IEEE Transactions on Industry Applications最新文献_第8页

Optimal Enhancement Control of Static Voltage Stability Margin for AC/DC Power System With Renewables Considering Control Mode Switching of DC Converter Stations 考虑直流换流站控制方式切换的可再生交直流电力系统静态电压稳定裕度优化增强控制

IF 4.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2025-01-17 DOI: 10.1109/TIA.2025.3530868

Wanbin Liu;Shunjiang Lin;Yuerong Yang;Ziqing Yang;Mingbo Liu;Qifeng Li

{"title":"Optimal Enhancement Control of Static Voltage Stability Margin for AC/DC Power System With Renewables Considering Control Mode Switching of DC Converter Stations","authors":"Wanbin Liu;Shunjiang Lin;Yuerong Yang;Ziqing Yang;Mingbo Liu;Qifeng Li","doi":"10.1109/TIA.2025.3530868","DOIUrl":"https://doi.org/10.1109/TIA.2025.3530868","url":null,"abstract":"In an AC/DC power system with high penetration of renewable energy stations (RESs), the injected bus power has high random fluctuation, which brings great challenges to ensuring the sufficient static voltage stability margin (SVSM) of the system. In this paper, considering the impact of uncertain RES output and DC converter stations’ control mode switching with the load growth, an optimal SVSM enhancement control model for AC/DC power system is proposed. In the model, the SVSM of the system under uncertain RES power is enhanced by regulating the power output and terminal voltage of generators and the parameters of multiple control modes of DC converter stations. By proposing a triangular convex hull relaxation method and using the uncertainty-aware model (UaM) method and the Karush–Kuhn–Tucker condition of inner-layer optimization model, the proposed bi-layer optimal SVSM enhancement control model is transformed into a single-layer nonlinear programming model, which can be directly solved by the CONOPT solver with high efficiency. Case study on the modified 39-bus AC/DC power system demonstrates that the obtained SVSM enhancement control scheme can accurately consider the influence of DC converter stations’ control mode switching with the load growth, identify the influence of uncertain injected bus power on the system SVSM, and enhance the SVSM to satisfy the required secure operation level.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"61 2","pages":"2565-2577"},"PeriodicalIF":4.2,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Factorization Machine Learning for Disaggregation of Transmission Load Profiles With High Penetration of Behind-the-Meter Solar 基于深度分解机器学习的高渗透太阳能输电负荷分布分解

IF 4.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2025-01-17 DOI: 10.1109/TIA.2025.3530864

Zhenyu Zhao;Daniel Moscovitz;Shengyi Wang;Liang Du;Xiaoyuan Fan

{"title":"Deep Factorization Machine Learning for Disaggregation of Transmission Load Profiles With High Penetration of Behind-the-Meter Solar","authors":"Zhenyu Zhao;Daniel Moscovitz;Shengyi Wang;Liang Du;Xiaoyuan Fan","doi":"10.1109/TIA.2025.3530864","DOIUrl":"https://doi.org/10.1109/TIA.2025.3530864","url":null,"abstract":"The ever-growing integration of distributed energy resources (DERs), especially behind-the-meter (BTM) solar generations, poses imperative operational challenges to system operators such as regional transmission organizations (RTOs). It is important for RTOs to effectively and accurately extract actual load profiles at the transmission level for a single node with significant BTM solar injection. This paper first illustrates the necessity of disaggregating the daily actual load profile of a single node. Furthermore, by segmenting nodes with selected time-series features, nodes with significant BTM solar generation are identified. Lastly, a bi-level framework is proposed, comprising reference node disaggregation and DeepFM nodal disaggregation, aimed at disaggregating the nodal load profiles from which system operators require more information. By adopting a hybrid Deep Factorization Machine (DeepFM) model, the model achieve accurate results by extracting both linear and nonlinear relations between nodes in the same region and the zonal load and nodal load profile. To overcome the lack of ground truth, this paper segments the load profile into daytime, nighttime, and zero-crossing points and utilizes the latter two for evaluation purposes. The proposed disaggregation procedure is validated using real-world, minute-level, normalized, and anonymized nodal data in the PJM service territory.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"61 2","pages":"2457-2466"},"PeriodicalIF":4.2,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Short-Term Transmission Capacity Prediction of Hybrid Renewable Energy Systems Considering Dynamic Line Rating Based on Data-Driven Model 基于数据驱动模型的考虑动态线路额定的混合可再生能源系统短期输电容量预测

IF 4.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2025-01-16 DOI: 10.1109/TIA.2025.3529824

