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• W2808068102 abstract "This paper presents a new Combined Modified Bat Search algorithm and artificial neural network control of grid-connected Hybrid Renewable Energy System (HRES). The HRES consists of photovoltaic, wind turbine, fuel cell, and Battery. Because of this resource utilization, the intermittent power generations are unpredictable and variable, which created a power fluctuation in HRES. To stabilize the power fluctuations, the intelligent controller is proposed. In the proposed technique, the modified bat search algorithm plays out the assessment procedure to establish the exact control signals for the system and builds up the control signals database for the offline way in light of the power variety between source side and the load side. The accomplished dataset is used to work the artificial neural network strategy on the online way and it leads the control procedure in less execution time. In the proposed control technique, the searching behavior of the bats is modified by using the efficient neighborhood search functions like crossover and mutation. Initially, the system behaviors are analyzed based on the objective function. For analyzing the power flow management, the equality and inequality constraints are defined, which specifies the availability of the renewable energy sources, power demand and the state of charge of storage elements. With this proper control, HRES is able to significantly enhance the dynamic security of the power system. In addition, Battery is utilized as an energy source to stabilize and permit the renewable power system units to keep running at a steady and stable output power. The proposed method is implemented in MATLAB/Simulink working platform and their performances are analyzed with the existing methods." @default.
• W2808068102 created "2018-06-21" @default.
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• W2808068102 date "2018-06-13" @default.
• W2808068102 modified "2023-10-01" @default.
• W2808068102 title "CMBSNN for Power Flow Management of the Hybrid Renewable Energy – Storage System-Based Distribution Generation" @default.
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• W2808068102 doi "https://doi.org/10.1080/02564602.2018.1465860" @default.
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