Electric Power Systems Research最新文献_第8页

Transient modeling and stability analysis of DFIG under different power control mode

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-02-03 DOI: 10.1016/j.epsr.2025.111449

Xiaoyang Deng , Linlin Wu , Xiao Wang , Han Gong , Ruibo Li , Jiaoxin Jia

{"title":"Transient modeling and stability analysis of DFIG under different power control mode","authors":"Xiaoyang Deng , Linlin Wu , Xiao Wang , Han Gong , Ruibo Li , Jiaoxin Jia","doi":"10.1016/j.epsr.2025.111449","DOIUrl":"10.1016/j.epsr.2025.111449","url":null,"abstract":"<div><div>As one of the dominant types of renewable energy generation, the transient characteristics of doubly fed induction generator (DFIG) affect the grid reliability significantly. The stator of DFIG is directly connected to the grid, and its current is strongly influenced by the output angle of the phase-locked loop (PLL). This unique structure results in the power angle of the DFIG more complex, as it cannot be simply derived from the stator voltage. Consequently, the transient process of the DFIG remains not fully characterized. To quantitatively analyze the transient stability of DFIG, a transient model of DFIG based on angle and magnitude is first established. Based on this magnitude-phase model (MPM), this study formulates the nonlinear equations for the DFIG's PLL and defines its power angle. Subsequently, a transient stability analysis method based on the equal area criterion (EAC) is developed, and its conservativeness is evaluated. The stability of the DFIG under different low voltage ride through (LVRT) strategies is compared and analyzed with the EAC, considering various rotor current-to-power angle relationships. Finally, simulations validate that the proposed MPM is accurate and applicable under transient process, and the proposed criterion can effectively estimate the stability of DFIG in transient processes.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"242 ","pages":"Article 111449"},"PeriodicalIF":3.3,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143131129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Integrated attentive Bi-LSTM conditional GAN for power system oscillation localization

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-02-01 DOI: 10.1016/j.epsr.2025.111456

Lokesh Saravana , Quang-Ha Ngo , Jianhua Zhang , Tuyen Vu , Thanh Long Vu

{"title":"Integrated attentive Bi-LSTM conditional GAN for power system oscillation localization","authors":"Lokesh Saravana , Quang-Ha Ngo , Jianhua Zhang , Tuyen Vu , Thanh Long Vu","doi":"10.1016/j.epsr.2025.111456","DOIUrl":"10.1016/j.epsr.2025.111456","url":null,"abstract":"<div><div>This paper presents an advanced deep learning framework that combines the Transformer model with either Long Short-Term Memory (LSTM) or Bidirectional Long Short-Term Memory (Bi-LSTM) in a Conditional Generative Adversarial Network (cGAN) architecture. This innovative framework is specifically designed to address forced oscillation (FO) source localization in power systems. The proposed methodology makes two key contributions to the field. Firstly, synthetic time series data during FO occurrences is generated by integrating the Transformer architecture with LSTM/ Bi-LSTM neural networks. Secondly, the cGAN’s Discriminator-Classifier component is employed to predict the location of the oscillation source. The experimental results validate our framework’s performance in two areas: generating synthetic oscillation datasets and enhancing FO source identification accuracy compared to traditional approaches. The proposed Transformer Bi-LSTM cGAN architecture outperforms the existing methods, particularly in challenging situations with deliberate faults and mixed fault scenarios, achieving an 87% to 96% accuracy in oscillation source identification, thus validating its viability for real-world power grid deployment.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"242 ","pages":"Article 111456"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143132331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Retraction notice to “Multi-objective economic environmental energy management microgrid using hybrid energy storage implementing and developed Manta Ray Foraging Optimization Algorithm” [Electric Power Systems Research 211 (2024) 108181]

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-02-01 DOI: 10.1016/j.epsr.2024.111135

Yanle Dong , Feichao Liu , Xiang Lu , Yantao Lou , Yuanshe Ma , Nasrin Eghbalian

引用次数: 0

Retraction notice to “Optimal unit commitment integrated energy storage system, renewable energy sources and FACTS devices with robust method” [Electric Power Systems Research 209 (2022) 107961]

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-02-01 DOI: 10.1016/j.epsr.2024.111136

Pan Liang , Navid Bohlooli

引用次数: 0

Retraction notice to “Fuzzy logic-based AGC and AVR for four-area interconnected hydro power system” [Electric Power Systems Research 224 (2023) 109494]

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-02-01 DOI: 10.1016/j.epsr.2024.111137

Abdulkerim Ali , Getachew Biru , Belachew Banteyirga

引用次数: 0

Retraction notice to “Characterizing Transformer HV – LV Winding FRA Curves through Derivation of Transfer Functions from FRA Data” [Electric Power Systems Research 228(2024) 110055]

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-02-01 DOI: 10.1016/j.epsr.2024.111010

Zhi Zhang

引用次数: 0

Allocation of BESS and state of charge management in unbalanced distribution networks considering the State of Health

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-02-01 DOI: 10.1016/j.epsr.2025.111467

Maria Elisa Fernandes Octaviano , Leandro Ramos de Araujo , Débora Rosana Ribeiro Penido

