Design of Hybrid System Power Management Based Operational Control System to Meet Load Demand

Zulfatman Has, N. Nurhadi, F. Faizal
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Abstract

Renewable energy is an energy of unlimited sources that covering wind, sunshine and water, which can be used as sources of renewable power plants. These power plants give several advantages, but also some disadvantages, such as expensive generation costs, etc. The difficulty of being raised, due instability of renewable energy resources (RER). Aim of this study is to design power management of a hybrid system based on operational control system due to load demand. In this study, Power Management of Hybrid System used 3 power plants: Photovoltaic (PV), Wind Power, and Micro Hydro Power Plant (PLTmH), while Battery was employed as storage system. Main focus of the work was to determine the activation of each plant using Artificial Neural Network (ANN) method to fulfill the load demand. Matlab Simulink was employed to developed and simulate the ANN on the system. From results of simulation can be concluded that ANN can reach target accuracy level in 80%. When interconnecting the entire plant, the ANN experienced a misreading due to the voltage drop in each generator that affected the ANN input.
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基于负载需求的混合动力系统电源管理运行控制系统设计
可再生能源是一种无限量的能源,涵盖了风、阳光和水,可以作为可再生发电厂的来源。这些发电厂有一些优点,但也有一些缺点,如昂贵的发电成本等。由于可再生能源(RER)的不稳定性,提高了难度。本研究的目的是设计基于运行控制系统的混合动力系统在负荷需求下的电源管理。在本研究中,混合动力系统的电源管理采用了光伏(PV)、风力发电(Wind Power)和微型水力发电厂(PLTmH) 3个电站,蓄电池作为存储系统。研究的重点是利用人工神经网络(ANN)方法确定各电厂的激活状态,以满足负荷需求。利用Matlab Simulink对该系统进行了开发和仿真。仿真结果表明,人工神经网络可以达到目标准确率的80%。当整个电厂互连时,由于每个发电机的电压降影响了人工神经网络的输入,人工神经网络经历了误读。
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