Design of a power system stabilizer for a synchronous generator using hybrid intelligent controller (original) (raw)
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Type-2 Fuzzy Self-Tuning Voltage Controller for a Synchronous Generator using Tabu Search
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IJSRD, 2014
The genetic local search technique (GLS) hybridizes the genetic algorithm (GA) and the local search (such as hill climbing) in order to eliminate the disadvantages in GA. The parameters of the PSS (gain, phase lead time constant) are tuned by considering the single machine connected to infinite bus system (SMIB). Here PSS are used for damping low frequency local mode of oscillations. Eigen value analysis shows that the proposed GLSPSS based PSS have better performance compared with conventional and the Genetic algorithm based PSS (GAPSS). Integral of time multiplied absolute value of error (ITAE) is taken as the performance index of the selected system. Genetic and Evolutionary algorithm (GEA) toolbox is used along with MATLAB/SIMULINK for simulation.
Application of Evolution Algorithm in Power System Control Design
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Power system controller design based on an evolutionary algorithm called population based incremental learning (PBIL) is proposed in this paper. The problem of controller design is transformed into an optimization problem and the parameters of the controller are determined via PBIL. The proposed method has the advantage that it is simpler, faster and more efficient than both the classical trial and error approach of designing PSS and the genetic algorithms (GAs). The PBIL-PSS is compared with the GA-PSS. Simulation results show that PBIL-PSS is more effective than GA-PSS in damping the system's oscillations.
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In this paper, an optimized Neuro-Fuzzy Power-System Stabilizer (NF PSS) is proposed to improve the transient and dynamic stability of synchronous machines. The NF PSS employs a five-layer Fuzzy-Neural Network (FNN). The learning scheme of this FNN is composed of three phases. The first phase uses a clustering algorithm for coarse identification of the initial membership functions of the Fuzzy Controller (FC). The second phase extracts the linguistic-fuzzy rules from the available training data. In the third phase, a Multi-Resolutional Dynamic Genetic Algorithm (MRD-GA) is used to fine-tune and optimize the membership functions of the FC. Extensive simulation studies have been carried out to show the performance of the NF PSS and to compare it with a Conventional PSS (CPSS) in a multi-machine power-system environment.