{"title":"Allocation of BESS and state of charge management in unbalanced distribution networks considering the State of Health","authors":"Maria Elisa Fernandes Octaviano , Leandro Ramos de Araujo , Débora Rosana Ribeiro Penido","doi":"10.1016/j.epsr.2025.111467","DOIUrl":"10.1016/j.epsr.2025.111467","url":null,"abstract":"<div><div>This paper presents a method for allocating Battery Energy Storage Systems (BESS) and managing their state of charge in unbalanced distribution systems. Various characteristics of distribution systems are considered to enhance result accuracy, including imbalances, neutrals, grounding, control devices, photovoltaic generation, and voltage-dependent loads. Several BESS features are also modeled, such as state of charge, state of health, depth of discharge, operating temperature impact, and cycle count. The proposed optimization framework is a mixed-integer nonlinear problem with temporal coupling, solved using a genetic algorithm combined with a quasi-static time series technique. Power flow equations are addressed using a Full-Newton AC power flow formulation, which offers a more precise representation of the distribution system, particularly under conditions with nonlinear loads, controls, and imbalances. Extensive tests are conducted on the IEEE Node Test Feeders. The results reveal that accounting for deep discharge and operating temperature significantly impacts BESS's operational cycle and lifetime.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"242 ","pages":"Article 111467"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143131124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Corrigendum to “Fast calculation of electromagnetic-thermal-fulid multiphysics coupling of GIL based on hybrid model” [238 (2025) 111074]

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-02-01 DOI: 10.1016/j.epsr.2024.111168

Xingxiong Yang, Yanpu Zhao, Shucan Cheng

引用次数: 0

Topology recognition of substation grounding grids based on small-sample electromagnetic induction images

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-02-01 DOI: 10.1016/j.epsr.2025.111435

Hengli Song , Qingpu Zhao , Haobin Dong

{"title":"Topology recognition of substation grounding grids based on small-sample electromagnetic induction images","authors":"Hengli Song , Qingpu Zhao , Haobin Dong","doi":"10.1016/j.epsr.2025.111435","DOIUrl":"10.1016/j.epsr.2025.111435","url":null,"abstract":"<div><div>To achieve intelligent recognition of the grounding grid topology structure, this paper addresses the challenge of limited measured samples, which makes it difficult for neural networks to be applied in grounding grid recognition. A classification system for grounding grid topology structure recognition is designed. By using the commercially available simulation software CDEGS, simulations are performed based on the parameters of the measured environment to quickly generate a large number of magnetic field intensity distribution images. Given the complex electromagnetic environment in substations and the presence of significant electromagnetic noise in the measured magnetic field intensity distribution images, a style transfer algorithm based on transfer learning is developed. This algorithm adds the electromagnetic noise from the measured images to the simulated images, thus creating a database of measured image samples. Based on the database of measured images, the AlexNet model, a deep learning algorithm for image recognition, is employed to investigate the grounding grid topology structure recognition method. This approach facilitates the extraction of topological structure features from the magnetic field intensity distribution images of the grounding grid, followed by classification and recognition. System test results demonstrate that the system achieves a high recognition rate for the measured images.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"242 ","pages":"Article 111435"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143131122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Parameter estimation and validation of cascaded DC-DC boost converters for renewable energy systems using the IGWO optimization algorithm

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-02-01 DOI: 10.1016/j.epsr.2025.111462

Seyit Alperen Celtek , Seda Kul , Selami Balci , Abdullah Dik

{"title":"Parameter estimation and validation of cascaded DC-DC boost converters for renewable energy systems using the IGWO optimization algorithm","authors":"Seyit Alperen Celtek , Seda Kul , Selami Balci , Abdullah Dik","doi":"10.1016/j.epsr.2025.111462","DOIUrl":"10.1016/j.epsr.2025.111462","url":null,"abstract":"<div><div>The voltage amplitude generated by renewable energy sources is often unstable, necessitating the use of power electronic circuits for effective grid integration. Among these, DC-DC converters play a critical role in maintaining a constant DC link voltage, typically 400 V or 800 V, at the input of inverter circuits that supply power to the load or the grid. The study focuses on the voltage gain behavior of a high-gain dual cascaded DC-DC boost converter designed for PV (photovoltaic) power systems. Using ANSYS Electronics software with its parametric solver, a comprehensive dataset was generated based on key parameters such as input voltage, power switch duty ratio, and switching frequency.</div><div>The Improved Grey Wolf Optimizer (IGWO) algorithm was employed to estimate mathematical models for this dataset using linear and quadratic equations. The accuracy of the proposed models was validated across six test scenarios, demonstrating superior performance compared to traditional optimization algorithms, including Harmony Search (HS), Particle Swarm Optimization (PSO), Differential Evolution (DE), and the standard Grey Wolf Optimizer (GWO). Experimental validations yielded output voltages of 23.5 V and 36.1 V for input voltages of 4.8 V and 6.2 V, respectively, closely aligning with simulation results of 23.113 V and 36.447 V.</div><div>The findings, supported by detailed simulations and graphical analyses, highlight the IGWO algorithm's precision and reliability in predicting converter output voltages under variable input conditions. This work advances renewable energy systems integration by enhancing the modeling and performance of cascaded DC-DC boost converters.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"242 ","pages":"Article 111462"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143131123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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