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[Introduction] In the low-voltage distribution network, the residual current protection device, as an important guarantee of electricity safety, can reduce the harm caused by the leakage fault of electrical appliances and prevent human electrocution accidents. The current residual current protection device relies on the residual current signal size as the basis for the action of the protection mechanism, but has no function to identify the electrocution characteristics. To address this problem, this paper proposes a method for electrocution signal feature extraction and electrocution diagnosis in low-voltage distribution networks based on wavelet decomposition and denoising, as well as GRU. [Method] In this paper, the residual currents collected from electrocution experiments were pre-processed by downsampling and discrete wavelet denoising; The time and frequency domain electrocution characteristic parameters of the residual currents were extracted by the sliding window method, and the Fourier transform was used to extract the characteristic parameters of residual currents to the second harmonic amplitude. All the extracted feature parameters were used to form a high-dimensional feature space vector; which was subject to dimensionality reduction using the method of principal component analysis to obtain a new set of three-dimensional feature vectors. A diagnostic model for electrocution was established, and the three-dimensional feature vectors representing electrocution features were input into the model. Comparison experiments were conducted on electrocution signals using five different electrocution diagnostic models, such as recurrent gated network (GRU). [Result] The experimental results show that the convergence of the GRU-based electrocution diagnosis model is good, and the recognition rate reaches 98.33%. [Conclusion] The method provides new insights for the research and development of a new generation of residual current protection devices and offers an effective guarantee for electrical safety.

Recent years, the tremendous number of distributed energy resources, electric vehicles are integrated into the Low-Voltage Distribution Network (LVDN), large amount of data are generated by edge devices in LVDN. The cloud data centers are unable to process these data timely and accurately, making it impossible to meet the demand for fine-grained control of LVDN. To solve the above problems, this paper proposes a flexible orchestration of lightweight artificial intelligence (AI) for edge computing in LVDN. Firstly, the application requirements of LVDN are analysed through feature extraction of its historical data, and a lightweight AI library is constructed to meet its requirements. Secondly, based on the multifactor priority, a flexible orchestration model is established, to allow the lightweight AI embedded in the edge devices of the LVDN. Finally, the particle swarm optimization algorithm is used to provide the best solution. The simulation results show that the method proposed in this paper can support the deployment of AI at the edge. It can significantly improve the utilization of edge computing resources, and reduce the pressure of cloud computing and the time of application processes. [ABSTRACT FROM AUTHOR]

Copyright of Southern Energy Construction is the property of Southern Energy Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the widespread use of new equipment such as distributed photovoltaics, distributed energy storage, electric vehicles, and distributed wind power, the control of low-voltage distribution networks (LVDNs) has become increasingly complex. Acquiring the most recent topological structure is essential for conducting accurate analysis and real-time control of LVDNs. The signal injection-based topology identification algorithm is favored for its speed and efficiency. This research introduces an innovative topology identification algorithm based on signal injection, specifically designed to address the challenges of incomplete and inaccurate identifications caused by the missing data in feature signal records (FSRs). Based on the correlations among FSRs at various devices, the algorithm introduces a dual-axis completion strategy—both vertical and horizontal—to effectively address missing data. Subsequently, an inclusion detection process is devised to process the completed FSRs, culminating in an accurate topology of LVDNs. Based on the study of actual LVDN data, the results indicate that the proposed algorithm markedly enhances the completeness and accuracy of topology identification. This advancement offers a robust solution tailored to accommodate the dynamic and swiftly changing topological configurations of LVDNs. [ABSTRACT FROM AUTHOR]

Residual current protection can detect and isolate the grounding (leakage) fault of low-voltage distribution networks in time, which is an essential technical measure to reduce electric shocks and fire accidents and improve power supply safety. This paper systematically analyzes the operating characteristics of low-voltage distribution networks and proposes a distributed residual current protection method based on closed sections. It utilizes the capabilities of a distribution IoT platform to provide comprehensive measurement information for the entire substation area. A method was introduced to divide the low-voltage distribution substation into different protection closed surfaces, defining the current phasor at the external contact point of the closed surface and the remaining current of the closed surface. A calculation method for the critical current based on the remaining current of the closed surface for fault detection was proposed. Case studies have shown that this method is less affected by the inherent current and can significantly improve the sensitivity of protection. For TN-C and TN-C-S systems, in the selection of closed surfaces, the repeated grounding point of the neutral line is excluded from the closed surface. This method can also overcome the impact of residual current changes when load switching, demonstrating the flexibility of the new principle. [ABSTRACT FROM AUTHOR]

To address the issue of inconspicuous electricity consumption characteristics among vacant users in low-voltage distribution networks (LVDNs), which hinders effective line–household relationship identification (LHRI), a method for identifying line–household relationship based on voltage clustering and electricity consumption characteristics is proposed. Initially, the paper employs Dynamic Time Warping (DTW) to analyze the similarity of user voltage profiles and utilizes the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster users. This approach identifies the topological relationship between vacant users and regular users to obtain multiple user categories. Subsequently, by analyzing the electricity consumption characteristic, the connection relationships between different user categories and phase lines are clarified based on the correlation between the electricity consumption characteristic vector of phase lines and the electricity consumption characteristic vector of user categories, thereby revealing the line–household relationship for all users. On the test dataset, the LHRI algorithm proposed in this article achieved 100% accuracy, within an allowable error range of 0.2%, and improved the accuracy by 20% compared to the traditional identification method. Finally, the LVDN simulation model established by OpenDSS 9.4.0.3 was used to verify the effectiveness of the proposed method, confirming its potential and advantages in practical applications. [ABSTRACT FROM AUTHOR]

