Comparison of LFC Optimization on Micro-hydro using PID, CES, and SMES based Firefly Algorithm

Kadaryono, Rukslin, Machrus Ali, Askan, A. Parwanti, Iwan Cahyono
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引用次数: 7

Abstract

Micro-hydro gets potential energy from water flow that has a certain height difference. Potential energy is strongly influenced by high water fall. Potential energy through pipes, incoming turbines converted into kinetic energy. The kinetic energy of the turbine coupled with the generator is converted into electrical energy. Some components used for micro-hydro power generation, among others; intake, settling basin, headrace, penstock, turbine, draft tube, generator, and control panel. Water flows through the pipe into the turbine house so it can rotate the turbine blades. Turbine rotation is used to rotate a generator at the micro hydro generator. The most common problem with micro-hydro generating systems is inconsistent generator rotation caused by changes in connected loads. Load changes can cause system frequency fluctuations and may cause damage to electrical equipment. Artificial Intelligence (AI) is used to obtain the right constants to obtain the best optimization. In this study compare the control method, namely; Proportional Integral Derivatives (PID), Capacitive Energy Storage (CES), and Superconducting Magnetic Energy Storage (SMES). This study also compared the method of artificial intelligence between Particle Swarm Optimization (PSO) method has been studied with the method of Firefly Algorithm (FA). Overall this study compares 11 methods, namely methods; uncontrolled, PID-PSO method, PID-FA method, CES-PSO method, CES-FA method, SMES-PSO method, SMES-FA method, PID-CES-PSO method, PID-CES-FA method, PID-SMES - PSO, and PID-SMES-FA method. The results of the simulation showed that from the 11 methods studied, it was found that the PID-CES-FA method has the smallest undershot value, ie -7.774e-03 pu, the smallest overshoot value, which is 4.482e-05 pu, and the fastest completion time is 7.11 s. These results indicate that the smallest frequency fluctuations are found in the PID-CES-FA controller. Thus it is stated that the PID-CES-FA method is the best method used in the previous method. This research will use other methods to get the best controller.
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基于PID、CES和SMES的萤火虫算法在微水电LFC优化中的比较
微水力从具有一定高差的水流中获取势能。位能受到高落差的强烈影响。势能通过管道,传入涡轮转化为动能。涡轮与发电机耦合的动能转化为电能。用于微型水力发电的一些部件;进气,沉降池,引水,压力管,涡轮,尾水管,发电机和控制面板。水通过管道流入涡轮室,这样就可以转动涡轮叶片。水轮旋转用于在微型水轮发电机上旋转发电机。微型水力发电系统最常见的问题是由于连接负荷的变化引起的发电机旋转不一致。负载变化会引起系统频率波动,并可能对电气设备造成损坏。人工智能(AI)用于获得正确的常数以获得最佳优化。在本研究中比较控制方法,即;比例积分导数(PID),电容储能(CES)和超导磁能储能(SMES)。本研究还比较了人工智能中粒子群优化(PSO)方法与萤火虫算法(FA)方法之间的关系。总体而言,本研究比较了11种方法,即方法;非受控、PID-PSO法、PID-FA法、CES-PSO法、CES-FA法、sme -PSO法、sme - fa法、PID-CES-PSO法、PID-CES-FA法、PID-SMES- PSO法、PID-SMES- PSO法、pid - sme - fa法。仿真结果表明,在所研究的11种方法中,PID-CES-FA方法的下冲值最小,为-7.774e-03 pu,超调值最小,为4.4820 -05 pu,最快完成时间为7.11 s。结果表明,PID-CES-FA控制器的频率波动最小。因此,PID-CES-FA法是上述方法中最好的方法。本研究将采用其他方法获得最佳控制器。
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