Power distribution network twinborn management method and system, storage medium and program product

Abstract

The invention provides a power distribution network twinning management method and system, a storage medium and a program product, and relates to the technical field of power digital twinning. The method comprises the steps of obtaining operation data of a power distribution network system, and when the operation data represents that the power distribution network system has power quality disturbance, performing power quality disturbance source tracing according to the operation data based on a disturbance source equivalent model to obtain a power quality disturbance source positioning result; according to the electric energy quality disturbance source positioning result, determining a target treatment measure for the electric energy quality disturbance source positioning result; performing panoramic simulation deduction on the electric energy quality of the target power distribution network region based on the target treatment measure to obtain an electric energy quality treatment simulation result; when the electric energy quality management simulation result meets a preset requirement, applying a target management measure in the target power distribution network area to carry out electric energy quality management; and visually displaying an electric energy quality comparison result before and after the treatment of the target power distribution network region. The method is used for improving power quality evaluation accuracy and traceability accuracy.

Description

Technical Field

[0001] This application relates to the field of power digital twin technology, and in particular to a distribution network digital twin management method, system, storage medium and program product. Background Technology

[0002] With the energy transition and the development of smart grids, power distribution systems are undergoing profound changes. These changes include the large-scale integration of distributed energy resources, the diversified growth of electricity demand, and more stringent requirements for power supply reliability, all driving the evolution of power distribution systems towards intelligence and efficiency. Against this backdrop, distribution network twin systems have emerged. By constructing a virtual model that maps in real-time to the physical distribution network system, these systems enable the monitoring, analysis, and prediction of the distribution network's operational status, providing strong support for optimizing and controlling the physical distribution network system.

[0003] Traditional distribution network twin systems are primarily designed for passive distribution network systems and are effective in assessing power quality in these systems. However, the uncertainties brought about by the large-scale integration of new energy sources make it difficult for traditional distribution network twin systems to meet the power quality assessment needs of active distribution network systems, resulting in problems such as low assessment accuracy and low source tracing accuracy. Summary of the Invention

[0004] This application provides a distribution network twin management method, system, storage medium, and program product to achieve high accuracy in power quality assessment and source tracing.

[0005] Firstly, this application provides a distribution network twin management method, including:

[0006] Acquire operational data of the distribution network system, which includes energy nodes, grid nodes, load nodes, and energy storage nodes;

[0007] When the operational data characterizes the existence of power quality disturbances in the distribution network system, the source of power quality disturbances is traced based on the equivalent model of the disturbance source and the operational data is used to obtain the source location results of the power quality disturbances. The equivalent model of the disturbance source is constructed based on the multi-timescale electromagnetic transient model corresponding to the distribution network system.

[0008] Based on the results of locating the sources of power quality disturbances, target mitigation measures are determined.

[0009] Based on the target governance measures, a panoramic simulation of the power quality of the target distribution network area is carried out to obtain the power quality governance simulation results corresponding to the target governance measures.

[0010] When the simulation results of power quality management meet the preset requirements, the target management measures are applied to the target distribution network area for power quality management.

[0011] The results of power quality comparison before and after the treatment of the target distribution network area are visualized.

[0012] In one possible implementation, based on an equivalent model of the disturbance source, the source of power quality disturbance is traced according to operational data to obtain the power quality disturbance source location results, including:

[0013] Based on the equivalent model of the disturbance source and the preset characteristic parameter library, the disturbance characteristics of the running data are determined.

[0014] An impedance matching algorithm is used to determine the characteristic impedance of each node in the distribution network based on operational data, and to identify multiple candidate disturbance sources.

[0015] Based on the perturbation characteristics, the target perturbation source is determined from multiple candidate perturbation sources using a Bayesian inference algorithm.

[0016] The output includes the power quality disturbance source location results, which contain the target disturbance source.

[0017] In one possible implementation, after identifying the target disturbance source, the method further includes:

[0018] Based on a multi-dimensional evaluation index system, the values ​​of each index corresponding to the target disturbance source are determined according to the operational data.

[0019] Based on the values ​​of each indicator and the set weights of the indicators, the power quality level corresponding to the area where the target disturbance source is located is determined.

[0020] In one possible implementation, based on the power quality disturbance source location results, target mitigation measures are determined targeting the power quality disturbance source location results, including:

[0021] Based on the mapping relationship between the power quality disturbance source location results and the mitigation measures, the target mitigation measures corresponding to the power quality disturbance source location results are determined.

[0022] In one possible implementation, based on the governance measures, a panoramic simulation of the power quality of the target distribution network area is performed to obtain the power quality governance simulation results corresponding to the target governance measures, including:

[0023] Based on a pre-defined multi-scenario library and target governance measures, a panoramic simulation of power quality in the target distribution network area is performed using the full electromagnetic transient simulation method. The simulation results of power quality governance corresponding to the target governance measures under different scenarios are obtained. The power quality governance simulation results include at least the power quality level, simulated power quality data, and the index values ​​corresponding to each index in the multi-dimensional evaluation index system.

[0024] In one possible implementation, the distribution network twin management method further includes:

[0025] Obtain real-time power quality data of the target distribution network area after governance;

[0026] Based on the root mean square error of real-time power quality data and simulated power quality data, the parameters of the multi-timescale electromagnetic transient model are corrected using the particle swarm optimization algorithm.

[0027] Secondly, this application provides a distribution network twin management system, comprising:

[0028] The acquisition module is used to acquire the operating data of the distribution network system, which includes energy nodes, grid nodes, load nodes and energy storage nodes.

