As energy-related problems continue to emerge,the need for stable energy supplies and issues regarding both environmental and safety require urgent consideration.Renewable energy is becoming increasingly important,wit...As energy-related problems continue to emerge,the need for stable energy supplies and issues regarding both environmental and safety require urgent consideration.Renewable energy is becoming increasingly important,with solar power accounting for the most significant proportion of renewables.As the scale and importance of solar energy have increased,cyber threats against solar power plants have also increased.So,we need an anomaly detection system that effectively detects cyber threats to solar power plants.However,as mentioned earlier,the existing solar power plant anomaly detection system monitors only operating information such as power generation,making it difficult to detect cyberattacks.To address this issue,in this paper,we propose a network packet-based anomaly detection system for the Programmable Logic Controller(PLC)of the inverter,an essential system of photovoltaic plants,to detect cyber threats.Cyberattacks and vulnerabilities in solar power plants were analyzed to identify cyber threats in solar power plants.The analysis shows that Denial of Service(DoS)and Manin-the-Middle(MitM)attacks are primarily carried out on inverters,aiming to disrupt solar plant operations.To develop an anomaly detection system,we performed preprocessing,such as correlation analysis and normalization for PLC network packets data and trained various machine learning-based classification models on such data.The Random Forest model showed the best performance with an accuracy of 97.36%.The proposed system can detect anomalies based on network packets,identify potential cyber threats that cannot be identified by the anomaly detection system currently in use in solar power plants,and enhance the security of solar plants.展开更多
An optimal method for prediction and adjustment on byproduct gasholder level and self-provided power plant gas supply was proposed.This work raises the HP-ENN-LSSVM model based on the Hodrick-Prescott filter,Elman neu...An optimal method for prediction and adjustment on byproduct gasholder level and self-provided power plant gas supply was proposed.This work raises the HP-ENN-LSSVM model based on the Hodrick-Prescott filter,Elman neural network and least squares support vector machines.Then,according to the prediction,the optimal adjustment process came up by a novel reasoning method to sustain the gasholder within safety zone and the self-provided power plant boilers in economic operation,and prevent unfavorable byproduct gas emission and equipment trip as well.The experiments using the practical production data show that the proposed method achieves high accurate predictions and the optimal byproduct gas distribution,which provides a remarkable guidance for reasonable scheduling of byproduct gas.展开更多
虚拟电厂(virtual power plant,VPP)可聚合多利益主体的分布式资源(distributed energy resources,DER)参与电力市场,以深入挖掘分布式能源的调度潜力,如何考虑各运行主体的协同调度,提高整体收益成为当前亟需解决的重要问题。基于此,...虚拟电厂(virtual power plant,VPP)可聚合多利益主体的分布式资源(distributed energy resources,DER)参与电力市场,以深入挖掘分布式能源的调度潜力,如何考虑各运行主体的协同调度,提高整体收益成为当前亟需解决的重要问题。基于此,提出了计及风光及市场电价不确定风险和多主体协同的虚拟电厂参与主辅市场联合优化策略。首先,建立电动汽车群体(electric vehicle duster,EVC)的可调度域评估模型,在此基础上考虑VPP与EVC两主体的利益均衡,提出VPP与EVC主从博弈的主能量市场日前竞标模型;其次,考虑电池储能系统(battery energy storage system,BESS)快速充放电的特性,进一步提出VPP参与主能量市场和辅助调频市场的日前联合投标模型和VPP实时调整及调频响应模型;最后,在日前联合投标模型中引入条件风险价值(condition value at risk,CVaR),衡量日前投标收益与不确定风险的关系。基于某省电力市场规则进行算例仿真表明,所提VPP优化策略能通过提供调频服务和鼓励EV响应调度提高VPP的综合收益。展开更多
巡检机器控制是电厂巡检自动化和智能化技术的核心,但现行方法在实际应用中存在一些不足和缺陷,不仅控制路径平滑系数较低,而且存在碰撞问题,智能巡检机器避障性能较差,无法达到预期的控制效果,为此提出基于SLAM(Simultaneous Localizat...巡检机器控制是电厂巡检自动化和智能化技术的核心,但现行方法在实际应用中存在一些不足和缺陷,不仅控制路径平滑系数较低,而且存在碰撞问题,智能巡检机器避障性能较差,无法达到预期的控制效果,为此提出基于SLAM(Simultaneous Localization And Mapping)算法和动静态规划的电厂智能巡检机器控制方法。利用激光雷达和相机获取巡检环境信息,采用YOLOv3对图像增强,通过点云旋转去除激光点云中离散点,实现对点云数据增强,采用SLAM算法对巡检环境图像和激光点云融合,构建巡检地图和定位巡检机器,采用动静态规划根据环境信息动态调整巡检机器运动轨迹,从而实现对电厂智能巡检机器导航跟踪控制。经实验证明,应用设计方法后,巡检机器路径平滑系数在0.9以上,未发生碰撞,该方法在电厂智能巡检机器控制方面具有良好的应用前景。展开更多
