Hybrid Sampling-Based Particle Filtering With Temporal Constraints

Hu, Chongyang; Liang, Yan; Xu, Linfeng

doi:10.1109/tsmc.2022.3192923

Cited by 5 publications

(1 citation statement)

References 36 publications

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“…This method can consider the impact of random changes in load, power source, and other factors on line loss rate, thus can accurately evaluating the distribution range of line loss rate in a specific power grid in the long, medium, and short term [18] . Moreover, probability analysis methods do not entirely rely on measured information and actual network structure information, simulation methods can be used to obtain system state and even network structure parameters, avoiding the collection of a large amount of data, thereby greatly improving the efficiency of line loss rate calculation [19]- [21] . Therefore, a monthly probability theoretical line loss calculation method of LVN based on simultaneous power and electricity is proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

Monthly Probability Theoretical Line Loss Calculation Method of Low Voltage Distribution Network Based on Simultaneous Power and Electricity

Feng,

Long,

Qian

2023

IEEE Access

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The probability analysis method of theoretical line loss (TLL) is an emerging TLL assessment method in recent years, which can consider the impact of random changes in load, power source, and other factors on the line loss rate, and can accurately evaluate the distribution range of line loss rates in a specific power grid in the long, medium, and short term. A monthly probability theoretical line loss calculation method of low-voltage network (LVN) based on simultaneous power and electricity is proposed in this paper. This method firstly converts hourly calculations of line loss into every 20 minutes calculations, which can shorten the calculation interval based on the first power, last power, and electricity collected in every hour by the user's smart meter. Then, based on improved K-means clustering method (KCM) and non-parametric Kernel density estimation, the load probability distribution model is established, and the acceptance-rejection sampling method (ARSM) is used to generate the power random samples of each user's load for every 20 minutes in a month. The three phase power flow of each simulation sample is calculated by using the injection Newton's method. Then, the monthly TLL is accumulated. Finally, based on the IEEE-13 buses and IEEE-33 buses standard distribution system, the simulation results showed that the proposed method can more accurately and effectively calculate the monthly probability TLL of LVN compared with the line loss calculation method based on daily and hourly electricity.

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Section: Introductionmentioning

confidence: 99%