CN-116089882-B - Cable fault prediction processing method and device and electronic equipment

CN116089882BCN 116089882 BCN116089882 BCN 116089882BCN-116089882-B

Abstract

The invention discloses a cable fault prediction processing method and device and electronic equipment. The method comprises the steps of obtaining partial discharge characteristic data of a high-voltage cable, respectively inputting first training set data in the partial discharge characteristic data into an initial radial basis function network model and an initial long-short-time memory network model for training, respectively inputting first test set data in the partial discharge characteristic data into the trained radial basis function network model and the trained long-short-time memory network model to obtain a first output result output by the trained radial basis function network model and a second output result output by the trained long-short-time memory network model, and determining a cable fault prediction model based on the first output result and the second output result. The method solves the technical problems of low cable fault prediction efficiency and poor prediction accuracy caused by poor cable fault prediction model performance in the related technology.

Inventors

LIU HONGJING
QIAN MENGDI
REN ZHIGANG
HE NAN
LIU KEWEN
MIAO WANG
LIU HONGLIANG
FANG LIE
XU YONGPENG

Assignees

国网北京市电力公司
国家电网有限公司
上海交通大学

Dates

Publication Date: 20260508
Application Date: 20221230

Claims (9)

1. A cable fault prediction processing method, characterized by comprising: Obtaining partial discharge characteristic data of a high-voltage cable; respectively inputting first training set data in the partial discharge characteristic data into an initial radial basis function network model and an initial long-short-term memory network model for training to obtain a trained radial basis function network model and a trained long-short-term memory network model; Respectively inputting first test set data in the partial discharge characteristic data into the trained radial basis function network model and the trained long-short-term memory network model to obtain a first output result output by the trained radial basis function network model and a second output result output by the trained long-short-term memory network model; Determining a cable fault prediction model based on the first output result and the second output result; the method comprises the steps of determining a cable fault prediction model based on the first output results and the second output results when the first test set data comprise a plurality of groups of test data, determining a comparison result between the plurality of first output results and the corresponding second output results respectively, wherein the comparison result is a first comparison result in which the first output results are consistent with the corresponding second output results respectively or a second comparison result in which the first output results are inconsistent with the corresponding second output results respectively, determining the total number of first results in the comparison results between the plurality of first output results and the corresponding second output results respectively and the number of the first comparison results, determining the first proportion of the number of the first comparison results to the total number of the first results, and building the cable fault prediction model according to the trained radial basis function network model and the trained time length network when the first proportion is larger than a preset proportion threshold.
2. The method according to claim 1, wherein the method further comprises: under the condition that the first proportion is not greater than a preset proportion threshold value, respectively updating the trained radial basis function network model and the trained long-short-time memory network model by adopting a gradient descent algorithm to obtain a new radial basis function network model and a new long-short-time memory network model; Respectively inputting second training set data in the partial discharge characteristic data into the new radial basis function network model and the new long-short-time memory network model for training to obtain a new trained radial basis function network model and a new trained long-short-time memory network model; Respectively inputting second test set data in the partial discharge characteristic data into the new trained radial basis function network model and the new trained long-short-term memory network model to obtain a third output result output by the new trained radial basis function network model and a fourth output result output by the new trained long-short-term memory network model; Determining comparison results between the plurality of third output results and the corresponding fourth output results respectively when the plurality of groups of test data are included in the second test set data, wherein the comparison results are third comparison results in which the third output results are consistent with the corresponding fourth output results respectively or fourth comparison results in which the third output results are inconsistent with the corresponding fourth output results respectively; Determining a total number of second results in comparison results between the plurality of third output results and corresponding fourth output results, respectively, and the number of third comparison results; determining a second proportion of the number of third comparison results to the total number of second results; And under the condition that the second proportion is larger than the preset proportion threshold value, constructing the cable fault prediction model according to the new trained radial basis function network model and the new trained long-and-short-term memory network model.
3. The method of claim 1, wherein before the first training set of data in the partial discharge feature data is input to an initial radial basis function network model and an initial long-short-term memory network model for training, respectively, to obtain a trained radial basis function network model and a trained long-short-term memory network model, the method further comprises: determining the characteristic type corresponding to the partial discharge characteristic data of the high-voltage cable; And determining initialization hyper-parameters corresponding to the initial radial basis function network model and the initial long-short time memory network model respectively based on the characteristic types, wherein the initialization hyper-parameters at least comprise initial node numbers and initial learning rates corresponding to the initial radial basis function network model and the initial long-short time memory network model respectively.
4. The method of claim 1, wherein after the determining a cable fault prediction model based on the first output result and the second output result, the method further comprises: Acquiring to-be-measured characteristic data of the to-be-measured cable; and inputting the characteristic data to be tested into the cable fault prediction model for testing to obtain the discharge type corresponding to the cable to be tested.
5. The method according to any one of claims 1 to 4, wherein the acquiring partial discharge characteristic data of the high voltage cable comprises: acquiring partial discharge fault history data of a high-voltage cable; Carrying out noise reduction treatment on the partial discharge fault historical data to obtain noise-reduced partial discharge fault historical data; and carrying out feature extraction processing on the noise-reduced partial discharge fault historical data to obtain the partial discharge feature data.
6. The method of claim 5, wherein the denoising the partial discharge fault history data to obtain denoised partial discharge fault history data comprises: judging whether missing data exists in the partial discharge fault historical data; under the condition that the missing data exists in the partial discharge fault historical data, filling the missing data by adopting an interpolation method to obtain filled partial discharge fault historical data; And carrying out noise reduction treatment on the filled partial discharge fault historical data to obtain the noise-reduced partial discharge fault historical data.
7. A cable fault prediction processing device, comprising: the first acquisition module is used for acquiring partial discharge characteristic data of the high-voltage cable; The training module is used for respectively inputting the first training set data in the partial discharge characteristic data into the initial radial basis function network model and the initial long-short-time memory network model for training to obtain a trained radial basis function network model and a trained long-short-time memory network model; the second acquisition module is used for respectively inputting the first test set data in the partial discharge characteristic data into the trained radial basis function network model and the trained long-short-time memory network model to obtain a first output result output by the trained radial basis function network model and a second output result output by the trained long-short-time memory network model; the determining module is used for determining a cable fault prediction model based on the first output result and the second output result; The determining module is further configured to determine a comparison result between the plurality of first output results and the corresponding second output results when the first test set data includes a plurality of sets of test data, where the comparison result is a first comparison result in which the first output results are consistent with the corresponding second output results, or a second comparison result in which the first output results are inconsistent with the corresponding second output results; determining a first result total number in comparison results between the plurality of first output results and corresponding second output results respectively, and the number of the first comparison results; and under the condition that the first proportion is larger than a preset proportion threshold value, constructing the cable fault prediction model according to the trained radial basis function network model and the trained long-short-time memory network model.
8. A non-volatile storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the cable fault prediction processing method of any one of claims 1 to 6.
9. An electronic device comprising one or more processors and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the cable fault prediction processing method of any of claims 1-6.

