IEEE Open Access Journal of Power and Energy最新文献

Survey of Load-Altering Attacks Against Power Grids: Attack Impact, Detection, and Mitigation 针对电网的负载改变攻击调查：攻击影响、检测和缓解

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-04-17 DOI: 10.1109/OAJPE.2025.3562052

Sajjad Maleki;Shijie Pan;Subhash Lakshminarayana;Charalambos Konstantinou

引用次数: 0

Characterization of the Long-Term Impedance Variations Due to Electric Vehicle Charging From 20 kHz to 500 kHz 电动汽车充电从20 kHz到500 kHz时的长期阻抗变化特性

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-04-17 DOI: 10.1109/OAJPE.2025.3562091

Jon González-Ramos;Itziar Angulo;Igor Fernández;Bernhard Grasel;Alexander Gallarreta;Amaia Arrinda;David de la Vega

{"title":"Characterization of the Long-Term Impedance Variations Due to Electric Vehicle Charging From 20 kHz to 500 kHz","authors":"Jon González-Ramos;Itziar Angulo;Igor Fernández;Bernhard Grasel;Alexander Gallarreta;Amaia Arrinda;David de la Vega","doi":"10.1109/OAJPE.2025.3562091","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3562091","url":null,"abstract":"This paper aims to empirically characterize the long-term grid impedance variations due to Electric Vehicle Charging Processes (EVCPs) in the frequency range from 20 kHz to 500 kHz. The study is supported by a measurement campaign performed in a controlled Low Voltage (LV) grid in Austria, composed of a Secondary Substation (SS) and four houses, which statistically represents the public LV grids in Austria. The results show that different impedance states (with different spectral patterns and amplitudes) can be identified during the charging processes of all the EVs under analysis. Additionally, time variability within each impedance state is also registered. The findings, which cover the still uncharacterized frequency band from 20 kHz to 500 kHz, have important implications for the performance of Narrowband Power Line Communications (NB-PLC), the propagation of Non-Intentional Emissions (NIEs) and the definition of a reference impedance in this frequency band.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"235-244"},"PeriodicalIF":3.3,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10967552","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913570","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

Soft Actor-Critic-Based MPPT Control of Solar PV Systems Under Partial Shading Conditions 部分遮阳条件下基于软因子临界的太阳能光伏系统MPPT控制

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-04-15 DOI: 10.1109/OAJPE.2025.3560626

Sampson E. Nwachukwu;Komla A. Folly;Kehinde O. Awodele

{"title":"Soft Actor-Critic-Based MPPT Control of Solar PV Systems Under Partial Shading Conditions","authors":"Sampson E. Nwachukwu;Komla A. Folly;Kehinde O. Awodele","doi":"10.1109/OAJPE.2025.3560626","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3560626","url":null,"abstract":"This paper presents a soft actor-critic (SAC)-based method for solving the solar photovoltaic (PV) Maximum Power Point Tracking (MPPT) control problem under partial shading conditions (PSCs). The MPPT method optimizes the solar PV power and ensures that it constantly operates at its “maximum power point (MPP),” regardless of the dynamics of weather conditions. Traditional MPPT methods, such as the perturb and observe (P&O) method, are commonly employed to solve the MPPT control problem. However, they often suffer from a slower convergence rate, significant oscillation near the MPP, drift problems. Additionally, in the presence of partial shading, they frequently fail to track the solar PV global maximum power point (GMPP). These problems were addressed using the deep Q-network (DQN) method. However, DQN cannot be applied to continuous action spaces. It also uses inefficient experience replay and suffers from Q-value overestimation. Thus, under PSCs and certain environmental conditions, DQN produces fluctuations of power close to the MPP or GMPP, resulting in power loss. To solve the MPPT control task, mathematical models of the Markov Decision Process, solar PV system, and boost converter were developed. Key hyperparameters affecting the SAC algorithm’s performance were also investigated. Furthermore, the P&O method was developed for comparison. Simulation results show that the SAC-based MPPT method achieved better tracking accuracy than the DQN method under standard testing conditions, varying irradiance levels, and PSCs. Also, it is shown that both the DQN and SAC methods have superior tracking performance compared to the P&O method under similar environmental conditions tested.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"194-208"},"PeriodicalIF":3.3,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10965705","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892359","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

A Novel Multi-Task Learning-Based Approach to Multi-Energy System Load Forecasting 基于多任务学习的多能源系统负荷预测新方法

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-04-14 DOI: 10.1109/OAJPE.2025.3559336

Zain Ahmed;Mohsin Jamil;Ashraf Ali Khan

引用次数: 0

Reinforcement Learning for Stability-Guaranteed Adaptive Optimal Primary Frequency Control of Power Systems Using Partially Monotonic Neural Networks 部分单调神经网络用于电力系统保稳定自适应最优一次频率控制的强化学习

