Electric line protector operation fault supervision system and method based on big data

Abstract

The invention relates to the field of protector data supervision, in particular to an electric line protector operation fault supervision system and method based on big data. According to the invention, data preprocessing is carried out on the collected real-time operation parameter data of the power utilization line protector to generate standard real-time operation parameter data of the power utilization line protector, and numerical value matching is carried out on the standard real-time operation parameter data of the power utilization line protector and the operation parameter analysis data of the power utilization line protector generated according to the historical operation parameter data of the power utilization line protector; according to the method, whether the power utilization line protector breaks down or not is judged, operation fault processing scheme analysis is carried out on the power utilization line protector with the fault, target power utilization line protector fault solution data are generated, intelligent recognition of the power utilization line protector fault is achieved, and a fault solution is scientifically generated.

Description

Technical Field

[0001] The present invention relates to the field of protector data supervision, and in particular to a power line protector operation fault supervision system and method based on big data. Background Art

[0002] Traditional methods of monitoring power line protector faults are often implemented through manual detection, which collects data from analog circuits in operation and analyzes the collected data to identify power line protector faults.

[0003] The Chinese invention patent with publication number CN115238925B introduces a motor equipment supervision method and system, which obtains historical data of the target motor equipment; inputs the historical operation data into the prediction model, and trains the prediction model to obtain the optimal prediction model; inputs the supervision data obtained in real time into the optimal prediction model, outputs the probability of various faults, and generates a motor operation status diagram through the fault probability; determines the fault area according to the motor operation status diagram, determines the comprehensive abnormal information, and generates a corresponding operation and maintenance strategy according to the comprehensive abnormal information, which can accurately judge the abnormality, predict the abnormality by comprehensively evaluating multiple parameters, and generate a motor operation status diagram, intuitively obtain the abnormal point, and generate the corresponding strategy according to the abnormality, but does not process the acquired data, so it is difficult to avoid the impact of abnormal data that may appear in the acquired data, and the accuracy and reliability of the generated operation and maintenance strategy cannot be guaranteed. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In order to solve the deficiencies in the background technology, the present invention provides a power line protector operation fault monitoring system and method based on big data, which can realize the intelligent identification of power line protector faults and scientifically generate fault solutions.

[0006] (II) Technical solution

[0007] A method for monitoring operation faults of power line protectors based on big data comprises the following steps:

[0008] S1. Collect historical operating parameter data of power line protectors;

[0009] S2. generating power line protector operation parameter analysis data based on the power line protector historical operation parameter data;

[0010] S3, collecting real-time operating parameter data of power line protectors;

[0011] S4, performing data preprocessing on the real-time operating parameter data of the power line protector to generate real-time operating parameter data of a standard power line protector;

[0012] S5, numerically matching the real-time operating parameter data of the standard power line protector with the operating parameter analysis data of the power line protector, analyzing whether the power line protector is faulty according to the matching result, and obtaining abnormal operating parameter data of the power line protector;

[0013] S6. When a power line protector fails, an operation fault handling solution analysis is performed on the power line protector to generate target power line protector fault solution data;

[0014] S7. Constructing power line protector operation fault data based on the power line protector abnormal operation parameter data and the target power line protector fault solution data, and pushing the data to the power line protector operation fault processing platform.

[0015] The present invention generates standard real-time operating parameter data of power line protectors by performing data preprocessing on collected real-time operating parameter data of power line protectors, and performs numerical matching with power line protector operating parameter analysis data generated based on historical operating parameter data of power line protectors, determines whether a power line protector fails, performs operating fault processing solution analysis on the failed power line protector, generates target power line protector fault solution data, combines the power line protector abnormal operating parameter data set with the target power line protector fault solution data to generate power line protector operating fault data, and pushes the data to a power line protector operating fault processing platform via the Internet of Things, thereby realizing intelligent identification of power line protector faults and scientifically generating fault solutions.

[0016] Preferably, the specific steps of collecting historical operating parameter data of the power line protector are as follows:

[0017] S11, set the power line protector operating parameter category set A = {a1, a2,, a i ,,a k}, where a i represents the operating parameter of the i-th type of power line protector, k represents the total number of power line protector operating parameter categories;

[0018] S12, collecting historical data of various types of power line protector operating parameters in the power line protector operating parameter category set during the normal operation of the power line protector, and generating a power line protector historical operating parameter data matrix B as follows:

[0019]

[0020] Among them, b ij represents the jth historical characteristic data of the operating parameters of the i-th type of power line protector during the normal operation of the power line protector, q i It represents the total number of historical characteristic data of the operating parameters of the i-th type of power line protector during the normal operation of the power line protector.

