Optimal power quality monitor placement using genetic algorithm and Mallow’s Cp A. Kazemi ⇑ , A. Mohamed, H. Shareef, H. Zayandehroodi Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia article info Keywords: Power quality monitor placement Genetic algorithm Mallow’s Cp abstract This study presents a method to determine the optimal number and placement of power quality monitors (PQMs) in power systems by using genetic algorithm (GA) and Mallow’s Cp which is a statistical criterion for selecting among many alternative subset regressions. This procedure helps to avoid the dependency of set voltage sag threshold values of PQMs in the conventional monitor reach area based (MRA) method. In the proposed GACp method, the fitness function for problem modeling aims to minimize allocated monitors and minimize the difference between the Mallow’s Cp and the number of variables used for the multivariable regression model during estimation of unmonitored buses. After obtaining the optimal placements of PQMs by using the GACp method, the observability and redundancy of the monitors are tested to further reduce the redundant PQMs. The IEEE 30 bus test system is simulated using the DIGSI- LENT power factory software to validate the proposed method. The simulated results show that the GACp method requires only two PQMs to observe all voltage sags that may appear at each bus in the test system without redundancy. 1. Introduction Significant economic losses associated with industrial equip- ment failure caused by voltage sags have raised concerns for utili- ties and their customers for the past decades. Voltage sag is defined as a decrease in RMS voltage or current at the power frequency for durations from 0.5 cycles to 1 min [1]. It is considered as one of the most common power quality disturbances that cause equipment malfunction and process interruption [2]. Voltage sags occur be- cause of short-circuit faults and the large motor starting in power systems [3]. Information about the actual cause and source of volt- age sags can help power engineers to decide on the resumption of systems to normal operation [4]. The implementation of power quality monitoring system in power supply networks is a main step in obtaining information about voltage sag disturbances [5]. Ideally, an entire power system should be monitored by a power quality monitor (PQM) at each bus through a communication facil- ity. However, such system produces huge amount of redundant data, making it cost inefficient and economically unreasonable. Therefore, methods that can select the number and location of monitored sites should be developed to minimize the number of monitors and to reduce monitoring costs without missing essential voltage sag information. In recent years, several studies have been attempted to solve the PQM placement problem by determining the optimal number and locations of PQMs [6,7]. A primary requisite in selecting the location of monitors is the guaranteed observability of the entire system to ensure the capture of any voltage sag event by at least one PQM [8]. In such case, the PQM placement methods can be di- vided into four main methods, namely, monitor reach area (MRA), covering and packing, graph theory, and multivariable regression (MVR). The concept of MRA was introduced to determine the opti- mal location of PQMs [9]. MRA is defined as the area of network that can be observed from a given monitor position. In [6,7], an im- proved optimal monitoring program is presented to optimize the MRA expression by using genetic algorithm (GA) for identifying optimal meter location [10,11]. Integer programming and fuzzy lo- gic have been applied to determine the optimal placement of PQMs in large transmission networks for the assessment of voltage sags [12]. The method expressed in [8] deals with uncovered line faults, which are ignored by the original MRA method. In [9], an approach based on monitor reach matrix (MRM) obtained from the solution of analytical expressions was presented for the determination of the optimal location of voltage sag monitors. Another optimal PQM placement method uses severity index, MRA matrix, and GA [13]. The covering and packing has been developed to determine the optimum number and location of PQMs by minimizing the cost of PQMs via the integer linear programming technique [4]. Simi- For Evaluation Only. Copyright (c) by Foxit Software Company, 2004 - 2007 Edited by Foxit PDF Editor