Method and device for repairing root cause of non-convergence of main grid power flow, equipment and storage medium

Abstract

The application provides a root cause repair method and device for main grid power flow divergence, equipment, and storage medium, and belongs to the technical field of power system automation analysis and dispatching. The method comprises the following steps: acquiring multi-source equipment account data, determining a core area of power flow divergence according to the multi-source equipment account data, and identifying target equipment from the core area; for each target equipment, performing multi-dimensional cross verification on the target equipment, determining associated cause group equipment of power flow divergence according to the verification result; for each associated cause group equipment, determining the standard voltage of the equipment; then determining the rated resistance unit value and the rated reactance unit value, combining the real-time load rate of the main transformer, and performing multi-cause collaborative repair on the associated cause group equipment to obtain a repair result. The application can automatically locate the root cause of the main grid power flow divergence and collaboratively repair the associated cause group equipment.

Description

Technical Field

[0001] This application belongs to the field of power system automation analysis and dispatching technology, and more specifically, it relates to a root cause repair method, device, equipment, and storage medium for main grid power flow non-convergence. Background Technology

[0002] Power flow analysis is a core technical means for main grid scheduling, operation, planning verification, and security assessment. While local main grids have accumulated massive amounts of equipment ledgers, real-time operational data, and topology data, the problem of power flow non-convergence persists in engineering practice. Even after basic ledger verification, iterative oscillations or ill-conditioned Jacobian matrices frequently occur under extreme conditions such as light and heavy loads, resulting in a lack of reliable data support for scheduling decisions. In cases of main grid power flow non-convergence, impedance mismatch at the main transformer ledger level (mismatch between ledger parameters and nameplate models) is one of the core contributing factors.

[0003] Existing solutions typically analyze parameters of a single type of equipment in isolation, failing to effectively and automatically locate the core areas and root causes of power flow non-convergence from multi-source equipment ledger data. This results in root cause investigation relying on manual item-by-item verification, which is inefficient. Furthermore, after locating the root cause, there is a lack of systematic and collaborative repair methods for scenarios with multiple coupled causes, such as missing voltage levels or incorrect impedance parameters. Manual trial and error adjustments of parameters are usually used, which makes it difficult to guarantee the convergence of power flow calculations after repair and cannot adapt to the dynamic parameter correction requirements under different operating conditions. Summary of the Invention

[0004] This application provides a method, apparatus, device, and storage medium for root cause repair of main network power flow non-convergence, in order to solve the problems of low efficiency in root cause investigation and difficulty in guaranteeing convergence after repair. It aims to achieve automatic location of associated cause group devices and multi-cause collaborative repair with adaptive operating conditions, thereby improving the convergence of power flow calculation.

[0005] To achieve the above objectives, the technical solutions provided in this application are as follows: Firstly, a root cause repair method for mainnet power flow non-convergence is provided, including: Obtain multi-source equipment ledger data for the target area, and based on the multi-source equipment ledger data, determine the core area of ​​power flow non-convergence in the target area. The multi-source equipment ledger data includes main transformer equipment ledger data and transmission line equipment ledger data. For each target device in the core area, multi-dimensional cross-validation is performed on the target device to obtain the corresponding validation results; Based on the verification results, identify the associated cause group of devices for power flow non-convergence from each target device; Perform a repair operation for each associated trigger group of devices: The repair operations include: Obtain the maximum allowable current carrying capacity and the average apparent power of the device; The assumed current is determined based on the maximum allowable current carrying capacity and the adaptive dynamic correction factor for operating conditions. The adaptive dynamic correction factor for operating conditions is used to characterize the coefficient for dynamically adjusting the maximum allowable current carrying capacity under different operating conditions of the equipment. The standard voltage of the device is determined based on the assumed average current and apparent power. Determine the per-unit values ​​of the rated resistance and rated per-unit reactance of the equipment based on the standard voltage. The ratio of the per-unit value of rated resistance to the per-unit value of rated reactance shall be used as the first ratio. Based on the first ratio, multi-factor collaborative repair is performed on the device to obtain the repair result.

