Monitoring Power Quality Events (PQE) is a crucial task for sustainable and resilient smart grid. This paper proposes a fast and accurate algorithm for monitoring PQEs from a pattern recognition perspective. The proposed method consists of two stages: feature extraction (FE) and decision-making. In the first phase, this paper focuses on utilizing a histogram based method that can detect the majority of PQE classes while combining it with a Discrete Wavelet Transform (DWT) based technique that uses a multi-resolution analysis to boost its performance. In the decision stage, Extreme Learning Machine (ELM) classifies the PQE dataset, resulting in high detection performance. A real-world like PQE database is used for a thorough test performance analysis. Results of the study show that the proposed intelligent pattern recognition system makes the classification task accurately. For validation and comparison purposes, a classic neural network based classifier is applied.

As the backbone of modern society and industry, the need for a more efficient and sustainable electrical grid is crucial for proper energy management. Governments have recognized this need and have included energy management as a key component of their plans. Decentralized Smart Grid Control (DSGC) is a new approach that aims to improve demand response without the need for major infrastructure upgrades. This is achieved by linking the price of electricity to the frequency of the grid. While DSGC solutions offer benefits, they also involve several simplifying assumptions. In this proposed study, an enhanced analysis will be conducted to investigate how data analytics can be used to remove these simplifications and provide a more detailed understanding of the system. The proposed data-mining strategy will use detailed feature engineering and explainable artificial intelligence-based models using a public dataset. The dataset will be analyzed using both classification and regression techniques. The results of the study will differ from previous literature in the ways in which the problem is handled and the performance of the proposed models. The findings of the study are expected to provide valuable insights for energy management-based organizations, as it will maintain a high level of symmetry between smart grid stability and demand-side management. The proposed model will have the potential to enhance the overall performance and efficiency of the energy management system.