[0021] Preferably, the specific steps of generating the power line protector fault category analysis data based on the power line protector historical operation parameter data are as follows:

[0022] S21, normalizing the power line protector historical operation parameter data in the power line protector historical operation parameter data matrix B by the maximum and minimum standardization method, and generating a standard power line protector historical operation parameter data matrix B' as follows:

[0023]

[0024] Among them, b' ij The standard power line protector historical operating parameter data is obtained after the j-th historical characteristic data of the operating parameters of the i-th type of power line protector during the normal operation of the power line protector is normalized; the normalization formula is as follows:

[0025]

[0026] Among them, b imin and b imax Respectively represent the minimum value and the maximum value of all the historical operating parameter data of the power line protector operating parameters of the i-th type of power line protector in the power line protector historical operating parameter data matrix;

[0027] S22, respectively arrange all the standard power line protector historical operating parameter data of each type of power line protector operating parameter in the standard power line protector historical operating parameter data matrix according to the numerical value from large to small, select the maximum value and the minimum value of all the standard power line protector historical operating parameter data of each type of power line protector operating parameter in the standard power line protector historical operating parameter data matrix, and construct a power line protector operating parameter analysis data set in, represents the analysis data interval of the operating parameters of the i-th type power line protector, and They respectively represent the minimum value and the maximum value of all the standard power line protector historical operating parameter data of the i-th type power line protector operating parameters in the standard power line protector historical operating parameter data matrix.

[0028] By processing and analyzing the collected historical operating parameter data of power line protectors, power line protector operating parameter analysis data is constructed to ensure the accuracy and reliability of the analysis data, providing a data basis for subsequent operations.

[0029] Preferably, the specific steps of collecting real-time operating parameter data of the power line protector are as follows:

[0030] S31, set a data collection cycle, and evenly divide the data collection cycle to obtain an operating parameter data collection time point set D = {d1, d2,, d i ,,d l}, where d i represents the i-th operating parameter data collection time point, l represents the total number of operating parameter data collection time points;

[0031] S32, using sensors to collect the operating parameters of the power line protector in real time during the operation process at each operating parameter data collection time point in the operating parameter data collection time point set, and obtaining a real-time operating parameter data matrix F of the power line protector as follows:

[0032]

[0033] Among them, f ij It represents the real-time data of the operating parameters of the i-th type of power line protector at the j-th operating parameter data collection time point during the working process of the power line protector.

[0034] Preferably, the specific steps of preprocessing the real-time operating parameter data of the power line protector to generate the real-time operating parameter data of the standard power line protector are as follows:

[0035] S41, normalizing the real-time operating parameter data of the power line protector in the real-time operating parameter data matrix F of the power line protector by using the maximum and minimum normalization method, and generating a normalized data matrix F' of the real-time operating parameters of the power line protector as follows:

[0036]

[0037] Among them, f' ij The normalized data of the real-time operating parameters of the power line protector obtained by normalizing the real-time data of the operating parameters of the i-th type of power line protector at the j-th operating parameter data collection time point during the working process of the power line protector;

[0038] S42, using a mean value formula to perform average numerical measurement processing on the normalized data of the real-time operating parameters of the power line protector in the normalized data matrix of the real-time operating parameters of the power line protector, to generate a standard real-time operating parameter data set of the power line protector in, Represents the real-time operating parameter data of the i-th standard power line protector; the mean value formula is as follows:

[0039]

[0040] Wherein, l represents the total number of time points for collecting operating parameter data.

[0041] The operating parameters of the power line protector during operation are collected in real time through sensors, and the collected data are normalized and averaged in turn to generate real-time operating parameter data of standard power line protectors, which ensures the accuracy of data acquisition and effectively avoids the impact of erroneous data.

[0042] Preferably, the specific steps of numerically matching the real-time operating parameter data of the standard power line protector with the operating parameter analysis data of the power line protector, analyzing whether the power line protector is faulty according to the matching result, and obtaining the abnormal operating parameter data of the power line protector are as follows:

[0043] S51. Match the operating parameter values ​​of the standard power line protector real-time operating parameter data in the standard power line protector real-time operating parameter data set with the power line protector operating parameter analysis data in the power line protector operating parameter analysis data set in sequence according to the numbers. If the standard power line protector real-time operating parameter data in the standard power line protector real-time operating parameter data set and the corresponding power line protector operating parameter analysis data are successfully matched, it is determined that the power line protector is not faulty; otherwise, it is determined that the power line protector is faulty, and the standard power line protector real-time operating parameter data that fails to match the corresponding power line protector operating parameter analysis data is filtered out from the standard power line protector real-time operating parameter data set to generate a power line protector abnormal operating parameter data set. in, represents the abnormal operation parameter data of the i-th power line protector, and p represents the total number of abnormal operation parameter data of the power line protector; the numerical matching process is as follows:

[0044] S511, if It is determined that the i-th standard power line protector real-time operating parameter data in the standard power line protector real-time operating parameter data set matches the corresponding power line protector operating parameter analysis data successfully; otherwise, it is determined that the i-th standard power line protector real-time operating parameter data in the standard power line protector real-time operating parameter data set matches the corresponding power line protector operating parameter analysis data unsuccessfully;

[0045] S512, repeat the operation in S511 until all the standard power line protector real-time operation parameter data in the standard power line protector real-time operation parameter data set are traversed, and the value matching operation is terminated.

