针对现有微电网V2G双向变换器运行数据的智能监测系统抗干扰性差、监测精度低等问题,提出一种新型配电自动化监测系统架构。该系统采用集成模拟电路的实时配电系统状态估计器(distribution system state estimator,DSSE)作为监测系统的...针对现有微电网V2G双向变换器运行数据的智能监测系统抗干扰性差、监测精度低等问题,提出一种新型配电自动化监测系统架构。该系统采用集成模拟电路的实时配电系统状态估计器(distribution system state estimator,DSSE)作为监测系统的关键部件。开发的DSSE针对配微电网V2G双向变换器监测系统进行了单元优化,在变电站实时执行监控,可通过多区域状态估计算法进行协调,提高监控事件的时间效率和鲁棒性,同时也保持了可接受的精度水平。设计了针对虚假噪声信号注入防护的试验,试验结果表明,该系统对于这类攻击具有较高稳定性,能有效保护微电网V2G双向变换器运行数据的质量。展开更多
The analog-to-information convertor (AIC) is a successful practice of compressive sensing (CS) theory in the analog signal acquisition. This paper presents a multi-narrowband signals sampling and reconstruction model ...The analog-to-information convertor (AIC) is a successful practice of compressive sensing (CS) theory in the analog signal acquisition. This paper presents a multi-narrowband signals sampling and reconstruction model based on AIC and block sparsity. To overcome the practical problems, the block sparsity is divided into uniform block and non-uniform block situations, and the block restricted isometry property and sub-sampling limit in different situations are analyzed respectively in detail. Theoretical analysis proves that using the block sparsity in AIC can reduce the restricted isometric constant, increase the reconstruction probability and reduce the sub -sampling rate. Simulation results show that the proposed model can complete sub -sampling and reconstruction for multi-narrowband signals. This paper extends the application range of AIC from the finite information rate signal to the multi-narrowband signals by using the potential relevance of support sets. The proposed receiving model has low complexity and is easy to implement, which can promote the application of CS theory in the radar receiver to reduce the burden of analog-to digital convertor (ADC) and solve bandwidth limitations of ADC.展开更多
Renewable energy production plays a major role in satisfying electricity demand.Wind power conversion is one of the most popular renewable energy sources compared to other sources.Wind energy conversion has two major ...Renewable energy production plays a major role in satisfying electricity demand.Wind power conversion is one of the most popular renewable energy sources compared to other sources.Wind energy conversion has two major types of generators such as the Permanent Magnet Synchronous Generator(PMSG)and the Doubly Fed Induction Generator(DFIG).The maximum power tracking algo-rithm is a crucial controller,a wind energy conversion system for generating maximum power in different wind speed conditions.In this article,the DFIG wind energy conversion system was developed in Matrix Laboratory(MATLAB)and designed a machine learning(ML)algorithm for the rotor and grid side converter.The ML algorithm has been developed and trained in a MATLAB environment.There are two types of learning algorithms such as supervised and unsupervised learning.In this research supervised learning is used to power the neural networks and analysis is made for various hidden layers and activation functions.Simulation results are assessed to demonstrate the efficiency of the proposed system.展开更多
文摘针对现有微电网V2G双向变换器运行数据的智能监测系统抗干扰性差、监测精度低等问题,提出一种新型配电自动化监测系统架构。该系统采用集成模拟电路的实时配电系统状态估计器(distribution system state estimator,DSSE)作为监测系统的关键部件。开发的DSSE针对配微电网V2G双向变换器监测系统进行了单元优化,在变电站实时执行监控,可通过多区域状态估计算法进行协调,提高监控事件的时间效率和鲁棒性,同时也保持了可接受的精度水平。设计了针对虚假噪声信号注入防护的试验,试验结果表明,该系统对于这类攻击具有较高稳定性,能有效保护微电网V2G双向变换器运行数据的质量。
基金supported by the National Natural Science Foundation of China(61172159)
文摘The analog-to-information convertor (AIC) is a successful practice of compressive sensing (CS) theory in the analog signal acquisition. This paper presents a multi-narrowband signals sampling and reconstruction model based on AIC and block sparsity. To overcome the practical problems, the block sparsity is divided into uniform block and non-uniform block situations, and the block restricted isometry property and sub-sampling limit in different situations are analyzed respectively in detail. Theoretical analysis proves that using the block sparsity in AIC can reduce the restricted isometric constant, increase the reconstruction probability and reduce the sub -sampling rate. Simulation results show that the proposed model can complete sub -sampling and reconstruction for multi-narrowband signals. This paper extends the application range of AIC from the finite information rate signal to the multi-narrowband signals by using the potential relevance of support sets. The proposed receiving model has low complexity and is easy to implement, which can promote the application of CS theory in the radar receiver to reduce the burden of analog-to digital convertor (ADC) and solve bandwidth limitations of ADC.
文摘Renewable energy production plays a major role in satisfying electricity demand.Wind power conversion is one of the most popular renewable energy sources compared to other sources.Wind energy conversion has two major types of generators such as the Permanent Magnet Synchronous Generator(PMSG)and the Doubly Fed Induction Generator(DFIG).The maximum power tracking algo-rithm is a crucial controller,a wind energy conversion system for generating maximum power in different wind speed conditions.In this article,the DFIG wind energy conversion system was developed in Matrix Laboratory(MATLAB)and designed a machine learning(ML)algorithm for the rotor and grid side converter.The ML algorithm has been developed and trained in a MATLAB environment.There are two types of learning algorithms such as supervised and unsupervised learning.In this research supervised learning is used to power the neural networks and analysis is made for various hidden layers and activation functions.Simulation results are assessed to demonstrate the efficiency of the proposed system.