Yi Su;Mao Tan;Jiashen Teh

{"title":"Short-Term Transmission Capacity Prediction of Hybrid Renewable Energy Systems Considering Dynamic Line Rating Based on Data-Driven Model","authors":"Yi Su;Mao Tan;Jiashen Teh","doi":"10.1109/TIA.2025.3529824","DOIUrl":"https://doi.org/10.1109/TIA.2025.3529824","url":null,"abstract":"The output capacity of Hybrid Renewable Energy Systems (HRES) is crucial for dispatching plans and spinning reserve capacity, but it's constrained by renewable energy generator output and transmission tie-line capacity. Considering both dynamic line rating for tie-lines and the entire HRES is beneficial due to their susceptibility to micro weather conditions. Unfortunately, considering them collectively for capacity forecasting involves long-term regular fluctuations and short-term uncertainty changes in weather factors, which reduces prediction accuracy. Thus, a novel data-driven model is introduced in this paper to address the aforementioned issue. Initially, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is employed to segment the capacity sequence into high-, medium-, and low-frequency components. Subsequently, the high-frequency component, characterized by wind-induced randomness, is predicted using Newton-Raphson-Based Optimizer (NRBO) - Bidirectional Gated Recurrent Unit (BiGRU); the medium-frequency component, reflecting seasonal regularities, is forecasted using Seasonal Autoregressive Integrated Moving Average (SAIMA); and the smooth and periodic low-frequency component is anticipated using Multivariable Linear Regression (MLR). Finally, the predictions from these models are combined to derive the ultimate predictive value. Case studies demonstrate that comprehensive consideration of transmission tie-lines equipped with DLR, as well as HRES, can enhance the external output capability of HRES, especially during periods of abundant wind resources. The proposed data-driven model can capture high-frequency fluctuations, medium-frequency periodicity, and low-frequency trends in capacity to enhance prediction accuracy.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"61 2","pages":"2410-2420"},"PeriodicalIF":4.2,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advanced Encoderless Control System-Based Split-Source Inverter for Induction Motor Drives 基于先进无编码器控制系统的异步电机分源逆变器

IF 4.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2025-01-16 DOI: 10.1109/TIA.2025.3529818

Mohamed G. Hussien;Lingling Cao;Junping He;Muhammed B. Shafik;Sherif M. Dabour;Khalil Alluhaybi;Md. Rabiul Islam;Abd El-Wahab Hassan

引用次数: 0

Improving Online Voltage Stability Monitoring in Smart Grids: A Physics-Informed Guided Deep Learning Model 改进智能电网的在线电压稳定性监测：一个物理信息引导的深度学习模型

IF 4.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2025-01-15 DOI: 10.1109/TIA.2025.3529813

Heng-Yi Su;Chia-Ching Lai

{"title":"Improving Online Voltage Stability Monitoring in Smart Grids: A Physics-Informed Guided Deep Learning Model","authors":"Heng-Yi Su;Chia-Ching Lai","doi":"10.1109/TIA.2025.3529813","DOIUrl":"https://doi.org/10.1109/TIA.2025.3529813","url":null,"abstract":"Amidst the increasing penetration of intermittent renewable generation and the persistent growth of load demands, voltage stability assumes a pivotal concern in smart grids. The real-time voltage stability assessment (VSA) under time-varying operating conditions becomes paramount. Recent strides in real-time VSA, utilizing intelligent data-driven learning with measurements, mark significant progress. However, a critical and unresolved challenge with purely data-driven methods is their susceptibility to performance degradation, especially in out-of-sample scenarios. To this end, this article presents a physics-informed guided deep learning (PGDL) paradigm for the practical and accurate assessment of voltage stability margins (VSMs), leveraging both physics-based and data-driven techniques. The PGDL architecture includes an improved temporal convolutional network (iTCN) for the automatic extraction of representative temporal features necessary for VSA from measurement data. Additionally, PGDL integrates physics-based features informed by domain-specific knowledge. A feature fusion scheme is then devised to merge deep-learned features with pertinent physics-based attributes. Acknowledging the unique contributions of these feature modalities to VSA, a novel twin attention mechanism (TAM) is proposed to adaptively adjust attention weights, prioritizing learned features and thus optimizing VSA performance. Substantial experiments on power systems of different scales, coupled with comparative analyses against state-of-the-art benchmarks, illustrate the efficacy and merits of the proposed approach.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"61 2","pages":"2397-2409"},"PeriodicalIF":4.2,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Perception Method of Voltage Spatial-Temporal Distribution for EV Enriched Distribution Network: A Priority Adaptive DNN Enhancement Approach 电动汽车富集配电网电压时空分布感知方法：一种优先自适应深度神经网络增强方法

