A carbon footprint constrained power distribution network ground fault line selection method and system

Abstract

The application discloses a carbon footprint constrained distribution network grounding fault line selection method and system, and the method comprises the following steps: acquiring electrical quantity data and operation scene parameters; calculating real-time power grid carbon emission factors based on real-time generator set output data; calculating new energy penetration based on load level and real-time generator set output data; judging whether a grounding fault occurs in the distribution network based on the electrical quantity data; determining a fault starting time when the grounding fault occurs; calculating the total carbon footprint of the grounding fault; acquiring line selection accuracy rates corresponding to different line selection strategies; optimizing through a pre-constructed multi-objective optimization model based on the whole-process carbon footprint and the line selection accuracy rates, generating a set containing multiple candidate line selection strategies; selecting an optimal line selection strategy from the set through a multi-attribute decision algorithm based on the load level, the power grid carbon emission factors and the new energy penetration; and executing the optimal line selection strategy.

Description

Technical Field

[0001] This application relates to the fields of power system automation and low-carbon energy management technology, and relates to, but is not limited to, a method and system for selecting grounding faults in distribution networks with carbon footprint constraints. Background Technology

[0002] The power distribution network is used to transmit electrical energy to users. Its cable lines are connected through ring main units to form a network with multiple branches. When a single-phase ground fault occurs in the cable lines of the distribution network, especially a low-current ground fault with high grounding resistance, the voltage and current signals representing the fault change very little and are easily interfered with by electrical signals during normal operation of the distribution network. This makes it difficult to detect ground faults in the distribution network in a timely manner and to determine the location of the fault.

[0003] In related technologies, methods for determining grounding faults include: steady-state methods based on the magnitude and direction of power frequency voltage and current after the fault; transient methods based on the characteristics of high-frequency signals at the moment the fault occurs; and multi-criteria fusion methods that use multiple judgment criteria for comprehensive decision-making. With the development of computer technology, analyzing fault data through artificial intelligence algorithms can improve the probability of correctly identifying faulty lines.

[0004] However, since the related technologies aim to improve the accuracy of line selection, they may use high-energy-consuming computing resources in pursuit of higher accuracy, or perform high-energy-consuming fault analysis when the distribution network is mainly powered by high-carbon energy, resulting in higher carbon emissions and increasing the carbon footprint of the distribution network operation and maintenance. Summary of the Invention

[0005] In view of this, embodiments of this application provide a method and system for selecting grounding faults in distribution networks with carbon footprint constraints, which at least solves the technical problem that fault selection methods in related technologies generate high carbon emissions while optimizing the accuracy of fault selection.

[0006] The technical solution of this application embodiment is implemented as follows: In a first aspect, embodiments of this application provide a method for selecting grounding faults in a distribution network under carbon footprint constraints, the method comprising: The system acquires real-time electrical quantity data and operating scenario parameters of the power distribution network; the operating scenario parameters include load level and real-time generator output data; based on the real-time generator output data, it calculates the real-time power grid carbon emission factor; and based on the load level and real-time generator output data, it calculates the renewable energy penetration rate. Based on the electrical quantity data, determine whether a ground fault has occurred in the distribution network; when it is determined that a ground fault has occurred in the distribution network, determine the fault initiation time; Based on the fault initiation time, the power grid carbon emission factor, and the pre-built carbon footprint quantification model, the total carbon footprint generated by the grounding fault during fault persistence loss, line selection operation, line selection device, and fault location patrol is calculated. Obtain the route selection accuracy corresponding to different route selection strategies; based on the total carbon footprint and the route selection accuracy corresponding to different route selection strategies, optimize through a pre-constructed multi-objective optimization model to generate a set containing multiple candidate route selection strategies, each of which corresponds to a set of total carbon footprint and route selection accuracy; Based on the load level, the power grid carbon emission factor, and the new energy penetration rate, the optimal route strategy is selected from the set through a pre-determined multi-attribute decision algorithm. Execute the optimal line strategy described above.

[0007] Secondly, embodiments of this application provide a carbon footprint-constrained distribution network grounding fault selection system, the system comprising: a data acquisition and calculation unit, a fault judgment unit, a carbon footprint calculation unit, a strategy optimization unit, a decision-making unit, and an execution unit; wherein: The data acquisition and calculation unit is used to acquire real-time electrical quantity data and operating scenario parameters of the distribution network; the operating scenario parameters include load level and real-time generator output data; based on the real-time generator output data, the real-time grid carbon emission factor is calculated; based on the load level and the real-time generator output data, the renewable energy penetration rate is calculated. The fault determination unit is used to determine whether a ground fault has occurred in the distribution network based on the electrical quantity data; and to determine the fault start time when it is determined that a ground fault has occurred in the distribution network. The carbon footprint calculation unit is used to calculate the total carbon footprint of the grounding fault during fault persistence loss, line selection operation, line selection device and fault location inspection based on the fault initiation time, the power grid carbon emission factor and the pre-built carbon footprint quantification model. The strategy optimization unit is used to obtain the route selection accuracy corresponding to different route selection strategies; based on the total carbon footprint and the route selection accuracy corresponding to different route selection strategies, it optimizes through a pre-constructed multi-objective optimization model to generate a set containing multiple candidate route selection strategies, each of which corresponds to a set of total carbon footprint and route selection accuracy. The decision-making unit is used to select the optimal route strategy from the set based on the load level, the power grid carbon emission factor and the new energy penetration rate through a pre-determined multi-attribute decision-making algorithm. The execution unit is used to execute the optimal route strategy.

