The benefits of distributed generation (DG) based on renewable energy sources leads to its high integration in the distribution network (DN). Despite its well-known benefits, mainly in improving the distribution system reliability and security, there are challenges encountered from a protection system perspective. Traditionally, the design and operation of the protection system are based on a unidirectional power flow in the distribution network. However, the integration of distributed generation causes multidirectional power flows in the system. Therefore, the existing protection systems require some improvement or modification to address this new feature. Various protection strategies for distribution system have been proposed so that the benefits of distributed generation can be fully utilized. This paper reviews the current progress in protection strategies to mitigate the impact of distributed generation in the distribution network. In general, the reviewed strategies in this paper are divided into: (1) conventional protection systems and (2) modifications of the protection systems. A comparative study is presented in terms of the respective benefits, shortcomings and implementation cost. Future directions for research in this area are also presented.

It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.

In zinc oxide (ZnO) surge arresters, leakage current usually flows across the arrester under normal operating condition. Leakage current is one of the factors which contribute towards degradation of surge arresters and therefore, it is very important to monitor the condition of surge arrester. In this work, the behaviour of leakage current in a ZnO surge arrester during normal operation, under different voltage amplitudes, wetness and pollution conditions was analysed. An 11 kV surge arrester model in three-dimensional space was subjected to finite element analysis (FEA) to determine the leakage current under different conditions. The results from the FEA model were compared with the measurement results to validate the model that has been developed. From comparison between the measurement and simulation results, physical parameters of a surge arrester that influence the leakage current under different conditions of the surge arrester were identified from the model. Through this work, a better understanding of leakage current behaviour can be attained, which may help in condition monitoring analysis on surge arrester in electrical utilities.