International Transactions on Electrical Energy Systems最新文献

{"title":"A Capability-Driven Sequential Optimization Framework for Electrical Machines Under Manufacturing Uncertainty","authors":"Chiweta E. Abunike,&nbsp;Ogbonnaya I. Okoro,&nbsp;Sumeet S. Aphale","doi":"10.1155/etep/2489065","DOIUrl":"https://doi.org/10.1155/etep/2489065","url":null,"abstract":"<p>This paper presents a capability-driven sequential optimization framework for the robust design of electrical machines under manufacturing uncertainty. The proposed methodology integrates surrogate modeling, NSGA-II-based Pareto front exploration, and Design for Six Sigma (DFSS)-driven refinement to ensure production-level reliability. Surrogate models constructed via Response Surface Methodology (RSM) enable efficient trade-off analysis across key performance metrics, including torque ripple, average torque, efficiency, and power factor. The first optimization stage applies NSGA-II to identify Pareto-optimal trade-offs, while statistical analysis reveals residual variability risks in critical objectives. To address this, the second stage introduces DFSS-based capability constraints within a sequential refinement process, driving capability indices (<i>C</i>_{<i>p</i><i>k</i>}) above 2.0 and reducing probability of failure (PoF) to below 0.01% across all objectives. Compared to baseline designs, the optimized solutions demonstrate significant reductions in variability: 36% in torque ripple, 40% in power factor, and 27% in torque spread. The proposed framework is validated as an effective and scalable approach for robust optimization of electrical machines subjected to real-world manufacturing tolerances, enabling production-ready solutions with high statistical confidence.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/2489065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-Stage Predictive Model Incorporating Extreme Gradient Boosting-Based Data Linearizer for High-Precision State-of-Charge Estimation of a Lithium-Ion Battery","authors":"Okemakinde Femi Emmanuel,&nbsp;Abeeb A. Adejare,&nbsp;Vincent Masabiar Tingbari,&nbsp;Ekuewa Oluwaseun Isaiah,&nbsp;Jaea Lee,&nbsp;Seok-Teak Yun,&nbsp;Jonghoon Kim","doi":"10.1155/etep/6774521","DOIUrl":"https://doi.org/10.1155/etep/6774521","url":null,"abstract":"<p>Most studies have primarily focused on state-of-charge (SOC) estimation models, often neglecting the improvement of battery datasets, resulting in raw data being commonly used to train these models. However, this study emphasizes the quality of the input data for SOC estimation models. A dual-stage predictive model was proposed in which the first stage, a Ridge data linearizer (RDL), is an algorithm designed to linearize inherently nonlinear lithium-ion battery (LIB) data, whereas the second stage employs an extreme gradient boosting (XGBoost) model, a machine learning (ML) algorithm used for SOC estimation using the linearized dataset from the RDL as the input. To assess the impact of the linearization process, a conventional XGBoost model was developed and trained on the raw dataset for performance comparison. In addition, a feature engineering technique was introduced during the data preprocessing stage to augment the battery features by manipulating existing data, thereby generating additional features automatically accessible to the estimation model. The SOC estimation comparison of both models highlighted the critical importance of data quality and the potential for data enhancement in improving the accuracy and reliability of SOC estimation models.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/6774521","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148943538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graph Neural Network–Based State-of-Health Estimation Applying Interpretable Distribution of Relaxation Times Features Extracted From Electrochemical Impedance Spectroscopy","authors":"Barancira Ted Doral,&nbsp;Oluwaseun I. Ekuewa,&nbsp;Femi E. Okemakinde,&nbsp;Sein Oh,&nbsp;Seok-Teak Yun,&nbsp;Jonghoon Kim","doi":"10.1155/etep/2012898","DOIUrl":"https://doi.org/10.1155/etep/2012898","url":null,"abstract":"<p>Multiple methods have been used for estimating the state-of-health (SOH) of a lithium-ion battery (LIB), but rarely do we see methods that compute the SOH and provide a physical interpretation of the battery. Therefore, we introduce in this study a framework that integrates graph-based and conventional learning techniques to assess the battery degradation by extracting the features from the distribution of relaxation times (DRTs). We first changed the electrochemical impedance spectroscopy (EIS) data into non-negative DRT features, which were obtained through the adaptive Tikhonov regularization with the regularized least square inversion. From this, a graph-structured representation is represented, and then we use a graph neural network (GNN) architecture, such as graph sample