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

A 0.18 µm CMOS on-chip integrated distributed MPPT (DMPPT) controller for cell-level photovoltaic solar systems 用于电池级光伏太阳能系统的0.18µm CMOS片上集成分布式MPPT （DMPPT）控制器

e-Prime - Advances in Electrical Engineering, Electronics and Energy Pub Date : 2026-03-01 Epub Date: 2025-12-04 DOI: 10.1016/j.prime.2025.101140

Hajjar Mamouni , Karim EL Khadiri , Mounir Ouremchi , Mohammed Ouazzani Jamil , Hassan Qjidaa

{"title":"A 0.18 µm CMOS on-chip integrated distributed MPPT (DMPPT) controller for cell-level photovoltaic solar systems","authors":"Hajjar Mamouni , Karim EL Khadiri , Mounir Ouremchi , Mohammed Ouazzani Jamil , Hassan Qjidaa","doi":"10.1016/j.prime.2025.101140","DOIUrl":"10.1016/j.prime.2025.101140","url":null,"abstract":"<div><div>This paper proposes an on-chip integrated power management system with a Distributed Maximum Power Point Tracking (DMPPT) controller for photovoltaic (PV) cells to enhance energy extraction efficiency in partial shading and inhomogeneous conditions.</div><div>Each PV cell is allocated a single MPPT unit to achieve localized power maximization and loss reduction in contrast to centralized tracking systems. The proposed DMPPT controller is realized in 0.18 µm CMOS and integrates Ripple Correlation Control (RCC) and a synchronous boost converter for efficient cell-level tracking. Cadence Virtuoso simulations were carried out using a single-diode PV model at irradiance values from 100 W/m² to 1200 W/m² and a constant temperature of 25°C. The converter runs with a 100 kHz switching frequency, achieving 92 % peak efficiency and stable voltage regulation.</div><div>The suggested scheme achieves a mean output voltage of 12.3 V, 986.6 mA of current, and offers nearly twice the normalized power compared to centralized MPPT techniques under partial shading. The chip occupies an area of approximately 1.73 mm². The results verify the engineering feasibility and high efficiency of cell-level Distributed MPPT (DMPPT) for maximizing energy output and operational reliability of photovoltaic systems under non-uniform irradiance.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"15 ","pages":"Article 101140"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705874","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

Artificial intelligence prediction of maximum power point tracking voltage and current based on battery for sensor reduction and complexity minimization for photovoltaic charge controller 基于电池的最大功率点跟踪电压和电流的人工智能预测，用于光伏充电控制器的传感器减少和复杂性最小化

e-Prime - Advances in Electrical Engineering, Electronics and Energy Pub Date : 2025-12-01 Epub Date: 2025-09-15 DOI: 10.1016/j.prime.2025.101110

Minh Long Hoang, Mirco Mongilli, Guido Matrella, Paolo Ciampolini

{"title":"Artificial intelligence prediction of maximum power point tracking voltage and current based on battery for sensor reduction and complexity minimization for photovoltaic charge controller","authors":"Minh Long Hoang, Mirco Mongilli, Guido Matrella, Paolo Ciampolini","doi":"10.1016/j.prime.2025.101110","DOIUrl":"10.1016/j.prime.2025.101110","url":null,"abstract":"<div><div>This research works on an Artificial Intelligence (AI)–based approach for Maximum Power Point Tracking (MPPT) in photovoltaic (PV) systems, focusing on the prediction of PV voltage (Vpv) and current (Ipv) from battery-side parameters rather than direct PV-side sensing. By applying Machine Learning (ML) and Deep Learning (DL) algorithms, the proposed framework eliminates the requirements for dedicated PV voltage–current sensors inside MPPT charge controllers, thereby reducing hardware cost, calibration requirements, and system complexity. An MPPT charge controller was employed to provide V<sub>MPPT</sub> and I<sub>MPPT</sub> values as ground truth for validation. Two experimental scenarios were designed: (i) using battery parameters alongside ambient temperature and humidity, and (ii) relying solely on battery parameters. A comprehensive evaluation of 10 ML and 7 DL algorithms was conducted, with the best-performing models selected via K-fold cross-validation. Results demonstrate that the Extra Trees Regressor achieved a root mean square error as low as 0.02 (normalized scale of 1), indicating strong accuracy in predicting PV operating points. The proposed approach highlight the practical system of PV sensor reduction, AI-driven MPPT strategy, offering a cost-effective and scalable alternative to traditional MPPT methods for both small-scale and potentially larger PV systems.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101110"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145121006","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 : 2025-12-01 Epub 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":"2025-12-01","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

