Search Results (633)

This research addresses the increasing need for efficient energy management in residential settings in response to the increasing global energy demands, focusing on the integration of artificial intelligence to identify energy burdens. We employ and compare some machine learning models, like Decision Trees, K-nearest neighbors, and Feedforward Neural Networks, with a primary focus on electrical current as a key parameter. The Fine K-NN model shows notable efficiency, achieving an accuracy of 99.1% in the identification of active household appliances using a single sensor. Our methodology encompasses rigorous data acquisition and preprocessing under controlled experimental conditions, ensuring the integrity and reliability of our results. This study contributes to the field by illustrating the effectiveness of specific AI models in energy management under controlled conditions, paving the way for future advancements in AI-driven energy conservation strategies. Full article

The primary focus of enhancing the efficiency of operations in the Industry 4.0 setting is Predictive and Preventive Maintenance (PPM). The paper introduces a predictive-maintenance system based on the Unified Namespace (UNS), which involves real-time sensor measurements, photogrammetry, and modelling of a digital twin to improve fault prediction and responsiveness to maintenance. This experiment was conducted over six months in a medium-sized discrete electromechanical production plant equipped with motors, Variable Speed Drives (VSDs), robot/cobots, precision grip systems, pipework systems, Magnemotion/linear motor drives, and a CNC machine. The continuous data, such as high-frequency vibration, temperature, current, and pressure, were monitored and analysed with machine-learning models, including support-vector machines, Gradient Boosting, long-short-term memory, and Random Forest, through which temporal degradation can be predicted. UNS architecture integrated all sensor and imaging data into a vendor-neutral data model through OPC UA to help ensure that all experiments could be integrated consistently and be updated in real time to real digital twins. The suggested system correctly identified mechanical and electrical failures and predicted failures before they really took place. Consequently, machine downtime was reduced by 42.25%, and Mean Time to Repair (MTTR) by 36%, compared to the prior six-month baseline period. These improvements were associated with earlier anomaly detection and digital-twin-supported pre-inspection. Overall, the findings indicate that the integration of UNS with multi-modal sensing and digital-twin technologies may enhance predictive maintenance performance in comparable industrial settings. The framework provides a data-driven, scalable solution to organisations that aim to modernise their maintenance processes, attain greater reliability and better equipment utilisation, as well as enhanced Industry 4.0 preparedness. Full article

Accurate long-term temperature prediction is critical for the reliable operation of mass-produced electrical machines. However, due to the randomness inherent in the manufacturing process, machines with identical design parameters often exhibit distinct thermal properties. The aging of the insulation system can also lead to variation in thermal performance. Conventional lumped-parameter thermal network (LPTN) models with fixed parameters fail to account for these factors, thus leading to biased prediction results for long-term temperature forecasting of mass-produced machines. To enhance the robustness of LPTN models, this paper proposes a methodology for adaptive online parameter updating. Based on the mathematical formulation of LPTN, a fast Jacobian matrix calculation method for model prediction errors is developed, which avoids the time-consuming numerical computation process. To further alleviate the computational burden, key parameters with significant impacts on prediction errors are screened prior to each optimization iteration. These improvements collectively reduce computational resource requirements and enable real-time online implementation. Finally, experimental verification is conducted on a 10 kW permanent magnet machine. Comparative analyses against the numerical method and extended Kalman filter (EKF) demonstrate that the proposed method can be efficiently realized and is more effective in estimating the model parameters online. Full article