[0046] Preferably, when a power line protector fails, the specific steps of analyzing the operation fault handling solution of the power line protector and generating the target power line protector fault solution data are as follows:

[0047] S61. Establish a power line protector fault solution data set E = {e1, e2,, e i ,,e k}, where e i represents the solution data when the operating parameters of the i-th type of power line protector are abnormal, and k represents the total number of power line protector fault solution data;

[0048] S62: when a power line protector fails, an operation fault processing scheme analysis is performed on the power line protector according to the power line protector abnormal operation parameter data in the power line protector abnormal operation parameter data set to generate a target power line protector fault solution data E. mubiao ;

[0049] S621, selecting any one of the abnormal operation parameter data of the power line protector in the abnormal operation parameter data set of the power line protector to perform operation fault processing solution analysis, and matching the selected abnormal operation parameter data of the power line protector with optimal power line protector fault solution data;

[0050] S6211, build a star population, set the star population size to N, the current number of iterations to t, and the maximum number of iterations to t max and the dimension of the fault solution data search space is P;

[0051] Each individual star in the star population corresponds to a power line protector fault solution data set in the power line protector fault solution data set, and randomly generates an initial position set X of the star population in the fault solution data search space = {X1, X2,, X i ,,X N}, where Xi represents the initial position of the i-th individual star in the star population;

[0052] S6212. Calculate the fitness function value of each individual star in the star population, and arrange the individual stars in the star population from large to small according to the fitness function value, and select the star individual with the highest fitness function value as the black hole individual; the fitness function formula is as follows:

[0053]

[0054] Among them, Fit i represents the fitness function value of the i-th star individual in the star population, x i represents the ability of the power line protector fault solution data corresponding to the i-th star individual in the star population to solve the selected power line protector abnormal operation parameter data, and φ represents the correction value;

[0055] S6213. When the black hole position in the fault solution data search space is determined, other individual stars in the star population will be affected by the gravitational force of the black hole and update their positions in the direction of the black hole; the position update formula is as follows:

[0056]

[0057] in, and denote the position of the i-th individual star in the star population during the t+1th and tth iterations, respectively; rand denotes a random number uniformly distributed between (0,1); X BH represents the position of the individual black hole;

[0058] S6214. After all the individual stars in the fault solution data search space have completed one position update, the distance between each individual star in the star population and the individual black hole is calculated. When the distance between the individual star and the individual black hole is less than the radius of the individual black hole, the individual star will be swallowed by the individual black hole, and a new individual star will be randomly generated in the fault solution data search space. The distance calculation formula and the black hole radius calculation formula are as follows:

[0059]

[0060] Among them, D i represents the distance between the i-th star individual and the black hole individual in the star population, X i,s represents the position of the i-th individual star in the star population in the s-dimensional fault solution data search space, X BH,s represents the position of the black hole individual in the s-th dimension fault solution data search space, R(t) represents the radius of the black hole individual, FitBH Represents the fitness function value of the black hole individual;

[0061] S6215, recalculating the fitness function value of each star individual in the star population. If the fitness function value of a star individual is greater than the fitness function value of a black hole individual, the star individual is used to replace the original black hole individual as a new black hole individual. The original black hole individual is transformed into a star individual again and moves in the fault solution data search space.

[0062] S6216: Determine whether the current number of iterations t is less than the maximum number of iterations t max , if the current number of iterations t is less than the maximum number of iterations t max , then the current iteration number t is increased by 1, and the process returns to S6213; otherwise, the black hole individual is taken as the global optimal solution, and the power line protector fault solution data corresponding to the global optimal solution is output as the optimal power line protector fault solution data;

[0063] S622, repeat the operation in S621 until all the abnormal operation parameter data of the power line protector in the power line protector abnormal operation parameter data set are traversed, and the optimal power line protector fault solution data corresponding to each power line protector abnormal operation parameter data are combined to generate the target power line protector fault solution data E mubiao .

[0064] The black hole algorithm is used to match the abnormal operating parameter data of the power line protector with the optimal power line protector fault solution data, which improves the speed and efficiency of the matching process, effectively avoids the optimization results from falling into the local optimal solution, and at the same time realizes the accurate identification of the power line protector fault solution, ensuring the quality and effectiveness of the power line protector fault handling operation.

