No abstract

Purpose The individual machine learning methods used for fault detection and classification have accuracy performance at a certain level. A combined learning model composed of different base classifiers rather than an individual machine learning model is introduced to ensure diversity. In this way, this study aims to improve the generalization capability of fault detection and classification scheme. Design/methodology/approach This study presents a probabilistic weighted voting model (PWVM) with multiple learning models for fault detection and classification. The working principle of this study’s proposed model relies on weight selection and per-class possibilities corresponding to predictions of base classifiers. Moreover, it can improve the power of the prediction model and cope with imbalanced class distribution through validation metrics and F-score. Findings The performance of the proposed PWVM was better than the performance of the individual machine learning methods. Besides, the proposed voting model’s performance was compared with different voting mechanisms involving weighted and unweighted voting models. It can be seen from the results that the presented model is superior to voting mechanisms. The performance results revealed PWVM has a powerful predictive model even in noisy conditions. This study determines the optimal model from among voting models with the prioritization method on data sets partitioned different ratios. The obtained results with statistical analysis verified the validity of the proposed model. Besides, the comparative results from different benchmark data sets verified the effectiveness and robustness of this study’s proposed model. Originality/value The contribution of this study is that PWVM is an ensemble model with outstanding generalization capability. To the best of the authors’ knowledge, no study has been performed using a PWVM composed of multiple classifiers to detect no-faulted/faulted cases and classify faulted phases.

Distributed energy resources (DERs) such as wind turbines and solar panels have fluctuating power generation profile. Also, uncertainty of DERs & loads can bring about complexity in functioning of protection for Microgrids (MGs). Thus, the operation of classical protection methods used in distribution networks may fail for MGs. In this context, this study presents a new protection methodology for discriminating between uncertain and asymmetrical / symmetrical events in MG. A detection method using covariance analysis of wavelet transform (CAWT) and a classification method based on fuzzy logic controller (FLC) using this analysis are presented. The proposed methods are evaluated under various conditions, including fault resistance, fault inception angle, and fault location. Furthermore, variation of solar irradiance levels and source strength has been taken into consideration to validate the performance of the proposed protection methodology. An advantage of the proposed methodology is that the relationship among phases using a covariance analysis proves the presence of the fault. Another advantage is that the discrimination between uncertain and ASF&SF events was discussed, and the faulted phase(s) was/were accurately identified through FLC. Besides, the efficiency of introduced CAWT&FLC-based methods was confirmed on MG configurations such as radial and mesh. In addition, the presented protection methodology became more efficient when combined with the detection of uncertain events.