【作者】 莫娜；

【导师】 田建设；

【作者基本信息】 华北电力大学（河北） ， 电力系统及其自动化， 2007， 硕士

【摘要】 针对常规模糊电力系统稳定器(C-FPSS)的适应性能差,不利于规则调整的缺点,提出了能在线调整量化因子的双模糊电力系统稳定器(D-FPSS)的设计方案。该控制器通过调整加权因子改变控制规则,再用优化的控制规则进行控制。在MATLAB环境下采用发电厂的参数进行非线性仿真,在不同故障运行条件下的结果分析表明D-FPSS具有较好的适应性和鲁棒性。在实际应用中,通过查表法在单片机上实现D-FPSS,不但能够对控制规则在线优化,而且成本低、设计简单,有实用价值。最后,结合神经网络的学习功能,设计了一个模糊神经网络电力系统稳定器(N-FPSS),仿真验证N-FPSS控制器在各种运行点和故障情况下均能保持良好的控制效果,具有较好动态品质。更多还原

【Abstract】 A double-fuzzy structure power system stabilizer(D-FPSS) is presented aim at the disadvantage of the common fuzzy PSS (C-FPSS) that contain bad steady capability, simple structure and make against the rule regulation. It has increased a fuzzy controller on C-FPSS basis and become double-fuzzy structure control system. It realizes control on-line according to rule optimized by adjusting weighted factor. Nonlinear simulation places the MATLAB with the parameters of the Power Plant under various fault conditions,and results indicates its strong adaptability. It is simple and feasible in the practical application,through looking up rule table to realize fuzzy inference in the kernel M68HC705P9 microcontroller .Finally,the fuzzy neural network PSS(N-FPSS),which combines fuzzy control and the self-study function of neural network,is proved the good dynamic quality by simulation.更多还原