PSO-based single multiplicative neuron model for time series prediction
Expert Systems with Applications: An International Journal
An improved binary particle swarm optimization for unit commitment problem
Expert Systems with Applications: An International Journal
IEEE Transactions on Fuzzy Systems
Expected value of fuzzy variable and fuzzy expected value models
IEEE Transactions on Fuzzy Systems
A new MOPSO to solve a multi-objective portfolio selection model with fuzzy value-at-risk
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part III
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Conventional power system optimization problems deal with the power demand and spinning reserve through real values. In this research, we employ fuzzy variables to better characterize these values in uncertain environment. In building the fuzzy power system reliable model, fuzzy Value-at-Risk (VaR) can evaluate the greatest value under given confidence level and is a new technique to measure the constraints and system reliability. The proposed model is a complex nonlinear optimization problem which cannot be solved by simplex algorithm. In this paper, particle swarm optimization (PSO) is used to find optimal solution. The original PSO algorithm is improved to straighten out local convergence problem. Finally, the proposed model and algorithm are exemplified by one numerical example.