[0029] The source tracing module is used to trace the source of power quality disturbances in the distribution network system based on the disturbance source equivalent model and the operating data when the operating data characterizes the presence of power quality disturbances. The disturbance source equivalent model is constructed based on the multi-timescale electromagnetic transient model corresponding to the distribution network system.

[0030] The processing module is used to determine the target mitigation measures based on the power quality disturbance source location results.

[0031] The panoramic simulation and deduction module is used to perform panoramic simulation and deduction of the power quality of the target distribution network area based on the target governance measures, and obtain the power quality governance simulation results corresponding to the target governance measures;

[0032] The governance module is used to apply target governance measures to power quality governance in the target distribution network area when the power quality governance simulation results meet the preset requirements.

[0033] The display module is used to visually display the power quality comparison results of the target distribution network area before and after the treatment.

[0034] In one possible implementation, the source tracing module is specifically used to: determine the disturbance characteristics of the operating data based on the disturbance source equivalent model and a preset characteristic parameter library; use an impedance matching algorithm to determine the characteristic impedance of each node in the distribution network based on the operating data, and determine multiple candidate disturbance sources; based on the disturbance characteristics, determine the target disturbance source from the multiple candidate disturbance sources using a Bayesian inference algorithm; and output the power quality disturbance source location result containing the target disturbance source.

[0035] In one possible implementation, the processing module is further configured to: determine the values ​​of each indicator corresponding to the target disturbance source based on the multi-dimensional evaluation index system and the operating data; and determine the power quality level corresponding to the area where the target disturbance source is located based on the values ​​of each indicator and the set indicator weights.

[0036] In one possible implementation, the processing module is further configured to: determine the target governance measures corresponding to the power quality disturbance source location results based on the mapping relationship between the power quality disturbance source location results and the governance measures.

[0037] In one possible implementation, the panoramic simulation and deduction module is specifically used to: based on a preset multi-scenario library and target governance measures, apply the full electromagnetic transient simulation method to perform panoramic simulation of power quality in the target distribution network area, and obtain power quality governance simulation results corresponding to the target governance measures under different scenarios. The power quality governance simulation results include at least the power quality level, simulated power quality data, and the index values ​​corresponding to each index in the multi-dimensional evaluation index system.

[0038] In one possible implementation, the processing module is further configured to: acquire real-time power quality data of the target distribution network area after governance; and correct the parameters of the multi-timescale electromagnetic transient model based on the root mean square error of the real-time power quality data and the simulated power quality data using a particle swarm optimization algorithm.

[0039] Thirdly, this application provides an electronic device, including: a memory and a processor;

[0040] The memory stores instructions that the computer executes;

[0041] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0042] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible embodiments of the first aspect.

[0043] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0044] The distribution network twin management method, system, storage medium, and program product provided in this application include: acquiring operational data of the distribution network system, which includes energy nodes, grid nodes, load nodes, and energy storage nodes; when the operational data indicates the existence of power quality disturbances in the distribution network system, tracing the power quality disturbance source based on the disturbance source equivalent model and the operational data to obtain the power quality disturbance source location result; the disturbance source equivalent model is constructed based on the multi-timescale electromagnetic transient model corresponding to the distribution network system; based on the power quality disturbance source location result, determining the target mitigation measures; based on the target mitigation measures, performing a panoramic simulation of the power quality in the target distribution network area to obtain the power quality mitigation simulation result corresponding to the target mitigation measures; when the power quality mitigation simulation result meets the preset requirements, applying the target mitigation measures to perform power quality mitigation in the target distribution network area; and visually displaying the power quality comparison results before and after mitigation in the target distribution network area. This application utilizes a multi-timescale electromagnetic transient model to accurately characterize the dynamic characteristics of energy nodes and load nodes, comprehensively describing the complex characteristics of the distribution network system. This provides a reliable foundation for power quality assessment, improving its accuracy. Based on this, it traces the sources of power quality disturbances, enhancing the accuracy of the tracing and improving the dynamic assessment capability of the distribution network system's power quality. Furthermore, it formulates different mitigation measures for different power quality disturbance source locations, enhancing the targeting of these measures. A panoramic simulation of power quality is then conducted to verify the effectiveness of the mitigation measures, improving the efficiency of power quality management in the distribution network system. Thus, it provides a complete solution from power quality problem identification and accurate source tracing to mitigation measure verification and implementation, supporting the reliable operation of new distribution network systems. Attached Figure Description

[0045] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0046] Figure 1 A flowchart illustrating the distribution network twin management method provided in this application embodiment;

[0047] Figure 2 This is a schematic diagram of the structure of the power distribution network twin management system provided in the embodiments of this application;

[0048] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0049] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0050] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0051] Traditional distribution network twin management systems are designed for passive distribution networks. However, with the development of new distribution network systems, which incorporate a large amount of renewable energy such as solar and wind power, the output of these renewable energy sources fluctuates significantly due to weather conditions, leading to voltage fluctuations and frequency deviations. New loads (such as electric vehicle charging stations) generate harmonics during operation, also affecting distribution network stability. Furthermore, traditional distribution network twin management systems rely on offline steady-state data for power quality assessment, making it difficult to accurately trace the source of disturbances. In summary, traditional distribution network twin systems struggle to meet the power quality assessment requirements of active distribution network systems (i.e., new distribution network systems), exhibiting low assessment accuracy and low source tracing accuracy.