Description

Cable fault prediction processing method and device and electronic equipment Technical Field The invention relates to the field of intelligent power grid safety monitoring, in particular to a cable fault prediction processing method and device and electronic equipment. Background The high-voltage cable is an important power device of the power system, and the running state of the high-voltage cable influences the safety and reliability of power supply of a power grid. However, insulation defects are inevitably generated in the cable system due to factors such as design defects, process defects in the installation process, external force damage, water tree invasion and the like. Partial discharge PD (Partial Discharge) is a major cause of insulation degradation, as well as an important characterization of cable insulation defects and insulation degradation. The possible partial discharge faults in the high-voltage cable can be timely and accurately identified, and the method plays an important role in safe and stable operation of the high-voltage cable. In the related art, the high-voltage cable partial discharge fault detection is mainly performed in a manual mode or a neural network prediction mode, so that the problems of high detection cost, poor model performance and the like exist, and the cable fault prediction and recognition efficiency are easy to be low, and the accuracy is poor. In view of the above problems, no effective solution has been proposed at present. Disclosure of Invention The embodiment of the invention provides a cable fault prediction processing method, a device and electronic equipment, which are used for at least solving the technical problems of low cable fault prediction efficiency and poor prediction accuracy caused by poor performance of a cable fault prediction model in the related technology. According to one aspect of the embodiment of the invention, a cable fault prediction processing method is provided, which comprises the steps of obtaining partial discharge characteristic data of a high-voltage cable, respectively inputting first training set data in the partial discharge characteristic data into an initial radial basis function network model and an initial long-short time memory network model for training to obtain a trained radial basis function network model and a trained long-short time memory network model, respectively inputting first test set data in the partial discharge characteristic data into the trained radial basis function network model and the trained long-short time memory network model to obtain a first output result output by the trained radial basis function network model and a second output result output by the trained long-short time memory network model, and determining a cable fault prediction model based on the first output result and the second output result. According to another aspect of the embodiment of the invention, a cable fault prediction processing device is provided, which comprises a first acquisition module, a training module, a determining module and a determining module, wherein the first acquisition module is used for acquiring partial discharge characteristic data of a high-voltage cable, the training module is used for respectively inputting first training set data in the partial discharge characteristic data into an initial radial basis function network model and an initial long-short time memory network model for training to obtain a trained radial basis function network model and a trained long-short time memory network model, the second acquisition module is used for respectively inputting first test set data in the partial discharge characteristic data into the trained radial basis function network model and the trained long-short time memory network model to obtain a first output result output by the trained radial basis function network model and a second output result output by the trained long-short time memory network model, and the determining module is used for determining the cable fault prediction model based on the first output result and the second output result. According to another aspect of the embodiment of the present invention, there is further provided a nonvolatile storage medium, wherein the nonvolatile storage medium stores a plurality of instructions, and the instructions are adapted to be loaded by a processor and execute any one of the cable fault prediction processing methods. According to another aspect of the embodiment of the present invention, there is further provided an electronic device, which is characterized by including one or more processors and a memory, where the memory is configured to store one or more programs, where the one or more programs, when executed by the one or more processors, cause the one or more processors to implement any one of the cable fault prediction processing methods described above. According to the embodiment of the invention, the partial discharge characteristi