Energy and AI最新文献

{"title":"A machine learning driven 3D+1D model for efficient characterization of proton exchange membrane fuel cells","authors":"Yuwei Pan ,&nbsp;Haijun Ruan ,&nbsp;Billy Wu ,&nbsp;Yagya N. Regmi ,&nbsp;Huizhi Wang ,&nbsp;Nigel P. Brandon","doi":"10.1016/j.egyai.2024.100397","DOIUrl":"10.1016/j.egyai.2024.100397","url":null,"abstract":"<div><p>The computational demands of 3D continuum models for proton exchange membrane fuel cells remain substantial. One prevalent approach is the hierarchical model combining a 2D/3D flow field with a 1D sub-model for the catalyst layers and membrane. However, existing studies often simplify the 1D domain to a linearized 0D lumped model, potentially resulting in significant errors at high loads. In this study, we present a computationally efficient neural network driven 3D+1D model for proton exchange membrane fuel cells. The 3D sub-model captures transport in the gas channels and gas diffusion layers and is coupled with a 1D electrochemical sub-model for microporous layers, membrane, and catalyst layers. To reduce computational intensity of the full 1D description, a neural network surrogates the 1D electrochemical sub-model for coupling with the 3D domain. Trained by model-generated large synthetic datasets, the neural network achieves root mean square errors of less than 0.2%. The model is validated against experimental results under various relative humidities. It is then employed to investigate the nonlinear distribution of internal states under different operating conditions. With the neural network operating at 0.5% of the computing cost of the 1D sub-model, the hybrid model preserves a detailed and nonlinear representation of the internal fuel cell states while maintaining computational costs comparable to conventional 3D+0D models. The presented hybrid data-driven and physical modeling framework offers high accuracy and computing speed across a broad spectrum of operating conditions, potentially aiding the rapid optimization of both the membrane electrode assembly and the gas channel geometry.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100397"},"PeriodicalIF":9.6,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000636/pdfft?md5=90b106f209226278c4c0202633628b96&pid=1-s2.0-S2666546824000636-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141706750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing hybrid electric vehicle coupling organic Rankine cycle energy management strategy via deep reinforcement learning","authors":"Xuanang Zhang ,&nbsp;Xuan Wang ,&nbsp;Ping Yuan ,&nbsp;Hua Tian ,&nbsp;Gequn Shu","doi":"10.1016/j.egyai.2024.100392","DOIUrl":"10.1016/j.egyai.2024.100392","url":null,"abstract":"<div><p>Trucks consume a lot of energy. Hybrid technology maintains a long range while realizing energy savings. Hybrid is therefore an effective energy-saving technology for trucks. Recovery of engine waste heat through the organic Rankine cycle further enhances engine efficiency and provides effective thermal management. However, the powertrain greatly increases the complexity of energy management system. In order to design an energy management system with high efficiency and robustness, this study proposes a deep reinforcement learning embedded rule-based energy management system. This method optimises the key parameters of the rule-based energy management system by inserting deep reinforcement learning into it. Therefore, this scheme combines the good optimization effect of deep reinforcement learning and the excellent robustness of rule. In order to verify the feasibility of this scheme, this study builds the system dynamic model and carries out a simulation study. Subsequently, a hybrid powertrain semi physical experimental bench was constructed and a rapid control prototype experimental study was carried out. The simulation results show that the deep reinforcement learning embedded rule-based energy management system can reduce the energy consumption by 4.31 % compared with the rule-based energy management system under the C-WTVC driving cycle. In addition, energy saving and safe operation can also be achieved under other unfamiliar untrained driving cycles. The rapid control prototype experimental study shows that the deep reinforcement learning embedded rule-based energy management system has good agreement in experiment and simulation, which demonstrates the potential for real vehicle engineering applications and promotes the engineering application of deep reinforcement learning.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100392"},"PeriodicalIF":9.6,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000582/pdfft?md5=02e8f26f646dcafab9c94b9440ca7815&pid=1-s2.0-S2666546824000582-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141709960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gas exchange optimization in aircraft engines using sustainable aviation fuel: A design of experiment and genetic algorithm approach","authors":"Zheng Xu ,&nbsp;Jinze Pei ,&nbsp;Shuiting Ding ,&nbsp;Longfei Chen ,&nbsp;Shuai Zhao ,&nbsp;Xiaowei Shen ,&nbsp;Kun Zhu ,&nbsp;Longtao Shao ,&nbsp;Zhiming Zhong ,&nbsp;Huansong Yan ,&nbsp;Farong Du ,&nbsp;Xueyu Li ,&nbsp;Pengfei Yang ,&nbsp;Shenghui Zhong ,&nbsp;Yu Zhou","doi":"10.1016/j.egyai.2024.100396","DOIUrl":"10.1016/j.egyai.2024.100396","url":null,"abstract":"<div><p>The poppet valves two-stroke (PV2S) aircraft engine fueled with sustainable aviation fuel is a promising option for general aviation and unmanned aerial vehicle propulsion due to its high power-to-weight ratio, uniform torque output, and flexible valve