This paper proposes a tube model predictive control method considering uncertainties of renewable energy generation in the low-voltage distribution network. For relatively low dependence on communication networks, a decentralized voltage control framework is urgently needed to be developed for voltage regulation in the low-voltage distribution network. Opposed to the traditional voltage control techniques, a novel decentralized tube model predictive voltage control (TMPC) strategy is proposed. First, this strategy entails the formulation of a discrete model encompassing distributed photovoltaic units and energy storage systems intertwined with the distribution grid model based on voltage sensitivity. Then, considering the worst case of voltage deviations caused by uncertain and stochastic power fluctuation, we specifically discuss a robust positively invariant to determine the control section of controllers. Further, the strategy employs tube model predictive controllers encompassing nominal and auxiliary controllers in distribution areas. The nominal controller generates a central path for different voltage operating points of each distribution area, and the auxiliary controller makes all trajectories lie in a bounded neighborhood called a tube. The effectiveness of the proposed control strategy is investigated on a test system containing renewable energy resources.

Correctly identifying the topology of a low-voltage distribution network aids in its management by power companies. However, the low collection rate and poor quality of the customer data collected by smart meters create difficulties in the application of data analysis algorithms. To address this, a new data-driven approach is proposed for topology identification in low-voltage distribution networks. First, a linear fitting method is used to approximate the noise-containing data. Next, the t-distributed stochastic neighbor embedding (t-SNE) method is used to reduce the dimensionality of the high-dimensional time-series voltage data. Then, the density-based spatial clustering of applications with noise (DBSCAN) method is applied to cluster the reduced dimensional data to perform topology identification. Finally, the simulated data and the actual data of a low-voltage distribution network in Dongguan, Guangdong Province are analyzed, and the results show the effectiveness and practicality of the proposed method.

The increasing integration of distributed household photovoltaics (PVs) and electric vehicles (EVs) may further aggravate voltage violations and unbalance of low-voltage distribution networks (LVDNs). DC distribution networks can increase the accommodation of PVs and EVs and mitigate mutilple power quality problems by the flexible power regulation capability of voltage source converters. This paper proposes schemes to establish hybrid AC/DC LVDNs considering the conversion of the existing three-phase four-wire low-voltage AC systems to DC operation. The characteristics and DC conversion constraints of typical LVDNs are analyzed. In addition, converter configurations for typical LVDNs are proposed based on the three-phase four-wire characteristics and quantitative analysis of various DC configurations. Moreover, an optimal planning method of hybrid AC/DC LVDNs is proposed, which is modeled as a bi-level programming model considering the annual investments and three-phase unbalance. Simulations are conducted to verify the effectiveness of the proposed optimal planning method. Simulation results show that the proposed optimal planning method can increase the integration of PVs while simultaneously reducing issues related to voltage violation and unbalance.

The increasing proportion of distributed photovoltaics (DPVs) and electric vehicle charging stations in low-voltage distribution networks (LVDNs) has resulted in challenges such as distribution transformer overloads and voltage violations. To address these problems, we propose a coordinated planning method for flexible interconnections and energy storage systems (ESSs) to improve the accommodation capacity of DPVs. First, the power-transfer characteristics of flexible interconnection and ESSs are analyzed. The equipment costs of the voltage source converters (VSCs) and ESSs are also analyzed comprehensively, considering the differences in installation and maintenance costs for different installation locations. Second, a bilevel programming model is established to minimize the annual comprehensive cost and yearly total PV curtailment capacity. Within this framework, the upper-level model optimizes the installation locations and capacities of the VSCs and ESSs, whereas the lower-level model optimizes the operating power of the VSCs and ESSs. The proposed model is solved using a non-dominated sorting genetic algorithm with an elite strategy (NSGA-II). The effectiveness of the proposed planning method is validated through an actual LVDN scenario, which demonstrates its advantages in enhancing PV accommodation capacity. In addition, the economic benefits of various planning schemes with different flexible interconnection topologies and different PV grid-connected forms are quantitatively analyzed, demonstrating the adaptability of the proposed coordinated planning method.

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.

Modern solutions for precise fault localisation in Low Voltage (LV) Distribution Networks (DNs) often rely on costly tools such as the micro-Phasor Measurement Unit (μ PMU), which is potentially impractical for the large number of nodes in LVDNs. This paper introduces a novel fault detection technique using a distribution network digital twin without the use of μ PMUs. The Digital Twin (DT) integrates data from Smart Meters (SMs) and network topology to create an accurate replica. In using SM voltage-magnitude readings, the pre-built twin compiles a database of fault scenarios and matches them with their unique voltage fingerprints. However, this SM-based voltage-only approach shows only a 70.7% accuracy in classifying fault type and location. Therefore, this research suggests using the cables' Currents Symmetrical Component (CSC). Since SMs do not provide direct current data, a Machine Learning (ML)-based regression method is proposed to estimate the cables' currents in the DT. Validation is performed on a 41-node LV distribution feeder in the Scottish network provided by the industry partner Scottish Power Energy Networks (SPEN). The results show that the current estimation regressor significantly improves fault localisation and identification accuracy to 95.77%. This validates the crucial role of a DT in distribution networks, thus enabling highly accurate fault detection when using SM voltage-only data, with further refinements being conducted through estimations of CSC. The proposed DT offers automated fault detection, thus enhancing customer connectivity and maintenance team dispatch efficiency without the need for additional expensive μ PMU on a densely-noded distribution network. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the continuous integration of renewable energy and the complexity of power system, dynamic voltage regulation of low-voltage distribution network has become an urgent problem to be solved. Through in-depth analysis and theoretical discussion, the mathematical model of low-voltage distribution network is established, and the control strategy based on renewable energy is designed for dynamic voltage regulation. Through simulation experiment and field verification, the superiority of the proposed method compared with the existing method is demonstrated. In addition, the sensitivity of the parameters and the stability of the control strategy are also comprehensively analyzed. Despite some limitations and challenges, the results of this study provide strong support for the sustainable, efficient and safe operation of low-voltage distribution networks, and further optimization and expansion in many aspects are needed in the future.

