Research On Insulation Monitoring And Fault Diagnosis Of Capacitive Equipments Considering Influence Of Environment

To monitor and diagnose the insulation condition of capacitive equipments on-line is the precondition and basis to realize the condition-based maintenance (CBM) for capacitive equipments. On-line monitoring and fault diagnosis of capacitive equipment's insulation would be influenced by environmental factors (such as temperature , humidity , contamination etc.). Therefore, it is very useful and meaningful to study the insulation monitoring and diagnosis method of capacitive equipments considering influence of environment. The test method of dielectric dissipation factor(tanδ)changing law in different environments, the tanδ's modified method and the method of capacitive equipment's fault diagnosis were researched in this paper. At the same time, on-line monitoring and fault diagnosis system was developed. The main work accomplished includes the following.In this paper, a new design proposal of fully insulated artificial climate chamber was put forward. The distribution of electric field in full space when insulators or bushing was hung in the chamber was analyzed. It was found that electrical field strength and potential around the the sample changed less in the fully insulated chamber than in conducting material phytotron, so it is closer the real condition. Based on the design, the first fully insulated large artificial climate chamber in China, in which temperature , humidity, and atmospheric pressure can be regulated, has been built in our laboratory. The paper developed an insulation parameters automatic test system of high voltage capacitive equipment based on LabVIEW. In the chamber, On-line tanδmeasurements of the capacitive transformer bushings were completed. The effects of environmental factors on tanδwere analyzed, and the curves between the tanδand the environment parameters were acquired. Then, a grey relational model to analyze the effects of main environmental factors on tanδwas built, and the grey relational grade between them was gained in this paper.In this paper, a modified model of the effects of main environmental factors on tanδbased on Least Squares Support Vector Machine( LS-SVM ) was put forward. So the tanδvalue measured in different climate conditions could be modified into a tanδvalue in standard atmospheric conditions, thereby the on-line measured data and the test data in laboratory under different climate conditions were equivalent. The model parameters were optimized by genetic algorithm, which improved the accuracy of modified calculation. The effectiveness of this method was validated by modified results of the test data and on-line monitoring data.Bayesian network method for capacitive equipment's fault Diagnosis was presented by means of combining all kinds of monitor data based on Bayesian network theory. It is the first time that the paper introduced the Bayesian network theory into capacitive equipment's fault diagnosis. Bayesian network's reasoning process was improved based on the characteristics of capacitive equipment's fault diagnosis in this paper. This method was proved to be effective by experiment.The feasibility that using the grey relational grade of main environmental factors on tanδas new characteristic quantity to diagnose the capacitive equipments was proved by examples. And the extension diagnosis method of capacitive equipment's faults based on matter-element model was advanced. This method can not only exclude the effects of environmental factors on tanδ, but also realize fault classification exactly. The method was proved to be successful by experiment.A new control method of the synchronous data sampling on multi-lower computer system was developed according to the needs of distributed capacitive equipment on-line monitor. Base on this method, we developed an on-line insulation monitoring and fault diagnosis system for capacitive equipment, which was then applied in practice. This system took the locally digitizing process to improve anti-disturbing capability. In practical project , the operation of the system was stable and reliable, and the data sampling was accurate.