Design Of Multimachine Power System Stabilizers Research Articles

Robust multi-machine power system stabilizer design using bio-inspired optimization techniques and their comparison

This paper reports a comparative study among four bio-inspired meta-heuristic techniques i.e. Sooty-Tern Optimization (STO), Grey Wolf Optimization (GWO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) to tune the robust Power System Stabilizer (PSS) parameters of the multi-machine power system. These approaches are successfully tested on two bench-mark systems: sixteen-machine, sixty-eight-bus New England Extended Power Grid (NEEPG) and three-machine, nine-bus Western System Coordinating Council (WSCC). The efficacy of planned PSS via STO and GWO is validated by extensive non-linear simulations, eigenvalue analysis, and performance indices for numerous operating conditions under decisive perturbations, and outcomes are matched with those of GA and PSO techniques. In addition, the robustness is also tested for these algorithms. The results indicate that the PSS design using STO and GWO improves the small-signal stability and damping performance for mitigating inter-area and local area modes of low-frequency oscillations compared to GA and PSO.

Interconnected multi-machine power system stabilizer design using whale optimization algorithm

The role of Power System Stabilizer (PSS) in the power system is to provide necessary damping torque to the system in order to suppress the oscillations caused by a variety of disturbances that occur frequently and maintain the stability of the system. In this paper, a PSS design technique is proposed using Whale Optimization Algorithm (WOA) by considering eigenvalue objective function. Two bench mark multi machine test systems: three- generator nine- bus system, two- area four- generator inter connected system working on various operating conditions are considered as case studies and tested with the proposed technique. Extensive simulation results are obtained and effectiveness of proposed WOA-PSS are compared with well - known PSO and DE based stabilizers under several disturbances.

A new optimization approach for multi-machine power system stabilizer design using a smart bacteria foraging algorithm

This paper presents a new optimization method for designing the parameters of a power system stabilizer (PSS) using a smart bacteria foraging algorithm (SBFA). The proposed technique, which is a modification of the bacteria foraging method (BFA), can direct bacteria by performing a tumble with a smart unit of length, decreasing the cost function better than the conventional BFA method. This algorithm not only considers social intelligence, but also emphasizes the individual intelligence of bacteria for finding a better nutritional path. A new cost function in the proposed SBFA has been used for specifying the direction of movement after a tumble. This approach led to a higher convergence speed and also better performance than the BFA. The effectiveness of the proposed method has been tested on a multi-machine power system while considering a frequency error-based objective function to enhance damping of the electromechanical oscillation modes. Simulation results for the proposed method are compared with conventional PSS, BFA- and fuzzy-based PSS methods. The results show the superior performance of the proposed SBFA-based PSS in comparison with other techniques for damping power system oscillations.

Multi-machine power system stabilizer design by using cultural algorithms

Conventional power system stabilizer (CPSS) has been widely used as a supplementary controller to damp out the low frequency oscillations. The tuning of CPSS parameters for nonlinear power systems in a robust way in order that the overall system stability can be improved is a major drawback of CPSS. Several meta-heuristic optimization techniques have been tried out to overcome this shortcoming. This paper presents a new technique named cultural algorithms (CAs) to tune the PSS parameters. This technique is robust and computationally efficient as compared with other meta-heuristic algorithms. Simulations are carried out on two typical multi-machine electric power systems. The results clearly verify that the proposed method improves the dynamic stability of the system, especially when the operating point changes.

