Adaptability Analysis of Particle Swarm Optimization Variants in Maximum Power Tracking for Solar PV Systems

2019 ◽  
pp. 397-409
Author(s):  
B. G. Dharshan ◽  
N. Rajasekar ◽  
R. Srinivasa Sankarkumar
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Maximum Power ◽  
Solar Pv ◽  
Swarm Optimization ◽  
Pv Systems ◽  
Maximum Power Tracking

Particle Swarm Optimization Based Solar PV Array Reconfiguration of the Maximum Power Extraction Under Partial Shading Conditions

2018 ◽  
Vol 9 (1) ◽  
pp. 74-85 ◽  
Author(s):  
Thanikanti Sudhakar Babu ◽  
J. Prasanth Ram ◽  
Tomislav Dragicevic ◽  
Masafumi Miyatake ◽  
Frede Blaabjerg ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Maximum Power ◽  
Solar Pv ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Power Extraction ◽  
Pv Array

scholarly journals Incremental Conductance Based Particle Swarm Optimization Algorithm for Global Maximum Power Tracking of Solar-PV under Nonuniform Operating Conditions

2020 ◽  
Vol 10 (13) ◽  
pp. 4575 ◽  
Author(s):  
Gajendra Singh Chawda ◽  
Om Prakash Mahela ◽  
Neeraj Gupta ◽  
Mahdi Khosravy ◽  
Tomonobu Senjyu
Keyword(s):  
Particle Swarm Optimization ◽  
Active Power ◽  
Operating Conditions ◽  
Maximum Power ◽  
Solar Pv ◽  
Swarm Optimization ◽  
Incremental Conductance ◽  
Global Power ◽  
Maximum Power Tracking ◽  
Photo Voltaic

In practical operating conditions, the Solar-Photo Voltaic (SPV) system experiences multifarious irradiation and temperature levels, which generate power with multiple peaks. This is considered as the nonuniform operating condition (NUOC). This requires accurate tracking of global power peaks to achieve maximum power from SPV, which is a challenging task. Hence, this paper presents an incremental Conductance based Particle Swarm Optimization (ICPSO) algorithm for accurate tracking of maximum global power from active power multiple peaks generated by the SPV. The proposed algorithm continuously adjusts the individual particle’s weight component, which depends on its distance from the global best position during the tracking process. The proposed algorithm has the merit of continuous adjustment of weight components which reduces active power oscillations at the optimal global position area. Proposed ICPSO algorithm has been successfully designed and implemented for Solar-photo voltaic (PV) under nonuniform operating condition. It is established that the proposed algorithm enhances the output power of the Solar-PV up to 7% with the maximum power tracking of 0.1 s compared to other maximum power point tracking algorithms.

scholarly journals Proportional-Integral Controllers Performance of a Grid-Connected Solar PV System with Particle Swarm Optimization and Ziegler–Nichols Tuning Method

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2516
Author(s):  
Klemen Deželak ◽  
Peter Bracinik ◽  
Klemen Sredenšek ◽  
Sebastijan Seme
Keyword(s):  
Particle Swarm Optimization ◽  
Distribution Network ◽  
Particle Swarm ◽  
Open Loop ◽  
Limited Area ◽  
Solar Pv ◽  
Pv System ◽  
Swarm Optimization ◽  
Proportional Integral ◽  
Tuning Method

This paper deals with photovoltaic (PV) power plant modeling and its integration into the medium-voltage distribution network. Apart from solar cells, a simulation model includes a boost converter, voltage-oriented controller and LCL filter. The main emphasis is given to the comparison of two optimization methods—particle swarm optimization (PSO) and the Ziegler–Nichols (ZN) tuning method, approaches that are used for the parameters of Proportional-Integral (PI) controllers determination. A PI controller is commonly used because of its performance, but it is limited in its effectiveness if there is a change in the parameters of the system. In our case, the aforementioned change is caused by switching the feeders of the distribution network from an open-loop to a closed-loop arrangement. The simulation results have claimed the superiority of the PSO algorithm, while the classical ZN tuning method is acceptable in a limited area of operation.

scholarly journals Maximum Power Point Tracking Method for Photovoltaic System Based on Enhanced Particle Swarm Optimization Algorithm Under Partial Shading Condition

2020 ◽  
Vol 13 (6) ◽  
pp. 241-254
Author(s):  
Anas Kamil ◽  
◽  
Mahmoud Nasr ◽  
Shamam Alwash ◽  
◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Maximum Power ◽  
Global Maximum ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Power Point