and aggregate (GraphSAGE), graph convolutional network (GCN), and graph attention network (GAT), for SOH regression. As a nongraph benchmark, we used the support vector regression (SVR) and long short-term memory (LSTM) networks to evaluate the performance of graph-based models. GraphSAGE achieved <i>R</i>² = 0.838 on B0005 and <i>R</i>² = 0.851 on B0006 in the optimal configuration, demonstrating competitiveness with SVR. The pipeline robustness was confirmed through a three-level bootstrap analysis with DRT peak uncertainty below 5% under realistic noise. The analysis also identified the solid electrolyte interface (SEI) and the charge-transfer band (3–10 ms) as the dominant SOH indicator in our case. Four additional battery groups, spanning from 4°C to 44°C, confirmed cross-condition transferability. Since the DRT-GNN method is also a computationally lightweight approach that provides physically interpretable features reflecting ongoing electrochemical degradation processes, it can potentially be extended to an online battery management system (BMS).</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/2012898","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148943536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on Distribution Network Line Loss Rate Prediction Based on a CNN-LSTM Model Optimized by an Improved Sparrow Search Algorithm","authors":"Lin Jiang,&nbsp;Chen Li,&nbsp;Wei Qiu,&nbsp;Yuyan Liu,&nbsp;Yanan Fei","doi":"10.1155/etep/3749664","DOIUrl":"https://doi.org/10.1155/etep/3749664","url":null,"abstract":"<p>Accurate short-term prediction of line loss rate is of great significance for improving the operational efficiency and economic performance of distribution networks. To address the limitations of conventional prediction models in nonlinear feature extraction and hyperparameter optimization, this paper proposes a CNN–LSTM prediction model optimized by an improved sparrow search algorithm incorporating logistic chaotic mapping and a linearly decreasing weight strategy, referred to as the LCSSA–CNN–LSTM model. First, a convolutional neural network is employed to extract local features from multidimensional input variables, and a long short-term memory network is then used to capture temporal dependencies in the line loss rate series. Second, logistic chaotic mapping is introduced to improve the initialization quality of the sparrow population, while a linearly decreasing weight is incorporated to enhance the balance between global exploration and local exploitation during iterative optimization. Finally, comparative experiments are conducted on a real line loss rate dataset. The results show that the proposed model significantly outperforms the baseline CNN–LSTM model, with the RMSE reduced by 55.79% and the prediction error of representative peak samples controlled within 3%. These results indicate that the proposed model can effectively improve the accuracy and robustness of short-term line loss rate prediction.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/3749664","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148943594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Multilevel Triple Interline Bidirectional DC Power Flow Controller","authors":"Ehsan Najafi,&nbsp;Hossein Tavakoli,&nbsp;Seyed Fariborz Zarei,&nbsp;Hamed Heydari-Doostabad","doi":"10.1155/etep/5277133","DOIUrl":"https://doi.org/10.1155/etep/5277133","url":null,"abstract":"<p>DC power flow control is becoming more essential with the increasing number of HVDC lines. In order to manage power transfer between lines, an interline DC power flow controller (IDPFC) is utilised. This paper proposes a novel multi-level triple interline DC power flow controller, or MT-IDCPFC, which has the ability to control load flow in DC lines and transfer power from an overloaded line to a less loaded line with more flexibility. To validate the proposed system, three DC lines are modelled and simulated by MATLAB/Simulink. The controller is proven to transfer power between three lines with better results in terms of duty cycle in comparison with available technology. Therefore, it is shown that the proposed topology provides a higher duty cycle, which facilitates running at higher frequencies with a limitation of switching time in semiconductor switches. The Topology and its control are novelties of this research which are explained and discussed by simulation results. Finally, the system is implemented on a hardware in the loop (HIL) Speedgoat platform, which verified its performance and results.