Application and comparison of computational intelligence techniques for optimal location and parameter setting of UPFC

Shaheen, H.I.; Rashed, Ghamgeen Izat; Cheng, Shijie

doi:10.1016/j.engappai.2009.12.002

Cited by 74 publications

(42 citation statements)

References 19 publications

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“…Recently, the growing interest in the methodology and techniques of computational intelligence (CI) has attracted attention in various areas of science and engineering (Pedrycz and Vasilakos 2000;Shaheen et al, 2010;Warne et al, 2004;del-Hoyo et al, 2009;Srinivasan et al, 2009;AlRashidi and El-Hawary, 2009). The research has demonstrated that various CI techniques can support the design of models with better model performance than those being obtained when using a single ''conventional'' technique.…”

Section: Introductionmentioning

confidence: 99%

Design of information granule-oriented RBF neural networks and its application to power supply for high-field magnet

Park

Chung

et al. 2011

Engineering Applications of Artificial Intelligence

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Section: Introductionmentioning

confidence: 99%

Design of information granule-oriented RBF neural networks and its application to power supply for high-field magnet

Park

Chung

et al. 2011

Engineering Applications of Artificial Intelligence

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“…Artificial Intelligence (AI) techniques, such as GA (Genetic Algorithm) and PSO (Particles Swarm Optimization), have been applied successfully to design heuristic search algorithms [15]. These both techniques are applied in the LQR controller based CSC-STATCOM and explained as below.…”

Section: Proposed Ai Techniques Based Lqr Controller Designmentioning

confidence: 99%

“…GA technique is a search method, which is used the idea of natural selection and sexual reproduction to obtain a new population from previous generation [15]. Although a good solution can be obtained by GA method, but it also has some negative aspects.…”

Section: Introductionmentioning

confidence: 99%

Optimal LQR controller in CSC based STATCOM using GA and PSO

Gupta¹,

Tripathi²

2014

Archives of Electrical Engineering

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Abstract:The static synchronous compensator (STATCOM) is the multipurpose FACTS device with the multiple input and multiple output system for the enhancement of its dynamic performance in power system. Based on artificial intelligence (AI) optimization technique, a novel controller is proposed for CSC based STATCOM. In this paper, the CSC based STATCOM is controlled by the LQR. But the best constant values for LQR controller's parameters are obtained laboriously through trial and error method, although time consuming. So the goal of this paper is to investigate the ability of AI techniques such as genetic algorithm (GA) and particle swarm optimization (PSO) methods to search the best values of LQR controller's parameters in a very short time with the desired criterion for the test system. Performances of the GA, PSO & ABC based LQR controllers are also compared. Applicability of the proposed scheme is demonstrated through simulation in MATLAB and the simulation results are shown an improvement in the input-output response of CSC-STATCOM.

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“…They can benefit the emerging power system in terms of improving the system stability, reducing the flow in heavily loaded lines, maintaining bus voltages at their desired level and thus enhance power system security under contingency conditions. However, the benefits of these devices are mainly dependent on their location in the system [9]. The proper location of FACTS devices is a key to maximize power system security.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Power System Security Enhancement via Optimal Installation of Flexible AC Transmission System (FACTS) Devices

et al. 2017

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Abstract:Increasing demand for electricity has placed heavy stress on power system security. Therefore, this paper focuses on the problem of how to maximize power system static security in terms of branch loading and voltage level under normal operation and even the most critical single line contingency conditions. This paper proposes a hybrid approach to find out the optimal locations and settings of two classical types of flexible AC transmission system (FACTS) devices, namely thyristor-controlled series compensators (TCSCs) and static var compensators (SVCs) for solving this problem. Our proposed approach requires a two-step strategy. Firstly, the min cut algorithm (MCA) and tangent vector technique (TVT) are applied to determine the proper candidate locations of TCSC and SVC respectively so as to reduce the search scope for a solution to the problem, and then the cuckoo search algorithm (CSA) is employed to solve this problem by simultaneously optimizing the locations and settings for TCSC and SVC installation. The proposed hybrid approach has been verified on the IEEE 6-bus and modified IEEE 14-bus test systems. The results indicate that CSA outperforms particle swarm optimization (PSO), proving its effectiveness and potential, and they also show that our proposed hybrid approach can find the best locations and settings for TCSC and SVC devices as an effective way for enhancing power system static security by removing or alleviating the overloads and voltage violations under normal operation and even the most critical single line contingency conditions. Using this hybrid approach, the search space for solution to the problem becomes limited hence the computational burden will be decreased.

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Application and comparison of computational intelligence techniques for optimal location and parameter setting of UPFC

Cited by 74 publications

References 19 publications

Design of information granule-oriented RBF neural networks and its application to power supply for high-field magnet

Design of information granule-oriented RBF neural networks and its application to power supply for high-field magnet

Optimal LQR controller in CSC based STATCOM using GA and PSO

A Hybrid Approach for Power System Security Enhancement via Optimal Installation of Flexible AC Transmission System (FACTS) Devices

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