The maximum power point tracking (MPPT) is an essential key to ensure that the photovoltaic (PV) system is operated at the highest possible power generation. This paper presents an efficient MPPT method for the PV system based on an enhanced particle swarm optimization algorithm to track the location of the global maximum power point, whatever its location changes in the search space under all environmental conditions, including the partial shading on strings. In this paper, the formulation of the conventional particle swarm optimization algorithm is enhanced to decrease the searching time and the oscillation of the generated output power as well as the power losses in the online tracking process. This enhancement can be achieved by utilizing a special time-varying weighting coefficient and removing the effect of some other coefficients in the conventional particle swarm optimization algorithm (PSO) that cause winding of the particles during the online tracking process. Test results verified the accuracy of the proposed method to track the global maximum power point with considering the effect of partial shading condition. The proposed method was also compared with other MPPT methods to verify the superiority of the proposed work. The obtained results reveal that the proposed method is effective to improve the tracking efficiency and reduce the tracking time and the number of iterations for the different irradiances and load conditions. The maximum number of iterations was 11 iteration and the highest tracking time was 0.273s with tracking efficiency of about 99.98%.

scholarly journals Enhanced Particle Swarm Optimization-Based Feeder Reconfiguration Considering Uncertain Large Photovoltaic Powers and Demands

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ying-Yi Hong ◽  
Faa-Jeng Lin ◽  
Fu-Yuan Hsu
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Distribution System ◽  
Urban Areas ◽  
Distribution Systems ◽  
Particle Swarm ◽  
Solar Pv ◽  
Swarm Optimization ◽  
Rural And Urban Areas ◽  
Enhanced Particle Swarm Optimization

The Kyoto protocol recommended that industrialized countries limit their green gas emissions in 2012 to 5.2% below 1990 levels. Photovoltaic (PV) arrays provide clear and sustainable renewable energy to electric power systems. Solar PV arrays can be installed in distribution systems of rural and urban areas, as opposed to wind-turbine generators, which cause noise in surrounding environments. However, a large PV array (several MW) may incur several operation problems, for example, low power quality and reverse power. This work presents a novel method to reconfigure the distribution feeders in order to prevent the injection of reverse power into a substation connected to the transmission level. Moreover, a two-stage algorithm is developed, in which the uncertain bus loads and PV powers are clustered by fuzzy-c-means to gain representative scenarios; optimal reconfiguration is then achieved by a novel mean-variance-based particle swarm optimization. The system loss is minimized while the operational constraints, including reverse power and voltage variation, are satisfied due to the optimal feeder reconfiguration. Simulation results obtained from a 70-bus distribution system with 4 large PV arrays validate the proposed method.

scholarly journals Group Teaching Optimization Algorithm Based MPPT Control of PV Systems under Partial Shading and Complex Partial Shading

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1962
Author(s):  
Muhammad Hamza Zafar ◽  
Thamraa Al-shahrani ◽  
Noman Mujeeb Khan ◽  
Adeel Feroz Mirza ◽  
Majad Mansoor ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Statistical Analysis ◽  
Optimization Algorithm ◽  
Maximum Power ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Pv Systems ◽  
Power Point

The most cost-effective electrical energy is produced by photovoltaic (PV) systems, and with the smallest carbon footprint, making it a sustainable renewable energy. They provide an excellent alternative to the existing fossil fuel-based energy systems, while providing 4% of global electricity demand. PV system efficiency is significantly reduced by the intrinsic non-linear model, maximum power point (MPP), and partial shading (PS) effects. These two problems cause major power loss. To devise the maximum power point tracking (MPPT) control of the PV system, a novel group teaching optimization algorithm (GTOA) based controller is presented, which effectively deals with the PS and complex partial shading (CPS) conditions. Four case studies were employed that included fast-changing irradiance, PS, and CPS to test the robustness of the proposed MPPT technique. The performance of the GTOA was compared with the latest bio-inspired techniques, i.e., dragon fly optimization (DFO), cuckoo search (CS), particle swarm optimization (PSO), particle swarm optimization gravitational search (PSOGS), and conventional perturb and observe (P&O). The GTOA tracked global MPP with the highest 99.9% efficiency, while maintaining the magnitude of the oscillation <0.5 W at global maxima (GM). Moreover, 13–35% faster tracking times, and 54% settling times were achieved, compared to existing techniques. Statistical analysis was carried out to validate the robustness and effectiveness of the GTOA. Comprehensive analytical and statistical analysis solidified the superior performance of the proposed GTOA based MPPT technique.

scholarly journals Maximum Power Point Tracking Method Based on Modified Particle Swarm Optimization for Photovoltaic Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Kuei-Hsiang Chao ◽  
Long-Yi Chang ◽  
Hsueh-Chien Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Maximum Power Point Tracking ◽  
Particle Swarm ◽  
Maximum Power ◽  
Photovoltaic Module ◽  
Maximum Power Point ◽  
Swarm Optimization ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

This study investigated the output characteristics of photovoltaic module arrays with partial module shading. Accordingly, we presented a maximum power point tracking (MPPT) method that can effectively track the global optimum of multipeak curves. This method was based on particle swarm optimization (PSO). The concept of linear decreases in weighting was added to improve the tracking performance of the maximum power point tracker. Simulation results were used to verify that this method could successfully track maximum power points in the output characteristic curves of photovoltaic modules with multipeak values. The results also established that the performance of the modified PSO-based MPPT method was superior to that of conventional PSO methods.

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