Feasibility and Application Study of Optical Voltage/Current Sensors Using FBG

This paper concludes the case study work on the optical sensor, which is a new method for voltage and current measurement. Fiber Bragg gratings (FBG) have been developed and used for decades in the telecommunication industry. In recent years, FBG sensors have found wide applications in monitoring strain, temperature, voltage and current across all industries. As the process of constructing a robust smart grid, thousands of miles of optical-fibers have been deployed along the power transmission lines for the purpose of power production communication. This paper focuses on using the power optical fiber as voltage/current sensors instead of those copper wired traditional current transformers. By using piezoelectric layers, the optical sensor is able to transform voltage/current magnitude into optical signal, as well as transmit the signal through the optical fiber. The application of using optical fiber will significantly reduce the cost of deploying traditional current transformers all around the power grid. Moreover, the optical sensor is more stable, more accurate and faster, with such characteristics, the smart grid monitoring system could be much better. The application of combining the optical composite low-voltage cable (OPLC) and the optical current sensor in the distribution network for smart distribution monitoring has been analyzed.

Fault separation and detection algorithm based on Mason Young Tracy decomposition and Gaussian mixture models

Purpose-For the large-scale power grid monitoring system equipment,its working environment is increasingly complex and the probability of fault or failure of the monitoring system is gradually increasing.This paper proposes a fault classification algorithm based on Gaussian mixture model(GMM),which can complete the automatic classification of fault and the elimination of fault sources in the monitoring system.Design/methodology/approach-The algorithm first defines the GMM and obtains the detection value of the fault classification through a method based on the causal Mason Young Tracy(MYT)decomposition under each normal distribution in the GMM.Then,the weight value of GMM is used to calculate weighted classification value of fault detection and separation,and by comparing the actual control limits with the classification result of GMM,the fault classification results are obtained.Findings-The experiment on the defined non-thermostatic continuous stirred-tank reactor model shows that the algorithm proposed in this paper is superior to the traditional algorithm based on the causal MYT decomposition in fault detection and fault separation.Originality/value-The proposed algorithm fundamentally solves the problem of fault detection and fault separation in large-scale systems and provides support for troubleshooting and identifying fault sources.