[0052] To address the aforementioned technical challenges, this application provides a distribution network twin management method that utilizes a multi-timescale electromagnetic transient model to characterize the dynamic characteristics of energy nodes and load nodes in the distribution network system. Based on this, it employs real-time operational data analysis to address power quality disturbances, simulates corresponding mitigation solutions to verify their effectiveness, and then implements the solutions to achieve precise management of power quality in the new distribution system.

[0053] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0054] Figure 1 This is a flowchart illustrating the distribution network twin management method provided in this application embodiment. The distribution network twin management method provided in this application embodiment is applied to the distribution network twin management system. The distribution network twin management system can be regarded as a virtual mapping of the physical distribution network system. It is used to evaluate, analyze and trace the source of power quality, manage, perform panoramic simulation and deduction, and predict the operation status of the physical distribution network system, supporting the precise planning and management of the new distribution network.

[0055] like Figure 1As shown, the distribution network twin management method includes the following steps:

[0056] S101. Obtain the operation data of the distribution network system, which includes energy nodes, grid nodes, load nodes and energy storage nodes.

[0057] New-type power distribution network systems typically include power generation resources, i.e., energy nodes, such as distributed photovoltaic, wind turbines, thermal power plants, and hydropower plants; grid nodes, such as power distribution equipment, which are responsible for power transmission, dispatch, and distribution; load nodes, such as electric vehicle charging stations, factory electrical equipment, and lighting equipment, which are considered the end users of electrical energy; and energy storage nodes, such as lithium battery, lead-acid battery, and hydrogen energy storage nodes, which are used to store electrical energy and regulate the difference between power supply and demand (e.g., releasing electrical energy during peak load periods to alleviate grid pressure). Therefore, this type of power distribution network topology is also called a new-type power distribution network system with a source-grid-load-storage coordinated model.

[0058] For example, smart measurement terminals (such as synchronous phasor measurement units, smart meters, SVG dynamic reactive power compensation device sensors, power quality analyzers, etc.) are deployed at various monitoring points in the distribution network system to collect operational data from each node in the distribution network system. This operational data is not limited to: distributed photovoltaic systems (e.g., output, illuminance, temperature, voltage, current, harmonics), wind turbines (e.g., wind speed, rotational speed, active power output, reactive power output, voltage, current, harmonics), electric vehicle charging stations (e.g., charging power, charging duration, access time, current, voltage, harmonics), and energy storage devices (e.g., charging and discharging power, SOC state of charge). Furthermore, to make the collected operational data more accurate and cover both transient and steady-state processes of the distribution network system, the sampling frequency can be adjusted to a suitable range, for example, setting the sampling frequency to 10kHz-1MHz to cover both transient and steady-state processes.

[0059] S102. When the operating data characterizes the power quality disturbance in the distribution network system, the power quality disturbance source is traced based on the disturbance source equivalent model and the operating data to obtain the power quality disturbance source location result. The disturbance source equivalent model is constructed based on the multi-timescale electromagnetic transient model corresponding to the distribution network system.

[0060] Among them, the data characteristics of the operational data can reflect whether there are power quality disturbances in the current system. Statistical analysis of the operational data can be performed. For example, a pre-trained deep learning model (built based on long short-term memory network, convolutional neural network or Transformer model) can be used to extract the operational data characteristics and determine the data characteristics of the operational data. The data characteristics can preliminarily characterize the type of power quality disturbance, thereby determining whether there are power quality disturbances in the distribution network system. For example, the data characteristics corresponding to the occurrence of power quality disturbances include voltage drop or rise, voltage interruption, harmonic pollution, three-phase imbalance, and excessive frequency fluctuation rate.

[0061] When power quality disturbances exist in the system, the location of the disturbance source is further analyzed and identified. In this step, the disturbance source equivalent model is constructed based on the power quality generation mechanism and typical forms. The disturbance source equivalent model is designed in the distribution network twin management system, and this disturbance source equivalent model is constructed based on a multi-timescale electromagnetic transient model. The disturbance source equivalent model is used to clarify the physical relationship between the disturbance and the system response. Among them, the electromagnetic transient process of the distribution network involves dynamic behavior at different time scales. When constructing the multi-timescale electromagnetic transient model, for example, for the transient scale (usually at the millisecond level): it involves simulating the switching action of power electronic equipment (such as inverter IGBT switching) and fault impact (such as short circuit), with a time step (e.g., set to 1e-6 seconds); for the dynamic scale (usually at the second to minute level): it involves simulating the output fluctuation of new energy sources and load changes, with a time step (e.g., set to 1 second); for the steady-state scale (usually at the hour level): it involves simulating power balance and voltage distribution, with a time step (e.g., set to 5 minutes).

[0062] The essence of step S102 is to map the complex disturbance behavior of the actual system into a parameterizable equivalent model of the disturbance source by integrating physical modeling and data-driven approaches, and then use the actual operating data to invert the model parameters to locate the disturbance source.

[0063] In one example, based on the identified power quality disturbance type, an equivalent model of the disturbance source matching that type is selected. For instance, the harmonic disturbance type corresponds to the Norton equivalent model (i.e., equivalent to a current source in parallel harmonic impedance), and the voltage drop disturbance type corresponds to the series impedance model (i.e., equivalent to the series impedance from the fault point to the monitoring point, the voltage drop caused by which is directly related to the measured voltage waveform). The operating data is preprocessed, such as time alignment and noise suppression. Using the operating data as input to the disturbance source equivalent model, the parameters of the disturbance source equivalent model are adjusted through an optimization algorithm to minimize the error between the model output and the measured data, thereby determining the location of the disturbance source (e.g., the disturbance source location is electric vehicle charging station No. 3). The optimization algorithm here can be a particle swarm optimization algorithm. Based on the topology of the distribution network system, the influence range of the disturbance source can be further determined (e.g., two downstream transformer substations).

