e-Prime - Advances in Electrical Engineering, Electronics and Energy最新文献

Multi-class imbalanced learning for short-term voltage stability assessment 短期电压稳定性评估的多级不平衡学习

e-Prime - Advances in Electrical Engineering, Electronics and Energy Pub Date : 2025-10-16 DOI: 10.1016/j.prime.2025.101128

Amir Hossein Babaali, Mohammad Taghi Ameli

引用次数: 0

Online estimation of wideband output impedance and control parameters of single-phase inverters using pseudo-random perturbation 基于伪随机摄动的单相逆变器宽带输出阻抗和控制参数在线估计

e-Prime - Advances in Electrical Engineering, Electronics and Energy Pub Date : 2025-10-14 DOI: 10.1016/j.prime.2025.101126

Ian Paul Gerber, Fredrick Mukundi Mwaniki, Hendrik Johannes Vermeulen

{"title":"Online estimation of wideband output impedance and control parameters of single-phase inverters using pseudo-random perturbation","authors":"Ian Paul Gerber, Fredrick Mukundi Mwaniki, Hendrik Johannes Vermeulen","doi":"10.1016/j.prime.2025.101126","DOIUrl":"10.1016/j.prime.2025.101126","url":null,"abstract":"<div><div>The growing deployment of single-phase inverters in residential low-voltage distribution networks poses new challenges to system stability and power quality. Accurate simulation models are essential for analysing these effects and enabling scenario assessment without costly and time-consuming physical testing. Wideband inverter models, in particular, are critical for capturing the inverter’s dynamic behaviour across a broad frequency range. The inverter’s output impedance profile plays a key role in identifying internal parameters, such as filter and control settings, typically not disclosed by manufacturers, and supports impedance-based stability analysis. This paper presents a methodology for online estimating an inverter’s wideband output impedance and internal control parameters. A pseudo-random impulse sequence is injected into the inverter AC terminals <em>in situ</em> to perturb the system, from which the output impedance is estimated. A case study on a standalone single-phase inverter supplying <span><math><mrow><mn>2</mn><mo>.</mo><mn>6</mn><mspace></mspace><msub><mrow><mi>A</mi></mrow><mrow><mtext>RMS</mtext></mrow></msub></mrow></math></span> demonstrates a strong correlation between the experimentally derived impedance and its analytical counterpart. The inverter’s impedance frequency response and time-domain output signals are further analysed to extract controller parameters using a three-step estimation process based on particle swarm optimisation. The approach is validated through both simulation and experimental results, confirming its accuracy and effectiveness in parameter identification.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101126"},"PeriodicalIF":0.0,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimization of pico-scale Turgo turbines for rural electrification: Design, performance, and applications in decentralized energy systems 农村电气化微尺度涡轮的优化：设计、性能和分散式能源系统的应用

e-Prime - Advances in Electrical Engineering, Electronics and Energy Pub Date : 2025-10-12 DOI: 10.1016/j.prime.2025.101127

Dendy Adanta , Dewi Puspita Sari , Imam Syofii , Muhammat Risky Ramadan , Amir Arifin , Ahmad Fudholi

{"title":"Optimization of pico-scale Turgo turbines for rural electrification: Design, performance, and applications in decentralized energy systems","authors":"Dendy Adanta , Dewi Puspita Sari , Imam Syofii , Muhammat Risky Ramadan , Amir Arifin , Ahmad Fudholi","doi":"10.1016/j.prime.2025.101127","DOIUrl":"10.1016/j.prime.2025.101127","url":null,"abstract":"<div><div>Indonesia’s energy transition necessitates decentralized solutions to address the electrification gap and reduce fossil fuel dependence. This study optimizes a pico-scale Turgo turbine for low-head hydropower generation by revising the traditional design ratio derived from Pelton turbine. Experimental testing of a 3D-printed prototype under controlled conditions (3 m head, 44 L per minute flow) combines velocity triangle analysis with response surface methodology to evaluate runner and blade geometries. Results derived from that adjusting the conventional size ratio improves efficiency, with a 23 cm runner and 4 cm blade achieving a peak efficiency of 19.95 % at optimal rotation. A predictive polynomial model shows diminishing returns with larger components. This optimized design offers a practical solution for remote communities, potentially replacing diesel generators while reducing costs and environmental impact. Although material and scalability limitations require further investigation, this study provides actionable guidance for small-scale hydropower systems, supporting Indonesia's renewable energy goals and global sustainable electrification efforts.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101127"},"PeriodicalIF":0.0,"publicationDate":"2025-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging IoT, digital twin and machine learning for smart energy audit in office building: a systematic literature review and recommendations 利用物联网、数字孪生和机器学习进行办公大楼智能能源审计：系统的文献综述和建议

e-Prime - Advances in Electrical Engineering, Electronics and Energy Pub Date : 2025-10-09 DOI: 10.1016/j.prime.2025.101124

