A Study on Power System Stability of SMIB System

Transcription

1 A Study on Power System Stability of SMIB System Swapna Dewangan M. Tech. Scholar In Power Electronics Electronics & Telecommunication Engineering Raipur Institute of Technology, Raipur (India) Ritesh Bohra Asst. Professor Electronics & Telecommunication Engineering Raipur Institute of Technology, Raipur (India) Abstract The initial stability problems were related to distant power plants feeding load hubs over expanded transmission lines. With slow exciters and noncontinuously acting voltage controllers, power transfer capability was often restricted by steady-state as well as transient rotor angle variability due to inadequate synchronizing torque. Because of the significance of the stability of the power systems, stabilizing control methods have been used for single machine infinite bus system (SMIB) with the help of intelligent methods. The optimal sequential design for SMIB system is very important. As a result, thoughtful deliberation is now being given the concern of stabilization control. This paper reviews about the power system stability (PSS) for SMIB system. Keywords AVR, KPSS, PSO, PSS, SMIB. I. INTRODUCTION For the continuous power supply, the stability of power system is a desirable key factor. Power system stability can be described as the attribute of a system that helps the system to maintain equilibrium under normal conditions and also retrieve the equilibrium condition under the condition of disturbance also. Various circumstances could lead to the conditions of instability in power system relying upon the mode of operation and system s configuration. Maintenance of synchronization is the major issue of concern particularly for those power systems that depend upon synchronous machines. The relationship between power and angle and the dynamics of generator angles affects the above mentioned synchronous attribute. Apart from the synchronization problem, the other issues that may be encountered are loading problems such as voltage collapse, etc. II. POWER SYSTEM STABILIZER (PSS) The electromechanical oscillations issue is resolved by accumulating to the generator a specific controller called: (Power System Stabilizer (PSS)). This controller detects the variations in rotor speed or electrical power of the generator and applies a signal adapted to the input of the voltage regulator (AVR). The generator can thus produce an additional damping torque that compensates for the negative effect of the excitation system on the oscillations. Introduction to PSS Controllers The additional auxiliary control of the AVR excitation system, loosely known as the PSS Stabilizer (Power System Stabilizer) has become the most common means for enhancing the damping of low-frequency oscillations in power systems (i.e. improvement of dynamic and static stability). The mechanical torque determines the output power of a generator. However, the latter can vary by the action of the field of excitation of the alternator. The PSS is added, it detects the variation of the electrical output power and controls the excitation so as to dampen the power oscillations rapidly [1]. A PSS is used to add a voltage signal proportional to the rotor speed variation in the input of the generator voltage regulator (AVR). Therefore, the entire excitation control system (AVR and PSS) must ensure the following [2]: 1. To support the first oscillations following a great disturbance; That is, ensure the transient stability of the system. 2. Maximize the damping of electromechanical oscillations associated with local modes - as well as interregional modes without negative effects on other modes. 3. Minimize the likelihood of adverse effects, namely: a. Interacting with high-frequency phenomena in the power system such as resonance in the transport network. b. Local instabilities in the band of the desired action of the control system.