[0065] Preferably, the specific steps of constructing the power line protector operation fault data based on the power line protector abnormal operation parameter data and the target power line protector fault solution data, and pushing it to the power line protector operation fault processing platform are as follows:

[0066] S71, the abnormal operation parameter data set of the power line protector The target power line protector fault solution data E mubiao Combine data to generate power line protector operation fault data

[0067]

[0068] S72. Push the power line protector operation fault data to the power line protector operation fault processing platform through the Internet of Things and execute the power line protector fault processing operation.

[0069] By constructing the power line protector operation fault data and pushing it to the power line protector operation fault processing platform through the Internet of Things, rapid feedback of the power line protector operation fault data can be achieved.

[0070] The present invention also discloses a power line protector operation fault monitoring system based on big data, including a historical operation parameter data collection module, an operation parameter analysis data generation module, a real-time operation parameter data acquisition module, a real-time operation parameter data preprocessing module, a fault determination module, an operation fault processing solution analysis module and an operation fault processing execution module;

[0071] The historical operation parameter data collection module generates the historical operation parameter data of the power line protector by collecting the historical data of the operation parameters of various power line protectors during the normal operation of the power line protector;

[0072] The operation parameter analysis data generation module normalizes the historical operation parameter data of the power line protector, and constructs the operation parameter analysis data of the power line protector according to the normalized historical operation parameter data of the power line protector;

[0073] The real-time operation parameter data acquisition module collects the operation parameters of the power line protector in real time during the operation process at each operation parameter data acquisition time point at the set operation parameter data acquisition time point through the sensor, and obtains the real-time operation parameter data of the power line protector;

[0074] The real-time operation parameter data preprocessing module generates standard power line protector real-time operation parameter data by performing normalization processing and average value measurement processing on the real-time operation parameter data of the power line protector in sequence;

[0075] The fault determination module numerically matches the real-time operating parameter data of the standard power line protector with the operating parameter analysis data of the power line protector, and determines whether the power line protector is faulty according to the matching result;

[0076] The operation fault handling solution analysis module matches the optimal power line protector fault solution data for the faulty power line protector through a black hole algorithm to generate target power line protector fault solution data;

[0077] The operation fault processing execution module generates power line protector operation fault data by combining the power line protector abnormal operation parameter data set with the target power line protector fault solution data, and pushes it to the power line protector operation fault processing platform through the Internet of Things.

[0078] (III) Beneficial effects

[0079] 1. The present invention performs data preprocessing on the collected real-time operating parameter data of the power line protector to generate standard real-time operating parameter data of the power line protector, and performs numerical matching with the power line protector operating parameter analysis data generated based on the historical operating parameter data of the power line protector to determine whether the power line protector has a fault, and performs an operating fault processing scheme analysis on the faulty power line protector to generate target power line protector fault solution data, and combines the power line protector abnormal operating parameter data set with the target power line protector fault solution data to generate power line protector operating fault data, and pushes it to the power line protector operating fault processing platform through the Internet of Things, so as to realize intelligent identification of power line protector faults and scientifically generate fault solutions;

[0080] 2. The operating parameters of the power line protector during operation are collected in real time through sensors, and the collected data are normalized and averaged in turn to generate real-time operating parameter data of the standard power line protector, which ensures the accuracy of the acquired data and effectively avoids the impact of erroneous data;

[0081] 3. The black hole algorithm is used to match the abnormal operating parameter data of the power line protector with the optimal power line protector fault solution data, which improves the speed and efficiency of the matching process, effectively avoids the optimization result from falling into the local optimal solution, and realizes the accurate identification of the power line protector fault solution, ensuring the quality and effect of the power line protector fault handling operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, they can also obtain drawings based on these drawings without paying creative work.

[0083] Figure 1 A flowchart of a method for monitoring operation faults of power line protectors based on big data provided by the present invention;

[0084] Figure 2 A module schematic diagram of a power line protector operation fault monitoring system based on big data provided by the present invention. DETAILED DESCRIPTION

[0085] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0086] In the description of the present invention, it is necessary to understand that the terms "opening", "upper", "lower", "top", "middle", "inside" and the like indicating orientation or positional relationship are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the invention.

[0087] Embodiment 1 is as follows:

[0088] See also Figure 1 A method for monitoring the operation fault of a power line protector based on big data comprises the following steps:

[0089] S1. Collect historical operating parameter data of power line protectors;

[0090] S11, set the power line protector operating parameter category set A = {a1, a2,, a i ,,a k}, where a i represents the operating parameter of the i-th type of power line protector, k represents the total number of power line protector operating parameter categories;

[0091] S12, collecting historical data of various types of power line protector operating parameters in the power line protector operating parameter category set during the normal operation of the power line protector, and generating a power line protector historical operating parameter data matrix B as follows:

[0092]

[0093] Among them, b ij represents the jth historical characteristic data of the operating parameters of the i-th type of power line protector during the normal operation of the power line protector, q i It represents the total number of historical characteristic data of the operating parameters of the i-th type of power line protector during the normal operation of the power line protector.

