Harsh working environments and wear between blades and other unit components can easily lead to cracks and damage on wind turbine blades. The cracks on the blades can endanger the shafting of the generator set, the tower and other components, and even cause the tower to collapse. To achieve high-precision wind blade crack detection, this paper proposes a crack fault-detection strategy that integrates Gated Residual Network (GRN), a fusion module and Transformer. Firstly, GRN can reduce unnecessary noisy inputs that could negatively impact performance while preserving the integrity of feature information. In addition, to gain in-depth information about the characteristics of wind turbine blades, a fusion module is suggested to implement the information fusion of wind turbine features. Specifically, each fan feature is mapped to a one-dimensional vector with the same length, and all one-dimensional vectors are concatenated to obtain a two-dimensional vector. And then, in the fusion module, the information fusion of the same characteristic variables in the different channels is realized through the Channel-mixing MLP, and the information fusion of different characteristic variables in the same channel is realized through the Column-mixing MLP. Finally, the fused feature vector is input into the Transformer for feature learning, which enhances the influence of important feature information and improves the model’s anti-noise ability and classification accuracy. Extensive experiments were conducted on the wind turbine supervisory control and data acquisition (SCADA) data from a domestic wind field. The results show that compared with other state-of-the-art models, including XGBoost, LightGBM, TabNet, etc., the F1-score of proposed gated fusion based Transformer model can reach 0.9907, which is 0.4%–2.09% higher than the compared models. This method provides a more reliable approach for the condition detection and maintenance of fan blades in wind farms.
{"title":"Gated Fusion Based Transformer Model for Crack Detection on Wind Turbine Blade","authors":"Wenyang Tang, Cong Liu, Bo Zhang","doi":"10.32604/ee.2023.040743","DOIUrl":"https://doi.org/10.32604/ee.2023.040743","url":null,"abstract":"Harsh working environments and wear between blades and other unit components can easily lead to cracks and damage on wind turbine blades. The cracks on the blades can endanger the shafting of the generator set, the tower and other components, and even cause the tower to collapse. To achieve high-precision wind blade crack detection, this paper proposes a crack fault-detection strategy that integrates Gated Residual Network (GRN), a fusion module and Transformer. Firstly, GRN can reduce unnecessary noisy inputs that could negatively impact performance while preserving the integrity of feature information. In addition, to gain in-depth information about the characteristics of wind turbine blades, a fusion module is suggested to implement the information fusion of wind turbine features. Specifically, each fan feature is mapped to a one-dimensional vector with the same length, and all one-dimensional vectors are concatenated to obtain a two-dimensional vector. And then, in the fusion module, the information fusion of the same characteristic variables in the different channels is realized through the Channel-mixing MLP, and the information fusion of different characteristic variables in the same channel is realized through the Column-mixing MLP. Finally, the fused feature vector is input into the Transformer for feature learning, which enhances the influence of important feature information and improves the model’s anti-noise ability and classification accuracy. Extensive experiments were conducted on the wind turbine supervisory control and data acquisition (SCADA) data from a domestic wind field. The results show that compared with other state-of-the-art models, including XGBoost, LightGBM, TabNet, etc., the F1-score of proposed gated fusion based Transformer model can reach 0.9907, which is 0.4%–2.09% higher than the compared models. This method provides a more reliable approach for the condition detection and maintenance of fan blades in wind farms.","PeriodicalId":35610,"journal":{"name":"Energy Engineering: Journal of the Association of Energy Engineers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135214880","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}
Jun Zhou, Shijie Fang, Jinghong Peng, Qing Li, G. Liang
{"title":"Investigation on the Long Term Operational Stability of Underground Energy Storage in Salt Rock","authors":"Jun Zhou, Shijie Fang, Jinghong Peng, Qing Li, G. Liang","doi":"10.32604/ee.2022.020317","DOIUrl":"https://doi.org/10.32604/ee.2022.020317","url":null,"abstract":"","PeriodicalId":35610,"journal":{"name":"Energy Engineering: Journal of the Association of Energy Engineers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69740122","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}
Li Luo, Yuhang Fan, Yu Wang, P. Ni, Xuewen Zhang, G. Xi
{"title":"Experiment Study on the Exhaust-Gas Heat Exchanger for Small and Medium-Sized Marine Diesel Engine","authors":"Li Luo, Yuhang Fan, Yu Wang, P. Ni, Xuewen Zhang, G. Xi","doi":"10.32604/ee.2022.022295","DOIUrl":"https://doi.org/10.32604/ee.2022.022295","url":null,"abstract":"","PeriodicalId":35610,"journal":{"name":"Energy Engineering: Journal of the Association of Energy Engineers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69743031","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}