[0008] The beneficial effects of the technical solutions provided in this application include at least the following: This application acquires real-time electrical quantity data and operational scenario parameters of the distribution network. The operational scenario parameters include load levels and real-time generator output data. Based on the real-time generator output data, the application calculates the real-time grid carbon emission factor and the renewable energy penetration rate. The grid carbon emission factor represents the amount of carbon dioxide emitted per unit of electricity produced by the distribution network at the current moment, and the renewable energy penetration rate reflects the proportion of renewable energy in the distribution network. Based on the electrical quantity data, the application determines whether a ground fault has occurred in the distribution network. When a ground fault is determined, the application determines the fault initiation time. The fault initiation time is the starting point of the ground fault. Based on the fault initiation time, the grid carbon emission factor, and a pre-built carbon footprint quantification model, the application calculates the total carbon footprint generated by the ground fault during fault persistence loss, line selection operation, line selection device, and fault location patrol. The application obtains the line selection accuracy corresponding to different line selection strategies. Based on the total carbon footprint and the line selection accuracy corresponding to different line selection strategies, the application optimizes the results using a pre-built multi-objective optimization model, generating a set containing multiple candidate line selection strategies. Each candidate line selection strategy corresponds to a set of total carbon footprints and line selection accuracy. By calculating the total carbon footprint and quantifying carbon emissions, both carbon emissions and route selection accuracy are considered as optimization objectives, thus simultaneously taking both into account in route selection decisions. Based on load levels, grid carbon emission factors, and renewable energy penetration rates, a pre-defined multi-attribute decision algorithm selects the optimal route selection strategy from a set. The multi-attribute decision algorithm determines the weights of route selection accuracy and carbon footprint in the decision-making process, selecting the optimal route selection strategy. The optimal route selection strategy is then executed. Therefore, this application can reduce carbon emissions during fault route selection while ensuring route selection accuracy. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 A schematic flowchart illustrating a carbon footprint-constrained distribution network grounding fault selection method provided in this application embodiment; Figure 2 This is a schematic diagram of a carbon footprint-constrained distribution network grounding fault location system provided in an embodiment of this application. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0011] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0012] It should be noted that the terms "first, second, and third" used in the embodiments of this application are merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, and third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0013] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments of this application pertain. It should also be understood that terms such as those defined in general dictionaries should be understood to have a meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0014] This application provides a carbon footprint-constrained method for selecting grounding faults in distribution networks. Figure 1 This is a flowchart illustrating a carbon footprint-constrained distribution network grounding fault location method provided in an embodiment of this application, as shown below. Figure 1 As shown, the method includes at least the following steps: Step S110: Obtain real-time electrical quantity data and operating scenario parameters of the distribution network; the operating scenario parameters include load level and real-time generator output data; calculate the real-time grid carbon emission factor based on the real-time generator output data; calculate the new energy penetration rate based on the load level and the real-time generator output data.

[0015] Electrical quantity data includes bus zero-sequence voltage, zero-sequence current of each outgoing line, three-phase voltage of the bus, and three-phase current of each outgoing line. Electrical quantity data can reflect the current electrical status of the distribution network.

[0016] The load level is the current total active load of the distribution network, calculated by summing the active power on the high-voltage side of the main transformer in the substation. The load level reflects the scale of current electricity demand. When a ground fault occurs, the higher the load level, the greater the scope of the power outage and the greater the power interruption losses caused by the fault. Therefore, the accuracy of the line selection should be emphasized when making line selection decisions in order to restore power supply as soon as possible and reduce losses.

[0017] Real-time power output data for generator units includes real-time power output data for thermal power units, gas power units, wind power units, photovoltaic units, and other units.

[0018] The grid carbon emission factor represents the amount of carbon dioxide emitted by the distribution network for each unit of electricity produced at the current moment.

[0019] The renewable energy penetration rate represents the proportion of total power output from wind turbines and solar PV units to the current load level. This rate reflects the share of renewable energy in the power distribution network, and this share influences the degree of emphasis placed on carbon emissions in route selection decisions.

[0020] Step S120: Based on the electrical quantity data, determine whether a ground fault has occurred in the distribution network; when it is determined that a ground fault has occurred in the distribution network, determine the fault start time.

[0021] A ground fault is an abnormal conductive path formed between a power distribution network cable line and the earth. The fault initiation time is the starting point of the ground fault occurrence.

[0022] After acquiring electrical quantity data, the system analyzes this data to determine whether the distribution network is in a ground fault state. If the determination indicates a ground fault has occurred, the system identifies the fault initiation time.

[0023] Step S130: Based on the fault initiation time, the power grid carbon emission factor, and the pre-built carbon footprint quantification model, calculate the total carbon footprint generated by the grounding fault during fault persistence loss, line selection operation, line selection device, and fault location patrol.

[0024] A pre-built carbon footprint quantification model is used to calculate the carbon emissions throughout the entire fault handling process. Fault persistence loss is the additional electrical energy loss caused by the persistence of the fault from the moment the fault begins until the faulty line is selected. Line selection operation is the electrical energy consumption generated by operations such as switching equipment during the execution of the line selection strategy. Line selection device is the carbon emissions of the equipment used for fault line selection amortized over its life cycle to a single fault. Fault location and line inspection is the carbon emissions generated when searching for the fault point along the line by personnel or vehicles.

[0025] By calculating the total carbon footprint, carbon emissions are quantified, and both carbon emissions and route selection accuracy are used as optimization targets, thus taking both carbon emissions and route selection accuracy into account in route selection decisions.

[0026] Step S140: Obtain the route selection accuracy corresponding to different route selection strategies; Based on the total carbon footprint and the route selection accuracy corresponding to different route selection strategies, optimize through a pre-constructed multi-objective optimization model to generate a set containing multiple candidate route selection strategies, each of which corresponds to a set of total carbon footprint and route selection accuracy.

[0027] Route selection strategies are used to identify faulty lines. Route selection accuracy is the probability that a given route selection strategy correctly identifies a faulty line; route selection accuracy is obtained through offline simulation or historical data statistics.

[0028] Total carbon footprint is the total carbon emissions generated by grounding faults during fault persistence loss, line selection operations, line selection devices, and fault location patrols.

[0029] A pre-built multi-objective optimization model is used to optimize the trade-off between the two objectives of total carbon footprint and route selection accuracy. Candidate route selection strategies are generated by the multi-objective optimization model, and each candidate route selection strategy corresponds to a set of total carbon footprint and route selection accuracy.

[0030] By using carbon emissions and route selection accuracy as optimization objectives, a multi-objective optimization model is employed to optimize the route selection accuracy while minimizing carbon emissions.

[0031] Step S150: Based on the load level, the power grid carbon emission factor, and the new energy penetration rate, the optimal line strategy is selected from the set using a pre-determined multi-attribute decision algorithm.

