Most Valuable Player Algorithm based Maximum Power Point Tracking for a Partially Shaded PV Generation System

Pervez, Imran; Shams, Immad; Mekhilef, Saad; Sarwar, Adil; Tariq, Mohd; Alamri, Basem

doi:10.1109/tste.2021.3069262

Cited by 80 publications

(38 citation statements)

References 39 publications

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“…Although MVPA is a new evolutionary algorithm (published in 2020), this algorithm has been used several times to solve other special problems. For example, problems such as Partially Shaded PV Generation System [19], Energy Control Center for Energy System Security [20], and Optimal Antenna Network Positioning [21]. This is one of our bases in deciding to use MVPA in this study.…”

Section: Most Valuable Player Algorithm (Mvpa)mentioning

confidence: 99%

MVPA and GA Comparison for State Space Optimization at Classic Tetris Game Agent Problem

Armanto

Putra

Pickerling

2022

Inf. J. Ilm. Bid. Teknol. Inf. dan Komun.

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Tetris is one of those games that looks simple and easy to play. Although it seems simple, this game requires strategy and continuous practice to get the best score. This is also what makes Tetris often used as research material, especially research in artificial intelligence. These various studies have been carried out. Starting from applying state-space to reinforcement learning, one of the biggest obstacles of these studies is time. It takes a long to train artificial intelligence to play like a Tetris game expert. Seeing this, in this study, apply the Genetic Algorithms (GA) and the most valuable player (MVPA) algorithm to optimize state-space training so that artificial intelligence (agents) can play like an expert. The optimization means in this research is to find the best weight in the state space with the minimum possible training time to play Tetris with the highest possible value. The experiment results show that GAs and MVPA are very effective in optimizing the state space in the Tetris game. The MVPA algorithm is also faster in finding solutions. The resulting state space weight can also get a higher value than the GA (MVPA value is 249 million, while the GA value is 68 million).

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Section: Most Valuable Player Algorithm (Mvpa)mentioning

confidence: 99%

MVPA and GA Comparison for State Space Optimization at Classic Tetris Game Agent Problem

Armanto

Putra

Pickerling

2022

Inf. J. Ilm. Bid. Teknol. Inf. dan Komun.

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“…In [25], a collaborative swarm algorithm (CSA) is adopted to improve the tracking speed and efficiency of MPP tracking by employing the excellent features of PSO, Jaya, and ACO-NUP collaboratively. Another recent strategy to counter the partial shading effects using a most valuable player algorithm (MVPA) is assumed in [26]. The performance of the proposed method is compared with the most widely used PSO and modified Jaya algorithm; although it exhibits comparatively better tracking capability, its MPP tracking duration still has high undershot oscillations.…”

Section: Introductionmentioning

confidence: 99%

An Improved Partial Shading Detection Strategy Based on Chimp Optimization Algorithm to Find Global Maximum Power Point of Solar Array System

2022

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A PV system’s operation highly depends on weather conditions. In case of varying irradiances or load changes, there is a power mismatch between various modules of the PV array. This power mismatch causes instability in the output of the PV system and deteriorates the overall system efficiency. To overcome instability and lower efficiency problems, and to extract maximum power from the PV system, various maximum power point tracking (MPPT) techniques are employed. The success of these techniques depends on the identification of the actual operating conditions of the system. This article proposes a hybrid maximum power point tracking (MPPT) technique that is capable of efficiently differentiating between uniform irradiance, non-uniform irradiance, and load variations on the PV system. Based on the identified operating conditions, the proposed method uses modified perturb and observe (Modified P&O) to cope with uniform irradiance variations and chimp optimization algorithms (ChOA) for non-uniform conditions to track the oscillation free maximum power-point. The proposed method is implemented and verified using a 4×3 PV array model in MATLAB Simulink software. Different cases of uniformly changing irradiance and non-uniformly changing irradiance are applied to test the performance of the proposed hybrid technique. The load varying conditions are performed by applying a variable load resistor. The authenticity of the proposed hybrid technique is critically evaluated against the well-known and most widely used optimization techniques of modified perturb and observe (Modified P&O), particle swarm optimization (PSO), flower pollination algorithm (FPA), and grey wolf optimization (GWO). The results demonstrate the superiority of the proposed technique in oscillation-free tracking of global maximum power point (GMPP) in a minimum tracking time of 0.4 s and 0.15 s, and steady-state MPPT efficiency of 96.92% and 99.54% under uniform and non-uniform irradiance conditions, respectively.

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“…Moreover, in [17], another improved variant of the Jaya algorithm known as adaptive Jaya (AJaya) is proposed that uses varying iteration weighting coefficients in the Jaya update equations, improving the convergence speed of the simple Jaya algorithm and reducing the number of power fluctuations. Finally, in [18], a Most Valuable Player Algorithm (MVPA) algorithm uses a clever strategy based on the sensitivity of the Power versus Voltage and duty ratio to limit the search space to a set of solutions with a high probability of finding the global maximum there, and thus significantly improving the convergence speed and reducing the power losses.…”

Section: Introductionmentioning

confidence: 99%

“…This work proposes a metaheuristic-based algorithm called the Low Burden Narrow Search (LBNS) to mitigate the power fluctuation and provide a more power-efficient MPP tracker. In addition, the Limited Search Strategy (LSS) proposed in [18] is modified to confine the search space around the actual optimum. By doing so, we reduce the number of update equations to one that avoid searching useless regions and reduce computational burden.…”

Section: Introductionmentioning

confidence: 99%

A Reduced Search Space Exploration Metaheuristic Algorithm for MPPT

2022

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The necessity for clean and sustainable energy has shifted the energy sector's interest in renewable energy sources. Photovoltaics (PV) is the most popular renewable energy source because the sun is ubiquitous. However, PV's power transfer efficiency varies with different load's electrical characteristics, temperatures on PV panels, and insolation conditions. Based on these factors, Maximum Power Point Tracking (MPPT) is a mechanism formulated as an optimization problem adjusting the PV to deliver the maximum power to the load. Under full insolation conditions, varying solar panel temperatures, and different loads MPPT problem is a convex optimization problem. However, when the PV's surface is partially shaded, multiple power peaks are created in the power versus voltage (P-V) curve making MPPT non-convex. Unfortunately, all optimization strategies for MPPT under partial shading applied in previous works, from traditional techniques to Machine Learning and the recently proposed Natureinspired algorithms, were either computationally expensive or/and led to extensive power losses. To this end, this work presents an algorithm that builds upon metaheuristic optimization algorithms to reduce their complexity further and mitigate the power losses during power tracking. Our experimental results demonstrated that the proposed algorithm converges faster to maximum power point with lower power losses during tracking compared to two very recently proposed MPPT algorithms under partial shading conditions. INDEX TERMSMetaheuristic Algorithms, improved Limited Search Strategy (iLSS), Photovoltaic (PV), Partial Shading (PS), Maximum Power Point Tracking (MPPT).

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Most Valuable Player Algorithm based Maximum Power Point Tracking for a Partially Shaded PV Generation System

Cited by 80 publications

References 39 publications

MVPA and GA Comparison for State Space Optimization at Classic Tetris Game Agent Problem

MVPA and GA Comparison for State Space Optimization at Classic Tetris Game Agent Problem

An Improved Partial Shading Detection Strategy Based on Chimp Optimization Algorithm to Find Global Maximum Power Point of Solar Array System

A Reduced Search Space Exploration Metaheuristic Algorithm for MPPT

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