Bounding Regression Errors in Data-Driven Power Grid Steady-State Models

Liu, Yuxiao; Xu, Bolun; Botterud, Audun; Zhang, Ning; Chen, Kang

doi:10.1109/tpwrs.2020.3017684

Cited by 23 publications

(5 citation statements)

References 31 publications

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“…The data read through smart electric meters of the users includes electrical energy data, voltage data, current data and so forth. The corresponding topological relation can be determined, and then the line and phase [54,55] of a user can be judged by the relation among the data on the same line.…”

Section: Topology Identification Based On Measurement Data 421 Measur...mentioning

confidence: 99%

Overview on topology identification technologies for a low-voltage distribution network

2024

Archives of Electrical Engineering

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The topology identification of low-voltage distribution networks is an important foundation for the intelligence of low-voltage distribution networks. Its accuracy fundamentally determines the effectiveness of functions such as power system state estimation, operational control, optimization planning, and intelligent electricity consumption. The low-voltage distribution network is composed of transformers, lines, and end users. The key task of topology identification is to distinguish the connection relationship between distribution transformers, low-voltage lines, and phase sequence with end users, which can be divided into transformer user relationship, line user relationship, and phase user relationship. At present, the main methods of low-voltage network topology identification can be divided into signal injection method and data analysis method. The signal injection method requires a large number of additional terminal devices and is difficult to promote. The data analysis method combines the characteristics of switch state, voltage, current, electrical energy, and other data to perform topology analysis. The commonly used methods include correlation analysis and feature learning. Finally, typical problems that urgently need to be solved in topology recognition and representation were proposed, providing a reference for the research and development of low-voltage distribution network topology automatic recognition technology.

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Section: Topology Identification Based On Measurement Data 421 Measur...mentioning

confidence: 99%

Overview on topology identification technologies for a low-voltage distribution network

2024

Archives of Electrical Engineering

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“…Smart grid is featured by the integration of advanced communication technologies and convectional power systems 3‐5 . The information is markedly enriched in generation, transmission, distribution, and energy consumption side.…”

Section: Data Visualization In Low‐carbon Energy Related Areasmentioning

confidence: 99%

“…Smart grid is featured by the integration of advanced communication technologies and convectional power systems. [3][4][5] The information is markedly enriched in generation, transmission, distribution, and energy consumption side. The amount of data is increased significantly given the extended information infrastructure, and thus how to process the data and visualize the information is becoming increasingly important.…”

Section: Data Visualization For Smart Gridmentioning

confidence: 99%

Data visualization in smart grid and low‐carbon energy systems: A review

Chen

2021

Int Trans Electr Energ Syst

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Summary Data visualization has been increasingly important with the deployment of advanced measurement system and big data analyses in smart grid and low carbon energy systems. Research on information design, related technology, and visualization tools have been lacking. Here we systematically review the analyses and practice on data visualization in power and energy systems. Visualization related to different energy system applications, including smart grid, electric vehicle, and building energy consumption, are summarized first. Design principles are presented for large screen, personal computer, or mobile device based interface respectively. Geographical information system technologies for visualization design are detailed. The application of 3‐D technologies, animations, and AR&VR for energy system visualization are also described.

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“…In order to meet the growing demand for electric energy, China's power construction scale has gradually increased, the power generation capacity has reached the first place in the world, and the installed capacity of power generation is far more than that of many developed countries, promoting the stable development of the national economy [1]. The side distributed power grid is a power generation, distribution and consumption system composed of distributed power supply, energy storage system, load and other structures, which has the function of self monitoring and controlling the operation status [2,3]. If there can be an effective optimization layered time-sharing configuration algorithm applied to the power grid, it will make the application scope of the power grid wider.…”

Section: Introductionmentioning

confidence: 99%

Grid Side Distributed Energy Storage Cloud Group End Region Hierarchical Time-Sharing Configuration Algorithm Based on Multi-Scale and Multi Feature Convolution Neural Network

Wen¹,

Zhu²,

Li³

et al. 2023

Energy Engineering

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There is instability in the distributed energy storage cloud group end region on the power grid side. In order to avoid large-scale fluctuating charging and discharging in the power grid environment and make the capacitor components show a continuous and stable charging and discharging state, a hierarchical time-sharing configuration algorithm of distributed energy storage cloud group end region on the power grid side based on multi-scale and multi feature convolution neural network is proposed. Firstly, a voltage stability analysis model based on multi-scale and multi feature convolution neural network is constructed, and the multi-scale and multi feature convolution neural network is optimized based on Self-Organizing Maps (SOM) algorithm to analyze the voltage stability of the cloud group end region of distributed energy storage on the grid side under the framework of credibility. According to the optimal scheduling objectives and network size, the distributed robust optimal configuration control model is solved under the framework of coordinated optimal scheduling at multiple time scales; Finally, the time series characteristics of regional power grid load and distributed generation are analyzed. According to the regional hierarchical time-sharing configuration model of "cloud", "group" and "end" layer, the grid side distributed energy storage cloud group end regional hierarchical time-sharing configuration algorithm is realized. The experimental results show that after applying this algorithm, the best grid side distributed energy storage configuration scheme can be determined, and the stability of grid side distributed energy storage cloud group end region layered timesharing configuration can be improved. KEYWORDSMultiscale and multi feature convolution neural network; distributed energy storage at grid side; cloud group end region; layered time-sharing configuration algorithm

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Bounding Regression Errors in Data-Driven Power Grid Steady-State Models

Cited by 23 publications

References 31 publications

Overview on topology identification technologies for a low-voltage distribution network

Overview on topology identification technologies for a low-voltage distribution network

Data visualization in smart grid and low‐carbon energy systems: A review

Grid Side Distributed Energy Storage Cloud Group End Region Hierarchical Time-Sharing Configuration Algorithm Based on Multi-Scale and Multi Feature Convolution Neural Network

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