Optimization of Renewable Energy Sources in a Microgrid Using Artificial Fish Swarm Algorithm

Kumar, K. Prakash; Saravanan, B.

doi:10.1016/j.egypro.2016.11.175

Cited by 58 publications

(22 citation statements)

References 7 publications

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“…In [49], an optimization algorithm is used for the optimal energy scheduling problem and the optimization of renewable energy sources in the micro-grid, called the Artificial Fish Swarm Algorithm. The efficiency of the algorithm is checked through a microgrid scenario to schedule generation.…”

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confidence: 99%

Sustainability and Optimization: From Conceptual Fundamentals to Applications

2020

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In recent years, both sustainability and optimization concepts have become inseparable developing topics with diverse concepts, elements, and aspects. The principal goal of optimization is to improve the overall sustainability including the environmental sustainability, social sustainability, economic sustainability, and energy resources sustainability through satisfying the objective functions. Therefore, applying optimization algorithms and methods to achieve the sustainable development have significant importance. This paper represents a considerable review on the employed optimization methodologies to sustainability and the sustainable development including sustainable energy, sustainable buildings, and sustainable environment. Since energy optimization is one of the major necessities of sustainability, sustainable development is investigated from the energy perspective. In addition, the concept, definitions, and elements of the sustainability and optimization have been presented, and the review of the optimization metaheuristic algorithms used in recent published articles related to sustainability and sustainable development was carried out. Thus, it is believed that this paper can be appropriate, beneficial, and practical for students, academic researchers, engineers, and other professionals.

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confidence: 99%

Sustainability and Optimization: From Conceptual Fundamentals to Applications

2020

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“…Too much crowding factor will affect the clustering behavior of artificial fish, as well as the convergence speed and accuracy. If the crowding factor is too small, the global performance of the algorithm will be affected [11].In the literature, by the author to crowded degree of factor analysis and verification, we can understand its mechanism of action, so, in this article can ignore the crowded degree factor, so that we can reduce complexity of algorithm can also ensure accuracy of convergence speed, and ability of global optimization [12]. In the following experimental data, the time performance of the standard AFSA and the improved AFSA algorithm is compared.…”

Section: Improve Artificial Fish Stocksmentioning

confidence: 99%

Study on Grade Prediction of Flotation Concentrate Based on Modified Artificial Fish Swarm Algorithm

Zhang¹,

Hong-yang²,

Li³

2019

Proceedings of the 2019 International Conference on Modeling, Simulation and Big Data Analysis (MSBDA 2019)

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Flotation is a process with nonlinear, large lag, strong coupling and other characteristics, concentrate grade is an important economic index to test the flotation production process, and it is difficult to establish accurate mathematical model, accurate prediction of concentrate grade is needed, which is of great significance to the quality prediction of flotation process.Support vector machine regression prediction was used as basic model, and modified basic artificial fish swarm algorithm(AFSA) was used to optimize parameters about the model, and GAFSA-SVM model was established to predict flotation concentrate grade.The initial value, crowding factor and visual field of the AFSA are improved, and the elimination mechanism is added.By simulating the historical data, the improved gafsa-svm model can predict the flotation concentrate grade well, effectively improve the prediction accuracy and speed of concentrate grade, and meet the requirements of field practice. IntroductionFlotation is the method of ore dressing in most of China's concentrators. Flotation has a wide range of application fields, fine particle size of flotation ore, high production index and a variety of flotation concentrates, and has a good development prospect. However, there are many physical and chemical changes in the process of flotation, with complex mechanism of action and many influencing factors, such as ore particle size, concentration of pulp, dosage of agents, concentration of foam and temperature. Concentrate grade is not only a parameter of the flotation process, but also an important index to test the flotation efficiency. Soft measurement method can obtain some physical quantities that cannot be measured by instruments, and predict some physical quantities that cannot be measured by establishing mathematical models. . In recent years, some progress has been made in the application of soft-sensing method to flotation prediction. Now there are many soft-sensing modeling methods based on intelligent algorithms. TongXi [1] by optimal ` ESN chaos time series RBF neural network to flotation prediction analysis of economic indicators. Zhang Xong, Zhu Jing [2], proposed the economic and technical index prediction of flotation process based on the combination of chaotic ant colony and least square method and simultaneously training neural network.Li Xiaolei proposed a new method -AFSA that about animal autonomy [3], which is an application of the idea of swarm intelligence and a bottom-up optimization method. AFSA includes random behavior, foraging behavior, clustering behavior and rear-end behavior, through which optimization is carried out. At present, the research of artificial fish swarm algorithm has penetrated into various application fields and obtained good practical application .Cui Qiang[4] et al. applied the AFSA to fault diagnosis of transformer. By improving the AFSA to train the neural network, the accuracy of transformer fault diagnosis was effectively improved. Zhu Xuhui[5] et al. optimized the SVM's parameters by improv...

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“…The food concentration in this position of fish is stated as the objective function value. The distance between the artificial fish is [25][26] expressed as,…”

Section: Preying Behaviourmentioning

confidence: 99%

Solving non-convex economic dispatch with prohibited zones using artificial fish swarm optimization

Ko¹,

Salkuti

Jung

2018

IJET

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In this paper, a novel approach is proposed to solve the non-convex and discontinuous economic dispatch (ED) problem of power system with thermal power plants. All the practical constraints (loss constraint, generators ramp rate constraints and network constraints) are considered for solving the ED problem. Here, the proposed ED problem is solved by considering the generators with valve point loading (VPL) effects and prohibited operating zones (POZs) effects. In this paper, to solve this practical ED problem, an evolutionary based Artificial Fish Swarm Optimization Algorithm (AFSOA) is utilized. The AFSOA is a global search algorithm based on the characteristics of fish swarm and its autonomous model. The detailed algorithm with its flow chart is presented in this paper. To show the effectiveness of the proposed ED approach, 3 test systems (3, 6 and 20 generating unit systems) are considered. The obtained results are compared with other algorithms reported in the literature.

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Optimization of Renewable Energy Sources in a Microgrid Using Artificial Fish Swarm Algorithm

Cited by 58 publications

References 7 publications

Sustainability and Optimization: From Conceptual Fundamentals to Applications

Sustainability and Optimization: From Conceptual Fundamentals to Applications

Study on Grade Prediction of Flotation Concentrate Based on Modified Artificial Fish Swarm Algorithm

Solving non-convex economic dispatch with prohibited zones using artificial fish swarm optimization

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