Power grid fault prediction method

A power grid fault and prediction method technology, applied in prediction, biological neural network model, chaotic model, etc., can solve problems such as slow convergence speed and falling into local optimum

Inactive Publication Date: 2018-11-27
张家港知航信息科技有限公司
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Problems solved by technology

[0003] Aiming at the defects and deficiencies in the prior art, the technical problem to be solved by the present invention is to provide a power grid fault prediction method to solve the problems of slow convergence and local optimum in the existing BP neural network fault prediction technology

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Embodiment Construction

[0038] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0039] like figure 1 Shown is a flow chart of a grid failure prediction method provided by the present invention:

[0040] Step 1, according to the input and output fault samples of the BP neural network system, its network structure is clarified.

[0041] The present invention adopts 3 layers of BP network structure, such as figure 2 shown. It consists of input layer X, hidden layer Y and output layer O. Nodes are x i 、y k , o j . x i with y k The weight between is ωik , the threshold value of neuron k in the hidden layer is θ k ;y k and o j The weight between is ω kj , the threshold value of neuron j in the output layer is θ j ,. The characteristics of the BP network structure are: neurons in each layer are only fully connected with neurons in adjacent layers, there is no connection between neurons in the same layer, and there...

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Abstract

The invention discloses a power grid fault prediction method, and belongs to the technical field of power grid fault prediction. The power grid fault prediction method includes: determining a networkstructure of a BP neural network system according to input and output fault samples of the BP neural network system; using the fault samples as training samples of the BP neural network; and using thechaotic ant colony algorithm to train the BP neural network. The BP neural network and the chaotic ant colony algorithm are combined to predict a power grid fault; chaos initialization is performed by using the chaos ergodicity; and the positive feedback principle of the ant colony algorithm and chaotic disturbance are used to solve the problem of slow network convergence and capture local optimum in power grid fault prediction.

Description

technical field [0001] The invention relates to the field of power grid fault prediction, in particular, a power grid fault prediction method is designed. Background technique [0002] The grid fault problem has attracted more attention in recent years. Since the occurrence of blackouts is usually accompanied by cascading faults, the analysis and research on how to predict grid faults is also increasing. The BP neural system has the advantages of self-learning, self-adaptation and nonlinear recognition, and is widely used in fault prediction. However, due to some defects in the BP neural system itself: the network converges slowly and often falls into local optimum. The ant colony algorithm is a new search algorithm in the field of combinatorial optimization, and it is found that the entire ant colony behavior is a periodic behavior but a single ant is a chaotic behavior, so the chaotic ant colony optimization algorithm is applied to BP neural network power grid faults Pre...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/00G06N3/04G06N7/08
CPCG06N3/006G06N7/08G06Q10/04G06Q50/06G06N3/044
Inventor 周旭
Owner 张家港知航信息科技有限公司
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