Four-order power system chaotic control method of bidirectional optimization BP neural network sliding mode variable structure

Abstract

The invention relates to a four-order power system chaotic control method of a bidirectional optimization BP neural network sliding mode variable structure. On the basis of a four-order power system chaotic mathematical model, the power system chaotic oscillation is controlled by using a control method combining a bidirectional optimization BP neural network and sliding mode variable structure control. In the neural network, the excitation function used in the forward direction and the learning rate used in the reverse direction of the algorithm are optimized respectively, so the buffeting phenomenon of sliding mode variable structure control is effectively suppressed, and the chaotic oscillation control is more ideal; compared with the traditional sliding mode variable structure control,the method keeps excellent characteristics of the BP neural network and the sliding mode variable structure control, and on the premise of overcoming the false saturation phenomenon of the neural network, the method improves the generalization ability and reasonably accelerates the learning process, weakens the buffeting phenomenon of the sliding mode variable structure control more effectively, and enables the chaotic control of the power system to be better.

Description

technical field [0001] The invention relates to the technical field of fourth-order power system chaos control, and relates to a fourth-order power system chaos control method with bidirectional optimization of BP neural network sliding mode variable structure. Background technique [0002] With the interconnection of large power grids and long-distance power transmission becoming the development trend of contemporary power grids, the structure of power systems is becoming increasingly complex. As a typical nonlinear system, the nonlinearity of the power system itself will cause chaotic oscillations under certain circumstances, which is a non-periodic and irregular low-frequency oscillation. At the same time, emergencies and uncertain factors may also cause During the operation of the system, chaotic oscillation occurs. At present, low-frequency oscillations have been observed many times at home and abroad, and the harm caused by chaotic oscillations cannot be ignored. Whe...

AI Technical Summary

Problems solved by technology

As one of the most widely used neural networks, the multi-layer feed-forward neural network (BP neural network) based on BP algorithm also has some problems, such as false saturation of the neural network, insufficient generalization ability, and unsatisfactory learning speed. It will directly affect the application effect of the neural network, and even the chattering problem of the latter cannot be solved after combining the sliding mode variable structure control, so it will not be able to achieve the ideal control of chaotic oscillation.

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