Ali Zaenal Abidin , I Ketut Agung Enriko , Aloysius Adya Pramudita

{"title":"Leveraging IoT, digital twin and machine learning for smart energy audit in office building: a systematic literature review and recommendations","authors":"Ali Zaenal Abidin , I Ketut Agung Enriko , Aloysius Adya Pramudita","doi":"10.1016/j.prime.2025.101124","DOIUrl":"10.1016/j.prime.2025.101124","url":null,"abstract":"<div><div>Energy audits play a pivotal role in improving energy efficiency and reducing carbon emissions in office buildings. However, conventional audits often suffer from fragmented insights, lack of system-level monitoring, establishing energy baseline, and insufficient incorporation of occupant behavior. To address these challenges, this study conducts a systematic literature review of recent applications of Internet of Things (IoT), machine learning (ML), and digital twin (DT) technologies in the energy audit domain. The review, guided by PRISMA methodology, analyzes eleven selected studies published between 2022 and 2024, revealing that while ML dominates in predictive modeling, IoT and DT remain underutilized in delivering integrated, efficiency recommendations. The analysis identifies three key engineering gaps: limited use of occupant behavior data, absence of continuous energy baseline modeling, and lack of systems capable of generating real-time efficiency recommendations. In response, this paper proposes a novel AIoT-based energy audit framework that combines real-time monitoring via IoT with ML-driven analytics and optimization, supported optionally by DT-based simulation. The proposed framework aims to enable continuous, system-level audits aligned with ISO 50000 standards, offering practical pathways for building managers to diagnose inefficiencies and implement energy-saving actions. Validating the model in real-world office environments, expanding input variables, and integration strategy with building automation systems are further important study to realize intelligent and scalable energy audit solutions.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101124"},"PeriodicalIF":0.0,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analytic and simulation results of a Gaussian analog random constant based on resistance dispersion 基于电阻色散的高斯模拟随机常数的分析与仿真结果

e-Prime - Advances in Electrical Engineering, Electronics and Energy Pub Date : 2025-10-08 DOI: 10.1016/j.prime.2025.101121

Riccardo Bernardini

引用次数: 0

Overview of key power system state estimation methods with a focus on artificial intelligence-based approaches 概述电力系统状态估计的关键方法，重点是基于人工智能的方法

e-Prime - Advances in Electrical Engineering, Electronics and Energy Pub Date : 2025-10-08 DOI: 10.1016/j.prime.2025.101125

Mohammad Amin Ranjbar , Sasan Azad , Morteza Nazari-Heris , Mostafa Mohammadpourfard

{"title":"Overview of key power system state estimation methods with a focus on artificial intelligence-based approaches","authors":"Mohammad Amin Ranjbar , Sasan Azad , Morteza Nazari-Heris , Mostafa Mohammadpourfard","doi":"10.1016/j.prime.2025.101125","DOIUrl":"10.1016/j.prime.2025.101125","url":null,"abstract":"<div><div>State estimation (SE) is the most critical part of power systems management and control centers because correct data from the equipment in the network is needed before any operation. Power systems in the past were less complex than today's systems, so simple methods were sufficient to solve the SE problem. As power systems have developed and distribution systems have become more interconnected to enhance reliability and handle growing loads and uncertainties, the methods for solving the SE problem have evolved over time. Many methods have been employed so far to address the state estimate problem; each of these techniques has the potential to be helpful in particular situations; therefore, identifying the strategies and getting familiar with their features can be crucial. Based on this issue, SE methods are divided into two categories. In one category, their dynamic and static characteristics are specified, and another category is based on the application of methods. Most of the methods have been studied, and the advantages and disadvantages of each have been thoroughly investigated to identify their strengths and weaknesses. The methods based on artificial intelligence (AI) can have good potential in solving SE problems, so they have been specifically investigated. This category of SE methods can be beneficial in solving future problems. Based on this, the existing challenges for the future of SE have been discussed. As a case study, we demonstrate how AI techniques, such as transfer learning (TL), can address one of these challenges—specifically in handling network reconfiguration in a 118-bus system. This example can guide those interested in the field to tackle similar challenges and provide direction for future research.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101125"},"PeriodicalIF":0.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advanced convolutional neural networks for fiber impairments compensation in long haul optical communication systems 基于卷积神经网络的长距离光通信系统光纤损伤补偿

e-Prime - Advances in Electrical Engineering, Electronics and Energy Pub Date : 2025-10-06 DOI: 10.1016/j.prime.2025.101123

