Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain models.It is especially important to evaluate and determine the particularly Weather Attribute(WA)...Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain models.It is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and authenticity.In this paper,a strategy is proposed to integrate three currently competitive WA's evaluation methods.First,a conventional evaluation method based on AEF statistical indicators is selected.Subsequent evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy c-means.Different AEF attributes contribute to a more accurate AEF classification to different degrees.The resulting dynamic weighting applied to these attributes improves the classification accuracy.Each evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation score.The integration in the proposed strategy takes the form of a score accumulation.Different cumulative score levels correspond to different final WA results.Thunderstorm imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm attributes.Empirical results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA evaluation.This is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.展开更多
Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial benefits.There is an unprecedented amo...Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial benefits.There is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced tools.The main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information security.The original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI mechanisms.Here,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting security.Then,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more effectively.The Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis function.Moreover,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security framework.The results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.展开更多
Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm activities.However,little attention has been paid to the ambiguous weather information impl...Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm activities.However,little attention has been paid to the ambiguous weather information implicit in AEFS changes.In this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by AEFS.First,a time series dataset is created in the time domain using AEFS attributes.The AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the FCM.Second,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF apparatus.Thus,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space domain.Finally,the rationality and reliability of the proposed method are verified by combining radar charts and expert experience.The results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first time.The detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 62171228in part by the National Key R&D Program of China under Grant 2021YFE0105500in part by the Program of China Scholarship Council under Grant 202209040027。
文摘Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain models.It is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and authenticity.In this paper,a strategy is proposed to integrate three currently competitive WA's evaluation methods.First,a conventional evaluation method based on AEF statistical indicators is selected.Subsequent evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy c-means.Different AEF attributes contribute to a more accurate AEF classification to different degrees.The resulting dynamic weighting applied to these attributes improves the classification accuracy.Each evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation score.The integration in the proposed strategy takes the form of a score accumulation.Different cumulative score levels correspond to different final WA results.Thunderstorm imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm attributes.Empirical results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA evaluation.This is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.
文摘Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial benefits.There is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced tools.The main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information security.The original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI mechanisms.Here,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting security.Then,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more effectively.The Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis function.Moreover,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security framework.The results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.
基金supported in part by the National Natural Science Foundation of China under Grant 62171228in part by the National Key R&D Program of China under Grant 2021YFE0105500in part by the Program of China Scholarship Council under Grant 202209040027。
文摘Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm activities.However,little attention has been paid to the ambiguous weather information implicit in AEFS changes.In this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by AEFS.First,a time series dataset is created in the time domain using AEFS attributes.The AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the FCM.Second,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF apparatus.Thus,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space domain.Finally,the rationality and reliability of the proposed method are verified by combining radar charts and expert experience.The results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first time.The detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.
