Muiz M. Zaben

The integration of inverter-interfaced sources into microgrids has led to new protection challenges due to fluctuating fault currents, bidirectional power flow, and low inertia. Traditional protection methods are inadequate for these evolving systems, necessitating the development of novel, fast-acting, and cost-effective strategies that are independent of fault current levels. Machine learning-based protection schemes offer potential for improved performance over traditional methods, but their success is highly dependent on the quality and nature of the input data. Raw fault signal data are often noisy and high-dimensional. Processing it without feature extraction or selection can result in poor performance, longer processing times, higher storage needs, overfitting, and reduced generalizability. The dimensionality reduction step, which encompasses feature extraction and/or feature selection algorithms, is crucial for machine learning-based protection schemes. Many such methods have been proposed in the last decade. This research aims to investigate available techniques for reducing the dimensionality of data used for fault diagnosis in AC microgrids. The research will review and compare different dimensionality reduction methods, highlighting their strengths and weaknesses. It will also explore the most used features and their effectiveness in different scenarios. This article highlights the key gaps in AC microgrid fault diagnosis, which include scalability, real-time performance, and generalization, among many others. It emphasizes that future research should focus on unexplored dimension reduction techniques, hybrid approaches, and improving deep learning interpretability for robust and accurate solutions.

AC microgrids are becoming increasingly important for providing reliable and sustainable power to communities. However, the evolution of distribution systems into microgrids has changed the way they respond to faults and hence their protection requirements. Faults in microgrids could hinder operation stability and damage the system components. The types, locations, and resistances of faults, as well as microgrid operation modes, distributed generation penetration levels, load changes, and system topologies, all affect how the microgrid responds to faults. In order to offer quick restoration and to protect the microgrid components, fault detection and classification are therefore essential for microgrids. In this direction, unconventional methods such as artificial intelligence have been increasing in popularity over the last years. Pattern recognition is a methodology that machine learning as an approach to artificial intelligence is concerned with. The combination of protection with machine learning may be motivating in order to achieve the goal of intelligent operation in the smart grid. In this paper, fault detection, classification and location methods are reviewed for microgrid application. Different methods applied for both fault location and fault classification are being classified by the implemented technique. Such methods are explained and analyzed providing the main advantages and disadvantages of each category. Additionally, the research trends in both fields are analyzed and state–of–the–art methods from each category are thoroughly compared. Finally, the research gaps and future directions are identified.