Lifetime prediction of Lithium-ion cells using electrochemical modeling with combined calendar and cyclic aging effects 结合日历和循环老化效应的锂离子电池电化学模型寿命预测

e-Prime - Advances in Electrical Engineering, Electronics and Energy Pub Date : 2025-12-01 Epub Date: 2025-09-08 DOI: 10.1016/j.prime.2025.101103

Prasath Raj , Ernst Richter , Kai Schofer , Julian Kempf , Florence Michel , Alexander Fill , Kai Peter Birke

{"title":"Lifetime prediction of Lithium-ion cells using electrochemical modeling with combined calendar and cyclic aging effects","authors":"Prasath Raj , Ernst Richter , Kai Schofer , Julian Kempf , Florence Michel , Alexander Fill , Kai Peter Birke","doi":"10.1016/j.prime.2025.101103","DOIUrl":"10.1016/j.prime.2025.101103","url":null,"abstract":"<div><div>Developing an aging model for Lithium-ion batteries (LIBs) that captures multifaceted degradation mechanisms and their interdependencies under real-world conditions is a complex challenge. This study introduces an advanced electrochemical model for a 43 Ah automotive-grade Lithium-ion pouch cell, parameterized at the anode, cathode, and electrolyte levels to predict both calendar and cyclic aging across diverse operating conditions. The model quantifies solid electrolyte interface (SEI) growth, distinguishing between passive formation during storage and accelerated growth during cycling, and incorporates a detailed representation of Lithium plating, assessing its influence under different charging rates and temperatures.</div><div>To validate the model, realistic driving profiles emulating Plug-in Hybrid Electric Vehicle (PHEV) usage were incorporated, ensuring direct comparison with empirical data. The model successfully captures capacity fade across different conditions varying in temperature, State of Charge (SoC), and applied current, replicating aging pathways dominated by SEI formation and Lithium plating. The ability to describe these fundamentally different degradation modes within a unified framework underscores the model’s robustness and predictive capability.</div><div>By accurately differentiating between calendar-induced SEI thickening and cycling-accelerated SEI growth, the model provides a mechanistic estimation of capacity loss without reliance on purely empirical fitting. The results reinforce the necessity of considering both SEI formation and Lithium plating in aging models to achieve a comprehensive and predictive understanding of Li-ion cell degradation.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101103"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027817","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

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 : 2025-12-01 Epub 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":"2025-12-01","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

A stabilized Benders decomposition approach for solving the multi-period secure stochastic AC optimal power flow for energy, reserves, and storage scheduling 一种求解多周期安全随机交流最优潮流的稳定Benders分解方法，用于能量、储备和存储调度

e-Prime - Advances in Electrical Engineering, Electronics and Energy Pub Date : 2025-12-01 Epub Date: 2025-11-10 DOI: 10.1016/j.prime.2025.101134

María del Pilar Buitrago-Villada, Carlos E. Murillo-Sánchez

{"title":"A stabilized Benders decomposition approach for solving the multi-period secure stochastic AC optimal power flow for energy, reserves, and storage scheduling","authors":"María del Pilar Buitrago-Villada, Carlos E. Murillo-Sánchez","doi":"10.1016/j.prime.2025.101134","DOIUrl":"10.1016/j.prime.2025.101134","url":null,"abstract":"<div><div>The growing penetration of renewable energy sources and the increasing complexity of modern power systems demand more accurate and computationally efficient operational planning tools, as the associated optimization problems are inherently high-dimensional and computationally intensive. Traditional optimization approaches often rely on simplified DC or convex formulations, which limit their ability to capture the nonlinear behavior of AC network model. This study addresses this gap by proposing a scalable solution framework for the Multi-Period Secure Stochastic AC Optimal Power Flow (MPSSOPF-AC). The proposed approach is based on Generalized Benders Decomposition (GBD) with reformulated AC subproblems that incorporate reserve and storage scheduling. Algorithmic performance is further enhanced through a bundle–trust-region stabilization technique and the parallel solution of subproblems that exploit the problem structure. The proposed methodology is validated on the real-size Colombian 96-bus power system under several wind generation scenarios and N-1 contingencies. Results demonstrate that the proposed GBD-based framework preserves modeling accuracy while reducing computational time by up to 94.8% compared with conventional methods. The outcomes highlight the potential of decomposition-based strategies to enable realistic large-scale stochastic AC-OPF applications in modern power system operation and planning.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101134"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519538","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 rule-based energy management for AC/DC hybrid microgrids using price-based demand response 基于价格的需求响应优化交直流混合微电网基于规则的能源管理

e-Prime - Advances in Electrical Engineering, Electronics and Energy Pub Date : 2025-12-01 Epub Date: 2025-11-05 DOI: 10.1016/j.prime.2025.101132

