Accelerated Particle Swarm Optimization for Photovoltaic Maximum Power Point Tracking under Partial Shading Conditions
2. Overview of Particle Swarm Optimization Algorithm
- Step 1:
- Initialize the particles randomly in the search space.
- Step 2:
- Evaluate the fitness value of each particle by sending the candidate solution to the objective function.
- Step 3:
- Update and .
- Step 4:
- Update the position and velocity of each particle.
- Step 5:
- Re-initialize the PSO algorithm unless the constrain is met. In other words, the algorithm stops when the is founded.
3. Proposed Accelerated PSO Algorithm
- Step 1
- (Parameter Selection): The number of particles is three. A complete optimization analysis has been done in , and it claimed that three particles deliver the best performance.
- Step 2
- (APSO Initialization): In the proposed APSO algorithm, the particles are placed on fixed positions. The first particle is set as 10% of the PV open circuit voltage (), the third particle is set as 90% of . The first and third particles defined the PSO search space. The second particle is randomly set between 10% and 90% of .
- Step 3
- (Fitness Evaluation): The purpose of the PSO-based MPPT method is to maximize the PV output power. PV voltage and current are measured to compute the PV output power as the fitness value for evaluation.
- Step 4
- (Update the Global Value): The particle which has the best fitness value is selected as the . In conventional PSO-based MPPT algorithms, is usually fixed. In this proposed APSO method, P&O MPPT algorithm is used to directly perturb the to accelerate the global MPP searching, so that will be moved towards a higher fitness value. In conventional PSO-based MPPT algorithms, the velocity of the particle is reducing when the particle is moving toward the . In this proposed APSO method, can move to a higher fitness value via P&O algorithm, and simultaneously attracts the remaining particle more rapidly to converge toward it. Therefore, the convergence time could be decreased.
- Step 5
- (Update the Velocity and Position of Each Particle): Once all the particles are assessed, the position and velocity of each particle need to be updated.
- Step 6
- (Convergence Determination): Two convergence criteria will be examined in this step. If the particle’s velocity becomes lower than a set value or if the maximum iteration number is reached, the algorithm computation will be stopped, and the global MPP is found.
- Step 7
- (Re-initialization): The global MPP position frequently changes with the environmental conditions. This requires the APSO algorithm to be reinitialized and search for the new global MPP. In this research, Equation (10) is used to identify the environmental conditions changes and reinitialize the APSO algorithm.
4. Experimental Results
4.1. Experimental Setup Configuration
4.2. Setting the Parameter Values of the Particle Swarm Optimization Algorithm
4.3. Case Studies
4.4. Test under Partial Shading Variations
Conflicts of Interest
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|Number of Particles||= 0.2||3||3|
|Sampling time||0.12 s||0.12 s||0.12 s|
|Shading Scenario||Method||Vmpp (V)||Impp (A)||Pmpp (W)||Rated Power (W)||Efficiency (%)||Tracking Time (s)|
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Alshareef, M.; Lin, Z.; Ma, M.; Cao, W. Accelerated Particle Swarm Optimization for Photovoltaic Maximum Power Point Tracking under Partial Shading Conditions. Energies 2019, 12, 623. https://doi.org/10.3390/en12040623
Alshareef M, Lin Z, Ma M, Cao W. Accelerated Particle Swarm Optimization for Photovoltaic Maximum Power Point Tracking under Partial Shading Conditions. Energies. 2019; 12(4):623. https://doi.org/10.3390/en12040623Chicago/Turabian Style
Alshareef, Muhannad, Zhengyu Lin, Mingyao Ma, and Wenping Cao. 2019. "Accelerated Particle Swarm Optimization for Photovoltaic Maximum Power Point Tracking under Partial Shading Conditions" Energies 12, no. 4: 623. https://doi.org/10.3390/en12040623