高压电除尘节能优化控制系统的设计 .pdfVIP

学兔兔 总第5O卷 第566期 电测与仪表 VOI．5O N0．566 2013年 第2期 Electrical Measurement Instrumentation Feb．2013 高压电除尘节能优化控制系统的设计 李志军，杨明印 (河北工业大学控制科学与工程学院，天津300130) 摘要：为实现电除尘器的节能优化运行，介绍了一种控制系统中嵌入二次电压优化算法的设计方案。系统以出 口粉尘浓度作为除尘效率的判据，以一次侧输入功率作为除尘器能耗的判据，用全监督RBF神经网络建立出口 浓度一供电电压模型，采用遗传算法GA寻找最佳二次电压的设定值。通过控制算法与优化算法的有效结合，系 统能够快速的寻找最优二次电压并且跟踪此最优电压。该方案对提高电除尘器的除尘效率，降低能耗，有重要 的理论价值。 关键词：RBF~O经网络；遗传算法；系统设计；控制算法；火花控制 中图分类号：TM89 文献标识码：A 文章编号：1001—1390(2013)02—0083—04 Energy—saVing Optimization and Controller Design for Electrostatic Precipitator Equipment LI Zhi-jnn，YANG Ming-yin (School of Control Science and Engineering，Hebei University of Technology，Tianjin 300130，China) Abstract：In order to achieve the energy optimization of the ESP，thiS paper introduced a design which embedded secondary voltage optimization algorithm into control system．This system adopted the expoa of electrostatic precipitator’S dust concentration as basis of the dust removal efficiency and precipitator primary input power as basis of energy consumption．An ESP outlet concentration—power supply voltage model based upon entire supervised RBF neural network was established and used genetic algorithm to find the best secondary voltage value．The system can quickly find the optimal secondary voltage and track it，for combination of control algorithms and optimization algorithm．This solution is useful to design precipitator control system which will be more effective，and lower energy consumption． Key words：RBF neural network，GA，system design，control algorithm，spark control 0 引 言 1 高压电源控制系统优化方案 随着我国工业水平的提高，工业生产所产生的烟 晶闸管相控调压整流供电