Rampelli Manojkumar , Chamakura Krishna Reddy , T Yuvaraj , Mohit Bajaj , Vojtech Blazek

{"title":"Optimized rule-based energy management for AC/DC hybrid microgrids using price-based demand response","authors":"Rampelli Manojkumar , Chamakura Krishna Reddy , T Yuvaraj , Mohit Bajaj , Vojtech Blazek","doi":"10.1016/j.prime.2025.101132","DOIUrl":"10.1016/j.prime.2025.101132","url":null,"abstract":"<div><div>The increasing integration of renewable energy sources (RESs) and battery energy storage systems (BESSs) into hybrid AC/DC microgrids offers opportunities for cost reduction and flexibility but poses challenges in control. This paper proposes a PSO-tuned rule-based energy management system (EMS) that coordinates photovoltaic (PV) generation, BESS, and the utility grid under dynamic pricing. The framework integrates price-based demand response (DR), adaptive battery operation rules, and real-time forecasts to minimize energy consumption cost (ECC). Compared with Genetic Algorithms, PSO achieves faster convergence and higher computational efficiency. A case study at an educational institution demonstrates significant seasonal ECC reductions—39.4 % in autumn, 76.5 % in winter, 65.0 % in summer, and 79.5 % in spring—resulting in annual savings of 64.97 % (from INR 3.40 million to INR 1.19 million). The EMS ensures intelligent load shifting, optimal battery utilization, and zero grid import during peak tariffs while enabling surplus PV injection. Results confirm the proposed approach as a scalable, efficient, and practical solution for reducing costs, improving renewable self-consumption, and enhancing resilience in next-generation hybrid microgrids.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101132"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520074","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

Optimal centralized scheduling strategy for bidirectional charging of PEV fleets in low-voltage microgrids 低压微电网电动汽车双向充电集中调度优化策略

e-Prime - Advances in Electrical Engineering, Electronics and Energy Pub Date : 2025-12-01 Epub Date: 2025-11-26 DOI: 10.1016/j.prime.2025.101136

Subhasis Panda , Buddhadeva Sahoo , Indu Sekhar Samanta , Pravat Kumar Rout , Binod Kumar Sahu , Mohit Bajaj , Cansu Ayvaz Güven , Vojtech Blazek , Lukas Prokop

{"title":"Optimal centralized scheduling strategy for bidirectional charging of PEV fleets in low-voltage microgrids","authors":"Subhasis Panda , Buddhadeva Sahoo , Indu Sekhar Samanta , Pravat Kumar Rout , Binod Kumar Sahu , Mohit Bajaj , Cansu Ayvaz Güven , Vojtech Blazek , Lukas Prokop","doi":"10.1016/j.prime.2025.101136","DOIUrl":"10.1016/j.prime.2025.101136","url":null,"abstract":"<div><div>Rapid growth of plug-in electric vehicles (PEVs) is reshaping demand in low-voltage microgrids where voltage stability and power-quality margins are tight. Uncoordinated charging deepens evening peaks, stresses feeder limits, and constrains renewable hosting. This paper proposes a centralized, optimization-based scheduling strategy for bidirectional charging coordinating grid-to-vehicle (G2V) and vehicle-to-grid (V2G) dispatch to jointly minimize energy cost and enhance voltage stability. A linear programming (LP) model optimizes charging/discharging over discrete intervals subject to realistic constraints: charger power limits, state-of-charge (SoC) bounds, nodal-voltage regulation, and line-flow limits. The optimization is embedded in a forward-backward sweep load-flow loop to respect feeder physics. Using the IEEE European LV 8-bus system, we evaluate five scenarios single tariff, time-of-use (ToU) tariff, holiday load growth, ToU under holiday load, and photovoltaic (PV) integration. Relative to an uncontrolled baseline, the centralized strategy shifts demand off-peak, reduces peaks by up to 40% (12.0 to 7.2 kW), lowers energy cost by up to 25% (₹192.0 to ₹144.0), and improves minimum node voltages to 400–407 V; with PV, energy cost reaches ₹96.0 and minimum voltage rises to 412 V, all within EN 50,160 (±10%) bounds. These results validate a practical, scalable demand-side management (DSM) approach that improves reliability, reduces operating cost, and facilitates renewable integration; extensions to real-time, data-driven, or decentralized variants for larger fleets are outlined.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101136"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683610","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

