Energy Conversion and Economics最新文献

Power system-oriented extreme weather scenario identification: Hybrid classical-quantum learning 面向电力系统的极端天气情景识别：经典-量子混合学习

IF 4.8

Energy Conversion and Economics Pub Date : 2026-09-06 Epub Date: 2026-08-29 DOI: 10.1049/enc2.70049

Tian Ni, Hui Hou, Zhenguo Wang, Wenzhe Zheng, Xixiu Wu

{"title":"Power system-oriented extreme weather scenario identification: Hybrid classical-quantum learning","authors":"Tian Ni, Hui Hou, Zhenguo Wang, Wenzhe Zheng, Xixiu Wu","doi":"10.1049/enc2.70049","DOIUrl":"https://doi.org/10.1049/enc2.70049","url":null,"abstract":"Weather-dependent renewable energy makes modern power systems more sensitive to extreme weather. Therefore, reliable identification of power system-oriented extreme weather scenarios is important for reliability assessment, resilience analysis, and risk-oriented operation. However, these scenarios are heterogeneous and suffer from severe class imbalance, which makes reproducible labelling and robust model training difficult. This paper proposes a hybrid classical-quantum learning framework for power system-oriented extreme weather scenario identification. First, a structured definition and labelling framework is developed to extract extreme weather scenarios from large-scale weather-power datasets by jointly considering meteorological abnormality, power system response, and external event evidence. Second, a quantum generative module is introduced as a training-only augmentation tool to synthesize minority-class samples, while validation and testing remain strictly based on real observations. Third, a hybrid classical-quantum classifier is constructed by embedding variational quantum circuits as trainable feature-mapping modules within a classical learning pipeline, aiming to examine their feasibility under small-sample and imbalanced-data conditions. In addition to conventional generation and classification metrics, risk-oriented indicators are used to evaluate whether the identified scenarios are meaningful for power system operation. The proposed framework is validated using real-world weather-power data from China. The results show that the quantum generative adversarial network preserves minority-class risk characteristics, while convolutional neural network-quantum recurrent neural network (CNN-QRNN) attains the numerically highest minority macro-F1 of 0.2998 among the compared models, with a macro-F1 of 0.3972.","PeriodicalId":100467,"journal":{"name":"Energy Conversion and Economics","volume":"7 4","pages":"241-259"},"PeriodicalIF":4.8,"publicationDate":"2026-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/enc2.70049","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148904369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An edge AI-powered power converter hardware platform for practical end-to-end AI-to-converter closed-loop deployment 一个边缘人工智能电源转换器硬件平台，用于实际端到端人工智能到转换器的闭环部署

IF 4.8

Energy Conversion and Economics Pub Date : 2026-09-06 Epub Date: 2026-08-29 DOI: 10.1049/enc2.70048

Pingyang Sun, Fan Zhang, Zihang Qiu, Junjie Chang, Hanwen Zhang, Gen Li

{"title":"An edge AI-powered power converter hardware platform for practical end-to-end AI-to-converter closed-loop deployment","authors":"Pingyang Sun, Fan Zhang, Zihang Qiu, Junjie Chang, Hanwen Zhang, Gen Li","doi":"10.1049/enc2.70048","DOIUrl":"https://doi.org/10.1049/enc2.70048","url":null,"abstract":"Modern power systems require increasingly flexible and responsive power conversion, driving the need for power converters with enhanced controllability and adaptability, where artificial intelligence (AI) plays a key role in enabling intelligent decision making, adaptive control and system level optimization. However, current AI applications in power systems remain largely restricted to cloud-based post processing or device-level demonstrations, lacking end-to-end hardware platforms that support the full pipeline from programming to deployment within a closed loop formed by physical converter hardware and AI modules. To address this gap, this paper proposes an edge AI-powered power converter (EAI-PC) hardware platform that utilizes locally deployed edge intelligence to establish bidirectional data exchange between converter low-level control and EAI upper-layer command dispatch. The platform features an end-to-end AI-to-converter closed-loop deployment framework, enabling AI-generated commands to be directly executed on physical converter hardware. By interconnecting converters (AC/DC and DC/DC) with an EAI module (using NVIDIA Jetson Orin Nano) via an industrial CAN protocol, this platform supports hardware-level algorithm verification in the power system domain. The proposed EAI-PC platform is experimentally evaluated across system configurations from single to multiple interconnected converters, assessing converter performance, command delivery and three AI deployment cases (two offline and one online): a multilayer perceptron (MLP) for power-sharing prediction, a spatio-temporal graph neural network (STGNN) for photovoltaic (PV) and load forecasting, and a bidirectional long short-term memory (Bi-LSTM) for online battery dispatch in load management and peak shaving.","PeriodicalId":100467,"journal":{"name":"Energy Conversion and Economics","volume":"7 4","pages":"260-278"},"PeriodicalIF":4.8,"publicationDate":"2026-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/enc2.70048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148904370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ENSO impacts on low wind power events: A long-term data-driven analysis using TabNet ENSO对低风力事件的影响：TabNet的长期数据驱动分析