timings. However, its high-altitude gas exchange performance remains unexplored, presenting new opportunities for optimization through artificial intelligence (AI) technology. This study uses validated 1D + 3D models to evaluate the high-altitude gas exchange performance of PV2S aircraft engines. The valve timings of the PV2S engine exhibit considerable flexibility, thus the Latin hypercube design of experiments (DoE) methodology is employed to fit a response surface model. A genetic algorithm (GA) is applied to iteratively optimize valve timings for varying altitudes. The optimization process reveals that increasing the intake duration while decreasing the exhaust duration and valve overlap angles can significantly enhance high-altitude gas exchange performance. The optimal valve overlap angle emerged as 93 °CA at sea level and 82 °CA at 4000 m altitude. The effects of operating parameters, including engine speed, load, and exhaust back pressure, on the gas exchange process at varying altitudes are further investigated. The higher engine speed increases trapping efficiency but decreases the delivery ratio and charging efficiency at various altitudes. This effect is especially pronounced at elevated altitudes. The increase in exhaust back pressure will significantly reduce the delivery ratio and increase the trapping efficiency. This study demonstrates that integrating DoE with AI algorithms can enhance the high-altitude performance of aircraft engines, serving as a valuable reference for further optimization efforts.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100396"},"PeriodicalIF":9.6,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000624/pdfft?md5=60d1010d2f983490e5527df283a897bf&pid=1-s2.0-S2666546824000624-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141703344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Topology reduction through machine learning to accelerate dynamic simulation of district heating","authors":"Dubon Rodrigue ,&nbsp;Mohamed Tahar Mabrouk ,&nbsp;Bastien Pasdeloup ,&nbsp;Patrick Meyer ,&nbsp;Bruno Lacarrière","doi":"10.1016/j.egyai.2024.100393","DOIUrl":"10.1016/j.egyai.2024.100393","url":null,"abstract":"<div><p>District heating networks (DHNs) provide an efficient heat distribution solution in urban areas, accomplished through interconnected and insulated pipes linking local heat sources to local consumers. This efficiency is further enhanced by the capacity of these networks to integrate renewable heat sources and thermal storage systems. However, integration of these systems adds complexity to the physical dynamics of the network, necessitating complex dynamic simulation models. These dynamic physical simulations are computationally expensive, limiting their adoption, particularly in large-scale networks. To address this challenge, we propose a methodology utilizing Artificial Neural Networks (ANNs) to reduce the computational time associated with the DHNs dynamic simulations. Our approach consists in replacing predefined clusters of substations within the DHNs with trained surrogate ANNs models, effectively transforming these clusters into single nodes. This creates a hybrid simulation framework combining the predictions of the ANNs models with the accurate physical simulations of remaining substation nodes and pipes. We evaluate different architectures of Artificial Neural Network on diverse clusters from four synthetic DHNs with realistic heating demands. Results demonstrate that ANNs effectively learn cluster dynamics irrespective of topology or heating demand levels. Through our experiments, we achieved a 27% reduction in simulation time by replacing 39% of consumer nodes while maintaining acceptable accuracy in preserving the generated heat powers by sources.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100393"},"PeriodicalIF":9.6,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000594/pdfft?md5=0c04fef1e20df0e384376520d8322eae&pid=1-s2.0-S2666546824000594-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141630244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Context-aware reinforcement learning for cooling operation of data centers with an Aquifer Thermal Energy Storage","authors":"Lukas Leindals,&nbsp;Peter Grønning,&nbsp;Dominik Franjo Dominković,&nbsp;Rune Grønborg Junker","doi":"10.1016/j.egyai.2024.100395","DOIUrl":"10.1016/j.egyai.2024.100395","url":null,"abstract":"<div><p>Data centers are often equipped with multiple cooling units. Here, an aquifer thermal energy storage (ATES) system has shown to be efficient. However, the usage of hot and cold-water wells in the ATES must be balanced for legal and environmental reasons. Reinforcement Learning has been proven to be a useful tool for optimizing the cooling operation at data centers. Nonetheless, since cooling demand changes continuously, balancing the ATES usage on a yearly basis imposes an additional challenge in the form of a delayed reward. To overcome this, we formulate a return decomposition, Cool-RUDDER, which relies on simple domain knowledge and needs no training. We trained a proximal policy optimization agent to keep server temperatures steady while minimizing operational costs. Comparing the Cool-RUDDER reward signal to other ATES-associated rewards, all models kept the server temperatures steady at around 30 °C. An optimal ATES balance was defined to be 0% and a yearly imbalance of −4.9% with a confidence interval of [−6.2, −3.8]% was achieved for the Cool 2.0 reward. This outperformed a baseline ATES-associated reward of 0 at −16.3% with a confidence interval of [−17.1, −15.4]% and all other ATES-associated rewards. However, the improved ATES balance comes with a higher energy consumption cost of 12.5% when comparing the relative cost of the Cool 2.0 reward to the zero reward, resulting in a trade-off. Moreover, the method comes with limited requirements and is applicable to any long-term problem satisfying a linear state-transition system.