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1 AUHTOR CONTACT INFORMATION 作者联系信息 Note the information in this page is only used for contact purpose. It will not appear in the final version of your paper. 注意：本页信息只作为联系用途，并不会出现您的论文最终稿中 The information of corresponding author (通讯作者信息) Name (姓名)： Cell Phone ()： E-mail (电子邮件)： Please provide us the above information in order to contact with you. 请务必准确提供以上信息，以便我们联系您 2 ARTIFICIAL INTELLIGENCE BASED TUNING OF SVC CONTROLLER FOR CO-GENERATED POWER SYSTEM 1FIRST AUTHOR, 2SECOND AUTHOR 1Asstt Prof., Department of Electrical Engineering, xxx, YYY 2Assoc. Prof., Department of Electrical Engineering, xxx, YYY E-mail: 1xxx@yahoo.co.in, 2xxx@ ABSTRACT The gain of SVC depends upon the type of reactive power load for optimum performance. As the load and input wind power conditions are variable, the gain setting of SVC needs to be adjusted or tuned. In this paper, an ANN based approach has been used to tune the gain parameters of the SVC controller over a wide range of load characteristics. The multi-layer feed-forward ANN tool with the error back-propagation training method is employed. Loads have been taken as the function of voltage. Analytical techniques have mostly been based on impedance load reduced network models, which suffer from several disadvantages, including inadequate load representation and lack of structural integrity. The ability of ANNs to spontaneously learn from examples, reason over inexact and fuzzy data and provide adequate and quick responses to new information not previously stored in memory has generated high performance dynamical system with unprecedented robustness. ANNs models have been developed for different hybrid power system configurations for tuning the proportional-integral controller for SVC. Transient responses of different autonomous configurations show that SVC controller with its gained tuned by the ANNs provide optimum system performance for a variety of loads. Keywords: Artificial Neural Network (ANN), Static Var Compensator (SVC), Autonomous Hybrid Power System (AHPS) 1.INTRODUCTION Applications of ANN to power systems are a growing area of interest. Considerable efforts have been placed on the applications of ANNs to power systems. Several interesting applications of ANNs to power system problems [1]-[5], indicate that ANNs have great potential in power system on-line and off-line applications. The feature of an ANN is its capability to solve a complicated problem very efficiently because the knowledge about the problem is distributed in the neurons and the connection weights of links between neurons, and information are processed in parallel. Back-propagation is an iterative, gradient search, supervised algorithm which can be viewed as multiplayer non-linear method that can re-code its input space in the hidden layers and thereby solve hard learning problems. The network is trained using ANN technique until a good agreement between predicted gain settings and actual gains is reached. Fig. 1. (a) first picture; (b) second picture During last three decades, the assessment of potential of the sustainable eco-friendly alternative sources and refinement in technology has taken place to a stage so that economical and reliable power can be produced. Different renewable sources are available at different geographical locations close to loads, therefore, the latest trend is to have distributed or dispersed power system. Examples of such systems are wind-diesel, wind- diesel-micro-hydro-system with or without multiplicity of generation to meet the load demand. 3 These systems are known as hybrid power systems. To have automatic reactive load voltage control SVC device have been considered. The multi-layer feed-forward ANN toolbox of MATLAB 6.5 with the error back-propagation training method is employed. Table 1. An example of a table An example of a column heading Column A (t) Column B (T) And an entry12 And another entry34 And another entry56 2.TRAINING OF ANN PARAMETERS The input to the ANN is the value of exponent of reactive power load-voltage characteristic (nq) and the output is the desired proportional gain (KP) and integral gain (KI) parameters of the SVC. Normalized values of nq are fed as the input to the ANN the normalized values of outputs are converted into the actual value. The process of REFRENCES: [Author Name(s), Paper Title, Conference/Journal Title (Vol/Issue), Date, Page Numbers] [1] T.S. Bhatti, R.C. Bansal, and D.P. Kothari, “Reactive Power Control of Isolated Hybrid Power Systems”, Proceedings of International Conference on Computer Application in Electrical Engineering Recent Advances (CERA), Indian Institute of Technology Roorkee (India), February 21-23, 2002, pp. 626- 632. [2] B.N. Singh, Bhim Singh, Ambrish Chandra, and Kamal Al-Haddad, “Digital Implementation of an Advanced Static VAR Compensator for Voltage Profile Improvement, Power Factor Correction and Balancing of Unbalanced Reactive Loads”, Electric Power Energy Research, Vol. 54, No.。