Ali Hayder Abdul Kareem , Ibrahim A. Murdas

{"title":"Advanced convolutional neural networks for fiber impairments compensation in long haul optical communication systems","authors":"Ali Hayder Abdul Kareem , Ibrahim A. Murdas","doi":"10.1016/j.prime.2025.101123","DOIUrl":"10.1016/j.prime.2025.101123","url":null,"abstract":"<div><div>This paper proposes a convolutional neural network (CNN) based equalization scheme for mitigating fiber nonlinear impairments in high-capacity coherent optical communication systems. Unlike traditional digital back-propagation (DBP), the proposed CNN learns nonlinear signal distortions such as self-phase modulation (SPM), cross-phase modulation (XPM), and four-wave mixing (FWM) directly from data, enabling a balance between accuracy and computational efficiency. The model was trained and validated using co-simulation between OptiSystem and MATLAB over a 16-channel DWDM system with 16QAM and 64QAM modulation formats, achieving a total capacity of 1.92 Tb/s across 5000 km. By analyzing the performance metrics, it was gained insights into the effectiveness of the CNN algorithm in compensating for fiber impairments and optimizing signal transmission. The results showed the best value in terms of the quality of the received signal in 16QAM at 5 dBm to reach the Q-factor 11.45 dB with 0.087 for EVM, while in 64QAM at 10 dBm reach 11.09 dB and 0.09, respectively, that is larger than hard decision forward error correction HD-FEC limits (Q-factor =8.5 dB).</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101123"},"PeriodicalIF":0.0,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cloud behavior prediction for solar power applications: A bibliometric analysis, categorized literature review, and future research directions 太阳能应用的云行为预测：文献计量分析、分类文献综述及未来研究方向

e-Prime - Advances in Electrical Engineering, Electronics and Energy Pub Date : 2025-10-06 DOI: 10.1016/j.prime.2025.101119

Maryam Nejati , Younes Mohammadi , Asghar Akbari Foroud , Thomas Olofsson

{"title":"Cloud behavior prediction for solar power applications: A bibliometric analysis, categorized literature review, and future research directions","authors":"Maryam Nejati , Younes Mohammadi , Asghar Akbari Foroud , Thomas Olofsson","doi":"10.1016/j.prime.2025.101119","DOIUrl":"10.1016/j.prime.2025.101119","url":null,"abstract":"<div><div>Accurate Cloud Behavior Prediction (CBP), also referred to as forecasting in this context, is essential for Solar Power Prediction (SPP), as well as for weather forecasting, climate analysis, and satellite imaging. However, the nonlinear and dynamic nature of clouds, combined with other limitations, presents significant challenges to advancing CBP. Recent developments, particularly the integration of Machine Learning (ML), Numerical Weather Prediction (NWP), and other innovative approaches, show strong potential for improving CBP and, in turn, enhancing SPP and related applications. This review presents a bibliometric analysis of 467 publications from 1970 to 2024, retrieved from the <em>Scopus database</em> using CBP-related keywords. It identifies trends, influential studies, major subject areas, leading authors, contributing countries, and key publishers. The study further categorizes the essential steps in CBP and provides a detailed review of the most relevant literature on cloud cover, cloud motion (including vector-based methods), and cloud image prediction. Additionally, it examines critical factors affecting model performance and introduces a framework for evaluating predictive methods based on input types, methodologies, prediction horizons, results, and evaluation metrics. Several key challenges are highlighted, including the nonlinearity of cloud behavior, limited data availability, image quality issues, and model accuracy. In response, actionable recommendations are offered, such as expanding data sources, applying hybrid imaging and modeling approaches, managing uncertainty, improving postprocessing techniques, and incorporating cloud content estimation. Given the relatively limited research in this field, this study serves as a valuable benchmark for researchers, engineers, and policymakers engaged in real-time SPP and other cloud-dependent domains.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101119"},"PeriodicalIF":0.0,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Economic dispatch of diesel generators considering photovoltaic energy and thermal congestion in distribution networks in isolated areas 考虑光伏发电和偏远地区配电网热拥塞的柴油发电机组经济调度

e-Prime - Advances in Electrical Engineering, Electronics and Energy Pub Date : 2025-10-04 DOI: 10.1016/j.prime.2025.101120

