Dynamic Optimization Method of Transmission Line Parameters Based on Grey Support Vector Regression

Qu, Zhaoyang; Li, Miao; Zhang, Zhenming; Cui, Mingshi; Zhou, Yuguang

doi:10.3389/fenrg.2021.634207

Cited by 13 publications

(10 citation statements)

References 25 publications

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“…MCMC and GA with residual-based loss function are applied for parameter identification firstly, the residual-based loss function refers to [22] 􏽘…”

Section: Resultsmentioning

confidence: 99%

“…Genetic algorithms (GA) combined with transient measurements have been proposed to identify electrical parameters in a power network [21]. 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%

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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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“…MCMC and GA with residual-based loss function are applied for parameter identification firstly, the residual-based loss function refers to [22] 􏽘…”

Section: Resultsmentioning

confidence: 99%

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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“…The power distribution side of the power system is connected with the power transmission system through the distribution substation, which is the last link to transfer the power resources from the transmission system to the users (Shen et al, 2019;Qu et al, 2021). In the process, the medium voltage grade power is transmitted to the distribution transformer located near the user's office by the primary distribution line.…”

Section: Power Distribution Internet Of Things Multi-source Data Proc...mentioning

confidence: 99%

Multi-Source Data Processing and Fusion Method for Power Distribution Internet of Things Based on Edge Intelligence

Yuan

Kou

et al. 2022

Front. Energy Res.

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With the rapid advancement of the Energy Internet strategy, the number of sensors within the Power Distribution Internet of Things (PD-IoT) has increased dramatically. In this study, an edge intelligence-based PD-IoT multi-source data processing and fusion method is proposed to solve the problems of confusing storage and insufficient fusion computing performance of multi-source heterogeneous distribution data. First, a PD-IoT multi-source data processing and fusion architecture based on edge smart terminals is designed. Second, the multi-source sensor data in the distribution network is unified in dimension and magnitude. By introducing the Box–Cox transform to improve the data offset problem in the Z-score normalization process, a multi-source heterogeneous data processing method for distribution networks based on the Box–Cox transform Z-score is proposed. Then, the conflicting phenomena of DS inference methods in data source fusion are optimally handled based on the PCA algorithm. A multi-source data fusion model based on DS inference with conflict optimization is constructed to ensure the effective fusion of distribution data sources from different domains. Finally, the effectiveness of the proposed method is verified by an experimental analysis of an IEEE39 node system in a regional distribution network in China.

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“…On the other hand, the use of renewable energy and microgrids is highly regarded and their integration with the distribution network can be very useful in meeting this increase in energy needs. However, another problem that persists even after the use of renewable energy is the permissible load capacity of transmission lines, which has reached its maximum after the increase in energy demand, and the construction of new transmission lines due to problems such as heavy costs and a lot of time spent on the design and implementation steps, is not economical Cao et al (2020); Lu et al (2021); Mohammadi et al (2021); Qu et al (2021).…”

Section: Introductionmentioning

confidence: 99%

Modeling the Price of Emergency Power Transmission Lines in the Reserve Market Due to the Influence of Renewable Energies

et al. 2022

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The law of free access to the transmission network obliges the transmission network to be in orbit, and on the other hand, the high loads in the transmission network, and economic uncertainties cause that the owners of transmission companies, don’t have sufficient motivation and resources to rebuild and develop the network. The main objective of this paper is the modeling the price of emergency power transmission lines in the reserve markets. This paper presents a method for calculating the reference price that a transmission line owner uses to bid on a price in excess of the nominal capacity of the transmission line under his ownership. For this purpose, first, the effects of operating a transmission line at a power greater than the rated power are described. After that, the reduction rate of the transmission line due to operation in these conditions is calculated, and finally the price determination is calculated based on the reduction rate of the generated life. In the next stage, this excess capacity is entered the two-stage model of energy market and reservation considering renewable energy sources as a price offer function. Numerical results of 6-Shin network show that the entry of renewable energy sources reduces energy costs, but the costs of the reserve market increases due to uncertainty. However, despite the emergency capacity, these costs are reduced due to the use of cheap resources in the network.

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Dynamic Optimization Method of Transmission Line Parameters Based on Grey Support Vector Regression

Cited by 13 publications

References 25 publications

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

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

Multi-Source Data Processing and Fusion Method for Power Distribution Internet of Things Based on Edge Intelligence

Modeling the Price of Emergency Power Transmission Lines in the Reserve Market Due to the Influence of Renewable Energies

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