Common-mode voltage (CMV) is a critical concern in motor drive applications employing multilevel inverters, as it can lead to significant issues such as high-frequency noise, electromagnetic interference, and motor bearing degradation. These effects can compromise the reliability, reduce the operational lifespan of electric machines, and introduce safety hazards. In this study, an enhanced Direct Torque Control (DTC) strategy incorporating Space Vector Modulation (SVM) is proposed to specifically address CMV-related challenges in induction motors (IM) driven by a three-level Neutral-Point-Clamped (NPC) inverter. The proposed DTC scheme utilizes a specialized modulation technique that effectively mitigates CMV while also minimizing current harmonic content, and torque and flux ripples with a constant switching frequency. The developed SVM algorithm simplifies the three-level space vector representation into six equivalent two-level diagrams, enabling more efficient control. The zero-voltage vector is synthesized virtually by combining two active vectors within a two-level hexagonal structure. The effectiveness of the proposed DTC approach is validated through both simulation and Hardware-In-the-Loop (HIL) testing. Compared to the conventional DTC method, the proposed solution demonstrates superior performance in CMV minimization and leakage current reduction. Notably, it limits the CMV amplitude to V_dc/6, a significant improvement over the V_dc/2 typically observed with the standard DTC approach. Full article

The growing penetration of Electric Vehicles (EVs) in power distribution networks presents both challenges and opportunities for grid operators and planners. This paper provides a comprehensive review of recent advances in optimization techniques and machine learning (ML) approaches for the efficient management of EV charging and integration in low- and medium-voltage distribution systems. Optimization methods are analyzed with reference to their objectives—such as load flattening, voltage regulation, loss minimization, and infrastructure cost reduction—and their capability to handle multi-objective, stochastic, and real-time constraints. Concurrently, the role of ML is explored in load forecasting, user behavior modeling, anomaly detection, and adaptive control strategies. Particular attention is given to hybrid approaches that combine optimization algorithms (e.g., MILP, heuristic methods) with data-driven models (e.g., neural networks, reinforcement learning), highlighting their effectiveness in enhancing grid flexibility and resilience. This review adopts a unified system-level perspective that links EV management objectives, optimization techniques, and machine learning-based solutions within distribution networks. In addition, particular attention is devoted to data availability, reproducibility, and practical deployment aspects, with the aim of identifying current limitations and providing actionable insights for future research and real-world applications. This study aims to support the development of intelligent energy management strategies for EVs, fostering a sustainable and reliable evolution of distribution networks. Full article

This paper presents a robust sensorless control strategy for a dual-inverter doubly fed induction motor (DFIM) designed for high-performance electric vehicle (EV) traction systems. The proposed scheme eliminates the mechanical speed sensor by employing a sliding-mode observer (SMO) for real-time estimation of rotor speed and flux, ensuring accurate feedback under load disturbances and thereby enhancing reliability while reducing implementation cost. The DFIM is powered by two voltage-source inverters that independently control the stator and rotor windings through space vector pulse-width modulation (SVPWM). A power-sharing strategy optimally distributes the electromagnetic power between the two converters, ensuring smooth transitions between sub-synchronous and super-synchronous operating modes. Furthermore, a stator-flux-oriented vector control (SFOC) scheme incorporating a graphical torque optimization algorithm is developed to maximize torque while satisfying inverter and machine constraints across both base-speed and flux-weakening regions. The stability of the SMO-based estimation and closed-loop control is rigorously validated using Lyapunov theory. Comprehensive MATLAB R2024b/Simulink simulations conducted under the WLTC-Class 3 driving cycle confirm high accuracy and robustness, showing fast dynamic response, precise speed estimation, and smooth torque behavior across the full speed range. The results demonstrate that the SMO-based DFIM drive offers a cost-effective and reliable solution for next-generation EV traction applications. Full article

Currently, distribution system scheduling faces significant uncertainty and dynamic complexity due to the large-scale integration of diverse heterogeneous entities, while conventional approaches suffer from limited capability in modeling user behavior responses and ensuring dispatch accuracy, making them inadequate for source–grid–load–storage collaborative optimization. To address this, this paper proposes a data-driven multi-scale coordinated scheduling method for distribution systems, in which distributed generation outputs, load responses, and energy storage states are extracted and modeled using an improved exponential smoothing technique; a hierarchical and time-divided optimization framework is then developed by combining machine learning and probabilistic modeling with spatial correlation analysis to enhance renewable generation and load forecasting accuracy; and finally, a two-stage robust optimization model considering scenario uncertainties is established through typical scenario generation and uncertainty set constraints to achieve dispatch strategies that balance economic efficiency and low-carbon objectives and supply reliability under fluctuating renewable outputs and dynamic load variations. Simulation results demonstrate that the proposed method reduces total operating cost by 16.4%, decreases carbon emissions by 10.7%, and lowers electricity purchase fluctuation by 8.75%, thereby significantly enhancing system flexibility and adaptability to renewable energy uncertainties and providing a novel pathway for the development of active and intelligent distribution systems. Full article