Carlos Arturo Páez , Didier Sierra , Isaac Dyner

{"title":"Economic dispatch of diesel generators considering photovoltaic energy and thermal congestion in distribution networks in isolated areas","authors":"Carlos Arturo Páez , Didier Sierra , Isaac Dyner","doi":"10.1016/j.prime.2025.101120","DOIUrl":"10.1016/j.prime.2025.101120","url":null,"abstract":"<div><div>The increase in extreme temperatures significantly affects electrical distribution networks, reducing both their transmission capacity and the efficiency of photovoltaic generation, thereby compromising operational security. In this context, the present study develops a computational model to evaluate the impact of ambient temperature on thermal congestion in power lines and on the efficiency of photovoltaic generation within the economic dispatch process of thermal generators. The model is formulated as a convex quadratic programming problem and implemented in Python using the IPOPT (Interior Point Optimizer) solver. It was applied to a case study in the city of Inírida, Colombia. The results indicate that the integration of distributed generation (DG) helps to mitigate thermal congestion in distribution networks by 6.91% and 12.10%, depending on the thermal conditions evaluated according to the IEEE 738 standard. Moreover, the efficiency of solar modules was found to decrease by 16.8% under elevated temperatures. Furthermore, operating costs were reduced by 36.7%, decreasing from USD 17,719.1 in the base scenario to USD 11,210.42 with the incorporation of distributed generation. Solar generation also contributed 7.9% of the total demand coverage, directly impacting the reduction of technical losses, which decreased from 553 kW to 362 kW. Similarly, a daily reduction in fuel consumption of 4,400.4 gallons and a reduction in CO₂ emissions of 43,641.4 kg were achieved. These findings demonstrate that the joint incorporation of climatic variables and renewable energy sources into the economic dispatch process enhances operational efficiency, improves the thermal resilience of the system, and promotes a more sustainable energy transition in isolated areas.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101120"},"PeriodicalIF":0.0,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimized estimation of Li-ion battery parameters to improve the Enhanced Self-Correcting model with nonlinear least-squares data fitting and SoC estimation using Invariant EKF 基于非线性最小二乘数据拟合的锂离子电池参数优化估计和基于不变EKF的电池剩余电量估计

e-Prime - Advances in Electrical Engineering, Electronics and Energy Pub Date : 2025-09-27 DOI: 10.1016/j.prime.2025.101115

Yassine Derouech, Abdelouahed Mesbahi

{"title":"Optimized estimation of Li-ion battery parameters to improve the Enhanced Self-Correcting model with nonlinear least-squares data fitting and SoC estimation using Invariant EKF","authors":"Yassine Derouech, Abdelouahed Mesbahi","doi":"10.1016/j.prime.2025.101115","DOIUrl":"10.1016/j.prime.2025.101115","url":null,"abstract":"<div><div>In recent years, batteries have become increasingly important, especially lithium batteries. Battery modeling is essential for many applications, such as state-of-charge (SoC) estimation. To obtain a high-performance model, the estimation of battery parameters must be precise. The Enhanced Self-Correcting model describes several battery dynamics, such as hysteresis cycling. This model can be further improved by making these parameters variable as a function of the SoC and temperature, making the model more efficient. In this study, the parameters of the single RC branch circuit during charging and discharging will be estimated, and these parameters will then be used to improve the “Enhanced Self-Correcting” model, which will enable it to better describe battery dynamics and improve accuracy. MATLAB/Simulink Toolboxes can simplify many tasks, and nonlinear least-squares data fitting using the Trust-Region-Reflective algorithm produces remarkable results. Then, this model is validated with the “SoC and voltage” estimation compared to other models, using the invariant extended Kalman filter (IEKF), which is reliable for nonlinear systems as its correction is independent of the output error, leading to greater accuracy and performance.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101115"},"PeriodicalIF":0.0,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0