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Frequency Stability Improvement of Micro Hydro Power System Using Hybrid SMES and CES Based on Cuckoo Search Algorithm

机译:基于布谷鸟搜索算法的混合SMES和CES改进微水电系统的频率稳定性

摘要

Micro hydro has been chosen because it has advantages both economically, technically and as well as in terms of environmental friendliness. Micro hydro is suitable to be used in areas that difficult to be reached by the grid. Problems that often occur in the micro hydro system are not the constant rotation of the generator that caused by a change in load demand of the consumer. Thus causing frequency fluctuations in the system that can lead to damage both in the plant and in terms of consumer electrical appliances. The appropriate control technology should be taken to support the optimum performance of micro hydro. Therefore, this study will discuss a strategy of load frequency control by using Energy Storage. Superconducting magnetic energy storage (SMES) and capacitor energy storage (CES) are devices that can store energy in the form of a fast magnetic field in the superconducting coil. For the optimum performance, it is necessary to get the optimum tuning of SMES and CES parameters. The artificial intelligence methods, Cuckoo Search Algorithm (CSA) are used to obtain the optimum parameters in the micro hydro system. The simulation results show that the application of the CSA that use to tune the parameters of hybrid SMES-CES-PID can reduce overshoot oscillation of frequency response in micro hydro power plant.
机译:选择微型水力发电是因为它在经济,技术以及环保方面均具有优势。微型水力发电适用于电网难以到达的区域。在微型水力系统中经常发生的问题不是由使用者的负载需求变化引起的发电机持续旋转。从而导致系统中的频率波动,这可能导致工厂内以及消费类电器方面的损坏。应该采用适当的控制技术来支持微水电的最佳性能。因此,本研究将讨论通过储能控制负载频率的策略。超导磁储能(SMES)和电容器储能(CES)是可以在超导线圈中以快速磁场形式存储能量的设备。为了获得最佳性能,必须对SMES和CES参数进行最佳调整。杜鹃搜索算法(CSA)的人工智能方法用于获得微水力系统中的最佳参数。仿真结果表明,将CSA用于混合SMES-CES-PID参数的调整,可以减少微水电厂频率响应的过冲振荡。

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