The inverter is one of the key components of wind turbine,and it is a complex circuit composed of a series of components such as a variety of electronic components and power devices.Therefore,it is difficult to accura...The inverter is one of the key components of wind turbine,and it is a complex circuit composed of a series of components such as a variety of electronic components and power devices.Therefore,it is difficult to accurately identify the operation states of inverter and some problems regarding its own circuit,especially in the early stages of failure.However,if the inverter temperature prediction model can be established,the early states can be identified through the judgment of the output temperature.Accordingly,considering whether the inverter heats up normally from the perspective of heat dissipation,a method for the early operation state identification of the inverter is provided in this paper.A variable selection method based on fusion analysis of correlation and physical relationship is adopted to extract variables as input variables,which have high correlation with inverter temperature.Then multi-input and multi-output temperature prediction model of inverter is established based on a nonlinear autoregressive exogenous model(NARX)network,and the prediction temperature residual is used as the real-time standard to evaluate the inverter states.For validating this,the validity and reliability of the established temperature prediction model are verified through case analysis,and the performance comparison with various models demonstrates that the proposed method has higher accuracy.The construction method of the prediction model can be used for reference to other aspects of wind turbine.All these bring huge benefits to wind energy industry.展开更多
As the demand for wind energy continues to grow at exponential rate, reducing operation and maintenance (O & M) costs and improving reliability have become top priorities in wind turbine maintenance strategies. Pr...As the demand for wind energy continues to grow at exponential rate, reducing operation and maintenance (O & M) costs and improving reliability have become top priorities in wind turbine maintenance strategies. Prediction of wind turbine failures before they reach a catastrophic stage is critical to reduce the O & M cost due to unnecessary scheduled maintenance. A SCADA-data based condition monitoring system, which takes advantage of data already collected at the wind turbine controller, is a cost-effective way to monitor wind turbines for early warning of failures. This article proposes a methodology of fault prediction and automatically generating warning and alarm for wind turbine main bearings based on stored SCADA data using Artificial Neural Network (ANN). The ANN model of turbine main bearing normal behavior is established and then the deviation between estimated and actual values of the parameter is calculated. Furthermore, a method has been developed to generate early warning and alarm and avoid false warnings and alarms based on the deviation. In this way, wind farm operators are able to have enough time to plan maintenance, and thus, unanticipated downtime can be avoided and O & M costs can be reduced.展开更多
为了提高抛丸设备的自动化控制与数据管理水平,本文基于数据采集与监视控制系统(Supervisory Control and Data Acquisition,SCADA)、数据库技术、网络与信息技术对抛丸设备进行智能化自动控制与数据监测。首先,合理的机械结构设计与电...为了提高抛丸设备的自动化控制与数据管理水平,本文基于数据采集与监视控制系统(Supervisory Control and Data Acquisition,SCADA)、数据库技术、网络与信息技术对抛丸设备进行智能化自动控制与数据监测。首先,合理的机械结构设计与电气控制设计是重要的前提。其次,基于SCADA对抛丸设备进行人机交互控制、实时数据监控、历史数据采集与存储、数据分析与可视化为系统主要功能。最后,系统平台可以利用服务器进行本地化部署与网络化访问。本文详细介绍了系统平台的架构、功能、实施过程与相关技术,来实现对抛丸设备的控制与管理。展开更多
