@inproceedings{9323eabefbc7486d992e3c9cc4f6916d,
title = "Enabling PID and SSSC for load frequency control using Particle Swarm Optimization",
abstract = "Rapid human population has led to increasing number of load demand. This load demand increased could potentially lead to instability of power system such as frequency stability. It is well known that governor has a significant role in controlling frequency on the generator. Generally, the governor usually used simple integral controller as the controller. However, with increasing number of load and rapid load changing, integral control only is not enough to handle the problems. Hence, deployment additional devices such as flexible alternating current transmission systems (FACTS) devices and changing the governor controller to PID controller is crucial. This paper proposed an enhancement of load frequency control using PID controller and Static Synchronous Series Compensator (SSSC). A Particle Swarm Optimization (PSO) is used as an optimization method to find the best parameter. Two area power system are used as a test system to analyze the performance of system with proposed method (PID and SSSC based on PSO). To examine the performance of the system with or without proposed method. From the simulation, it is found by using PID and SSSC based on PSO the frequency performance of the system is enhanced.",
keywords = "FACTS Devices, LFC, PSO, SSSC",
author = "Widodo, {Dwi Lastomo} and Herlambang Setiadi",
note = "Funding Information: V. CONCLUSIONS This paper shows the application of PID controller and SSSC to improve the frequency stability of the system. From the case studies, it is found that PID and SSSC have a significant impact on improving frequency stability of the system. It was also found that PSO can provide the optimum value of PID and SSSC parameter. Further research is required to utilize PID controller and SSSC in bigger systems such as 3 area load frequency control. Also combining PSO with another metaheuristic algorithm such as genetic algorithm or differential evolution algorithm ACKNOWLEDGMENT The first author is very grateful to PGRI Adi Buana University for funding the research and publication. Publisher Copyright: {\textcopyright} 2017 IEEE.; 3rd International Conference on Science in Information Technology, ICSITech 2017 ; Conference date: 25-10-2017 Through 26-10-2017",
year = "2017",
month = jul,
day = "1",
doi = "10.1109/ICSITech.2017.8257107",
language = "English",
series = "Proceeding - 2017 3rd International Conference on Science in Information Technology: Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "182--187",
editor = "Riza, {Lala Septem} and Andri Pranolo and Wibawa, {Aji Prasetyo} and Enjun Junaeti and Yaya Wihardi and Hashim, {Ummi Raba'ah} and Shi-Jinn Horng and Rafal Drezewski and Lim, {Heui Seok} and Goutam Chakraborty and Leonel Hernandez and Shah Nazir",
booktitle = "Proceeding - 2017 3rd International Conference on Science in Information Technology",
address = "United States",
}