Hydropower Generating Unit Control System Identification And Fault Diagnosis

The control system of hydropower generating unit is tightly associated with the stability, safety and efficient operation of hydropower generating unit (HGU), and the precise modeling of this system is foundation of dynamic process simulation, adaptive control, stability analysis of HGU and power system. The control system of hydropower generating unit is a non-minimum phase and time-varying nonlinear system, precise modeling of which is a difficult problem. In addition, the trend of HGU to be large and complicated brings significant risk of fault, thus problem of fault diagnosis of HGU is focused by researches widely.In researches of HGU system identification, traditional methods focus on linear system identification based on models which have almost been simplified by omitting nonlinearity sections, and the deficiency of research on nonlinear system identification of HGU restricts the precise modeling of HGU. Thus development of theory of nonlinear system identification and building the system of system identification of HGU are necessary. In this paper, based on comprehensive analysis of models of HGU, scientific problems in system identification of control system of hydropower generating unit (CSHGU) are proposed. Based on fuzzy theory and intelligent optimization methods, parameter identification and system identification of CSHGU are researched, furthermore fault diagnosis strategy based on model and system identification, fault pattern recognition are researched. The main contents and innovative results are listed as follows:(1) In considering of characteristics and system identification of HGU, models of all sections of governing system of HGU are researched and nonlinear sections are analyzed with emphasis. Models in different operating condition of CSHGU are discussed, and SIMULINK simulation platforms of linear and nonlinear models of CSHGU are built, providing foundation of system identification research.According to the special requirements of parameter identification, continuous system identification methods are studied and deduced to identify physical parameters directly, based on differential transform and integral transform. Continuous system identification strategy based on Hartley transform is researched and applied in control parameter identification of control system of HGU, then Haar transform with simple structure and high computing efficiency is studied and applied in parameter identification of objects in CSHGU.(3) In fact control system of HGU is a complicated nonlinear system. System identification of control system of HGU is studied under the condition of keeping all nonlinear sections of model of CSHGU. Gravity search algorithm (GSA) is introduced and improved by combining merits of particle swarm optimization, the search ability of improved GSA is enhanced by combination of gravity search, information sharing and ability of memory. The improved GSA is applied in nonlinear parameter identification of CSHGU and the identification strategy based on intelligent optimization method is proposed, realizing the precise identification of governing system of HGU under complicated operating conditions.(4) In addition, system identification of CSHGU based on nonlinear model is researched, focusing precise modeling using fuzzy model. On the basis of T-S fuzzy model, mutative scale chaos optimization is used to optimize the structure and parameters of T-S fuzzy model. In order to improve the linearity of fuzzy partition, fuzzy clustering methods based on linear regressive model and hyperplane are proposed, realizing precise identification of T-S fuzzy model, finally the effectiveness of proposed methods are verified in system identification of CSHGU.(5) In the end, fault diagnosis of dynamic system based on model and system identification is studied, building the foundation for further research. Fault diagnosis based pattern recognition based on fuzzy clustering analysis is emphasized, while weighted chaos optimization based fuzzy clustering method (WCOFCM) and weighted fuzzy kernel clustering (WFKC) algorithm are proposed. In WCOFCM, the global search ability of chaos optimization and local search ability of gradient operator are combined to improve the ability of obtaining more excellent partition solutions. In WFKC, samples in original space are mapped to high-dimension feature space by mercer kernel, and then a similarity based weighting method is used to assign weight to features of the transferred samples, and finally weighted fuzzy clustering in feature space is realized. WCOFCM and WFKC are applied in fault pattern recognition of HGU, the results show that the accuracy of fault pattern recognition are significantly improved.