With the ongoing digitalization and intelligence of power systems,there is an increasing reliance on large-scale data-driven intelligent technologies for tasks such as scheduling optimization and load forecasting.Neve...With the ongoing digitalization and intelligence of power systems,there is an increasing reliance on large-scale data-driven intelligent technologies for tasks such as scheduling optimization and load forecasting.Nevertheless,power data often contains sensitive information,making it a critical industry challenge to efficiently utilize this data while ensuring privacy.Traditional Federated Learning(FL)methods can mitigate data leakage by training models locally instead of transmitting raw data.Despite this,FL still has privacy concerns,especially gradient leakage,which might expose users’sensitive information.Therefore,integrating Differential Privacy(DP)techniques is essential for stronger privacy protection.Even so,the noise from DP may reduce the performance of federated learning models.To address this challenge,this paper presents an explainability-driven power data privacy federated learning framework.It incorporates DP technology and,based on model explainability,adaptively adjusts privacy budget allocation and model aggregation,thus balancing privacy protection and model performance.The key innovations of this paper are as follows:(1)We propose an explainability-driven power data privacy federated learning framework.(2)We detail a privacy budget allocation strategy:assigning budgets per training round by gradient effectiveness and at model granularity by layer importance.(3)We design a weighted aggregation strategy that considers the SHAP value and model accuracy for quality knowledge sharing.(4)Experiments show the proposed framework outperforms traditional methods in balancing privacy protection and model performance in power load forecasting tasks.展开更多
With the popularisation of intelligent power,power devices have different shapes,numbers and specifications.This means that the power data has distributional variability,the model learning process cannot achieve suffi...With the popularisation of intelligent power,power devices have different shapes,numbers and specifications.This means that the power data has distributional variability,the model learning process cannot achieve sufficient extraction of data features,which seriously affects the accuracy and performance of anomaly detection.Therefore,this paper proposes a deep learning-based anomaly detection model for power data,which integrates a data alignment enhancement technique based on random sampling and an adaptive feature fusion method leveraging dimension reduction.Aiming at the distribution variability of power data,this paper developed a sliding window-based data adjustment method for this model,which solves the problem of high-dimensional feature noise and low-dimensional missing data.To address the problem of insufficient feature fusion,an adaptive feature fusion method based on feature dimension reduction and dictionary learning is proposed to improve the anomaly data detection accuracy of the model.In order to verify the effectiveness of the proposed method,we conducted effectiveness comparisons through elimination experiments.The experimental results show that compared with the traditional anomaly detection methods,the method proposed in this paper not only has an advantage in model accuracy,but also reduces the amount of parameter calculation of the model in the process of feature matching and improves the detection speed.展开更多
A downburst is a strong downdraft generated by intense thunderstorm clouds,producing radially divergent and highly destructive winds near the ground.Its characteristic scales are expressed through random variations in...A downburst is a strong downdraft generated by intense thunderstorm clouds,producing radially divergent and highly destructive winds near the ground.Its characteristic scales are expressed through random variations in jet height,velocity,and diameter during an event.In this study,a reduced-scale parked wind turbine is exposed to downburst wind fields to investigate the resulting extreme wind loads.The analysis emphasizes both the flow structure of downbursts and the variations of surface wind pressure on turbine blades under different jet parameters.Results show that increasing jet velocity markedly enhances the maximum horizontal wind speed,while greater jet height reduces the horizontal wind speed and shifts the peak velocity closer to the jet center.Increasing jet diameter primarily affects the radial position of the maximum horizontal wind speed.For the wind turbine,the maximum equivalent stress and blade displacement increase almost linearly with jet velocity,but exhibit the opposite trend with jet diameter.Specifically,as jet velocity rises from 10 m/s to 20 m/s,the surface pressure coefficient at the blade tip increases by approximately 4.5 times.Changes in jet diameter indirectly alter the turbine’s relative position within the wind field,leading to variations in wind load direction and exposure area.Conversely,increasing jet height extends the dissipation path of the downdraft,thereby reducing the intensity of the airflow acting on the blades.For example,when jet height increases from 0.3 m to 1.2 m,the surface pressure coefficient at the blade tip decreases by nearly 50%.展开更多