[0064] S103. Based on the results of locating the power quality disturbance sources, determine the target mitigation measures for addressing the power quality disturbance sources.

[0065] The results of power quality disturbance source location are not limited to including the disturbance source name, disturbance type, disturbance source location, and disturbance time. Different disturbance sources have different physical mechanisms, therefore the corresponding mitigation measures will differ depending on the power quality disturbance source location results.

[0066] For example, in some embodiments, based on the power quality disturbance source location results, target governance measures are determined for the power quality disturbance source location results. Specifically, this includes: based on the mapping relationship between the power quality disturbance source location results and governance measures, determining the target governance measures corresponding to the power quality disturbance source location results.

[0067] In one implementation, a mapping table between the location results of power quality disturbance sources and mitigation measures is pre-established and stored in a database, based on expert experience, theoretical research, and historical experience. After the system determines the current location result of the power quality disturbance source, field matching is performed in the database, and the mitigation measure corresponding to the power quality disturbance source location result with the highest matching degree is taken as the target mitigation measure. For example, if the power quality disturbance source location result is "the 5th harmonic current of electric vehicle charging station No. 3 exceeds the standard, affecting two downstream transformer areas," the target mitigation measure is "to install a 5th harmonic filter with a capacitor capacity of 20μF to suppress the 5th harmonic current to within the industry standard."

[0068] In another implementation, a pre-trained governance measure recommendation model is used. This model is obtained by iteratively training the model using historical disturbance source location data and governance measure labels through classification or regression algorithms. This model can then provide external users with the function of extracting features of the current power quality disturbance source location results and automatically recommending the optimal governance measures based on these features.

[0069] In response to the problem in related technologies that the effectiveness and feasibility of various power quality improvement schemes cannot be evaluated and verified when conducting power quality analysis of distribution network systems, this application embodiment, after determining the target governance measures, conducts full electromagnetic transient simulation analysis of the new distribution network system based on the digital twin model of the source-grid-load-storage equipment of the new distribution network system, and uses panoramic simulation and deduction technology to evaluate and verify the effectiveness and feasibility of the power quality simulation improvement scheme, see S104.

[0070] S104. Based on the target governance measures, a panoramic simulation of the power quality of the target distribution network area is performed to obtain the power quality governance simulation results corresponding to the target governance measures.

[0071] The digital twin model of the power generation, grid, load, and storage equipment in the new distribution network system is a multi-timescale electromagnetic transient model, which can accurately characterize the dynamic characteristics of energy nodes and load nodes and comprehensively describe the complex characteristics of the distribution network system. Panoramic simulation and deduction, through a highly realistic virtual environment (covering transient, dynamic, and steady-state scenarios), simulates the dynamic behavior and potential risks of complex systems in real physical environments.

[0072] Taking the target mitigation measure as "installing a 5th harmonic filter with a capacitance of 20μF to suppress the 5th harmonic current to within industry standards" as an example, this target mitigation measure is simulated on a multi-timescale electromagnetic transient model to obtain the simulation results of the improved power quality. Optionally, parallel acceleration techniques (such as using GPU parallel computing) can be combined to shorten the simulation time.

[0073] For example, regarding harmonic pollution, before the power quality improvement project, "the fifth harmonic current of electric vehicle charging station No. 3 exceeded the standard." After the power quality improvement project, "the fifth harmonic current of electric vehicle charging station No. 3 was reduced to 3A, and the total harmonic distortion rate was reduced from 5% to 2%", meeting the industry standard requirements.

[0074] Optionally, multiple dimensions can be integrated to generate power quality management simulation results. For example, power quality management simulation results include effectiveness (the 5th harmonic current of electric vehicle charging station No. 3 is reduced to 3A, and the total harmonic distortion rate is reduced from 5% to 2%), feasibility (cost-effectiveness), and dynamic simulation trajectory (such as the wave current recovery curve within 0~10 seconds).

[0075] S105. When the simulation results of power quality management meet the preset requirements, apply the target management measures to power quality management in the target distribution network area.

[0076] The target distribution network area includes the area where the disturbance source of the power quality disturbance is located.

[0077] The effectiveness of target governance measures can be quickly verified in a virtual environment (distribution network twin management system). When the power quality governance simulation results meet the preset requirements, the target governance measures can be applied in the real physical environment, thereby reducing the cost and time of on-site testing and improving governance efficiency.

[0078] For example, preset requirements can be based on industry standards, user needs, and business requirements. For instance, the total harmonic distortion (THD) after treatment should be less than 30%, the voltage deviation should be less than 5%, and the 5th harmonic current should be less than 2%.

[0079] S106. Visualize the comparison results of power quality before and after the treatment of the target distribution network area.

[0080] For example, the power distribution network twin management system provides users with a front-end interface for power quality simulation and evaluation, supports real-time information interaction, and can display the operating data, simulation data, historical data, asset data, and business transmission data of the power distribution network system.

[0081] The comparison results of power quality before and after the treatment can be visually displayed in various forms such as charts and reports, showing the changes in the system's operating status. Users can also click to view details.