[0032] Multi-attribute decision-making algorithms select the optimal route selection strategy from a set of multiple candidate strategies based on multiple attributes. The set contains multiple candidate strategies. The optimal route selection strategy is the one most suitable for the current operating scenario, chosen from the set.

[0033] The three parameters—load level, grid carbon emission factor, and renewable energy penetration rate—reflect the different priorities of the distribution network in terms of route selection accuracy and carbon emissions. For example, when the load level is high, the power outage losses caused by faults are greater, so more emphasis should be placed on route selection accuracy; when the grid carbon emission factor is high, more emphasis should be placed on reducing carbon emissions; and when the renewable energy penetration rate is high, the transient characteristics of the grid may change, requiring corresponding adjustments to the route selection strategy.

[0034] By combining load level, grid carbon emission factor and new energy penetration rate through multi-attribute decision-making algorithm, the weight of route selection accuracy and carbon footprint in decision-making can be determined, and the optimal route selection strategy can be selected, thereby reducing carbon emissions in fault handling process while ensuring route selection accuracy.

[0035] Step S160: Execute the optimal line strategy.

[0036] The optimal line selection strategy is executed. According to the line selection algorithm type, data window length, sampling frequency, start threshold and whether the location function is included in the optimal line selection strategy, the number or location of the faulty line is determined, and the identification of the distribution network cable line with grounding fault is completed.

[0037] This application acquires real-time electrical quantity data and operational scenario parameters of the distribution network. The operational scenario parameters include load levels and real-time generator output data. Based on the real-time generator output data, the application calculates the real-time grid carbon emission factor and the renewable energy penetration rate. The grid carbon emission factor represents the carbon emissions generated per unit of electricity produced by the distribution network at the current moment, and the renewable energy penetration rate reflects the proportion of renewable energy in the distribution network. Based on the electrical quantity data, the application determines whether a ground fault has occurred in the distribution network. When a ground fault is determined, the application determines the fault initiation time. The fault initiation time is the starting point of the ground fault. Based on the fault initiation time, the grid carbon emission factor, and a pre-built carbon footprint quantification model, the application calculates the total carbon footprint generated by the ground fault during fault persistence loss, line selection operation, line selection device, and fault location patrol. The application obtains the line selection accuracy corresponding to different line selection strategies. Based on the total carbon footprint and the line selection accuracy corresponding to different line selection strategies, the application optimizes the results using a pre-built multi-objective optimization model, generating a set containing multiple candidate line selection strategies. Each candidate line selection strategy corresponds to a set of total carbon footprints and line selection accuracy. By calculating the total carbon footprint and quantifying carbon emissions, carbon emissions and route selection accuracy are used as optimization objectives, thus simultaneously considering both carbon emissions and route selection accuracy in route selection decisions. Based on load levels, grid carbon emission factors, and renewable energy penetration rates, a pre-defined multi-attribute decision algorithm selects the optimal route selection strategy from a set. The multi-attribute decision algorithm determines the weights of route selection accuracy and carbon footprint in the decision-making process, selecting the optimal route selection strategy. The optimal route selection strategy is then executed. Therefore, this application can reduce carbon emissions during the fault route selection process while ensuring route selection accuracy.

[0038] Optionally, calculating the real-time grid carbon emission factor based on the real-time output data of the generator sets includes: determining the total power generation based on the real-time output data of the generator sets; determining the total carbon emissions of the distribution network based on the real-time output data of the generator sets and the unit power generation carbon emission factor of each generator set; and determining the grid carbon emission factor based on the total carbon emissions and the total power generation.

[0039] Based on the real-time output data of the generator sets and the unit carbon emission factor of each generator set, specifically, the carbon emissions of each generator set are calculated based on the real-time output data and the corresponding unit carbon emission factor. Then, based on the carbon emissions of each generator set, the total carbon emissions of the distribution network are calculated.

[0040] Table 1 Electrical Quantity Data Requirements and Sampling Configuration Parameters ; Table 1 lists the electrical quantity data, including bus zero-sequence voltage, zero-sequence current of each outgoing line, bus three-phase voltage, and three-phase current of each outgoing line. Bus zero-sequence voltage is collected from the open delta winding of the bus voltage transformer; zero-sequence current of each outgoing line is collected from its respective zero-sequence current transformer; bus three-phase voltage is collected from the bus voltage transformer; and three-phase current of each outgoing line is collected from its respective current transformer. The sampling frequency for bus zero-sequence voltage and zero-sequence current of each outgoing line is no less than 6.4 kHz, with no less than 128 points per cycle, to meet the requirements for transient fault characteristic analysis. The sampling frequency for bus three-phase voltage and three-phase current of each outgoing line is 128 points per cycle, meaning that no less than 128 data points are collected within each power frequency cycle.

[0041] Table 2 shows the parameters for real-time power output data acquisition for generator units. The real-time power output data in Table 2 includes that of thermal power units, gas turbine units, wind turbine units, photovoltaic units, and other units. All of the above data is acquired through real-time telemetry accumulation. Regarding data sources, the real-time power output data for thermal power units, gas turbine units, and other units originates from the data acquisition and monitoring control system, while the real-time power output data for wind turbine units and photovoltaic units, as real-time power output data for new energy units, originates from the new energy monitoring system.

[0042] Table 2. Generator Set Real-time Output Data Acquisition Parameter Table ; The data acquisition cycle for real-time generator output is 1-5 minutes. For areas where individual generator output data acquisition is not feasible, regional aggregated data released by the provincial power grid can be used.

[0043] The calculation of the carbon emission factor of the power grid is shown in formula (1): EF(t)= Formula (1); Wherein, EF(t) is the carbon emission factor of the power grid; Carbon emission factor per unit of electricity generated by thermal power units; The carbon emission factor per unit of electricity generated by a gas turbine generator set; Carbon emission factor per unit of electricity generated by new energy units; The carbon emission factor per unit of electricity generated by other generating units; Let t be the total power generation of all generating units at time t; the numerator of the power grid carbon emission factor represents the total carbon emissions of all generating units at time t. Provides real-time power output data for new energy generating units.

[0044] Among them, the unit carbon emission factor of each generator set represents the amount of carbon emissions generated by that type of generator set producing a unit of electricity.

