Assessment of voltage stability based on power transfer stability index (PTSI) using computational intelligence models

Ahmed Majeed Ghadban, Ghassan Abdullah Salman, Husham Idan Hussein

Abstract


In this paper, the importance of voltage stability is explained, which is a great problem in the electrical power system. The estimation of voltage stability is made a priority so as to make the power system stable and prevent it from reaching voltage collapse. The Power Transfer Stability Index (PTSI) is used as a predictor or for voltage stability assessment in this paper. The PTSI is an indicator utilized in a power system network to detect the instability of voltages on weakened buses. A power stability index is used to obtain a voltage assessment of the power system networks. Two hybrid algorithms are developed in this paper: The cultural-neural network (CA-NN) and the particle swarm optimization-neural network (PSO-NN). After developing the algorithms, they are compared with the actual values of PTSI NR method. The hybrid algorithms are then installed on the 24 bus Iraqi power system. The actual values of PTSI are the targets needed. They are obtained from the Newton Raphson algorithm when the input data is Vi, δi, Pd, Qd for the algorithm. After our simulations, the results indicate that a weak bus that approaches voltage collapse and all results were approximately the same. There was only a slight difference with the actual results. The results demonstrated that classical methods are slower and less accurate than the hybrid algorithms. It also demonstrates the validation and effectiveness of Hybrid Intelligent Algorithms (CA-NN, and PSO-NN) for assessing voltage-prioritizing Hybrid Intelligent Algorithms (CA-NN). The MATLAB-programming was utilized to obtain most of the results.

Keywords


ANN; culture algorithm; hybrid algorithm; PSO; system stability; voltage assessment;



DOI: http://doi.org/10.11591/ijece.v11i4.pp%25p

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578