2 c. Be robust enough to enable the control system to meet its objectives for various probable operating points of the power system. Hence, various techniques based on modern commands have been applied for the design of the PSS. Also includes optimal control, adaptive control, variable structure control and intelligent control which are developed in [3, 4 and 5]. Despite these new modern control techniques with different structures, power system exploiters prefer the conventional PSS advance / delay (Conventional Power System Stabilizer) because of its simple and reliable structure. III. DIFFERENT CONFIGURATIONS OF PSS The type of a PSS can be identified by the nature of its input signal. The most widespread are those having as input the power variation ΔP. However, recently, input signals such as Δω (variation in velocity) and Δf (variation in frequency) have been adopted to improve the stability of the inter-zone modes given the ever increasing increase in interconnections in electrical networks. The choice of the type of PSS to adopt is according to the oscillations and modes to be damped [2]. The most common type of PSS is known as the conventional PSS (or PSS advance/delay). This type has shown its great efficiency in maintaining stability at small disturbances. This PSS uses the rotor speed variation as input. It is usually composed of four blocks, Figure 1 [6]: An amplifier block. A high-pass filter block "washout filter." A phase compensation block. A limiter Figure 1: Conventional PSS model Amplifier KPSS varies from 0.01 to 50. Ideally, its value (KPSS) must correspond to the maximum damping. The value of the gain should satisfy the damping of the leading modes of the system without risk of degrading the stability of the other modes or the transitory stability [7]. High-Pass Filter Washout Filter. It eliminates very low-frequency oscillations. The time constant of this filter (T W ) must be large enough to allow the signals, whose frequency is located in the useful band, to be transmitted without attenuation. However, it should not be too large to avoid leading to undesirable variations in generator voltage during the stand-by conditions. Generally, T W varies from 1 to 20 seconds [8]. Here it is set to 10 seconds. Phase Compensation Block Composed of two advance/phase delay compensators as shown in Figure 1. The phase advance is used to compensate for the phase delay introduced between the electric torque of the generator and the input of the excitation system. The time constants (T 1, T 3 ) and delay times (T 2, T 4 ) are adjustable. The range of each time constant generally ranges from 0.01 to 6 seconds. The Limiter The PSS is a limiter to reduce its unwanted influence during transient phases. The minimum and maximum values of the limiter range from ± 0.02 to 0.1 per-unit [7]. The function of the transfer of the PSS and described as follows: st w (1+sT 1 ) (1+sT 3 ) 1+sT w V PSS = K PSS input (1) (1+sT 2 ) (1+sT 4 ) Where, V PSS : Output signal of the corrector K PSS : Gain of the corrector T w = Time constant of the high pass filter T 1, T 2, T 3, T 4 : Time Constant delay input: Correction input signal IV. LITERATURE REVIEW Small signal stability of a synchronous machine connected to an infinite bus has been improved using variable structure control. Rashmi et al. presented the design of variable structure control (VSC) based Power System Stabilizer (PSS) for Single Machine Infinite Bus (SMIB) system. Optimal VSC based PSS has been tuned to minimize the low-frequency oscillations in torque angle deviation using Particle Swarm Optimization (PSO). The robustness and effectiveness of the designed controller are verified by the change in the operating points [9]. Critical slowing down (CSD) is the phenomenon in which a system recovers more slowly from small perturbations. CSD, as evidenced by increasing signal variance and autocorrelation, has been observed in many dynamical systems approaching a critical transition, and thus can be a useful signal of proximity to transition. Ghanavati et al. derive autocorrelation functions for the state variables of a stochastic single machine infinite bus system (SMIB). The results show that both autocorrelation and variance increase as this system approaches a saddle-node bifurcation. The autocorrelation functions help to explain why CSD

