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Summary A microgrid is a trending small‐scale power system comprising of distributed power generation, power storage, and load. This article presents a brief overview of the microgrid and its operating characteristics. The integration of microgrids with the existing power system has been challenging and requires time to time modifications. To analyze the changing demand, the article discusses the modifications made in IEEE 1547 integration standards. The article recollects and reviews the control strategies of certain microgrid testbeds installed all around the world. A focus has been drawn toward the integration of microgrids in a developing country like India. An overview of the policies followed and the challenges faced to integrate the microgrid in the Indian power industry is presented. The article concludes with the worthy findings for the future aspects of research to improve the overall performance along with the smooth integration of the microgrid.

Summary Integration of the distributed generators into a distribution system encounters various system issues, and out of those islanding detection is 1 of the major protection problems to focus on. Many detection schemes have been proposed in the recent past, which possess a nondetection zone (NDZ) and usually neglect to provide a justification for the selected detecting features among all possible measures. Sensitive feature selection and minimization of NDZ are the 2 major objectives of this study. This paper comprises of 2 operational modes of designed IEEE 13‐bus test feeder (offline mode and online mode of operation). The offline mode of system operation focuses on selecting the optimal feature vectors using the proposed “modified multiobjective differential evolution algorithm” coupled with an extreme learning machine classifier. The modified multiobjective differential evolution algorithm‐extreme learning machine is applied to find out 2 optimum feature vectors, one by considering accuracy and minimal features and another one by dependability with a single feature as its objective functions. The online mode concentrates on the proposed new hybrid islanding detection method comprised of both passive and active detection techniques. Passive technique implements a decision tree designed by using the obtained accuracy‐based feature vector. Decision tree triggers the active method on suspecting the runtime instances as non‐islanding events to reduce the NDZ. Active method uses the obtained dependability‐based feature vector as an injecting parameter. The test results indicate the efficiency and accuracy of the proposed approach under different circumstances of power mismatch.

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