault credibility level is medium, and proceed to step S3; where the fifth threshold is the fault threshold of the high-voltage winding, the sixth threshold is the fault threshold of the low-voltage winding, and the fifth threshold < the sixth threshold < the third threshold < the fourth threshold < the first threshold < the second threshold; Step S244: If the first change amount is less than the fifth threshold, or the second change amount is less than the sixth threshold, then increment the counter and start timing. Statistically analyze the value of the counter within a preset time period. If it exceeds the preset value, then the first fault credibility level is low, and proceed to step S3.

6. The distributed wireless control method for transformer winding deformation detection according to claim 3, characterized in that, The step S31 includes: Step S311: Query the fault confirmation signal from the database, and the fault confirmation signal includes a timestamp; Step S312: Obtain the fault time series signal according to the timestamp signal; Step S313: Obtain the first time period between each adjacent confirmed fault; Step S314: Obtain the sequence of N short-circuit impedance values within the N first time periods closest to the current moment of the first fault signal before the current moment, where N is a preset positive integer; Step S315: Compare the size relationship between the N first time periods and the fault-free time period before the current moment of the first fault signal respectively, and take the first time period with the smallest absolute value difference from the fault-free time period among the N first time periods as the second time period; Step S316: Intercept data with the same maximum time length within the second time period and the fault-free time period, and obtain the first trend curve and the second trend curve through curve fitting; Step S317: Conduct a correlation analysis on the first trend curve and the second trend curve, and calculate the correlation degree, which is the longitudinal fault credibility.

7. The distributed wireless control method for transformer winding deformation detection according to claim 3, wherein The step S32 includes: Step S321: Trace the control historical data corresponding to the other two-phase windings of the transformer corresponding to the first fault signal and the fault-free time period; Step S322: Determine whether there is a fault confirmation signal in the control historical data. If there are fault confirmation signals in both of the other two phases, proceed to step S41, otherwise proceed to step S42; Step S323: Query other transformers of the same model, obtain the short-circuit impedance historical data, and return to step S3 until the control historical data without a fault confirmation signal is found; Step S324: Fit the control historical data into a third trend curve through curve fitting, and fit the data of the winding corresponding to the first fault signal within the fault-free time period into a fourth trend curve through curve fitting; Step S325: Conduct a correlation analysis on the third trend curve and the fourth trend curve, and calculate the correlation degree, which is the horizontal fault credibility.

8. The distributed wireless control method for transformer winding deformation detection according to claim 3, wherein The step S4 includes: Step S41: If the first fault credibility level is low, and when the longitudinal fault credibility is less than the preset value or the horizontal fault credibility is less than the preset value, proceed to step S5; Step S42: If the first fault credibility level is medium, and when the longitudinal fault credibility is less than the preset value and the horizontal fault credibility is less than the preset value, proceed to step S5; Step S43: If the first fault credibility level is high, directly generate a fault confirmation signal.

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