基金supported by the Korea Institute of Energy Technology Evaluation and Planning(KETEP)grant funded by the Korea government(MOTIE)(20224B10100140,50%)the Nuclear Safety Research Program through the Korea Foundation of Nuclear Safety(KoFONS)using the financial resource granted by the Nuclear Safety and Security Commission(NSSC)of the Republic of Korea(No.2106058,40%)the Gachon University Research Fund of 2023(GCU-202110280001,10%)。
文摘As energy-related problems continue to emerge,the need for stable energy supplies and issues regarding both environmental and safety require urgent consideration.Renewable energy is becoming increasingly important,with solar power accounting for the most significant proportion of renewables.As the scale and importance of solar energy have increased,cyber threats against solar power plants have also increased.So,we need an anomaly detection system that effectively detects cyber threats to solar power plants.However,as mentioned earlier,the existing solar power plant anomaly detection system monitors only operating information such as power generation,making it difficult to detect cyberattacks.To address this issue,in this paper,we propose a network packet-based anomaly detection system for the Programmable Logic Controller(PLC)of the inverter,an essential system of photovoltaic plants,to detect cyber threats.Cyberattacks and vulnerabilities in solar power plants were analyzed to identify cyber threats in solar power plants.The analysis shows that Denial of Service(DoS)and Manin-the-Middle(MitM)attacks are primarily carried out on inverters,aiming to disrupt solar plant operations.To develop an anomaly detection system,we performed preprocessing,such as correlation analysis and normalization for PLC network packets data and trained various machine learning-based classification models on such data.The Random Forest model showed the best performance with an accuracy of 97.36%.The proposed system can detect anomalies based on network packets,identify potential cyber threats that cannot be identified by the anomaly detection system currently in use in solar power plants,and enhance the security of solar plants.
基金Project(51066002/E060701) supported by the National Natural Science Foundation of ChinaProject(U0937604) supported by the NSFC-Yunnan Joint Fund of China
文摘An optimal method for prediction and adjustment on byproduct gasholder level and self-provided power plant gas supply was proposed.This work raises the HP-ENN-LSSVM model based on the Hodrick-Prescott filter,Elman neural network and least squares support vector machines.Then,according to the prediction,the optimal adjustment process came up by a novel reasoning method to sustain the gasholder within safety zone and the self-provided power plant boilers in economic operation,and prevent unfavorable byproduct gas emission and equipment trip as well.The experiments using the practical production data show that the proposed method achieves high accurate predictions and the optimal byproduct gas distribution,which provides a remarkable guidance for reasonable scheduling of byproduct gas.
文摘虚拟电厂(virtual power plant,VPP)可聚合多利益主体的分布式资源(distributed energy resources,DER)参与电力市场,以深入挖掘分布式能源的调度潜力,如何考虑各运行主体的协同调度,提高整体收益成为当前亟需解决的重要问题。基于此,提出了计及风光及市场电价不确定风险和多主体协同的虚拟电厂参与主辅市场联合优化策略。首先,建立电动汽车群体(electric vehicle duster,EVC)的可调度域评估模型,在此基础上考虑VPP与EVC两主体的利益均衡,提出VPP与EVC主从博弈的主能量市场日前竞标模型;其次,考虑电池储能系统(battery energy storage system,BESS)快速充放电的特性,进一步提出VPP参与主能量市场和辅助调频市场的日前联合投标模型和VPP实时调整及调频响应模型;最后,在日前联合投标模型中引入条件风险价值(condition value at risk,CVaR),衡量日前投标收益与不确定风险的关系。基于某省电力市场规则进行算例仿真表明,所提VPP优化策略能通过提供调频服务和鼓励EV响应调度提高VPP的综合收益。
文摘巡检机器控制是电厂巡检自动化和智能化技术的核心,但现行方法在实际应用中存在一些不足和缺陷,不仅控制路径平滑系数较低,而且存在碰撞问题,智能巡检机器避障性能较差,无法达到预期的控制效果,为此提出基于SLAM(Simultaneous Localization And Mapping)算法和动静态规划的电厂智能巡检机器控制方法。利用激光雷达和相机获取巡检环境信息,采用YOLOv3对图像增强,通过点云旋转去除激光点云中离散点,实现对点云数据增强,采用SLAM算法对巡检环境图像和激光点云融合,构建巡检地图和定位巡检机器,采用动静态规划根据环境信息动态调整巡检机器运动轨迹,从而实现对电厂智能巡检机器导航跟踪控制。经实验证明,应用设计方法后,巡检机器路径平滑系数在0.9以上,未发生碰撞,该方法在电厂智能巡检机器控制方面具有良好的应用前景。
