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10kV feeder line fault prediction method based on CNN and LightGBM

A fault prediction and feeder technology, applied in the field of intelligent distribution network, can solve problems such as poor objectivity and real-time performance, insufficient rapidity, flexibility and predictability, and complex distribution network structure, and achieve good rapidity and timeliness. sexual effect

Active Publication Date: 2020-03-27
STATE GRID CHONGQING ELECTRIC POWER +2
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The risk assessment of the distribution network needs to establish risk indicators, which mostly rely on expert experience and fault-related static data, and the quantification rules often use AHP, DeFiel method and fuzzy evaluation method, etc., which leads to the objectivity and The real-time performance is poor; statistical analysis, correlation analysis and multidimensional analysis are generally used in the analysis of distribution network historical fault-related data. The structure of the distribution network is complex, and it is very difficult to predict the fault of the distribution network based on the mechanism modeling. The common mechanism modeling includes the temperature rise model of the power equipment and the oil and gas analysis model. Compared with the mechanism modeling, It is relatively easy to use intelligent algorithms based on data processing technology to predict faults in distribution networks, such as regression algorithms, clustering algorithms, support vector machines, and artificial neural networks. Variables and multivariate discrete time variables are modeled, lack of fusion processing of distribution network time series features and non-time series features, and lack of feature extraction method for distribution network time series variables, and with the distribution network data Accumulated year by year, some algorithms are no longer suitable for large-scale data scenarios

Method used

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  • 10kV feeder line fault prediction method based on CNN and LightGBM
  • 10kV feeder line fault prediction method based on CNN and LightGBM
  • 10kV feeder line fault prediction method based on CNN and LightGBM

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0054] see Figure 1 to Figure 8 , a 10kV feeder fault prediction method based on CNN and LightGBM, mainly includes the following steps:

[0055] 1) Obtain the original data of the distribution network and preprocess the original data of the distribution network.

[0056] Further, the distribution network raw data includes meteorological data and distribution network information data. The original data of the distribution network is divided into three categories based on the feeder faults, which respectively represent x under different types of feeder faults i year i Raw data of the feeder. The feeder faults include faults caused by operating factors, faults caused by equipment failures and faults caused by weather factors. The feeders described in this embodiment are all 10kV feeders.

[0057] Further, the steps of preprocessing the raw data of the distribution network are as follows:

[0058] 1.1) Complement the original data of the distribution network by using the te...

Embodiment 2

[0102] A 10kV feeder fault prediction method based on CNN and LightGBM mainly includes the following steps:

[0103] 1) Obtain the original data of the distribution network and preprocess the original data of the distribution network.

[0104] 2) Extract features from the raw data of the distribution network, and construct a feature set f{f 1 , f 2 … f 15 , f 16 , L}; L is the label, indicating whether the feeder is faulty or not. Among them, the element f 1 , element f 2 , element f 3 , element f 4 , element f 5 , element f 6 is an inherent attribute. element f 7 , element f 8 , element f 9 , element f 10 , element f 11 , element f 12 , element f 13 characteristics for statistical analysis. element f 14 , element f 15 , element f 16 Deep Temporal Features Extracted for Convolutional Neural Networks (CNN).

[0105] 3) Using the LightGBM algorithm to establish a distribution network fault prediction model.

[0106] 4) Input the real-time data of the 10kV ...

Embodiment 3

[0108] A 10kV feeder fault prediction method based on CNN and LightGBM, the main steps are shown in Example 2, wherein the distribution network original data set feature parameters include inherent attribute features, statistical analysis features and deep time series features.

[0109] The inherent attribute features include line properties f 1 , line length f 2 , Substation f 3 , line commissioning time f 4 , the number of line equipment f 5 and line equipment manufacturers f 6 .

[0110] The statistical analysis features include time statistical features, meteorological statistical features and fault autocorrelation statistical features, wherein the time statistical features include summer characterization parameters f 7 and the weekend representation parameter f 8 . Meteorological statistical features include the maximum value of temperature in a day f 9 , the minimum temperature f in a day 10 , the maximum value of humidity f 11 and the maximum wind speed f 12 ...

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Abstract

The invention discloses a 10kV feeder line fault prediction method based on a CNN (Convolutional Neural Network) and LightGBM (LightGBM), and the method mainly comprises the steps: 1), obtaining the original data of a power distribution network, and carrying out the preprocessing of the original data of the power distribution network; 2) extracting features from the original data of the power distribution network, and constructing a feature set f {f1, f2... F15, f16, L}, wherein L is a label and represents whether the feeder line has a fault or not, the element f1, the element f2, the elementf3, the element f4, the element f5 and the element f6 are inherent attribute characteristics, the element f7, the element f8, the element f9, the element f10, the element f11, the element f12 and theelement f13 are statistical analysis characteristics, and the element f14, the element f15 and the element f16 are depth time sequence features extracted by a convolutional neural network CNN; 3) establishing a power distribution network fault prediction model; and 4) inputting the real-time data of the 10kV feeder line of the power distribution network into the power distribution network fault prediction model to obtain a feeder line fault prediction result. The 10kV feeder line fault prediction method has the advantages that the rapidity and timeliness are better, and the prediction result can provide auxiliary decision support for the operation and maintenance management personnel of the power distribution network on the premise of meeting the accuracy requirement.

Description

technical field [0001] The invention relates to the field of intelligent distribution networks, in particular to a CNN and LightGBM-based 10kV feeder fault prediction method. Background technique [0002] With the continuous improvement of industrialization, informatization and intelligence in modern society, electric energy, as an important part of secondary energy, not only plays a key role in social development, but also is irreplaceable in people's lives. The power system integrates five links of power generation, power transmission, power distribution, and power consumption. The network structure is complex and the operating environment is changeable. In recent years, the access of distributed power sources and charging piles has also brought great challenges to the power system. Any failure of any part of the power grid will affect users, ranging from economic losses to life-threatening threats. The distribution network is located at the end of the power system, close...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/04
CPCG06Q10/04G06Q50/06G06N3/045Y04S10/50
Inventor 黄虎冯德伦范敏杨青刘亚玲苑吉河张曦彭港贾世韬
Owner STATE GRID CHONGQING ELECTRIC POWER
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