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Multi-microgrids (MMGs) suffer from power shortages due to the loss of utility grid support when an unintentional transition occurs. This imposes a transient shock on the system voltage and frequency. To maintain the frequency stability and power balance of an islanded MMG, this paper presents an underfrequency load shedding (UFLS) strategy with adaptive variation. A comprehensive load evaluation method based on a composite least squares support vector machine (CLS-SVM) is proposed to ensure uninterrupted power for critical loads. This method considers the comprehensive evaluation influence factors (CEIFs) of loads. Then, a least squares support vector machine (LS-SVM) provides the load shedding determination factors, transforming the problem of determining critical loads into a 0-1 planning problem. A method with adaptive variation is proposed to solve the UFLS model. The effectiveness of the proposed strategy is verified for an MMG model based on a modified IEEE 33-bus system. The test results show that: 1) the average accuracy of the proposed method is 21.05% higher than that based on LS-SVM; 2) compared with UFLS strategies based on the load level alone and on an intelligent algorithm, the frequency fluctuation range of the proposed strategy is 12.50% and 19.23% lower, respectively, and the frequency recovery time is 3.90% and 5.73% shorter, respectively; 3) compared with PSO, GOA and GA, the standard deviation of the iterative mean of the proposed algorithm decreases by 36.22%, 53.42%, and 34.00%, respectively. The proposed strategy can reduce the frequency fluctuation range and frequency recovery time while maintaining strong adaptability.INDEX TERMS Adaptive solution method, comprehensive evaluation, load shedding, microgrid, power shortage.

The occurrence of unintentional islanding will seriously threaten the stable operation of a microgrid (MG). Therefore, detecting the islanding of an microgrid timely is an important premise to ensure the microgrid operates safely and stably. However, the problem of dead zone exists in the traditional islanding detection process because the threshold of various electrical feature quantities of the point of common coupling (PCC) cannot be determined effectively. To solve this problem, an islanding detection method based on CatBoost is proposed for an microgrid. The novelty of this method lies in two aspects: 1) To reduce the error brought by the electrical feature quantities with weak correlation in the process of islanding detection, an analysis method based on the Spearman correlation coefficient is used to extract the electrical feature quantities closely related to islanding detection. 2) To determine the threshold of the electrical feature quantities more accurately and reduce the dead zone of island detection, an integrated learning machine is used to dig out correlations between the electrical feature quantities and the operation of an microgrid. The performance of the proposed islanding detection method is verified based on the modified IEEE13-bus system. The results of the example verify that the proposed islanding detection can achieve higher detection accuracy in cases of grid-connected interference and line faults.

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