Optimal Power Flow Solution Using Self-Evolving Brain-Storming Inclusive Teaching-Learning-Based Algorithm

摘要：

　　In this paper, a new hybrid self-evolving algorithm is presented with its application to a highly nonlinear problem in electrical engineering.The optimal power flow problem described here focuses on the minimization of the fuel costs of the thermal units while maintaining the voltage stability at each of the load buses.There are various restrictions on acceptable voltage levels, capacitance levels of shunt compensation devices and transformer taps making it highly complex and nonlinear.The hybrid algorithm discussed here is a com bination of the learning principles from Brain Storming Optimization algorithm and Teaching-Learning-Based Optimization algorithm, along with a self evolving principle applied to the control parameter.The strategies used in the proposed algorithm makes it self-adaptive in performing the search over the multi-dimensional problem domain.The results on an IEEE 30 Bus system indicate that the proposed algorithm is an excellent candidate in dealing with the optimal power flow problems.

关键词： Brain-Storming Optimization Non-dominated sorting Optimal power flow Teaching-learning-based optimization

作者: K.R.Krishnanand Syed Muhammad Farzan Hasani Bijaya Ketan Panigrahi Sanjib Kumar Panda

作者单位: Electrical and Computer Engineering, National University of Singapore,Singapore Department of Electrical Engineering, Indian Institute of Technology, Delhi, India

会议类型: 国际会议

会议名称: 4th international Conference,ICSI2013(第4届群体智能国际会议)

会议地点: 哈尔滨

会议语种:英文

页码: 338-345

在线出版日期: 2013-06-12（万方平台首次上网日期，不代表论文的发表时间）