Advances in Electrical and Computer Engineering Volume 18, Number 4, 2018 Simplified Model and Genetic Algorithm Based Simulated Annealing Approach for Excitation Current Estimation of Synchronous Motor Orhan KAPLAN* 1 , Emre ÇELİK 2 1 Department of Electrical and Electronics Engineering, Gazi University, 06500, Ankara, Turkey 2 Department of Electrical and Electronics Engineering, Duzce University, 81620, Duzce, Turkey *okaplan@gazi.edu.tr 1 Abstract—Reactive power demanded by many loads besides active power is one of the important issue in terms of the efficient use of energy. The optimal solution of reactive power demand can be performed by tuning the excitation current of synchronous motor available in power system. This paper presents an effective application of genetic algorithm-based simulated annealing (GASA) algorithm to solve the problem of excitation current estimation of synchronous motors. Firstly, the multiple linear regression model used in a few studies for estimation of excitation current of synchronous motor, is considered and regression coefficients of this model are optimized by GASA algorithm using training data collected from experimental setup performed. The supremacy of GASA over some recently reported algorithms such as gravitational search algorithm, artificial bee colony and genetic algorithm is widely illustrated by comparing the estimation results. Owing to the observation of weak regression coefficient of load current indicating that it is not much beneficial to excitation current, load current is removed from the regression model. Then, the remaining regression coefficients are tuned to accommodate new modification. It is seen from the findings that both training and testing performance of the simplified model are improved further. The major conclusions drawn from this study are that it introduces a new efficient algorithm for the concerned problem as well as the multiple linear regression model, which has the advantages of simplicity and cost-friendliness. Index Terms—reactive power compensation, power factor, artificial intelligence, genetic algorithms, simulated annealing. I. INTRODUCTION Power quality deals with several problems in power systems to ensure the utilization of the available capacity and substructure efficiently. Because of the widespread of loads consuming reactive power in the industry and daily life, poor power factor is one of the power quality problem [1]. The poor power factor causes overloading of transformers and generators, increase of resistive losses, deterioration of voltage stability, and inadequate use of line transmission capacity [2]. Besides, if consumers exceed the allowable limit of reactive power, they have to pay penalty to electric utility. The negative effects of poor power factor can be overcome using reactive power compensation. There are several techniques to compensate the reactive power [3]. However, it is difficult to quickly and precisely determine the required reactive power changed depending upon loads in the power system [4]. Due to the low-cost of installation and operation, fixed capacitor groups are the most preferred method to supply reactive power [5]. However, slow response and stepped compensation are important deficiencies of this method [6]. In addition, although the capacitors are not harmonic sources, they may amplify the existing harmonic current components and lead to bigger problems in power system [7]. Another solution to provide the dynamic reactive power is thyristor-based static VAr compensator (SVCs). These systems provide much faster and smoother reactive power to grid compared to capacitor groups [8]. The main drawback of the SVCs is to cause harmonic components with lower order during their operation [9]. Static synchronous compensator (STATCOM) has fastest response time and lowest harmonic component to improve poor power factor among all solution methods [10]. Nevertheless, STATCOM offers a solution approximately 30% more expensive than SVCs for the same VA power rate [11]. Finally, synchronous motor (SM) is a prominent way to compensate reactive power in the literature [6]. If there is a SM in a power system, optimal solution is to provide the dynamic reactive power required by adjusting its excitation current [9]. The greatest advantage of SM is that the desired power factor value can be easily obtained by changing its excitation current without adding or subtracting any component if there is a need for a varying reactive power. In addition, the energy stored in the rotor can help stabilize the system during disturbances [12]. However, the determination of SM excitation current without delay time is main difficulty taking into account the reactive power demand occasionally altering in a power system [9, 13-20]. Various control approaches have been used to detect the excitation current in case of the variable reactive power required [6, 9, 12-21]. The experimental studies show that classical control systems proportional integral (PI) controller and proportional integral derivative (PID) controller can achieve to approximately designate the excitation current of SM under variable reactive power demand, however the artificial intelligence (AI)-based algorithms precisely converge the excitation current value performing the unity power factor [6, 9, 15-17]. I. Colak et al introduced the use of a fuzzy logic controlled SM for reactive power compensation. The obtained results proved that the proposed system can give a very fast response to the reactive power demand of inductive loads [6]. S. Sagiroglu et al suggested the usage of artificial neural networks (ANNs) to forecast the excitation current of SM, and then they investigated the 75 1582-7445 © 2018 AECE Digital Object Identifier 10.4316/AECE.2018.04009 [Downloaded from www.aece.ro on Wednesday, December 05, 2018 at 00:58:09 (UTC) by 78.136.203.11. Redistribution subject to AECE license or copyright.]