Abstract
The ultraviolet pulse detection technology has superiorities in response speed and antijamming capability, so it is extensively applied to insulation detection for electrical equipment. The existing detection and analysis methods have defects, such as failure of precise modeling caused by incomplete analysis of influence factor, poor model adaptability, and complex operation. In allusion to the problems, the ultraviolet pulse detection circuit was optimized and its sensitivity was analyzed through tests. Then, a partial discharge intensity evaluation method for electrical equipment based on the improved adaptive network based fuzzy inference system (improved ANFIS) was proposed combined with the detected pulse count (P), temperature (T) and humidity (H). The initial fuzzy inference system structure was established with the subtractive clustering method (SCM) and fuzzy C-means (FCM) algorithm, and the traditional ANFIS learning algorithm was improved via Fletcher-Reeves conjugate gradient method. In this way, the model parameters were optimized continuously, and the system ability of ignoring small changes in the network was improved. Finally, the effectiveness and practicability of the method were verified through field test. The experimental results demonstrated that the improved ANFIS reduced the model error by 2% when compared with traditional ANFIS, and the model accuracy is improved. Besides, the quantitative precision of the discharge intensity is higher than that of the traditional ANFIS by the contrastive analysis of field test data, indicating that the improved ANFIS evaluate the partial discharge intensity of electrical equipment more accurately. Thus, a decision basis can be provided for the equipment protection and charged maintenance of the electric system.
Highlights
The safe and reliable operation of electrical equipment is directly related to its insulation conditions
The results show that the improved adaptive network based fuzzy inference system (ANFIS) has higher prediction accuracy when applied to discharge intensity evaluation for electrical equipment via ultraviolet pulse detection, and that effective suggestions can be provided for online monitoring and overhaul of equipment insulation
IMPROVED METHOD AND MODELING ANALYSIS OF ANFIS ANFIS is a neuro-fuzzy inference system based on the Takagi-Sugeno model, which has organically combined the self-study function of artificial neural network with the fuzzy language expression ability of fuzzy inference system to complement each other’ advantages
Summary
The safe and reliable operation of electrical equipment is directly related to its insulation conditions. Under the influences of environmental factors and electric field distortion in the operating process, the insulation will degrade leading to the partial discharge phenomenon. The insulation fault of electrical equipment is the most common fault in power system. The partial discharge degradation of external insulation of high voltage electric power equipment will. Numerous experimental studies show that detecting partial discharge is an important method to discover equipment insulation fault and potential hidden dangers. It has important significance for the safe operation of electric system to evaluate the discharge intensity of electrical equipment accurately and rapidly [1]–[5]
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