Coordinated tuning of power system controllers using parallel genetic algorithms

This paper presents an investigation into the optimisation of power system stabiliser (PSS) parameters using multi-power flow conditions. A simple objective function takes into account all the critical operating conditions. The steepest descent optimisation method based on eigen-value sensitivity is used for the minimisation of the objective function. Two types of PSSs are evaluated, namely, the optimised PSS (OPSS) based on multi-power flow conditions and the conventional PSS (CPSS). It is shown that the OPSS performs better than the CPSS on a single machine infinite bus (SMIB) system.

Damping of electro-mechanical oscillations in interconnected power systems is important to guarantee secure and stable operation of the system. Power system stabilizers (PSSs) are applied to damp these oscillations. To overcome flows associated with conventional algorithm of PSS design, a modified version of non-dominated sorting genetic algorithm is used to regulate the PSS parameters in this paper. Additionally, fuzzy logic principle is employed to develop PSS in order to improve the stability and reliability of the power systems subjected to disturbances. Two-area (four-machine) power system is considered as the case study in this paper. PSS parameters are obtained for four PSS connected to four generators. The effectiveness of the proposed algorithm in damping the system oscillations during overall disturbances is evaluated. The simulation results illustrate this issue.

Power system stabilizer (PSS) and flexible AC transmission system (FACTS) damping controller to improve the stability of the power system has been widely used. A coordinated control method based on the combined computational evolutionary theory is proposed to overcome some of damping controllers simultaneously so as to keep the allowable level of power system damping. It works by making full use of favorable interaction between the controlling and minimizing adverse interactions so that the power system oscillations can be suppressed effectively. Proportional integral derivative (PID) controller tuning based power system stabilizer types PSS3B (PIDPSS3B), static var compensator (SVC) and automatic voltage regulator (AVR) presented in this paper. PID controller gain parameters such as proportional, integral factor, differential coefficient and get AVR selected and optimized by BF-PSOTVAC. The integral time absolut error (ITAE) standards of optimization design as objective function. The results of simulations show that performance index of system the proposed method is 42.7890. The BF-PSOTVAC method has the capability to damping optimally and suppresses error to minimum.

The paper describes an optimal parameter selection method of automatic voltage regulator (AVR) for a large power plant in order to improve power system stability. The AVR is generally used to control the output voltage of the generator by regulating the excitation voltage in a generator rotor. In order to improve transient stability in power system, the paper proposes the method to select optimal parameters of the AVR based on exact power system modeling. The performances of the proposed method are carried out by timedomain simulation by applying fault to a power system.

Introduction. In an electrical power system, the output of the synchronous generators varies due to disturbances or sudden load changes. These variations in output severely affect power system stability and power quality. The synchronous generator is equipped with an automatic voltage regulator to maintain its terminal voltage at rated voltage. Several control techniques utilized to improve the response of the automatic voltage regulator system, however, proportional integral derivative (PID) controller is the most frequently used controller but its parameters require optimization. Novelty. In this paper, the chaotic sequence based on the logistic map is hybridized with particle swarm optimization to find the optimal parameters of the PID for the automatic voltage regulator system. The logistic map chaotic sequence-based initialization and global best selection enable the algorithm to escape from local minima stagnation and improve its convergence rate resulting in best optimal parameters. Purpose. The main objective of the proposed approach is to improve the transient response of the automatic voltage regulator system by minimizing the maximum overshoot, settling time, rise time, and peak time values of the terminal voltage, and eliminating the steady-state error. Methods. In the process of parameter tuning, the Chaotic particle swarm optimization technique was run several times through the proposed hybrid objective function, which accommodates the advantages of the two most commonly used objective functions with a minimum number of iterations, and an optimal PID gain value was found. The proposed algorithm is compared with current metaheuristic algorithms including conventional particle swarm optimization, improved kidney algorithm, and others. Results. For performance evaluation, the characteristics of the integral of time multiplied squared error and Zwe-Lee Gaing objective functions are combined. Furthermore, the time-domain analysis, frequency-domain analysis, and robustness analysis are carried out to show the better performance of the proposed algorithm. The result shows that automatic voltage regulator tuned with the chaotic particle swarm optimization based PID yield improvement in overshoot, settling time, and function value of 14.41 %, 37.91 %, 1.73 % over recently proposed IKA, and 43.55 %, 44.5 %, 16.67 % over conventional particle swarm optimization algorithms. The improvement in transient response further improves the automatic voltage regulator system stability for electrical power systems.