[0094] S2. generating power line protector operation parameter analysis data based on the power line protector historical operation parameter data;

[0095] S21, normalizing the power line protector historical operation parameter data in the power line protector historical operation parameter data matrix B by the maximum and minimum standardization method, and generating a standard power line protector historical operation parameter data matrix B' as follows:

[0096]

[0097] Among them, b' ij The standard power line protector historical operating parameter data is obtained after the j-th historical characteristic data of the operating parameters of the i-th type of power line protector during the normal operation of the power line protector is normalized; the normalization formula is as follows:

[0098]

[0099] Among them, b imin and b imax Respectively represent the minimum value and the maximum value of all the historical operating parameter data of the power line protector operating parameters of the i-th type of power line protector in the power line protector historical operating parameter data matrix;

[0100] S22, respectively arrange all the standard power line protector historical operating parameter data of each type of power line protector operating parameter in the standard power line protector historical operating parameter data matrix according to the numerical value from large to small, select the maximum value and the minimum value of all the standard power line protector historical operating parameter data of each type of power line protector operating parameter in the standard power line protector historical operating parameter data matrix, and construct a power line protector operating parameter analysis data set in, represents the analysis data interval of the operating parameters of the i-th type power line protector, and They respectively represent the minimum value and the maximum value of all the standard power line protector historical operating parameter data of the i-th type power line protector operating parameters in the standard power line protector historical operating parameter data matrix.

[0101] S3, collecting real-time operating parameter data of power line protectors;

[0102] S31, set a data collection cycle, and evenly divide the data collection cycle to obtain an operating parameter data collection time point set D = {d1, d2,, d i ,,d l}, where d i represents the i-th operating parameter data collection time point, l represents the total number of operating parameter data collection time points;

[0103] S32, using sensors to collect the operating parameters of the power line protector in real time during the operation process at each operating parameter data collection time point in the operating parameter data collection time point set, and obtaining a real-time operating parameter data matrix F of the power line protector as follows:

[0104]

[0105] Among them, f ij It represents the real-time data of the operating parameters of the i-th type of power line protector at the j-th operating parameter data collection time point during the working process of the power line protector.

[0106] S4, performing data preprocessing on the real-time operating parameter data of the power line protector to generate real-time operating parameter data of a standard power line protector;

[0107] S41, normalizing the real-time operating parameter data of the power line protector in the real-time operating parameter data matrix F of the power line protector by using the maximum and minimum normalization method, and generating a normalized data matrix F' of the real-time operating parameters of the power line protector as follows:

[0108]

[0109] Among them, f' ij The normalized data of the real-time operating parameters of the power line protector obtained by normalizing the real-time data of the operating parameters of the i-th type of power line protector at the j-th operating parameter data collection time point during the working process of the power line protector;

[0110] S42, using a mean value formula to perform average numerical measurement processing on the normalized data of the real-time operating parameters of the power line protector in the normalized data matrix of the real-time operating parameters of the power line protector, to generate a standard real-time operating parameter data set of the power line protector in, Represents the real-time operating parameter data of the i-th standard power line protector; the mean value formula is as follows:

[0111]

[0112] Wherein, l represents the total number of time points for collecting operating parameter data.

[0113] S5, numerically matching the real-time operating parameter data of the standard power line protector with the operating parameter analysis data of the power line protector, analyzing whether the power line protector is faulty according to the matching result, and obtaining abnormal operating parameter data of the power line protector;

[0114] S51. Match the operating parameter values ​​of the standard power line protector real-time operating parameter data in the standard power line protector real-time operating parameter data set with the power line protector operating parameter analysis data in the power line protector operating parameter analysis data set in sequence according to the numbers. If the standard power line protector real-time operating parameter data in the standard power line protector real-time operating parameter data set and the corresponding power line protector operating parameter analysis data are successfully matched, it is determined that the power line protector is not faulty; otherwise, it is determined that the power line protector is faulty, and the standard power line protector real-time operating parameter data that fails to match the corresponding power line protector operating parameter analysis data is filtered out from the standard power line protector real-time operating parameter data set to generate a power line protector abnormal operating parameter data set. in, represents the abnormal operation parameter data of the i-th power line protector, and p represents the total number of abnormal operation parameter data of the power line protector; the numerical matching process is as follows:

[0115] S511, if It is determined that the i-th standard power line protector real-time operating parameter data in the standard power line protector real-time operating parameter data set matches the corresponding power line protector operating parameter analysis data successfully; otherwise, it is determined that the i-th standard power line protector real-time operating parameter data in the standard power line protector real-time operating parameter data set matches the corresponding power line protector operating parameter analysis data unsuccessfully;

[0116] S512, repeat the operation in S511 until all the standard power line protector real-time operation parameter data in the standard power line protector real-time operation parameter data set are traversed, and the value matching operation is terminated.