[0045] The calculation of real-time output data of new energy units is shown in formula (2): Formula (2); The total power generation of all generator units at time t is calculated as shown in formula (3): Formula (3); Optionally, the real-time power output data of the generator sets includes real-time power output data of wind turbines and real-time power output data of photovoltaic units; the step of calculating the new energy penetration rate based on the load level and the real-time power output data of the generator sets includes: determining the real-time power output data of new energy units based on the real-time power output data of wind turbines and real-time power output data of photovoltaic units; and calculating the new energy penetration rate based on the real-time power output data of new energy units and the load level.

[0046] New energy penetration rate The calculation is shown in formula (4): Formula (4); in, The load level refers to the current total active load of the distribution network.

[0047] A renewable energy penetration rate of 30% or higher indicates a high penetration rate. At this rate, transient interferences such as harmonics and interharmonics in the distribution network increase, potentially reducing the accuracy of line selection methods based on transient parameters.

[0048] When the penetration rate of new energy sources is less than 15%, it indicates a low penetration rate. At this point, the transient characteristics of the current distribution network are close to those of a traditional distribution network, and the transient route selection method is highly reliable.

[0049] Therefore, when the penetration rate of new energy sources is high, the weight of route selection accuracy can be increased.

[0050] Optionally, determining whether a ground fault has occurred in the distribution network based on the electrical quantity data includes: monitoring the bus zero-sequence voltage, and determining that a ground fault has occurred in the distribution network when the absolute value of the bus zero-sequence voltage exceeds the zero-sequence voltage initiation threshold; and / or monitoring the bus zero-sequence current mutation, and determining that a ground fault has occurred in the distribution network when the absolute value of the bus zero-sequence current mutation exceeds the zero-sequence current initiation threshold.

[0051] The instantaneous value of zero-sequence voltage is used as the primary criterion for judging grounding faults in the distribution network. Real-time monitoring of bus zero-sequence voltage is also required. When the condition of formula (5) is met, it is determined that a single-phase ground fault has occurred in the distribution network: > Formula (5); in, The zero-sequence voltage start-up threshold. This is the zero-sequence voltage of the bus.

[0052] Determining the zero-sequence voltage initiation threshold requires consideration of two conditions. Condition 1: To ensure the distribution network does not erroneously start during normal operation, the zero-sequence voltage initiation threshold should be greater than the maximum unbalanced voltage during normal operation. During normal operation of the distribution network, due to the non-symmetrical capacitance to ground of the three-phase lines, a zero-sequence unbalanced voltage exists on the bus. The zero-sequence unbalanced voltage during normal operation does not exceed 5% of the rated phase voltage. Condition 2: The zero-sequence voltage initiation threshold should meet the detection sensitivity requirements for high-resistance grounding faults. When a high-resistance grounding fault occurs, the zero-sequence voltage amplitude is inversely proportional to the transition resistance. If the zero-sequence voltage initiation threshold is set too high, the zero-sequence voltage during a high-resistance grounding fault may be lower than the zero-sequence voltage initiation threshold, resulting in the grounding fault being undetectable.

[0053] Combining conditions 1 and 2, the range of the zero-sequence voltage start-up threshold is set to 5% to 30% of the rated phase voltage.

[0054] Preferably, 15% of the rated phase voltage is taken as the zero-sequence voltage initiation threshold. The zero-sequence voltage initiation threshold is greater than the maximum unbalanced voltage during normal operation of the distribution network, thus preventing misjudgment of ground faults due to unbalanced voltage under normal distribution network conditions. The maximum unbalanced voltage during normal operation of the distribution network is approximately 5% of the rated phase voltage. Simultaneously, the zero-sequence voltage initiation threshold is less than the zero-sequence voltage amplitude during a high-resistance ground fault, ensuring reliable initiation during a high-resistance ground fault and guaranteeing sufficient detection sensitivity for most ground faults, including high-resistance ground faults with a certain resistance value.

[0055] The zero-sequence voltage start-up threshold can be adaptively corrected, as shown in formula (6): Formula (6); in, The baseline threshold; is the effective value of the zero-sequence unbalanced voltage during real-time monitoring of normal operation; s is the reliability coefficient, and the value of s ranges from 1.2 to 1.5.

[0056] To compensate for the blind spot in the zero-sequence voltage start-up criterion under high-resistance grounding or voltage transformer fault scenarios, the bus zero-sequence current mutation is added as an auxiliary criterion for judging grounding faults, as shown in formula (7): > Formula (7); in, = The zero-sequence current change of the busbar is T, which is the power frequency period, which can be 20ms. Let be the zero-sequence current of the bus at time t. for The zero-sequence current of the bus at time t; where, the zero-sequence current of the bus at time t. It is the zero-sequence current of each outgoing line. The vector sum, where k is the number of each outgoing line; The zero-sequence current initiation threshold is defined as follows. The value of the zero-sequence current initiation threshold ranges from 5A to 20A, with 10A being the preferred value.

[0057] When a ground fault is determined to have occurred in the distribution network, the start time of the fault is recorded as follows: .

[0058] The calculation process for the fault initiation time includes: (1) Detect the abrupt change in zero-sequence voltage. Continuously sample the zero-sequence voltage and calculate the difference between adjacent sampling points, as shown in formula (8): Formula (8); in, Let be the zero-sequence voltage change at the z-th sampling time. Let be the zero-sequence voltage of the bus at time t. The sampling time is The bus zero-sequence voltage value at that time. When the difference between adjacent sampling points exceeds a preset threshold, the current sampling point is marked as a candidate fault point. For example, the preset threshold is 0.1 times the rated phase voltage.

[0059] (2) A multi-point confirmation mechanism is adopted to avoid misjudgment caused by noise interference. When the zero-sequence voltage mutation values ​​of three consecutive sampling points are detected to exceed the zero-sequence voltage mutation threshold, and the polarity of these three zero-sequence voltage mutation values ​​is consistent, the moment is confirmed as the fault start moment.

[0060] (3) Determine the time resolution. The sampling frequency is 128 points per cycle, which corresponds to 6.4kHz at 50Hz. Therefore, the time resolution at the time of the fault is determined to be approximately 0.156ms.

[0061] Waveform data from three power frequency cycles before and after the fault initiation moment were selected as the analysis data window. The total length of the data window is six power frequency cycles, which is 120ms for a 50Hz system.