3 can be used as an indicator of proximity to criticality in power systems revealing, for example, how nonlinearity in the SMIB system causes these signs to appear [10]. Jalilzadeh et al. presented a paper which focuses on multi-objective designing of multimachine Thyristor Controlled Series Compensator (TCSC) using Strength Pareto Evolutionary Algorithm (SPEA). The TCSC parameters designing problem is converted to an optimization problem with the multi-objective function including the desired damping factor and the desired damping ratio of the power system modes, which is solved by a SPEA algorithm. The effectiveness of the proposed controller validates on a multi-machine power system over a wide range of loading conditions [11]. Sambariya et al. presented a method to obtain simplified models of the systems by using different reduction techniques. In this paper, an analysis is presented by the use of Differentiation Method in Time & Frequency Domain. It also shows the preservation of stability and other characteristic parameters of a single machine infinite bus system (SMIB) with power system stabilizer, whose 2 nd & 3 rd order reduced model are obtained [12]. Mahmud et al. presented a full-order nonlinear observer-based excitation controller design for interconnected power systems. Exact linearization approach of feedback linearization is used to design the nonlinear observer when the power system is fully linearized. The excitation control law is derived for the exactly linearized power system model. The states of power system are directly used as the input to the controller where the control law does not need to be expressed in terms of all measured variables. A single machine infinite bus (SMIB) system is used as test system and all the states are observed for SMIB system. To validate the effectiveness of this control scheme on a large system, a benchmark 3 machine 11 bus system is also simulated [13]. The low frequency oscillations (LFOs) are related to the small signal stability of a power system and are detrimental to the goals of maximum power transfer and power system security. As power systems began to be operated closer to their stability limits, the weakness of a synchronizing torque among the generators was recognized as a major cause of system instability instead of damping torque. Automatic voltage regulators (AVRs) can improve the steady-state stability of the power systems. The addition of a supplementary controller into the control loop, such as power system stabilizers (PSSs) to the AVRs on the generators, provides the means to reduce the inhibiting effects of low frequency oscillations. The power system stabilizers work well at the particular network configuration and steady state conditions for which they were designed. Once conditions change the performance degrades to overcome the drawbacks of power system stabilizer (PSS), numerous techniques are available in the literature. Surjan et al. presented the comparison of effectiveness for conventional PSS and PID-PSS [14]. With constraints on data availability and for study of power system stability it is adequate to model the synchronous generator with field circuit. Gudla et al. presented a systematic procedure for modelling and simulation of a single-machine infinite-bus power system installed with a Power System Stabilizer (PSS) and Fuzzy Logic Power System Stabilizer (FLPSS) where the synchronous generator is represented, so that impact of PSS on power system stability can be more reasonably evaluated. The model of the example power system is developed using MATLAB/SIMULINK which can be used for teaching the power system stability phenomena, and also for research works especially to develop generator controllers using advanced technologies. An analytical approach is developed for the determination of PSS parameters [15]. Damping of Low frequency oscillations using Swarm optimized controller for Single Machine Infinite Bus (SMIB) system is presented in this paper. Suguna et al. implemented a framework for Power System Stabilizer (PSS) based on speed and electrical power deviation. Particle Swarm Optimization (PSO) has gained growing popularity in the recent years and is finding a wide range of important applications. Like other population based stochastic meta-heuristics, PSO has a few algorithm parameters that need to be carefully set to achieve best execution results. Power system stabilizer based on the Particle Swarm Optimization (PSO) algorithm is presented for tuning dual input Power System Stabilizer parameters. In this method, based on the optimization of a suitable objective function, optimal values for PSS controlling parameters including lead-lag compensator time constants as well as the controller gain are calculated. The employed objective function is the error between the reference voltage and the signal produced from the terminal voltage (i.e. to minimize the overshoot of low-frequency oscillations). This algorithm is applied to a single machine power system and for various operating conditions [16].

4 Vijay et al. proposed a modified method of designing power system stabilizers (PSS) for interconnected power system. Conventional method using a single machine infinite bus approximation involves the frequency response estimation called the GEP(s) between the AVR input and the resultant electrical torque. This requires the knowledge of equivalent external reactance and infinite bus voltage or their estimated values at each machine. In this method, information available at the high voltage bus of the step-up transformer is used to set up a modified Heffron-Phillip s model. With this model it is possible to decide the structure of the PSS compensator and tune its parameters at each machine in the multi-machine environment, using only those signals that are available at the generating station [17]. Funso et al. presented a paper which simulates the behaviour of power system stabilizer (PSS) on automatic voltage regulator (AVR) and excitation system. It also developed an algorithm to investigate the transient and dynamic stability of the power systems. This was with a view to providing information of damping rotor oscillations of synchronous generators. Three types of power systems were investigated: a single-machineinfinite-bus system with and without PSS, two generators connected to a load with various types of excitation controls and a multi-machine power system typified by Nigerian 330-kV electrical network. Tabu-search technique was used to tune the PSS-parameters for a single machine connected to an infinite bus operating at three different loading conditions [18]. V. CONCLUSION Dynamic stability analysis of a large interconnected power system is extremely time consuming and laborious and may even exceed the storage capacity of modern fast computers because of the high order of the system matrix. The complexity often makes it difficult to obtain a good understanding of the behaviour of a system. The exact analysis of most of high order system is both tedious and costly; it poses a great challenge to both system analyst and control engineer. The preliminary design and optimization of such systems can often be accomplished with greater ease if a low order linear model is derived which provides a good approximation to the system. In this paper, ensuring system stability, in order to provide faster responses over a wide range of power system operation a power system stability (PSS) of SMIB system is reviewed. REFERENCE [1] E.V. Larsen and D.A. Swann, Applying power system stabilizers part-ii: Performance Objectives and Tuning Concepts, IEEE Trans. Power App. Sys, 100 (6) (1981) [2] Anders Hammer, Analysis of IEEE Power System Stabilizer Models, Master of Science in Electric Power Engineering, Norwegian University of Science and Technology Department of Electric Power Engineering, June [3] Do Bomfim, Antonio LB, Glauco N. Taranto, and Djalma M. Falcao. 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