scholarly journals A Novel MPPT Design for a Partially Shaded PV System Using Spotted Hyena Optimization Algorithm

2021 ◽  
Vol 11 (6) ◽  
pp. 7776-7781
Author(s):  
B. Korich ◽  
A. Benaissa ◽  
B. Rabhi ◽  
D. Bakria
Keyword(s):  
Spotted Hyena ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Pv Systems ◽  
Remarkable Improvement ◽  
Perturb And Observe ◽  
Power Point

Partial shading is a common problem in photovoltaic (PV) systems, known for its difficulty. Numerous attempts have been conducted to mitigate this problem. Some of these efforts deploy metaheuristic optimization with a view to tracking the multiple-peak P–V curve in a partial shading PV system. Hence, this paper proposes a novel metaheuristic algorithm to track the maximum power point of PV systems using the Spotted Hyena Optimization (SHO) algorithm. When evaluated, the SHO algorithm proved to be very fast, robust, and accurate in standard conditions, Partial Shading Conditions (PSCs), and irradiance variations. Also, the results reveal a remarkable improvement in the performance when we compare the SHO algorithm with the Grey Wolf Optimization (GWO) algorithm and the Perturb and Observe (P&O) algorithm.

scholarly journals A Modification of the Perturb and Observe Method to Improve the Energy Harvesting of PV Systems under Partial Shading Conditions

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2521
Author(s):  
Alfredo Gil-Velasco ◽  
Carlos Aguilar-Castillo
Keyword(s):  
Maximum Power ◽  
Global Maximum ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Pv Systems ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point ◽  
Computational Resources

There are multiples conditions that lead to partial shading conditions (PSC) in photovoltaic systems (PV). Under these conditions, the harvested energy decreases in the PV system. The maximum power point tracking (MPPT) controller aims to harvest the greatest amount of energy even under partial shading conditions. The simplest available MPPT algorithms fail on PSC, whereas the complex ones are effective but require high computational resources and experience in this type of systems. This paper presents a new MPPT algorithm that is simple but effective in tracking the global maximum power point even in PSC. The simulation and experimental results show excellent performance of the proposed algorithm. Additionally, a comparison with a previously proposed algorithm is presented. The comparison shows that the proposal in this paper is faster in tracking the maximum power point than complex algorithms.

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 A Simple Method for Detecting Partial Shading in PV Systems

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4938
Author(s):  
Waleed Al Abri ◽  
Rashid Al Abri ◽  
Hassan Yousef ◽  
Amer Al-Hinai
Keyword(s):  
Accurate Method ◽  
Maximum Power ◽  
Global Maximum ◽  
Pv System ◽  
Maximum Power Point ◽  
Simple Method ◽  
Partial Shading ◽  
Pv Systems ◽  
Power Point ◽  
Global Peak

Partial shading conditions (PSCs) can significantly reduce the output energy produced by photovoltaic (PV) systems. Moreover, when such conditions occur, conventional and advanced maximum power point tracking (MPPT) systems fail to operate the PV system at its peak because the bypassing diodes may cause the PV system to become trapped at a low power point when they are in conduction mode. The PV system can be operated at the global maximum power point (MPP) with the help of global peak searching tools. However, the frequent use of these tools will reduce the output of PV systems since they force the PV system to operate outside its power region while scanning the I-V curve in order to determine the global MPP. Thus, the global peak searching tools should be deployed only when a PSC occurs. In this paper, a simple and accurate method is proposed for detecting PSCs by means of monitoring the sign of voltage changes (positive or negative). The method predicts a PSC if the sign of successive voltage changes is the same for a certain number of successive changes. The proposed method was tested on two types of PV array configurations (series and series–parallel) with several shading patterns emulated on-site. The proposed method correctly and timely identified all emulated shading patterns. It can be used to trigger the global MPP searching techniques for improving the PV system’s output under PSCs; furthermore, it can be used to notify the PV system’s operator of the occurrence of PSCs.

scholarly journals Enhanced Grey Wolf Optimizer based MPPT Algorithm of PV system under Partial Shaded Condition

2017 ◽  
Vol 6 (3) ◽  
pp. 203 ◽  
Author(s):  
Santhan Kumar Cherukuri ◽  
Srinivasa Rao Rayapudi
Keyword(s):  
Maximum Power ◽  
Global Maximum ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

Partial shading condition is one of the adverse phenomena which effects the power output of photovoltaic (PV) systems due to inaccurate tracking of global maximum power point. Conventional Maximum Power Point Tracking (MPPT) techniques like Perturb and Observe, Incremental Conductance and Hill Climbing can track the maximum power point effectively under uniform shaded condition, but fails under partial shaded condition. An attractive solution under partial shaded condition is application of meta-heuristic algorithms to operate at global maximum power point. Hence in this paper, an Enhanced Grey Wolf Optimizer (EGWO) based maximum power point tracking algorithm is proposed to track the global maximum power point of PV system under partial shading condition. A Mathematical model of PV system is developed under partial shaded condition using single diode model and EGWO is applied to track global maximum power point. The proposed method is programmed in MATLAB environment and simulations are carried out on 4S and 2S2P PV configurations for dynamically changing shading patterns. The results of the proposed method are analyzed and compared with GWO and PSO algorithms. It is observed that proposed method is effective in tracking global maximum power point with more accuracy in less computation time compared to other methods.Article History: Received June 12nd 2017; Received in revised form August 13rd 2017; Accepted August 15th 2017; Available onlineHow to Cite This Article: Kumar, C.H.S and Rao, R.S. (2017 Enhanced Grey Wolf Optimizer Based MPPT Algorithm of PV System Under Partial Shaded Condition. Int. Journal of Renewable Energy Development, 6(3), 203-212.https://doi.org/10.14710/ijred.6.3.203-212

scholarly journals Maximum Power Point Tracking using Grey Wolf Technique Under Fast-Changing Irradiance

2020 ◽  
Vol 9 (11) ◽  
pp. 365-371
Keyword(s):  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Pv System ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Nature Inspired Algorithm ◽  
Power Point

In this paper, maximum power point tracking (MPPT) using Grey wolf optimization (GWO) algorithm is presented using MATLAB/Simulink. As we know that meta-heuristic or nature-inspired algorithm has proven to be superior in performance compared to the conventional MPPT methods. Grey Wolf optimization algorithm is a meta-heuristic algorithm based on the hunting behaviour of grey wolves. The proposed system includes modelling of PV system under changing irradiance and the MPPT control is driven by GWO algorithm. Most of the conventional MPPTs are unable to track multiple peaks and also shows oscillations on the output side, for this reason proposed MPPT algorithm is used in this paper. For eliminating oscillations, this algorithm has proven to be better compared to perturb and observe (P&O) and particle swarm optimization (PSO). The results are compared in terms of output power.

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