Terminal Sliding Mode Controllers for Hydraulic Turbine Governing System with Bifurcated Penstocks under Input Saturation

Terminal sliding mode controller method is introduced to enhance the regulation performance of the hydraulic turbine governing system(HTGS).For the purpose of describing the characteristics of controlled system and deducing the control rule,a nonlinear mathematic model of hydraulic turbine governing system with bifurcated penstocks(HTGSBF)under control input saturation is established,and the input/output state linearization feedback approach is used to obtain the relationship between turbine speed and controller output.To address the control input saturation problem,an adaptive assistant system is designed to compensate for controller truncation.Numerical simulations have been conducted under fixed point stabilization and periodic orbit tracking conditions to compare the dynamic performances of proposed terminal sliding mode controllers and conventional sliding mode controller.The results indicate that the proposed terminal sliding mode controllers not only have a faster response and accurate tracking results,but also own a stronger robustness to the system parameter variations.Moreover,the comparisons between the proposed terminal sliding mode controllers and current most often used proportional-integral-differential(PID)controller,as well its variant NPID controller,are discussed at the end of this paper,where the superiority of the terminal sliding mode controllers also have been verified.

Research on Power Consumption Anomaly Detection Based on Fuzzy Clustering and Trend Judgment

Among the end-users of the power grid,especially in the rural power grid,there are a large number of users and the situation is complex.In this complex situation,there are more leakage caused by insulation damage and a small number of users stealing electricity.Maintenance staff will take a long time to determine the location of the abnormal user meter box.In view of this situation,themethod of subjective fuzzy clustering and quartile difference is adopted to determine the partition threshold.The power consumption data of end-users are divided into three regions:high,normal and low,which can be used to screen users in the area of abnormal power consumption.Then the trend judgment method is used to further accurately screen to improve the accuracy and reduce the number of users in the abnormal range.Finally according to abnormal power consumption auxiliary locate abnormal electricity users list box.Then the simulation environment is set to verify the application of membership fuzzy clustering and trend judgment in power consumption data partition.