A modulated triple vectors model predictive controllers for PMSM drives based on voltage reference position selection 基于电压基准位置选择的调制三向量模型预测控制器

e-Prime - Advances in Electrical Engineering, Electronics and Energy Pub Date : 2025-12-01 Epub Date: 2025-11-10 DOI: 10.1016/j.prime.2025.101133

Muhammad Syahril Mubarok , Nur Vidia Laksmi B． , Ananta Adhi Wardana , Agus Mukhlisin , Dimas Herjuno

{"title":"A modulated triple vectors model predictive controllers for PMSM drives based on voltage reference position selection","authors":"Muhammad Syahril Mubarok , Nur Vidia Laksmi B． , Ananta Adhi Wardana , Agus Mukhlisin , Dimas Herjuno","doi":"10.1016/j.prime.2025.101133","DOIUrl":"10.1016/j.prime.2025.101133","url":null,"abstract":"<div><div>This study introduces a modulated triple vectors model predictive current controller. The proposed method employs a triple voltage vector approach with an optimized duty cycle modulation scheme to improve current prediction accuracy while minimizing torque and current ripples. The control algorithm utilizes cost function minimization for eight possible voltage vectors to determine the optimal current prediction and properly selects voltage vector combination, thereby enhancing dynamic response and steady-state performance. By properly selecting voltage vectors based on the reference position in a stationary reference frame, the proposed method reduces computational complexity compared to conventional single vector and dual vector approaches. In addition, a model predictive speed controller with constraints is implemented to improve the dynamic speed controller. Experimental results confirm the advantages of the proposed method by significantly reducing total harmonic distortion and torque ripple, which are 5,26 % and 0105 N.m, respectively. Additionally, the proposed method exhibits improved robustness under different speed and load disturbance conditions, making this proposed method become a possible solution for high-performance permanent magnet synchronous motor drive applications.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101133"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617743","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

Fault diagnosis in renewable-integrated distribution systems using EMD-GAF and ANN 基于EMD-GAF和ANN的可再生集成配电系统故障诊断

e-Prime - Advances in Electrical Engineering, Electronics and Energy Pub Date : 2025-12-01 Epub Date: 2025-09-04 DOI: 10.1016/j.prime.2025.101101

Dhanunjayudu N. , Eswaramoorthy K. Varadharaj , Mohana Rao M. , Krishnaiah J.

{"title":"Fault diagnosis in renewable-integrated distribution systems using EMD-GAF and ANN","authors":"Dhanunjayudu N. , Eswaramoorthy K. Varadharaj , Mohana Rao M. , Krishnaiah J.","doi":"10.1016/j.prime.2025.101101","DOIUrl":"10.1016/j.prime.2025.101101","url":null,"abstract":"<div><div>The increasing integration of distributed renewable energy sources and dynamic loads has made fault detection in modern distribution systems significantly more challenging. Traditional protection schemes often fail to accurately distinguish between faults and non-fault disturbances such as switching events, islanding, or power quality anomalies, which can lead to delayed or incorrect responses. This paper proposes a fast and reliable fault diagnosis technique integrating Empirical Mode Decomposition (EMD), Gramian Angular Fields (GAF), and Artificial Neural Networks (ANN) to detect, classify, and locate faults in renewable-integrated distribution networks. Three-phase current and voltage signals are first decomposed using EMD to extract low-frequency residues, that are then transformed into two-dimensional GAF visual patterns. Cosine similarity compares these patterns against reference healthy conditions for fault detection.</div><div>For fault localization, an ANN is trained using statistical features from four levels of EMD residues. The proposed method achieves over 99.5% accuracy in fault detection and classification using only 0.25 cycles of post-fault data and single-point current and voltage measurements at the substation, even under noisy (20 dB SNR) and high-impedance (up to 5 <span><math><mi>Ω</mi></math></span>) conditions. It outperforms existing signal-analysis-based and visual-pattern-based techniques by accurately distinguishing faults from switching and islanding events, making it a robust and scalable solution for real-time smart grid protection. Furthermore, the method achieves up to 99.04% bus-level fault localization accuracy and reduces distance-to-fault errors by over 25% compared to existing techniques, further enhancing suitability for protection and precise fault location.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101101"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097341","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