{"title":"A Single-Ended Protection Principle for LCC-VSC-MTDC System with High Resistance Fault Tolerance","authors":"Dahai Zhang, Chuanjian Wu, Jinghan He","doi":"10.32604/ee.2022.023304","DOIUrl":"https://doi.org/10.32604/ee.2022.023304","url":null,"abstract":"","PeriodicalId":35610,"journal":{"name":"Energy Engineering: Journal of the Association of Energy Engineers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69743289","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}
Tinnawat Hongphan, Matthew O. T. Cole, C. Chamroon, Z. Brand
{"title":"Control System Design for Low Power Magnetic Bearings in a Flywheel Energy Storage System","authors":"Tinnawat Hongphan, Matthew O. T. Cole, C. Chamroon, Z. Brand","doi":"10.32604/ee.2022.022821","DOIUrl":"https://doi.org/10.32604/ee.2022.022821","url":null,"abstract":"","PeriodicalId":35610,"journal":{"name":"Energy Engineering: Journal of the Association of Energy Engineers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69743328","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}
Lately, in modern smart power grids, energy demand for accurate forecast of electricity is gaining attention, with increased interest of research. This is due to the fact that a good energy demand forecast would lead to proper responses for electricity demand. In addition, proper energy demand forecast would ensure efficient planning of the electricity industry and is critical in the scheduling of the power grid capacity and management of the entire power network. As most power systems are been deregulated and with the rapid introduction and development of smart-metering technologies in Oman, new opportunities may arise considering the efficiency and reliability of the power system; like price-based demand response programs. These programs could either be a large scale for household, commercial or industrial users. However, excellent demand forecasting models are crucial for the deployment of these smart metering in the power grid based on good knowledge of the electricity market structure. Consequently, in this paper, an overview of the Oman regulatory regime, financial mechanism, price control, and distribution system security standard were presented. More so, the energy demand forecast in Oman was analysed, using the econometric model to forecasts its energy peak demand. The energy econometric analysis in this study describes the relationship between the growth of historical electricity consumption and macro-economic parameters (by region, and by tariff), considering a case study of Mazoon Electricity Distribution Company (MZEC), which is one of the major power distribution companies in Oman, for effective energy demand in the power grid.
{"title":"Overview of the Global Electricity System in Oman Considering Energy Demand Model Forecast","authors":"A. Al-Abri, Kenneth E. Okedu","doi":"10.32604/ee.2023.020375","DOIUrl":"https://doi.org/10.32604/ee.2023.020375","url":null,"abstract":"Lately, in modern smart power grids, energy demand for accurate forecast of electricity is gaining attention, with increased interest of research. This is due to the fact that a good energy demand forecast would lead to proper responses for electricity demand. In addition, proper energy demand forecast would ensure efficient planning of the electricity industry and is critical in the scheduling of the power grid capacity and management of the entire power network. As most power systems are been deregulated and with the rapid introduction and development of smart-metering technologies in Oman, new opportunities may arise considering the efficiency and reliability of the power system; like price-based demand response programs. These programs could either be a large scale for household, commercial or industrial users. However, excellent demand forecasting models are crucial for the deployment of these smart metering in the power grid based on good knowledge of the electricity market structure. Consequently, in this paper, an overview of the Oman regulatory regime, financial mechanism, price control, and distribution system security standard were presented. More so, the energy demand forecast in Oman was analysed, using the econometric model to forecasts its energy peak demand. The energy econometric analysis in this study describes the relationship between the growth of historical electricity consumption and macro-economic parameters (by region, and by tariff), considering a case study of Mazoon Electricity Distribution Company (MZEC), which is one of the major power distribution companies in Oman, for effective energy demand in the power grid.","PeriodicalId":35610,"journal":{"name":"Energy Engineering: Journal of the Association of Energy Engineers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69744037","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}