[0082] This application's embodiments utilize a multi-timescale electromagnetic transient model to accurately characterize the dynamic characteristics of energy nodes and load nodes, comprehensively describing the complex characteristics of the distribution network system. This provides a reliable foundation for power quality assessment, improving its accuracy. Based on this, power quality disturbance sources are traced. Since the disturbance source equivalent model can accurately describe the relationship between the disturbance source and the system response, it provides a reliable theoretical basis for source tracing analysis, thereby improving tracing accuracy and enhancing the dynamic assessment capability of the distribution network system's power quality. Furthermore, different mitigation measures are formulated for different power quality disturbance source locations, enhancing the targeting of these measures. A panoramic simulation of power quality is then conducted to verify the effectiveness of the mitigation measures, improving the efficiency of power quality management in the distribution network system. This provides a complete solution from power quality problem identification, accurate source tracing, mitigation measure verification to implementation, supporting the reliable operation of new distribution network systems.

[0083] Optionally, the distribution network twin management method provided in this application embodiment can also be used in the demonstration application of intelligent power quality assessment of new distribution network systems. For example, in the scenario where industrial parks or transformer substations with high penetration rates of high energy consumption and high pollution are used as demonstration sites, a digital twin model (multi-timescale electromagnetic transient model) of the industrial park or transformer substation can be established to carry out power quality assessment, accurate source tracing, and verification of the effectiveness and feasibility of governance and improvement schemes.

[0084] In some embodiments, based on the equivalent model of the disturbance source, the source of power quality disturbance is traced according to the operating data to obtain the power quality disturbance source location result, which may include:

[0085] S1021. Based on the equivalent model of the disturbance source and the preset characteristic parameter library, determine the disturbance characteristics of the running data.

[0086] The characteristic parameter library contains attribute parameters and operational characteristic parameters of nodes in the distribution network system. Attribute parameters include, for example, distributed photovoltaic (250W per panel, 38.5V open-circuit voltage, reference impedance, etc.), wind turbines (2MW rated power, 25m / s cut-off wind speed, reference impedance, etc.), and electric vehicle charging stations (60kW charging power, 5Ω harmonic impedance). Optionally, it uses a relational database MySQL for storage and supports indexing by "node type-voltage level-operation mode". Operational characteristic parameters include, for example, the photovoltaic output temperature coefficient (-0.35% / ℃) and wind turbine power curves (wind speed-output fitting function). Optionally, it uses a time-series database InfluxDB for storage and supports querying by time dimension (e.g., typical parameters for summer / winter). The characteristic parameter library is used to support rapid matching and inversion of disturbance sources.

[0087] The disturbance source equivalent model processes the operating data and determines the disturbance characteristics by combining the preset characteristic parameter library. The disturbance characteristics describe the power quality problems reflected by the operating data, such as "the third harmonic mainly comes from the three-phase rectifier equipment" and "the voltage sag is caused by the operation of the wind turbine converter switch". It is used to represent the mapping relationship between the disturbance type and the node, and provides a basis for locating the target disturbance source.

[0088] S1022. Using an impedance matching algorithm, the characteristic impedance of each node in the distribution network is determined based on the operating data, and multiple candidate disturbance sources are identified.

[0089] For example, the running data includes voltage and current. Based on the running data, the characteristic impedance of each node is calculated. The characteristic impedance can be expressed as... , For voltage, For each node, if the absolute difference between the characteristic impedance and the reference impedance (impedance during normal operation) is greater than a set threshold (e.g., 20%), the node is identified as a candidate disturbance source.

[0090] S1023. Based on the disturbance characteristics, determine the target disturbance source from multiple candidate disturbance sources using the Bayesian inference algorithm.

[0091] The Bayesian inference algorithm is used to fuse the operational data of multiple candidate disturbance sources, calculate the disturbance probability of each candidate disturbance source, and determine the candidate disturbance source, i.e. the real disturbance source, as the disturbance probability is greater than the threshold (e.g., 80%) and the disturbance characteristics are met.

[0092] S1024. Output the power quality disturbance source location results, including the target disturbance source.

[0093] The power quality disturbance source location results include the physical location of the target disturbance source (e.g., the disturbance source is located at electric vehicle charging station No. 3), and can further determine the influence range of the target disturbance source (such as two downstream transformer substations) based on the topology of the distribution network system.

[0094] In some embodiments, after identifying the target disturbance source, the method further includes: determining the values ​​of each indicator corresponding to the target disturbance source based on a multi-dimensional evaluation index system and operational data; and determining the power quality level corresponding to the area where the target disturbance source is located based on each indicator value and the set indicator weights.

[0095] The multi-dimensional evaluation index system includes multi-level indicators, including voltage deviation, harmonic distortion rate, voltage stability margin, and economic cost (specifically, network loss rate).

[0096] Based on the operational data corresponding to the target disturbance source, the value of each indicator is calculated separately. For example, a multi-level indicator quantification model can be pre-set, which integrates the calculation formulas of different indicators. Through this quantification model, the operational data can be extracted and transformed into different indicator values.

[0097] Each indicator has a corresponding weight, which is determined by expert scoring. The values ​​of each indicator are weighted and summed to obtain the overall power quality score for the area where the target disturbance source is located. The power quality level is determined based on the range of the overall power quality score. For example, an overall score less than 1 indicates a very poor power quality level; an overall score greater than or equal to 1 and less than 5 indicates a poor power quality level; an overall score greater than or equal to 5 and less than 7 indicates a medium power quality level; and an overall score greater than or equal to 7 indicates a good power quality level. No specific limitations are set here.

