The Study Of Online Monitoring For 10kV Switchgear Cabinet On Coal Mine Ground Based On CAN Bus

As a key equipment connecting the power grid and the coal mine power supply system, coal mine ground 10 kV switchgear cabinets' operation conditions have a great influence on the stability and reliability of the coal mine power supply system. Therefore, the online monitoring and diagnosis for real-time running status of coal mine ground 10 kV switchgear cabinets are very necessary.This paper did further research on online monitoring and diagnosis for coal mine ground 10 kV switchgear cabinets to make them networked and intellectual under the foundation support of Project Production and Study of Henan Province(project number: 132107000027), then comes up with the “Online Monitoring and Diagnosis System for Real-time Running Status of Coal Mine Ground 10 kV Switchgear Cabinets Based on the CAN Bus”. According to the monitoring requirement for status data, this paper designs the overall structure of the whole system, selects a variety of suitable sensors, data acquisition module and communication interface card, installs them in the appropriate location of the 10 kV switchgear cabinets to build the hardware system platform, then uses an industrial control computer as the upper machine and writes the data processing and monitoring software in the LabVIEW 2011 programming environment. All the works above are finished under the condition that the safety distance is guaranteed and the normal operation of switchgear is not affected. With the advantages of high real-time, high reliability and extensible topological structure of CAN Bus, the system can achieve the online monitoring of quite a few operating parameters of more than one switchgear cabinets comprehensively at the same time.In addition, this paper improves the empirical mode decomposition method by boundary extension to overcome the end effect during the decomposition process. With adopting the improved EMD decomposition method, mechanical vibration signals of the circuit breakers are decomposed into a finite number of independent IMFs. Then the energy entropies contained in each IMF are calculated and used as the input of RBF neural network. The RBF neural network is trained by alternating gradient method, which has the advantages of fast convergence and good classification ability, to realize the mechanical fault type identification of circuit breaker.Finally, this paper conducts a large number of tests on the KYN28A- 12(Z) type high voltage switchgear cabinet and the ZN63A- 12 type vacuum circuit breaker. The repeated debugging results show that the system can achieve all the online monitoring functions with high reliability and measuring precision, which can comprehend realtime health conditions of the switchgear cabinets. Besides, the combining method(the improved EMD,the energy entropy of IMFs and the RBF neural network trained by alternating gradient method) can identify several common faults type of the vacuum circuit breaker rapidly and accurately, providing a practical decision-making basis for the maintenance of coal mine high-voltage switchgear cabinet.