{"title":"AC-DC Fuzzy Linear Active Disturbance Rejection Control Strategy of Front Stage of Bidirectional Converter Based on V2G","authors":"Guosheng Li, Qingquan Lv, Zhenzhen Zhang, Haiying Dong","doi":"10.32604/ee.2023.023770","DOIUrl":"https://doi.org/10.32604/ee.2023.023770","url":null,"abstract":"","PeriodicalId":35610,"journal":{"name":"Energy Engineering: Journal of the Association of Energy Engineers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69746290","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}
Yunlong Ma, Junwei Niu, Bo Xu, Xingtao Song, Wei Huang, Guoqiang Sun
In the power distribution system, the missing or incorrect file of users-transformer relationship (UTR) in low-voltage station area (LVSA) will affect the lean management of the LVSA, and the operation and maintenance of the distribution network. To effectively improve the lean management of LVSA, the paper proposes an identification method for the UTR based on Local Selective Combination in Parallel Outlier Ensembles algorithm (LSCP). Firstly, the voltage data is reconstructed based on the information entropy to highlight the differences in between. Then, the LSCP algorithm combines four base outlier detection algorithms, namely Isolation Forest (I-Forest), One-Class Support Vector Machine (OC-SVM), Copula-Based Outlier Detection (COPOD) and Local Outlier Factor (LOF), to construct the identification model of UTR. This model can accurately detect users’ differences in voltage data, and identify users with wrong UTR. Meanwhile, the key input parameter of the LSCP algorithm is determined automatically through the line loss rate, and the influence of artificial settings on recognition accuracy can be reduced. Finally, this method is verified in the actual LVSA where the recall and precision rates are 100% compared with other methods. Furthermore, the applicability to the LVSAs with difficult data acquisition and the voltage data error in transmission are analyzed. The proposed method adopts the ensemble learning framework and does not need to set the detection threshold manually. And it is applicable to the LVSAs with difficult data acquisition and high voltage similarity, which improves the stability and accuracy of UTR identification in LVSA.
{"title":"Identification Method for Users-Transformer Relationship in Station Area Based on Local Selective Combination in Parallel Outlier Ensembles Algorithm","authors":"Yunlong Ma, Junwei Niu, Bo Xu, Xingtao Song, Wei Huang, Guoqiang Sun","doi":"10.32604/ee.2023.024719","DOIUrl":"https://doi.org/10.32604/ee.2023.024719","url":null,"abstract":"In the power distribution system, the missing or incorrect file of users-transformer relationship (UTR) in low-voltage station area (LVSA) will affect the lean management of the LVSA, and the operation and maintenance of the distribution network. To effectively improve the lean management of LVSA, the paper proposes an identification method for the UTR based on Local Selective Combination in Parallel Outlier Ensembles algorithm (LSCP). Firstly, the voltage data is reconstructed based on the information entropy to highlight the differences in between. Then, the LSCP algorithm combines four base outlier detection algorithms, namely Isolation Forest (I-Forest), One-Class Support Vector Machine (OC-SVM), Copula-Based Outlier Detection (COPOD) and Local Outlier Factor (LOF), to construct the identification model of UTR. This model can accurately detect users’ differences in voltage data, and identify users with wrong UTR. Meanwhile, the key input parameter of the LSCP algorithm is determined automatically through the line loss rate, and the influence of artificial settings on recognition accuracy can be reduced. Finally, this method is verified in the actual LVSA where the recall and precision rates are 100% compared with other methods. Furthermore, the applicability to the LVSAs with difficult data acquisition and the voltage data error in transmission are analyzed. The proposed method adopts the ensemble learning framework and does not need to set the detection threshold manually. And it is applicable to the LVSAs with difficult data acquisition and high voltage similarity, which improves the stability and accuracy of UTR identification in LVSA.","PeriodicalId":35610,"journal":{"name":"Energy Engineering: Journal of the Association of Energy Engineers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69746414","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}
{"title":"Line Fault Detection of DC Distribution Networks Using the Artificial Neural Network","authors":"Xun Zhang, Chuanyan Liu, Zuo Sun","doi":"10.32604/ee.2023.025186","DOIUrl":"https://doi.org/10.32604/ee.2023.025186","url":null,"abstract":"","PeriodicalId":35610,"journal":{"name":"Energy Engineering: Journal of the Association of Energy Engineers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69746960","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}
{"title":"Research on AC Electronic Load with Energy Recovery Based on Finite Control Set Model Predictive Control","authors":"Jian Wang, Jianzhong Zhu, Xueyu Dong, Chenxi Liu, Jiazheng Shen","doi":"10.32604/ee.2023.025490","DOIUrl":"https://doi.org/10.32604/ee.2023.025490","url":null,"abstract":"","PeriodicalId":35610,"journal":{"name":"Energy Engineering: Journal of the Association of Energy Engineers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69747387","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：