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This paper proposes an efficient optimization algorithm named chaotic teaching–learning algorithm (CTLA), to solve multimachine power system stabilizers design problem. The original teaching learning algorithm as competitive to other optimization algorithms, used two phases to proceed to the global optimal solution: ‘teacher phase’ and ‘learner phase’. However, during the second phase an adequate interaction between the teacher and the learners in entire search space are not guaranteed and the algorithm may be trapped in local optima. Thus, in the proposed CTLA a new phase named “chaotic phase” is added in order to overcome this drawback. The performance of the CTLA is investigated by using a set of benchmark functions. To demonstrate the effectiveness of the proposed algorithm in power systems, the conventional lead-lag power system stabilizers (PSSs) are tuned for: three machines nine bus system (WSCC) and the ten machine thirty-nine bus New England power systems. The performance of the proposed CTLA-based PSS (CTLAPSSs) under different loading conditions and disturbances is investigated through eigen-value analysis, non-linear time domain-simulations and some performance indices. [ABSTRACT FROM AUTHOR]

Abstract: The industrialization and the growth of the population are the main factors, which increase the electricity consumption. This phenomenon comes with a deep restructuring of the electricity sector. Indeed, new constraints concerning reduction of the gas emissions and the use of renewable sources are imposed. The electric networks become more and more large and complex. The grid operation appears as non-linear problems, multi-objective, having a lot of variables and constraints. Complexity arises from the problem formulation and evolutionary algorithms appear as an efficient tool for the resolution. This paper presents three problems, which are related to the electric networks. The economic environmental dispatch (EED) problem consists to minimize the fuel cost and the gas emissions. The optimal reactive dispatch (RD) consists to minimize the active losses in the transmission lines, the voltage deviations and the cost of compensation devices. Finally the active, reactive and environmental dispatch problem (ARED) consists to minimize the cost of fuel, gas emissions and the active losses in the transmission lines. To solve these problems an Improving Strength Pareto Evolutionary Algorithm (SPEA2) method is applied to the New-England Power System (39 buses, 10 thermal generators). The results demonstrate the ability of the proposed approach to find and maintain a diverse Pareto optimal solution in one single run. [Copyright &y& Elsevier]

Abstract: In this paper, we develop a method aimed to impose an acceptable voltages profile and to reduce active losses of an electrical supply network including wind generators in real time. These tasks are ensured by acting on capacitor and reactor banks implemented in the load nodes. To solve this problem, we minimize multi-objective functions associated to the active losses and the compensation devices cost under constraints imposed on the voltages and the reactive productions of the various banks. The minimization procedure was realised by the use of evolutionary algorithms. After a training phase, a neural model has the capacity to provide a good estimation of the voltages, the reactive productions and the losses for actual curves of the load and the wind speed, in real time. [Copyright &y& Elsevier]