IF 4.8

Energy Conversion and Economics Pub Date : 2026-09-06 Epub Date: 2026-08-29 DOI: 10.1049/enc2.70047

Kai Chen, Han Zhou, Yan Jiang, Xufei Liu, Shijie Chen, Zhenyu Zhao, Congtong Zhang, Junkai Liang

{"title":"ENSO impacts on low wind power events: A long-term data-driven analysis using TabNet","authors":"Kai Chen, Han Zhou, Yan Jiang, Xufei Liu, Shijie Chen, Zhenyu Zhao, Congtong Zhang, Junkai Liang","doi":"10.1049/enc2.70047","DOIUrl":"https://doi.org/10.1049/enc2.70047","url":null,"abstract":"As power systems transition towards decarbonised energy mixes with increasing wind penetration, prolonged low-wind-power (LWP) events have emerged as a critical source of adequacy risk. However, a comprehensive analysis of LWP events remains constrained by the scarcity of long-term historical wind-power generation records in regions with high renewable penetration. To address this limitation, this study proposes a TabNet-based wind power modelling framework for 90 wind farms in Guangxi, China, leveraging historical meteorological records and measured operational data. ERA5 reanalysis data are used to reconstruct hourly wind-power generation over a 43-year period (1980–2022). An adaptive mean-below-threshold method featuring a tolerance mechanism is proposed to identify LWP events over the 43-year period. Based on this, the intra-annual and inter-annual characteristics of LWP events are analysed, and their relationship with the El Niño–Southern Oscillation and the Eastern Pacific (EP) index is investigated. The results indicate that both the annual duration and frequency of LWP events exhibit upward trends over the study period, with the upward trends becoming more pronounced after 2000. Furthermore, the EP index demonstrates a lagged association with LWP duration and frequency in Guangxi, with decaying La Niña years exhibiting the clearest positive anomalies in LWP duration and frequency among the investigated ENSO categories. These findings offer climate-informed insights for wind power adequacy assessment and renewable integration planning.","PeriodicalId":100467,"journal":{"name":"Energy Conversion and Economics","volume":"7 4","pages":"279-292"},"PeriodicalIF":4.8,"publicationDate":"2026-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/enc2.70047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148904371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pricing and hedging renewable power purchase agreement floors under stochastic generation 随机发电下可再生能源购买协议下限的定价与对冲

IF 4.8

Energy Conversion and Economics Pub Date : 2026-09-06 Epub Date: 2026-08-23 DOI: 10.1049/enc2.70045

Ezio Lauro, Helyette Geman

{"title":"Pricing and hedging renewable power purchase agreement floors under stochastic generation","authors":"Ezio Lauro, Helyette Geman","doi":"10.1049/enc2.70045","DOIUrl":"https://doi.org/10.1049/enc2.70045","url":null,"abstract":"Renewable power purchase agreements (PPAs) expose generators and offtakers to capture-price risk because realised revenue depends on prices during renewable production hours. Downside protection written on the production-weighted settlement price is economically relevant, but difficult to mark and hedge because the index is non-tradable and depends jointly on power prices and renewable output. This article develops a futures-anchored statistical mark-to-model and hedging framework for long-dated Asian floors embedded in renewable PPAs when renewable generation remains stochastic. The framework is not interpreted as a unique arbitrage-free price in a complete market. Instead, it isolates the hedgeable component through liquid German baseload futures and updates the remaining price-generation exposure using deterministic seasonal profiles and a vector autoregression for deseasonalised German day-ahead price and wind-generation residuals. Moment matching and a delta-method approximation yield a tractable Bachelier-style floor mark and operational futures hedge ratios. In pre-delivery backtests for the 2024 and 2025 delivery years, the stochastic hedge reduces daily residual variance by 94.7% and 89.0%, respectively compared with 69.1% and 47.9% for a deterministic-generation hedge. The results show that stochastic renewable production is a first-order input for practical risk management of renewable PPA optionality.","PeriodicalId":100467,"journal":{"name":"Energy Conversion and Economics","volume":"7 4","pages":"312-323"},"PeriodicalIF":4.8,"publicationDate":"2026-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/enc2.70045","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148904374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Degradation-aware energy management for photovoltaic-storage-charging microgrids under extreme operating environments: A comprehensive review 极端运行环境下光伏-储能-充电微电网的退化感知能量管理：综合综述