[0098] Optionally, the analytic hierarchy process (AHP) can be used to dynamically calculate the indicator weights. Specifically, a judgment matrix is ​​constructed (using a 1-9 scale to quantify the importance of the indicators), such as "voltage deviation" being more important than "harmonic distortion rate," with a scale of 3. The largest eigenvalue and its corresponding eigenvector of the judgment matrix are solved using the power method, and the eigenvector is the indicator weight (e.g., voltage deviation weight 0.35, harmonic distortion weight 0.25). Furthermore, to avoid logical contradictions in the judgment matrix, a consistency check is performed. When the consistency ratio is less than 0.1, it indicates that the consistency check has passed, the judgment matrix is ​​valid, and the aforementioned eigenvector is determined as the final indicator weight.

[0099] This application embodiment quantifies power quality through multi-dimensional indicators and weights, achieving more accurate and intuitive power quality assessment, providing a basis for disturbance source management from qualitative judgment to quantitative decision-making, and improving the efficiency of power quality management and user satisfaction in the distribution network system.

[0100] In some embodiments, based on the target governance measures, a panoramic simulation of the power quality of the target distribution network area is performed to obtain the power quality governance simulation results corresponding to the target governance measures. This includes: based on a preset multi-scenario library and the target governance measures, a full electromagnetic transient simulation method is applied to perform a panoramic simulation of the power quality of the target distribution network area to obtain the power quality governance simulation results corresponding to the target governance measures under different scenarios. The power quality governance simulation results include at least the power quality level, simulated power quality data, and the index values ​​corresponding to each index in the multi-dimensional evaluation index system.

[0101] The preset scenario library is used to simulate the power quality management effects under different load conditions and various extreme scenarios, verifying the robustness of the management measures. For example, the preset scenario library contains 1000 sets of random scenario simulation data, such as voltage and harmonic data under scenarios like "high photovoltaic output + low load level" (PV output 90%-100%, load level 10%-20%) and "low photovoltaic output + high load level" (PV output 0%-10%, load level 80%-90%), as well as probabilistic indicators (such as a voltage exceedance probability of 7%). This is used to cover various extreme scenarios and solve the problem of incomplete simulation of a single scenario. The construction of the preset scenario library involves probabilistic power flow calculation, Monte Carlo simulation algorithm, and Latin hypercube sampling algorithm.

[0102] The dynamic behavior of a real power distribution network can be accurately reproduced using a full electromagnetic transient simulation method. Based on this, target mitigation measures are configured for different scenarios, and power quality simulations are performed on the target power distribution network area to obtain power quality mitigation simulation results corresponding to the target mitigation measures under different scenarios. The target power distribution network area represents a virtual mirror environment corresponding to the area where the disturbance source of the power quality disturbance is located, represented by a multi-timescale electromagnetic transient model. The power quality mitigation simulation results include at least the mitigated power quality level, simulated power quality data, and the corresponding index values ​​of each indicator in the multi-dimensional evaluation index system. The calculation of the power quality level and the corresponding index values ​​of each indicator in the multi-dimensional evaluation index system can be found in the implementation method described in the above embodiments, and will not be repeated here.

[0103] Optionally, during the panoramic simulation, the operating status of the target distribution network area and the entire distribution network system under different scenarios can be monitored in real time to identify potential problems in a timely manner, thereby allowing for timely adjustment and optimization of governance strategies.

[0104] This application embodiment verifies the effectiveness of power quality management solutions by simulating the effects of different scenarios in a virtual environment, thereby reducing the cost and time of on-site testing and improving the efficiency of power quality management in the distribution network system.

[0105] In some embodiments, the distribution network twin management method further includes: acquiring real-time power quality data of the target distribution network area after governance; and correcting the parameters of the multi-timescale electromagnetic transient model based on the root mean square error of the real-time power quality data and the simulated power quality data using a particle swarm optimization algorithm.

[0106] For example, a power quality detector is deployed in the target distribution network area to collect real-time power quality data of the target distribution network area. The simulated power quality data is generated based on the multi-timescale electromagnetic transient model in the distribution network twin management system. Therefore, the parameter accuracy of the multi-timescale electromagnetic transient model can be iteratively optimized by the deviation between the real-time power quality data and the simulated power quality data.

[0107] During iterative optimization, the real-time power quality data and simulated power quality data can be preprocessed first. For example, synchronous phasor measurement technology can be used to align the timestamps of the simulated and measured data, and wavelet transform can be applied to remove high-frequency noise. The key parameters of the multi-timescale electromagnetic transient model can be iteratively corrected using the particle swarm optimization algorithm until the root mean square error of the real-time power quality data and the simulated power quality data is less than a threshold.

[0108] In this embodiment, real-time power quality data is used to drive the parameter correction of a multi-timescale electromagnetic transient model, ensuring the dynamic consistency between the multi-timescale electromagnetic transient model and the actual distribution network, improving the accuracy of the distribution network twin management system, and providing closed-loop optimization capabilities for power quality governance.

[0109] In some embodiments, the distribution network twin management method further includes: predicting the operating status based on the operating data of the distribution network system to obtain the operating status prediction result of the distribution network system.

[0110] For example, real-time operating data of the distribution network system is acquired. Based on a pre-trained operating status prediction model, features of the real-time operating data are extracted to predict the operating status of the distribution network system. The predicted operating status results are output, such as load or renewable energy output prediction curves for the next 24 hours, to identify potential power quality risks and abnormal operating conditions in advance. The operating status prediction model is built on a Long Short-Term Memory (LSTM) network and is iteratively trained based on historical operating data (such as historical load, weather data, voltage, current, output, etc.).

