Based On Trends In Health Assessment And Prediction Of Deterioration Hydropower Unit Fault Diagnosis System

The real-time diagnosis of hydropower units operating state is directly related to the important economic and social benefits of the hydropower station, such as security and stability running, power quality and electricity production costs, etc.. With the continuous expansion of power plant scale and monitoring auxiliary systems, information of unit’s control and monitoring data is growing significantly, making it more and more difficult to the real-time monitoring of unit operating status and the quick and accurate determination of equipment failure for the operating personnels. Therefore, it’s of great necessarity for us to research on health assessment of operating status and performance degradation trend forecast of hydropower units.Taking into account the actual situation that hydropower units condition monitoring technology has been widely used while hydropower units fault samples are lack currently, and existing diagnostic technology can not meet the project applications, a new idea for hydropower units fault diagnosis based on health assessment and deterioration trend forecastideas was proposed. Starting from the normal feature research of unit operation, we achieved the health diagnosis of hydropower units by establishing health samples with sound monitoring features. Diagnostic methods focus on real-time health diagnosis of the equipment operating status, with emphasis on monitoring anomalies and predicting abnormal. This diagnostic method is quite different from traditional fault symptom-based methods. Since the probability of failure is small in the actual operation of hydropower units, diagnostic system developed based on this diagnostic concept is very practical. Moreover, the system with functions of real-time health diagnosis and quantified degradation trend forecasting, can not only monitor abnormal real-time, but also predict abnormalities, which meets the engineering applications at the present stage. Research on fault diagnosis of hydropower units based on health assessment and deterioration trend predicted was carried out in the paper from the study of the following six aspects including the running state feature extraction, establishment of health standards and health samples, feature sample based health assessment, state trend forecast based on timing decomposition model, LS-SVM based parameters performance degradation assessment and the research and application of integrated fault diagnosis system.With hydraulic turbine and generator as the object of study, and on the basis of summarizing various feature performances in operation abnormal of hydropower units, the paper put forward characteristic parameters to characterize the operational status of the hydropower units, as well as its three characteristic indexes that could be quantified: amplitude, frequency and waveform shape, and the corresponding calculation methods were given.Three evaluation criteria for operating state health assessment of hydropower units were proposed:absolute evaluation criteria, relative evaluation standard and analog evaluation criteria, and the corresponding value calculation methods of the standard limit given. According to Probability Theory and Limit theory of mathematical statistics, also the Shewhart control charts theory, we suggested the health standards for feature quantity that take the sample mean as the feature’s standard value, and Xc=X±3σ determined by3a criteria to as alarm limit value.In the paper, we analyzed the influences from unit operating conditions (power, head) to monitoring parameters characteristic indices illustrated by the case of vibration monitoring samples under normal operating conditions in unit’s prophase, proposed to partition the operating condition s adopting the method to control the sample variance, and established specific methods and procedures for health samples of each part. Thus to ensure the accuracy of the sample abnormal judgment, and to reduce the sample space dimension (the number of samples) at the same time.Also, characteristics trend forecasting model of hydropower units based on time series variation decomposition was established, and algorithms of trend forecast and performance degradation prediction based on this model were proposed as well. We verified this decomposition model and algorithms on power station monitoring data, results showing that the forecasting trends are in good agreement with monitoring trends, can meet the needs of trend forecasting and performance degradation prediction of monitor characteristic quantities in hydropower units, and are with good usability on the early warning of unit potential abnormalities.Taking vibration of hydropower units as an example,3D standard model (power-head-vibration) for vibration parameters performance degradation assessment of hydropower unit based on LS-SVM was proposed. We can get the judgement whether the unit vibration under current conditions has deviated from normal state once the run-time active power and water head are written into the trained model, and achieve health assessment of unit’s operating status. On the basis of performance degradation time series of vibration parameters, we established LS-SVM based vibration parameters performance degradation prediction model of hydropower unit, and employed the on-site monitoring data of upper guide swing and upper bracket vibration parameters to validate the proposed model. The results showed that the model could assess and predict vibration parameters performance degradation of hydropower units well.In the end, the research and application of integrated fault diagnosis system was introduced illustrated by the case of remote status monitoring and fault diagnosis system of Three Gorges Corporation, and proposed the overall structure of the distributed fault diagnosis system, composed of on site monitoring layer-plant and substation integration layer-the central diagnostic layer. To achieve data communication between different monitoring devices, we standardized the data format. Through the integration of standardized data platform, state information sharing and multi-information fusion diagnosis were achieved. Real-time health assessment and performance degradation prediction of unit operating status can be achieved through the establishment of its healthy sample library, realizing the purpose of the quantitative assessment of equipment health status, and can provide technical basis for the guidance of condition-based maintenance of the unit.