Integrating Artificial Intelligence for Enhanced Fault Detection in Power Transmission Systems: A Smart Grid Approach
Authors: Taghizade Nijat , Lio Pietro 
Published: December 2024

Modern electrical systems rely heavily on sensors and relays for fault detection in three-phase transmission lines and distribution transformers. However, these devices often need more time complexity and are prone to false alarms (erroneous signals). The study was guided by the PRISMA guidelines and methodology. The study's objective was to compare the levels of accuracy of fault detection presented by different models studied between 2023 and 2024. The general objective was further divided into sub-objectives, which included determining types of faults in power transmission systems, examining fault detection machine-learning models in power transmission systems, and assessing levels of accuracy of fault detection techniques in power transmission systems. The study used a qualitative research design, which entailed a systematic literature review that involved identifying scholarly articles from respectable international journal websites that aligned with examining how the deep-learning model enhanced fault detection capabilities in a three-phase transmission line simulated dataset. The inclusion criteria were that the study used journals published between 2023 and 2024. The study also included journals that tested Artificial Intelligence systems' accuracy in fault detection in three-phase transmission lines and distribution transformers. The study excluded sources that were older than 2023. The sources of information were journals written by professionals and scholars in the field of Artificial intelligence and engineering. The sourced journals used simulated and real databases to test fault detection accuracy in three-phase transmission lines and distribution transformers. The source of data for review was obtained from the internet via Google search with various credible academic journal websites being searched by the researcher. A total of 12 sources were searched. To assess the risk of bias in the included studies, the researcher used the Robvis methods that visualized risk-of-bias during the inclusion criteria for sources used in the systematic review. The techniques used to present the findings were narratives, graphs, and tables. The method used to synthesize results was comparative data analysis. The study included two studies, the sample size from the three possible journals the researcher identified. The characteristics of the studies were that they both informed the study's general and specific sub-objectives. The two studies also used the same methods to arrive at their findings, which made them ideal for comparative analysis. Finally, both studies compared older systems to new systems regarding Artificial Intelligence accuracy in fault detection. The study's findings were as follows: The study ranked the Novel glass box-based model as the best artificial intelligence machine-learning approach for accurately detecting electric power transmission lines and systems faults. The Novel glass box-based approach's accuracy was 99%, followed by the Convolution Neural Network model's accuracy of 98%, the Gated Recurrent Unit model's accuracy of 92%, the Random Forest model's accuracy of 90%, the Logistic Regression model's accuracy of 74%, and the Support Vector Classifier model's accuracy of 63%. The review was adequate to conclude that the Novel glass-box-based proposed approach was the most advanced and suitable model for detecting faults in electric power transmission lines. The study had one limitation: it reported the results based on two sources, which made the exclusion criteria a point of bias. The survey results implied that builders of new Artificial intelligence systems for fault detection in electric line transmitters should aim to produce 99% accurate systems to meet the recommendations and fill the gap that this study has reported. This study was funded by the researcher using family contributions. The study's author is Nijat Taghizad (ORCID ID: 0009-0005-0505-8767).