The Linkage Analysis of Electric Load Characteristic Statistical Indexes Based on VAR

Xie, Zhengchao; Ma, Rui; Liu, Dao Xin; Wang, Zijian; Wang, Jun; Xu, Hui; Wang, Xi Liang

doi:10.4028/www.scientific.net/amm.268-270.1401

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…In Reference [3], the article aims at the problem that some load appliances have overlapping characteristics in the current low-frequency nonintrusive load decomposition. In Reference [4], low-level correlation indexes are extracted by using correlation analysis, then the linkage analysis theory in econometrics is applied, and the load characteristic indexes are analyzed by using vector auto-regression model. Te synchronous movement trend and causal leading relationship among electric load characteristic indexes are confrmed by cointegration test and granger-casualty analysis.…”

Section: Introductionmentioning

confidence: 99%

Multi-Factor Load Classification Method considering Clean Energy Power Generation

Zang

Wang

et al. 2023

International Transactions on Electrical Energy Systems

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The analysis of load characteristics is the basis and premise of load actively participating in power grid regulation. This paper proposes a multi-factor load classification method considering the load of clean energy power generation, the rapidity of load classification, and various subjective and objective factors that may affect the behavior of load consumption. First, it describes the characteristic index of load consumption behavior and analyzes the subjective and objective factors that affect the power grid consumption behavior. The effect of clean energy generation on load side is considered. Based on the load characteristics, the K-means algorithm is used for main clustering. Then, the confidence level of the uncertainty of the actual load adjustable capacity is analyzed by quantifying the load adjustable potential index and the fuzzy C-means clustering method was used for secondary clustering of the adjustable capacity. Finally, DBI and SC indexes are used to evaluate the clustering results, standard values of evaluation indexes are set, and unqualified clustering results are recalculated and corrected. 31 industrial users in a province are selected as research objects, and the load data of the past 365 days are collected to verify the effectiveness and practicability of the proposed method. The classification results show that the classification accuracy is still good when the noise is 30%, and the maximum deviation between the clustering results and the actual load regulation potential is 12%. It can meet the actual engineering error standard.

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Section: Introductionmentioning

confidence: 99%

Multi-Factor Load Classification Method considering Clean Energy Power Generation

Zang

Wang

et al. 2023

International Transactions on Electrical Energy Systems

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Thermo-driven self-healable organic/inorganic nanohybrid polyurethane film with excellent mechanical properties

Wang

et al. 2021

Polym J

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Cationic Emulsion Polymerization of Octamethylcyclotetrasiloxane (D4) in Mixtures with Alkoxysilanes

et al. 2022

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The cationic emulsion polymerization of octamethylcyclotetrasiloxane (D4) in mixtures with methyltriethoxysilane (MTES) and vinyltriethoxysilane (VTES) was studied by FTIR ATR, GC, the development of a toluene insoluble fraction of the polymer and a gravimetric analysis. The polymerization of D4 alone was also conducted for comparison and, additionally, the development of molecular weight of polydimethylsiloxane (PDMS) obtained in that process was studied by GPC. Dodecylbenzenesulphonic acid (DBSA) was used as a surfactant and catalyst. The process was carried out in a “starved feed” mode by adding dropwise the monomer mixture to the aqueous solution of DBSA. FTIR ATR spectra were recorded by the sensor placed in the probe tip of a ReactIR 15TM apparatus. It was found that the silicone polymer formation proceeded faster when D4 was polymerized in the mixture with alkoxysilanes, especially in the beginning of the process, and that already at the beginning of the process, the partly crosslinked polymer was formed. The induction period of ca. 30 min was observed and the concentration of cyclic siloxanes (D4 and decamethylcyclopentasiloxane—D5) remained at a very low level in the course of the reaction and only traces were detected in the final product. The particle size development in the course of the reaction was also studied and it was found that the particle size distribution was bimodal and was broadening as the reaction proceeded, though this phenomenon was less distinct when D4 was polymerized in the mixtures with alkoxysilanes. The structure of the reaction product was confirmed by 29Si NMR.

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The Linkage Analysis of Electric Load Characteristic Statistical Indexes Based on VAR

Cited by 3 publications

References 4 publications

Multi-Factor Load Classification Method considering Clean Energy Power Generation

Multi-Factor Load Classification Method considering Clean Energy Power Generation

Thermo-driven self-healable organic/inorganic nanohybrid polyurethane film with excellent mechanical properties

Cationic Emulsion Polymerization of Octamethylcyclotetrasiloxane (D4) in Mixtures with Alkoxysilanes

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