[0006] Secondly, a root cause repair device for mainnet power flow non-convergence is provided, comprising: The core area determination module is used to acquire multi-source equipment ledger data of the target area, and based on the multi-source equipment ledger data, determine the core area of ​​power flow non-convergence from the target area. The multi-source equipment ledger data includes main transformer equipment ledger data and transmission line equipment ledger data. The verification module is used to perform multi-dimensional cross-verification on each target device in the core area and obtain the corresponding verification results. The associated cause group device determination module is used to determine the associated cause group devices for power flow non-convergence from each target device based on the verification results. The repair module is used to perform repair operations for each associated cause group of devices; Specifically, the repair module, when performing repair operations, is used for: Obtain the maximum allowable current carrying capacity and the average apparent power of the device; The assumed current is determined based on the maximum allowable current carrying capacity and the adaptive dynamic correction factor for operating conditions. The adaptive dynamic correction factor for operating conditions is used to characterize the coefficient for dynamically adjusting the maximum allowable current carrying capacity under different operating conditions of the equipment. The standard voltage of the device is determined based on the assumed average current and apparent power. Determine the per-unit values ​​of the rated resistance and rated per-unit reactance of the equipment based on the standard voltage. The ratio of the per-unit value of rated resistance to the per-unit value of rated reactance shall be used as the first ratio. Based on the first ratio, multi-factor collaborative repair is performed on the device to obtain the repair result.

[0007] Thirdly, embodiments of this application also provide an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the root cause repair method for main network power flow non-convergence provided by any possible implementation of the first aspect.

[0008] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the root cause repair method for main network power flow non-convergence provided by any possible implementation of the first aspect.

[0009] The beneficial effects of the technical solution provided in this application are as follows: The root cause repair method, apparatus, equipment, and storage medium for main grid power flow non-convergence provided in this application embodiment, compared with related technologies, determines the core area of ​​power flow non-convergence through multi-source equipment ledger data, and then performs multi-dimensional cross-verification on target equipment within the core area, automatically identifying associated cause groups of equipment with multiple coupled causes such as missing voltage levels and incorrect impedance parameters, thus improving the efficiency of root cause investigation and location. Secondly, after locating the associated cause group of equipment, this embodiment determines the per-unit values ​​of rated resistance and reactance and their ratios based on the operating condition adaptive dynamic correction factor combined with the maximum allowable current carrying capacity to back-calculate the standard voltage. Then, it performs multi-cause collaborative repair based on the ratio and the real-time load rate of the main transformer. This method can dynamically adjust the correction weights of resistance and reactance according to different load ranges (light load, heavy load, etc.), avoiding convergence problems such as Jacobian matrix distortion under light load and reactive power oscillation under heavy load, and achieving stable convergence of power flow across the entire load range. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below.

[0011] Figure 1 A flowchart illustrating the root cause repair method for mainnet power flow non-convergence provided in this application embodiment; Figure 2 A flowchart illustrating the repair method provided in an embodiment of this application; Figure 3 A structural block diagram of the root cause repair device for main network power flow non-convergence provided in the embodiments of this application; Figure 4 A schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] The embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the embodiments described below with reference to the accompanying drawings are exemplary descriptions for explaining the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions of the embodiments of this application.