IF 4.8

Energy Conversion and Economics Pub Date : 2026-09-06 Epub Date: 2026-08-26 DOI: 10.1049/enc2.70046

Qiong Liu, Jun Wang, Jiaxu Duan, Yunhui Liu, Yue Chen

{"title":"Degradation-aware energy management for photovoltaic-storage-charging microgrids under extreme operating environments: A comprehensive review","authors":"Qiong Liu, Jun Wang, Jiaxu Duan, Yunhui Liu, Yue Chen","doi":"10.1049/enc2.70046","DOIUrl":"https://doi.org/10.1049/enc2.70046","url":null,"abstract":"Photovoltaic-storage-charging microgrids (PSCMs) are being increasingly deployed in extreme environments, including desert, polar, coastal and high-altitude regions, to provide a reliable and sustainable power supply. However, these harsh environments significantly accelerate the degradation of photovoltaic arrays, battery storage systems and electric vehicle charging infrastructures. Effectively managing PSCMs under these conditions requires energy management systems with control strategies that explicitly account for environment-dependent degradation dynamics. This paper provides a comprehensive review of degradation-aware energy management for PSCMs operating in extreme environments. First, extreme operating environments are classified using quantitative thresholds, and their specific degradation effects on PSCM components are systematically analysed. Next, degradation modelling approaches comprising analytical models, data-driven approximations and physics-informed enhancements are reviewed and compared. Current energy management strategies, ranging from model-based methods to data-driven reinforcement learning and hybrid architectures, are then examined. Finally, future research directions are outlined, focusing on generalizable multi-stress degradation models, principled degradation-aware control frameworks and comprehensive datasets and digital twin testbeds.","PeriodicalId":100467,"journal":{"name":"Energy Conversion and Economics","volume":"7 4","pages":"293-311"},"PeriodicalIF":4.8,"publicationDate":"2026-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/enc2.70046","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148904387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bi-level capacity planning for park IES considering energy supply reliability 考虑供电可靠性的园区IES双层容量规划

IF 4.8

Energy Conversion and Economics Pub Date : 2026-07-02 Epub Date: 2026-06-04 DOI: 10.1049/enc2.70040

Chenxi Wu, Hao Li, Yaolin Lou, Walter Angelis, Xinfu Li, Dongmin Zhu

{"title":"Bi-level capacity planning for park IES considering energy supply reliability","authors":"Chenxi Wu, Hao Li, Yaolin Lou, Walter Angelis, Xinfu Li, Dongmin Zhu","doi":"10.1049/enc2.70040","DOIUrl":"https://doi.org/10.1049/enc2.70040","url":null,"abstract":"Carbon emissions and energy consumption of urban buildings represent a notable fraction of global energy utilization. In pursuit of the “carbon peaking and carbon neutrality” goals, urban park building integrated energy system (IES) energy saving technology is regarded as a key technology to achieve sustainable urban development. Therefore, a bi-level optimal capacity allocation method for the park energy supply/storage equipment has been developed in this paper. Flexible load demand response and energy supply reliability are fully considered. First, heterogeneous data analysis of smart park is conducted and models of energy conversion devices and storage devices based on the energy supply/storage characteristics of park IES are established, along with operational constraint models for various equipment. Second, at the upper level, the optimization aims to minimize the park IES cost. The operational cost is minimized in the lower level. The source/storage capacity configurations are optimized by genetic algorithm. In this paper, operational optimization is incorporated into planning, and flexible load control schemes are applied to peak load shifting, contributing to alleviated energy demand pressure during peak periods. Finally, the superiority role of the proposed bi-level optimization method is proved by case studies demonstrated by a real park IES and the effectiveness and feasibility of the data processing methods in improving the quality and accuracy of load data were also verified. The developed methodology enhances economic benefits, fully meeting the multi-energy demand of the system.","PeriodicalId":100467,"journal":{"name":"Energy Conversion and Economics","volume":"7 3","pages":"194-206"},"PeriodicalIF":4.8,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/enc2.70040","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148373323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

State of health estimation for lithium-ion batteries using modern heuristic algorithm optimized multiple kernel extreme learning 基于优化多核极值学习的现代启发式锂离子电池健康状态估计算法