[Introduction] The correct topology information recorded by the power supply department can help the staff monitor the power grid information, analyze the faults, and optimize the operation of the power grid to meet the needs of lean and intelligent management of low-voltage distribution networks. At present, the addition of various new types of electricity-using equipment and users has caused the low-voltage distribution network structure to show a continuous change in characteristics, and the line maintenance cost is greatly increased. [Method] Therefore, the identification method of low-voltage distribution network attribution relationship based on density clustering was proposed. First, the effective voltage data collected by smart meters were extracted to generate a high-dimensional time-series voltage matrix. Then, the t-distributed Stochastic Neighbor Embedding algorithm (t-SNE) and Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN) were applied to cluster the voltage data to achieve identification of low-voltage distribution network attribution relationship. Finally, the actual data of a low-voltage distribution network in Sanya City, Hainan Province were analyzed, and the proposed method is compared with other mainstream topology identification methods. [Result] The analysis results show that the proposed method can achieve more than 95% of identification accuracy, which is higher than other mainstream topology identification methods. [Conclusion] The proposed method is effective and advantageous in solving such problems, and can provide reference for practical engineering applications and offer a different research idea in the field of topology identification of low-voltage distribution network.

Line loss is directly responsible for the management profitability of the grid company. The traditional method of calculating the theoretical line loss for Low Voltage Distribution Networks (LVDN) necessitates more electrical parameters. which cannot be obtained easily. Besides, due to the backward communication conditions of LVDN, the problem of smart meter data missing is significant, which poses a challenge to an exact theoretical line loss calculation. In an attempt to solve the issues above, a theoretical line loss computation approach via matrix completion and ReliefF-convolutional neural network (CNN) for LVDN is proposed. Firstly, a feature weighting algorithm based on ReliefF is presented to analyze the relevance of the electrical parameters, which can be obtained easily. Secondly, a theoretical line loss calculation method is proposed for CNN-based. In the view of the data missing problem, a matrix completion method based on singular value thresholding (SVT) is introduced to obtain the high-precision data, in order to enhance the calculation accuracy of the theoretical line loss calculation. Finally, the proposed method is tested on the data sample of 789 LVDNs. The results show that comparing with CNN, back-propagation and other methods, the mean absolute percentage error (MAPE) of the presented method can reduce by more than 90%. When data missing, the MAPE of the proposed method can reduce by more than 95% compared with the method without considering the data completion.

Driven by the goal of energy transformation and carbon neutralization, more and more distributed photovoltaics (PVs) are integrated into low-voltage distribution networks (LVDNs). However, the integration of high-proportion PVs may lead to overload problem of distribution transformer (DT) and voltage violation problem, bringing many challenges to the safe and stable operation of LVDN. DC distribution network has the advantages of large transmission capacity and flexible power regulation, which can also realize the interconnection and power transfer between AC low-voltage lines. Therefore, structures and control methods suitable for hybrid AC/DC LVDN are proposed to alleviate the above problems caused by PVs. Firstly, the structure of hybrid AC/DC LVDN is analyzed, including the structure with parallel DC line within distribution area and the structure of DC-interconnected distribution networks between distribution areas. Secondly, a local control method of voltage source converter (VSC) is proposed for the comprehensive improvement of overload problem of DT and voltage violation problem. Finally, an actual LVDN is used to verify the effectiveness of the proposed structure and control method of hybrid AC/DC LVDN. According to the simulation results, the proposed structure of hybrid AC/DC LVDN and VSC control method can improve the voltage level and overload problems of LVDN, decrease the power loss and increase proportion of PV accommodation.

Copyright of Southern Energy Construction is the property of Southern Energy Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the widespread use of new equipment such as distributed photovoltaics, distributed energy storage, electric vehicles, and distributed wind power, the control of low-voltage distribution networks (LVDNs) has become increasingly complex. Acquiring the most recent topological structure is essential for conducting accurate analysis and real-time control of LVDNs. The signal injection-based topology identification algorithm is favored for its speed and efficiency. This research introduces an innovative topology identification algorithm based on signal injection, specifically designed to address the challenges of incomplete and inaccurate identifications caused by the missing data in feature signal records (FSRs). Based on the correlations among FSRs at various devices, the algorithm introduces a dual-axis completion strategy—both vertical and horizontal—to effectively address missing data. Subsequently, an inclusion detection process is devised to process the completed FSRs, culminating in an accurate topology of LVDNs. Based on the study of actual LVDN data, the results indicate that the proposed algorithm markedly enhances the completeness and accuracy of topology identification. This advancement offers a robust solution tailored to accommodate the dynamic and swiftly changing topological configurations of LVDNs.

