基于RBF的深度调峰机组电机变频控制优化

RBF-Based Motor Frequency Conversion Control Optimization of Deep Peak-Shaving Unit

针对东北某电厂600 MW机组深度调峰期间给水泵电机变频控制出现的给水流量不精确、调速差、实时性差、易造成安全隐患等问题,在1号机组原有变频方案的基础上,提出采用RBF模糊神经网络与SVPWM相结合的控制方案对还未改造的2号机组电机进行优化设计。仿真结果表明:在50%负荷运行条件下,与传统PID-SVPWM相比,RBF-SVPWM策略达到稳态的响应时间缩短了40%;电磁转矩、转速最大超调量分别降低25.7%、3.33%,它们的稳态波动幅度均下降了50%。证明了RBF-SVPWM策略适用于600 MW给水泵电机且能有效提高系统运行的精确性、快速性和稳定性;为同类型机组的变频改造提供参考。

Aiming at the problems of inaccurate feed water flow,poor speed regulation,poor real-time performance,and potential safety hazards during the deep peak regulation of the feed water pump motor,with the deep peak regulation of a 600 MW unit in a power plant in Northeast China,based on the original frequency conversion scheme of Unit 1,a technique of RBF fuzzy neural network combined with SVPWM was proposed to optimize the design of the motor of Unit 2 that has not yet been transformed.The simulation results show that:Under 50%load operation conditions,compared with the traditional PID-SVPWM,the steady-state response time of the RBF-SVPWM strategy is shortened by 40%;the maximum overshoot of electromagnetic torque and rotor speed are reduced by 25.7%and 3.33%,respectively.The amplitude of steady-state fluctuations both have been reduced by 50%.It proves that the RBF-SVPWM strategy is suitable for 600 MW feed water pump motors and can effectively improve the accuracy,rapidity and stability of system operation;it provides a reference for the frequency conversion transformation of the same type of units.