IF 4.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2025-01-15 DOI: 10.1109/TIA.2025.3529825

Yuntian Zhang;Tiance Zhang;Siwei Liu;Gengyin Li;Ming Zhou

{"title":"Perception Method of Voltage Spatial-Temporal Distribution for EV Enriched Distribution Network: A Priority Adaptive DNN Enhancement Approach","authors":"Yuntian Zhang;Tiance Zhang;Siwei Liu;Gengyin Li;Ming Zhou","doi":"10.1109/TIA.2025.3529825","DOIUrl":"https://doi.org/10.1109/TIA.2025.3529825","url":null,"abstract":"With rapid growth of electric vehicles (EVs) and the integration of large-scale distributed energy sources in distribution networks (DNs), their stochastic and disorderly integration presents a significant challenge to real-time voltage perception. Accordingly, this paper puts forth a priority adaptive deep neural network (DNN) enhancement approach for voltage real-time perception, with the objective of establishing the spatial-temporal mapping relationship between bus voltage of DN with renewable energy, load, and EV data. Firstly, a DNN model is proposed as a solution to the problem of inaccurate voltage perception in EV-enriched DNs caused by the limitations of fuzzy power flow models. Then, to address the issue of the deep learning (DL) method's inability to achieve precise results in extreme circumstances, a power flow model is integrated with the DL method through iteration to support the voltage perception model in making well-informed decisions. And, in order to tackle the difficulties presented by large-scale, complex, non-convex, non-linear problems on resource-constrained devices, a mix precision DNN quantization method is proposed to enable the real-time, high-precision sensing of bus voltages in EV-rich DNs. Finally, the effectiveness of the proposed method is demonstrated by a 141-bus DN test case.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"61 2","pages":"2386-2396"},"PeriodicalIF":4.2,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MAPIRL: A Hyperbolic Tangent-Enforced Physical-Informed RL for Multi-IESs Optimal Dispatch MAPIRL：一种双曲切线强制物理信息RL，用于多节点最优调度

IF 4.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2025-01-15 DOI: 10.1109/TIA.2025.3529675

Jiaming Dou;Xiaojun Wang;Zhao Liu;Yi Han;Wei Ma;Jinghan He

引用次数: 0

Applications of Data-Driven Dynamic Modeling of Power Converters in Power Systems: An Overview 数据驱动的电力变换器动态建模在电力系统中的应用综述

IF 4.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2025-01-15 DOI: 10.1109/TIA.2025.3529797

Sunil Subedi;Yonghao Gui;Yaosuo Xue

{"title":"Applications of Data-Driven Dynamic Modeling of Power Converters in Power Systems: An Overview","authors":"Sunil Subedi;Yonghao Gui;Yaosuo Xue","doi":"10.1109/TIA.2025.3529797","DOIUrl":"https://doi.org/10.1109/TIA.2025.3529797","url":null,"abstract":"Power electronic converter (PEC)–based resources are growing ubiquitously in power systems and there is a vital necessity for precise dynamic models to comprehend their dynamics to different events and control strategies. Inaccurate modeling can lead to instability, higher costs, and reliability issues. Anticipating the increase in PECs in the near future, detailed modeling becomes computationally and mathematically complex, requiring extensive computing power and knowledge of vendor-specific PECs. To overcome these challenges, data-driven machine learning/artificial intelligence (ML/AI) approaches are widely used, tracking the dynamic responses of PECs operating in various modes with limited knowledge. These models find applications in protection, stability, fault diagnosis, optimization, control and monitoring, and power quality. While the literature on power systems often emphasizes the advantages of data-driven modeling, an in-depth look at the limitations, challenges, and opportunities related to converter-dominated grids is still lacking. The purpose of this survey is to conduct a comprehensive review of ML/AI methodologies in PECs and investigate their applications in power systems. The article introduces various PEC types, their roles, and modeling approaches. It then provides an in-depth overview of how ML/AI can be applied to PECs in power systems. Finally, the survey highlights gaps in the field's knowledge and suggests potential directions for future research.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"61 2","pages":"2434-2456"},"PeriodicalIF":4.2,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Novel Data-Driven Models for Detecting and Mitigating False Data Injection Attacks in Automatic Generation Control Considering Nonlinearities 考虑非线性的自动生成控制中检测和减轻假数据注入攻击的新型数据驱动模型

IF 4.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2025-01-15 DOI: 10.1109/TIA.2025.3529819

Ahmed Abughali;Abdullahi Oboh Muhammed;Ameena Saad Al-Sumaiti;Mohamed Shawky El Moursi

