Optimization of power system stabilizer parameters using population-based incremental learning

The population-based incremental learning (PBIL) is a novel evolutionary algorithm combined the mechanisms of the Genetic Algorithm with competitive learning. In this paper, the influence of the number of selected best solutions on the convergence speed of the PBIL is studied by experiment. Based on experimental results, a PBIL algorithm with elitist strategy, named Double Learning PBIL (DLPBIL), is proposed. The new algorithm learns both the selected best solutions in current population and the optimal solution found so far in the algorithm at same time. Experimental results show that the DLPBIL out-performs the standard PBIL. Both the convergence speed and the solution quality are improved.

Performance evaluation of power system stabilizers based on Population-Based Incremental Learning (PBIL) algorithm

This paper presents the study related to the power system stabilizer (PSS) design for northern regional power grid (NRPG) of India. The location of local PSS is suggested by participation factor approach. The power-voltage reference (PVr) characteristic of the suggested generator is used to obtain the compensator block of the local PSS. Internal states are lost while performing model order reduction therefore, the shaft dynamics are not available in the reduced order model for disabling. Therefore compensating block has to be design by utilizing full order system. But, the gain of the PSS can be tuned by utilizing the reduced order model. Next, the model order reduction (MOR) is performed with a target on preserving the characteristics and behavior of dominant inter-area mode in reduced order model (ROM). Thereafter, the tuned gain using ROM is combined with compensator and washout filter to obtain PSS for 44th generator (G44). The performance of designed PSS is investigated under five cases of disturbances.

Evolutionary algorithms (EAs) have become the standards and paradigms for solving inverse problems. However, their two inherited operations, namely, the crossover and mutation operations, are complicated and difficult, both in theory and in numerical implementations. In this regard, increasing efforts have been devoted to EAs which are based on probabilistic models (EAPMs) to overcome the shortcomings of available EAs. The population-based incremental learning (PBIL) is an EAPM; moreover, it can bridge the gap between machine learning and the EAs, hence enjoying several merits compared with other EAs. However, lukewarm efforts have been devoted to PBILs, especially the real coded PBILs, in the study of inverse problems in electromagnetics. In this regard, a novel real coded PBIL is being proposed in this paper. In the proposed real coded PBIL, a probability matrix is proposed to randomly generate a population, and the updating formulas for this probability matrix using the so far searched best solution and the best solution of the current population are introduced to strike a balance between convergence performance and solution quality. The proposed real coded PBIL algorithm is numerically experimented on several case studies and promising results are reported in this paper.

This chapter is concerned with the stability enhancement of a power system using power system stabilizers (PSSs) designed based on four evolutionary algorithms (EAs), namely, genetic algorithms (GAs), breeder genetic algorithm (BGA), population-based incremental learning (PBIL), and differential evolution (DE). GAs have been widely applied in many fields of engineering and science and have shown to be a robust and powerful adaptive search algorithm. However, GAs are known to have several limitations. To deal with these limitations, many variant forms of GAs have been suggested often tailored to specific problems. In this research, we investigated the performances of GA-PSS and three other EAs-based PSSs (i.e., BGA-PSS and PBIL-PSS and DE-PSS) in improving the small-signal stability of a power system. These EAs have been selected on the basis of their simplicity, efficiency, and effectiveness in solving the optimization problem at hand. Frequency domain and time-domain simulation results show that DE-PSS, PBIL-PSS, and BGA-PSS performed better than GA-PSS. Time domain simulations suggest that overall, DE-PSS performs better than PBIL-PSS and BGA-PSS in terms of undershoot and subsequent swings, albeit with a relatively large first swing overshoot. The performances of BGA-PSS and PBIL-PSS are similar. On the other hand, GA-PSS gives a better response than the conventional PSS (CPSS).

In this paper a robust method is presented for the combined design of STATCOM and Power System Stabilizer (PSS) controllers in order to enhance the damping of the low frequency oscillations in power systems. The combined design problems among PSS and STATCOM internal ac and dc voltage controllers has been taken into consideration. The equations that describe the proposed system have been linearized and a Fuzzy Logic Controller (FLC) has been designed for the PSS. Then, the Particle Swarm Optimization technique (PSO) which has a strong ability to find the most optimistic results is employed to search for the optimal STATCOM controller parameters. The proposed controllers are evaluated on a single machine infinite bus power system with the STATCOM installed in the midpoint of the transmission line. The results analysis reveals that the combined design has an excellent capability in damping a power system's low frequency oscillations, and that it greatly enhances the dynamic stability of power systems. Moreover, a system performance analysis under different operating conditions and some performance indices studies show the effectiveness of the combined design.

As an alternative to traditional evolutionary algorithms (EAs), population-based incremental learning (PBIL) maintains a probabilistic model of the best individual(s). Originally, PBIL was applied in binary search spaces. Recently, some work has been done to extend it to continuous spaces. In this paper, we review two such extensions of PBIL. An improved version of the PBIL based on Gaussian model is proposed that combines two main features: a new updating rule that takes into account all the individuals and their fitness values and a self-adaptive learning rate parameter. Furthermore, a new continuous PBIL employing a histogram probabilistic model is proposed. Some experiments results are presented that highlight the features of the new algorithms.