[0062] The data from the three cycles prior to the fault represents the normal operating conditions before the fault, serving as a benchmark for comparative analysis before and after the fault. This data is used to calculate the zero-sequence voltage and zero-sequence current surges. The data from the three cycles prior to the fault also provides a benchmark value for fault loss analysis in carbon footprint calculations.

[0063] The selection criteria for the three cycles following a fault: According to power system transient analysis theory, the fault transient process mainly occurs within 0.5 to 1 power frequency cycle after the fault, including high-frequency components and attenuated DC components, which are the main information sources for transient line selection methods. Taking three cycles after the fault can completely cover the transient process. According to the requirements for calculating the effective value of the power frequency, the transient components have basically attenuated by the second to third cycles after the fault, entering the steady-state process. This data segment is suitable for line selection methods based on steady-state quantities, including the fifth harmonic method and the zero-sequence power direction method. The data window length of three cycles simultaneously meets the data requirements of both transient and steady-state methods, providing a unified input for multi-criteria fusion line selection.

[0064] The data window extraction process is as follows. The start time of data window positioning is equal to the fault start time minus 3T, and the end time is equal to the fault start time plus 3T. The total duration of the data window is equal to 6T, which is 120ms. During waveform data extraction, waveform data of bus zero-sequence voltage, zero-sequence current of each outgoing line, and three-phase voltage of the bus are read from the time-series database. Data integrity checks include: whether the number of data points is consistent with expectations, i.e., the sampling frequency multiplied by the data window duration; whether there are missing data or outliers; and whether the timestamps are continuous.

[0065] Optionally, the step of calculating the total carbon footprint of the grounding fault during fault persistence loss, line selection operation, line selection device, and fault location patrol based on the fault initiation time, the power grid carbon emission factor, and a pre-built carbon footprint quantification model includes: calculating, based on the real-time power grid carbon emission factor, the first carbon footprint generated by the fault persistence loss, the second carbon footprint generated by the line selection operation, the third carbon footprint generated by the line selection device, and the fourth carbon footprint generated by the fault location patrol during the period from the fault initiation time to the fault line being selected; and determining the total carbon footprint based on the first carbon footprint, the second carbon footprint, the third carbon footprint, and the fourth carbon footprint.

[0066] The total carbon footprint is calculated as shown in formula (9): Formula (9); in, The first carbon footprint of the fault-prone losses corresponds to the additional power loss during the period from the occurrence of the fault to the successful selection of the faulty line. The second carbon footprint generated by the line selection operation corresponds to the carbon emissions generated by the operation of switching equipment during the line selection process; The third carbon footprint generated by the line selection device corresponds to the carbon emissions of the line selection device itself during the production, operation, and disposal stages, which are allocated to a single failure. The fourth carbon footprint generated during fault location patrols corresponds to the carbon emissions generated by maintenance personnel patrolling to find fault points.

[0067] The calculation of the first carbon footprint generated by the failure persistence loss is shown in formula (10): Formula (10); in, The time when the fault begins. The moment when the faulty line is successfully selected. As a carbon emission factor of the power grid, This represents the total active power loss during the period from the occurrence of the fault to the selection of the faulty line.

[0068] The calculation of the total active power loss during the period from the occurrence of the fault to the selection of the faulty line is shown in formula (11): Formula (11); in, This is the effective value of the line voltage; Rated phase voltage, Let n be the active power loss of the kth outgoing line under rated voltage, and n be the total number of outgoing lines, which can be calculated based on the line parameters. This is the tangent of the dielectric loss angle; This represents the real-time active power loss of the grounding coil.

[0069] Every route selection operation generates carbon emissions, including the carbon emissions from the power consumption of the operating mechanism's energy storage motor. Gas-insulated GIS equipment or circuit breakers may cause minor damage with each operation. Gas leak.

[0070] The calculation of the second carbon footprint generated by the line selection operation is shown in formula (12): Formula (12); in, To reduce the carbon footprint of line selection operations, for Carbon footprint of gas leaks.

[0071] The calculation of the carbon footprint of power consumption during line selection is shown in formula (13): Formula (13); in, This represents the total number of switching operations during the line selection process. The energy consumption for a single switching operation depends on the type of operating mechanism, such as a spring-operated mechanism or a hydraulic mechanism, and the typical energy consumption for a single switching operation is 0.1 to 1.0 kWh / operation. The grid carbon emission factor at the time of line selection operation is EF(t) or the average factor.

[0072] The carbon footprint of gas leakage is calculated as shown in formula (14): Formula (14); in, For a single operation Gas leakage rate, typically 0.001–0.005 kg / time; for The global warming potential, taken as 23500, represents 1 kg of Equivalent to 23,500 kg .

[0073] The calculation of the third carbon footprint generated by the line selection device is shown in formula (15): Formula (15); in, This refers to the embodied carbon of the plant, including the total carbon emissions from raw material acquisition, manufacturing, transportation, and installation; estimated based on the plant's material inventory and a carbon emission factor database, typically ranging from 150 to 500 kg. ; The active power consumption during device operation is typically 20-200W, based on the device nameplate value. The number of operating hours within the design life of the device is calculated as design life in years × 8760h; The average carbon emission factor during the operation of the device can be taken as the annual average value of the regional power grid; Carbon emissions are reduced by replacing virgin materials with recyclable materials after the equipment is decommissioned; The expected number of faults to be handled within the design life of the device can be determined based on historical failure rate statistics or design target values.

[0074] The calculation of the fourth carbon footprint generated by fault location and line inspection is shown in formula (16): Formula (16); Where D is the patrol mileage; FC is the fuel consumption per unit mile of the patrol vehicle, typically 0.08 to 0.15 L / km, depending on the vehicle model and road conditions; As a carbon emission factor of fuel, gasoline accounts for approximately diesel .

[0075] If electric vehicles are used for line inspection, the calculation of the fourth carbon footprint generated by fault location line inspection is changed to the calculation of energy consumption, as shown in formula (17): Formula (17); in, The value range at this time is 0.15~0.25kWh / km; This refers to the carbon emission factors of electric vehicles.

