Optimal power quality monitor placement in power systems using an adaptive quantum-inspired binary gravitational search algorithm

https://doi.org/10.1016/j.ijepes.2013.12.019Get rights and content

Highlights

  • An adaptive QBGSA search algorithm to solve the optimal PQ monitor placement is proposed.

  • The topological monitor reach area is introduced to generalize the PQ monitor placement method.

  • A multi-objective function is created to deal with the monitor overlapping and sag severity.

  • Adaptive QBGSA gives the best optimal PQM placement results.

Abstract

This paper presents a novel adaptive quantum-inspired binary gravitational search algorithm (QBGSA) to solve the optimal power quality monitor (PQM) placement problem in power systems. In this algorithm, the standard binary gravitational search algorithm is modified by applying the concepts and principles of quantum behavior to improve the search capability with a fast convergence rate. QBGSA is integrated with an artificial immune system, which acts as an adaptive element to improve the flexibility of the algorithm toward economic capability while maintaining the quality of the solution and speed. The optimization involves multi-objective functions and handles the observability constraints determined by the concept of the topological monitor reach area. The objective functions are based on the number of required PQM, monitor overlapping index, and sag severity index. The proposed adaptive QBGSA is applied on several test systems, which include both transmission and distribution systems. To evaluate the effectiveness of the proposed adaptive QBGSA method, its performance is compared with that of the conventional binary gravitational search algorithm, binary particle swarm optimization, quantum-inspired binary particle swarm optimization, and genetic algorithm.

Introduction

In conventional power quality monitoring practice, power quality monitors (PQMs) are usually installed in locations where the utility or customer wishes to measure the power quality of the system by detecting and analyzing power quality events [1]. Voltage sag is the most frequent type of event captured among all power quality events [2]. Voltage sag is defined by the Institute of Electrical and Electronics Engineers (IEEE) standard 1159-1995 as a voltage reduction in the root mean square (RMS) voltage from 0.1 to 0.9 per unit for a duration of between half of a cycle and <1 min. Voltage sag has become a significant concern because it creates huge economic losses resulting from the failure or malfunction of sensitive equipment in industries. The installation of PQMs at selected buses in a power system is important to monitor and detect the occurrence of voltage sags.

In a distributed power quality monitoring scheme, selecting the number and location of PQMs is a critical problem because it is directly related to the efficiency of the monitoring system. Installing PQMs at all buses in a power distribution network to monitor voltage sags is uneconomical and inefficient. Thus, the number of PQMs must be decreased to reduce the total cost of the power quality monitoring system and the redundancy of the data being measured by monitors [3]. In the past, the procedure for selecting the minimum number and best locations for PQM installation is usually performed manually by power quality experts through their experience and knowledge on power quality and system topology. However, such a procedure is unreliable and inconsistent. Therefore, an automated approach to determine the optimal number and location of PQMs is necessary to establish how many PQMs are required to monitor the entire power network with the lowest possible redundancy. Each possible voltage sag that may occur in the power network can be observed by at least one of the installed monitors. The minimum number and optimal location of PQMs are often linked together because the number of monitors required is reduced by installing the monitors in strategic network buses with the highest observability capacities. The concept of monitor observability based on the monitor reach area (MRA) has been utilized to determine the optimal placement of PQMs in transmission networks [3], [4], [5], [6], [7], [8], [9]. In other applications similar to PQM placement, deciding where to place the optimal phasor measurement unit only applies to transmission networks [10], [11], [12]. Not enough evidence has been provided to prove that the concept is applicable to radial distribution networks. Therefore, a new optimal PQM placement method that is applicable for both transmission and distribution networks and caters to the system topology issue must be developed.

