Abstract-At present around the world including Indonesia have an energy crisis that is necessary to find renewable energy as a replacement. One of renewable energy is solar energy that use photovoltaic (PV) system to convert into electrical energy. The weakness of this PV system is the low energy conversion efficiency. . To increase the efficiency of PV panels, it must operate around the maximum power point which is influenced by cell temperature and sun irradiation. A controller therefore is needed to determine MPP and control PV output voltage according MPP voltage although there change in temperature and sun irradiation. The aim of this paper is design neural fuzzy controller for control the PV system output voltage using the buck converter to operate at the MPP although occur disturbance with MATLAB/SIMULINK. Neural fuzzy define MPP point and the MPPT controlling done by adjusting the duty cycle of converter so that the PV array voltage remains at MPP operating point. In particular, the simulation of neural fuzzy will be discussed.

This paper presents a technique for Medium Term Load Forecasting (MTLF) using Particle Swarm Optimization (PSO) algorithm based on Least Squares Regression Methods to forecast the electric loads of the Jordanian grid for year of 2015. Linear, quadratic and exponential forecast models have been examined to perform this study and compared with the Auto Regressive (AR) model. MTLF models were influenced by the weather which should be considered when predicting the future peak load demand in terms of months and weeks. The main contribution for this paper is the conduction of MTLF study for Jordan on weekly and monthly basis using real data obtained from National Electric Power Company NEPCO. This study is aimed to develop practical models and algorithm techniques for MTLF to be used by the operators of Jordan power grid. The results are compared with the actual peak load data to attain minimum percentage error. The value of the forecasted weekly and monthly peak loads obtained from these models is examined using Least Square Error (LSE). Actual reported data from NEPCO are used to analyze the performance of the proposed approach and the results are reported and compared with the results obtained from PSO algorithm and AR model.

No abstract

No abstract

No abstract