Maximum Power Point Tracking of Partial Shading Photovoltaic Array Base on Particle Swarm Optimization

This paper presents an accelerated particle swarm optimization (PSO)-based maximum power point tracking (MPPT) algorithm to track global maximum power point (MPP) of photovoltaic (PV) generation under partial shading conditions. Conventional PSO-based MPPT algorithms have common weaknesses of a long convergence time to reach the global MPP and oscillations during the searching. The proposed algorithm includes a standard PSO and a perturb-and-observe algorithm as the accelerator. It has been experimentally tested and compared with conventional MPPT algorithms. Experimental results show that the proposed MPPT method is effective in terms of high reliability, fast dynamic response, and high accuracy in tracking the global MPP.

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Efficiency Improvement of Photovolataic by Using Maximum Power Point Tracking Based on Particle Swarm Optimization Algorithm Under Normal and Partial Shading

JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) ◽

10.21070/jeee-u.v2i2.1703 ◽

2018 ◽

Vol 2 (2) ◽

pp. 71

Author(s):

Machmud Effendy ◽

Khusnul Hidayat ◽

Wahyu Dianto Pramana

Keyword(s):

Particle Swarm Optimization ◽

Maximum Power Point Tracking ◽

Particle Swarm ◽

Maximum Power ◽

Maximum Power Point ◽

Partial Shading ◽

Swarm Optimization ◽

Point Tracking ◽

Power Point Tracking ◽

Power Point

Photovoltaic (PV) is a device which is capable to converts solar irradiance into Direct Current (DC) electricity energy. To increase the power result of PV, it needs a method to track the Maximum Power Point(MPP) which is usually called Maximum power Point Tracking(MPPT). So that, the power result increased with low cost. The purpose of this research is to conduct MPPT modeling by Particle Swarm Optimization (PSO). The proposed method is implemented in DC to DC converter. This research used SEPIC converter. The purpose of using SEPIC converter is in order the output of current and voltage have smallest ripple. The modelling system is conducted by using MATLAB 2016b software to find out performance of PSO and SEPIC converter. The evaluation of PSO and SEPIC converter performance has been done. The simulation result shows that the proposed system has been working very well. The PSO has good accurateness in tracking and capable to to track the power produced by PV with velocity around ±4,2 seconds when in conditions STC, ±9,2 seconds when in conditions partial shading, despite a fluctuating irradiance change. While in SEPIC converter is able to reach efficiency of ≥ 80%.

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PARTICLE SWARM OPTIMIZATION WITH INDIVIDUALLY BIASED PARTICLES FOR RELIABLE AND ROBUST MAXIMUM POWER POINT TRACKING UNDER PARTIAL SHADING CONDITIONS

Eurasian Physical Technical Journal ◽

10.31489/2020no2/128-137 ◽

2020 ◽

Vol 17 (2) ◽

pp. 128-137

Author(s):

K. Baktybekov ◽

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Keyword(s):

Particle Swarm Optimization ◽

Maximum Power Point Tracking ◽

Particle Swarm ◽

Maximum Power ◽

Maximum Power Point ◽

Partial Shading ◽

Swarm Optimization ◽

Point Tracking ◽

Power Point Tracking ◽

Power Point

Efficient power control techniques are an integral part of photovoltaic system design. One of the means of managing power delivery is regulating the duty cycle of the DC to DC converter by various algorithms to operate only at points where power is maximum power point. Search has to be done as fast as possible to minimize power loss, especially under dynamically changing irradiance. The challenge of the task is the nonlinear behavior of the PV system under partial shading conditions. Depending on the size and structure of the photovoltaic panels, PSC creates an immense amount of possible P-V curves with numerous local maximums - requiring an intelligent algorithm for determining the optimal operating point. Existing benchmark maximum power point tracking algorithms cannot handle multiple peaks, and in this paper, we offer an adaptation of particle swarm optimization for the specific task.

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