A Classification Identification Method Based on Phasor Measurement for Distribution Line Parameter Identification Under Insufficient Measurements Conditions

Sun, Jinping; Xia, Mingchao; Chen, Qifang

doi:10.1109/access.2019.2950461

Cited by 24 publications

(11 citation statements)

References 26 publications

(60 reference statements)

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“…In this paper, different results can be obtained with each snapshot PMU data based on (12) and (13). Then, a reliable identification result can be obtained with the above median estimation.…”

Section: B Median Estimationmentioning

confidence: 99%

Robust Identification Method for Transmission Line Parameters That Considers PMU Phase Angle Error

Xue

Martin

et al. 2020

IEEE Access

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Accurate transmission line parameters are the basis of power system calculations. The measured phasor measurement unit (PMU) phase angle data at both ends of a line may contain large errors caused by synchronization problems, which can seriously affect the accuracy of parameter identification. This paper proposes a robust PMU-based method for calculating transmission line parameters from PMU data at both ends of a line in such a way that a synchronization error between the ends does not degrade the results. Specifically, a π -equivalent model for the transmission line is established, and a least square objective function with nonlinear equations for positive parameter identification is derived. Furthermore, to reduce the impact of noise and biased data, median estimation is used to obtain the final result. Finally, a simulation shows the effectiveness and robustness of the proposed method, and its practicality is demonstrated in a case study using measured PMU data.

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Section: B Median Estimationmentioning

confidence: 99%

Robust Identification Method for Transmission Line Parameters That Considers PMU Phase Angle Error

Xue

Martin

et al. 2020

IEEE Access

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“…Besides, some methods based on machine learning, such as support vector regression [22], ensemble Kalman filtering [23], and interior point method, are combined with discrete particle swarm optimal [24]. Sun et al [25] take advantage of the time invariant characteristics of PDN line parameters in a short time and proposed a method of convolutional neural network (CNN) for regression calculations for line parameter identification.…”

Section: Introductionmentioning

confidence: 99%

The New Definitions of Loss Function for the Model-Based Parameter Identification Method in Power Distribution Network

et al. 2022

International Transactions on Electrical Energy Systems

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Accurate device parameters play critical roles in calculation and analysis of power distribution network (PDN). However, device parameters are always affected by the operating status and influenced by manual entry; besides, the distribution area of PDN is very wide with many points, which brings more challenges to PDN parameter identification. Most of the proposed algorithms recently assume that the parameters of PDN contribute in a nonlinear probability space and optimize parameters by the power flow model with a loss function. Although these algorithms can achieve satisfying results in PDN analysis, the relationship between the power flow model and loss functions remains unclear. In this paper, the outputs of the power flow model have been analyzed firstly by experimental data, which includes the head and end voltages, as well as active and reactive power on the low-voltage side. It is revealed that the loss functions used by current algorithms are not suitable and reasonable for power flow model in PDN calculation, which constitutes one of the main findings of this work. Subsequently, this work proposes four novel loss functions combined with genetic algorithm (GA) and Markov Chain Monte Carlo (MCMC) to identify PDN parameters. Compared with the published algorithms, our experimental results show that the loss function defined in this paper can achieve better and more stable performance with about two times lower in MAE, RMSE, and RMPE evaluation functions to identify PDN parameters.

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“…Therefore, the parameter identification methods of the transmission network may not be suitable for the calculation of the PDN parameters. With the rapid development of artificial intelligence technology, some researchers have developed PDN parameter identification methods based on deep learning and machine learning [11]- [14]. Artificial intelligence methods consider the fact that PDN measurement devices lack parts of the original data of the line and exhibit some deviations.…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification for a Power Distribution Network Based on MCMC Algorithm

et al. 2021

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The calculation and analysis of a power distribution network (PDN) require accurate device parameters. However, a PDN has many points, and the distribution area is very wide. The PDN parameters are influenced by manual entry, and most are relatively random. Additionally, these parameters are affected by the operating status. Thus, this paper proposes an algorithm that accurately identifies PDN parameters based on the Markov chain and Monte Carlo (MCMC) method. The algorithm assumes that the PDN parameters conform to a nonlinear probability space. The parameters are the line resistance L R , line reactance L X , short-circuit loss k P , short-circuit voltage percentage % k U , no-load loss 0 P , no-load current percentage 0 % I , etc. The algorithm in this paper uses the Monte Carlo method to provide parameter values that conform to the initial probability distribution and then combines the data collected from the actual feeder to perform power flow calculations to obtain the loss function. The data include the head and end voltages and active and reactive power on the low voltage side. The Markov chain and loss function update the initial parameter probability distribution. The low voltage side voltage of the power flow calculation is iteratively calculated under the new given parameters to obtain the new loss function, and finally, the PDN line and transformer parameter values are identified. Actual feeder data verification results show that this MCMC PDN parameter identification method can obtain high-precision parameter values without phase angle information; additionally, this method is insensitive to the initial values and exhibits fast convergence.

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A Classification Identification Method Based on Phasor Measurement for Distribution Line Parameter Identification Under Insufficient Measurements Conditions

Cited by 24 publications

References 26 publications

Robust Identification Method for Transmission Line Parameters That Considers PMU Phase Angle Error

Robust Identification Method for Transmission Line Parameters That Considers PMU Phase Angle Error

The New Definitions of Loss Function for the Model-Based Parameter Identification Method in Power Distribution Network

Parameter Identification for a Power Distribution Network Based on MCMC Algorithm

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