In this paper a conceptual analysis on the effect of Automatic Voltage Regulators (AVR) and Power System Stabilizers (PSS) for small signal stability (SSS) of power systems is developed. A modal formulation approach including the use of the Participation Matrix is used. The Modal Formulation is performed considering synchronous generator (SG) equations for both the Electromotive Forces (EMF) model and the Flux Linkages (FL) model as well as AVR and PSS equations. Then, a selected study case is evaluated to study the effect of the AVR and PSS on the small signal stability. For pedagogical purposes a comparison between the basic RLC circuit and a synchronous generator connected to an infinite bus, to clarify the concepts of synchronizing torque and damping torque, is presented.

This paper presents the modelling and simulation of Centurion electric power system using MATLAB/SIMULINK package. The basic system is tested under large and small disturbances to study the dynamic behaviour of the system and the stability margins associated with the different configurations of the system. The single-machine infinite bus (SMIB) power system model is considered, with the synchronous generator being represented by model 1 with both the generator main field winding and the damper winding in q-axis, and developed to evaluate the impact of power system stabilizer (PSS) with automatic voltage regulator (AVR) and flexible ac transmission system (FACTS) based controllers on power system stability. As a suggested solution to increase stability margins of the system, PSS with AVR are installed in the network. FACTS controllers are added, modelled and tested to validate the effectiveness on the different stability margins under both large and small disturbances.

The work described herein has an aim to control the reactive power of the power system by employing fuzzy aided proportional integral derivative with filter (PIDF) controller. The automatic voltage regulator (AVR) is utilized to control the reactive power and thereby maintaining the terminal voltage of the alternator at the desired limit. To enhance the system performance, the controller parameters, initially, are optimized by JAYA algorithm. JAYA is a Sanskrit word meaning victory. Furthermore, to improve the solution of JAYA, quasi-oppositional based learning (Q-OBL) has been housed with it which results in QJAYA. An extensive comparative study between QJAYA with original JAYA and genetic algorithm has been performed. To show the mastery of QJAYA tuned F-PIDF, the output results are compared with PID and fuzzy logic controller (FLC). Finally, power system stabilizer (PSS) is designed by QJAYA and included in the AVR loop to improve the system stability.

This study presents the design of intelligent coordinators for the automatic voltage regulator (AVR) and power system stabiliser (PSS) in a multi‐machine power system. The intelligent coordinators are designed to update the gains of AVR and PSS in severe disturbances to guarantee the stability of the studied power system. Three potent intelligent coordinators are proposed: (a) fuzzy logic coordinator, (b) artificial neural network coordinator, and (c) brain emotional learning coordinator. Since the intelligent coordinators are based on the knowledge of the experts, desirable scaling factors are considered in the output signals of the coordinators to achieve optimal results. The scaling factors are optimised using a new evolutionary approach known as the sine–cosine algorithm. To evaluate the efficiency of the proposed intelligent approaches, the performances of coordinators are analysed on a two‐area four‐machine power system. A range of power system signals, such as rotor speed, terminal voltages, acceleration power and rotor angle of generators are demonstrated to approve and compare the performance of the intelligent coordinators. The simulation results indicate that the intelligent coordinators can guarantee the stability of the power system and satisfy performance objectives, such as desired transient and steady‐state errors.

Summary form only given. This article gives a brief overview of theoretical advances, computing trends, applications and future perspectives in parallel genetic algorithms. It explains basic terms and behavior of (parallel) genetic algorithms. Genetic algorithms are easily parallelized algorithms, therefore two kinds of possible parallelism, data parallelism and control parallelism, are mentioned and described towards them. Parallelism of genetic algorithms brings many advantages and gains. Classifications of these algorithms are often based on the type of computing model, a walk strategy and the used computing machinery. Afterwards significant milestones in the theory with latest advances are briefly mentioned. Then current trends in parallel computing with stress computer architectures of parallel systems, interconnection topologies, operating systems, parallel (genetic) libraries and programming paradigms are reviewed shortly. The sufficient space is devoted to the latest applications of parallel genetic algorithms. After the discussion section, perspectives of the algorithms are predicted till the year 2005. The information in the article is segregated into two periods before and after the year 2000 in all chapters. The second period is more interesting and of higher importance, because it highlights recent research efforts and gives some hints about possible future trends. That is why we devote much space to the second period. As there is no such an overview of the recent period of parallel genetic algorithms, our investigation could be appealing and useful in many aspects.