文摘With the ongoing digitalization and intelligence of power systems,there is an increasing reliance on large-scale data-driven intelligent technologies for tasks such as scheduling optimization and load forecasting.Nevertheless,power data often contains sensitive information,making it a critical industry challenge to efficiently utilize this data while ensuring privacy.Traditional Federated Learning(FL)methods can mitigate data leakage by training models locally instead of transmitting raw data.Despite this,FL still has privacy concerns,especially gradient leakage,which might expose users’sensitive information.Therefore,integrating Differential Privacy(DP)techniques is essential for stronger privacy protection.Even so,the noise from DP may reduce the performance of federated learning models.To address this challenge,this paper presents an explainability-driven power data privacy federated learning framework.It incorporates DP technology and,based on model explainability,adaptively adjusts privacy budget allocation and model aggregation,thus balancing privacy protection and model performance.The key innovations of this paper are as follows:(1)We propose an explainability-driven power data privacy federated learning framework.(2)We detail a privacy budget allocation strategy:assigning budgets per training round by gradient effectiveness and at model granularity by layer importance.(3)We design a weighted aggregation strategy that considers the SHAP value and model accuracy for quality knowledge sharing.(4)Experiments show the proposed framework outperforms traditional methods in balancing privacy protection and model performance in power load forecasting tasks.
文摘With the popularisation of intelligent power,power devices have different shapes,numbers and specifications.This means that the power data has distributional variability,the model learning process cannot achieve sufficient extraction of data features,which seriously affects the accuracy and performance of anomaly detection.Therefore,this paper proposes a deep learning-based anomaly detection model for power data,which integrates a data alignment enhancement technique based on random sampling and an adaptive feature fusion method leveraging dimension reduction.Aiming at the distribution variability of power data,this paper developed a sliding window-based data adjustment method for this model,which solves the problem of high-dimensional feature noise and low-dimensional missing data.To address the problem of insufficient feature fusion,an adaptive feature fusion method based on feature dimension reduction and dictionary learning is proposed to improve the anomaly data detection accuracy of the model.In order to verify the effectiveness of the proposed method,we conducted effectiveness comparisons through elimination experiments.The experimental results show that compared with the traditional anomaly detection methods,the method proposed in this paper not only has an advantage in model accuracy,but also reduces the amount of parameter calculation of the model in the process of feature matching and improves the detection speed.
基金the National Natural Science Foundation of China(Grant Nos.52276197, 52166014)Gansu Province Key Research and Development Program—Industrial Project(Grant No.23YFGA0069).
文摘A downburst is a strong downdraft generated by intense thunderstorm clouds,producing radially divergent and highly destructive winds near the ground.Its characteristic scales are expressed through random variations in jet height,velocity,and diameter during an event.In this study,a reduced-scale parked wind turbine is exposed to downburst wind fields to investigate the resulting extreme wind loads.The analysis emphasizes both the flow structure of downbursts and the variations of surface wind pressure on turbine blades under different jet parameters.Results show that increasing jet velocity markedly enhances the maximum horizontal wind speed,while greater jet height reduces the horizontal wind speed and shifts the peak velocity closer to the jet center.Increasing jet diameter primarily affects the radial position of the maximum horizontal wind speed.For the wind turbine,the maximum equivalent stress and blade displacement increase almost linearly with jet velocity,but exhibit the opposite trend with jet diameter.Specifically,as jet velocity rises from 10 m/s to 20 m/s,the surface pressure coefficient at the blade tip increases by approximately 4.5 times.Changes in jet diameter indirectly alter the turbine’s relative position within the wind field,leading to variations in wind load direction and exposure area.Conversely,increasing jet height extends the dissipation path of the downdraft,thereby reducing the intensity of the airflow acting on the blades.For example,when jet height increases from 0.3 m to 1.2 m,the surface pressure coefficient at the blade tip decreases by nearly 50%.
