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Micro-analysis method for transformer device based on BP nerve network and manual shoal

A BP neural network, substation equipment technology, applied in the field of microscopic analysis of substation equipment state, can solve the problems of falling into local extreme value, slow convergence speed of BP algorithm, etc.

Inactive Publication Date: 2012-07-11
YUN NAN ELECTRIC TEST & RES INST GRP CO LTD ELECTRIC INST
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Problems solved by technology

Although the neural network has a good state microscopic analysis ability, the BP algorithm has a slow convergence speed and is easy to fall into a local extremum

Method used

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  • Micro-analysis method for transformer device based on BP nerve network and manual shoal
  • Micro-analysis method for transformer device based on BP nerve network and manual shoal
  • Micro-analysis method for transformer device based on BP nerve network and manual shoal

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Embodiment Construction

[0050] In the present invention, the fish shoal size of each iterative operation of the weight and threshold of BP neural network is set as: N=18; = 0; the step size of the artificial fish is set as: AF Step = 0.005; the perception range of the artificial fish is set as: Visual = 0.01; the maximum number of trials for the artificial fish is set as: try_number = 30; The crowding degree factor of fish school is set as: δ=0.618; BP neural network input is 14, output is 4, three-layer network structure.

[0051] The present invention randomly initializes the weights and thresholds of the BP neural network, and forms a data set E={X containing N artificial fishes respectively i},in Represents the position of the i-th artificial fish, and it is initialized with the fish swarm algorithm. The individual extremum of each artificial fish in the fish school i=1, 2,..., N, and the optimal position of the fish school P gmost ={P gmost} are equal to the initial position of the weight...

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Abstract

A micro-analysis method for a transformer device based on a BP nerve network and a manual shoal comprises the following steps: obtaining the data such as furfural, brackish water, disruptive voltage and acid value; optimizing the weight and the threshold of the BP nerve network by utilizing a manual shoal optimizing AFSO algorithm, so as to define the weight and the threshold; extracting the state micro-analysis result of a breakdown case base of the transformer device to train the BP nerve network; and inputting the newly obtained furfural, brackish water, disruptive voltage, acid value, hydrogen content, acetylene content, methane content, ethylene content, commissioning time, breakdown frequency in the recent three years, and breakdown grade in the recent three years into the BP nerve network for micro-analysis of the state of the transformer device, so as to define the state of the transformer device. The method has the advantages of scientificity, efficiency and accuracy.

Description

technical field [0001] The invention relates to a microcosmic analysis method for the status of substation equipment, in particular to a microcosmic analysis method for the status of substation equipment based on BP neural network and artificial fish swarm optimization. Background technique [0002] As an asset-intensive enterprise, the power grid company's core competitiveness is to maximize asset efficiency and minimize costs. From early after-event fault repairs to preventive maintenance that emphasizes prior maintenance, the awareness of refined management of power grid equipment assets is gradually being established. The microscopic analysis of the status of substation equipment is an important basis for realizing the life cycle management and fine management of the company's substation equipment. [0003] Lean management of substation equipment is a systematic project, which involves a wide range of points and is highly technical. way to optimize performance. The mi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01D21/02G06N3/02
Inventor 于虹吴毅魏杰姜虹云赵现平孙鹏王达达马仪陈磊侯亚非崔志刚张少泉何程
Owner YUN NAN ELECTRIC TEST & RES INST GRP CO LTD ELECTRIC INST
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