[0013] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the terms “comprising” and “including” as used in embodiments of this application mean that the corresponding feature can be implemented as the presented feature, information, data, step, operation, element, and / or component, but do not exclude implementation as other features, information, data, step, operation, element, component, and / or combinations thereof supported by the art. It should be understood that when we say that an element is “connected” or “coupled” to another element, the one element can be directly connected or coupled to the other element, or it can mean that the one element and the other element establish a connection relationship through an intermediate element. Furthermore, “connected” or “coupled” as used herein can include wireless connection or wireless coupling. The term “and / or” as used herein indicates at least one of the items defined by the term; for example, “A and / or B” can be implemented as “A,” or as “B,” or as “A and B.” When describing multiple (two or more) items, if the relationship between the multiple items is not explicitly defined, the multiple items can refer to one, several or all of the multiple items. For example, the description of "parameter A includes A1, A2, A3" can be implemented as parameter A includes A1 or A2 or A3, or it can be implemented as parameter A includes at least two of the three items A1, A2 and A3.

[0014] This application provides a root cause repair method for main network power flow non-convergence, which can be executed by electronic devices, such as... Figure 1 As shown, the method may include: S101: Obtain multi-source equipment ledger data for the target area, and based on the multi-source equipment ledger data, determine the core area of ​​non-converging power flow from the target area.

[0015] In this embodiment, multi-source equipment ledger data refers to a diverse and heterogeneous data set stored in a power dispatch automation system (such as an EMS system or a PMS system), describing the inherent attributes, static parameters, and operating status of main grid equipment (such as main transformer equipment and transmission line equipment). This multi-source equipment ledger data may include main transformer equipment ledger data and transmission line equipment ledger data. Specifically, main transformer equipment ledger data refers to data describing the physical characteristics and electrical parameters of the main transformer, and transmission line equipment ledger data refers to data describing the physical characteristics and electrical parameters of the transmission line. The target area refers to the power system subnet or the entire network to be analyzed for power flow non-convergence, such as a municipal power grid, the power supply area of ​​a substation, or a designated dispatch jurisdiction area.

[0016] The multi-source equipment ledger data may also include real-time operating data (e.g., three-phase current, active power, reactive power, and voltage amplitude), topology data (e.g., electrical connections between substations / buses and equipment), and environmental data (e.g., ambient temperature and altitude of the area where the equipment is located).

[0017] In this embodiment, after acquiring multi-source equipment ledger data, the data is cleaned and standardized. Specifically, for the same measurement quantity (e.g., three-phase current) of the same equipment, its mean μ and standard deviation σ over the past 30 days are calculated. Data exceeding the range of [μ-3σ, μ+3σ] are marked as outliers and removed. For the missing values ​​after removal, if the missing proportion is less than or equal to a preset missing proportion threshold (e.g., 30%), linear interpolation is used to fill the missing values; if the missing proportion is greater than the preset missing proportion threshold, the average value of equipment of the same model and operating conditions is used to fill the missing values ​​(e.g., the average current of main transformers of the same capacity and cooling method under similar load rates).

[0018] In this embodiment, the unit of voltage data is kV, the unit of power data is MW or Mvar, the unit of impedance data (resistance, reactance) is Ω (nominal value) or per-unit value (pu), and the unit of temperature data is ℃.

[0019] In this embodiment, real-time operating data is aligned according to a preset time interval (e.g., 15 minutes) based on the apparent power formula. Determine the actual apparent power, where, P represents actual apparent power (MVA), P represents active power (MW), and Q represents reactive power (Mvar); according to the basic formula for apparent power... Determine the apparent power, where, This indicates the calculation of apparent power (MVA). This indicates the candidate voltage level (kV) corresponding to the equipment model. This represents the average three-phase current (A). The verification deviation is determined based on the actual apparent power and the calculated apparent power, using the verification deviation calculation formula. This verification deviation calculation formula can be... If the verification deviation is greater than the preset first verification deviation threshold (e.g., 20%), the section is directly removed; if the verification deviation is not less than the preset second verification deviation threshold (e.g., 5%) and not greater than the preset first verification deviation threshold, the weight of the section is reduced (e.g., the weight coefficient is 1). Then, the weighted average method is used to calculate the average of the core operating indicators over the past 30 days.

