Automatic Generation Control (AGC) of Wind Power System: An Least Squares-Support Vector Machine (LS-SVM) Radial Basis Function (RBF) Kernel Approach

Sharma Gulshan; Nasiruddin Ibraheem; Niazi K. R.; Bansal R. C.

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Automatic Generation Control (AGC) of Wind Power System: An Least Squares-Support Vector Machine (LS-SVM) Radial Basis Function (RBF) Kernel Approach

机译：风力发电系统的自动发电控制（AGC）：最小二乘支持向量机（LS-SVM）径向基函数（RBF）核方法

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The present paper discusses the structural, operational and control complexity of present day modern power systems in the wake of addition of electrical energy from wind turbines. The automatic generation control (AGC) problem of interconnected power system incorporating doubly fed induction generator (DFIG)-based wind turbines has been formulated and the investigations under various system operating conditions are presented. A two-area power system with DFIG-based wind turbines in each area are considered for the investigations. The system non-linearities such as governor dead-band and generation rate constraint are incorporated in the system dynamic model development. A novel AGC scheme using a non-linear least squares-support vector machine is proposed in the work. The proposed regulator is trained using a reliable data set consisting of wide range of operating conditions and area load changes generated by robust control technique. The results obtained with proposed regulator are compared with that achieved with multi-layer perceptron (MLP) and conventional PI regulators under various system operating conditions. The investigations carried out in the work have effectively demonstrated the superiority over MLP and conventional PI-based AGC regulators.

机译：本文讨论了随着风力涡轮机电能的增加，当今现代电力系统的结构，操作和控制的复杂性。提出了基于双馈感应发电机（DFIG）的风力发电机组的互联电力系统的自动发电控制（AGC）问题，并提出了在各种系统运行条件下的研究。考虑在每个区域中使用基于DFIG的风力涡轮机的两区域电力系统。系统动态模型开发中包含了系统非线性，例如调速器死区和发电率约束。在工作中，提出了一种使用非线性最小二乘支持向量机的新颖AGC方案。所建议的调节器是使用可靠的数据集进行训练的，该数据集包括各种运行条件以及通过鲁棒控制技术生成的区域负载变化。将拟议的调节器获得的结果与多层感知器（MLP）和常规PI调节器在各种系统操作条件下获得的结果进行比较。在这项工作中进行的调查有效地证明了优于MLP和传统的基于PI的AGC调节器。

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来源
《Electric Power Components and Systems》 |2018年第15期|1621-1633|共13页
作者
Sharma Gulshan; Nasiruddin Ibraheem; Niazi K. R.; Bansal R. C.;
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作者单位

Durban Univ Technol, Dept Elect Power Engn, Durban, South Africa;

Qassim Univ, Dept Elect Engn, Qassim Engn Coll, Buraydah, Saudi Arabia;

MNIT, Dept Elect Engn, Jaipur, Rajasthan, India;

Univ Pretoria, Dept Elect Elect & Comp Engn, Room 14-27,Engn Bldg 1, Pretoria, South Africa;

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正文语种 eng
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关键词
automatic generation control; DFIG support wind turbines; robust control; ANN; SVM; LS-SVM; RBF kernel; ANN; SVM; LS-SVM; RBF kernel;

机译：自动发电控制;DFIG支持风机;鲁棒控制;ANN;SVM;LS-SVM;RBF内核;ANN;SVM;LS-SVM;RBF内核;

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1. Non-linear LS-SVM with RBF-kernel-based approach for AGC of multi-area energy systems [J] . Gulshan Sharma, Akhilesh Panwar, Ibraheem Nasiruddin, Generation, Transmission & Distribution, IET . 2018,第14期

机译：基于RBF核的非线性LS-SVM用于多区域能源系统的AGC
2. An improved radial basis function neural network control strategy-based maximum power point tracking controller for wind power generation system [J] . Sitharthan R., Parthasarathy T., Rani S. Sheeba, Transactions of the Institute of Measurement and Control . 2019,第11期

机译：一种改进的径向基函数神经网络控制策略的风力发电系统最大功率点跟踪控制器
3. Adaptive data-driven nonlinear synchro squeezed transform with single class radial basis function kernel support vector machine applied to wind turbine planetary gearbox fault diagnostics [J] . Maheswari R. Uma, Umamaheswari R. Proceedings of the Institution of Mechanical Engineers . 2020,第7期

机译：自适应数据驱动的非线性同步挤压变换与单级径向基函数内核支持向量机应用于风力涡轮机行星齿轮箱故障诊断
4. Radial Basis Function (RBF) tuned Kernel Parameter of Agarwood Oil Compound for Quality Classification using Support Vector Machine (SVM) [C] . Mohamad Amirul Aiman Ngadilan, Nurlaila Ismail, Mohd Hezri Fazalul Rahiman, IEEE Control and System Graduate Research Colloquium . 2018

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5. Support vector machines, radial basis functions neural networks, and fuzzy granulation in pattern recognition. [D] . Ramirez, Lino Martin. 2002

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6. Leaf identification using radial basis function neural networks and SSA based support vector machine [O] . Ali Ahmed, Sherif E. Hussein 2020

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7. Detecção de mudanças em dados de imagens multitemporais de sensoriamento remoto empregando Support Vector Machines com o uso de kernel polinomial e kernel RBF (kernel função de base radial) [O] . Rute Henrique da Silva FERREIRA, Neide Pizzolato ANGELO 2018

机译：利用多项式内核和内核RBF采用支持向量机的多立体遥感图像数据的变化检测（内核径向基础函数）

Automatic Generation Control (AGC) of Wind Power System: An Least Squares-Support Vector Machine (LS-SVM) Radial Basis Function (RBF) Kernel Approach

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