The reliability of magnet wire insulation is critical for the safe and efficient operation of aerospace electric machines exposed to extreme electrical and environmental conditions. Polyimide-based insulations are widely used due to their excellent thermal and dielectric properties; however, they face challenges such as space charge accumulation, partial discharge activity, and accelerated aging under combined stressors. This study investigates the dielectric breakdown behavior of MW35-C class magnet wire subjected to both AC and DC electrical stress under sub-atmospheric pressures representative of aerospace environments. Experimental measurements were performed on 13 AWG, 15 AWG, and 20 AWG wires, all sourced from the same manufacturer but differing in core conductor radius and total insulation thickness. The results were statistically analyzed using the Weibull distribution. To complement the experimental analysis, 3D finite element simulations were conducted to evaluate electric field distributions at the contact interface between wires. The results demonstrate that breakdown strength is significantly affected by ambient pressure, wire geometry (core radius and insulation thickness), and the volume effect. Among the tested wires, 20 AWG exhibited the highest breakdown strength, attributed to its favorable conductor-to-insulation ratio and reduced insulation volume, which lowers the probability of critical defects. These findings provide valuable insights for the design and qualification of robust insulation systems in all-electric and more-electric aircraft operating in low-pressure environments. Full article

The rapid growth of electric vehicle (EV) deployment has created a strong demand for charging systems capable of handling higher power levels while preserving grid stability and maintaining satisfactory energy quality. In this work, a fast-charging architecture for 400 V battery systems is developed using a Vienna rectifier on the AC front end and a DC–DC buck converter on the DC stage. To enhance the performance of this topology, two complementary control techniques are combined: the Crystal Structure Algorithm (CryStAl), used for offline optimization of switching behavior, and a Random Decision Forest (RDF) model, employed for real-time adaptation to operating conditions. A clear, step-oriented derivation of the converter state–space equations is included to support controller design and ensure reproducibility. This control framework improves the key performance indices, including Total Harmonic Distortion (THD), ripple suppression, efficiency, and power factor correction. Specifically, the Vienna rectifier works on input current shaping and enhances the power quality, while the buck converter maintains a constant DC output appropriate for reliable battery charging. The simulation studies show that the combined CryStAl–RDF approach outperforms the conventional PI- and Particle Swarm Optimization (PSO)-based controllers. The proposed method achieves THD less than 2%, conversion efficiency higher than 97.5%, and a power factor close to unity. The voltage and current ripples are also significantly reduced, which justifies the extended life of the batteries and reliable charging performance. Overall, the results portray the potential of the combined metaheuristic optimization with machine learning-based decision techniques to enhance the behavior of power electronic converters for EV fast-charging applications. The proposed control method offers a practical and scalable route for next-generation EV charging infrastructure. Full article

