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Energy Systems Analysis

A Novel Combined Particle Swarm Optimization and Genetic Algorithm MPPT Control Method for Multiple Photovoltaic Arrays at Partial Shading

[+] Author and Article Information
Liqun Liu

Assistant Professor
College of Electronic and
Information Engineering,
Taiyuan University of
Science and Technology,
Waliu Road 66,
Taiyuan, Shanxi Province 030024, China
e-mail: llqd2004@163.com

Chunxia Liu

Assistant Professor
College of Computer
Science and Technology,
Taiyuan University of
Science and Technology,
Waliu Road 66,
Taiyuan, Shanxi Province 030024, China
e-mail: lcx456@163.com

1Corresponding author.

Contributed by the Advanced Energy Systems Division of ASME for publication in the Journal of Energy Resources Technology. Manuscript received March 18, 2012; final manuscript received October 24, 2012; published online December 12, 2012. Assoc. Editor: Kau-Fui Wong.

J. Energy Resour. Technol 135(1), 012002 (Dec 12, 2012) (5 pages) Paper No: JERT-12-1055; doi: 10.1115/1.4007940 History: Received March 18, 2012; Revised October 24, 2012

The output characteristics of multiple photovoltaic (PV) arrays at partial shading are characterized by multiple steps and peaks. This makes that the maximum power point tracking (MPPT) of a large scale PV system becomes a difficult task. The conventional MPPT control method was unable to track the maximum power point (MPP) under random partial shading conditions, making the output efficiency of the PV system is low. To overcome this difficulty, in this paper, an improved MPPT control method with better performance based on the genetic algorithm (GA) and adaptive particle swarm optimization (APSO) algorithm is proposed to solve the random partial shading problem. The proposed genetic algorithm adaptive particle swarm optimization (GAAPSO) method conveniently can be used in the real-time MPPT control strategy for large scale PV system, and the implementation of the collect circuit is easy to gain the global peak of multiple PV arrays, thereby resulting in lower cost, higher overall efficiency. The proposed GAAPSO method has been experimentally validated by using several illustrative examples. Simulations and experimental results demonstrate that the GAAPSO method provides effective, fast, and perfect tracking.

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Figures

Grahic Jump Location
Fig. 1

The output characteristic of PV array at uniform and partial shading

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Fig. 2

Performance comparison of PSO, APSO, and GAAPSO in terms of optimization error

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Fig. 3

Equivalent circuit model of a PV cell

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Fig. 4

Different partial shading conditions and ideal power output using the different MPPT method

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Fig. 5

Multiple arrays controlled by single centralized MPPT controller

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Fig. 6

Flowchart of GAAPSO MPPT control method experimental realization

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Fig. 7

Output power of PV system at the initial partial shading using the different MPPT method

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Fig. 8

Output power of PV system using the different MPPT method at the sudden changing of partial shading

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