Journal ArticleDOI
Reactive power tracing in pool-based power system utilising the hybrid genetic algorithm and least squares support vector machine
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TLDR
In this article, a hybrid GA and least squares support vector machine (GA-LSSVM) was used for reactive power tracing in a pool-based power system by introducing the hybrid genetic algorithm and least square support vector machines (GA and LSSVM), where GA was used to obtain the optimal values of regularisation parameter, γ, and kernel radial basis function (RBF) parameter, σ2, and adopted a supervised learning approach to train the LSSV model.Abstract:
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.read more
Citations
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Journal ArticleDOI
Integrating commercial demand response resources with unit commitment
TL;DR: In this paper, a dynamic approach is proposed for participating DR service providers in power markets in order to maximize their profits by employing the proposed DRPs model as a new concept in electricity market.
Journal ArticleDOI
Novel approaches using evolutionary computation for sparse least square support vector machines
TL;DR: Two new approaches to building sparse least square support vector machines (LSSVM) based on genetic algorithms (GAs) for classification tasks are introduced to leave out outliers, non-relevant patterns or those ones which can be corrupted with noise and thus prevent classifiers to achieve higher accuracies along with a reduced set of support vectors.
Journal ArticleDOI
Reactive power cost allocation by power tracing based method
Mala De,Swapan Kumar Goswami +1 more
TL;DR: In this paper, a reactive power cost allocation method based on power tracing principle is proposed to solve the problem of bidirectional reactive power flow to enable the application of power tracing method to reactive power flows.
Proceedings ArticleDOI
LS-SVM hyper-parameters optimization based on GWO algorithm for time series forecasting
TL;DR: A new metaheuristic algorithm, viz., Grey Wolf Optimizer (GWO), is employed to optimize the parameters of interest, and empirical results suggested that the GWO-LSSVM is capable to produce lower error rates as compared to the identified algorithms for the price of interested time series data.
Journal ArticleDOI
Random fully connected layered 1D CNN for solving the Z-bus loss allocation problem
TL;DR: This study presents a novel convolutional neural network architecture that is highly effective for z-bus loss allocation that uses the Z-bus matrix as input is 1D and the performance of it is higher than other state-of-the-art methods.
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