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

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Abstract

Unified power flow controller (UPFC) is one of the most effective flexible AC transmission systems (FACTS) devices for enhancing power system security. However, to what extent the performance of UPFC can be brought out, it highly depends upon the location and parameter setting of this device in the system. This paper presents a new approach based on computational intelligence (CI) techniques to find out the optimal placement and parameter setting of UPFC for enhancing power system security under single line contingencies (N−1 contingency). Firstly, a contingency analysis and ranking process to determine the most severe line outage contingencies, considering lines overload and bus voltage limit violations as a performance index, is performed. Secondly, a relatively new evolutionary optimization technique, namely: differential evolution (DE) technique is applied to find out the optimal location and parameter setting of UPFC under the determined contingency scenarios. To verify our proposed approach and for comparison purposes, simulations are performed on an IEEE 14-bus and an IEEE 30-bus power systems. The results, we have obtained, indicate that DE is an easy to use, fast, robust and powerful optimization technique compared with genetic algorithm (GA) and particle swarm optimization (PSO). Installing UPFC in the optimal location determined by DE can significantly enhance the security of power system by eliminating or minimizing the number of overloaded lines and the bus voltage limit violations.

Introduction

The secure operation of power system has become an important and critical issue in today's large, complex, and load-increasing systems. Security constraints such as thermal limits of transmission lines and bus voltage limits must be satisfied under all system operation conditions. Commonly, Power systems are planned and operated based on the N−1 security criterion, which implies that the system should remain secure under all important first contingencies. One solution to cope with this problem is to design the system to meet the N−1 security criterion which is somewhat conservative and costly. An alternative solution to improve the security of power system is the flexible AC transmission systems (FACTS) devices which is a concept proposed by Hingorani (1988).

FACTS devices can reduce the flows of heavily loaded lines, maintain the bus voltages at desired levels, and improve the stability of the power network. Consequently, they can improve the power system security under contingency situations. Unified power flow controller (UPFC) is a versatile FACTS's device which can independently or simultaneously control the active power, the reactive power, and the bus voltage to which it is connected (Gyugyi, 1992). However, to achieve such functionality of UPFC, it is highly important to determine the optimal location of this device in the power system with the appropriate parameter setting. Since UPFC can be installed in different locations, its effectiveness will be different. Therefore, we will face the problem of where we should install UPFC. For this reason, some performance indices must be satisfied. The following are some factors that can be considered in the selection of the optimal installation and parameter setting of UPFC: The stability margin improvement, the power transmission capacity increasing, and the power blackout prevention, etc. Therefore, conventional power flow algorithm (Puerle-Esquivel and Acha, 1997) should incorporate with UPFC and the optimization should consider one, two, or all of the above-mentioned factors. However, in this paper, we only consider blackout prevention, in other words, enhancing the security of power system under single line contingencies through installing UPFC in an optimal location with optimal parameter setting.

In the last decade, new algorithms have been developed for the optimal power flow incorporating with UPFC device as well as for the optimal placement of UPFC. Some of them are: A sensitivity-based approach which has been developed for finding suitable placement of UPFC (Singh and Erlich, 2005), an evolutionary-programming-based load flow algorithm for systems containing unified power flow controllers (Wang et al., 2003), a genetic algorithm which proposed for solving the optimal location problem of UPFC (Arabkhaburi et al., 2006), and a particle swarm optimization (PSO) for optimal location of FACTS devices (Saravanan, 2005).

Also a lot of work has been done in the contingency analysis research area. Operation scheme of FACTS devices to enhance the power system steady-state security level considering a line contingency analysis is suggested in Song et al. (2004). A method for contingency selection and security enhancement of power systems by optimal placement of FACTS devices using GA is presented in Sudersan et al. (2004).

Recently, a relatively new, easy to implement, reasonably fast, and robust evolutionary algorithms (EAs) technique, known as differential evolution (DE) has been developed (Storn and Price, 1995; Price et al., 2005). DE has shown great promise in several applications including the field of power system (Gamperle et al., 2002; Babu and Jehan, 2003; Ursem and Vadstrup, 2003; Onwubolu, 2004; Wong and Dong, 2005). To the best of the author's knowledge, the applications of the DE technique for optimal allocation of FACTS devices in general and UPFC in particular are not existed in the open literature.

In this paper, one of the newest EAs techniques, namely DE, is applied to find out the optimal location and parameter setting of UPFC device for enhancing system security under single line contingencies through eliminating or minimizing the overloaded lines and the bus voltage limit violations.

Section snippets

UPFC power flow model

Fig. 1 shows the equivalent circuit of a UPFC power flow model, this circuit consists of two coordinated synchronous voltage sources represent the UPFC adequately for the purpose of fundamental steady-state analysis (Enrique et al., 2004), the UPFC voltage sources are:EvR=VvR(cosδvR+jsinδvR)EcR=VcR(cosδcR+jsinδcR)where VvR is the shunt voltage source magnitude; δvR is the shunt voltage source angle; VcR is the series voltage source magnitude; and δcR is the series voltage source angle.

The

Overview of DE

DE is a parallel direct search method proposed by Storn and Price (1995). Similar to other EAs techniques, DE is a heuristic, population-based optimization method that uses a population of points to search for a global minimum of a function over continuous search space. Basically, DE generates new vectors of parameters by adding the weighted difference between two population vectors to a third one. If the resulting individual provides a smaller objective function value than a predetermined

Simulation tools and power systems

Matlab programming codes for DE, GA, PSO, and modified power flow algorithm to include UPFC are developed and incorporated together for the simulation purposes in this research. To investigate the validation and performance of the applied techniques, DE, GA, and PSO have been tested on the following two test systems: An IEEE 14-bus system and an IEEE 30-bus system. The data of the above-mentioned systems are taken from Freris and Sasson (1968) and Wu et al. (1998), respectively. Simulations are

Conclusion

In this paper, the effectiveness of the optimal location of UPFC for enhancing the security of power systems under single line contingencies has been investigated. Determinations of the severest contingency scenarios were performed based on the contingency selection and ranking process. One of the newest computational intelligence techniques, namely: DE has been successfully applied to the problem under consideration. Maximization of power system security is considered as the optimization

Acknowledgements

This work was supported in part by the Chinese CSC and in part by the Electric Power Security and High Efficiency Lab., Huazhong University of Science and technology.

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