Application of artificial intelligent systems for real power transfer allocation
来源期刊：中南大学学报(英文版)2014年第7期
论文作者：Shareef Hussain Abd. Khalid Saifulnizam Sulaiman Herwan Mohd Mustafa Wazir Mohd
文章页码：2719 - 2730
Key words：artificial intelligence; power tracing; support vector machine; power system deregulation
Abstract: The application of various artificial intelligent (AI) techniques, namely artificial neural network (ANN), adaptive neuro fuzzy interface system (ANFIS), genetic algorithm optimized least square support vector machine (GA-LSSVM) and multivariable regression (MVR) models was presented to identify the real power transfer between generators and loads. These AI techniques adopt supervised learning, which first uses modified nodal equation (MNE) method to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to estimate the power transfer using AI techniques. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of various AI methods compared to that of the MNE method.
Shareef Hussain1, Abd. Khalid Saifulnizam2, Sulaiman Herwan Mohd3, Mustafa Wazir Mohd2
(1. Faculty of Electrical and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia;
2. Department of Electrical Power System, Faculty of Electrical Engineering,
Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia;
3. School of Electrical System Engineering, University Malaysia Perlis (UniMAP), Perlis,
Kangar 01000, Malaysia)
Abstract:The application of various artificial intelligent (AI) techniques, namely artificial neural network (ANN), adaptive neuro fuzzy interface system (ANFIS), genetic algorithm optimized least square support vector machine (GA-LSSVM) and multivariable regression (MVR) models was presented to identify the real power transfer between generators and loads. These AI techniques adopt supervised learning, which first uses modified nodal equation (MNE) method to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to estimate the power transfer using AI techniques. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of various AI methods compared to that of the MNE method.
Key words:artificial intelligence; power tracing; support vector machine; power system deregulation