Multi-machine power system stabilizer design using improved particle swarm optimization (PSO) with time-varying acceleration coefficients

An efficient and most famous tool to enhance damping of the power system low frequency oscillations is the conventional widely used lead-lag Power System Stabilizer (PSS). To achieve the desired level of robust performance under transient situation, selecting a suitable design method for optimal tuning of PSS parameters is very important in multi-machine power system. Because, it is a multimodal and difficult combinatorial optimization problem, this paper presents a novel parameter automation strategy for Particle Swarm Optimization (PSO) called PSO with Time-Varying Acceleration Coefficients (PSO-TVAC). This optimization method has a strong ability to successfully control both global and local search in each iteration process for considerably increasing the probability of finding the global optimum solution. The PSO-TVAC algorithm is applied to optimal tuning PSS parameters problem in order to reduce the PSS design effort and find the best possible solution within a reasonable computation time. For this reason, the robustly selection of PSSs parameters is converted as an optimization problem based on the time domain-based objective function under different operating conditions. The robustness of the proposed method is demonstrated on a multi-machine power system in comparison with the classical PSO and conventional method based designed PSSs. It is shown through the nonlinear time domain simulation and some performance indices for a wide range of loading condition. The analysis of the results shows that the improved PSO-TVAC is not only very effective but also provides an excellent ability for damping low frequency oscillations and greatly enhance the dynamic stability of the power system. Moreover, the proposed PSO-TVAC algorithm is superior than that of the classical PSO one in terms of accuracy, convergence and computational effort. Key words: Power system stabilizer (PSS) design, particle swarm optimization, power system dynamics, time-varying acceleration coefficients.

Multi-machine power system stabilizer design by rule based bacteria foraging

Several power system stabilizers (PSS) connected in number of machines in a multi-machine power systems, pose the problem of appropriate tuning of their parameters so that overall system dynamic stability can be improved in a robust way. Based on the foraging behavior of Escherichia coli bacteria in human intestine, this paper attempts to optimize simultaneously three constants each of several PSS present in a multi-machine power system. The tuning is done taking an objective function that incorporates a multi-operative condition, consisting of nominal and various changed conditions, into it. The convergence with the proposed rule based bacteria foraging (RBBF) optimization technique is superior to the conventional and genetic algorithm (GA) techniques. Robustness of tuning with the proposed method was verified, with transient stability analysis of the system by time domain simulations subjecting the power system to different types of disturbances.

Power System Stabilizer Design based on Structured Singular Values

Decentralized control and robustness are two important issues in power system operation and planning. A recent study shows that, for power system load frequency control, a design method based on the structured singular values can be applied to address these two issues effectively. This article shows that, for another power system engineering problem, power system stabilizer (PSS) design, the structured singular values can also be applied to develop a decentralized multimachine PSS design method. A simulation study and frequency-response analysis on a model of a practical 10-machine power system have demonstrated that the system with decentralized PSSs designed in this article has good robustness in stability.

00/00821 A new reduced order multimachine power system stabilizer design

00/00821 A new reduced order multimachine power system stabilizer design

A new reduced order multimachine power system stabilizer design

This paper presents a new design method for a multimachine power system stabiliser. The stabiliser is dynamic, low order and robust. The order of the stabiliser can be as low as the number of those machines for which a stabiliser is necessary. The design method is systematic, simple and easy to implement, using standard MATLAB ™ functions. A three machine power system is used as a case study. A comprehensive treatment of design, and analysis is given.

98/01298 Applying structured singular value to multi-machine power system stabilizer design: Yang, T. C. Electric Power Systems Research, 43, (2), 113–123

98/01298 Applying structured singular value to multi-machine power system stabilizer design: Yang, T. C. Electric Power Systems Research, 43, (2), 113–123

Applying structured singular value to multi-machine power system stabilizer design

A power system stabilizer design method for multi-machine power systems is proposed in this paper. In this design method, the design problem is translated into an equivalent problem of decentralized controller design for Multi-Input Multi-Output (MIMO) control systems. Subject to a condition based on the structured singular values, each stabilizer can be designed independently. The robust stability condition for power systems with stabilizers on can be easily stated as to achieve a sufficient interaction margin defined in this paper, and a sufficient gain and phase margin defined in the classical feedback theory during each independent design. Within this general framework, the conventional stabilizer design methodology based on the concept of synchronous and damping torques is used to decide the design details of each stabilizer. The suggested design method is applied to a model of a practical 10 machine power system.