This paper presents a comparative analysis among various Power System Stabilizers (PSSs) for the enhancement of small signal stability of power systems. The most efficient approach of countering small signal instability dilemma is to add a supplementary controller known as PSS to the Automatic Voltage Regulator (AVR) on the generator. The conventional PSS seems to perform well at particular network configurations for which they are tuned. However, when system parameters change due to disturbance, unambiguous condition arises. The above limitations can be overcome by improving PSS design technique using artificial intelligence. In this paper, the effectiveness of various PSS design techniques has been analyzed in MATLAB/Simulink environment by performing nonlinear simulations utilizing a synchronous machine-infinite bus model. Among various PSSs the Adaptive Neuro Fuzzy Inference System (ANFIS) design technique is found better compare to the other design techniques.

Design of Robust Power System Stabilizer using Genetic Algorithm-based Fixed-Structure H∞ Loop Shaping Control

This paper presents a dynamic model of the equivalent Great Britain (GB) 36-zone power system, which can be used for reliable and realistic assessment of emerging load frequency control mechanisms. Flexible architecture of the presented dynamic test system permits a broad range of security of supply and small-signal stability studies for design of future power grids. It can be particularly useful for academic research, but also for undertaking feasibility studies in power industries. The proposed dynamic test system, which is obtained through network reduction of the original full-scale GB transmission power system developed by National Grid Electricity System Operator (NGESO) Company, provides detailed information about the GB power system. In this regard, the required data and modelling approaches to develop the 36-zone system are provided in detail. The presented dynamic test system represents the system topology, impedance characteristics and electromechanical oscillations of the original GB power system however, it is not an exact equivalent of the master GB system. Illustrative dynamic models of the key system components, including synchronous generators, automatic voltage regulators, power system stabilizers, hydro and steam turbines models along with speed governing systems are presented. Dynamic behavior of 36-zone test system in response to infeed loss contingencies is investigated. Particularly, the impact of changes in the system inertia on the system electromechanical modes is examined using the modal analysis approach. In this context, the mode shape concept is employed to determine dominant generators and contribution of different zones in the low frequency oscillations. Moreover, time-domain simulations are undertaken to validate the modal analysis results. Additionally, the condition of different zones from the viewpoint of frequency nadir and maximum rate of change of frequency for various contingencies and extreme cases are examined.

The Power System can become unstable due to disturbances. To enhance system stability the Unified Power Flow Controller (UPFC) is tuned and allocated in the System. In this paper, a brief review of UPFC tuning and allocation studies for power systems stability is presented. The databases consulted for literature are the IEEE Xplore, ScienceDirect, Google Scholar and IOP Publications. The search terms used are Allocation, Tuning, UPFC, Power System and Stability to find the literature used in this review. A total of 26 Journal articles and conference papers were found and reviewed based on tuning and allocation studies. The Researchers applied Fuzzy coordination, Genetic Algorithm (GA), Particles Swarm Optimization (PSO), Grey Wolf Optimization (GWO) and Linear Quadratic Tracker (LQT) to tune the UPFC for enhancing power system stability. For studies on UPFC allocation in power systems, the Researchers applied frequency response of power system transfer function, power flow, Tabu Search (TS), PSO and GA. For allocation based on optimization, the Researchers minimized power losses, voltage index and investment costs considering equality and inequality constraints.

Power system stabilizers (PSSs) have become very common in the operation of large electric power systems. The stability of electric power systems is increased by the use of power system stabilizers. It is a challenging task to design a stabilizer that delivers good performance in a wider range of operating conditions and under disturbances. This paper presents a fuzzy logic power system stabilizer (FLPSS) and demonstrates the good performance of this stabilizer under a wide range of operating conditions and disturbances. The performance of FLPSS is compared with that of a speed deviation (delta-omega) PSS in the variation of four different operating conditions, i.e., inertia constant (H), automatic voltage regulator (AVR) gain (Ka), internal reactance (Xe), and reactive power (Q).