IF 4.8

Energy Conversion and Economics Pub Date : 2026-07-02 Epub Date: 2026-06-19 DOI: 10.1049/enc2.70041

Yumin Zhang, Yongtao Liu, Pingfeng Ye, Xingquan Ji, Yunqi Wang, Kezhuang Xue, Xuchen Ma

{"title":"State of health estimation for lithium-ion batteries using modern heuristic algorithm optimized multiple kernel extreme learning","authors":"Yumin Zhang, Yongtao Liu, Pingfeng Ye, Xingquan Ji, Yunqi Wang, Kezhuang Xue, Xuchen Ma","doi":"10.1049/enc2.70041","DOIUrl":"https://doi.org/10.1049/enc2.70041","url":null,"abstract":"Accurate state of health (SOH) estimation is crucial for the safe operation of lithium-ion batteries. To address the limited capability of traditional models in characterizing nonlinear degradation, this study proposes a novel data-driven SOH estimation framework. First, an indirect health indicator (IHI) extraction strategy constructs aging-sensitive features directly from operational data, avoiding complex electrochemical modeling. Second, a hybrid multiple kernel extreme learning machine (HMKELM) integrates radial basis, polynomial, and sigmoid kernels with adaptive mixing weights to capture both local and global characteristics of degradation trajectories. Furthermore, to alleviate the hyperparameter-tuning bottleneck of HMKELM, a gold rush-inspired grey wolf optimizer (GRGWO), incorporating a leader-update strategy and a dynamic convergence factor, is developed to escape local optima and jointly optimize kernel parameters and mixing weights. Validation on the NASA ARC-FY08Q4 dataset shows that the proposed GRGWO-HMKELM framework enables accurate long-horizon SOH estimation using only early-cycle data for training and consistently outperforms representative baseline models, indicating improved generalization for practical in-service SOH monitoring.","PeriodicalId":100467,"journal":{"name":"Energy Conversion and Economics","volume":"7 3","pages":"180-193"},"PeriodicalIF":4.8,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/enc2.70041","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148373464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HSL-CFS: Hybrid stacked learning with cooperative feature selection for cyberattack detection in smart grids HSL-CFS：基于协同特征选择的智能电网网络攻击检测混合堆叠学习

IF 4.8

Energy Conversion and Economics Pub Date : 2026-07-02 Epub Date: 2026-06-19 DOI: 10.1049/enc2.70042

Qize Gao, Qiuyu Lu, June Li, Zhao Peng, Guo Shen, Ming Ni

{"title":"HSL-CFS: Hybrid stacked learning with cooperative feature selection for cyberattack detection in smart grids","authors":"Qize Gao, Qiuyu Lu, June Li, Zhao Peng, Guo Shen, Ming Ni","doi":"10.1049/enc2.70042","DOIUrl":"https://doi.org/10.1049/enc2.70042","url":null,"abstract":"An effective method for detecting cyberattacks is essential to the security of smart grids (SGs). In SGs, data from both cyber and physical domains can support attack detection. However, existing works insufficiently consider the heterogeneity, high dimensionality, and cross-domain correlations of multi-source data, affecting model generalization, stability, and detection accuracy. To address these limitations, this paper proposes a hybrid stacked learning method with cooperative feature selection (HSL-CFS). First, a cooperative feature selection approach is proposed for feature extraction from multi-source heterogeneous data. An embedded method with adaptive threshold adjustment and a wrapper method with dynamic bidirectional search extract key features from complementary perspectives, and their feature sets are merged via set union to output representative features. Second, a hybrid stacked model combining Extreme Random Trees (ET) and an improved Convolutional Neural Network (CNN) is proposed. The CNN is enhanced with Euclidean-norm regularization (L2 regularization) and Squeeze-and-Excitation (SE) block attention mechanism to mitigate overfitting and recalibrate channel responses. Furthermore, probability alignment calibrates base classifier outputs before stacking, enabling better capture of complementary patterns in SGs cyber-physical data. Experimental results show that HSL-CFS selects fewer features and achieves enhanced stability, generalization and accuracy, outperforming existing methods on the selected dataset.","PeriodicalId":100467,"journal":{"name":"Energy Conversion and Economics","volume":"7 3","pages":"207-220"},"PeriodicalIF":4.8,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/enc2.70042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148373465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Full-stage risk management of forecast deviation for large-scale renewable energy participating in the spot market 大型可再生能源参与现货市场预测偏差的全阶段风险管理

