Detection and Classification of Power Quality Disturbances by Using Discrete Teager Energy Operator in the PV-Based DC Microgrid

Nowadays the usage of circuit breakers, switches, converters, and non-linear loads is increasing rapidly. These are frequently caused by power quality disturbances (PQ) in the electrical network. It is necessary to detect and classify the different PQ disturbances (i.e. single and mixed) in order to prevent the network and its connected equipment. This paper presents PQ disturbances detection and classification by using a discrete Teager energy operator (DTEO). Initially, voltage signals of PQ disturbances are captured from the proposed microgrid i.e. PV based DC microgrid at point of common coupling (PCC). Further, these signals are passed through DTEO for detection with the help of a first peak. These detected PQ signals are passed through the statistical features i.e. Teager energy kurtosis and Teager energy mean for classification by using the simple decision tree. The performance and superiority of the proposed method are verified in terms of classification accuracy (CA) when compared to other existing methods. Here it is evidenced that the proposed method is simple, more flexible and it reduces the complexity by avoiding the other signal processing algorithms.