RBF dual neural network adaptive sliding mode control method for active power filter

A dual neural network, power filter technology, applied in active power filtering, AC network circuits, AC networks to reduce harmonics/ripples, etc. Deterministic, reliable control, the effect of strong control effects

Active Publication Date: 2017-09-08
HOHAI UNIV CHANGZHOU
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] At present, there is no advanced control theory system for systematic active power filters at home and abroad. The modeling methods of active power fi

Method used

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  • RBF dual neural network adaptive sliding mode control method for active power filter
  • RBF dual neural network adaptive sliding mode control method for active power filter
  • RBF dual neural network adaptive sliding mode control method for active power filter

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Experimental program
Comparison scheme
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Embodiment

[0101] Combining the dynamic model of active power filter and the design method of adaptive RBF dual neural network controller of fractional-order sliding mode control, the main program is designed by Matlab / Simulink software.

[0102] The designed fractional order sliding mode controller parameter λ 1 =50,λ 2 =10,λ 1 =1, the adaptive parameter takes η 1 =100,η 2 =100, fractional order α=0.85, and the number of hidden nodes in the RBF neural network is 6. Power supply voltage V s1 =V s2 =V s3 =220V, f=50Hz. The resistance of the nonlinear load is 40Ω, and the inductance is 5mH. The compensation circuit has an inductance of 10mH and a capacitance of 100μF.

[0103] At 0.04S (S stands for seconds), the compensation circuit access switch is closed, the active filter starts to work, and an identical additional non-linear load is connected at 0.1S and 0.2S.

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Abstract

The invention discloses an RBF dual neural network adaptive sliding mode control method for an active power filter. The method is characterized by comprising the following steps: step (1), establishing a mathematical model of the active power filter; (2) designing an adaptive RBF dual neural network based on a fractional order sliding mode surface, and separately approximating the nonlinear function and the upper bound of interference of the system by using the two RBF neural networks; and step (3) controlling the active power filter according to a fractional order RBF dual neural network sliding mode controller. According to the method disclosed by the invention, the characteristics that the fractional order can get rid of the dependence of system functions and improve the control response of the system are adopted; on this basis, the nonlinear function and the upper bound of interference values of the system can be approximated by adopting the characteristic that the RBF neural networks do not relay on the model of the system; and moreover, the stability of a system controller can be proved by designing the Lyapunov function, the real-time tracking compensation can be performed for the instruction current, and high reliability, high robustness to parameter variation and high stability can be achieved.

Description

technical field [0001] The invention relates to an active power filter RBF double neural network self-adaptive sliding mode control method. Background technique [0002] With the progress and development of society, people's living standards are improving day by day, and a large number of electrical equipment is put into daily production and life, followed by a large number of harmonic and reactive power pollution in the power grid, which Seriously affect the quality of electric energy. The existence of harmonic voltage or harmonic current in the power grid will increase the additional loss of power system equipment, cause problems such as failure of measurement and automatic control instruments, affect the efficiency of equipment, and may cause fires due to overheating of lines in severe cases. [0003] At present, external harmonic compensation devices are mainly used to compensate harmonics, and filters are divided into passive filters and active filters. The control ef...

Claims

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Application Information

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IPC IPC(8): H02J3/01
CPCH02J3/01H02J2203/20Y02E40/20
Inventor 刘倪宣费峻涛
Owner HOHAI UNIV CHANGZHOU
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