{"title":"Novel Data-Driven Models for Detecting and Mitigating False Data Injection Attacks in Automatic Generation Control Considering Nonlinearities","authors":"Ahmed Abughali;Abdullahi Oboh Muhammed;Ameena Saad Al-Sumaiti;Mohamed Shawky El Moursi","doi":"10.1109/TIA.2025.3529819","DOIUrl":"https://doi.org/10.1109/TIA.2025.3529819","url":null,"abstract":"Increasing cyber vulnerabilities pose various concerns regarding the stability and reliability of power systems. Conventionally, Automatic Generation Control (AGC) is employed to maintain the frequency of power systems within a predefined range. However, it is susceptible to cyber-attacks due to its reliance on data transmitted through communication links. Consequently, designing robust protection mechanism to detect, locate and mitigate such attacks is crucial. This paper proposes three data-driven architectures to detect, locate and mitigate False Data Injection (FDI) and Denial of Service (DoS) attacks against AGC systems. First, the proposed models are trained and evaluated using diverse Pulse and Ramp stealthy attacks scenarios in a two-area AGC system, considering the AGC nonlinearities. The detection model exhibits exemplar capability for detecting and locating individual and particularly, multiple coordinated stealthy cyber-attacks, that can significantly undermine the effectiveness of detection systems, with <inline-formula><tex-math>$F_{1}$</tex-math></inline-formula>-score of 93.46% and 96.32% AUC score. The second and third models, attacked class-based mitigation model (ACM) and comprehensive mitigation model (CMM), are employed to accurately recover the corrupted measurements, attaining RMSEs of 0.003463 and 0.003218, respectively. Furthermore, this paper is the first to innovatively examine the impact of PV power system injections on the effectiveness of the proposed detection model, which accurately classified 75,100 out of 75,131 no-attack instances, showcasing its proficiency in distinguishing PV injections from cyber-attacks. Finally, the proposed models are further evaluated using three-area AGC system under mixed FDI and DoS attack scenarios. The obtained results demonstrate their capability to handle larger systems while meeting practical operational requirements.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"61 2","pages":"2731-2745"},"PeriodicalIF":4.2,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Model-Data-Driven Approach for Achieving Decoupled Power Flow and Its Application in Asymmetric Bipolar DC Distribution Networks 模型数据驱动的潮流解耦方法及其在非对称双极直流配电网中的应用

IF 4.2 2区工程技术

IEEE Transactions on Industry Applications Pub Date : 2025-01-15 DOI: 10.1109/TIA.2025.3529803

Yiyao Zhou;Qianggang Wang;Chao Lei;Jianquan Liao;Tao Huang;Yuan Chi;Niancheng Zhou

{"title":"Model-Data-Driven Approach for Achieving Decoupled Power Flow and Its Application in Asymmetric Bipolar DC Distribution Networks","authors":"Yiyao Zhou;Qianggang Wang;Chao Lei;Jianquan Liao;Tao Huang;Yuan Chi;Niancheng Zhou","doi":"10.1109/TIA.2025.3529803","DOIUrl":"https://doi.org/10.1109/TIA.2025.3529803","url":null,"abstract":"Bipolar DC distribution networks (Bi-DCDNs) offer a promising alternative to medium and low voltage distribution networks by enhancing both the loadability and the access capability of renewable energy sources. However, coupled power flow in asymmetric Bi-DCDNs poses challenges for system-level optimal operation problems. Hence, this paper proposes a model-date-driven decoupling framework and employs it to construct the static voltage stability region (SVSR), a representative operational challenge in asymmetric Bi-DCDNs. More specifically, the model-driven approach defines the decoupling coefficient and derives its analytical expression through branch flow analysis. The expression denotes a parametric equation governing pole voltage, functioning as a posteriori indicator of the state of Bi-DCDNs. This equation manifests as a highly nonlinear expression, which can be further determined through a data-driven approach. Various operational scenarios of Bi-DCDNs are simulated using Monte Carlo sampling, without making assumptions about the distribution of loads. The distribution of the decoupling coefficient is derived from power flow calculations, with the decoupling coefficient determined as the expected value within an acceptable confidence interval. Subsequently, the optimal power flow problem for decoupled Bi-DCDNs is formulated, serving as the basis for constructing the SVSR of Bi-DCDNs. The numerical results indicate that the proposed decoupling framework achieves both computational efficiency and accuracy. Furthermore, it exhibits advantageous applications for asymmetric operational Bi-DCDNs.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"61 2","pages":"2504-2514"},"PeriodicalIF":4.2,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0