Time‐domain resonant characteristics between the disturbances and the RS422 communication signals in Tesla pulse driver, and analysis on the caused RS422 communication interference

Zhang, Yu; Cheng, Jie; Su, Jiancang; Zheng, Liu; Qiu, Xiang; Shang, W.

doi:10.1049/iet-smt.2019.0498

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…Firstly, this angle is used to realize the timing for the SEC controlling the electronic detection system for powering on and preheating, and secondly, it is used to realize the moment when the SEC controls the turntable through the TEC to bring the sunlight into the guide mirror's field of view. The TEC obtains the azimuth and pitch angles according to SEC through the RS422 (its full name is Electrical Characteristics of Balanced Voltage Digital Interface Circuits, published by the ANSI Telecommunication Industry Association/Electronic Industries Association (TIA/EIA) in 1994) [35][36][37] communication bus and makes the turntable rotate according to these angles. The corresponding encoder's location information is utilized for closed-loop.…”

Section: The Solar Tracking Principle Of the Spectrometermentioning

confidence: 99%

Prediction Aerospace Software to Detect Solar Activity and the Fast Tracking of Solar Activity Using Remote Sensing Instruments in Orbit

Shao

Yang

et al. 2023

Remote Sensing

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At present, solar remote sensing instruments face problems regarding the short detection time for solar activity and the need to preheat the electronics before detection. The accurate orbit prediction function ensures that the maximum error of the payload electronics system preheating time is less than 8 s, enabling the electronics to obtain stable and reliable solar remote sensing data, and allowing the prediction function to adapt to the changes in preheating time and the limit position of turntable rotation. To achieve the complete detection of solar activity by a payload in orbit, a turntable with two directions of rotation was accurately controlled to achieve rapid capture and track the Sun’s activity, and the delay time for tracking the Sun successfully was less than 8 s to ensure that the payload made full use of the solar detection time for full detection. The turntable’s tracking error was less than 0.002°, allowing the payload to obtain high-precision solar remote sensing data in orbit. The above methods enable the payload to realize the complete, stable, and high-precision detection of solar activity in orbit for wavelengths ranging approximately from 165 nm to 2400 nm.

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Section: The Solar Tracking Principle Of the Spectrometermentioning

confidence: 99%

Prediction Aerospace Software to Detect Solar Activity and the Fast Tracking of Solar Activity Using Remote Sensing Instruments in Orbit

Shao

Yang

et al. 2023

Remote Sensing

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“…It needs to forward data packets while also transmitting data packets. The network centered DC needs to transmit to the network through switches and routers [25]. VDC is used to describe the application that tenants are deploying.…”

Section: A Dcn Structurementioning

confidence: 99%

Application of KNCMA Algorithm Integrating Network Features in Data Resource Scheduling

Wang,

Chai

2023

IEEE Access

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To improve the efficiency of data resource scheduling in virtual data centers, this study analyzes the network structure of the data center, constructs a set of key nodes, and selects the importance of nodes. And a mapping algorithm based on key nodesis proposed as the mapping algorithm for the virtual data center. The effectiveness of virtual data center compression and network feature validation are analyzed. In the DCell structure, after compressing the virtual data center, the request acceptance rate has improved. The compressed virtual data center significantly consumes less physical bandwidth during mapping compared to the case of direct virtual data center mapping. In the BCube structure, after compressing the virtual data center, the request acceptance rate has also improved. A comparative experiment is conducted between the mapping algorithm based on key nodes and the mapping algorithm based ontherandom experiments. Under the three architectures of DCell, BCube, and Fattree, the acceptance rate of key node mapping algorithms is higher than that ofthe random experimental mapping algorithms. Its bandwidth utilization rate is also lower than the mapping algorithm in random experiments. After introducing network features, the request acceptance rate has improved to varying degrees. The bandwidth utilization rate increases with the increase of acceptance rate.

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“…e analysis of power signals can be mainly divided into time domain analysis [33], frequency domain analysis [34], and time-frequency domain analysis [35]. Considering the complexity of power load, this paper extracts the time domain features and frequency domain features of power data to form the joint features of power load, and on this basis, WOA is introduced for feature screening.…”

Section: Joint Features Of Time Domain and Frequency Domainmentioning

confidence: 99%

Joint Feature Selection of Power Load in Time Domain and Frequency Domain Based on Whale Optimization Algorithm

Zhou

et al. 2022

International Transactions on Electrical Energy Systems

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Accurate classification of power load is the premise of demand-side response capability evaluation, which is of great significance for demand-side management. Therefore, this study proposes a selection method of joint characteristics of power load in time domain and frequency domain based on whale optimization algorithm. The electrical measurement data of six typical electrical equipment are selected to form the original power load identification dataset. Firstly, the time domain features and frequency domain features of the power load data are extracted, and then the joint characteristics of the power load are screened by Whale optimization algorithm (WOA). Finally, the selected feature information is used as the input to verify the performance of WOA on the joint characteristics of power load under back propagation neural network (BP), extreme learning machine (ELM), support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), and naive Bayesian (NB) classifiers. The results show that WOA can effectively screen out the 15 most helpful feature attributes for power load identification, which can not only improve the accuracy of power load identification but also effectively reduce the time cost of the algorithm, which is of reference value for further demand-side response strategy. At the same time, it is of great significance for intelligent dispatching of power system and improving the economic operation of the power system.

show abstract

Time‐domain resonant characteristics between the disturbances and the RS422 communication signals in Tesla pulse driver, and analysis on the caused RS422 communication interference

Cited by 8 publications

References 11 publications

Prediction Aerospace Software to Detect Solar Activity and the Fast Tracking of Solar Activity Using Remote Sensing Instruments in Orbit

Prediction Aerospace Software to Detect Solar Activity and the Fast Tracking of Solar Activity Using Remote Sensing Instruments in Orbit

Application of KNCMA Algorithm Integrating Network Features in Data Resource Scheduling

Joint Feature Selection of Power Load in Time Domain and Frequency Domain Based on Whale Optimization Algorithm

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