Hybrid Cuckoo Search Algorithm for Optimal Placement and Sizing of Static VAR Compensator

Hybrid Cuckoo Search Algorithm for Optimal Placement and Sizing of Static VAR Compensator

Khai Phuc Nguyen (Shibaura Institute of Technology, Japan), Dieu Ngoc Vo (Ho Chi Minh University of Technology, Vietnam) and Goro Fujita (Shibaura Institute of Technology, Japan)
DOI: 10.4018/978-1-4666-9644-0.ch011
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Abstract

This chapter proposes a Hybrid Cuckoo search algorithm to determine optimal location and sizing of Static VAR Compensator (SVC). Hybrid Cuckoo search algorithm is a simple combination of the Cuckoo search algorithm (CSA) and Teaching-learning-based optimization (TLBO), where the learner phase of TLBO is added to improve performance of Cuckoo eggs. The proposed method is applied for optimizing location and sizing of SVC in electric power system. This problem is a kind of discrete and combinatorial problem. The objective function considers loss power, voltage deviation and operational cost of SVC and other operating constraints in power system. Numerical results from three various tested systems show that the proposed method is better than the conventional CSA and TLBO in finding the global optimum solutions and its performance is also high than others.
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Introduction

This chapter presents a hybrid version of the Cuckoo search algorithm (HCSA) and its application for determining optimal placement and sizing of Static VAR compensator (SVC) devices in the electric power system. The Cuckoo search algorithm is a nature-inspired optimization technique, which works on the parasitic behavior of Cuckoo species. The conventional method includes two probability-generating stages. In the first stage, The Lévy flight generates random Cuckoo eggs, and then these eggs will be laid into the neighbors’ nests to create new solutions. The better solutions will carry over the next generation. The second stage describes the action of the host birds to abandon Cuckoo eggs in their nests. In this proposed Hybrid Cuckoo search algorithm, we combine the learner stage of the Teaching-learning-based optimization to improve performances of Cuckoo eggs instead of abandoning them. The proposed method is applied for optimizing location and sizing of SVC devices to enhance the voltage profile of the power system. This problem combines discrete and continuous unknowns with many equal and unequal constraints. The discrete unknowns are natural numbers representing the location of installed SVC devices, and the continuous unknowns represent injected reactive power of SVC devices. The proposed method is evaluated on three IEEE standard systems and compared with both of conventional Cuckoo search algorithm and Teaching-learning-based optimization. Numerical results show that the proposed Hybrid Cuckoo search algorithm gives better solutions and has higher performance than other methods.

This chapter divides into seven sections. Section 2 gives the literature review of the Cuckoo search algorithm and the problem of optimal placement and sizing of SVC devices. Section 3 proposes the Hybrid Cuckoo search algorithm. Section 4 describes the structure of a SVC device and the objective function. In Section 5, the implement of Hybrid Cuckoo search algorithm for the problem has been discussed. Section 6 shows numerical results on three benchmark problems. Finally, conclusions are made.

Key Terms in this Chapter

Static VAR Compensator: A type of shunt connected FACTS. Static VAR Compensator includes many capacitors or reactors and it is often controlled by thyristors.

Voltage Deviation: A sum of voltage deviations at all buses in the power system from reference values. It is an important index in operating the electric power system.

Flexible AC Transmission System (FACTS): A system concludes power electronic and other static elements to enhance quality and efficiency of the AC transmission system.

Power Flow: A numerical analysis of the electric power flow in steady-state operation. Power flow study determines voltage angles and magnitudes at buses in power system for specified load demand, real power and voltage magnitude of generators.

Power Loss: The amount of power lost in transmission lines. Resistive elements in transmission lines consume most of power loss in a power system.

Teaching-Learning-Based Optimization: An evolutionary algorithm, which inspires behaviors of teachers and learners in class. Learners get knowledge from teachers and discuss together to improve their marks.

Cuckoo Search Algorithm: An optimization technique developed by Yang and Deb in 2009. This method inspires parasitic behaviors of Cuckoo species.

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