The fiber optic current sensor (FOCS) is susceptible to external temperature in actual operation, which will lead to its accuracy deviation, even malfunction. In order to improve the temperature stability of FOCS’s ratio error, a temperature compensation method based on RBF neural network is established by taking the temperature as input and the ratio error as output to the network. Compared with BP neural network, the simulation results show that the temperature compensation model based on RBF neural network has better accuracy whose prediction error is less than 3%. At the same time, the experimental results show that the drift deviation of ratio error can remain as low as ±0.1% in the range of -40 °C to 70°C, and the 0.2S-level accuracy of GBT20840.8 standard can be achieved.
The engineering application number of fiber optic current sensor (FOCS) is decreasing year by year since 2012 in China due to its reliability problems. However, the researchers and related enterprises have also made some constructive attempts on the study of fault diagnosis of FOCS. In this paper, the application status and the common fault modes of FOCS are analyzed. Three ways to diagnosing the soft and hard fault of FOCS are reviewed, including based on analytical model, on signal processing and on knowledge. Finally, the research direction of FOCS fault diagnosing is prospected. It is concluded that the diversified and intelligent fault diagnosis method based on knowledge has more advantages compared with the other two methods. In addition, the development of FOCS for integrated optical path is of great help in improving its reliability and will be a research hotspot in the future.
The fiber optic current transformer (FOCT) is affected by complex environment in actual operation which will lead performance deteriorate or even malfunction. This paper introduces the application status and common fault modes of FOCT, and a fault diagnosis algorithm of FOCT based on Wavelet-Allan variance is presented. Wavelet transform is used to identify the jump signal for the mutation fault diagnosis, and Allan variance analysis is used to analyze the noise distribution for the gradual fault diagnosis. By combining the two methods above, the condition monitoring and fault diagnosis of FOCT can be realized without changing the structure of the original transformer or adding additional detection equipment. This method is proved to be effective and accurate by diagnosing faults from FOCTs in substation.