Black hole optimised cascade proportional derivative-proportional integral derivative controller for frequency regulation in hybrid distributed power system

IF 0.8 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Swarm Intelligence Research Pub Date : 2019-12-06 DOI:10.1504/ijsi.2019.10025731
Tulasichandra Sekhar Gorripotu, R. Pilla
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引用次数: 3

Abstract

This manuscript presents a novel black hole optimised (BHO) proportional derivative-proportional integral derivative controller (PD-PID) is provided for the optimal solution of the frequency regulation of hybrid power system. At first, a two area power system is considered in which area-1 having thermal, distributed units and in area-2 includes thermal, hydel and nuclear units. Appropriate nonlinearities such boiler dynamics, governor dead band (GDB) and generation rate constraint (GRC) are considered. In the next step, PD-PID controller is considered as a secondary controller and its preeminence is shown by comparing with proportional integral derivate (PID) and proportional integral double derivate (PIDD) controllers for the same model having integral time multiplied absolute error (ITAE) as an error function. Finally, sensitivity of the proposed controller is investigated over a wide variation of system parameters and loading condition. For more examination of the proposed controller is also analysed under random step load and sinusoidal disturbances.
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黑洞优化串级比例导数-比例积分导数混合分布式电力系统频率调节控制器
本文提出了一种新的黑洞优化(BHO)比例导数-比例积分导数控制器(PD-PID),用于混合动力系统的频率调节。首先,考虑一个两区电力系统,其中区1有热电、分布式机组,区2包括热电、水电和核电机组。适当考虑了锅炉动力学、调速器死区和发电速率约束等非线性因素。接下来,将PD-PID控制器作为二级控制器,并将其与以积分时间乘绝对误差(ITAE)为误差函数的同一模型的比例积分衍生(PID)和比例积分双衍生(PIDD)控制器进行比较,表明其优越性。最后,研究了该控制器在系统参数和负载条件变化情况下的灵敏度。为了进一步检验所提出的控制器,还分析了在随机阶跃负载和正弦干扰下的性能。
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来源期刊
International Journal of Swarm Intelligence Research
International Journal of Swarm Intelligence Research COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
2.50
自引率
0.00%
发文量
76
期刊介绍: The mission of the International Journal of Swarm Intelligence Research (IJSIR) is to become a leading international and well-referred journal in swarm intelligence, nature-inspired optimization algorithms, and their applications. This journal publishes original and previously unpublished articles including research papers, survey papers, and application papers, to serve as a platform for facilitating and enhancing the information shared among researchers in swarm intelligence research areas ranging from algorithm developments to real-world applications.
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