[0076] Optionally, the pre-built multi-objective optimization model takes minimizing the route selection error rate and minimizing the total carbon footprint of the entire fault process as optimization objectives, and solves the model with route selection strategy as decision variables; the route selection strategy includes route selection algorithm type, data window length, sampling frequency, start threshold, and whether it includes positioning function.

[0077] The line selection algorithm type can be selected as transient energy method, fifth harmonic method, injection method, or multi-criteria fusion method. The data window length is 1 to 5 cycles. The sampling frequency is 5kHz, 10kHz, or 20kHz, and the value of the sampling frequency affects the transient feature resolution. The start-up threshold is 5% to 30% of the rated phase voltage, and the value of the start-up threshold affects the start-up sensitivity. Whether it includes positioning function is selected as no positioning or with positioning. This encoding is used for the crossover, mutation, and selection operations of the genetic algorithm.

[0078] The first objective function for minimizing the line selection error rate is calculated as shown in formula (18): Formula (18); in, The first objective function value, The accuracy rate of route selection for strategy S is obtained through offline simulation or historical data statistics. A higher accuracy rate indicates better route selection performance and a lower error rate. The accuracy rate ranges from 95% to 95%.

[0079] The second objective function for minimizing the total carbon footprint of the entire failure process is calculated as shown in Equation (19): Formula (19); in, The value of the second objective function. The first carbon footprint of strategy S depends on the time spent on line selection. It is related to the data window length and algorithm complexity; The second carbon footprint of strategy S depends on the false trigger probability, and the false trigger rate varies for different strategies; The third carbon footprint of strategy S depends on the device hardware configuration; the injection method requires additional hardware. The fourth carbon footprint of strategy S depends on whether it has a positioning function.

[0080] The first and second objective functions are solved using a pre-constructed multi-objective optimization model. The pre-constructed multi-objective optimization model is an improved non-dominated sorting genetic algorithm.

[0081] The solution steps of the improved non-dominated sorting genetic algorithm include: initializing the population, calculating fitness, non-dominated sorting and crowding distance calculation, genetic operations, elite retention, and iteration termination.

[0082] (1) Initialize the population. 100 chromosomes are randomly generated as the initial population, and each chromosome represents a candidate line selection strategy. To speed up convergence, the candidate line selection strategies are injected into the initial population.

[0083] (2) Fitness calculation. For each candidate line selection strategy, calculate the first objective function value and the second objective function value.

[0084] (3) Perform non-dominated sorting. Perform non-dominated sorting of the population based on the first objective function value and the second objective function value: if the first objective function value and the second objective function value of candidate line selection strategy 1 are both better than those of candidate line selection strategy 2, then candidate line selection strategy 1 is said to dominate candidate line selection strategy 2; candidate line selection strategies that are not dominated by any candidate line selection strategy constitute the first layer of the Pareto front; after removing the first layer of the Pareto front, the remaining candidate line selection strategies that are not dominated constitute the second layer of the Pareto front, and so on.

[0085] (4) Calculate the crowding distance. To maintain the diversity of solutions, assume that the current non-dominated layer has a total of The congestion distance for candidate route selection strategies at the same layer is calculated as shown in formula (20): Formula (20); in, The distance is the congestion distance, where i represents the index of the candidate route selection strategy in this layer after sorting by a certain target, i=2,3,… -1; j represents the index of the objective function, j=1 is related to the accuracy of line selection, j=2 is related to the total carbon footprint; These are the function values ​​of the sorted adjacent candidate line selection strategies on target j; These are the maximum and minimum values ​​of target j in the current layer, respectively.

[0086] The greater the crowding distance, the sparser the surrounding area of ​​the candidate line selection strategy, and the more likely it should be retained during the evolution process.

[0087] (5) Perform genetic operations. A tournament selection method is used to select candidate parents from the current population, employing a crossover probability strategy. Perform single-point crossover, with mutation probability Gene locus mutations are performed to generate a progeny population. The crossover and mutation probabilities can be set within commonly used ranges based on actual optimization results; for example, setting... =0.9, =0.1.

[0088] (6) Perform elite preservation. Merge the parent and offspring populations to form a temporary population of size 2N. Perform non-dominated sorting and crowding distance calculation on the temporary population, and select the N best candidate line selection strategies as the new generation population to ensure that excellent candidate line selection strategies are not lost in the evolution process.

[0089] (7) Termination of iteration. Repeat the above steps. When the Pareto front shows no significant change for several consecutive generations, generate a set containing multiple candidate line selection strategies.

[0090] The set of multiple candidate line selection strategies is shown in Equation (21): Formula (21); in, Let m be a set containing m candidate route selection strategies. Each corresponds to a candidate line selection strategy.

[0091] Optionally, the step of selecting the optimal route selection strategy from the set based on the load level, the power grid carbon emission factor, and the new energy penetration rate using a pre-determined multi-attribute decision algorithm includes: constructing a decision matrix based on a set of total carbon footprints and route selection accuracy corresponding to each candidate route selection strategy; standardizing the decision matrix to obtain a standardized decision matrix; weighting the standardized decision matrix to obtain a weighted decision matrix; determining the positive ideal solution and the negative ideal solution of the weighted decision matrix, and calculating the closeness between each candidate route selection strategy and the positive ideal solution and the negative ideal solution; and determining the candidate route selection strategy corresponding to the maximum closeness as the optimal route selection strategy.

[0092] A decision matrix is ​​constructed based on the total carbon footprint and the route selection accuracy corresponding to each candidate route selection strategy. This decision matrix includes a comprehensive operating condition coefficient and an environmental value orientation coefficient.

[0093] The calculation of the comprehensive coefficient of operating conditions is shown in formula (22): Formula (22); The environmental value orientation coefficient is calculated as shown in formula (23): Formula (23); In formulas (22) and (23), This is the comprehensive coefficient for operating conditions. For environmental value orientation coefficient, , representing the normalized load value; , representing the normalized value of penetration rate; , representing the normalized value of the carbon emission factor; , , The coefficients are constant and satisfy the following conditions: Preferably, , , .

[0094] The standardized decision matrix includes the normalized comprehensive coefficient of operating conditions and the normalized environmental value orientation coefficient.

