Combined Economic and Emission Dispatch using RBF neural network
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 computer science topics Active In SP Posts: 610 Joined: Jun 2010 22-06-2010, 09:35 PM   Combined Economic and Emission Dispatch using RBF neural network.ppt (Size: 1.39 MB / Downloads: 143) Combined Economic and Emission Dispatch using RBF neural network Under the guidance of Dr. N. Subrahmanyam Presented by B. Shiva Kumar Topics Â¢ Introduction Â¢ CEED Â¢ RBF Â¢ Algorithms Â¢ OVERALL PROCESS DESCRIPTION Â¢ CEED USING LAMBDA TECHNIQUE Â¢ CLUSTERING TECHNIQUE Â¢ CEED USING RBF Â¢ Case studies Â¢ 3GEN Â¢ 6GEN Â¢ 15GEN Â¢ Conclusion Introduction of work Â¢ Formulating Combined economic and emission dispatch using RBF network Â¢ comparing results using BPA network and conventional lambda technique Combined Economic and Emission Dispatch The multi-objective CEED problem is converted into single optimization problem by introducing price penalty factor, h Minimize F = F + hpd* E Â¢ The modified price penalty factor hpd, combines the emission with the normal fuel costs Price Penalty Factor Â¢ The price penalty factor for a particular load demand PD : Â¢ Find the ratio between maximum fuel cost and maximum emission of each generator. Â¢ sort hi values in ascending order. Â¢ Add the maximum capacity of each unit (Pi,max) one at a time, starting from the smallest hi unit until Pi,max = PD Â¢ At this stage, hi associated with the last unit in the process is the price penalty factor for the given load. Modified Price Penalty Factor Â¢ The computation steps for hpd : Â¢ Find the ratio between maximum fuel cost and maximum emission of each generator Â¢ sort hi values in ascending order Â¢ Form an array, m by adding Pi,max one by one from the lowest hi value unit Â¢ Add the elements of mi one at a time, starting from the smallest hi unit until S m > PD Â¢ The modified price penalty factor hpd is computed by interpolating the values of hi for last two units by satisfying the corresponding load demand. Numerical example Â¢ hi = [h3 h2 h1] ; hi = [1.1909 2.6221 3.1057] Â¢ The corresponding maximum limits of generating units are Pi,max = [180 150 200] Â¢ m is formed by adding maximum capacity of the units one by one m = [180 330 530] Â¢ For PD = 259MW; (180+330) MW >259MW Â¢ The modified price penalty factor hm is computed by interpolating the values of hi for last two units by satisfying the corresponding load demand. ie, hpd = 1.1909 + ((2.6221- 1.1909)/(330- 180))*(259- 180) = 1.9446 MEMORISING FOR TRAINING Â¢ Weight training: 1. First run the process with weights randomised with restrictions on itermax and epsilon 2. Repeat above step for a few trials say 6 - 9 trials and memorize all the finalised weights and Errorrate after complete training 3. Choose the best trial that is most successful among all trials 4. then run process using finalised weights after best run trial as initial set of weights instead of taking randomised weights 5. Repeation of above step 4 for itself for a few times (say 5 â€œ 10) improves convergence time Conclusion Â¢ Combined economic and emission dispatch is formulated using RBF network. â€œ Centres were apart â€œ Memorising weights and centres, results in faster convergence â€œ Network is tested for 3gen, 6gen, 15gen systems Â¢ Can be combined with Unit Commitment Problem for complete study References An RBF Network With OLS and EPSO Algorithms for Real-Time Power Dispatch by Chao-Ming Huang and Fu-Lu Wang IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 22, NO. 1, FEBRUARY 2007 Â¢ R. Ramanathan, Emission constrained economic dispatch, IEEE Trans. Power Syst., vol. 9, no. 4, pp. 1994â€œ2000, Nov. 1994. Â¢ P. S. Kulkarni, A. G. Kothari, and D. P. Kothari, Combined economic and emission dispatch using improved backpropagation neural network, Elect. Mach. Power Syst., vol. 28, no. 1, pp. 31â€œ44, Jan. 2000. Â¢ P. S. Kulkarni, A. G. Kothari, and D. P. Kothari, Application of radial basis function neural network for economic dispatch, J. Inst. Eng. (India): Elect. Eng. Div., vol. 83, pp. 81â€œ86, Sep. 2002. Â¢ Solutions to Practical Unit Commitment Problems with Operational, Power Flow and Environmental Constraints I. Jacob Raglend and Narayana Prasad Padhy IEEE 2006 Â¢ Economic Power Dispatch Of Power System With Pollution Control Using Multiobjective Particle Swarm Optimization Tarek Bouktir, Rafik Labdani and Linda Slimani June 2007 University of Sharjah Journal of Pure & Applied Sciences Volume 4, No. 2 Use Search at http://topicideas.net/search.php wisely To Get Information About Project Topic and Seminar ideas with report/source code along pdf and ppt presenaion