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100395"},"PeriodicalIF":9.6,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000612/pdfft?md5=b17bfa78652179749ed19203f3f51d82&pid=1-s2.0-S2666546824000612-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141623301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative analysis of time series neural network methods for three-way catalyst modeling","authors":"Zhuoxiao Yao,&nbsp;Tao Chen,&nbsp;Weipeng Lin,&nbsp;Yifang Feng,&nbsp;Zengchun Wei","doi":"10.1016/j.egyai.2024.100394","DOIUrl":"https://doi.org/10.1016/j.egyai.2024.100394","url":null,"abstract":"<div><p>Relative Oxygen Level of the Three-Way Catalyst is an important parameter that affects the conversion efficiency of pollutants. ROL is a time-varying hidden state variable that is difficult to directly observe in practice. Therefore, it is common to use a method of clearing oxygen storage to simplify control in vehicles. However, this method negates the positive effects of ROL on pollutant treatment. ROL can be indirectly observed through modeling methods. Chemical modeling methods involve extensive computational requirements that cannot meet the demands of practical control. In contrast, time-series neural networks offer computational speed advantages when dealing with similar problems. Therefore, the ROL observation models using both NARX and LSTM neural networks are developed and compared in this study. The results indicate that the NARX neural network exhibits higher precision with a smaller number of neurons and time steps. The LSTM neural network demonstrates greater stability when dealing with data error fluctuations. In practical applications, the ROL model can monitor the TWC operating status and assist in the development of intelligent pollutant aftertreatment control strategies.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100394"},"PeriodicalIF":9.6,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000600/pdfft?md5=e0f28356e08298507b446cd855bbbd46&pid=1-s2.0-S2666546824000600-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141606210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling Anti-Corrosion Coating Performance of Metallic Bipolar Plates for PEM Fuel Cells: A Machine Learning Approach","authors":"Pramoth Varsan Madhavan ,&nbsp;Samaneh Shahgaldi ,&nbsp;Xianguo Li","doi":"10.1016/j.egyai.2024.100391","DOIUrl":"https://doi.org/10.1016/j.egyai.2024.100391","url":null,"abstract":"<div><p>Proton exchange membrane (PEM) fuel cells have significant potential for clean power generation, yet challenges remain in enhancing their performance, durability, and cost-effectiveness, particularly concerning metallic bipolar plates, which are pivotal for lightweight compact fuel cell stacks. Protective coatings are commonly employed to combat metallic bipolar plate corrosion and enhance water management within stacks. Conventional methods for predicting coating performance in terms of corrosion resistance involve complex physical-electrochemical modelling and extensive experimentation, with significant time and cost. In this study machine learning techniques are employed to model metallic bipolar plate coating performance, diamond-like-carbon coatings of varying thicknesses deposited on SS316L are considered, and coating performance is evaluated using potentiodynamic polarization and electrochemical impedance spectroscopy. The obtained experimental data is split into two datasets for machine learning modelling: one predicting corrosion current density and another predicting impedance parameters. Machine learning models, including extreme gradient boosting (XGB) and artificial neural networks (ANN), are developed, and optimized to predict coating performance attributes. Data preprocessing and hyperparameter tuning are carried out to enhance model accuracy. Results show that ANN outperforms XGB in predicting corrosion current density, achieving an R² &gt; 0.98, and accurately predicting impedance parameters with an R² &gt; 0.99, indicating that the models developed are very promising for accurate prediction of the corrosion performance of coated metallic bipolar plates for PEM fuel cells.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100391"},"PeriodicalIF":9.6,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000570/pdfft?md5=e9602a2d4bdcbf9edd1ed121d42ef9f7&pid=1-s2.0-S2666546824000570-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141542907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing four machine learning algorithms for household non-intrusive load monitoring","authors":"Thomas Lee Young,&nbsp;James Gopsill,&nbsp;Maria Valero,&nbsp;Sindre Eikevåg,&nbsp;Ben Hicks","doi":"10.1016/j.egyai.2024.100384","DOIUrl":"https://doi.org/10.1016/j.egyai.2024.100384","url":null,"abstract":"<div><p>The combination of Machine Learning (ML), smart energy meters, and availability of household appliance energy profile data has opened new opportunities for Non-Intrusive Load Monitoring (NILM). However, the number of options makes it challenging in selecting optimal combinations for different energy applications, which requires studies to examine their trade-offs.