[0020] In one embodiment of this application, determining the core region of power flow non-convergence from the target region based on multi-source device ledger data includes: Key features are extracted from multi-source equipment ledger data, an adjacency list is generated based on the key features, and core indicators are determined based on the key features and the adjacency list. Based on the core indicators and the power flow equation, the core region where the power flow does not converge is determined.

[0021] In this embodiment, key metrics include average apparent power, real-time transformer load factor, and line transmission power density. An adjacency table is used to characterize the connection relationships between transformer equipment and transmission line equipment.

[0022] In this embodiment, key features include basic identity and type features, core electrical parameter features, and topology-related auxiliary features. For basic identity and type features, the main transformer equipment extracts the number of windings, cooling method, number of phases, voltage regulation method, manufacturer code, and product serial number; the transmission line equipment extracts the conductor material (e.g., steel-cored aluminum stranded wire LGJ, steel-cored aluminum alloy stranded wire JL / G1A), number of splits, nominal cross-section, and line length. For core electrical parameter features, the main transformer equipment extracts the rated voltage, rated capacity, short-circuit loss, and short-circuit voltage percentage for the high-voltage side, medium-voltage side, and low-voltage side; the transmission line equipment extracts the rated voltage, unit length resistance reference value, and reactance reference value. For topology-related auxiliary features, the equipment's substation code, bay number, connecting bus number, main transformer winding connection group (e.g., YNd11), and the starting and ending substation identifiers of the transmission line equipment are extracted.

[0023] In this embodiment, the method for generating an adjacency list (topological association adjacency list) based on key features can be as follows: Integrate topological association features such as plant codes, bay numbers, and connecting bus numbers extracted from equipment models, as well as the equipment physical connection relationship table exported from the EMS system; Define the power plant as the root node, the busbar as the secondary node, and the main transformer and transmission line as the tertiary node to construct a four-level undirected adjacency matrix. In this matrix, an element value of 1 indicates a direct electrical connection, and an element value of 0 indicates no connection. The adjacency matrix is ​​converted into a linked adjacency list based on the sparse matrix compression storage method. Each node stores the ID, connection type and voltage level of all its directly connected nodes. Topology verification is performed based on bus power balance and voltage level consistency. If the deviation between the bus power balance and the theoretical calculation value is no greater than 5%, and the deviation between the voltage level consistency and the standard voltage level is no greater than 2%, then the verification is considered successful, and a topology association adjacency table is generated.

[0024] This embodiment is based on the formula for calculating the average apparent power. Calculate the mean apparent power, where, It represents the average active power (MW). This represents the average reactive power (Mvar). This represents the average apparent power (MVA). Based on the rated capacity and average apparent power of the main transformer, and using the formula for calculating the real-time load rate of the main transformer, the real-time load rate is calculated. The formula for calculating the real-time load rate of the main transformer can be: in, This represents the rated capacity (MVA) of the main transformer equipment. Based on the average apparent power and the line length of the transmission line equipment, and using the line transmission power density calculation formula, the line transmission power density of the transmission line equipment is calculated. The line transmission power density calculation formula can be... ,in, Indicates the length of the line (km).

[0025] In this embodiment, the specific method for determining the core region of non-convergent power flow based on core indicators and power flow equations is as follows: The preprocessed data is then connected to a multi-node power system network. The node power balance equations of the Newton-Raphson power flow calculation model are used, and in this embodiment, the maximum number of iterations is set to 50. A multi-node power system network refers to a topology network containing several nodes, where node i and node j refer to any two interconnected electrical nodes in the topology network.

[0026] In this embodiment, the node power balance equation can be: ,in, This represents the injected active power (MW) at node i. This represents the injected reactive power (Mvar) at node i. Let represent the voltage amplitude (pu) at node i and node j, respectively. This represents the voltage phase angle difference (rad) between node i and node j. These represent the conductance and susceptance in the i-th row and j-th column of the node admittance matrix, respectively. The node admittance matrix refers to the electrical connection relationship between node i and node j.

