Jul 2026· International Scientific Journal of Engineering and Management· Vol 05, pp. 1-9· 0 citations
TL;DR
Results indicate that artificial intelligence can significantly strengthen the resilience and automation of next-generation smart grid infrastructures.
Abstract
ABSTRACT -The increasing integration of renewable energy resources, distributed generation, electric vehicles, and intelligent monitoring devices has significantly enhanced the complexity of modern smart grids, making conventional fault detection techniques inadequate for ensuring reliable and secure power system operation. This paper presents an AI-based fault detection framework for smart grids that utilizes machine learning and deep learning techniques to identify, classify, and localize electrical faults in real time. The proposed framework collects operational data from intelligent electronic devices (IEDs), phasor measurement units (PMUs), smart meters, and IoT-enabled sensors. The acquired data undergo preprocessing, normalization, and feature extraction before being analyzed using a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The CNN effectively extracts spatial fault characteristics, while the LSTM captures temporal variations in electrical signals for accurate fault prediction. The trained model is deployed on an edge-cloud architecture to enable low-latency fault detection, rapid decision-making, and remote monitoring. Experimental evaluation demonstrates that the proposed system achieves high fault detection accuracy, reduced false alarm rates, and faster response times compared with conventional rule-based and statistical approaches. The framework also enhances grid reliability, minimizes outage duration, supports predictive maintenance, and improves operational efficiency under dynamic grid conditions. These results indicate that artificial intelligence can significantly strengthen the resilience and automation of next-generation smart grid infrastructures.
Keywords— Smart Grid, Artificial Intelligence (AI), Fault Detection, Deep Learning, Machine Learning, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Internet of Things (IoT), Predictive Maintenance, Edge Computing.
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.
N. Sridhar, R. Devarajan, G. S. Nagesha et al.· International Conference on...· 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
The rapid expansion of offshore wind farms has introduced significant challenges to operation and maintenance (O&M), particularly under harsh marine environments where reliable electromagnetic information transmission and constrained wireless communication resources directly affect intelligent monitoring performance. Traditional Supervisory Control and Data Acquisition (SCADA) systems relying on cloud-centric architectures often encounter excessive latency and bandwidth bottlenecks when transmitting high-frequency vibration signals, limiting real-time fault diagnosis. To address these issues, this study proposes an Internet of Things (IoT)-based intelligent monitoring and fault detection method built upon an Edge-Cloud collaborative architecture. A lightweight Adaptive One-Dimensional Convolutional Neural Network (A-1D-CNN) is developed for deployment on edge gateway devices, enabling direct extraction of fault characteristics from raw vibration signals without manual feature engineering. Combined with an “ Edge-Training, Cloud-Update” strategy, the proposed framework continuously optimizes diagnostic performance while substantially reducing communication overhead across wireless sensing and electromagnetic transmission infrastructures. Experimental evaluation on a standard bearing fault dataset demonstrates that the proposed method achieves a fault diagnosis accuracy of 99.25% with a compact model size of only 0.45 MB, providing an effective balance between diagnostic precision and deployment efficiency. The results indicate that the proposed framework offers a practical solution for real-time intelligent monitoring in bandwidth-limited offshore environments and provides technical support for reliable electromagnetic-enabled sensing networks and distributed fault diagnosis in next-generation offshore energy systems.
Y. Ouyang, W. Liang· Advanced Electromagnetics· 0 citations
The rapid expansion of Internet of Things (IoT) networks has introduced unprecedented connectivity across smart cities, healthcare systems, industrial automation, and intelligent transportation. However, this widespread deployment has also increased the vulnerability of IoT infrastructures to cyberattacks, operational faults, and abnormal behaviors. Traditional anomaly detection techniques, which rely heavily on static rules or handcrafted features, struggle to adapt to the dynamic and heterogeneous nature of IoT environments. To address these challenges, this paper presents a comprehensive study on real-time anomaly detection in IoT networks using deep neural models. The proposed framework leverages deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Autoencoders to identify anomalous traffic patterns with high accuracy and low latency. The methodology emphasizes real-time data acquisition, feature normalization, model training, and deployment within resource-constrained IoT environments. Extensive experimental evaluations demonstrate that deep neural models significantly outperform traditional machine learning approaches in terms of detection accuracy, false positive reduction, and scalability. The findings confirm the suitability of deep learning-based anomaly detection systems for securing next-generation IoT networks while maintaining operational efficiency.
Oluwaseun B Adeyemi, F. Adebayo, Ibrahim Sadiq Bello· International Journal of App...· 0 citations
Abstract. The explosive growth of grid-connected renewable energy systems (RES) has increased the complexity of the operation of the modern power infrastructure, making the detection of the faults reliably an inevitable condition of the stable functioning and safety. Traditional single-algorithm and rule-based monitoring systems are not sufficiently flexible or discriminatory to distinguish between the many varieties of faults that occur in photovoltaic (PV) arrays, wind turbines, battery management systems, and grid-tie inverters. The paper suggests 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. Publicly available SCADA and lab bench data were used to create a curated multi-source dataset of 8,400 labelled samples to represent five operational states. The proposed framework achieved an accuracy of 97.8, a macro-averaged F1-score of 97.1, and a Matthews Correlation Coefficient (MCC) of 0.972, outperforming all the compared baseline methods at least by 3.3 percentage points. The findings verify the effectiveness of the hybrid stacking paradigm in identifying faults in real-time and multiple classes in heterogeneous renewable energy settings.
A. Gopalakrushna· Materials Research Proceedin...· 0 citations
The rapid expansion of smart networks, encompassing the Internet of Things (IoT), software-defined networking (SDN), and 5G-enabled edge infrastructure, has dramatically increased the attack surface available to malicious actors, while simultaneously producing high-velocity, heterogeneous traffic that traditional signature-based intrusion detection systems struggle to analyze in real time. This paper proposes a Hybrid Deep Learning and Machine Learning (DL-ML) framework for intelligent cyber threat detection that fuses a Convolutional Neural Network combined with a Bidirectional Long Short-Term Memory (CNN-BiLSTM) branch, which captures spatial and temporal traffic patterns, with a gradient-boosted ensemble branch (XGBoost/Random Forest), which captures statistical flow-level signatures. The outputs of both branches are combined through a weighted feature-fusion and ensemble layer that produces a unified threat classification and severity score. The framework was evaluated on a large-scale smart-network intrusion dataset comprising over 1.8 million labeled flow records spanning six traffic classes: normal, DDoS, botnet, port scanning, malware communication, and spoofing. Experimental results show that the proposed hybrid model achieves 98.8% accuracy, 96.4% precision, 95.6% recall, and a 96.0% F1-score, exceeding the strongest individual baseline (LSTM) by 3.7 percentage points in F1-score and achieving an AUC of 0.992.
Rajesh Yadav, Dinesh Kumar, Sanjeev Kumar et al.· International journal of com...· 1 citation