[0117] S6. When a power line protector fails, an operation fault handling solution analysis is performed on the power line protector to generate target power line protector fault solution data;

[0118] S61. Establish a power line protector fault solution data set E = {e1, e2,, e i ,,e k}, where e i represents the solution data when the operating parameters of the i-th type of power line protector are abnormal, and k represents the total number of power line protector fault solution data;

[0119] S62: When a power line protector fails, an operation fault processing scheme is analyzed for the power line protector according to the power line protector abnormal operation parameter data in the power line protector abnormal operation parameter data set, and a target power line protector fault solution data E is generated. mubiao ;

[0120] S621, selecting any one of the abnormal operation parameter data of the power line protector in the abnormal operation parameter data set of the power line protector to perform operation fault processing solution analysis, and matching the selected abnormal operation parameter data of the power line protector with optimal power line protector fault solution data;

[0121] S6211, build a star population, set the star population size to N, the current number of iterations to t, and the maximum number of iterations to t max and the dimension of the fault solution data search space is P;

[0122] Each individual star in the star population corresponds to a power line protector fault solution data set in the power line protector fault solution data set, and randomly generates an initial position set X of the star population in the fault solution data search space = {X1, X2,, X i ,,X N}, where X i represents the initial position of the i-th individual star in the star population;

[0123] S6212. Calculate the fitness function value of each individual star in the star population, and arrange the individual stars in the star population from large to small according to the fitness function value, and select the star individual with the highest fitness function value as the black hole individual; the fitness function formula is as follows:

[0124]

[0125] Among them, Fit i represents the fitness function value of the i-th star individual in the star population, x i represents the ability of the power line protector fault solution data corresponding to the i-th star individual in the star population to solve the selected power line protector abnormal operation parameter data, and φ represents the correction value;

[0126] S6213. When the black hole position in the fault solution data search space is determined, other individual stars in the star population will be affected by the gravitational force of the black hole and update their positions in the direction of the black hole; the position update formula is as follows:

[0127]

[0128] in, and denote the position of the i-th individual star in the star population during the t+1th and tth iterations, respectively; rand denotes a random number uniformly distributed between (0,1); X BH represents the position of the individual black hole;

[0129] S6214. After all the individual stars in the fault solution data search space have completed one position update, the distance between each individual star in the star population and the individual black hole is calculated. When the distance between the individual star and the individual black hole is less than the radius of the individual black hole, the individual star will be swallowed by the individual black hole, and a new individual star will be randomly generated in the fault solution data search space. The distance calculation formula and the black hole radius calculation formula are as follows:

[0130]

[0131] Among them, D i represents the distance between the i-th star individual and the black hole individual in the star population, X i,s represents the position of the i-th individual star in the star population in the s-dimensional fault solution data search space, X BH,s represents the position of the black hole individual in the s-th dimension fault solution data search space, R(t) represents the radius of the black hole individual, Fit BH Represents the fitness function value of the black hole individual;

[0132] S6215, recalculating the fitness function value of each star individual in the star population. If the fitness function value of a star individual is greater than the fitness function value of a black hole individual, the star individual is used to replace the original black hole individual as a new black hole individual. The original black hole individual is transformed into a star individual again and moves in the fault solution data search space.

[0133] S6216: Determine whether the current number of iterations t is less than the maximum number of iterations t max , if the current number of iterations t is less than the maximum number of iterations t max , then the current iteration number t is increased by 1, and the process returns to S6213; otherwise, the black hole individual is taken as the global optimal solution, and the power line protector fault solution data corresponding to the global optimal solution is output as the optimal power line protector fault solution data;

[0134] S622, repeat the operation in S621 until all the abnormal operation parameter data of the power line protector in the power line protector abnormal operation parameter data set are traversed, and the optimal power line protector fault solution data corresponding to each power line protector abnormal operation parameter data are combined to generate the target power line protector fault solution data E mubiao .

[0135] S7, constructing power line protector operation fault data based on the power line protector abnormal operation parameter data and the target power line protector fault solution data, and pushing the data to the power line protector operation fault processing platform;

[0136] S71, the abnormal operation parameter data set of the power line protector The target power line protector fault solution data E mubiao Combine data to generate power line protector operation fault data

[0137]

[0138] S72. Push the power line protector operation fault data to the power line protector operation fault processing platform through the Internet of Things and execute the power line protector fault processing operation.

