There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Integrating commercial demand response resources with unit commitment

A new data classification method based on chaotic particle swarm optimization and least square-support vector machine

An improved Shapley value-based profit allocation method for CHP-VPP

Novel approaches using evolutionary computation for sparse least square support vector machines

This study presents a new method for reactive power tracing in a pool-based power system by introducing the hybrid genetic algorithm and least squares support vector machine (GA–LSSVM). The idea is to use GA to obtain the optimal values of regularisation parameter, γ, and kernel radial basis function (RBF) parameter, σ2, and adopt a supervised learning approach to train the LSSVM model. The technique that uses proportional sharing method (PSM) is used as a teacher. To obtain a lossless system, the concept of virtual load is proposed. Prior to that, the equivalent transmission line model is introduced. It integrates the nodal reactive power with the power produced by shunt admittances. Based on power-flow solution and reactive power tracing procedure by PSM, the description of inputs and outputs for training and testing data is created. The generators’ shares to reactive loads in the test system are expected can be determined accurately by proposed GA–LSSVM model. In this study, five-bus system is used to illustrate the concept of virtual load and equivalent transmission line model whereas the 25-bus equivalent system of southern Malaysia is used to illustrate the effectiveness of the proposed GA–LSSVM model compared to PSM and artificial neural network.

Reactive power tracing in pool-based power system utilising the hybrid genetic algorithm and least squares support vector machine

This paper proposes a new method to trace the transmission loss in deregulated power system by applying Genetic Algorithm (GA) and Least Squares Support Vector Machine (LS-SVM). The idea is to use GA as an optimizer to find the optimal values of hyper-parameters of LS-SVM and adopt a supervised learning approach to train the LS-SVM model. The well known proportional sharing method (PSM) is used to trace the loss at each transmission line which is then utilized as a teacher in the proposed hybrid technique called GA-SVM method. Based on load profile as inputs and PSM output for transmission loss allocation, the GA-SVM model is expected to learn which generators are responsible for transmission losses. In this paper, IEEE 14-bus system is used to show the effectiveness of the proposed method.

An application of Genetic Algorithm and Least Squares Support Vector Machine for tracing the transmission loss in deregulated power system

Transmission power loss has become a progressing issue these days due to the changes from monopolized to deregulation environment. This can be due to the large transmission power loss which leads to monetary losses. The presence of deregulated power system requires the participants to make decision on transmission line charge. Hence it is necessary to have a method to determine fair, transparent and equitable charges among market participants respectively. This paper proposed a heuristic technique termed as Artificial Bee Colony (ABC) algorithm for real power line allocation for solving the line loss allocation among market participants. In this study ABC Algorithm optimization engine is utilized to solve the real power loss allocation. The proposed technique was validated on IEEE 30 Bus Reliability Test System. Results obtained from the study indicated that the proposed ABC algorithm technique is superior as compared to Particle Swarm Optimization (PSO) technique.

Implementation of Artificial Bees Colony algorithm on real power line loss allocation

This paper attempts to allocate the generators' contributions to loads in pool based power system by incorporating the Least Squares Support Vector Machine (LS-SVM). The idea is to use supervised learning approach to train the LS-SVM. The technique that uses proportional tree method (PTM) which is applying the convention of proportional sharing principle is utilized as a teacher. Based on converged load flow and followed by PTM for power tracing procedure, the description of inputs and outputs of the training data for the LS-SVM are created. The LS-SVM will learn to identify which generators are supplying to which loads. The proposed technique is demonstrated using IEEE 14-bus system to illustrate the effectiveness of the LS-SVM technique compared to that of the PTM. The comparison result with Artificial Neural Network (ANN) technique is also will be discussed.

S. N. Abd. Khalid

Papers

Reactive power tracing in pool-based power system utilising the hybrid genetic algorithm and least squares support vector machine

An application of Genetic Algorithm and Least Squares Support Vector Machine for tracing the transmission loss in deregulated power system

Implementation of Artificial Bees Colony algorithm on real power line loss allocation

Determination of generators' contributions to, loads in pool based power system using Least Squares Support Vector Machine