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Support vector machine (SVM) is very efficacious in pattern recognition and function estimation problems. However, its application to design efficient automatic generation control (AGC) scheme has not been investigated till date by the researchers and power engineers worldwide. Hence, in this study an early attempt has been made to present a combined design of optimal control and optimisation problem of least-squares SVM (LS-SVM)-based AGC of multi-area energy systems. The radial basis function (RBF) kernel is used to train the LS-SVM. To validate the efficacy of the approach, the proposed control method is implemented on various 2-area system models such as non-reheat thermal, reheat thermal and hydro-thermal energy systems. The trained LS-SVM RBF-kernel-based AGC is realised with 1% load perturbation in one of the control area and the obtained results are compared with multi-layer perceptron artificial neural network and the conventional integral based controller in order to show the supremacy of the proposed control design. Furthermore, the performance of the AGC design is examined considering the system non-linearities such as governor dead-band and generation rate constraint.
Inspec keywords: power engineering computing; hydrothermal power systems; least squares approximations; control system synthesis; power generation control; support vector machines; optimisation; radial basis function networks; optimal control; automatic gain control; learning (artificial intelligence); control engineering computing; hybrid power systems
Other keywords: governor dead-band; nonreheat thermal energy systems; radial basis function kernel; trained LS-SVM RBF-kernel-based AGC design; hydro-thermal energy systems; function estimation problems; generation rate constraint; optimisation problem; multilayer perceptron artificial neural network; nonlinear LS-SVM; least-squares SVM; integral based controller; 2-area system models; automatic generation control scheme; control design; multiarea energy systems; support vector machine; pattern recognition; optimal control design; reheat thermal energy systems
Subjects: Knowledge engineering techniques; Optimal control; Power engineering computing; Interpolation and function approximation (numerical analysis); Interpolation and function approximation (numerical analysis); Control system analysis and synthesis methods; Thermal power stations and plants; Hydroelectric power stations and plants; Optimisation techniques; Phase and gain control; Optimisation techniques; Neural computing techniques