Neuro-Wavelet Based Islanding Detection Technique

Abstract

Integrating distributed generator into the existing distribution network is predicted to play an important role in the near future. Distributed generators, specifically renewable energy technologies, such as photovoltaic, wind-turbine and fuel cells are entering a stage of fast expansion. The Electric Power Research Institute estimates that distributed generations account for 20% of all new generations going online in the US. Connecting distributed generator to the distribution network has many benefits such as increasing the capacity of the grid and enhancing the power quality. However, it gives rise to many problems. This is mainly due to the fact that distribution networks are designed without any generation units at that level. Hence, integrating distributed generators into the existing distribution network is not problem-free. Unintentional islanding is one of the encountered problems. Islanding is the situation where the distribution system containing both distributed generator and loads is separated from the main grid as a result of many reasons such as electrical faults and their subsequent switching incidents, equipment failure, or pre-planned switching events like maintenance. In this thesis a passive Neuro-wavelet based islanding detection technique has been developed. The proposed method utilizes and combines wavelet analysis and artificial neural network to detect islanding. The technique is based on the transient voltage signals generated during the islanding event. Discrete wavelet transform is capable of decomposing the signals into different frequency bands. It can be utilized in extracting discriminative features from the acquired voltage signals. The features are then fed to a trained artificial neural network model which is if well trained capable of differentiating between islanding event and any other transient events such as switching or temporary fault. The trained classifier was then tested using novel voltage waveforms. The test results indicated that this approach can detect islanding events with high degree of accuracy.