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-04-02 DOI: 10.1109/OAJPE.2025.3556142

Hamad Alduaij;Yang Weng

{"title":"Reinforcement Learning for Stability-Guaranteed Adaptive Optimal Primary Frequency Control of Power Systems Using Partially Monotonic Neural Networks","authors":"Hamad Alduaij;Yang Weng","doi":"10.1109/OAJPE.2025.3556142","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3556142","url":null,"abstract":"Deepening the deployment of distributed energy resources requires the large-scale integration of inverter-based resources, which can deteriorate the frequency stability. Recent studies propose using neural Lyapunov-based reinforcement learning for control. While this method can be trained offline with performance guarantees, it is only optimal for specific values of system parameters, as it omits critical modeling factors like decreasing inertia and damping variation over time. To maintain the performance at varying operation points, we consider an adaptive neural Lyapunov framework that adapts the controller’s output in the presence of varying parameters. Neural networks require flexibility to maximize adaptive control performance, while stability demands monotonicity, creating an inherent conflict. In this paper, we design a partially monotonic controller that maintains stability with maximal representation capacity for parameter adaptation. Stability is ensured by having monotonicity retained for frequency while non-monotonicity is allowed for the system parameters, such as damping and inertia. The structural form of partially monotonic neural networks is used for the controller design to that end. Flexibility is allowed by the design when adaptation to changes to the system parameters is made, while the Lyapunov stability guarantee is retained. The non-monotonic layers are chosen through an adaptive layer that is designed for damping and inertia based on their relationship to control in the system equation, by which optimized output for different operating conditions is allowed.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"259-269"},"PeriodicalIF":3.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947581","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073416","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

Graph Learning-Based Power System Health Assessment Model 基于图学习的电力系统健康评估模型

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-03-31 DOI: 10.1109/OAJPE.2025.3556004

Koji Yamashita;Nanpeng Yu;Evangelos Farantatos;Lin Zhu

{"title":"Graph Learning-Based Power System Health Assessment Model","authors":"Koji Yamashita;Nanpeng Yu;Evangelos Farantatos;Lin Zhu","doi":"10.1109/OAJPE.2025.3556004","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3556004","url":null,"abstract":"As the power transmission system’s energy sources become increasingly diversified, the grid stability is experiencing increased fluctuations, thereby necessitating more frequent and near real-time monitoring by grid operators. The power system security has been monitored through real-time contingency analysis and dynamic security assessment framework, both of which are typically based on time-domain simulations or power flow calculations. Achieving higher accuracy in grid health level prediction often requires time-consuming simulation and analysis. To improve computational efficiency, this paper develops machine learning models with phasor measurement unit (PMU) data to monitor the power system health index, focusing on rotor angle stability and frequency stability. The proposed machine learning models accurately predict frequency and angle stability indicators, essential for evaluating grid health considering various contingencies, even when dealing with limited PMU deployment in transmission grids. The proposed framework leverages a physics-informed graph convolution network and graph attention network with ordinal encoders, which are benchmarked with multi-layer perceptron models. These models are trained on dataset derived from an augmented IEEE 118-bus system with different demand levels and fuel mix, including tailored dynamic generator models, generator controller models, and grid protection models. The numerical studies explored the performance of the proposed and baseline machine learning models under both full PMU coverage and various partial PMU coverage conditions, where different data imputation methods are used for substations without PMUs. The findings from this study offer valuable insights, such as machine learning model selection and critical PMU locations regarding power equipment, into the design of data-driven grid health index prediction models for power systems.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"181-193"},"PeriodicalIF":3.3,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10945887","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800742","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

Data Driven Reduced Pi-Model of Feeders for Distribution Network Representation With DERs for Fast Reconfiguration 数据驱动的带der的配电网络馈线简化pi模型

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-03-22 DOI: 10.1109/OAJPE.2025.3572718

