Modern Intelligent Computation And Its Application In Fault Diagnosis Of Hydropower Generating Unit

The modern hydro-electric power unit is evolving toward the large, complex and supercritical parameter direction, its operation with safety, reliability and stability has become a research hotspot of great concern and engineering application problem in the hydro-electric power industry. With the in-depth study of the artificial intelligence theory and the rapid development of information science, domestic and foreign scholars have acquired a complete theoretical and methodological system in aspects of pattern recognition and signal processing, which has not only laid a solid theoretical foundation for the intelligent fault diagnosis technology, but also brought new challenges to the traditional diagnostic methods.For the scientific phenomena and difficult problems in the fault diagnosis of hydroelectric generating units, including shaft orbit recognition, signal detection and extraction, the advanced intelligent algorithms, such as chaos, support vector machines, rough sets, information fusion, etc, are introduced in this paper, and the in-depth and systematic diagnosis research has been performed, and the main contents and innovative results are listed as follows.(1) Shaft orbit, as a mapping way of status information, reflects the operation state of the hydroelectric generating unit from the viewpoint of its graphical features. To extract shaft orbit's features invariant to rotation, scaling and translation, the unique strengths of Haar orthogonal matrix type are found, the horizontal and vertical coordinates of shaft orbit are fast proceeded by Haar transform, respectively, and the slopes with different resolutions are obtained at different positions by using the corresponding transform coefficients, and then the multi-resolution angles between the adjacent slope straight line are extracted skillfully and used for the hierarchical recognition, the recognition process accords with the principle of human's recognizing things by using hierarchy analysis approach.(2) Taking into account the requirements of the real-time identification of the orbit sample and the engineering reality of the insufficient prior knowledge, the Walsh spectrum features of the shaft orbit are fast extracted by calculating and transforming the distance vector, then the mapping relations between the orbit features and the orbit types are learned by utilizing support vector machine theory, and the classifier gotten is used for the identification of shaft orbits. Examples analysis shows that the proposed method is superior to the traditional extraction methods, and has a strong generalization ability and small-sample learning ability.(3) To address the contradiction that it is easy to omit important information for a single feature extraction method while for the complex methods the redundancy information is often added, the geometric features and frequency domain features of shaft orbit are extracted and integrated, then the combination feature vectors are discretized, reduced and reasoned by introducing rough set theory, the rules achieved are used for the diagnostic test. The theoretical analysis as well as the experimental results fully displays the prominent data reduction function of the rough set theory, and shows the rapidness and accuracy of the proposed method.(4) Haar type orthogonal transforms (HTOTs), owning the efficient algorithm and the potential value in the engineering application, may be obtained conveniently. However, these advantages cause little attention of scholars' since the mathematical abstract of HTOT. To this end, HTOT theory is introduced into the fault signal analysis, and the classification performance of its transform coefficients is compared with that of other transforms under the related evaluation criteria. As a result, the conclusion that certain transform fits the analysis of corresponding signals is drawn, and the adaptive diagnosis strategy that the transform has been adopted in accordance with the type of fault signal is offered. On this basis, the statistical feature vectors constituted by the transform coefficients are proceeded by rough set theory, the diagnostic test demonstrates that the proposed approach is flexible and effective, and has a very clear superiority over the traditional methods in both the diagnosis accuracy and speed.(5) Considering the defect that the weak signal cannot be detected comprehensively by the traditional methods, a fault signal detection method based on the information fusion and chaotic oscillator is put forward by exploring the response characteristics of the nonlinear system sensitive to the initial value.â‘ the frequency and phrase of the weak signal are determined by the chaotic oscillator array.â‘¡various chaotic oscillators and various methods are introduced to the amplitude detection of the same weak periodic signal, then the detection outcomes are fused by the adaptive weighted fusion method.â‘¢ by using the statistics distance, two dimensional entropy and Walsh transform, the state recognition of chaotic oscillator is carried out from the viewpoint of time domain and frequency domain. Then the recognition outcomes are fused.â‘£the singular features of the strong amplitude signal are extracted according to the modulus maximum principle of the transform coefficients, then combined with the signal time-domain statistical features and the advantage that the weak signal can be detected by the chaotic oscillator, the diagnosis method based on the k/l fusion technology is provided, which can represent information more comprehensively and diagnose fault more accurately.