In this paper we study two different meta-heuristics to solve a real-word frequency assignment problem (FAP) in GSM networks. We have used a precise mathematical formulation in which the frequency plans are evaluated using accurate interference information coming from a real GSM network. We have developed an improved version of the scatter search (SS) algorithm in order to solve this problem. After accurately tuning this algorithm, it has been compared with a version fixed for the FAP problem of the population-based incremental learning (PBIL) algorithm. The results show that SS obtains better frequency plannings than PBIL for all the experiments performed.

This article deals with the comparative performance of some established multi-objective evolutionary algorithms (MOEAs) for structural topology optimization. Four multi-objective problems, having design objectives like structural compliance, natural frequency and mass, and subjected to constraints on stress, etc., are posed for performance testing. The MOEAs include Pareto archive evolution strategy (PAES), population-based incremental learning (PBIL), non-dominated sorting genetic algorithm (NSGA), strength Pareto evolutionary algorithm (SPEA), and multi-objective particle swarm optimization (MPSO). The various MOEAs are implemented to solve the problems. The ground element filtering (GEF) technique is used to suppress checkerboard patterns on topologies. The results obtained from the various optimizers are illustrated and compared. It is shown that PBIL is far superior to the others. The optimal topologies from using PBIL can be compared with those obtained by employing the classical gradient-based approach. It can be considered as a powerful tool for structural topological design.

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.

Finding interactions among genes is one of the main problems in molecular biology. In this paper, we use a novel approach to model the gene's regulations, or Gene Regulatory Networks (GRNs). We use a Recursive Neural Network (RNN) to model the networks, and then use Population Based Incremental Learning (PBIL) enhanced with K-means to find the optimum parameters of the Neural Network. We present experiments with real data, we compare our algorithm with others approaches by calculating different statistics for the solutions.

This paper reports a new technique for achieving optimized design for power system stabilizers. In any large scale interconnected systems, disturbances of small magnitudes are very common and low frequency oscillations pose a major problem. Hence small signal stability analysis is very important for analyzing system stability and performance. Power System Stabilizers (PSS) are used in these large interconnected systems for damping out low-frequency oscillations by providing auxiliary control signals to the generator excitation input. In this paper, collective decision optimization (CDO) algorithm, a meta-heuristic approach based on the decision making approach of human beings, has been applied for the optimal design of PSS. PSS parameters are tuned for the objective function, involving eigenvalues and damping ratios of the lightly damped electromechanical modes over a wide range of operating conditions. Also, optimal locations for PSS placement have been derived. Comparative study of the results obtained using CDO with those of grey wolf optimizer (GWO), differential Evolution (DE), Whale Optimization Algorithm (WOA) and crow search algorithm (CSA) methods, established the robustness of the algorithm in designing PSS under different operating conditions.

An evolutionary algorithm-based power system stabilizers (PSS) design for low-frequency oscillations (LFO) damping in multi-machine power system networks (MMPSNs) is presented in this paper. A damping ratio-based objective function is developed to enhance the system damping where the widely employed lead-lag type PSS is considered in the problem formulation. The equilibrium Optimizer (EO), a recently developed metaheuristic algorithm that is capable of finding optimal solutions in complex engineering problems, is employed in this article. The algorithm's resilience is demonstrated by its ability to lead to the best PSS design regardless of the initial assumption made by the user. Two distinct multi-machine networks 2-area 4-machine and IEEE 10-machine 39-bus are used in this research. EO-based PSS results are compared with traditional PSS results to investigate which one yields better results for stability. According to the simulation findings, the EO technique reduces the settling time and overshoot significantly over the other techniques.

The design of power system stabilizer (PSS) is load-dependent and needs continuous adjustment at each operating condition. This paper aims at introducing a robust non-fragile PSS for interconnected power systems. The proposed controller has the capability of adaptively tuning online its rule-base through a variable-structure direct adaptive control algorithm in order to rigorously attain the desired objectives. The PSS controller acts on damping the electromechanical modes of oscillations not only through a wide range of operating conditions but also in presence of different disturbances. Using MATLABTM-Simulink, simulation results significantly verify that the proposed controller provides favorable performance and efficiently contributes towards enhancing the system dynamic behavior when applied to the four machines two-area power system that mimics the typical system behavior in actual operation. The interaction between the variable-structure adaptive fuzzy-based power system stabilizer (VS-AFPSS) and the existed typical ones inside the interconnected power systems has been explicitly discussed. Compared to other conventional controllers, VS-AFPSS enables better damping characteristics to both local and inter-area oscillation modes considering different operating conditions and sever disturbances.

A suboptimal H/sub /spl infin// PSS tuning method has been developed. This method can improve overall damping factor and robustness by using the existing PSS structure. One of the optimization methods applied to this tuning procedure was sequential quadratic programming (SQP). Since SQP only deals with convex problems, it is not suitable for multi-machine power systems. To address the shortcomings of the method an optimization method called PBIL (population based incremental learning) with the key features of its ability to optimize nonconvex, multi-variable problems has been applied to the tuning method developed.