Islanding Detection Methods In Microgrid

Large amounts of greenhouse gas emissions that are produced by fossil energy cause serious problems to environment and survival of human beings. Renewable energy generation is considered as an important approach to solve energy and environment problems in the future. Distributed generation(DG) technique is a primary way to utilize renewable energy. Microgrid which is integrated with different kinds of distributed resources can improve energy efficiency and reduce the negative impact on power grid. Micorgird has two operation mode, including grid-connected mode and islanding mode, running on quite different strategies. Effective islanding detection methods are indispensable to realize optimal control of microgrid. Islanding detection methods are researched in the paper. It is a premise for islanding detection methods to detect islanding timely and effectively, assuring security of microgrid. Therefore, islanding detection technologies is critical and indispensible in microgrid. Research works are summarized as follows:Firstly, performance indices and critical technique problems of islanding detection methods which are commonly used are discussed in the paper. The discussed Islanding detection methods are also classified into local methods and remote methods. Local methods are based on distributed generation side, including passive methods and active methods. Remote methods are based on grid side. Advantages, disadvantages and applications of islanding detection methods are analyzed in the paper. Due to summarization of existed islanding detection methods, some technique problems are proposed in the paper. For example, performance indices are not considered completely in many existed islanding detection methods. Some indices might be ignored in researches of improving performance of islanding detection methods. Improvements of different performance indices face many difficult problems that they would be restricted by each other. Improving a performance index might cause the decrease of other fields of performance. As a result, the effect of islanding detection method is not significantly improved.Secondly, a group of performance indices are summarized in the paper to evaluate performance of islanding detection methods, including non-detection zone(NDZ), detection time, error detection ratio and power quality. Effectiveness of islanding detection methods in multiple-inverter cases is also analyzed. According to proposed performance indices above, the paper aims to discuss the improvements of performance among existed islanding detection methods. The paper concludes that performance indices are not considered completely in existed islanding detection methods. As a result, it is significant for researchers to consider performance indices completely when propose a new islanding detection method or improve performance.Finally, a novel hybrid method is proposed, which combines decision tree(DT) with voltage and frequency positive feedback for islanding detection in DGs. In the proposed method, the feature indices of prescribed events are captured and recorded in a target location to train a DT classifier, and the classifier can then be used for categorizing islanding mode or grid-connected mode. The voltage and frequency positive feedback techniques are employed to regulate the DG power outputs based on voltage and frequency deviation between DG outputs and rated values. Furthermore, the disturbances are introduced to change the feature indices significantly when DG is switched to island mode, which can increase the sensitivities of feature indices and improve detection accuracy. The proposed hybrid method has been fully evaluated and tested on a representative distributed generation system with two DGs. Simulation studies demonstrate that the proposed method can detect the islanding more accurately in situations where the power is balanced between DG output and load consumption, and can also improve detection accuracy in situations where DG system operates in minimum or maximum loading states.