Practical Method for Data-Driven User Phase Identification in Low-Voltage Distribution Networks

Yu, Huang; Wu, Yao; Guan, Weiling; Zhang, Daolu; Yu, Tao; Liu, Qianjin

doi:10.3389/fenrg.2021.752571

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…It is critical to ensuring the quality of the power supply and improving the consumer experience. However, the consumer phase connectivity information in LVDN is generally missing or inaccurate, which has become a bottleneck that restricts the planning and operation management of LVDN [3]. With the rapid application of smart meters in LVDN, a large number of researchers have studied the phase identification of LVDN by analyzing the data from smart meters.…”

Section: A Setsmentioning

confidence: 99%

Phase Identification of Low-voltage Distribution Network Based on Stepwise Regression Method

Yi¹,

Liu²,

Zhang³

et al. 2023

Journal of Modern Power Systems and Clean Energy

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Accurate information for consumer phase connectivity in a low-voltage distribution network (LVDN) is critical for the management of line losses and the quality of customer service. The wide application of smart meters provides the data basis for the phase identification of LVDN. However, the measurement errors, poor communication, and data distortion have significant impacts on the accuracy of phase identification. In order to solve this problem, this paper proposes a phase identification method of LVDN based on stepwise regression (SR) method. First, a multiple linear regression model based on the principle of energy conservation is established for phase identification of LVDN. Second, the SR algorithm is used to identify the consumer phase connectivity. Third, by defining a significance correction factor, the results from the SR algorithm are updated to improve the accuracy of phase identification. Finally, an LVDN test system with 63 consumers is constructed based on the real load. The simulation results prove that the identification accuracy achieved by the proposed method is higher than other phase identification methods under the influence of various errors.

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Section: A Setsmentioning

confidence: 99%

Phase Identification of Low-voltage Distribution Network Based on Stepwise Regression Method

Yi¹,

Liu²,

Zhang³

et al. 2023

Journal of Modern Power Systems and Clean Energy

View full text Add to dashboard Cite

show abstract

“…Voltage stability [175]- [178] Security and cascading failure assessment [179]- [182] Transient stability [183]- [186] Building-level event detection Occupancy and activity [187]- [190] Fault diagnosis in appliances [191]- [194] Optimal power flow End-to-end learning [195]- [198] Learning-augmented [199]- [202] Energy management & control Demand side management Heating/Cooling loads [203]- [206] Demand-response [207]- [210] Buildings [211]- [214] Communities [215]- [218] Microgrids [219]- [222] Load frequency control [223]- [226] Voltage control [227]- [230] Grid variables estimation/ identification Phase [231]- [234] Topology and lines parameters [235]- [238] State estimation [239]- [242] Voltage calculation [243]- [246] FIGURE 9: Further decomposition of analytics services (II). Four indicative references are given for each analytics category.…”

Section: Analytics Cybersecuritymentioning

confidence: 99%

“…• Grid variables estimation: Data-driven methods that leverage the deployment of AMI to calculate grid variables/ parameters in conditions of partial or no awareness of grid topology-related information without using model-based techniques. In the literature, four distinct services are identified: phase estimation (identify the phase of grid-connected loads) [231]- [234], topology and lines parameters estimation (estimate grid parameters when missing) [235]- [238], state estimation of the partial observable grid (estimation of branch currents and bus voltages) [239]- [242], and calculation of voltages (where no information about grid topology exists) [243]- [246].…”

Section: Analytics Cybersecuritymentioning

confidence: 99%

Data-Driven Analytics for Reliability in the Buildings-to-Grid Integrated System Framework: A Systematic Text-Mining-Assisted Literature Review and Trend Analysis

Bachoumis,

Mylonas,

Plakas

et al. 2023

IEEE Access

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Data-driven machine learning-based methods have provided immense capabilities, revolutionizing sectors like the Buildings-to-grid (B2G) integrated system. Since the penetration rate of distributed energy resources increases towards a net-zero emissions power system, so does the need for advanced services that ensure B2G-integrated system reliability. The convergence of advancements in machine learning, computational resources at the entire cloud-edge continuum, and large datasets from sensing devices enable the development of these data-driven energy analytics services. This work conducts a systematic text-mining-based literature review to examine the diverse range and trends of machine learning methods used to enhance reliability in B2G-integrated systems. While traditional manual sampling and analysis approaches have limited the effectiveness of previous literature review papers in this field, this systematic literature review work aims to synthesize and summarize the existing body of research more efficiently and effectively. To achieve this, this study collected almost 10,500 papers from Scholar and Scopus databases. It employed text-mining-assisted BERTopic-based topic modelling and statistical trend analysis techniques to uncover semantic patterns and explore the temporal evolution of research themes. A two-dimensional taxonomy was derived to analyze the technical papers from a business and machine learning-related perspective. By quantifying the temporal trends within these topics, the study unveiled insights about the state-of-the-art analytics that ensure reliability in the B2G-integrated system domain while proposing future research directions.

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Distribution Network Topology Identification Based on Time Series Voltage Mutual Information and Probability Density Function

Lin,

Cong,

Diao

et al. 2024

2024 IEEE 7th International Electrical and Energy Conference (CIEEC)

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Practical Method for Data-Driven User Phase Identification in Low-Voltage Distribution Networks

Cited by 5 publications

References 20 publications

Phase Identification of Low-voltage Distribution Network Based on Stepwise Regression Method

Phase Identification of Low-voltage Distribution Network Based on Stepwise Regression Method

Data-Driven Analytics for Reliability in the Buildings-to-Grid Integrated System Framework: A Systematic Text-Mining-Assisted Literature Review and Trend Analysis

Distribution Network Topology Identification Based on Time Series Voltage Mutual Information and Probability Density Function

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