[0139] Embodiment 2 is as follows:

[0140] See also Figure 2 , an operation fault monitoring system for power line protectors based on big data, including a historical operation parameter data collection module, an operation parameter analysis data generation module, a real-time operation parameter data acquisition module, a real-time operation parameter data preprocessing module, a fault determination module, an operation fault processing solution analysis module, and an operation fault processing execution module;

[0141] The historical operation parameter data collection module generates the historical operation parameter data of the power line protector by collecting the historical data of the operation parameters of various power line protectors during the normal operation of the power line protector;

[0142] The operation parameter analysis data generation module normalizes the historical operation parameter data of the power line protector, and constructs the operation parameter analysis data of the power line protector according to the normalized historical operation parameter data of the power line protector;

[0143] The real-time operation parameter data acquisition module collects the operation parameters of the power line protector in real time during the operation process at each operation parameter data acquisition time point at the set operation parameter data acquisition time point through the sensor, and obtains the real-time operation parameter data of the power line protector;

[0144] The real-time operation parameter data preprocessing module generates standard power line protector real-time operation parameter data by performing normalization processing and average value measurement processing on the real-time operation parameter data of the power line protector in sequence;

[0145] The fault determination module numerically matches the real-time operating parameter data of the standard power line protector with the operating parameter analysis data of the power line protector, and determines whether the power line protector is faulty according to the matching result;

[0146] The operation fault handling solution analysis module matches the optimal power line protector fault solution data for the faulty power line protector through a black hole algorithm to generate target power line protector fault solution data;

[0147] The operation fault processing execution module generates power line protector operation fault data by combining the power line protector abnormal operation parameter data set with the target power line protector fault solution data, and pushes it to the power line protector operation fault processing platform through the Internet of Things.

[0148] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0149] The preferred embodiments of the invention disclosed above are only used to help explain the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the invention, so that those skilled in the art can understand and use the invention well.

Claims

1. A method for monitoring the operation fault of a power line protector based on big data, characterized in that: The steps include: S1. Collect historical operating parameter data of power line protectors; S2. generating power line protector operation parameter analysis data based on the power line protector historical operation parameter data; S3, collecting real-time operating parameter data of power line protectors; S4, performing data preprocessing on the real-time operating parameter data of the power line protector to generate real-time operating parameter data of a standard power line protector; S5, numerically matching the real-time operating parameter data of the standard power line protector with the operating parameter analysis data of the power line protector, analyzing whether the power line protector is faulty according to the matching result, and obtaining abnormal operating parameter data of the power line protector; S6. When a power line protector fails, an operation fault handling solution analysis is performed on the power line protector to generate target power line protector fault solution data; S7. Constructing power line protector operation fault data based on the power line protector abnormal operation parameter data and the target power line protector fault solution data, and pushing the data to the power line protector operation fault processing platform.

2. According to the big data-based power line protector operation fault monitoring method of claim 1, it is characterized in that: The S1 comprises the following steps: S11, setting a power line protector operating parameter category set; S12, collecting historical data of various types of power line protector operating parameters in the power line protector operating parameter category set during the normal operation of the power line protector, and generating a power line protector historical operating parameter data matrix B.

3. The method for monitoring the operation fault of a power line protector based on big data according to claim 2 is characterized in that: The S2 comprises the following steps: S21, normalizing the power line protector historical operating parameter data in the power line protector historical operating parameter data matrix B by a maximum and minimum standardization method to generate a standard power line protector historical operating parameter data matrix B'; S22, respectively arrange all the standard power line protector historical operating parameter data of each type of power line protector operating parameter in the standard power line protector historical operating parameter data matrix according to the numerical value from large to small, select the maximum value and the minimum value of all the standard power line protector historical operating parameter data of each type of power line protector operating parameter in the standard power line protector historical operating parameter data matrix, and construct a power line protector operating parameter analysis data set in, represents the analysis data interval of the operating parameters of the i-th type power line protector, and They respectively represent the minimum value and the maximum value of all the standard power line protector historical operating parameter data of the i-th type power line protector operating parameters in the standard power line protector historical operating parameter data matrix.

4. The method for monitoring the operation fault of a power line protector based on big data according to claim 3 is characterized in that: The S3 comprises the following steps: S31, setting a data collection cycle, and evenly dividing the data collection cycle to obtain an operating parameter data collection time point set; S32. Using sensors to collect operating parameters of the power line protector in real time during operation at each operating parameter data collection time point in the operating parameter data collection time point set, and obtain a real-time operating parameter data matrix F of the power line protector.