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/5277133","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fault Resistance Estimation and Multifault Classification in 11-kV Distribution Networks: A Noise-Resilient Machine Learning Framework","authors":"Jacob Wekalao,&nbsp;Tobias Topisia","doi":"10.1155/etep/5687183","DOIUrl":"https://doi.org/10.1155/etep/5687183","url":null,"abstract":"<p>Accurate estimation of fault resistance and reliable discrimination of fault type are foundational to the protection and self-healing capability of medium-voltage distribution networks, yet both tasks are severely confounded by high-impedance faults (HIFs) whose low and variable current signatures evade conventional overcurrent and distance protection. This paper presents a noise-resilient machine learning framework that performs continuous fault resistance regression and multiclass fault-type classification on a simulated 11-kV radial distribution feeder. A high-fidelity electromagnetic model was constructed in MATLAB/Simulink–Simscape, comprising a 35 MVA Thévenin-equivalent source, two cascaded PI-section lines totaling 10 km, a 5-MVA distribution transformer, and an aggregated downstream load. An automated Monte Carlo pipeline generated 6000 single-phase-to-ground (AG) fault cases, with fault resistance log uniformly sampled over 0.01–500 Ω, and a further 3500 cases spanning seven canonical fault types. From pre- and postfault voltage and current windows sampled at 10 kHz, 22 physically interpretable features were extracted, encompassing faulted- and healthy-phase magnitudes, asymmetry indices, zero-sequence components, and derived impedance quantities. Gradient boosting machine (GBM), random forest (RF), and support vector machine/regression (SVM/SVR) models were trained and benchmarked under clean and noise-contaminated measurement conditions with additive white Gaussian noise spanning 10–50 dB SNR. Under clean conditions, the GBM regressor achieved <i>R</i>² = 0.9961, RMSE = 8.73 Ω, and MAE = 4.21 Ω; the RF classifier attained 99.34% accuracy on fault-severity classification. The framework maintained <i>R</i>² &gt; 0.97 and classification accuracy &gt; 96% down to 30 dB SNR, which serves as a qualitative reference level representative of metering-class instrument transformer measurement quality, establishing practical sensor requirements for field deployment. Multifault-type extension yielded 98.91% identification accuracy with per-type regression <i>R</i>² consistently exceeding 0.98. All results are based on synthetic simulation data from a single feeder configuration, one fault location, and AWGN-based noise modeling; field validation remains future work. The results demonstrate that interpretable, ensemble-based learning on physically grounded features can deliver simultaneous resistance estimation and fault typing suitable for integration into intelligent protection and smart-grid monitoring systems.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/5687183","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Microgrid Energy Management Framework Using Proximal Policy Optimization Combined With Renewable Scenario–Based Stochastic Programming","authors":"Pallavi Jha,&nbsp;Deepak Gupta,&nbsp;Pranda Prasanta Gupta,&nbsp;Kavita Tukaram Patil,&nbsp;Sharmila Kishor Wagh,&nbsp;Suvarna Patil,&nbsp;Akhilesh Kumar Sharma,&nbsp;Srikanth Prabhu","doi":"10.1155/etep/8748240","DOIUrl":"https://doi.org/10.1155/etep/8748240","url":null,"abstract":"<p>An essential component of modern microgrid operation is the energy management system. This work proposes a stochastic programming–based proximal policy optimisation (PPO) technique for a typical wind–solar–diesel–storage microgrid, accounting for the significant uncertainty associated with wind energy. Using Latin Hypercube Sampling, scenario sets for wind power with different probabilities are generated by simulating potential errors in predicted wind power. A Markov decision process with high-dimensional state and action spaces is used to model the energy management optimisation problem. In this manner, day-ahead power scheduling for each generation/storage unit is optimised using PPO, which maximises economic gain while satisfying security constraints. The proposed method’s day-ahead scheduling, evaluated on a real-world microgrid, demonstrates the energy management system’s high efficiency and robustness. The results show that the proposed framework is competitive in solution quality and constraint satisfaction, and it achieves a positive balance among computational efficiency, robustness, operational feasibility and long-term system reliability under renewable uncertainty.