The MPPT Control Method by Using BP Neural Networks in PV Generating System

Author

Yong, Zhao ; Hong, Li ; Liqun, Liu ; XiaoFeng, Gao

fYear

2012

Firstpage

1639

Lastpage

1642

Abstract

An efficiency method of Maximum Power Point Tracking (MPPT) is extremely important to improve the output characteristic of photovoltaic (PV) power generation system and reduce the cost of the system. The nonlinear and time-varying output characteristics of PV in the changing weather cause the difficult MPPT process. Neural networks algorithm is suitable for solving the nonlinear relation, and the result of comparing with the traditional disturbance observation shows that neural networks has better MPPT characteristics. The MPPT controller based on Back Propagation (BP) networks play an effective role to improve the efficiency and reduce the output vibration of PV power system.

Keywords

backpropagation; maximum power point trackers; neurocontrollers; nonlinear control systems; photovoltaic power systems; power generation control; time-varying systems; vibration control; BP neural networks; MPPT control method; PV generating system; back propagation; cost reduction; disturbance observation; maximum power point tracking; nonlinear output characteristics; nonlinear relation; output vibration reduction; photovoltaic power generation system; time-varying output characteristics; Industrial control; Artificial neural networks; Maximum power point tracking; PV power system;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Control and Electronics Engineering (ICICEE), 2012 International Conference on

Conference_Location

Xi´an

Print_ISBN

978-1-4673-1450-3

Type

conf

DOI

10.1109/ICICEE.2012.433

Filename

6322723