Residual current protection can detect and isolate the grounding (leakage) fault of low-voltage distribution networks in time, which is an essential technical measure to reduce electric shocks and fire accidents and improve power supply safety. This paper systematically analyzes the operating characteristics of low-voltage distribution networks and proposes a distributed residual current protection method based on closed sections. It utilizes the capabilities of a distribution IoT platform to provide comprehensive measurement information for the entire substation area. A method was introduced to divide the low-voltage distribution substation into different protection closed surfaces, defining the current phasor at the external contact point of the closed surface and the remaining current of the closed surface. A calculation method for the critical current based on the remaining current of the closed surface for fault detection was proposed. Case studies have shown that this method is less affected by the inherent current and can significantly improve the sensitivity of protection. For TN-C and TN-C-S systems, in the selection of closed surfaces, the repeated grounding point of the neutral line is excluded from the closed surface. This method can also overcome the impact of residual current changes when load switching, demonstrating the flexibility of the new principle.

The abnormal behavior of end-users is one of the main causes of abnormal line loss in distribution networks. The integration of a large amount of distributed renewable energy into a low-voltage distribution network (LVDN) complicates line loss analysis. Traceability analysis for abnormal line loss aims to identify the specific end-user responsible for the anomaly in line loss. This paper proposes, for LVDNs with incomplete topology and line parameters, a practical traceability analysis approach using a data-driven power flow model. A data-driven power flow model based on a neural network is first established to capture the power flow mapping relationship without topology and line parameter information. A backpropagation algorithm is then presented to correct the actual power consumption data according to the measured voltage data. By comparing actual power consumption data with measured power data, users with abnormal behavior can be accurately identified and tracked. Finally, the effectiveness of the proposed approach is verified by actual data.

Precisely identifying the household-transformer relationship is of significant importance for both the stability of the power system and the quality of customer electricity consumption. However, the complex network structures and frequent reconfigurations may lead to inaccurate records of household-transformer relationships. In this paper, a novel data-driven similarity assessment solution is proposed to enhance the accuracy and scalability of identifying household-transformer relationships. Initially, a data processing method based on dynamic temporal regularization with sliding windows is employed to optimize dataset quality as well as enhance the efficiency of data processing. Then, a two-stage solution is proposed for identifying the household-transformer relationship. The first stage involves initial normalized clustering based on the basic information of power distribution substations, while the second stage assesses the similarity between households and transformer operational states based on Hausdorff distance. The superior performance of the proposed method is extensively assessed through real historical datasets, compared to benchmarks.

The long-term three-phase imbalance in distribution systems can lead to increased energy losses during transmission, reduced efficiency in energy utilization, and have serious implications for power supply security, power quality, and economic operations of the power system. The automatic load commutation device is an automatic device designed to address the issue of three-phase load imbalances in low-voltage distribution networks. By adjusting the phase of users without interrupting the power supply, this automatic device evenly distributes the load across all phases and effectively resolves the three-phase imbalance problem. Therefore, aiming at the above issue, a robust optimization method is adopted to address the control problem of automatic load commutation devices for a low-voltage distribution network. First, using historical data from photovoltaic and wind power generation systems as well as user load, a forecast analysis is conducted and uncertainty models for renewable energy and load demand are established. Then, a robust control strategy for the automatic load commutation device is proposed, which considers uncertainties of both source and load using the robust optimization method. The proposed model is then linearized using second-order cone technology and strong dual theory, and the column-and-constrained generation (C&CG) algorithm is employed to solve the problem iteratively. Finally, a modified IEEE 33-bus system is taken to verify the effectiveness of the proposed strategy. The simulation results show that the proposed load commutation device robust control model can enhance the ability of the distribution network to respond to load demand and renewable energy fluctuations while ensuring the economic operation of the distribution network. In addition, by adjusting the deviation amount and uncertainty parameters of load demand and renewable energy, a good balance between the robustness and economy of the proposed model can be achieved. [ABSTRACT FROM AUTHOR]

With the gradual advancement towards the goal of carbon neutrality, photovoltaic power generation, as a relatively mature zero-carbon power technology, will be connected to the grid in an increasing proportion. A voltage control strategy, involving distributed energy storage, is proposed in order to solve the voltage deviation problem caused by the high proportion of PV connected to the low voltage distribution network (LVDN). A voltage calculation method of the LVDN node with a high proportion of PV is proposed. According to the voltage distribution of the LVDN nodes running throughout the day, fuzzy cluster analysis was used to partition the nodes in the LVDN. A two-objective mathematical model for optimizing distributed energy storage in a partition was constructed, and a particle swarm optimization algorithm was used to solve the model. A voltage control strategy based on sensitivity analysis is proposed, and a mathematical model is established to analyze the relationship between the node current increment and the node off-limit voltage. The mathematical relationship among the node off-limit voltage, node off-limit power and energy storage exchange power is derived, and the voltage deviation of the distribution network can be suppressed by adjusting the injected power of the distributed energy storage. Through case analysis, it is verified that the proposed voltage control strategy can reduce the voltage deviation of the distribution network nodes, effectively solve the problem of the distribution network voltage deviation, and reduce the active power loss. [ABSTRACT FROM AUTHOR]

In order to form a collaborative operation between village power grid and the end users, a two-stage management strategy is proposed where residential photovoltaic power systems have been linked to the distribution network in a rural area. At stage one, a regulation method considering active and reactive power is formed with a purpose of maintaining power grid within a security domain in operation; at stage two, to maximize the benefits of both the grid and its costumers, a “grid-user” interactive strategy is established so as to allow both the grid and users have opportunities and privileges equally. At the second stage, the power grid takes economics as an objective while delivering the extra benefits to the user as an incentive reward to improve the costumers income while avoiding extra resource investment to reach the same purpose. The test results show that the proposed strategy can effectively ensure the security of the network while improving the economy of the network operator and users.