Growing pressure on electricity grids and the increasing availability of smart meter data have intensified the need for accurate, interpretable, and scalable methods to analyze and forecast household electricity consumption. In this context, this study presents a general, data-agnostic methodology for predicting and classifying household energy consumption. The proposed workflow unifies data preparation, feature engineering, and machine learning techniques (including clustering, classification, regression, and time series forecasting) within a single interpretable pipeline that supports actionable insights. Rather than proposing new prediction algorithms, this work contributes a fully reproducible, end-to-end methodological pipeline that enables the controlled evaluation of the impact of contextual variables, customer segmentation, and cold-start conditions on household energy forecasting. A distinctive aspect of the pipeline is the explicit use of household- and dwelling-level contextual variables to derive customer typologies via clustering and to enrich forecasting models. The models are evaluated for predictive accuracy, reliability under varying conditions, and suitability for operational use. The results show that incorporating contextual variables and clustering significantly improves forecasting accuracy, particularly in cold-start scenarios where no historical consumption data are available. Although numerous public datasets of residential electricity consumption exist, they rarely provide, in an openly accessible form, both detailed load histories and rich contextual attributes, while many are subject to privacy or licensing restrictions. To ensure full reproducibility and to enable controlled experiments where contextual variables can be switched on and off, the experiments are conducted on a synthetically generated dataset that reproduces realistic behavior and seasonal usage patterns. However, the proposed methodology is independent of the specific data source and can be directly applied to any real or synthetic dataset with similar structure. The approach enables applications such as short- and long-term demand forecasting, estimation of household energy costs, and forecasting demand for new customers. These findings demonstrate that the proposed pipeline provides a transparent and effective framework for end-to-end analysis of household electricity consumption. Full article

In the face of escalating energy demand, this research proposes a demand-side management (DSM) strategy that focuses on appliance-level load shifting in residential environments. The proposed approach utilizes detailed energy consumption forecasts that are generated by ensemble machine learning models, which predict usage at both whole-household and individual appliance levels. This granular forecasting enables the development of customized load-shifting schedules for controllable devices. These schedules are optimized using a metaheuristic genetic algorithm (GA) with the objectives of minimizing consumer energy costs and reducing peak demand. The iterative nature of GA allows for continuous fine-tuning, thereby adapting to dynamic energy market conditions. The implemented DSM technique yields significant results, successfully reducing the daily energy consumption cost for shiftable appliances. Overall, the proposed system decreases the per-day consumer electricity cost from 237 cents (without DSM) to 208 cents (with DSM), achieving a 12.23% cost saving. Furthermore, it effectively mitigates peak demand, reducing it from 3.4 kW to 1.2 kW, which represents a substantial 64.7% reduction. These promising outcomes demonstrate the potential for substantial consumer savings while concurrently enhancing the overall efficiency and reliability of the power grid. Full article

Electric Water Pumps (EWPs) are being adopted more widely to improve thermal management in internal combustion engines and electrified powertrain systems. In this context, the drive motor must deliver high efficiency and reliability despite a strict volume constraint. This paper addresses a key drawback of coreless printed circuit board (PCB) stator axial-flux permanent-magnet machines for EWP use: the PCB traces are directly exposed to the magnet flux, which increases AC loss, while the required phase resistance also leads to non-negligible DC copper loss. To mitigate both loss components within the same conductor design space, a pyramid trace concept is introduced. A magnetic equivalent circuit (MEC) based model is first used to estimate the baseline performance as the number of PCB stator modules changes, and the resulting scalability is examined in terms of module commonality. The final design then applies the pyramid trace layout with a layer-dependent trace width that is narrower on the layers closer to the magnets and wider on the layers farther away—the trade-off between AC loss and DC loss is optimized using 3D finite element analysis. Torque predictions from the simplified MEC model are cross-checked against 3D finite element analysis (FEA), and finally, a prototype is built to validate the analysis with experimental measurements; for the final selected model, the torque prediction error is 2.37% compared with the validation result. Full article

