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Content Provider | IET Digital Library |
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Author | Sharma, Gulshan Panwar, Akhilesh Nasiruddin, Ibraheem Bansal, Ramesh C. |
Abstract | 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. |
Starting Page | 3510 |
Ending Page | 3517 |
Page Count | 8 |
ISSN | 17518687 |
Volume Number | 12 |
e-ISSN | 17518695 |
Issue Number | Issue 14, Aug (2018) |
Journal | IET Generation, Transmission & Distribution |
Publisher Date | 2018-05-17 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | 2-area System Models Automatic Gain Control Automatic Generation Control Scheme Control Design Control Engineering Computing Control System Analysis Control System Synthesis Function Estimation Problem Generation Rate Constraint Governor Dead-band Hybrid Power System Hydro-thermal Energy System Hydroelectric Power Stations And Plant Hydrothermal Power System Integral Based Controller Interpolation And Function Approximation Knowledge Engineering Technique Learning in AI Least Squares Approximation Least-squares SVM Multiarea Energy System Multilayer Perceptron Artificial Neural Network Neural Computing Technique Nonlinear LS-SVM Nonreheat Thermal Energy System Numerical Analysis Optimal Control Optimal Control Design Optimisation Optimisation Problem Optimisation Technique Pattern Recognition Phase And Gain Control Power Engineering Computing Power Generation Control Radial Basis Function Kernel Radial Basis Function Network Reheat Thermal Energy System Support Vector Machine Synthesis Method Thermal Power Stations And Plant Trained LS-SVM RBF-kernel-based AGC Design |
Content Type | Text |
Resource Type | Article |
Subject | Energy Engineering and Power Technology Control and Systems Engineering Electrical and Electronic Engineering |
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