%0 Electronic Article %A Gulshan Sharma %A Akhilesh Panwar %A Ibraheem Nasiruddin %A Ramesh C. Bansal %K hydro-thermal energy systems %K 2-area system models %K integral based controller %K function estimation problems %K optimisation problem %K reheat thermal energy systems %K pattern recognition %K nonlinear LS-SVM %K least-squares SVM %K multiarea energy systems %K governor dead-band %K generation rate constraint %K nonreheat thermal energy systems %K trained LS-SVM RBF-kernel-based AGC design %K optimal control design %K support vector machine %K automatic generation control scheme %K radial basis function kernel %K control design %K multilayer perceptron artificial neural network %X 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. %@ 1751-8687 %T Non-linear LS-SVM with RBF-kernel-based approach for AGC of multi-area energy systems %B IET Generation, Transmission & Distribution %D August 2018 %V 12 %N 14 %P 3510-3517 %I Institution of Engineering and Technology %U https://digital-library.theiet.org/;jsessionid=37ib7m8uwl397.x-iet-live-01content/journals/10.1049/iet-gtd.2017.1402 %G EN