[0111] This research focuses on a novel power distribution system operation status prediction technology based on historical load time series data and artificial intelligence algorithms. On this basis, it combines the power output curves of new energy sources and digital twins of operating equipment to study a panoramic dynamic power flow simulation technology for power distribution networks driven by data and knowledge, thereby obtaining real-time power quality simulation data and issuing alarms for power quality risk points and abnormal operating conditions in the system.

[0112] In summary, this application has at least the following advantages:

[0113] First, multi-timescale electromagnetic transient models can accurately characterize the dynamic characteristics of energy nodes and load nodes, comprehensively describe the complex characteristics of distribution network systems, provide a reliable foundation for power quality assessment, and improve the accuracy of power quality assessment. Based on this, power quality disturbance sources can be traced. Since the equivalent model of the disturbance source can accurately describe the relationship between the disturbance source and the system response, it provides a reliable theoretical basis for source tracing analysis, thereby improving the accuracy of source tracing and enhancing the dynamic assessment capability of power quality in distribution network systems. Furthermore, different mitigation measures can be formulated for different power quality disturbance source locations, enhancing the pertinence of mitigation measures. A panoramic simulation of power quality can be further conducted to verify the effectiveness of mitigation measures, improving the efficiency of power quality mitigation in distribution network systems. This provides a complete solution from power quality problem identification, accurate source tracing, mitigation measure verification to mitigation measure implementation, supporting the reliable operation of new distribution network systems.

[0114] Second, by quantifying power quality through multi-dimensional indicators and weights, we can achieve more accurate and intuitive power quality assessment, providing a basis for disturbance source management from qualitative judgment to quantitative decision-making, and improving the efficiency of power quality management and user satisfaction in the distribution network system.

[0115] Third, by simulating the effects of power quality management schemes under different scenarios in a virtual environment, the effectiveness of management measures can be verified, reducing the cost and time of on-site testing and improving the efficiency of power quality management in the distribution network system.

[0116] Fourth, by using real-time power quality data to drive the parameter correction of the multi-timescale electromagnetic transient model, the dynamic consistency between the multi-timescale electromagnetic transient model and the actual distribution network is ensured, the accuracy of the distribution network twin management system is improved, and closed-loop optimization capability is provided for power quality governance.

[0117] Figure 2 This is a schematic diagram of the structure of the power distribution network twin management system provided in an embodiment of this application. Figure 2 As shown, the distribution network twin management system 20 provided in this embodiment includes:

[0118] The acquisition module 21 is used to acquire the operating data of the distribution network system, which includes energy nodes, grid nodes, load nodes and energy storage nodes.

[0119] The source tracing module 22 is used to trace the source of power quality disturbance based on the disturbance source equivalent model and the operating data when the operating data characterizes the existence of power quality disturbance in the distribution network system, and obtain the power quality disturbance source location result. The disturbance source equivalent model is constructed based on the multi-timescale electromagnetic transient model corresponding to the distribution network system.

[0120] Processing module 23 is used to determine target mitigation measures based on the power quality disturbance source location results.

[0121] The panoramic simulation and deduction module 24 is used to perform panoramic simulation and deduction of the power quality of the target distribution network area based on the target governance measures, and obtain the power quality governance simulation results corresponding to the target governance measures.

[0122] The governance module 25 is used to apply target governance measures to power quality governance in the target distribution network area when the power quality governance simulation results meet the preset requirements.

[0123] Display module 26 is used to visually display the comparison results of power quality before and after the treatment of the target distribution network area.

[0124] It should be noted that the architecture of the distribution network twin management system adopts the MQTT protocol (lightweight IoT protocol), and the measurement data is uploaded to the cloud (where the distribution network twin management system is deployed) through the edge gateway. The graph database Neo4j is used to construct a "device-data-business" relationship graph (e.g., the relationship between "photovoltaic inverter-output data-power quality assessment"). Furthermore, Docker container technology is used to isolate different business modules (e.g., the governance module and the panoramic simulation module), and Kubernetes is used to manage container resources to ensure access control during data interaction. For the front-end interface framework, Vue.js can be used to develop a single-page application, integrating ECharts to draw dynamic topology diagrams (real-time display of node voltage colors: green for normal, red for exceeding limits). For visualization, Three.js can be used to build a 3D model of the distribution network (including substations, line spatial distribution, etc.), and it supports mouse drag-and-drop rotation. Additionally, real-time interaction of the front-end interface can use the WebSocket protocol; for example, the front-end retrieves the latest simulation data from the back-end server every 2 seconds and refreshes the interface display.

[0125] In one possible implementation, the source tracing module 22 is specifically used to: determine the disturbance characteristics of the operating data based on the disturbance source equivalent model and the preset characteristic parameter library; use an impedance matching algorithm to determine the characteristic impedance of each node in the distribution network according to the operating data, and determine multiple candidate disturbance sources; based on the disturbance characteristics, determine the target disturbance source from the multiple candidate disturbance sources using a Bayesian inference algorithm; and output the power quality disturbance source location result containing the target disturbance source.

[0126] In one possible implementation, the processing module 23 is further configured to: determine the values ​​of each indicator corresponding to the target disturbance source based on the multi-dimensional evaluation index system and the operating data; and determine the power quality level corresponding to the area where the target disturbance source is located based on the values ​​of each indicator and the set index weights.

[0127] In one possible implementation, the processing module 23 is further configured to: determine the target governance measures corresponding to the power quality disturbance source location results based on the mapping relationship between the power quality disturbance source location results and the governance measures.

