Performance of PSO Based Variants in Tracking Optimal Power in a Solar PV based Generation System under Partial Shading Condition

Iqbal, Danish; Tauheed, A.; Pervez, Imran; Malick, Ifham H.; Sarwar, Adil; Tariq, Mohd

doi:10.1080/23080477.2019.1700067

Cited by 10 publications

(3 citation statements)

References 13 publications

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“…The traditional analysis found it difficult in the uncertain complex power system to operate/control for the future development of microgrid. Conventional optimization methods like a genetic algorithm (GA), 11,12 ant colony optimization (ACO), 13,14 and particle swarm optimization (PSO) 15,16 were categories under metaheuristic method and its convergence to attain an optimal solution to specified problems are less in robustness and its lack in computation with machine learning. AI-based models are trained with big data collected from the wide-area monitoring system (WAMS) to attain deep learning.…”

Section: Need Of Algorithm In Microgridmentioning

confidence: 99%

Deep learning and reinforcement learning approach on microgrid

Kumar¹,

Kandasamy

Ramanathan

2020

Int Trans Electr Energ Syst

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Microgrid is a new era in the power system and it has more scope of investigation on research. Due to an increase in demand and future expansion of the power system, analyzing the complexities of the network becomes a challenging task. Artificial intelligence plays a vital role in resolving such issues in a microgrid in various aspects. Owing to the rapid growth of periodical update in computational cost reduction, enhanced data analysis-based algorithm artificial intelligence enters into new epoch Artificial Intelligence AI 2.0. Based on such approach, machine learning has been evolved as AI 2.0 initially. Now, it develops branches like deep learning, reinforcement learning, and a combination of both deep reinforcement learning algorithms. These algorithms are precise to attain higher priority in decision-making under a complex network. This paper deals with numerous challenges of the above algorithm to state the significance of AI 2.0 and summarization of their application toward microgrid is useful to analyze the power system. K E Y W O R D S deep learning, deep reinforcement learning, microgrid, reinforcement learning 1 | INTRODUCTION Microgrid is a cluster of distributed generators (DG), stored energy system, local loads along with protecting and monitoring devices. Microgrid has its own specialty in operating under two modes of operation: autonomous and grid-connected mode, mode selection attained by the use of static transfer switch that bridges operation selection in microgrid. The presence of multiple DGs creates a distribution access problem in multipoint is transformed into a concentrated

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Section: Need Of Algorithm In Microgridmentioning

confidence: 99%

Deep learning and reinforcement learning approach on microgrid

Kumar¹,

Kandasamy

Ramanathan

2020

Int Trans Electr Energ Syst

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“…A detailed model that specifically defines the behavior of photovoltaic panels for various industrial applications (array fault detection, power controller and controller configuration, maximum power point tracking techniques, grid integration, etc.) is quite essential and thus increases the overall performance of PV systems [3]. Accurate modeling of PV modules is therefore required to reflect their characteristics for further study.…”

Section: Introductionmentioning

confidence: 99%

Identification of Solar Photovoltaic Model Parameters Using an Improved Gradient-Based Optimization Algorithm With Chaotic Drifts

Premkumar¹,

Jangir²,

Ramakrishnan³

et al. 2021

IEEE Access

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When discussing the commercial applications of photovoltaic (PV) systems, one of the most critical problems is to estimate the efficiency of a PV system because current (I)voltage (V) and power (P) voltage (V) characteristics are highly non-linear. It should be noted that most of the manufacturer's datasheets do not have complete information on the electrical equivalent parameters of PV systems that are necessary for simulating an effective PV module. Compared to conventional approaches, computational optimization and global research strategies are more acceptable as an effective alternative to parameter estimation of solar PV modules. Recently, a Gradient-based optimizer (GBO) is reported to solve the engineering design optimization problems. However, the basic GBO algorithm is stuck in local optima when handling complex non-linear problems. In this sense, this paper presents a new optimization technique called the Chaotic-GBO (CGBO) algorithm to derive the parameters of PV modules while offering precise I-V and P-V curves. To this end, the CGBO algorithm is based on a chaotic generator to obtain the PV parameters combined with the GBO algorithm. There are five case studies considered to validate the performance of the proposed CGBO algorithm. A quantitative and qualitative performance evaluation reveals that the proposed CGBO algorithm has improved results than other state-of-the-art algorithms in terms of accuracy and robustness when obtaining PV parameters. The average RMSE values and runtime of five case studies are equal to 9.8427E-04, 2.3700E-04, 2.4251E-03, 4.3524E-03 and 1. 8349E-03, and 18.44, 17.78, 18.18, 18.28 and 17.97, respectively. The results proved the superiority of the proposed CGBO algorithm over the different selected algorithms. For future research, this study will be backed up with external support at https://premkumarmanoharan.wixsite.com/mysite.

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“…Hence, conventional optimization algorithms often fail to assess the damage correctly. However, it is possible to overcome this difficulty by implementing guided random search techniques like genetic algorithm (GA) [9][10][11][12][13]. Most of these metaheuristic algorithms are nature-inspired stochastic search techniques and are computationally efficient, as they possess the ability to implement distributed computing.…”

Section: Introductionmentioning

confidence: 99%

Teaching–learning-based optimization algorithm for solving structural damage detection problem in frames via changes in vibration responses

Kundu

Mishra

Maity

2021

Archit. Struct. Constr.

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Optimization algorithms are primarily responsible for efficiency in vibration-based damage detection particularly when utilizing the inverse approach. A complex problem of damage detection tends to converge into local minima, generated by a false damaged state which produces a response that is almost similar to the actual damaged state. Hence, there is a need for an efficient and accurate soft computing technique that can find the global minima or the actual damaged state. Recently, the teaching-learning based optimization (TLBO) algorithm has become quite popular due to its superior performance especially when compared to other metaheuristic algorithms. In this paper, damage estimation capability of the TLBO for frame structures and a benchmark problem of cantilever beam is studied and comparisons are made with some established soft computing techniques. TLBO is observed to produce better results relative to the other artificial intelligence-based techniques used for structural health monitoring.

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Performance of PSO Based Variants in Tracking Optimal Power in a Solar PV based Generation System under Partial Shading Condition

Cited by 10 publications

References 13 publications

Deep learning and reinforcement learning approach on microgrid

Deep learning and reinforcement learning approach on microgrid

Identification of Solar Photovoltaic Model Parameters Using an Improved Gradient-Based Optimization Algorithm With Chaotic Drifts

Teaching–learning-based optimization algorithm for solving structural damage detection problem in frames via changes in vibration responses

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