随着嵌入式系统的广泛应用,选择适当的轻量级安全防护措施以提升资源受限终端的安全水平变得尤为重要。为此,在考虑网络安全防护措施的资源消耗属性、时间延迟属性及安全收益属性的基础上,建立轻量级防护措施决策的属性目标函数,利用层...随着嵌入式系统的广泛应用,选择适当的轻量级安全防护措施以提升资源受限终端的安全水平变得尤为重要。为此,在考虑网络安全防护措施的资源消耗属性、时间延迟属性及安全收益属性的基础上,建立轻量级防护措施决策的属性目标函数,利用层次分析法确定各属性的权重,构建基于多属性决策的决策模型,根据决策值完成对备选防护措施的优选,解决了传统防护方案选择防护措施主观性强的问题。于搭建的模拟控制系统(supervisory control and data acquisition, SCADA)数据采集与监视控制系统中应用所提轻量级安全防护措施决策模型,结果表明所提模型能够有效选择轻量级安全防护措施。展开更多
我国新能源近年发展迅速,风电已成为了目前主要的发电方式之一。随着大数据、人工智能、物联网、云计算、自然语言处理技术的发展和国家政策的支持,大数据分析在风电智能化领域逐步得到广泛应用。通过对风电数据采集与监视控制(Supervis...我国新能源近年发展迅速,风电已成为了目前主要的发电方式之一。随着大数据、人工智能、物联网、云计算、自然语言处理技术的发展和国家政策的支持,大数据分析在风电智能化领域逐步得到广泛应用。通过对风电数据采集与监视控制(Supervisory Control and Data Acquisition,SCADA)系统重新进行架构设计,搭建了一套基于SCADA的风电数据分析平台,将数据进行归类、采集、传输、预处理、存储。平台将风电数据进行重新定义,从多个维度对设备及系统进行实时评价,同时引入多元状态估计技术验证了平台可以对模型的性能指标有更好的提升。该研究旨在通过大数据分析进行故障预警及操作指导,建立具有新型能源体系的智能管控平台,进而提高风电场的运维效率和可靠性。展开更多
As quantum computing continues to advance,traditional cryptographic methods are increasingly challenged,particularly when it comes to securing critical systems like Supervisory Control andData Acquisition(SCADA)system...As quantum computing continues to advance,traditional cryptographic methods are increasingly challenged,particularly when it comes to securing critical systems like Supervisory Control andData Acquisition(SCADA)systems.These systems are essential for monitoring and controlling industrial operations,making their security paramount.A key threat arises from Shor’s algorithm,a powerful quantum computing tool that can compromise current hash functions,leading to significant concerns about data integrity and confidentiality.To tackle these issues,this article introduces a novel Quantum-Resistant Hash Algorithm(QRHA)known as the Modular Hash Learning Algorithm(MHLA).This algorithm is meticulously crafted to withstand potential quantum attacks by incorporating advanced mathematical and algorithmic techniques,enhancing its overall security framework.Our research delves into the effectiveness ofMHLA in defending against both traditional and quantum-based threats,with a particular emphasis on its resilience to Shor’s algorithm.The findings from our study demonstrate that MHLA significantly enhances the security of SCADA systems in the context of quantum technology.By ensuring that sensitive data remains protected and confidential,MHLA not only fortifies individual systems but also contributes to the broader efforts of safeguarding industrial and infrastructure control systems against future quantumthreats.Our evaluation demonstrates that MHLA improves security by 38%against quantumattack simulations compared to traditional hash functionswhilemaintaining a computational efficiency ofO(m⋅n⋅k+v+n).The algorithm achieved a 98%success rate in detecting data tampering during integrity testing.These findings underline MHLA’s effectiveness in enhancing SCADA system security amidst evolving quantum technologies.This research represents a crucial step toward developing more secure cryptographic systems that can adapt to the rapidly changing technological landscape,ultimately ensuring the reliability and integrity of critical infrastructure in an era where quantum computing poses a growing risk.展开更多
为了辨识抽水蓄能电站发电机多种运行工况下的模型参数,通过建立发电机的微分方程模型,并经过数学推导建立了发电机实测数据变量与发电机待辨识参数数学关系。基于时域最小二乘法的原理,给出了发电机各工况下适用的参数辨识方法,并通过...为了辨识抽水蓄能电站发电机多种运行工况下的模型参数,通过建立发电机的微分方程模型,并经过数学推导建立了发电机实测数据变量与发电机待辨识参数数学关系。基于时域最小二乘法的原理,给出了发电机各工况下适用的参数辨识方法,并通过抽水蓄能电站的SCADA(supervisory control and data acquisition)数据采集系统获得运行数据并对各工况参数进行辨识。仿真验算运用南方电网某抽水蓄能在发电和抽水两种工况下的实际运行数据,通过编制C++程序分析计算得到发电工况和抽水工况下的抽水蓄能整个发电机系统的参数值。将参数值与实际铭牌值相比,误差不超过5%。仿真结果表明,运用高采样速度的实时运行数据,能够激发抽水蓄能机组的暂态和次暂态过程。这种经过采样数据和公式推导相结合的辨识方法比通过传统做实验的辨识方法更简单、方便、有效,且适合抽水蓄能机组多工况运行的特点。展开更多
基金This work is supported by the National Natural Science Foundation of People’s Republic of China(Grant No.51875199)Hunan Provincial Natural Science Foundation(Grant No.2019JJ50154)the Key Research and Development Program of Hunan Province,China(Grant No.2018GK2073).
