Active power filter global sliding mode control method based on double-feedback fuzzy neural network

A fuzzy neural network and power filter technology, applied in active power filtering, AC network circuits, and AC networks to reduce harmonics/ripples, etc., can solve problems such as low robustness, system instability, and inconvenience

Active Publication Date: 2018-07-27
HOHAI UNIV CHANGZHOU
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Problems solved by technology

However, the robustness of adaptive control to external disturbances is very low, and it is easy to make the system unstable.
[0004] This shows that above-mentioned existing active power filter obviously still has inconvenience and defect in use, a

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  • Active power filter global sliding mode control method based on double-feedback fuzzy neural network
  • Active power filter global sliding mode control method based on double-feedback fuzzy neural network
  • Active power filter global sliding mode control method based on double-feedback fuzzy neural network

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Embodiment Construction

[0088] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

[0089] Such as figure 1 is the basic circuit topology diagram of the three-phase three-wire parallel voltage-type active power filter, v s1 ,v s2 ,v s3 is the grid voltage, i s1 ,i s2 ,i s3 is the supply current, i L1 ,i L2 ,i L3 is the load current, v 1 ,v 2 ,v 3 is the voltage at the common connection point, i 1 ,i 2 ,i 3 is the filter output compensation current, C is the DC side capacitance, v dc is the DC side capacitor voltage, i dc is the DC side capacitor voltage, L c is the AC side inductance, R c is the equivalent resistance.

[0090] A fully-adjustable fuzzy neural network with a double feedback structure of the present invention, although the traditional neural net...

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Abstract

The invention discloses an active power filter global sliding mode control method based on a double-feedback fuzzy neural network. The active power filter global sliding mode control method based on the double-feedback fuzzy neural network comprises the following steps: (1) establishing a mathematical model of an active power filter; (2) establishing an active power filter global sliding mode controller based on the double-feedback fuzzy neural network, designing a control law, and taking the control law as control input of the active power filter; and (3) designing an adaptive law on the basis of a Lyapunov function, and verifying the stability of the active power filter global sliding mode controller based on the double-feedback fuzzy neural network. Compensating current tracking performance of the active power filter system under the circumstances of existing of parameter perturbation and external disturbance and system robustness can be improved.

Description

technical field [0001] The invention relates to a control method for an active power filter, in particular to a global sliding mode control method for an active power filter based on a double-feedback fuzzy neural network. Background technique [0002] With the popularization and application of modern power electronic technology, there are more and more various power electronic devices. The working performance and service life of equipment and electrical equipment, and even endanger the safety of the power system. Currently, external filters are mainly used for treatment. Filters are divided into passive filters and active filters. Since the passive filter can only compensate specific harmonics and other defects, the current research on the control of electric energy problems mainly focuses on the active filter. The active filter can track and compensate the harmonics with changing frequency and amplitude. It can not only compensate each harmonic, but also suppress flicker...

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Application Information

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IPC IPC(8): G06F17/50H02J3/01
CPCG06F30/20H02J3/01H02J2203/20Y02E40/20
Inventor 储云迪费峻涛王欢冯治琳
Owner HOHAI UNIV CHANGZHOU
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