[0128] In one possible implementation, the panoramic simulation and deduction module 24 is specifically used to: based on a preset multi-scenario library and target governance measures, apply the all-electromagnetic transient simulation method to perform panoramic simulation of power quality in the target distribution network area, and obtain power quality governance simulation results corresponding to the target governance measures under different scenarios. The power quality governance simulation results include at least the power quality level, simulated power quality data, and the index values ​​corresponding to each index in the multi-dimensional evaluation index system.

[0129] In one possible implementation, the processing module 23 is further configured to: acquire real-time power quality data of the target distribution network area after treatment; and correct the parameters of the multi-timescale electromagnetic transient model based on the root mean square error of the real-time power quality data and the simulated power quality data using a particle swarm optimization algorithm.

[0130] The distribution network twin management system provided in this embodiment can execute the methods provided in the above method embodiments. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0131] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 3 As shown, the electronic device 30 provided in this embodiment includes at least one processor 301 and a memory 302. Optionally, the electronic device 30 further includes a communication component 303. The processor 301, memory 302, and communication component 303 are connected via a bus 304.

[0132] In a specific implementation, at least one processor 301 executes computer execution instructions stored in memory 302, causing at least one processor 301 to perform the above-described method.

[0133] The specific implementation process of processor 301 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0134] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0135] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0136] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0137] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0138] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0139] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0140] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0141] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0142] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0143] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0144] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0145] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0146] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for managing distribution network twins, characterized in that, include: Acquire operational data of the distribution network system, which includes energy nodes, grid nodes, load nodes, and energy storage nodes; When the operating data indicates that there is a power quality disturbance in the distribution network system, the power quality disturbance source is traced based on the disturbance source equivalent model and the operating data to obtain the power quality disturbance source location result. The disturbance source equivalent model is constructed based on the multi-timescale electromagnetic transient model corresponding to the distribution network system. Based on the power quality disturbance source location results, target mitigation measures are determined for the power quality disturbance source location results; Based on the target governance measures, a panoramic simulation of the power quality of the target distribution network area is performed to obtain the power quality governance simulation results corresponding to the target governance measures. When the power quality management simulation results meet the preset requirements, the target management measures are applied to the target power distribution network area to manage power quality. The power quality comparison results before and after the treatment of the target distribution network area are displayed visually.

2. The distribution network twin management method according to claim 1, characterized in that, The method of tracing the source of power quality disturbance based on the equivalent model of the disturbance source and the operating data to obtain the source location result of the power quality disturbance, includes: Based on the equivalent model of the disturbance source and the preset characteristic parameter library, the disturbance characteristics of the operating data are determined; An impedance matching algorithm is used to determine the characteristic impedance of each node in the distribution network based on the operating data, and to identify multiple candidate disturbance sources. Based on the aforementioned disturbance characteristics, and using a Bayesian inference algorithm, the target disturbance source is determined from the plurality of candidate disturbance sources; The output includes the power quality disturbance source location results, which contain the target disturbance source.

3. The distribution network twin management method according to claim 2, characterized in that, After determining the target disturbance source, the method further includes: Based on the multi-dimensional evaluation index system, the values ​​of each index corresponding to the target disturbance source are determined according to the operational data. Based on the values ​​of each indicator and the set indicator weights, the power quality level corresponding to the area where the target disturbance source is located is determined.

4. The distribution network twin management method according to any one of claims 1 to 3, characterized in that, The step of determining target mitigation measures based on the power quality disturbance source location results includes: Based on the mapping relationship between the power quality disturbance source location results and the mitigation measures, the target mitigation measures corresponding to the power quality disturbance source location results are determined.

5. The distribution network twin management method according to claim 4, characterized in that, Based on the target governance measures, a panoramic simulation of the power quality in the target distribution network area is performed to obtain the power quality governance simulation results corresponding to the target governance measures, including: Based on a pre-defined multi-scenario library and the target governance measures, a panoramic simulation of power quality is performed on the target distribution network area using a full electromagnetic transient simulation method. The simulation results of power quality governance corresponding to the target governance measures under different scenarios are obtained. The simulation results of power quality governance include at least the power quality level, simulated power quality data, and the index values ​​corresponding to each index in the multi-dimensional evaluation index system.

6. The distribution network twin management method according to claim 5, characterized in that, Also includes: Obtain real-time power quality data of the target distribution network area after treatment; Based on the root mean square error of the real-time power quality data and the simulated power quality data, the parameters of the multi-timescale electromagnetic transient model are corrected using the particle swarm optimization algorithm.

7. A distribution network twin management system, characterized in that, include: The acquisition module is used to acquire the operating data of the distribution network system, which includes energy nodes, grid nodes, load nodes and energy storage nodes; The source tracing module is used to trace the source of power quality disturbance based on the disturbance source equivalent model and the operating data when the operating data indicates that there is power quality disturbance in the distribution network system, and to obtain the power quality disturbance source location result. The disturbance source equivalent model is constructed based on the multi-timescale electromagnetic transient model corresponding to the distribution network system. The determination module is used to determine the target mitigation measures based on the power quality disturbance source location results. The panoramic simulation and deduction module is used to perform panoramic simulation and deduction of the power quality of the target distribution network area based on the target governance measures, and obtain the power quality governance simulation results corresponding to the target governance measures; The governance module is used to apply the target governance measures to perform power quality governance in the target distribution network area when the power quality governance simulation results meet the preset requirements. The display module is used to visually display the power quality comparison results of the target distribution network area before and after the treatment.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed, are used to implement the method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes a computer program that, when executed, implements the method of any one of claims 1 to 6.

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