Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 2024-2029· 0 citations· 22 references
Abstract
The incorporation of smart technologies, renewable energy sources, and distributed systems is making abstract-Modern power grids more complex than ever before and fault detection and management are becoming harder than ever. To help solve these problems, this paper has suggested an Intelligent Fault Diagnosis System (IFDS) that will integrate real-time monitoring, based on Internet of Things (IoT) systems, with advanced Artificial Intelligence (AI) methods. The system is based on a hybrid deep learning architecture, which combines the Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to correctly learn both spatial and temporal dynamics in power system data. The parameters (voltage, current, temperature, etc.) measured by IoT sensors are constantly updated, which allows real-time analysis and a more rapid decision-making process. The suggested solution does not only identify and categorize the faults properly, but also integrates proactive maintenance to project possible breakdowns before they happen. It was experimentally proven that the system is able to achieve high accuracy of 97.4% in fault classification and has a shorter response time of 58 ms that is better than both traditional and standalone machine learning methodologies. The predictive model also has 96.3 percent accuracy, which is a good guarantee of early fault prediction. These findings underscore how the proposed AI-IoT integrated framework is effective in improving the reliability of the power grid, downtime reduction, and proactive and smarter power grid maintenance technologies.
Results indicate that artificial intelligence can significantly strengthen the resilience and automation of next-generation smart grid infrastructures.
T. Anvesh, Akshaya Chelpuri, Ambati Chandu· International Scientific Jou...· 0 citations
With the development of modern energy systems, there is a need to operate increasingly intelligent electricity systems. Smart grids are advanced electricity networks that allow for integrating new technologies, increasing the reliability and performance of the power system, addressing the issues of renewable energy sources, growing electricity consumption, and real-time monitoring of the operating processes. Artificial Intelligence (AI) can be applied in different areas of smart grid to make the electricity system more reliable and competitive. The methods include Machine Learning, Deep Learning, Artificial Neural Networks, Reinforcement Learning, Fuzzy Logic, and Expert Systems. They are used to perform demand side management, predict electricity consumption, forecast renewable energy production, detect and diagnose grid failures, optimize maintenance, enhance protection and security, and manage energy consumption. However, there are challenges for implementing AI in the smart grid, such as data privacy, security, complexity, lack of transparency, and legislation. The current research explores the different aspects of applying AI technologies to smart grids. The paper provides some of the most common applications of AI and discusses the challenges and future outlook for AI-driven smart grids. The study emphasizes the significance of artificial intelligence for developing the next-generation smart grid.
A. Faiz, A. S, A. T· International Journal of Res...· 1 citation
A new stacking-ensemble hybrid machine learning model that will combine a one-dimensional convolutional neural network with a bidirectional long short-term memory (CNN-BiLSTM) module, a Random Forest classifier, and an XGBoost gradient booster as base learners under the guidance of a logistic regression meta-learner is suggested.
A. Gopalakrushna· Materials Research Proceedin...· 0 citations
A hybrid deep learning-based model that combines convolutional neural networks and long short-term memory with explainable artificial intelligence to detect and classify faults accurately and interpretably to intelligent fault management in a contemporary smart grid is suggested.
Udit Mamodiya, Divyanshu Sinha, I. Kishor et al.· Scientific Reports· 0 citations
Non-technical losses (NTLs) are a major problem in modern smart grids, damaging revenue and operational functionality. This study proposes an integrated IoT-edge-cloud framework to improve fraud detection, analyze electricity usage patterns, and enhance data reliability in distributed smart grids. The approach extracts multiple features from smart meter data and uses a hybrid machine learning method that combines classification and clustering. To test the system’s robustness, the study included simulated fraud scenarios in difficult circumstances. Results show that the system achieved high detection accuracy (96.4%) and an AUC of 0.98. The framework also reduced false alarms by 86% compared to traditional rule-based methods, improving consistency and productivity. It supports near-real-time operation with response times around 125 ms and is scalable for larger smart grid environments. Behavioral segmentation further improved reliability by identifying differences in electricity consumption and reducing incorrect classifications. Overall, the study shows that combining data quality management, behavioral analysis, and distributed processing yields a more reliable and resilient solution for operational smart grid systems.
F. Otosi, Celestine A. Udie, F. Faithpraise· E3S Web of Conferences· 0 citations
The development of smart devices and the integration of Internet of Things (IoT) technology into modern energy systems have enabled real-time acquisition of high-frequency smart meter data, which supports the development of sustainable and efficient power grids. This real time data monitoring enhances operational efficiency and optimal utilization of energy. However, these smart devices, such as smart meters, increase the complexity of the system, which introduces challenges in identifying anomalies such as energy theft, meter malfunctions, and irregular consumption patterns, which negatively impact energy sustainability. Energy theft is one of the major concerns because it leads to unnecessary non-technical losses and inefficient utilization of electrical resources. This paper presents a method to detect energy theft with the help of machine learning and deep learning techniques. The techniques implemented are Support Vector Machine (SVM) and Convolutional Neural Network (CNN). These techniques are used to analyze real-world energy consumption patterns. The available dataset was imbalanced due to the scarcity of energy theft cases. To address the imbalance in smart meter datasets, synthetic data is generated by assuming certain conditions that may represent the energy theft scenarios. The models that are proposed here are trained on one year data taken from smart meters to accurately learn and predict load patterns reflecting actual consumer behaviour. To validate these proposed models and to evaluate the performance of models (SVM & CNN), quality indices such as the confusion matrix, accuracy, precision, and recall metrics are utilized. The experimental results demonstrate that the CNN model presents better results than the SVM, particularly in detecting minority classes associated with energy theft. The accuracy observed for the CNN model is 98%, and a recall of 87 %. These results validate the CNN model for detecting energy theft cases, which is actually a non-technical loss, hence supporting sustainable, reliable, and environmentally responsible smart grid operations.
S. Naqvi, Sarvottam Dixit, Pooja Tripathi et al.· International journal of com...· 0 citations
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