</p><p>This paper contributes one such study that investigated four established ML approaches – K Nearest Neighbour (KNN), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost) and Convolutional Neural Network (CNN) – and their performance in classifying appliance events from Alternating Current (AC) and Root Mean Square (RMS) energy data where the sampling frequency and training dataset set size was varied (10 Hz–1 kHz and 50–2000 examples per class, respectively). The computational expense during training, testing and storage was also assessed and evaluated with reference to real-world applications.</p><p>The CNN classifier trained on AC data at 500 Hz and 11,000 examples gave the best F1-score <span><math><mfenced><mrow><mn>0</mn><mo>.</mo><mn>989</mn></mrow></mfenced></math></span> followed by the KNN classifier <span><math><mfenced><mrow><mn>0</mn><mo>.</mo><mn>940</mn></mrow></mfenced></math></span>. The storage size required by the CNN models was 3̃MB, which is very close to fitting on cost-effective embedded system microcontrollers. This would prevent high-rate data needing to be sent to the cloud as analysis could be performed on edge computing Internet-of-Things (IoT) devices.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100384"},"PeriodicalIF":9.6,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000508/pdfft?md5=240b289da6cfc06f2620e646326a2a01&pid=1-s2.0-S2666546824000508-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141481313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of spatial disaggregation methods for climate action planning","authors":"Shruthi Patil ,&nbsp;Noah Pflugradt ,&nbsp;Jann M. Weinand ,&nbsp;Detlef Stolten ,&nbsp;Jürgen Kropp","doi":"10.1016/j.egyai.2024.100386","DOIUrl":"https://doi.org/10.1016/j.egyai.2024.100386","url":null,"abstract":"<div><p>National-level climate action plans are often formulated broadly. Spatially disaggregating these plans to individual municipalities can offer substantial benefits, such as enabling regional climate action strategies and for assessing the feasibility of national objectives. Numerous spatial disaggregation approaches can be found in the literature. This study reviews and categorizes these. The review is followed by a discussion of the relevant methods for the disaggregation of climate action plans. It is seen that methods employing proxy data, machine learning models, and geostatistical ones are the most relevant methods for the spatial disaggregation of national energy and climate plans. The analysis offers guidance for selecting appropriate methods based on factors such as data availability at the municipal level and the presence of spatial autocorrelation in the data.</p><p>As the urgency of addressing climate change escalates, understanding the spatial aspects of national energy and climate strategies becomes increasingly important. This review will serve as a valuable guide for researchers and practitioners applying spatial disaggregation in this crucial field.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100386"},"PeriodicalIF":9.6,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000521/pdfft?md5=b85d13c3337101d8dea3c12c85754bdd&pid=1-s2.0-S2666546824000521-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141438719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discriminative features based comprehensive detector for defective insulators","authors":"Yalin Li,&nbsp;Xinshan Zhu,&nbsp;Bin Li,&nbsp;Junting Zeng,&nbsp;Shuai Wang","doi":"10.1016/j.egyai.2024.100387","DOIUrl":"https://doi.org/10.1016/j.egyai.2024.100387","url":null,"abstract":"<div><p>Insulators are essential equipment to ensure the safety and reliability of power transmission systems. Defective insulators may cause partial discharge and even lead to serious safety accidents. Hence it is necessary to accurately identify the defective insulator from a string of insulators. However, small defect poses significant challenges for recognizing the defective insulator from a large number of insulators. To address these issues, we collect and annotate the randomly generated defect dataset (RGDD). Further, the discriminative feature learning-based detector (DFLD) is constructed based on the pattern of backbone-neck-head. Specifically, considering the simultaneous existence of many insulators, attention-based bidirectional feature pyramid (ABFP) is designed to capture the discriminative information. Considering the small size of defective part, the efficient receptive field adaptation (ERFA) module is constructed to enhance the perception of contextual information related to defective insulators. Meanwhile, the two-stage detection head is designed to correct the location of defective insulators. It also adapts to the shape variation of insulators by the deformable convolution. On this basis, the keypoints method is introduced to more accurately represent the location of defective insulators. Due to the imbalance between positive and negative samples, the Adaptive Threshold Sample Assignment (ATSA) Strategy is proposed for selecting the best positive samples. DFLD has achieved good detection performance compared with classical object detection networks on the RGDD dataset and CPLID dataset. The ablation experiments are conducted on the RGDD dataset. It is verified that the discriminative features from DFLD can effectively recognize the small defect from insulators.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100387"},"PeriodicalIF":9.6,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000533/pdfft?md5=ee18da9af0fcff760d49779875c4d300&pid=1-s2.0-S2666546824000533-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141481292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}