[0095] The comprehensive coefficient of operating conditions is normalized to obtain the normalized comprehensive coefficient of operating conditions. As shown in formula (24): Formula (24); The environmental value orientation coefficient is normalized to obtain the normalized environmental value orientation coefficient. As shown in formula (25): Formula (25); Pareto Solution Set There is a set of m candidate route selection strategies, and each candidate route selection strategy includes a route selection accuracy. Total carbon footprint .

[0096] The decision matrix is ​​constructed as shown in formula (26): Formula (26); in, For decision matrix; middle, The element in the decision matrix is ​​j, which takes the value 2, and m is the number of candidate line selection strategies. "2" indicates that there are two evaluation metrics, namely: and . Let represent the element in the first column of the decision matrix, which is the accuracy of the ith candidate route selection strategy; Let represent the element in the second column of the decision matrix, which is the total carbon footprint of the i-th candidate route selection strategy.

[0097] Since a higher route selection accuracy is better, while a lower total carbon footprint is better, a differential processing is needed to eliminate the influence of dimensions. Therefore, a reciprocal transformation is applied to the total carbon footprint to obtain the reciprocal-transformed total carbon footprint. As shown in formula (27): Formula (27); The decision matrix is ​​standardized to obtain the standardized decision matrix. As shown in formula (28): Formula (28); in, Standardized decision matrix The primary indicator; Standardized decision matrix The second indicator.

[0098] Based on the normalized comprehensive coefficient of operating conditions For standardized decision matrices The first indicator We perform weighted analysis to obtain the first index value corresponding to the i-th candidate route selection strategy. As shown in formula (29): Formula (29); Based on the normalized environmental value orientation coefficient For standardized decision matrices The second indicator We perform weighted analysis to obtain the second index value corresponding to the i-th candidate route selection strategy. As shown in formula (30): Formula (30); Based on the first indicator value Second indicator value Determine the weighted decision matrix .

[0099] The positive ideal solution of the weighted decision matrix is ​​determined as shown in formula (31): Formula (31); in, For the positive ideal solution of the weighted decision matrix, The positive ideal solution for the first indicator. This is the positive ideal solution for the second indicator. Let be the first index value corresponding to the i-th candidate route selection strategy. Let be the second index value corresponding to the i-th candidate route selection strategy. Let be the maximum value of the first indicator corresponding to the i candidate line selection strategies. It represents the maximum value of the second indicator corresponding to the i candidate line selection strategies.

[0100] The negative ideal solution of the weighted decision matrix is ​​determined as shown in formula (32): Formula (32); in, The negative ideal solution of the weighted decision matrix. The negative ideal solution for the first indicator. This is the negative ideal solution for the second indicator. Let be the minimum value of the first indicator corresponding to the i candidate line selection strategies. Let be the minimum value of the second indicator corresponding to the i candidate line selection strategies.

[0101] Calculate the Euclidean distance from each candidate route selection strategy to the positive and negative ideal solutions, as shown in formula (33): Formula (33); in, Let Euclidean distance be the distance from the candidate route selection strategy to the positive ideal solution. Let Euclidean distance be the distance from the candidate line selection strategy to the negative ideal solution.

[0102] The proximity of each candidate route selection strategy to the positive and negative ideal solutions is calculated as shown in formula (34): Formula (34); in, For the degree of closeness.

[0103] Sort the candidates by proximity from highest to lowest, and select the candidate with the highest proximity as the optimal route selection strategy for the current scenario.

[0104] Optionally, the predetermined multi-attribute decision algorithm is an approximation of the ideal solution sorting method.

[0105] Figure 2 A schematic diagram of a carbon footprint-constrained distribution network grounding fault location system provided in this application embodiment is shown below. Figure 2 As shown, this application proposes a carbon footprint-constrained distribution network grounding fault selection system. The system 200 includes: a data acquisition and calculation unit 210, a fault judgment unit 220, a carbon footprint calculation unit 230, a strategy optimization unit 240, a decision-making unit 250, and an execution unit 260; wherein: The data acquisition and calculation unit is used to acquire real-time electrical quantity data and operating scenario parameters of the distribution network; the operating scenario parameters include load level and real-time generator output data; based on the real-time generator output data, the real-time grid carbon emission factor is calculated; based on the load level and the real-time generator output data, the renewable energy penetration rate is calculated. The fault determination unit is used to determine whether a ground fault has occurred in the distribution network based on the electrical quantity data; and to determine the fault start time when it is determined that a ground fault has occurred in the distribution network. The carbon footprint calculation unit is used to calculate the total carbon footprint of the grounding fault during fault persistence loss, line selection operation, line selection device and fault location inspection based on the fault initiation time, the power grid carbon emission factor and the pre-built carbon footprint quantification model. The strategy optimization unit is used to obtain the route selection accuracy corresponding to different route selection strategies; based on the total carbon footprint and the route selection accuracy corresponding to different route selection strategies, it optimizes through a pre-constructed multi-objective optimization model to generate a set containing multiple candidate route selection strategies, each of which corresponds to a set of total carbon footprint and route selection accuracy. The decision-making unit is used to select the optimal route strategy from the set based on the load level, the power grid carbon emission factor and the new energy penetration rate through a pre-determined multi-attribute decision-making algorithm. The execution unit is used to execute the optimal route strategy.

[0106] It should be noted that the description of the above system embodiments is similar to the description of the above method embodiments, and has similar beneficial effects. For technical details not disclosed in the system embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0107] It should be noted that, in the embodiments of this application, if the above-mentioned carbon footprint-constrained distribution network grounding fault selection method is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to the related technology, 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 an electronic device to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware and software combination.

[0108] Correspondingly, embodiments of this application provide a computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the steps in the carbon footprint-constrained distribution network grounding fault location method described in any of the above embodiments. Correspondingly, embodiments of this application also provide a computer program product, which, when executed by a processor of an electronic device, is used to implement the steps in the carbon footprint-constrained distribution network grounding fault location method described in any of the above embodiments.

[0109] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0110] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0111] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0112] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected to achieve the purpose of the embodiments of this application according to actual needs. In addition, each functional unit in the embodiments of this application may be fully integrated into one processing unit, or each unit may be a separate unit, or two or more units may be integrated into one unit; the integrated unit may be implemented in hardware or in the form of hardware plus software functional units.

