Data-Driven Feature Analysis in Control Design for Series-Compensated Transmission Systems

Li, Xinya; Fan, Xiaoyuan; Ren, Huiying; Hou, Zhangshuan; Huang, Qiuhua; Wang, Song; Ciniglio, Orlando

doi:10.1109/tpwrs.2019.2912711

Cited by 8 publications

(2 citation statements)

References 7 publications

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“…In the formula,   karepresents the congestion mark value carried by the a packet, d represents the statistical value of the congestion mark, R represents the weight of the congestion mark value carried by the packet. When the statistics are completed, the congestion mark value collected in the synchronization interval is cleared to avoid interference with the statistics of the next synchronization period [10].…”

Section: Congestion Determination Of Conjugate Symmetric Data Transmi...mentioning

confidence: 99%

Conjugate Symmetric Data Transmission Control Method based on Machine Learning

Wang¹

2023

IJACSA

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In conjugate symmetric data transmission, due to insufficient judgment of congestion during transmission, the amount of data is large and the transmission rate is low. In order to improve the data transmission rate, a conjugate symmetric data transmission control method based on machine learning is designed. Firstly, the data to be transmitted is tracked and determined, and then conjugate symmetric data fusion is completed according to the calculation result of the best tracking signal. According to the fusion results, the framework of the conjugate symmetric data coding system is established, and the data coding is completed. The average congestion mark value is calculated by the machine learning method, and the congestion judgment of data transmission is completed. On the basis of congestion determination, the efficient transmission control of conjugate symmetric data is realized by specifying the conjugate symmetric data transmission protocol. Experimental results show that compared with traditional control methods, this control method has the advantages of a high delivery rate, low message transmission overhead and low data transmission delay. Compared to the traditional two-way path model, the scheduling method proposed in this study increases the transmission delivery rate by 5%, while reducing the transmission cost and delay by 0.7 cost index and 1.1 min delay, respectively. In comparison with the performance of accurate error tracking equalization, the transmission delivery rate of the research method increased by 21%, and in transmission cost index and delay analysis, it also decreased by 3.1 and 2.9 minutes. Based on the above performance comparison analysis, it can be concluded that the machine learning method has more superior transmission control performance.

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Section: Congestion Determination Of Conjugate Symmetric Data Transmi...mentioning

confidence: 99%

Conjugate Symmetric Data Transmission Control Method based on Machine Learning

Wang¹

2023

IJACSA

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“…Monte Carlo (MC) and Latin hypercube sampling (LHS) (and their variants) are two widely used sampling methods in power system analyses [6], [9]- [13]. MC sampling recreates the input distributions through sampling, however, a problem of clustering arises when a small number of iterations are performed.…”

Section: Introductionmentioning

confidence: 99%

Smart Sampling for Reduced and Representative Power System Scenario Selection

Sun

Datta

et al. 2021

IEEE Open J. Power Energy

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With increasing penetration of renewable energy and active market participation, power system operation scenarios and patterns have increased exponentially. This has led to challenges in identifying a good subset of scenarios for routine planning, operation, and emerging machine learning applications. To address these challenges, we develop an approach integrating comprehensive exploratory data analyses and smart sampling techniques to identify and select a small subset of representative power system scenarios that maintain the coverage of system scenarios and operation envelope, therefore, leading to very efficient, yet representative studies and analysis. We propose a hierarchical Latin Hypercube Sampling (LHS) technique for smart sampling, which allows free-form distributions of system load and considers generator commitment status along with generation levels. A set of performance metrics are also defined for systematic evaluation of the adequacy and efficiency of the sampled cases. The developed approach and metrics are demonstrated using the Texas 2000 bus system in this paper and will be extended to the more complex real world systems such as Western Interconnect System.

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Urban traffic prediction using metrological data with fuzzy logic, long short-term memory (LSTM), and decision trees (DTs)

AlKheder

Alomair

2021

Nat Hazards

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Data-Driven Feature Analysis in Control Design for Series-Compensated Transmission Systems

Cited by 8 publications

References 7 publications

Conjugate Symmetric Data Transmission Control Method based on Machine Learning

Conjugate Symmetric Data Transmission Control Method based on Machine Learning

Smart Sampling for Reduced and Representative Power System Scenario Selection

Urban traffic prediction using metrological data with fuzzy logic, long short-term memory (LSTM), and decision trees (DTs)

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