Modern factories still struggle with unexpected machine failures because traditional maintenance systems depend on fixed rules and threshold-based alerts. These older approaches often overlook subtle or complex patterns in multimodal sensor data, causing them to miss early signs of wear and leading to late or incorrect maintenance decisions. As a result, production can slow down, costs increase and equipment reliability suffers. To address this challenge, this study introduces a smart and interpretable fault diagnosis and predictive maintenance framework designed to detect wear, degradation and potential failures before they disrupt operations. The proposed framework integrates multiscale feature extraction, multimodal sensor fusion and cross-sensor correlation analysis with advanced temporal modeling using a Temporal Convolutional Network (TCN). By jointly performing tool-health classification and Remaining Useful Life (RUL) estimation, the framework provides a comprehensive assessment of machine condition. When evaluated on the NASA Ames milling dataset, the model achieved an overall accuracy of 86%, correctly classifying healthy and failed tools in more than 88% of cases and worn tools in over 75%, demonstrating consistent performance across different stages of tool wear. Explainable artificial intelligence (XAI) techniques, including attention-based visualizations and SHAP-based feature attribution, reveal that electrical and vibration signals are the most influential early indicators of tool degradation. The proposed framework exhibits low computational latency and minimal memory requirements, making it suitable for real-time fault diagnosis and deployment on industrial edge devices. Overall, the framework balances predictive accuracy, interpretability and practical applicability, enabling proactive and reliable maintenance decisions that enhance machine uptime and support efficient smart manufacturing operations. Full article

End-of-Line (EoL) quality control plays a crucial role in ensuring the reliability, safety, and performance of electric motors in modern industrial production. Increasing product complexity, tighter manufacturing tolerances, and rising production quantities have exposed the limitations of conventional EoL inspection systems, which rely primarily on manually crafted features, expert-defined thresholds, and rule-based decision logic. In recent years, artificial intelligence (AI) techniques, including machine learning (ML), deep learning (DL), and transfer learning (TL), have emerged as promising solutions to overcome these limitations by enabling data-driven, adaptive, and scalable quality inspection. This paper presents a comprehensive and structured review of the latest advances in intelligent EoL quality inspection for electric motor production. It systematically surveys the non-invasive measurement techniques that are commonly employed in industrial environments and examines the evolution from traditional signal processing-based inspection to AI-based approaches. ML methods for feature selection and classification, DL models for raw signal-based fault detection, and TL strategies for data-efficient model adaptation are critically analyzed in terms of their effectiveness, robustness, interpretability, and industrial applicability. Furthermore, this work identifies key challenges that prevent the widespread adoption of AI-based EoL inspection systems, including dependence on expert knowledge, limited availability of labeled fault data, generalization between motor variants and production condition, and the lack of standardized evaluation methodologies. Based on the identified research gaps, this review outlines research directions and emerging concepts for developing robust, interpretable, and data-efficient intelligent inspection systems suitable for real-world manufacturing environments. By synthesizing recent advances and highlighting open challenges, this review aims to support researchers and experts in designing next-generation intelligent EoL quality control systems that enhance production efficiency, reduce operational costs, and improve product reliability. Full article

This study proposes and evaluates a data-driven framework for short-term System Marginal Price (SMP) forecasting in the Chilean National Electric System (NES), a power system characterized by high penetration of variable renewable generation and persistent transmission congestion. Using publicly available hourly operational data for 2024, multiple machine learning regressors including Linear Regression (base case), Bayesian Ridge, Automatic Relevance Determination, Decision Trees, Random Forests, and Support Vector Regression are implemented under a node-specific modeling strategy. Two alternative approaches for predictor selection are compared: a system-wide methodology that exploits lagged SMP information from all network nodes; and a spatially filtered methodology that restricts SMP inputs to correlated subsystems identified through nodal correlation analysis. Model robustness is explicitly assessed by reserving January and July as out-of-sample test periods, capturing contrasting summer and winter operating conditions. Forecasting performance is analyzed for representative nodes located in the northern, central, and southern zones of the NES, which exhibit markedly different congestion levels and generation mixes. Results indicate that non-linear and ensemble models, particularly Random Forest and Support Vector Regression, provide the most accurate forecasts in well-connected areas, achieving mean absolute errors close to 10 USD/MWh. In contrast, forecast errors increase substantially in highly congested southern zones, reflecting the structural influence of transmission constraints on price formation. While average performance differences between M1 and M2 are modest, a paired Wilcoxon signed-rank test reveals statistically significant improvements with M2 in highly congested zones, where M2 yields lower absolute errors for most models, despite relying on fewer inputs. These findings highlight the importance of congestion-aware feature selection for reliable price forecasting in renewable-intensive systems. Full article