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In this article, a nonlinear control method based on a virtual synchronous inverter is presented in order to maintain the stability of the microgrid in the island and connected mode with the presence of solar renewable energy sources. Uncertainty about solar renewable energy sources is modeled using the distribution function. Also, in order to optimize the parameters of the nonlinear control method, a sailfish optimization algorithm has been used. The proposed method is implemented on a microgrid with three distributed generation sources. The obtained results show the effectiveness of the proposed method in damping power and voltage fluctuations in both islanded and grid-connected modes, as well as when changing the working mode of the microgrid.

Aimed at the problem that the existing K-means clustering (KMC) algorithm is greatly affected by initialization, and the randomly generated clustering center can easily make the clustering result fall into local optimum and stop iterating, resulting in low clustering accuracy and poor robustness, a K-means hybrid iterative clustering algorithm based on memory transfer sailfish optimization (MTSFO-HIKMC) is proposed. First, learning from the existing improvement ideas, the maximum and minimum distance product is introduced to initialize the KMC cluster center, to avoid the uncertainty caused by random initialization. At the same time, in the iterative process, the current optimal solution is made to locally perform adaptive memory transfer correction to solve the problem of poor global optimization ability and insufficient search accuracy caused by the single search path of the sailfish algorithm. Using the Iris, Seeds, CMC and Wine international standard data sets, the MTSFO-HIKMC, the sailfish optimized K-means hybrid iterative clustering (SFO-KMC) algorithm, the introduction of the improved Moth-to-fire K-means cross iterative clustering (IMFO-KMC) algorithm, the KMC algorithm, and the fuzzy C-means (FCM) algorithm are compared and tested. From the obtained convergence curves and performance indicators, it can be seen that the MTSFO-HIKMC algorithm proposed in this paper has a faster convergence speed than IMFO-KMC. Compared with the IMFO-KMC algorithm, the dimensional space has a higher search accuracy. Compared with the KMC algorithm and FCM, it has a higher search accuracy. Compared with the SFO-KMC algorithm, its convergence speed and search accuracy are significantly improved, especially in high-dimensional data sets.

• Sailfish algorithm (SFO) is used to optimize VMD parameters (k) and (α). • Gini index is used in SFO as a fitness function as it is sensitive to impulses. • Determination of the relevant mode was carried out based on Gini index. • The proposed method can optimize the VMD parameters for high decomposition quality. Variational mode decomposition (VMD) method is a recently employed signal processing technique for fault identification from the vibration signal of rolling bearings. However, the selection of VMD parameters namely the number of modes (k) and the quadratic penalty factor (α) still represents a challenge to obtain proper decomposition modes with the most relevant and denoised fault information. This paper presents a framework using sailfish optimization (SFO) algorithm and Gini index (GI) as a criterion to adaptively select the optimum VMD parameters for each fault signal. The proposed algorithm is tested using three experimental signals of faulty bearings, and the most appropriate mode containing fault information is automatically extracted based on maximum GI values. The obtained results indicate high efficiency of the proposed method in extracting fault feature and in exclusion of noise effect as compared to conventional fixed-parameter VMD, local mean decomposition (LMD), and ensemble empirical mode decomposition (EEMD). [ABSTRACT FROM AUTHOR]