A few optimization techniques have been utilized in the last few years to solve the optimal PQM placement problem. In [3], a PQM placement method based on covering and packing was developed with the GAMS software as an integer linear program. In [4], [5], [6], the branch and bound algorithm was applied by dividing the solution space into small spaces for easy solving. However, this algorithm may provide an incorrect solution when a branch is incorrectly selected in the earlier stages. In [7], [8], [9], the genetic algorithm (GA) was used to solve the optimal PQM placement problem. GA is commonly utilized to solve the optimization problem; however, the disadvantage of GA is its slow convergence rate. Thus, an alternative optimization technique with a faster convergence rate, such as particle swarm optimization (PSO), is recommended [13]. A relatively new heuristic optimization technique known as the gravitational search algorithm (GSA) is gaining popularity because it has been reported to provide a solution better than that of PSO in solving certain problems [14]. Therefore, GSA is examined in this study to evaluate its performance in solving the optimal PQM placement problem.

The main aim of this study is to develop a new method to solve the optimal PQM placement problem in both transmission and distribution networks through a new heuristic optimization technique that considers three concepts, namely, quantum behavior, binary gravitational search algorithm (BGSA), and artificial immune system (AIS). The observability concept based on the topological monitor reach area (TMRA) is introduced in the proposed optimal PQM placement method to allow for the application of observability to both transmission and distribution systems [15]. In addition, the monitor coverage control parameter is employed to provide flexibility to the search algorithms in complying with sensitivity and economic capability. Control parameter α is defined as a voltage threshold level in p.u. at a monitored bus to indicate whether a fault occurs inside or outside the monitor’s coverage area. A PQM usually detects and captures voltage variations when the measured RMS voltage reaches 0.9 p.u. [16]. In this study, the maximum α value is set at 0.85 p.u. to allow some overlapping of the monitor coverage area at the boundary. This approach will help overcome the boundary issues and non-monitored fault on the line segment at the boundary.

This paper is organized as follows. The core subject, which refers to the monitor coverage concept in the PQM placement method, is explained in Section 2. The existing MRA concept is briefly reviewed, and then the proposed TMRA concept is described. The problem formulation for optimal PQM placement is discussed in Section 3. The overview and procedures of BGSA, quantum-inspired binary gravitational search algorithm (QBGSA), and AIS are presented in Section 4. The test results on the power systems under study and the optimal solutions are provided and discussed in Section 5.

Section snippets

Monitor coverage concept

Monitor coverage is the most important entity in the determination of PQM placement. This concept is employed to evaluate the placement and guarantee the observability of the entire power network. The monitoring coverage concept is called MRA [4]. Residual voltages at each bus for all fault cases are required in the formation of MRA. Therefore, residual voltages should be saved in the form of the fault voltage (FV) matrix where the matrix columns (j) represent the bus numbers of residual

Optimal PQM placement problem formulation

The three common elements required in the binary optimization technique are decision vectors, objective functions, and optimization constraints. Each element is formulated and explained to obtain the optimal solution of PQM placement. The optimization technique explores the optimal solution as defined in the objective function through the manipulation of the bits of the decision vector subject to the constraints in each generation. The process is iterated for a fixed number of times or until a

Adaptive QBGSA

This section provides a brief overview of BGSA, QBGSA, and adaptive QBGSA using AIS. The implementation of the proposed adaptive QBGSA to solve the optimal PQM placement problem is also described.

Test results and discussion

The performance of QBGSA is compared with that of other heuristic optimization techniques, namely, GA [15], BPSO [25], QBPSO [26], and BGSA [27], to demonstrate the effectiveness of QBGSA in solving the optimal PQM placement problem. Two test systems, namely, the 69-bus distribution system and the IEEE 118-bus transmission system, are utilized in the case study. The new adaptive QBGSA technique is then compared with the original QBGSA to evaluate its performance in terms of computation time and

Conclusions

The performances of GA, BPSO, BGSA, QBPSO, QBGSA, and a novel adaptive QBGSA in solving the multi-objective optimization problem for optimal PQM placement were compared in this study. The optimization problem formulation was mainly based on the use of TMRA and two placement evaluation indices, namely, SSI and MOI. Five different optimal PQM placement programs were implemented on the 69-bus and IEEE 118-bus test systems to reveal the most suitable optimization techniques. QBGSA provides the best

Acknowledgment

The authors are grateful to Universiti Kebangsaan Malaysia (UKM) for supporting this study under grants DIP-2012-30 and ETP-2013-044.

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