Photovoltaic Cell Anomaly Detection Enabled by Scale Distribution Alignment Learning and Multiscale Linear Attention Framework

IEEE Internet of Things Journal, Год журнала: 2024, Номер 11(16), С. 27816 - 27827

Опубликована: Июнь 14, 2024

Язык: Английский

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Unlocking new horizons, challenges of integrating machine learning to energy conversion and storage research

Indian Chemical Engineer, Год журнала: 2025, Номер unknown, С. 1 - 18

Опубликована: Янв. 9, 2025

In recent times, artificial intelligence (AI) and machine learning (ML) have emerged as revolutionary technologies with wide-ranging applications across various fields, including energy conversion storage (ECS) systems. These methods utilise large amounts of data computational power to predict material properties, optimise systems, develop control algorithms for devices. This literature analysis focuses on the latest advancements methodologies in AI/ML ECS encompassing design discovery, property prediction, system optimisation. Furthermore, study examines main challenges integrating ML into these problems include issues related availability quality, model interpretability, transfer learning, experimental integration, ethics. Despite challenges, has potential revolutionise enhance performance. Advancements ML-driven sustainable are fostering interdisciplinary collaboration research, offering promising solutions energy.

Язык: Английский

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Capacity estimation of lithium-ion batteries based on segment IC curve data dimensionality reduction and reconstruction methods

Ionics, Год журнала: 2025, Номер 31(3), С. 2457 - 2471

Опубликована: Янв. 11, 2025

Язык: Английский

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Adaptive Multi-domain Capacity Estimation for Battery Energy Storage System based on Multi-scale Random Sequence Feature Fusion

Energy, Год журнала: 2025, Номер unknown, С. 134997 - 134997

Опубликована: Фев. 1, 2025

Язык: Английский

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Battery health prognosis in data-deficient practical scenarios via reconstructed voltage-based machine learning

Cell Reports Physical Science, Год журнала: 2025, Номер unknown, С. 102442 - 102442

Опубликована: Фев. 1, 2025

Язык: Английский

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Fault diagnosis of mechanical seals using graph neural networks with multi-sensor data fusion

Advances in Mechanical Engineering, Год журнала: 2025, Номер 17(2)

Опубликована: Фев. 1, 2025

Mechanical seals are critical components in the mechanical industry, and their operational status directly impacts performance of pumps, compressors, other machinery. Therefore, conducting research on fault diagnosis is essential. To enhance accuracy assessment model, we propose an integrated approach that leverages fusion multiple graph neural networks (GNNs). Firstly, recognizing diversity among different sensors, utilize multi-channel data to comprehensively represent state seal. These channels include various types sensors such as acoustic emission force sensors. Secondly, employ methods transform original into data, thereby continuously increasing datasets used for training. Finally, after training GNNs, output these through obtain evaluation results. The effectiveness our demonstrated using seal test data.

Язык: Английский

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A Novel Capacity Estimation Method for Lithium-Ion Batteries Based on the Adam Algorithm

Batteries, Год журнала: 2025, Номер 11(3), С. 85 - 85

Опубликована: Фев. 20, 2025

Accurate estimation of the capacity lithium-ion batteries is crucial for battery management and secondary utilization, which can ensure healthy efficient operation system. In this paper, we propose multiple machine learning algorithms to estimate using incremental (IC) curve features, including adaptive moment (Adam) model, root mean square propagation (RMSprop) support vector regression (SVR) model. The Kalman filter algorithm first used construct IC curve, peak corresponding voltages correlated with life were analyzed extracted as features. three models then learn relationship between aging features capacity. Finally, cycle data validate performance proposed models. results show that Adam model performs better than other two models, balancing efficiency accuracy in throughout entire lifecycle.

Язык: Английский

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Robust Clustering and Anomaly Detection of User Electricity Consumption Behavior Based on Correntropy

IET Generation Transmission & Distribution, Год журнала: 2025, Номер 19(1)

Опубликована: Янв. 1, 2025

ABSTRACT Anomaly detection in power systems is crucial for ensuring the safety and stability of electrical grids. Traditional methods struggle to extract meaningful features from electricity consumption data due significant differences usage patterns across various user types, such as residential industrial users. Applying a single model all categories increases feature complexity computational demands. Additionally, non‐Gaussian outliers caused by equipment measurement noise can significantly deviate normal patterns, making them difficult filter using standard methods. To address these challenges, this paper proposes robust, user‐type‐specific anomaly method. After preprocessing, correntropy‐based K‐means clustering method used separate users with noisy data. A two‐stage framework combining fuzzy logic convolutional neural network (CNN)‐long short‐term memory (LSTM) enhances both efficiency accuracy. The experiments were conducted open‐source datasets, results demonstrated that our achieved an accuracy 95%, which approximately 4% higher than traditional Isolation Forest This indicates approach effectively balances detection, its generalizability further validated on additional dataset.

Язык: Английский

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Confidence-aware Quantile Transformer for reliable degradation prediction of battery energy storage systems

Reliability Engineering & System Safety, Год журнала: 2025, Номер unknown, С. 111019 - 111019

Опубликована: Март 1, 2025

Язык: Английский

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Addressing unknown faults diagnosis of transport ship propellers system based on adaptive evolutionary reconstruction metric network

Advanced Engineering Informatics, Год журнала: 2025, Номер 65, С. 103287 - 103287

Опубликована: Апрель 8, 2025

Язык: Английский

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