A photovoltaic (PV)-rich low-voltage (LV) distribution network poses a limit on the export power of PVs due to the voltage magnitude constraints. By defining a customer export limit, switching off the PV inverters can be avoided, and thus reducing power curtailment. Based on this, this paper proposes a mixed-integer nonlinear programming (MINLP) model to define such optimal customer export. The MINLP model aims to minimize the total PV power curtailment while considering the technical operation of the distribution network. First, a nonlinear mathematical formulation is presented. Then, a new set of linearizations approximating the Euclidean norm is introduced to turn the MINLP model into an MILP formulation that can be solved with reasonable computational effort. An extension to consider multiple stochastic scenarios is also presented. The proposed model has been tested in a real LV distribution network using smart meter measurements and irradiance profiles from a case study in the Netherlands. To assess the quality of the solution provided by the proposed MILP model, Monte Carlo simulations are executed in OpenDSS, while an error assessment between the original MINLP and the approximated MILP model has been conducted.

Building a robust and capable village power networks plays an important role for responding to national strategic goals of “village revitalization”, “peak carbon dioxide emission” and “carbon neutrality”. At present, user-side distributed photovoltaic (PV) power is encouraged to be implemented in rural areas in China which brings about tremendous pressure to village power grids. Those power grids are already under vulnerable and inferior conditions in reality. Since PV power systems featuring volatility and randomness are commonly implemented in different lines across low-voltage distribution networks (LVDN) in village power networks in Chinese rural area, power imbalance affects the reliability of power supply. There is an urgent need for developing adaptive control strategies for managing this kind of distributed PV systems. This paper firstly studies the topology of a three-phase-four-line LVDN with regulated parameters. Afterwards, by analyzing the operational costs of the power grid and equipment reliability, a two-stage control scheme is proposed. Furthermore, the imbalance of three phase power systems, network losses, transformer losses are considered to form a multi-objective optimization problem which is solved by tuning transformer voltage, reactive power from PV inverters, energy storage system and demand response schemes. An actual LVDN and associated parameters from a real power distribution networks in Bijie region in Guizhou province, China are used in this study. The effectiveness of the proposed method is tested where 32.1% of power loss is reduced with three-phase imbalance being obviously mitigated accordingly.

The large-scale access of distributed power puts forward higher requirements for the monitoring of the distribution networks, and the topology identification of low-voltage power lines can effectively promote the integration of monitoring data and the distribution network information, effectively realizing the rapid identification of faults and ensuring the safety of users. In this paper, the method of graph theory was used to simplify the analysis of low-voltage lines, and the full topology identification strategy was proposed. Based on IEC 61850 SCL topology configuration information, line topology identification within the region was realized, and the correlation between regions was determined by the injection method. According to the configuration information, regional association information, and user's collection information, the low-voltage station area line topology was divided into known regional topology and unknown regional topology. Aiming for the identification of line topology in the unknown region, according to the similarity of voltage fluctuations over short electrical distances, clustering analysis of user's voltage data in the unknown region was carried out based on the k-means clustering algorithm. The test results showed that this scheme can realize the identification of topology in the region. [ABSTRACT FROM AUTHOR]

Modern solutions for precise fault localisation in Low Voltage (LV) Distribution Networks (DNs) often rely on costly tools such as the micro-Phasor Measurement Unit (μPMU), which is potentially impractical for the large number of nodes in LVDNs. This paper introduces a novel fault detection technique using a distribution network digital twin without the use of μPMUs. The Digital Twin (DT) integrates data from Smart Meters (SMs) and network topology to create an accurate replica. In using SM voltage-magnitude readings, the pre-built twin compiles a database of fault scenarios and matches them with their unique voltage fingerprints. However, this SM-based voltage-only approach shows only a 70.7% accuracy in classifying fault type and location. Therefore, this research suggests using the cables’ Currents Symmetrical Component (CSC). Since SMs do not provide direct current data, a Machine Learning (ML)-based regression method is proposed to estimate the cables’ currents in the DT. Validation is performed on a 41-node LV distribution feeder in the Scottish network provided by the industry partner Scottish Power Energy Networks (SPEN). The results show that the current estimation regressor significantly improves fault localisation and identification accuracy to 95.77%. This validates the crucial role of a DT in distribution networks, thus enabling highly accurate fault detection when using SM voltage-only data, with further refinements being conducted through estimations of CSC. The proposed DT offers automated fault detection, thus enhancing customer connectivity and maintenance team dispatch efficiency without the need for additional expensive μPMU on a densely-noded distribution network.

