This paper provides a comprehensive and systematic review of fault localization methods based on artificial intelligence (AI) in power distribution networks described in the literature. The review is organized into several sections that cover different aspects of the methods proposed. It first discusses the advantages and disadvantages of various techniques used, including neural networks, fuzzy logic, and reinforcement learning. The paper then compares the types of input and output data generated by these algorithms. The review also analyzes the data-gathering systems, including the sensors and measurement equipment used to collect data for fault diagnosis. In addition, it discusses fault type and DG considerations, which, together with the data-gathering systems, determine the applicability range of the methods. Finally, the paper concludes with a discussion of future trends and research gaps in the field of AI-based fault location methods. Highlighting the advantages, limitations, and requirements of current AI-based methods, this review can serve the researchers working in the field of fault location in power systems to select the most appropriate method based on their distribution system and requirements, and to identify the key areas for future research.

Nowadays, modern power systems are facing increasing complexity with the rapid growth of renewable energy sources (RESs), the expansion of small and distributed energy resources (DERs) and the evolution towards cyber-physical smart grids. These developments have made the need for intelligent and flexible systems to support the decision-making processes. Advanced large linguistic models (LLM), as a new generation of artificial intelligence (AI), can understand and process complex text data and have been proposed as an innovative solution in the energy industry. These technologies can create a fundamental transformation in power systems management and optimization. This paper analyzes in depth the application of LLM in power systems. The current research tracks the evolution of AI in this field from simple methods, based on basic rules, to more advanced technologies, such as deep learning and transformer models. Within the paper, the basic principles of transformer models are described, and three important capabilities of this technology are emphasized: the ability to interpret the results, the ability to expand for different applications, and the flexibility to face different conditions. The conducted analysis highlights how the new generation of AI can bring a fundamental transformation in the management of power systems.

The integration of Distributed Energy Resources (DERs) in power distribution networks poses challenges for protection systems due to dynamic bidirectional fault currents. This paper presents a novel wide-area protection scheme for modern Doubly Feed Induction Generator (DFIG)-integrated distribution networks that simplifies fault location and relay coordination. The system employs Artificial Neural Networks (ANNs) for fault detection and a Fault Zone Relay Condition (FZRC) matrix for fault location. The simulation results demonstrate the effectiveness of the proposed approach in fault detection and isolation.

Utilization of active power filters (APFs) is the most efficient method to reduce harmonic pollution in distribution networks. Previous approaches utilized APFs in integrated control schemes based on broad data-gathering systems. Since a broad data-gathering system is not available in most practical distribution networks, previously proposed approaches may not readily be implemented. This paper presents the utilization of stand-alone controlled APFs (SACAPFs) in radial distribution networks. Utilizing APFs with a stand-alone control system decreases implementation costs and complexity by making them autonomous and independent of integrated control systems, which are complicated and expensive in practical applications. In this paper, a single SACAPF is modeled as a dependent current source where its injection current is equal in amplitude but opposite in phase compared to the harmonic content of the current passing through the point of common coupling (reference current). Due to the presence of both linear and nonlinear loads in the distribution network, the reference current changes after injection by SACAPF, so it is necessary to modify the injection current until reaching a constant value in the reference current. This is considered via an iterative procedure in the modeling scheme. Operation of multiple SACAPFs is handled using a backward procedure based on a priority list. Simulation results on an IEEE 18-bus test system show the proper operation of the stand-alone control systems for both single and multiple SACAPF implementation. Furthermore, optimal allocation of the proposed SACAPFs is performed in an IEEE 33-bus test network and a 9-bus test network, and the results are discussed and compared with the allocation of integrated control system APFs.