文摘The inverter is one of the key components of wind turbine,and it is a complex circuit composed of a series of components such as a variety of electronic components and power devices.Therefore,it is difficult to accurately identify the operation states of inverter and some problems regarding its own circuit,especially in the early stages of failure.However,if the inverter temperature prediction model can be established,the early states can be identified through the judgment of the output temperature.Accordingly,considering whether the inverter heats up normally from the perspective of heat dissipation,a method for the early operation state identification of the inverter is provided in this paper.A variable selection method based on fusion analysis of correlation and physical relationship is adopted to extract variables as input variables,which have high correlation with inverter temperature.Then multi-input and multi-output temperature prediction model of inverter is established based on a nonlinear autoregressive exogenous model(NARX)network,and the prediction temperature residual is used as the real-time standard to evaluate the inverter states.For validating this,the validity and reliability of the established temperature prediction model are verified through case analysis,and the performance comparison with various models demonstrates that the proposed method has higher accuracy.The construction method of the prediction model can be used for reference to other aspects of wind turbine.All these bring huge benefits to wind energy industry.
文摘As the demand for wind energy continues to grow at exponential rate, reducing operation and maintenance (O & M) costs and improving reliability have become top priorities in wind turbine maintenance strategies. Prediction of wind turbine failures before they reach a catastrophic stage is critical to reduce the O & M cost due to unnecessary scheduled maintenance. A SCADA-data based condition monitoring system, which takes advantage of data already collected at the wind turbine controller, is a cost-effective way to monitor wind turbines for early warning of failures. This article proposes a methodology of fault prediction and automatically generating warning and alarm for wind turbine main bearings based on stored SCADA data using Artificial Neural Network (ANN). The ANN model of turbine main bearing normal behavior is established and then the deviation between estimated and actual values of the parameter is calculated. Furthermore, a method has been developed to generate early warning and alarm and avoid false warnings and alarms based on the deviation. In this way, wind farm operators are able to have enough time to plan maintenance, and thus, unanticipated downtime can be avoided and O & M costs can be reduced.
