Due to the continuous increase in global energy demand,photovoltaic solar energy generation and associated maintenance requirements have significantly expanded.One critical maintenance challenge in photovoltaic instal...Due to the continuous increase in global energy demand,photovoltaic solar energy generation and associated maintenance requirements have significantly expanded.One critical maintenance challenge in photovoltaic installations is detecting hot spots,localized overheating defects in solar cells that drastically reduce efficiency and can lead to permanent damage.Traditional methods for detecting these defects rely on manual inspections using thermal imaging,which are costly,labor-intensive,and impractical for large-scale installations.This research introduces an automated hybrid system based on two specialized convolutional neural networks deployed in a cascaded architecture.The first convolutional neural network efficiently detects and isolates individual solar panels from high-resolution aerial thermal images captured by drones.Subsequently,a second,more advanced convolutional neural network accurately classifies each isolated panel as either defective or healthy,effectively distinguishing genuine thermal anomalies from false positives caused by reflections or glare.Experimental validation on a real-world dataset comprising thousands of thermal images yielded exceptional accuracy,significantly reducing inspection time,costs,and the likelihood of false defect detections.This proposed system enhances the reliability and efficiency of photovoltaic plant inspections,thus contributing to improved operational performance and economic viability.展开更多
Maintenance operations have a critical influence on power gen-eration by wind turbines(WT).Advanced algorithms must analyze large volume of data from condition monitoring systems(CMS)to determine the actual working co...Maintenance operations have a critical influence on power gen-eration by wind turbines(WT).Advanced algorithms must analyze large volume of data from condition monitoring systems(CMS)to determine the actual working conditions and avoid false alarms.This paper proposes different support vector machine(SVM)algorithms for the prediction and detection of false alarms.K-Fold cross-validation(CV)is applied to evaluate the classification reliability of these algorithms.Supervisory Control and Data Acquisition(SCADA)data from an operating WT are applied to test the proposed approach.The results from the quadratic SVM showed an accuracy rate of 98.6%.Misclassifications from the confusion matrix,alarm log and maintenance records are analyzed to obtain quantitative information and determine if it is a false alarm.The classifier reduces the number of false alarms called misclassifications by 25%.These results demonstrate that the proposed approach presents high reliability and accuracy in false alarm identification.展开更多
Emerging sub-synchronous interactions(SSI)in wind-integrated power systems have added intense attention after numerous incidents in the US and China due to the involvement of series compensated transmission lines and ...Emerging sub-synchronous interactions(SSI)in wind-integrated power systems have added intense attention after numerous incidents in the US and China due to the involvement of series compensated transmission lines and power electronics devices.SSI phenomenon occurs when two power system elements exchange energy below the synchro-nous frequency.SSI phenomenon related to wind power plants is one of the most significant challenges to main-taining stability,while SSI phenomenon in practical wind farms,which has been observed recently,has not yet been described on the source of conventional SSI literature.This paper first explains the traditional development of SSI and its classification as given by the IEEE,and then it proposes a classification of SSI according to the current research status,reviews several mitigation techniques and challenges,and discusses analysis techniques for SSI.The paper also describes the effect of the active damping controllers,control scheme parameters,degree of series compensation,and various techniques used in wind power plants(WPPs).In particular,a supplementary damping controller with converter controllers in Doubly Fed Induction Generator based WPPs is briefly pronounced.This paper provides a real-istic viewpoint and a potential outlook for the readers to properly deal with SSI and its mitigation techniques,which can help power engineers for the planning,economical operation,and future expansion of sustainable development.展开更多
Heterogeneous vehicular clustering integrates multiple types of communication networks to work efficiently for various vehicular applications.One popular form of heterogeneous network is the integration of long-term e...Heterogeneous vehicular clustering integrates multiple types of communication networks to work efficiently for various vehicular applications.One popular form of heterogeneous network is the integration of long-term evolution(LTE)and dedicated short-range communication.The heterogeneity of such a network infrastructure and the non-cooperation involved in sharing cost/data are potential problems to solve.A vehicular clustering framework is one solution to these problems,but the framework should be formally verified and validated before being deployed in the real world.To solve these issues,first,we present a het-erogeneous framework,named destination and interest-aware clustering,for vehicular clustering that integrates vehicular ad hoc networks with the LTE network for improving road traffic efficiency.Then,we specify a model system of the proposed framework.The model is formally verified to evaluate its performance at the functional level using a model checking technique.To evaluate the performance of the proposed framework at the micro-level,a heterogeneous simulation environment is created by integrating state-of-the-art tools.The comparison of the simulation results with those of other known approaches shows that our proposed framework performs better.展开更多
基金funded by the Spanish Ministerio de Ciencia,Innovación y Universidades,grant number RTC2019-007364-3(FPGM)by the Comunidad de Madrid through the direct grant with ref.SI4/PJI/2024-00233 for the promotion of research and technology transfer at the Universidad Autónoma de Madrid。
文摘Due to the continuous increase in global energy demand,photovoltaic solar energy generation and associated maintenance requirements have significantly expanded.One critical maintenance challenge in photovoltaic installations is detecting hot spots,localized overheating defects in solar cells that drastically reduce efficiency and can lead to permanent damage.Traditional methods for detecting these defects rely on manual inspections using thermal imaging,which are costly,labor-intensive,and impractical for large-scale installations.This research introduces an automated hybrid system based on two specialized convolutional neural networks deployed in a cascaded architecture.The first convolutional neural network efficiently detects and isolates individual solar panels from high-resolution aerial thermal images captured by drones.Subsequently,a second,more advanced convolutional neural network accurately classifies each isolated panel as either defective or healthy,effectively distinguishing genuine thermal anomalies from false positives caused by reflections or glare.Experimental validation on a real-world dataset comprising thousands of thermal images yielded exceptional accuracy,significantly reducing inspection time,costs,and the likelihood of false defect detections.This proposed system enhances the reliability and efficiency of photovoltaic plant inspections,thus contributing to improved operational performance and economic viability.
