Journal of Wind Energy

Volume 2014 (2014), Article ID 570234, 12 pages

http://dx.doi.org/10.1155/2014/570234

## Comparative Assessment of SVC and TCSC Controllers on the Small Signal Stability Margin of a Power System Incorporating Intermittent Wind Power Generation

Department of Electrical Engineering, Faculty of Technology, University of Ibadan, Ibadan 0037, Nigeria

Received 9 August 2014; Accepted 24 November 2014; Published 11 December 2014

Academic Editor: Ujjwal K. Saha

Copyright © 2014 T. R. Ayodele. 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

Wind power is highly variable due to the stochastic behavior of wind speeds. This intermittent nature could excite the electromechanical modes resulting in the small signal instability of a power system. In this study, the performance of static VAR compensation (SVC) and thyristor controlled series capacitor (TCSC) controllers in the damping of electromechanical modes is analyzed and compared. The study employs probabilistic modal analysis method using Monte Carlo simulation and Latin hypercube sampling techniques. Various scenarios are created to get insight into the study. The results obtained from the modal analysis are verified by using the time-domain simulation. Some of the key results show that SVC is more robust in the damping of electromechanical modes compared to TCSC. The result also reveals that allocation of power system stabilizer (PSS) using probabilistic method is more effective and robust compared to deterministic approach.

#### 1. Introduction

The complexity of power system has increased due to high rate of growth of power demands, and the increasing penetration of renewable energy generators (REGs) on the grid in recent years has further pushed the operation of the existing transmission lines closer to their operating limit. This has necessitated the need to understand the impact of REGs on the overall stability of a power system. The output power of most REGs (wind generators, PV, small hydrogenerators, etc.) is highly variable and can have considerable effect on the dynamic behavior of a power system leading to power swing and less synchronizing coupling [1]. Wind generators have been the most proliferated REG in recent time. This is a result of its technological maturity and the availability of wind resources in many regions of the world [2]. The present and the progressive scales of integration have generated concern about the possible impact it may have on the power system grid integrity. Most of the large wind farms are located where the wind resources are found; hence they are far from the load center and are connected to relatively weak grid [3]. The presence of wind generators connected to such weak transmission networks incurs serious concern about system stability, system security, and power quality. Small signal stability is a key issue in the study of grid impact of wind power integration as a result of its intermittent nature [4, 5]. Small signal stability is concerned with the ability of a power system to ascertain a stable operating condition following a small perturbation around its operating equilibrium [6]. Modal analysis is widely recognized for the analysis of small signal stability of a power system. The analysis is carried out based on the system nonlinear equations describing the dynamic behavior of the system, linearized about a chosen operating point.

In order to take into consideration the stochastic nature of wind power, probabilistic modal analysis method via Monte Carlo simulation (MCS) is employed using MATLAB based power system toolbox (PST). The wind speeds used for the generation of intermittent wind power are drawn from Weibull distribution using Latin hypercube sampling (LHS) techniques. The use of LHS reduces the number of iterations required in traditional MCS and hence reduces computational effort and cost. The basic outline of the procedures for generating LHS as used in this paper is given in Appendix A. However, details on the application of LHS techniques to probabilistic small signal stability can be found in Ayodele et al.’s work [7].

FACTS controllers have been extensively used to improve the steady-state control problems and enhancing power system stability in addition to the main function of power flow control [8]. Damping electromechanical power oscillations has been recognized as an important issue in electric power system operation. Application of power system stabilizers (PSS), with increasing transmission line loading over long distances, may in many cases not provide sufficient damping for interarea power swings. In such cases, other solutions for power oscillation damping are needed. Fuzzy logic controlled energy storage (energy capacitor system) has been used to enhance the overall stability of electric power system [9]. Hossain et al. compared SVC and STATCOM in the improvement of voltage stability and concluded that STATCOM provides better response during low voltage compared to SVC [1]. The performance of three FACTS controllers, namely, static compensator (STATCOM), static synchronous series compensator (SSSC), and the unified power flow controller (UPFC), was studied in [10] using current injection model. The model was applied to damping electromechanical modes. According to their results, UPFC is the most effective FACTS controller for damping interarea oscillations and SSSC is more effective than STATCOM. Comparison of proportional integrated derivative (PID), power system stabilizer (PSS), and thyristor controlled dynamic brake (TCDB) in small signal stability study has been presented by Balwinder [11]. It was concluded that combination of PID with TCDB offers better improvement in oscillation damping. Application of controllable series compensator (CSC) in damping power system oscillation was investigated on the basis of Philip-Heffron model by Swift and Wang [12]. In their work, the capability of CSC controller was analyzed in terms of its damping torque contribution both for single machine infinite bus and for multimachine power system. The same authors later compared the effectiveness of damping torque of FACTS devices, namely, SVC, CSC, and phase shifter (PS), to power system [13]. It was concluded that SVC and CSC damping provide more damping torque during high load conditions while PS damping control does not depend on system load condition. However, none of the aforementioned study compared SVC and thyristor controlled series capacitor (TCSC) controllers. Moreover, none of the studies incorporates wind power in their study. In the present study, SVC and TCSC controllers are compared for the damping of electromechanical mode of a wind generator connected power system considering the intermittent nature of wind power using probabilistic approach.

#### 2. Model Description

Analysis of small signal stability in an interconnected power system requires adequate model describing various components making up the system. Therefore, this section presents the model approach of these components.

##### 2.1. Synchronous Generator Model

Synchronous machine can be mathematically modeled as either elementary classical models or detailed ones. In the detailed models, transient and subtransient phenomena are considered [14, 15]. In this study, detailed model is employed to model all the synchronous generators.

The mechanical variables are linked with the electrical variables using the following [16]: where and represent the damping constant and the inertia time constant, respectively; stands for the input mechanical torque; and represent the rotational speed and rotor angle, respectively; and correspond to the subtransient generated voltage in the direct and quadrature axes; and and stand for the armature current in the direct and quadrature axes, respectively.

##### 2.2. Power System Stabilizer (PSS) Model

The input signal to PSS may be rotor speed, rotor angle, or a combination of this signal. The linearized differential equation of PSS can be written as follows [11]: where represent the output of washout filter, represent the output of first lag-lead network, is the output signal of PSS block, and is the washout time constant. and are lead-time constants while and are the lag time constants of lag-lead network. Figure 1 depicts the PSS block diagram design.