Saifulnizam Abd. Khalid

Power flow allocation method with the application of hybrid genetic algorithm-least squares support vector machine

This paper proposes a new power flow allocation method in pool based power system with the application of hybrid genetic algorithm (GA) and least squares support vector machine (LS-SVM), namely GA-SVM. GA is utilized to find the optimal values of regularization parameter, γ and Kernel RBF parameter, σ2, which are embedded in LS-SVM model so that the power flow allocation problem can be solved by using machine learning adaptation approach. The supervised learning paradigm is used to train the LS-SVM model where the proportional sharing principle (PSP) method is utilized as a teacher. Based on converged load flow and followed by PSP technique for power tracing procedure, the description of inputs and outputs of the training data are created. The GA-SVM model will learn to identify which generators are supplying to which loads. In this paper, the 25-bus equivalent system of southern Malaysia is used to illustrate the proposed method. The comparison result with artificial neural network (ANN) technique is also will be presented.

Real power transfer allocation via Continuous Genetic Algorithm-Least Squares Support Vector Machine technique

This paper proposes a new hybrid technique, Continuous Genetic Algorithm and Least Squares Support Vector Machine to allocate the real power transfer from generators to loads, namely CGA-LSSVM. CGA is used to obtain the optimal value of hyper-parameters of LS-SVM and supervised learning approach is adopted in the training of LS-SVM model. The technique that uses proportional sharing principle (PSP) is utilized as a teacher. Based on load profile of the system and followed by PSP technique for power tracing procedure, the description of inputs and outputs of the training data are created. The CGA-LSSVM is expected to be able to assess which generators are supplying to which specific loads. In this paper, the 25-bus equivalent system of southern Malaysia is used to illustrate the effectiveness of the CGA-LSSVM technique compared to that of the PSP technique.

Tracing the real power transfer of individual generators to loads using Least Squares Support Vector Machine with Continuous Genetic Algorithm

This paper attempts to trace the real power transfer of individual generators to loads in pool based power system by incorporating the hybridization of Least Squares Support Vector Machine (LS-SVM) with Continuous Genetic Algorithm (CGA)- CGA-LSSVM. The idea is to use CGA to find the optimal values of regularization parameter, γ and Kernel RBF parameter, σ2, and adapt a supervised learning approach to train the LS-SVM model. The technique that uses proportional sharing principle (PSP) is utilized as a teacher. Based on converged load flow and followed by PSP technique for power tracing procedure, the description of inputs and outputs of the training data are created. The CGA-LSSVM will learn to identify which generators are supplying to which loads. In this paper, the 25-bus equivalent system of southern Malaysia is used to illustrate the effectiveness of the CGA-LSSVM technique compared to that of the PSP technique.

An ANFIS Approach for Real Power Transfer Allocation

This paper proposes an adaptive neurofuzzy interface system (ANFIS) approach to identify the real power transfer between generators. Based on solved load flow results, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to train the designed ANFIS. It also incorporated an enhanced feature extraction method called principle component analysis (PCA) to reduce the input features to the ANFIS. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of the ANFIS output compared to that of the MNE method. The ANFIS output provides promising results in terms of accuracy and computation time. Furthermore, it can be concluded that the ANFIS with enhanced feature extraction method reduces the time taken to train the ANFIS without affecting the accuracy of the results.

Preference comparison of ai power tracing techniques for deregulated power markets

This paper compares the two preference artificial intelligent (AI) techniques, namely, artificial neural network (ANN) and genetic algorithm optimized least square support vector machine (GA-LSSVM) approach, to allocate the real power output of individual generators to system loads. Based on solved load flow results, it first uses modified nodal equation method (MNE) 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 the AI techniques compared to those of the MNE method. The AI methods provide the results in a faster and convenient manner with very good accuracy.

Hybrid Genetic Algorithm-Support Vector Machine Technique for Power Tracing in Deregulated Power Systems

The electric power industry is under deregulation in response to changes in the law, technology, market and competition. The aim of deregulation is to optimize the system welfare by introducing competitive environment, mainly among the power producers. Developing fair and transparent power flow and loss allocation method has been an active topic of research, with many transactions taking place at any time.

Application of ANN to Real and Reactive Power Allocation Scheme

This chapter describes the implementation of ANN for real and reactive power transfer allocation. The 25 bus equivalent power system of south Malaysia region and IEEE 118 bus system are used to demonstrate the applicability of the ANN output compared to that of the Modified Nodal Equations (MNE) which is used as trainers for real and reactive power allocation. The basic idea is to use supervised learning paradigm to train the ANN. Then the descriptions of inputs and outputs of the training data for the ANN are easily obtained from the load flow results and each method used as teachers respectively. The proposed ANN based method provides promising results in terms of accuracy and computation time. Artificial intelligence has been proven to be able to solve complex processes in deregulated power system such as loss allocation. So, it can be expected that the developed methodology will further contribute in improving the computation time of transmission usage allocation for deregulated system.