Advances in Electrical Engineering

Volume 2017, Article ID 7873491, 11 pages

https://doi.org/10.1155/2017/7873491

## A New Hybrid UPFC Controller for Power Flow Control and Voltage Regulation Based on RBF Neurosliding Mode Technique

^{1}Unité de Recherche d’Automatique et d’Informatique Appliquée (LAIA), Département de Génie Electrique, IUT FOTSO Victor Bandjoun, Université de Dschang, BP 134, Bandjoun, Cameroon^{2}Unité de Recherche de Matière Condensée, d’Electronique et de Traitement du Signal (LAMACETS), Département de Physique, Faculté des Sciences, Université de Dschang, BP 69, Dschang, Cameroon

Correspondence should be addressed to Godpromesse Kenne; moc.liamg@essemorpdog

Received 16 May 2017; Revised 17 July 2017; Accepted 17 September 2017; Published 22 October 2017

Academic Editor: George E. Tsekouras

Copyright © 2017 Godpromesse Kenne et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This paper presents a new technique to design a Unified Power Flow Controller (UPFC) for power flow control and DC voltage regulation of an electric power transmission system which is based on a hybrid technique which combines a Radial Basis Function (RBF) neural network (online training) with the sliding mode technique to take advantage of their common features. The proposed controller does not need the knowledge of the perturbation bounds nor the full state of the nonlinear system. Hence, it is robust and produces an optimal response in the presence of system parameter uncertainty and disturbances. The performance of the proposed controller is evaluated through numerical simulations on a Kundur power system and compared with a classical PI controller. Simulation results confirm the effectiveness, robustness, and superiority of the proposed controller.

#### 1. Introduction

Presently, it is well established in the scientific community that the UPFC has the ability to increase the power flow capacity and improve the stability of an electric power transmission system through the proper design of its controller [1]. Over the past several decades, linear and nonlinear control techniques have been successfully proposed and applied in the literature for the control of UPFC based on modern and classical control theories [2–10]. However, the main drawback of such techniques is that their application requires the development of mathematical models which are difficult to obtain. Thus, only partial and quite weak results have been obtained in terms of online implementation feasibility.

Faced with these difficulties, intelligent controls such as fuzzy logic and artificial neural networks have emerged as better alternatives to the conventional linear and nonlinear control methods. However, the complexities associated with the adaption of membership functions and computation requirements for defuzzification have hindered the application of fuzzy logic [11–15]. Hence, recent studies have turned to artificial neural networks (ANN) to achieve the desired goals [16–18].

Artificial neural networks have an inherent capability to learn and store information regarding the nonlinearities of the system and to provide this information whenever required. This renders the neural networks suitable for system identification and control applications [19–21]. Although intelligent and hybrid algorithms are already being implemented in the domains of image processing, robotics, financial management, and so on, their application in the field of FACTS devices for power flow control is fairly recent. Some recent results can be found in [12, 16, 17, 22, 23].

In [16], a radial basis function neural network has been designed to control the operation of the UPFC in order to improve its dynamic performance. Simulation and experimental results were presented to demonstrate the robustness of the proposed controller against changes in the transmission system operating conditions. However, large memory and long computation time are required for its proper functioning and, in addition, the controller is designed under the assumption that the upper bound of the disturbance is known. A comparative study of transient stability and reactive power compensation issues in an autonomous wind-diesel-photovoltaic based hybrid system using robust fuzzy-sliding mode based Unified Power Flow Controller has been presented in [12], but it has the limitation that a linearized small-signal model of the hybrid system is considered for the transient stability analysis. Hence, the system will suffer from performance degeneracy when the operating condition changes. In [22], the recently proposed -learning method for updating the parameter of a single neuron radial basis function neural network has been used as a control scheme for the UPFC to improve the transient stability performance of a multimachine power system. However, the updating control parameters are optimized for each perturbation using a generic algorithm which increases the computational burden and makes the control implementation less feasible. A neural network predictive controller for the UPFC has been designed in [23] to improve the transient stability performance of the power system. Nevertheless, the neural network controller is implemented only on the series branch of the UPFC which limits the performance of the device. In [17], a neural network controller based on a feedback linearization autoregression average model is used to design an adaptive-supplementary unified power flow control for two interconnected areas of a power system. However, in this paper and many others, the bounds of system uncertainty and disturbances are assumed to be known. But in practice, it is always difficult to determine the exact upper limit of system uncertainty and disturbances. Hence, the above controllers cannot provide satisfactory results.

From the above drawbacks, in this paper, a new hybrid approach which combines RBF neural network with the sliding mode technique to design a UPFC controller for power flow control and DC voltage regulation of an electric power transmission system with unknown bounds of system uncertainty and disturbances is proposed. The advantages of this design philosophy are that the controller is suitable for practical implementation and it makes the design useful for the real world complex power system. The remaining sections of this paper are organized as follows. In Section 2, the mathematical model of a UPFC in reference frame is described. The design of the RBF neurosliding mode controller is developed in Section 3. In Section 4, simulation results in a Kundur two-area four-machine power system are presented. Finally, in Section 5, some concluding remarks end the paper.

#### 2. System Modeling

Figure 1(a) shows a schematic diagram of a UPFC system, while Figure 1(b) shows a single-phase representation of the power circuit of the UPFC which consists of two back-to-back self-commutated voltage source converters connected through a common DC-link [24, 25]. The series converter is coupled to the AC system through a series transformer and the shunt converter is coupled through a shunt transformer. In Figure 1(b), the series and shunt converters are represented by the voltage sources and , respectively. The subscripts “,” “,” and “” are used to represent the sending-end bus, receiving end bus, and the three-phase quantities (phases ), respectively. Also and represent the resistance and leakage inductance of series converter, respectively, is the line current, , , and are the resistance, inductance, and current of the shunt converter, respectively. The series and shunt branch currents of the circuit in Figure 1(b) can be expressed by the following three-phase system of differential equations [24–26]: