Based on the system dynamic model, a full system dynamics estimation method is proposed for a chain shell magazine driven by a permanent magnet synchronous motor(PMSM). An adaptive extended state observer(AESO) is pro...Based on the system dynamic model, a full system dynamics estimation method is proposed for a chain shell magazine driven by a permanent magnet synchronous motor(PMSM). An adaptive extended state observer(AESO) is proposed to estimate the unmeasured states and disturbance, in which the model parameters are adjusted in real time. Theoretical analysis shows that the estimation errors of the disturbances and unmeasured states converge exponentially to zero, and the parameter estimation error can be obtained from the extended state. Then, based on the extended state of the AESO, a novel parameter estimation law is designed. Due to the convergence of AESO, the novel parameter estimation law is insensitive to controllers and excitation signal. Under persistent excitation(PE) condition, the estimated parameters will converge to a compact set around the actual parameter value. Without PE signal, the estimated parameters will converge to zero for the extended state. Simulation and experimental results show that the proposed method can accurately estimate the unmeasured states and disturbance of the chain shell magazine, and the estimated parameters will converge to the actual value without strictly continuous PE signals.展开更多
文摘Based on the system dynamic model, a full system dynamics estimation method is proposed for a chain shell magazine driven by a permanent magnet synchronous motor(PMSM). An adaptive extended state observer(AESO) is proposed to estimate the unmeasured states and disturbance, in which the model parameters are adjusted in real time. Theoretical analysis shows that the estimation errors of the disturbances and unmeasured states converge exponentially to zero, and the parameter estimation error can be obtained from the extended state. Then, based on the extended state of the AESO, a novel parameter estimation law is designed. Due to the convergence of AESO, the novel parameter estimation law is insensitive to controllers and excitation signal. Under persistent excitation(PE) condition, the estimated parameters will converge to a compact set around the actual parameter value. Without PE signal, the estimated parameters will converge to zero for the extended state. Simulation and experimental results show that the proposed method can accurately estimate the unmeasured states and disturbance of the chain shell magazine, and the estimated parameters will converge to the actual value without strictly continuous PE signals.
文摘电站辅机设备健康状态评估与故障预警对新型电力系统火电机组的安全运行具有重要意义。以某超临界660 MW火电机组送风机为研究对象,提出了一种基于多重特征参数的送风机故障模型动态记忆矩阵构建方法,该方法可在确保计算结果精度的同时有效提升模型计算速度。同时引入权重系数改进多元状态估计(multivariate state estimation technique,MSET)算法,提出了一种权重系数计算方法;采用总体相似度和参数相似度指标进行故障预警和定位,构建了基于动态记忆矩阵和加权MSET算法的送风机故障预警模型。运用该模型对送风机故障进行仿真,仿真结果表明:加权MSET算法不仅能够有效提高故障工况下异常参数的预测精度,还能降低异常参数对正常参数预测结果的影响,进而在实现送风机故障提前预警的同时准确定位出故障点参数。