With the intensive integration of photovoltaic (PV) sources into the low-voltage distribution networks (LVDN), the nodal voltage limit violations and fluctuation problem cause concerns on the safety operation of a power system. The intermittent, stochastic, and fluctuating characteristics of PV output power leads to the frequent and fast fluctuation of nodal voltages. To address the voltage limit violation and fluctuation problem, this paper proposes a distributed nodal voltage regulation method based on photovoltaic reactive power and on-load tap changer transformers (OLTC). Using the local Q/V (Volt/Var) feedback controller derived from the grid sensitivity matrix, the voltage magnitude information is adopted to adjust the output of PV systems. Moreover, in order to share the burden of voltage regulation among distributed PV systems, a weighted distributed reactive power sharing algorithm is designed to achieve the voltage regulation according to the rated reactive power. Theoretical analysis is provided to show the convergence of the proposed algorithm. Additionally, the coordination strategy for distributed PV systems and OLTC is provided to reduce the reactive power outputs of PV systems. Five simulation case studies are designed to show the effectiveness of the proposed voltage regulation strategy, where the voltage regulation and proportional reactive power sharing can be achieved simultaneously. [ABSTRACT FROM AUTHOR]

• Proposing a recognition scheme based on the combination of IEC 61850 and K-means. • Propose a region delineation method that can identify only unknown regions. • Propose a configuration method for phase-line relationships. • Improving k-means clustering accuracy. • Identify only unknown areas to avoid wasting resources. Accurate phase line topology identification of LV lines facilitates the handling of line faults, which is of great significance for the safe and stable operation of distribution networks. In this paper, we model and analyze the LV lines based on graph theory, and propose a phase line relationship identification scheme based on the combination of HPLC and k-means clustering for the problem of missing or dynamically changing phase line relationships of LV lines in the station area. Aiming at the known phase-line relationship in the station area such as distribution panel, two phase-line configuration schemes, Terminal extension and new phase-line description logical node PPLD, are proposed. The area is divided by combining the configuration information of IEC 61850 SCL and the information of metering automation system. If HPLC is configured in the unknown region, HPLC is used to identify the phase line relationship. If HPLC is not configured, k-means based algorithm is used for correlation analysis between voltage measurements. Example results from a place in Shandong show that the accuracy of the method proposed in this paper is 93.96 % on average, which is higher compared to the existing methods. Significance: Accurate identification of phase-line relationships in LV lines is essential for optimising load through phase-change switches. In this study, we propose a novel phase line relationship identification scheme that combines HPLC and k-means algorithm based on the existing equipment in the station area. Leveraging the known topology within the existing equipment (LTU, switchboard, etc.) in the station area, we introduce two phase line configuration schemes, Terminal extension (extending the known phase line relationship) and the new phase line description logical node PPLD (a novel approach for configuring known phase line relationships). Additionally, we suggest a method of regional division to recognise phase line relationships in unknown regions. If HPLC is configured, it is used for phase line identification; otherwise, a k-means based algorithm analyses voltage measurements. By tailoring identification schemes to the actual characteristics of the station area, we significantly reduce the amount of data to be identified, improving accuracy and resource efficiency. [Display omitted] [ABSTRACT FROM AUTHOR]

Accurate user-transformer connectivity relationship (UTCR) plays a key role in fine management of low-voltage distribution network (LVDN) i.e., load expansion, line loss management, and electrical service restoration after outage. Limited data and low discriminability and noise in data increase the difficulty to identify UTCR for the existing data analytics methods. To overcome these hurdles, this paper proposes a novel UTCR algorithm which combining the data preprocessing with multi-dimensional priori knowledge based on voltage characteristics in LVDN. Firstly, the prior knowledge related to UTCR are refined on account of voltage correlation characteristics of users at different locations to provide theoretical foundation. Then, Z-score and principal component analysis are combined to standardize and extract features from the original voltage data to magnify the differences between data and reduce the impact of data noise. Further, on the basis of the prior knowledge of voltage correlation characteristics, a knowledge-driven identification model is proposed to identify users with wrong UTCR and their real UTCR. Finally, the performance of the proposed algorithm is verified on simulated LVNDs. The comparison analysis between the proposed method and other published methods and the impact of the number of principal components on the identification accuracy are also investigated. The results indicate that the proposed method achieves higher recognition accuracy than other published methods with low discriminability and noise in data.

Maintaining and monitoring low-voltage overhead power lines are of great importance because such lines are the key link between the power system and low-voltage power users. At present, few networks can be detected accurately on intelligent edge identification devices because of the complex backgrounds and limited characteristics in unmanned aerial vehicle images as well as the low computing abilities of hardware. In order to give consideration to accuracy and speed, a novel power line detection method was proposed, denoted by Gabor-YOLONet, used for intelligent edge identification devices available to UAV. Unlike existing methods, the proposed method uses the Gabor algorithm to extract foreground of power lines from cluttered backgrounds automatically and predict power lines and their auxiliary targets such as insulators in the foreground scene. In addition, a new inference method was introduced, which can summarize the average location and orientation of auxiliary targets by clustering to verify the rationality of the predicted results for power lines. The experiment results showed that the proposed method had the higher accuracy and consumed less computing resources; compared with other methods, the mAP of identification for power lines was 86.6% and the running time was only 25 ms, with excellent performance on intelligent edge devices.