[0113] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to related technologies, 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 the device automatic test line to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0114] The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined to obtain new method embodiments without conflict. The features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined to obtain new method embodiments or device embodiments without conflict.

[0115] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A carbon footprint-constrained method for selecting grounding faults in distribution networks, characterized in that, The method includes: The system acquires real-time electrical quantity data and operating scenario parameters of the power distribution network; the operating scenario parameters include load level and real-time generator output data; based on the real-time generator output data, it calculates the real-time power grid carbon emission factor; and based on the load level and real-time generator output data, it calculates the renewable energy penetration rate. Based on the electrical quantity data, determine whether a ground fault has occurred in the distribution network; when it is determined that a ground fault has occurred in the distribution network, determine the fault initiation time; Based on the fault initiation time, the power grid carbon emission factor, and the pre-built carbon footprint quantification model, the total carbon footprint generated by the grounding fault during fault persistence loss, line selection operation, line selection device, and fault location patrol is calculated. Obtain the route selection accuracy corresponding to different route selection strategies; based on the total carbon footprint and the route selection accuracy corresponding to different route selection strategies, optimize through a pre-constructed multi-objective optimization model to generate a set containing multiple candidate route selection strategies, each of which corresponds to a set of total carbon footprint and route selection accuracy; Based on the load level, the power grid carbon emission factor, and the new energy penetration rate, the optimal route strategy is selected from the set using a pre-determined multi-attribute decision algorithm. Execute the optimal line strategy described above.

2. The method according to claim 1, characterized in that, The calculation of the real-time grid carbon emission factor based on the real-time output data of the generator set includes: The total power generation is determined based on the real-time output data of the generator set; Based on the real-time output data of the generator sets and the unit power generation carbon emission factor of each generator set, the total carbon emissions of the distribution network are determined. The carbon emission factor of the power grid is determined based on the total carbon emissions and the total power generation.

3. The method according to claim 1, characterized in that, The real-time power output data of the generator sets includes real-time power output data of wind turbines and real-time power output data of photovoltaic units; the calculation of the renewable energy penetration rate based on the load level and the real-time power output data of the generator sets includes: Based on the real-time output data of the wind turbine and the real-time output data of the photovoltaic unit, the real-time output data of the new energy unit is determined. The new energy penetration rate is calculated based on the real-time output data of the new energy units and the load level.

4. The method according to claim 1, characterized in that, The determination of whether a ground fault has occurred in the distribution network based on the electrical quantity data includes: Monitor the zero-sequence voltage of the bus, and determine that a ground fault has occurred in the distribution network when the absolute value of the zero-sequence voltage of the bus exceeds the zero-sequence voltage initiation threshold; and / or monitor the sudden change in the zero-sequence current of the bus, and determine that a ground fault has occurred in the distribution network when the absolute value of the sudden change in the zero-sequence current of the bus exceeds the zero-sequence current initiation threshold.

5. The method according to claim 1, characterized in that, The total carbon footprint of the grounding fault, including fault persistence loss, line selection operation, line selection device, and fault location patrol, is calculated based on the fault initiation time, the power grid carbon emission factor, and a pre-built carbon footprint quantification model. Based on the real-time power grid carbon emission factor, the first carbon footprint generated by the continuous loss of the fault, the second carbon footprint generated by the line selection operation, the third carbon footprint generated by the line selection device, and the fourth carbon footprint generated by the fault location and line inspection are calculated from the fault initiation time to the fault line being selected. The total carbon footprint is determined based on the first carbon footprint, the second carbon footprint, the third carbon footprint, and the fourth carbon footprint.

6. The method according to claim 1, characterized in that, The pre-built multi-objective optimization model takes minimizing the route selection error rate and minimizing the total carbon footprint of the entire fault process as optimization objectives, and uses the route selection strategy as the decision variable for solution; the route selection strategy includes the route selection algorithm type, data window length, sampling frequency, start threshold, and whether it includes positioning function.

7. The method according to claim 1, characterized in that, The step of selecting the optimal route strategy from the set based on the load level, the power grid carbon emission factor, and the new energy penetration rate through a pre-determined multi-attribute decision algorithm includes: A decision matrix is ​​constructed based on a set of total carbon footprints and route selection accuracy corresponding to each candidate route selection strategy. The decision matrix is ​​standardized to obtain a standardized decision matrix; The standardized decision matrix is ​​weighted to obtain a weighted decision matrix; Determine the positive and negative ideal solutions of the weighted decision matrix, and calculate the closeness of each candidate line selection strategy to the positive and negative ideal solutions; The candidate line selection strategy corresponding to the maximum proximity is determined as the optimal line selection strategy.

8. The method according to claim 7, characterized in that, The predetermined multi-attribute decision algorithm is the sorting method for approximating the ideal solution.

9. A carbon footprint-constrained distribution network grounding fault location system, characterized in that, The system includes: a data acquisition and calculation unit, a fault diagnosis unit, a carbon footprint calculation unit, a strategy optimization unit, a decision-making unit, and an execution unit; wherein: The data acquisition and calculation unit is used to acquire real-time electrical quantity data and operating scenario parameters of the distribution network; the operating scenario parameters include load level and real-time generator output data; based on the real-time generator output data, the real-time grid carbon emission factor is calculated; based on the load level and the real-time generator output data, the renewable energy penetration rate is calculated. The fault determination unit is used to determine whether a ground fault has occurred in the distribution network based on the electrical quantity data; and to determine the fault start time when it is determined that a ground fault has occurred in the distribution network. The carbon footprint calculation unit is used to calculate the total carbon footprint of the grounding fault during fault persistence loss, line selection operation, line selection device and fault location inspection based on the fault initiation time, the power grid carbon emission factor and the pre-built carbon footprint quantification model. The strategy optimization unit is used to obtain the route selection accuracy corresponding to different route selection strategies; based on the total carbon footprint and the route selection accuracy corresponding to different route selection strategies, it optimizes through a pre-constructed multi-objective optimization model to generate a set containing multiple candidate route selection strategies, each of which corresponds to a set of total carbon footprint and route selection accuracy. The decision-making unit is used to select the optimal route strategy from the set based on the load level, the power grid carbon emission factor and the new energy penetration rate through a pre-determined multi-attribute decision-making algorithm. The execution unit is used to execute the optimal route strategy.

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