Andi Imran

Optimal placement and tuning power system stabilizer using Participation Factor and Imperialist Competitive Algorithm in 150 kV South of Sulawesi system

In this study will be explained determination of the location of the power system stabilizer (PSS) and the tuning parameters of the PSS in South Sulawesi electrical system using the Imperialist Competitive Algorithm (ICA) for tuning and Participation Factor for determining the placement of PSS. From the simulation results obtained placement and proper tuning PSS parameters can improve the stability of South Sulawesi generator electrical system, which can reduce the overshoot and settling time speed up the frequency response to speed up the system reaches steady state conditions. The critical eigenvalue of the system using a conventional PSS and PSS ICA, which showed an increase with increasing eigenvalue damping ratio. Damping values greater than 0.1 indicate that the eigenvalue condition is secure and allows stable higher. The proposed of method can be improved stability south of Sulawesi system. The method used here will also be used without PSS and comparison with conventional methods.

Application of Fuzzy-Flower Pollination Algorithmfor Peak Load Forecasting on National Holiday

Application of Type-2 Fuzzy Logic System (T2FLS) has became attention for a short-term load forecasting problems solution. This paper presentsapplication optimization membership function of antecedent (X,Y) and consequent (Z) interval type-2 Fuzzy Logic System using Flower Pollination Algorithm (FPA) for short-term load forecasting on national holiday. This method has being implemented on the historical peak load data during 14 national holidays case study in JawaBaliIndonesia electrical power system in 2011. Flower Pollination Algorithm (FPA) will be applied to optimize interval Footprint of Uncertainty (FOU) membership functions of interval type-2 fuzzy logic system. The test result showed Main Absolute Percentage Error (MAPE) is less than type-2 Fuzzy Logic System (FLS) and optimization type-2 FLS-Big Bang Big Crunch Algorithm. Finally, this paper defined Main Absolute Percentage Error (MAPE) 2.040612143% for type-2 FLS, 1.279257143% for optimization type-2 FLS-Big Bang Big Crunch Atgorithm and 1.091543571% for optimization type2 FLS-Flower Pollination Algorithm.