Study On Artificial Immune System Based Intelligent Diagnosis Technology Of Hydroelectric Generating Unit

The demands on energies from the fast-developing national economy are increasing and the freely competing mechanism has been introduced into the electric power market. Hydropower enterprises have to try to improve their producing efficiency and reduce the production cost, especially the unpredictable economical costs and social effects, which results from the breakdown of Hydroelectric Generating Unit (HGU) due to casualties or maintenance actions. The researches on fault diagnosis should advance towards the direction of prognosis, emphasizing on how to determine the probability of the faults before their occurrence, how to avoid the faults, when the faults will occur, and how to prevent their development at their primary states. In order to improve the correctness, real-timeness, and robustness of the fault diagnosis of HGU, on the one hand, the current approaches should be improved and optimized. On the other hand, new approaches should be resorted to, especially those inspired by biological intelligences.Artificial Immune System (AIS) includes the system forms or algorithm structures to solve some engineering technique problems, inspired from the structure characteristics and function mechanism of Biological Immune System (BIS).Previous research works on the modern intelligent diagnostic techniques and the theory and applications of AIS have been summarized first in the dissertation. Aiming to solve the diagnosis problems under the conditioned maintenance of HGU, diagnosis techniques are combined with AIS appropriately. Several immune diagnostic approaches are researched, and have been applied to the fault diagnosis, state prognosis and maintenance decision of HGU. The outline of this dissertation is as follows:The problems, purposes and significance of this research are firstly presented. The intension and significance of the intelligent diagnosis are discussed under the maintenance system where conditioned maintenance takes the predominant place and other maintenance forms coexist. Then the characteristics of the fault of HGU and the status in researches on intelligent diagnostic techniques are summarized, followed with the existing problems in intelligent diagnosis. After the description of the status in both theoretic research and application of AIS, the universal thought and the main content of this dissertation are presented.BIS and its function mechanism are described in details as well as its main characteristics. Then the basic principles which include shape-space model and immunocyte model and several typical AIS algorithms are present. The potentials of AIS in the intelligent diagnosis field are analyzed and emphasized on.In order to overcome the limitations of the traditional FFT in non-stationary signal processing and overdependence on the energy characteristics during the analysis and fault diagnosis for HGU, the concept of combined feature is proposed, and a novel fault diagnosis model integrating the combined feature and RBFNN is built. How to extract relative energy feature of the stability signals via wavelet transform and how to evaluate the influences of the processing parameter changes on the stability state and extract the relationship symptoms are described in details, as well as the construction of the proposed fault diagnosis model based on RBFNN. Application results show that this proposed method is feasible and efficient in the overall feature extraction of HGU as well as the appropriate evaluation of fault types and severity degrees.In order to solve the problems such as large sample demands and lacking ability of active learning in the fault diagnosis techniques, a new diagnosis method based on immune response mechanisms is proposed. It is inspired from the recognition progress of antibodies with antigens. The mapping relations between the diagnosis system of HGU and AIS are set up. The diagnostic schemes corresponding to the primary response and the second response are discussed detail. This method can not only recognize the known states, but also remember the unknown ones and recognize them with fast reactions and robustness. The proposed method was applied to the vibration diagnosis of hydroelectric generating unit. Results indicate the high diagnosis accuracy with low demands on fault samples, and the merits of this method in application as well.The analysis on similarities between the structure design and the immune mechanism is implemented at the view of the immune system. A novel immune optimization algorithm is proposed. It is used to select and determine the structure of RBFNN, and jointed with the recursive least squares (RLS) algorithm to form the hybrid learning algorithm of immune-optimized RBFNN. The prediction of the vibration state at the water guide bearing of HGU based on the above immune-optimized RBFNN is performed. The results are compared with those by K-mean clustering RBFNN and BPNN, which show the high accuracy, correctness and feasibility of the proposed immune optimization algorithm. The immune agent model is proposed after the comparison of AIS and MAS. The approaches to acquire, organize and manage relative knowledge are discussed. Then the frame of the immune agent based conditioned maintenance system of HGU is presented, followed with the detailed description of each immune agent with different functions. Finally the immune co-operation diagnostic strategy under the frame is expounded.Study on AIS based intelligent diagnosis technology of HGU integrates the theory and approaches of AIS and those of intelligent diagnosis. It is a brave try in the research field of conditioned maintenance of HGU. Some research fruits are achieved. Because the theoretic and application researches on AIS start relatively late, there remains a large quantity of work to do, to implement the highly fusion of AIS and intelligent diagnosis technology, to realize the active diagnosis, the high-accuracy prognosis, and the optimal maintenance decision making.At the end of this dissertation, the future research directions in the field of AIS based intelligent diagnosis technology are pointed out.