IF 4.8

Energy Conversion and Economics Pub Date : 2026-07-02 Epub Date: 2026-06-26 DOI: 10.1049/enc2.70044

Sicheng Xu, Ruoyi Zhang, Jingwei Chen, Si Zhang, Li Yang, Zhenzhi Lin

{"title":"Full-stage risk management of forecast deviation for large-scale renewable energy participating in the spot market","authors":"Sicheng Xu, Ruoyi Zhang, Jingwei Chen, Si Zhang, Li Yang, Zhenzhi Lin","doi":"10.1049/enc2.70044","DOIUrl":"https://doi.org/10.1049/enc2.70044","url":null,"abstract":"Against the backdrop of renewable energy achieving full market integration, renewable energy forecasting deviations have become a critical bottleneck constraining the clearing efficiency of electricity spot markets, hindering the rational allocation of costs, and threatening the secure operation of the power system. A framework comprising a full-stage governance system covering planning, medium- to long-term trading, spot markets, and settlement is adopted in this paper. The experiences of global markets in addressing forecast deviations of renewable energy through multi-stage, tiered governance are systematically reviewed in the paper. During the planning stage, a probabilistic physical defence boundary is established to address hidden physical gaps arising from random fluctuations that deterministic planning cannot account for. In the medium- to long-term stage, risks are mitigated through a combination of physical resource aggregation and financial hedging tools, including bilateral contracts, forward contracts, reliability options, and parametric insurance. In the spot market, approaches such as self-forecast verification, rolling forecast corrections, and a multi-level substitution mechanism for abnormal data are adopted. During the settlement stage, incentive-compatible mechanisms are used to improve the accuracy of renewable energy forecasting. Finally, suggestions on full-stage risk management for large-scale renewable energy participating in the spot market are proposed in this paper.","PeriodicalId":100467,"journal":{"name":"Energy Conversion and Economics","volume":"7 3","pages":"221-240"},"PeriodicalIF":4.8,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/enc2.70044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148373539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards practical oscillation detection in wind farms: Comparative study of AI models, novel metrics, and edge implementations 风力发电场的实际振荡检测：人工智能模型、新指标和边缘实现的比较研究

IF 4.8

Energy Conversion and Economics Pub Date : 2026-07-02 Epub Date: 2026-06-22 DOI: 10.1049/enc2.70043

Shyam Yathirajam, Arash Peighambari, Valeria Romero, Christopher Rubin, Nikil Balaji, Hamed Nademi, Sreedevi Gutta, Justin Morris, Ali Ahmadinia

{"title":"Towards practical oscillation detection in wind farms: Comparative study of AI models, novel metrics, and edge implementations","authors":"Shyam Yathirajam, Arash Peighambari, Valeria Romero, Christopher Rubin, Nikil Balaji, Hamed Nademi, Sreedevi Gutta, Justin Morris, Ali Ahmadinia","doi":"10.1049/enc2.70043","DOIUrl":"https://doi.org/10.1049/enc2.70043","url":null,"abstract":"Fast oscillations observed in wind farms and other renewable energy systems interfaced with power grids must be addressed promptly to prevent devastating shutdown and damage. Existing approaches mainly target high-magnitude oscillations using data-intensive convolutional neural networks (CNNs), leading to delayed detection and overlooking mild oscillations that can still cause long-term degradation. This paper proposes a dual detection framework that rapidly identifies high-magnitude oscillations using time-domain analysis while detecting mild oscillations through FFT-based frequency-domain feature extraction. The framework leverages hyperdimensional computing (HDC) and machine learning models, including one-class support vector machine (SVM), 1D-CNN, 2D-CNN, and a one-class autoencoder. To enhance performance evaluation, we introduce true positive confidence (TPC) and false positive confidence (FPC) metrics. Unlike prior work limited to server-based evaluation, we deploy HDC on edge platforms, including a Jetson Nano graphics processing unit (GPU), tensor processing unit (TPU), and field programmable gate array (FPGA). Experimental results on real-world wind farm data and Simulink-generated datasets show that HDC outperforms all baseline models, achieving at least 99% confidence in both TPC and FPC for high-magnitude and mild oscillations. Notably, HDC operates up to 2100 times faster than 1D-CNN on GPU and 6800 times faster on FPGA, while being four times smaller in model size, demonstrating that FPGA-based HDC is the minimum suitable hardware for reliable real-time deployment in wind farms.","PeriodicalId":100467,"journal":{"name":"Energy Conversion and Economics","volume":"7 3","pages":"163-179"},"PeriodicalIF":4.8,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/enc2.70043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148373049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0