基金supported financially by the Ministerio de Ciencia e Innovación(Spain)and the European Regional Development Fund under the Research Grant WindSound Project(Ref.:PID2021-125278OB-I00).
文摘Maintenance operations have a critical influence on power gen-eration by wind turbines(WT).Advanced algorithms must analyze large volume of data from condition monitoring systems(CMS)to determine the actual working conditions and avoid false alarms.This paper proposes different support vector machine(SVM)algorithms for the prediction and detection of false alarms.K-Fold cross-validation(CV)is applied to evaluate the classification reliability of these algorithms.Supervisory Control and Data Acquisition(SCADA)data from an operating WT are applied to test the proposed approach.The results from the quadratic SVM showed an accuracy rate of 98.6%.Misclassifications from the confusion matrix,alarm log and maintenance records are analyzed to obtain quantitative information and determine if it is a false alarm.The classifier reduces the number of false alarms called misclassifications by 25%.These results demonstrate that the proposed approach presents high reliability and accuracy in false alarm identification.
基金supported financially by the Ministerio de Ciencia e Innovación(Spain)and the European Regional Development Fund,under Research Grant WindSound project(Ref.:PID2021-125278OB-I00).
文摘Emerging sub-synchronous interactions(SSI)in wind-integrated power systems have added intense attention after numerous incidents in the US and China due to the involvement of series compensated transmission lines and power electronics devices.SSI phenomenon occurs when two power system elements exchange energy below the synchro-nous frequency.SSI phenomenon related to wind power plants is one of the most significant challenges to main-taining stability,while SSI phenomenon in practical wind farms,which has been observed recently,has not yet been described on the source of conventional SSI literature.This paper first explains the traditional development of SSI and its classification as given by the IEEE,and then it proposes a classification of SSI according to the current research status,reviews several mitigation techniques and challenges,and discusses analysis techniques for SSI.The paper also describes the effect of the active damping controllers,control scheme parameters,degree of series compensation,and various techniques used in wind power plants(WPPs).In particular,a supplementary damping controller with converter controllers in Doubly Fed Induction Generator based WPPs is briefly pronounced.This paper provides a real-istic viewpoint and a potential outlook for the readers to properly deal with SSI and its mitigation techniques,which can help power engineers for the planning,economical operation,and future expansion of sustainable development.
基金the European Project H2020(No.H2020-MG-2018-2019-2020,ENDURUNS).Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature。
文摘Heterogeneous vehicular clustering integrates multiple types of communication networks to work efficiently for various vehicular applications.One popular form of heterogeneous network is the integration of long-term evolution(LTE)and dedicated short-range communication.The heterogeneity of such a network infrastructure and the non-cooperation involved in sharing cost/data are potential problems to solve.A vehicular clustering framework is one solution to these problems,but the framework should be formally verified and validated before being deployed in the real world.To solve these issues,first,we present a het-erogeneous framework,named destination and interest-aware clustering,for vehicular clustering that integrates vehicular ad hoc networks with the LTE network for improving road traffic efficiency.Then,we specify a model system of the proposed framework.The model is formally verified to evaluate its performance at the functional level using a model checking technique.To evaluate the performance of the proposed framework at the micro-level,a heterogeneous simulation environment is created by integrating state-of-the-art tools.The comparison of the simulation results with those of other known approaches shows that our proposed framework performs better.
