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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS)
Balasim M. Hussein
University Of Diyala
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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A new scaled fuzzy method using PSO segmentation (SePSO) applied for two area power system Balasim M. Hussein
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 2: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (919.861 KB) | DOI: 10.11591/ijece.v9i2.pp815-825

Abstract

The balance of the power supply and demand (frequency control) is one of the most ancient approaches for the power systems, which is considered as a highly complex system.The power systems frequency response is a perfect indicator of the resilience to the multi-disturbances. In this work, the fuzzy logichas been scaledusing PSO segmentation (SePSO) and suggested to get high performance of frequency stability. PSO has participated into multi-segments for calculating the scald-fuzzy membership with basic rules. Two identical interconnectedpower areas wereselected to exam the new scaled fuzzy method. The time response of the results has undertaken the effectiveness of the controller reactionusing the MATLAB Simulink. The work feed back proved that the proposed SePSO optimization for the controlhas significantly faster with low undershot concerningthe classical controllers in differenttime schedules and disturbance values.
Short term load forecasting using evolutionary algorithm for Tajikistan Balasim M. Hussein; Hatim Ghadhban Abood; Mahmadjonov Firuz; Ivan Ivanovich Nadtoka
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i3.pp1894-1900

Abstract

Load forecasting is a significant element in the energy management system of power systems. Precise load forecasting aids electric utilities to conduct decisions of unit commitment, reduction of spinning reserve capacity, and schedule device maintenance plan. Furthermore, load forecasting contributes to reducing the generation cost, and it is fundamental to the reliability of the power systems. On the other hand, short-term load forecasting is substantial for economic running. The forecasting precision directly affects the reliability, economy running and supplying power quality of the power system. Hence, finding the required load forecasting method to enhance the accuracy is valuable for forecasting precision. This paper proposed particle swarm optimization (PSO) to improve working support vector machine (SVM), SVM regression model is derived; also derived SVM with PSO. Support vector machine (SVM) model is adopted with and without PSO based on the historical load data and meteorological data of Tajikistan country, analysis the various factors affecting the forecast. The historical data and the load forecasting factors to be considered are normalized. The two parameters of SVM significantly influenced the model, and therefore it optimized using evolutionary algorithm.
Co-Authors Hatim Ghadhban Abood Ivan Ivanovich Nadtoka Mahmadjonov Firuz