International Transactions on Electrical Energy Systems最新文献

{"title":"Hybrid Bi-LSTM–GRU Framework for Solar Energy Forecasting in Modern Power Systems","authors":"Syed Zarak Shah,&nbsp;Kaleem Ullah,&nbsp;Syed Mustafa Ali,&nbsp;Syed Ahmad Saleem Bokhari,&nbsp;Zafar Ullah Khan,&nbsp;Shehzad Ahmad","doi":"10.1155/etep/1276713","DOIUrl":"https://doi.org/10.1155/etep/1276713","url":null,"abstract":"<p>Solar irradiance exhibits intermittent and stochastic characteristics, posing significant challenges to the operational resilience, stability and reliability of photovoltaic (PV) systems and modern power grids. With the increasing penetration of large-scale PV installations, accurate solar power forecasting has become essential for maintaining grid balance, optimising energy dispatch and enhancing overall system efficiency. In this paper, a hybrid Bi-LSTM–GRU architecture is proposed for solar forecasting in renewable-integrated power systems due to its accuracy. The hybrid architecture takes advantage of the temporal learning power of Bi-LSTM layers to learn bidirectional relationships in irradiance and generation data, whereas GRU layers are used to improve sequential representations and faster convergence through the alleviation of the vanishing gradient problem. The model is trained and tested on the UNISOLAR dataset at La Trobe University, Australia, which offers time-series measurements of the PV generation at the large scale, as well as of the meteorological parameters. Comprehensive evaluations are conducted against multiple baseline models. The proposed Bi-LSTM–GRU hybrid architecture consistently demonstrated superior predictive accuracy and robustness, achieving an RMSE of 0.298, MAE of 0.287 and MAPE of 0.033. These results indicate that the proposed model effectively captures complex nonlinear temporal dependencies and irradiance variability inherent in solar energy data. Consequently, the model enables more accurate short-term forecasting and supports adaptive grid management strategies.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/1276713","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942747","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":"Comparative Modeling and Hybrid Analytical–Metaheuristic–Machine Learning Identification With Experimental Validation of Photovoltaic Solar Cell Models","authors":"Rabeb Abid,&nbsp;Fatma Ben Salem,&nbsp;Jihed Hmad,&nbsp;Motab Turki Almousa,&nbsp;Riyadh Bouddou,&nbsp;Seif Al Bustanji,&nbsp;Nabil Derbel,&nbsp;Mebratu Sintie Geremew","doi":"10.1155/etep/6089011","DOIUrl":"https://doi.org/10.1155/etep/6089011","url":null,"abstract":"<p>Accurate photovoltaic cell modeling is essential for reliable performance prediction, yet parameter estimation remains challenging for widely used electrical equivalent models. This work investigates four PV cell models: (i) ideal, (ii) single-diode model (SDM), (iii) double-diode model (DDM), and (iv) three-diode model (TDM). Each one undergoes a rigorous investigation of its intrinsic, unobservable parameters employing two distinct methodologies: (i) the identification approach and (ii) the adjustment technique. Several numerical and metaheuristic techniques are employed, including MATLAB nonlinear solvers, Newton–Raphson, genetic algorithms (GAs), and the gradient evolution (GE) method. Furthermore, an artificial neural network (ANN)–based identification scheme also serves to improve the quality of such hybrid solutions. A comparative analysis of the four models is conducted and validated through theoretical and experimental results. The findings show that the ideal electrical model combined with ANN provides the best performance, achieving a very low maximum deviation (<i>ξ</i>_<i>r</i> = 2.1 × 10⁻⁹) and a high fill factor of 78.97%, demonstrating its effectiveness for accurate PV parameter estimation and PV behavior prediction.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/6089011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942605","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":"Sensitivity-Based Siting Strategy for Multiple Improved Phase Shifting Transformers Under Renewable Energy Uncertainty","authors":"Yuxuan Huang,&nbsp;Haoran Xu,&nbsp;Jinyang Li,&nbsp;Xin Zhao,&nbsp;Shan Gao,&nbsp;Ningyu Zhang,&nbsp;Qun Li","doi":"10.1155/etep/7621451","DOIUrl":"https://doi.org/10.1155/etep/7621451","url":null,"abstract":"<p>Large-scale renewable energy integration intensifies operating uncertainty and uneven branch power flow distributions, creating a growing need for more flexible power flow control and the coordinated siting of multiple power flow control devices. Compared to conventional phase-shifting transformers, improved phase-shifting transformers (IPSTs) incorporate voltage magnitude ratio control in addition to phase angle control, thereby providing a wider power flow control range. This paper proposes a siting strategy for multiple IPSTs under renewable energy uncertainty. An IPST equivalent circuit and power injection model are established by explicitly considering the equivalent impedance, voltage magnitude ratio, and phase angle shift. To evaluate candidate installation locations under uncertain operating conditions, a hybrid probabilistic power flow (PPF) method integrating Latin hypercube sampling (LHS), the cumulant method (CM), and piecewise linearization is developed. An IPST siting sensitivity index is then derived from the active power flow performance index. A dominance criterion and a joint control capability measure are introduced to quantify the interactions among multiple IPSTs, and the individual and joint control capabilities of the devices are incorporated into a chance-constrained siting model. Case studies on a modified IEEE 30-bus system and the IEEE 118-bus system show that the proposed strategy can identify coordinated installation locations for multiple IPSTs, eliminate existing branch overloads without causing new violations, and achieve a more balanced power flow distribution among transmission paths. Sensitivity analyses further demonstrate the robustness of the proposed siting strategy.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/7621451","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942824","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":"Distribution Network Fault Risk Suppression Strategy Based on Charging Station Electricity Purchase Guidance Considering the Scheduling Potential Uncertainty","authors":"Kewen Li,&nbsp;Qianyi Chen,&nbsp;Shifeng Ou,&nbsp;Zongtao Qin","doi":"10.1155/etep/5605148","DOIUrl":"https://doi.org/10.1155/etep/5605148","url":null,"abstract":"<p>The large-scale development of electric vehicles (EVs) provides new means for fault defense and rapid handling in distribution networks. This paper proposes a distribution network fault defense strategy for charging stations (CSs) electricity purchase guidance, considering the uncertainty of scheduling potential. First, a generalized energy storage model for CSs is established based on Minkowski sum theory, and the scheduling potential probabilistic characteristics of CSs are analyzed. Subsequently, under the constraints of CS scheduling potential uncertainty and distribution network fault risk, a two-stage stochastic programming strategy for multi-CS collaborative electricity purchase guidance is constructed by integrating Latin hypercube sampling and Stackelberg game theory, incorporating fault risk suppression in distribution networks. Finally, an analytical target cascading (ATC) algorithm is employed to propose a decoupling solution strategy for the multi-CS collaborative electricity purchase guidance model considering the fault risk suppression of distribution networks. Simulation results demonstrate that the proposed method effectively motivates CSs to participate in distribution network load regulation, reducing electricity purchase costs by 13.57%–21.17% while ensuring the distribution network’s fault risk is reduced to a safe level.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/5605148","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942191","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 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}