Performance Enhancement of Wind Farms Using Tuned SSSC Based on Artificial Neural Network

International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 5, Nº 7 Performance Enhancement of Wind Farms Using Tuned SSSC Based on Artificial Neural Network Yousry Ibrahim1, Salah Kamel1,2, Ahmed Rashad1, Loai Nasrat1, Francisco Jurado3* Department of Electrical Engineering, Faculty of Engineering, Aswan University, 81542 Aswan (Egypt) Department of Electrical Engineering, University of Jaén, 23700 EPS Linares, Jaén (Spain) 3 State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400030 (China) 1 2 Received 4 December 2018 | Accepted 1 May 2019 | Published 9 May 2019 Abstract Keywords Recently, power systems are confronting a lot of challenges. Increasing the dependence on renewable energy sources especially wind energy and its impact on the stability of electrical systems are the most important challenges. Flexible alternating current transmission systems (FACTS) can be used to improve the relationship between wind farms and electrical grids. The performance of these FACTS depends on the parameters of its control system. These parameters can be tuned using modern methods like Artificial Neural Network (ANN). In this paper, ANN is used to improve the performance of static synchronous series compensator (SSSC) integrated into combined wind farm (CWF). This CWF is composed of squirrel cage induction generators (SCIG) and doubly fed induction generators (DFIG) wind turbines. This wind farm is collecting the advantage of SCIG and DFIG wind turbines. To view out the motivation of this paper, a comparison is done among the performances of combined wind farm (CWF) with ANN-SSSC, CWF with ordinary SSSC and CWF with SSSC tune by Multiobjective genetic algorithm (MOGA SSSC). The root mean square Error (RMSE) is used to evaluate the results. The results illustrate that the performance of CWF can be improved using SSSC adjusted by ANN. Squirrel Cage Induction Generator (SCIG), Doubly Fed Induction Generator (DFIG), Combined Wind Farm (CWF), Static Synchronous Series Compensator (SSSC), Artificial Neural Network (ANN). I. Introduction R enewable energy is an important source for the power generation. Solar energy, and wind energy are the most famous forms of this technology. Wind energy plays an important role in producing electric power in all the world so that its injection on the grid represents a wide range of studies. This injection depends on the induction generator of the wind turbines. There are two types of induction generator, first type is squirrel cage induction generators (SCIG) which are suitable to fixed speed wind turbines and second type is doubly fed induction generators (DFIG) that are used with variable speed wind turbines. The stability of wind farms is affected by the exchange in the reactive power between the interconnected grid and the wind farms. The compensation devices of the reactive power consider a fundamental element in SCIG wind turbines (SCIG-WT). The flexible alternating current transmission systems (FACTS) were used to damp power oscillation and, enhance power stability. In Ref. [1] a dual STATCOM had been used to damp power oscillations. Tuning parameters of SSSC had been proposed in [2] to damp power oscillations. In Ref [3] a unified power flow controller has been used to damp power oscillations between two areas. The SSSC used to damp power oscillation, enhance power stability and control the power flow of DFIG-WF is studied in [4]-[5].The effect of FACTS such as Static VAR Compensator (SVC), Static Synchronous Compensator (STATCM) and SSSC on the performance of wind farms * Corresponding author. E-mail address: DOI: 10.9781/ijimai.2019.05.001 were studied in [6]-[10]. The impact of SSSC on the performance of different types of wind farms had been discussed in [11]. The main advantage of Artificial intelligence (AI) is solving complex problems in less time and with high precision, such as using optimization methods to solve the complex control problem. Also, AI can easily predict and take the correct decisions with little margin of error. It can be used for predicting the change in wind speed and its impact on stability of power system. In this paper, AI has been used to predict and determine the optimal value of the control gains of SSSC which can enhance the performance of CWF. On other side, AI represents high technology so that it is storage costly. In last years, Artificial Intelligence (AI) has been used extensively in improving the performance of FACTS and enhancing the performance of wind farms interconnected grid. A genetic algorithm has been implemented to tune different type of FACTS interconnected wind farms and photovoltaic solar plant in [12]. In ref [13] [14] multi-objective genetic algorithm is used to improve the performance of DFIG. Also, multi-objective genetic algorithm is used to find the optimal gains of SSSC in [15]. Adaptive-network-based fuzzy inference system (ANFIS), ANN and genetic algorithm are proposed in [16] to improve the reactive power control of STATCOM. The whale optimization algorithm, genetic algorithm and ANN were used in [17] to determine the optimal parameters of STATCOM integrated with CWF. In Ref [18] particle swarm optimization is used to tune and damp power system oscillation of DFIG wind farms integrated with SSSC. A new control strategy based on ANFIS is proposed in [19] to improve the performance of DFIG wind farm integrated with SSSC. - 118 - Regular Issue This paper aims to improve the performance of CWF which is based on SCIG and DFIG using SSSC controlled by ANN (ANNSSSC). Also, in this paper the control parameters which had been investigated in [15] are used for implementing ANN. Moreover, a comparison is done between the performances of CWF with ordinary SSSC, CWF associated with SSSC tuned by multi-objective genetic algorithm (SSSC MOGA) investigated in [15] and CWF associated with proposed ANN-SSSC during three phase-faults. The rest of the paper is organized as follows. Section II presents a brief summary of ANN. Section III presents modelling of wind turbines. Section IV explains the construction, operation and control system of SSSC. Section V introduces the proposed ANN control, which is applied to SSSC. The last two sections present the results and conclusion. Fig. 1. ANN principle operation. II. Artificial Neural Network (ANN) The artificial neural network is a modest simulation of the effect, form and content of the neural network found in the human brain. It consists of nodes called neurons and connected together by bonds called weights. Each set of neurons forms a single layer; the ANN is composed of different types of layers. From Fig. 1, it can be observed that it consists of input layer, hidden layer (processing element) and output layer. The hidden layer could be single layer or multi-layers. The input signal is passed from input layer to the output layer through the (...truncated)