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/8748240","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New Method for Cyber Resilience in Microgrids by Considering Electricity Market","authors":"Rahim Falahzadeh,&nbsp;Afshin Lashkarara,&nbsp;Hajar Bagheri Tolabi","doi":"10.1155/etep/9096034","DOIUrl":"https://doi.org/10.1155/etep/9096034","url":null,"abstract":"<p>This paper develops a cyberphysical resilience model for microgrids to facilitate energy exchange with the upstream electrical power network and participation in the wholesale market, essential for energy management systems. Managing energy across multiple microgrids poses a significant challenge for operators. To address this, a new method is proposed to model the cyberphysical resilience for microgrids to engage in energy buying and selling within the wholesale market. The proposed model consists of three different functions. First, the wholesale market price is forecasted using a two-stage game theory approach. Subsequently, the second function employs a two-stage game theory technique to determine local exchange requests for microgrids. Following this, the intermediary entity determines the necessary exchange amounts on behalf of virtual microgrids in the third function. The intermediary institution establishes price limits for the wholesale market, serving as the basis for competitive pricing. The proposed model is implemented and solved numerically using MATLAB and general algebraic modeling system (GAMS) software. The results highlight the optimal technical and economic connection between the upstream electrical power network and the downstream microgrids.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/9096034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Renewable Energy, Storage Resources, and Demand-Side Management in Residential Settings Under Dynamic Pricing","authors":"Sana Iqbal,&nbsp;Fareed Ahmad,&nbsp;Mohammad Sarfraz,&nbsp;Sanjay Kumar,&nbsp;Tousif Khan Nizami,&nbsp;Satya Veerendra Arigela","doi":"10.1155/etep/4534983","DOIUrl":"https://doi.org/10.1155/etep/4534983","url":null,"abstract":"<p>The residential sector accounts for a major share of global electricity demand, and this demand is projected to rise sharply with the spread of automated appliances and heatwave-driven cooling loads. While fuzzy-logic HVAC control, dynamic pricing, and renewable-storage integration have each been studied individually, few works jointly quantify how a fuzzy-logic energy management controller interacts with real-time and time-of-use pricing and with renewable generation, storage, and load shifting within a single, reproducible residential simulation framework, leaving the combined saving potential and its attribution across mechanisms unclear. This study addresses that gap by developing a fuzzy-logic-based energy management controller for an interruptible HVAC load and a smart load simulator (SLS)–based model of critical and noncritical residential loads, evaluated under real-time pricing (RTP) and time-of-use (TOU) tariffs with and without local solar PV, wind, and battery storage. A MATLAB/Simulink GUIDE-based platform was used to run the simulations over a representative one-month operating horizon, with a 72-rule fuzzy inference base built on four input variables: outdoor temperature, price, demand, and occupancy. Simulation results show that the fuzzy HVAC controller reduces HVAC energy consumption by approximately 28% relative to manual thermostat operation under RTP and that combining renewable generation with noncritical load shifting reduces total household energy consumption and cost by 42.28% and 38.37%, respectively. The contribution of this work is a unified, reproducible residential energy management framework that jointly evaluates fuzzy HVAC control, dynamic-pricing response, and renewable-storage-shifting strategies within one consistent simulation environment, providing quantitative guidance for residential demand-side management design.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/4534983","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Model-Free Adaptive Control Approach for Secondary Control of Four Distributed Generation Units in AC Islanded Microgrid","authors":"Rahman Abasszadeh Gorany,&nbsp;Seyed Ali Hosseini,&nbsp;Javad Pourhossein","doi":"10.1155/etep/7068752","DOIUrl":"https://doi.org/10.1155/etep/7068752","url":null,"abstract":"<p>This paper addresses the problem of secondary voltage and frequency control in islanded AC microgrids under uncertain operating conditions and limited system modeling information. Conventional secondary controllers typically rely on accurate mathematical models and centralized communication structures, which reduce flexibility and robustness in practical applications. To overcome these limitations, a distributed model-free adaptive control (MFAC) strategy is proposed for a four-unit islanded AC microgrid. The proposed approach regulates voltage and frequency using only real-time local input–output measurements without requiring an explicit mathematical model of the microgrid. The controller employs online dynamic linearization and pseudogradient estimation to adapt to parameter uncertainties, load variations, and communication delays in real time. In addition, each distributed generation (DG) unit computes its control action using only local and neighboring information, improving scalability and reducing dependence on centralized coordination.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/7068752","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}