Due to the lack of data quality in real production environment, the traditional line loss calculation method cannot be applied, thus through the investigation of various information systems' operation in power supply enterprises, a short-term low-voltage distribution network line loss prediction algorithm based on Kmeans-LightGBM is proposed. Operating data quality evaluation system of low-voltage distribution network was set up based on Hadoop platform, the feature dimensions were expanded by feature engineering, then those with no multicollinearity and high correlation with the line loss were selected, data normalization was again performed, Kmeans clustering algorithm was used to cluster the area and then, LightGBM algorithm was used to predict the classes within the area of line loss. Finally, the line loss of the numerical inverse normalization was found and validated with Beijing Power Grid of a low-voltage distribution network. By comparison, the model's prediction accuracy is found to be higher than BPNN, FOA-SVR and traditional LightGBM. [ABSTRACT FROM AUTHOR]

The long-term three-phase imbalance in distribution systems can lead to increased energy losses during transmission, reduced efficiency in energy utilization, and have serious implications for power supply security, power quality, and economic operations of the power system. The automatic load commutation device is an automatic device designed to address the issue of three-phase load imbalances in low-voltage distribution networks. By adjusting the phase of users without interrupting the power supply, this automatic device evenly distributes the load across all phases and effectively resolves the three-phase imbalance problem. Therefore, aiming at the above issue, a robust optimization method is adopted to address the control problem of automatic load commutation devices for a low-voltage distribution network. First, using historical data from photovoltaic and wind power generation systems as well as user load, a forecast analysis is conducted and uncertainty models for renewable energy and load demand are established. Then, a robust control strategy for the automatic load commutation device is proposed, which considers uncertainties of both source and load using the robust optimization method. The proposed model is then linearized using second-order cone technology and strong dual theory, and the column-and-constrained generation (C&CG) algorithm is employed to solve the problem iteratively. Finally, a modified IEEE 33-bus system is taken to verify the effectiveness of the proposed strategy. The simulation results show that the proposed load commutation device robust control model can enhance the ability of the distribution network to respond to load demand and renewable energy fluctuations while ensuring the economic operation of the distribution network. In addition, by adjusting the deviation amount and uncertainty parameters of load demand and renewable energy, a good balance between the robustness and economy of the proposed model can be achieved.

The structure of the low-voltage distribution network often changes. The change of topology will affect fault detection, fault location, line loss calculation, etc. It leads to fault detection error, inaccurate positioning and abnormal line loss calculation. This paper presents a new method to automatically identify the topology of a low-voltage power grid by using the injection current signal. When the disturbance current signal is injected into the low-voltage line, the current upstream of the injection point will change, and the current downstream of the injection point will not be affected. It is proved theoretically by using the superposition principle. With this method, the disturbance current signal can be injected into the line in turn, and the topology can be identified by observing the change of the current in line. The correctness of the method is proved by Matlab simulation and laboratory verification.

This paper proposes a tube model predictive control method considering uncertainties of renewable energy generation in the low-voltage distribution network. For relatively low dependence on communication networks, a decentralized voltage control framework is urgently needed to be developed for voltage regulation in the low-voltage distribution network. Opposed to the traditional voltage control techniques, a novel decentralized tube model predictive voltage control (TMPC) strategy is proposed. First, this strategy entails the formulation of a discrete model encompassing distributed photovoltaic units and energy storage systems intertwined with the distribution grid model based on voltage sensitivity. Then, considering the worst case of voltage deviations caused by uncertain and stochastic power fluctuation, we specifically discuss a robust positively invariant to determine the control section of controllers. Further, the strategy employs tube model predictive controllers encompassing nominal and auxiliary controllers in distribution areas. The nominal controller generates a central path for different voltage operating points of each distribution area, and the auxiliary controller makes all trajectories lie in a bounded neighborhood called a tube. The effectiveness of the proposed control strategy is investigated on a test system containing renewable energy resources. • A novel decentralized control structure is proposed to serve voltage control for distribution networks, which can cooperate for accurate voltage control with random and fluctuant renewable energy. • In the nominal controller, operation points of DGs are generated considering the generation and load scheduling. In the auxiliary controller, detailed control objectives and constraints are proposed for suppressing disturbances of DGs. • To balance the conservation and performance, a worst-case-based robust positively invariant is developed to quantify the range of tubes to cope with the randomness. [ABSTRACT FROM AUTHOR]

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There is a need for induction filter technology in the field of low-voltage distribution networks. This paper proposes an induction filter distribution transformer with a unique Dd0yn11 three-winding configuration. This approach allows for the control of load side harmonics in low-voltage distribution networks. At present, the induction filter technology proposed in the known literature is mostly used in the high-voltage power system, but there is still no research on the application of induction filter technology to the distribution transformer with Dyn11 under the low-voltage distribution network. In this paper, the wiring scheme of inductive filter distribution transformer and filter system in low-voltage distribution network is established firstly, then the mathematical relationship between the harmonic current generated by the load side and the current on the grid side is derived according to the winding model, and it can be deduced that the induction filter distribution transformer with its filtering system has good filtering performance and reactive power compensation characteristics. Finally, the experiment simulates the three-phase harmonic load, detects the current waveform and other parameters of the actual grid side of the new transformer, verifies the correctness of the theoretical analysis. The induction filter distribution transformer proposed in this paper provides a new solution for harmonic suppression and reactive power compensation in low-voltage distribution network, which fills the vacancy of the application of induction filter technology in low-voltage distribution network, This paper reveals that the inductive filter technique has a good filtering performance when applied to low-voltage distribution network. And it provides a reference for further research on improving power grid efficiency, saving energy cost and improving transformer electromagnetic environment under low-voltage distribution network.