5. The method for monitoring the operation fault of a power line protector based on big data according to claim 4 is characterized in that: The S4 comprises the following steps: S41, normalizing the real-time operating parameter data of the power line protector in the real-time operating parameter data matrix F of the power line protector by a maximum and minimum normalization method to generate a normalized data matrix F' of the real-time operating parameters of the power line protector; S42, using a mean value formula to perform average numerical measurement processing on the normalized data of the real-time operating parameters of the power line protector in the normalized data matrix of the real-time operating parameters of the power line protector, to generate a standard real-time operating parameter data set of the power line protector in, Represents the real-time operating parameter data of the i-th standard power line protector.

6. A method for monitoring operation faults of power line protectors based on big data according to claim 5, characterized in that: The S5 comprises the following steps: S51. Match the operating parameter values ​​of the standard power line protector real-time operating parameter data in the standard power line protector real-time operating parameter data set with the power line protector operating parameter analysis data in the power line protector operating parameter analysis data set in sequence according to the numbers. If the standard power line protector real-time operating parameter data in the standard power line protector real-time operating parameter data set and the corresponding power line protector operating parameter analysis data are successfully matched, it is determined that the power line protector is not faulty; otherwise, it is determined that the power line protector is faulty, and the standard power line protector real-time operating parameter data that fails to match the corresponding power line protector operating parameter analysis data is filtered out from the standard power line protector real-time operating parameter data set to generate a power line protector abnormal operating parameter data set. The numerical matching process is as follows: S511, if It is determined that the i-th standard power line protector real-time operating parameter data in the standard power line protector real-time operating parameter data set matches the corresponding power line protector operating parameter analysis data successfully; otherwise, it is determined that the i-th standard power line protector real-time operating parameter data in the standard power line protector real-time operating parameter data set matches the corresponding power line protector operating parameter analysis data unsuccessfully; S512, repeat the operation in S511 until all the standard power line protector real-time operation parameter data in the standard power line protector real-time operation parameter data set are traversed, and the value matching operation is ended.

7. A method for monitoring operation faults of power line protectors based on big data according to claim 6, characterized in that: The S6 comprises the following steps: S61. Establish a data set of solutions for power line protector faults; S62: When a power line protector fails, an operation fault processing scheme is analyzed for the power line protector according to the power line protector abnormal operation parameter data in the power line protector abnormal operation parameter data set, and a target power line protector fault solution data E is generated. mubiao ; S621, selecting any one of the abnormal operation parameter data of the power line protector in the abnormal operation parameter data set for power line protector to perform operation fault processing solution analysis, and matching the selected abnormal operation parameter data of the power line protector with optimal power line protector fault solution data; S622, repeat the operation in S621 until all the abnormal operation parameter data of the power line protector in the power line protector abnormal operation parameter data set are traversed, and the optimal power line protector fault solution data corresponding to each power line protector abnormal operation parameter data are combined to generate the target power line protector fault solution data E mubiao .

8. The method for monitoring the operation fault of a power line protector based on big data according to claim 7 is characterized in that: The S621 includes the following steps: S6211, build a star population, set the star population size to N, the current number of iterations to t, and the maximum number of iterations to t max and the dimension of the fault solution data search space is P; Each individual star in the star population corresponds to one power line protector fault solution data in the power line protector fault solution data set, and randomly generates an initial position set of the star population in the fault solution data search space; S6212, calculating the fitness function value of each individual star in the star population, and arranging the individual stars in the star population from large to small according to the fitness function value, and selecting the individual star with the highest fitness function value as the black hole individual; S6213. When the position of the black hole in the fault solution data search space is determined, other individual stars in the star population will be affected by the gravitational force of the black hole and update their positions in the direction of the black hole; S6214. After all the individual stars in the fault solution data search space have completed one position update, the distance between each individual star in the star population and the individual black hole is calculated. When the distance between the individual star and the individual black hole is smaller than the radius of the individual black hole, the individual star will be swallowed by the individual black hole, and a new individual star will be randomly generated in the fault solution data search space. S6215, recalculating the fitness function value of each star individual in the star population. If the fitness function value of a star individual is greater than the fitness function value of a black hole individual, the star individual is used to replace the original black hole individual as a new black hole individual. The original black hole individual is transformed into a star individual again and moves in the fault solution data search space. S6216, determine whether t is less than t max , if t is less than t max , then t is increased by 1, and S6213 is returned; otherwise, the black hole individual is taken as the global optimal solution, and the power line protector fault solution data corresponding to the global optimal solution is output as the optimal power line protector fault solution data.

9. A method for monitoring operation faults of power line protectors based on big data according to claim 8, characterized in that: The S7 comprises the following steps: S71, the abnormal operation parameter data set of the power line protector The target power line protector fault solution data E mubiao Combine data to generate power line protector operation fault data S72. Push the power line protector operation fault data to the power line protector operation fault processing platform through the Internet of Things and execute the power line protector fault processing operation.

10. A system for implementing the method for supervising operation faults of power line protectors based on big data as described in any one of claims 1 to 9.

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