Tharmini Thavaratnam;Bala Venkatesh

{"title":"Data Driven Reduced Pi-Model of Feeders for Distribution Network Representation With DERs for Fast Reconfiguration","authors":"Tharmini Thavaratnam;Bala Venkatesh","doi":"10.1109/OAJPE.2025.3572718","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3572718","url":null,"abstract":"Deep electrification by 2050 is expected to increase distribution systems by three to five times and include innumerable distributed energy resources (DERs). Robust methods for operations will be required. Reconfigurations, well researched for 50+ years, are created given the size and importance of present distribution systems. This paper proposes a network configuration method which is significantly dense, heavily loaded, societally important, and has innumerable loads and DERs. This method reduces sections of feeders with DERs to equivalent reduced Pi-Model representations. It then uses a regression model to correlate loading scenarios of the distribution to reduced Pi-Model parameters feeder sections. A regression model yields reduced Pi-Models of feeder sections, and they are used to construct a complete distribution system representation, with this reduced model used for reconfiguration. The proposed method was tested on modified 33-, 69- and 123-Bus data networks and reduced the number of buses to around 50%. Computing time was reduced by 26.30%, 58.54% and 67.33%, respectively while providing accuracy of 97.35%, 97.30%, and 99.05%, respectively. The computation time was lowered by 46.45% when the methodology was expanded to the North Dakota 880-Bus network. As the method scales for larger distribution systems, it should increasingly perform better.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"330-340"},"PeriodicalIF":3.3,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11010128","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170809","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

Assessing Oscillatory Stability With Dominant Grid-Forming Power Systems for Active Power Imbalances 有功不平衡下优势并网电力系统振荡稳定性评估

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-03-21 DOI: 10.1109/OAJPE.2025.3571108

Sander Lid Skogen;José Luis Rueda Torres

{"title":"Assessing Oscillatory Stability With Dominant Grid-Forming Power Systems for Active Power Imbalances","authors":"Sander Lid Skogen;José Luis Rueda Torres","doi":"10.1109/OAJPE.2025.3571108","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3571108","url":null,"abstract":"As the integration of renewable energy accelerates, ensuring power system stability becomes increasingly critical. This research utilized a Root Mean Square (RMS) synthetic model of the future 380 kV Dutch power system towards 2050 to analyze its oscillatory stability under high renewable penetration and the impact of grid-forming converters under various parametrizations. The presented case study shows that grid-forming (GFM) converters significantly improve frequency stability and damping performance across different perturbations, particularly at higher GFM penetration levels, improving frequency and damping parameters. However, various oscillatory modes present potential stability risks at high penetration levels. The case study also shows minimal differences in controller selection in large-scale models, except under certain conditions. Additionally, the analysis of controller parameters highlighted the critical importance of tuning active power parameters to ensure system stability. The investigation provides essential insights for future power systems, where large-scale integration of renewable energy will necessitate the implementation of converters able to provide ancillary services. The findings emphasize the importance of optimizing GFM converter settings and penetration levels to maintain system resilience, offering valuable guidance for future system planning and regulatory frameworks.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"318-329"},"PeriodicalIF":3.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11008671","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170986","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

Energy-Efficient Direct Instantaneous Torque Control of Switched Reluctance Generator at Low Speeds 低速开关磁阻发电机的节能直接瞬时转矩控制

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-03-20 DOI: 10.1109/OAJPE.2025.3553408

Elmer O. Hancco Catata;Marcelo Vinícius De Paula;Ernesto Ruppert Filho;Tárcio André Dos Santos Barros

{"title":"Energy-Efficient Direct Instantaneous Torque Control of Switched Reluctance Generator at Low Speeds","authors":"Elmer O. Hancco Catata;Marcelo Vinícius De Paula;Ernesto Ruppert Filho;Tárcio André Dos Santos Barros","doi":"10.1109/OAJPE.2025.3553408","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3553408","url":null,"abstract":"An efficient switching method is proposed for Direct Instantaneous Torque Control (DITC) in Switched Reluctance Generators (SRG) operating at low speeds, aiming to enhance system efficiency and reduce torque ripple. In the traditional DITC strategy, the magnetization state in the outgoing phase is enabled at low operating speeds, leading to decreased efficiency and unnecessary torque ripple. The proposed DITC strategy improves efficiency at low speeds while maintaining low torque ripple levels. It prioritizes the freewheeling and demagnetization states during the outgoing period. When the back electromotive force (back EMF) is small, the magnetization state is disabled, using the freewheeling state to smoothly increase torque and the demagnetization state to decrease torque. The magnetization state is reintroduced as the back EMF increases. To implement the modified DITC, an artificial neural network is used to estimate electromagnetic torque. Experimental tests were conducted for both fixed and variable SRG speeds. The proposed method is compared with other methods in the literature. Experimental tests carried out at fixed and variable SRG speeds show that the proposed method significantly enhances efficiency by up to 20% and reduces torque ripple by up to 21% compared to existing methods.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"171-180"},"PeriodicalIF":3.3,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10935298","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740298","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

Composite Index for Identifying Anomalies in Low Voltage Systems Using Smart Meter Measurement Data 利用智能电表测量数据识别低压系统异常的综合指数

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-03-16 DOI: 10.1109/OAJPE.2025.3570834

Felipe B. B. Rolim;Fernanda C. L. Trindade;Vinicius C. Cunha

引用次数: 0