A Comprehensive Database and Smart-Learning Framework for Monitoring Failure Risk Factors, Maintenance, and Protection in Electrical Networks

A Standardized Framework for Machine Learning in Power System Protection

Studies of machine-learning-based power-system protection increasingly report near-perfect scores, yet the meaning of those scores depends strongly on the evaluation setting. Protection task, physical scope, measurements, timing, targets, preprocessing, and validation often vary jointly and remain incompletely specified. This paper proposes a standardization-oriented framework that treats evaluation design as part of the scientific contribution. It defines seven required study dimensions: protection objective, physical scope, observability, timing and decision windows, targets and sample validity, validation protocol, and evaluation outputs. The framework is instantiated in a bounded case study on the public PROTECT-90 electromagnetic-transient benchmark, comprising 9022 simulated episodes from a 90 kV double-line topology, for onset-conditioned fault classification and localization. Under centralized sensing, simulation-metadata-aligned 20 ms windows, and episode-grouped validation, a multi-layer perceptron (MLP) achieved a five-fold mean macro-averaged F1 score of 0.991 +/- 0.001 for classification and a localization mean absolute error of 10.20 +/- 0.25% of line length (mean +/- std across episode-grouped folds). Extending the decision horizon to 50 ms preserved this task-dependent performance asymmetry, while reduced observability approximately doubled the MLP localization error but had little effect on classification. A synchronized two-ended conventional locator outperformed the learning locators under its richer clean information set, and measurement degradation showed that clean predictive performance did not determine robustness. The framework turns evaluation assumptions into explicit, reproducible evidence and provides a basis for more comparable, auditable evaluation and future certification-oriented assessment of machine-learning protection functions.

A simulation based dataset of faults and events for machine learning in power systems

The integration of inverter-based renewable energy sources into electric grids challenges conventional power system protection. Machine learning-based solutions can address these challenges by utilizing available data in modern smart grids. However, the lack of open datasets prevents reproducibility and fair comparisons between different approaches and their results, which hinders further progress. Therefore, this paper presents EvEMTBench, a synthetic dataset of faults and events generated using electromagnetic transient simulations. The physical plausibility of the power system simulation is ensured by validating the simulation parameters against established literature and providing comprehensive documentation of the simulation procedure. The dataset is designed for training, fine-tuning, and benchmarking machine learning models by providing synchronized point-on-wave voltage and current measurements at 9600 Hz across a diverse set of topologies and voltage levels. The inclusion of a wide range of fault and operating events allows the utilization of EvEMTBench for different tasks like incipient fault detection, fault localization, or event detection.

An Intelligent predictive maintenance framework for fault prediction in industrial internet of things environments

A Smart Predictive Maintenance Architecture for Industrial Equipment Monitoring Using IIoT, Machine Learning, and Digital Twin Models

An Industrial Internet of Things (IIoT)-based Predictive Maintenance System that integrates smart sensors, edge computing, cloud analytics, artificial intelligence, and digital twin technology is proposed that contributes to the development of intelligent and self-optimizing industrial environments aligned with Industry 4.0 objectives.

Predictive Maintenance in Industry 4.0 Using Machine Learning Techniques

Predictive Maintenance (PdM) is a key component of Industry 4.0, enabling intelligent and data-driven management of industrial assets. Traditional maintenance strategies are no longer sufficient for complex cyber-physical systems, where reliability and efficiency are critical. With the rise of Industrial IoT (IIoT), large volumes of data can be analyzed using machine learning (ML) techniques to predict equipment failures, estimate remaining useful life (RUL), and optimize maintenance schedules. This paper provides a comprehensive study of ML-based PdM frameworks, covering data-driven, physics-based, and hybrid approaches, including supervised, unsupervised, and deep learning models. A structured methodology is proposed, involving data acquisition, preprocessing, feature engineering, model development, and deployment. Key aspects such as degradation modeling, anomaly detection, and performance evaluation are discussed. Challenges including data imbalance, interpretability, scalability, cybersecurity, and real-time implementation are also analyzed. The paper concludes with future directions such as explainable AI, digital twins, federated learning, and autonomous maintenance systems, offering valuable insights for developing efficient and scalable PdM solutions.

AI-DRIVEN PREDICTIVE MAINTENANCE AND INTELLIGENT MONITORING OF MECHANICAL SYSTEMS USING MACHINE LEARNING AND IOT

An end-to-end predictive maintenance system is proposed for high stress mechanical drivetrain and rotating machinery and the architecture proposed combines an industrial Internet of Things edge sensory network and hybrid machine learning and deep learning pipelines.