In recent years, distributed photovoltaic facilities and distributed energy storage are widely connected to a low-voltage distribution network, which makes the topology of the low-voltage distribution network change faster, and the online identification and monitoring of the impedance of each branch are becoming more difficult. With the widespread installation of new distribution automation terminals and smart electricity meters, the technical gap of power data collection has been filled, the state perception ability of the distribution network has been improved, and the data support for further exploration of the topology and impedance of the distribution network has been provided. Therefore, the low-voltage distribution network topology and impedance identification method based on smart meter measurements, the physical topology model of distribution transformer-branch-meter box-user and the impedance exclusion model are proposed. Line loss and branch node power flow are calculated on the basis of the two models. Finally, the low-voltage distribution network topology recognition algorithm and power flow partition backtracking impedance estimation technology are developed based on the data mining method of distribution transformer terminal and smart meter. The example results show that the physical topology model of distribution transformer-branch-meter box-user and the impedance exclusion model proposed in this study can effectively identify the branch topology and impedance distribution in the low-voltage distribution network with a large amount of measurement information, realize the online perception of the distribution network, and provide the basis for the fine management of the power grid and line loss.

Low‐voltage power line carrier network has the advantages of wide distribution and easy access. However, the dynamic topology of intelligent distribution network directly affects the reliability of low‐voltage power line carrier communication. In view of this, this article first analyzes the topological structure and business requirements of intelligent distribution network communication system. Then, based on the traditional power line carrier ant colony routing algorithm, the state transition factor and logistic function in the ant colony algorithm are combined to optimize. An ant colony optimization algorithm for power line carrier based on logistic function is proposed. It improves the reliability of the carrier communication route. It is of great significance to the development of power line carrier communication in smart grid. [ABSTRACT FROM AUTHOR]

The large-scale access of distributed power puts forward higher requirements for the monitoring of the distribution networks, and the topology identification of low-voltage power lines can effectively promote the integration of monitoring data and the distribution network information, effectively realizing the rapid identification of faults and ensuring the safety of users. In this paper, the method of graph theory was used to simplify the analysis of low-voltage lines, and the full topology identification strategy was proposed. Based on IEC 61850 SCL topology configuration information, line topology identification within the region was realized, and the correlation between regions was determined by the injection method. According to the configuration information, regional association information, and user’s collection information, the low-voltage station area line topology was divided into known regional topology and unknown regional topology. Aiming for the identification of line topology in the unknown region, according to the similarity of voltage fluctuations over short electrical distances, clustering analysis of user’s voltage data in the unknown region was carried out based on the k-means clustering algorithm. The test results showed that this scheme can realize the identification of topology in the region.

For low-voltage distribution networks (LVDNs), accurate models depicting network and phase connectivity are crucial to the analysis, planning, and operation of these networks. However, phase connectivity data in the LVDN are usually incorrect or missing. Wrong or incomplete phase information collected could lead to unbalanced operation of three-phase distribution systems and increased power loss. Based on the advanced measurement infrastructure (AMI) in the development of smart grids, in this study, a novel data-driven phase identification algorithm is proposed. Firstly, the method involves extracting features from voltage–time matrices using a non-linear dimension reduction algorithm. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to divide customers into clusters with arbitrary shape. Finally, the algorithms were tested with the IEEE European Low Voltage Test Feeder of the IEEE PES AMPS DSAS Test Feeder working group. The results showed an accuracy of over 90% for the method.

With the intensive integration of photovoltaic (PV) sources into the low-voltage distribution networks (LVDN), the nodal voltage limit violations and fluctuation problem cause concerns on the safety operation of a power system. The intermittent, stochastic, and fluctuating characteristics of PV output power leads to the frequent and fast fluctuation of nodal voltages. To address the voltage limit violation and fluctuation problem, this paper proposes a distributed nodal voltage regulation method based on photovoltaic reactive power and on-load tap changer transformers (OLTC). Using the local Q/V (Volt/Var) feedback controller derived from the grid sensitivity matrix, the voltage magnitude information is adopted to adjust the output of PV systems. Moreover, in order to share the burden of voltage regulation among distributed PV systems, a weighted distributed reactive power sharing algorithm is designed to achieve the voltage regulation according to the rated reactive power. Theoretical analysis is provided to show the convergence of the proposed algorithm. Additionally, the coordination strategy for distributed PV systems and OLTC is provided to reduce the reactive power outputs of PV systems. Five simulation case studies are designed to show the effectiveness of the proposed voltage regulation strategy, where the voltage regulation and proportional reactive power sharing can be achieved simultaneously.

As residential PV generation penetrates in the low‐voltage distribution network (LVDN), the unbalance issue may be intensified due to the asymmetry of generations/loads in different phases. Phase‐reconfiguration device (PRD), which can reconfigure connected phases of residential customers, provides an effective method to address this issue. Noting that although the benefit brought by PRDs can vary if they are placed at different locations in the network, little literature has been reported on this topic. To bridge the research gap, this paper presents a novel method to optimally place PRDs in an LVDN aiming at minimizing the power unbalance running through the distribution transformer. The problem considers both installation, operational constraints, and boils down to a challenging mixed‐integer non‐convex programming problem, which is then reformulated as an efficient solvable mixed‐integer linear programming problem. Moreover, operational constraints are relaxed with slack variables penalized in the objective function, which makes sure a feasible solution is always available without or with minimal operational violations. Case studies based on a modified IEEE system and a practical system in Australia demonstrate that an efficient strategy can be provided to address the unbalance issue while improving the network's power supply qualities. [ABSTRACT FROM AUTHOR]