Power distribution network fault power failure influence factor sensitivity analysis method based on BP neural network

A BP neural network and distribution network fault technology, applied in the field of power systems, can solve problems such as poor investment purpose

Inactive Publication Date: 2019-06-07
CHINA AGRI UNIV +1
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Problems solved by technology

Therefore, in view of the problem of poor investment purpose in the current distribution network investment process, the prese...

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  • Power distribution network fault power failure influence factor sensitivity analysis method based on BP neural network
  • Power distribution network fault power failure influence factor sensitivity analysis method based on BP neural network
  • Power distribution network fault power failure influence factor sensitivity analysis method based on BP neural network

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Embodiment Construction

[0044] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0045] Step 1: Sort out the investment-related distribution network failure and outage influencing factors set, and form the relationship between investment-influencing factors-per household failure and outage indicators;

[0046] The main influencing factors related to investment that affect the reliability index of distribution network failure and outage can be summarized as grid structure factors, equipment level factors, emergency repair level (operation and maintenance management level) factors, and depreciation factors. Among them, the grid structure factors include; the average load ratio of each section of the line, the average length of the line, the average number of segments, the proportion of transferable feeder lines, the average power supply radius of a single substation, the average installed capacity of medium-voltage lines, and the average cap...

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Abstract

The invention relates to a power distribution network fault power failure influence factor sensitivity analysis method based on a BP neural network algorithm, and the method comprises the steps: 1, sorting a power distribution network fault power failure influence factor set related to investment, and forming association relationship of investment-influence factor-household average fault power failure indexes; 2, taking each influence factor as the input of a BP neural network algorithm, taking the household average fault power failure index as the output of the BP neural network algorithm, and establishing a fault power failure prediction model; 3, analyzing each influence factor influencing the power failure index of the household average fault by adopting a sensitivity analysis method;4, sorting the absolute values of the sensitivity of the influence factors according to the size; a plurality of influence factors having the maximum influence on the household average fault power failure index are obtained, and the higher the absolute value sequence of the sensitivity of the influence factors is, the weaker the influence factors are, so that the direction guidance can be providedfor the investment of the power distribution network.

Description

technical field [0001] The invention belongs to the field of electric power systems, and in particular relates to a method for analyzing the sensitivity of factors affecting distribution network faults and outages based on a BP neural network algorithm. Background technique [0002] In recent years, marked by the "Action Plan for Construction and Renovation of Distribution Networks (2015-2020)" issued by the National Energy Administration in 2015, my country's modern distribution network construction and transformation process has advanced rapidly, and improving power supply reliability will be the core of construction and transformation one of the goals. How to scientifically and reasonably identify the weak links of the distribution network and improve the reliability of power supply more targetedly with limited funds is an urgent problem to be solved and studied. [0003] In the past, some studies carried out diagnostic analysis on the actual power outage data of the dist...

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q50/06G06N3/08
Inventor 杜松怀刘杨涛刘博苏娟张光儒刘丽娟武子超
Owner CHINA AGRI UNIV
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