文摘为了提高抛丸设备的自动化控制与数据管理水平,本文基于数据采集与监视控制系统(Supervisory Control and Data Acquisition,SCADA)、数据库技术、网络与信息技术对抛丸设备进行智能化自动控制与数据监测。首先,合理的机械结构设计与电气控制设计是重要的前提。其次,基于SCADA对抛丸设备进行人机交互控制、实时数据监控、历史数据采集与存储、数据分析与可视化为系统主要功能。最后,系统平台可以利用服务器进行本地化部署与网络化访问。本文详细介绍了系统平台的架构、功能、实施过程与相关技术,来实现对抛丸设备的控制与管理。
文摘随着嵌入式系统的广泛应用,选择适当的轻量级安全防护措施以提升资源受限终端的安全水平变得尤为重要。为此,在考虑网络安全防护措施的资源消耗属性、时间延迟属性及安全收益属性的基础上,建立轻量级防护措施决策的属性目标函数,利用层次分析法确定各属性的权重,构建基于多属性决策的决策模型,根据决策值完成对备选防护措施的优选,解决了传统防护方案选择防护措施主观性强的问题。于搭建的模拟控制系统(supervisory control and data acquisition, SCADA)数据采集与监视控制系统中应用所提轻量级安全防护措施决策模型,结果表明所提模型能够有效选择轻量级安全防护措施。
文摘我国新能源近年发展迅速,风电已成为了目前主要的发电方式之一。随着大数据、人工智能、物联网、云计算、自然语言处理技术的发展和国家政策的支持,大数据分析在风电智能化领域逐步得到广泛应用。通过对风电数据采集与监视控制(Supervisory Control and Data Acquisition,SCADA)系统重新进行架构设计,搭建了一套基于SCADA的风电数据分析平台,将数据进行归类、采集、传输、预处理、存储。平台将风电数据进行重新定义,从多个维度对设备及系统进行实时评价,同时引入多元状态估计技术验证了平台可以对模型的性能指标有更好的提升。该研究旨在通过大数据分析进行故障预警及操作指导,建立具有新型能源体系的智能管控平台,进而提高风电场的运维效率和可靠性。
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R343),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabiathe Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia for funding this research work through the project number NBU-FFR-2025-1092-10.
文摘As quantum computing continues to advance,traditional cryptographic methods are increasingly challenged,particularly when it comes to securing critical systems like Supervisory Control andData Acquisition(SCADA)systems.These systems are essential for monitoring and controlling industrial operations,making their security paramount.A key threat arises from Shor’s algorithm,a powerful quantum computing tool that can compromise current hash functions,leading to significant concerns about data integrity and confidentiality.To tackle these issues,this article introduces a novel Quantum-Resistant Hash Algorithm(QRHA)known as the Modular Hash Learning Algorithm(MHLA).This algorithm is meticulously crafted to withstand potential quantum attacks by incorporating advanced mathematical and algorithmic techniques,enhancing its overall security framework.Our research delves into the effectiveness ofMHLA in defending against both traditional and quantum-based threats,with a particular emphasis on its resilience to Shor’s algorithm.The findings from our study demonstrate that MHLA significantly enhances the security of SCADA systems in the context of quantum technology.By ensuring that sensitive data remains protected and confidential,MHLA not only fortifies individual systems but also contributes to the broader efforts of safeguarding industrial and infrastructure control systems against future quantumthreats.Our evaluation demonstrates that MHLA improves security by 38%against quantumattack simulations compared to traditional hash functionswhilemaintaining a computational efficiency ofO(m⋅n⋅k+v+n).The algorithm achieved a 98%success rate in detecting data tampering during integrity testing.These findings underline MHLA’s effectiveness in enhancing SCADA system security amidst evolving quantum technologies.This research represents a crucial step toward developing more secure cryptographic systems that can adapt to the rapidly changing technological landscape,ultimately ensuring the reliability and integrity of critical infrastructure in an era where quantum computing poses a growing risk.
文摘为了辨识抽水蓄能电站发电机多种运行工况下的模型参数,通过建立发电机的微分方程模型,并经过数学推导建立了发电机实测数据变量与发电机待辨识参数数学关系。基于时域最小二乘法的原理,给出了发电机各工况下适用的参数辨识方法,并通过抽水蓄能电站的SCADA(supervisory control and data acquisition)数据采集系统获得运行数据并对各工况参数进行辨识。仿真验算运用南方电网某抽水蓄能在发电和抽水两种工况下的实际运行数据,通过编制C++程序分析计算得到发电工况和抽水工况下的抽水蓄能整个发电机系统的参数值。将参数值与实际铭牌值相比,误差不超过5%。仿真结果表明,运用高采样速度的实时运行数据,能够激发抽水蓄能机组的暂态和次暂态过程。这种经过采样数据和公式推导相结合的辨识方法比通过传统做实验的辨识方法更简单、方便、有效,且适合抽水蓄能机组多工况运行的特点。