[0027] In this embodiment, the active power residual and reactive power residual of the power flow equation are determined based on the difference between the node injected power and the calculated power after each iteration. If the active power residual or reactive power residual exceeds the preset residual threshold, the device that exceeds the preset residual threshold and the device / bus connected to the device are regarded as the core region of power flow non-convergence. All main transformer devices and transmission line devices in the core region are extracted as target devices.

[0028] S102: For each target device in the core area, perform multi-dimensional cross-validation on the target device to obtain the corresponding validation results.

[0029] In one embodiment of this application, before performing cross-validation on the target device and obtaining the validation result, the method further includes: Obtain the target equipment's rated capacity, high-voltage side rated voltage, short-circuit loss, short-circuit voltage percentage, and voltage level; The target device was cross-validated to obtain the following validation results: The rated capacity, high-voltage side rated voltage, short-circuit loss, short-circuit voltage percentage, and voltage level of the target equipment are verified according to the preset verification method to obtain the parameter integrity verification result. If the parameter integrity verification result fails, the target equipment is determined to be an abnormal equipment with missing parameters. Obtain the resistors, reactances, standard nameplate resistors, and standard nameplate reactances from the equipment ledger; Based on the resistance, reactance, standard nameplate resistance, and standard nameplate reactance, determine the ledger resistance deviation and ledger reactance deviation respectively; If the deviation of the ledger resistance or the deviation of the ledger reactance is higher than the preset deviation threshold, the target equipment is determined to be a device with abnormal parameter consistency. If the target equipment is a main transformer, then obtain the measured active power, measured reactive power, average measured current, rated resistance, rated reactance, measured active power and measured reactive power on the high-voltage side of the main transformer. Based on the measured active power on the high-voltage side, the average measured current on the high-voltage side, and the rated resistance, the theoretical active power on the low-voltage side is determined. Based on the theoretical active power on the low-voltage side and the measured active power on the low-voltage side, the power deviation is determined. If the power deviation is higher than the preset power deviation threshold, the main transformer is determined to be a device with abnormal operating parameters. If the target equipment is a power transmission line, then obtain the measured active power, measured reactive power, reference voltage, and measured current value of the power transmission line. The theoretical current value is determined based on the measured active power, measured reactive power, and rated voltage of the transmission line. If the difference between the theoretical current value and the measured current value is higher than the preset current deviation threshold, the transmission line equipment is determined to be an equipment with abnormal operating parameters.

[0030] In this embodiment, the core parameters of the acquired target equipment (rated capacity, high-voltage side rated voltage, short-circuit loss, short-circuit voltage percentage, voltage level) are verified for completeness. A three-tiered progressive verification logic is adopted: field existence verification → non-empty verification → numerical reasonableness verification. If any step fails, the core parameter verification is deemed to have failed. Specifically, each core parameter field is traversed to check if it exists in the standardized ledger. If all fields exist in the standardized ledger, the field existence verification is deemed to have passed. The next step is to check if the field value corresponding to each field is empty, NaN, or not in a numeric format. If all field values ​​are not empty, NaN, or not in a numeric format, the non-empty verification is deemed to have passed. The next step is to check if the field value falls within the reasonable numerical range commonly used in the power industry (e.g., the voltage level must be a standard value of 35kV, 110kV, 220kV, 500kV, etc., and must not be ≤0). If all field values ​​fall within the reasonable numerical range commonly used in the power industry, the numerical reasonableness verification is deemed to have passed. If any of the above conditions are not met, the parameter is deemed to be missing.

[0031] Specifically, the criteria for passing the rated capacity verification are: the field exists and is not empty, the value is positive, and it falls within the standard capacity range of the main transformer of the corresponding voltage level; The standard for passing the high-voltage side rated voltage verification is: the field exists and is not empty, and the value is the main grid standard voltage level specified in DL / T5003-2017; The short-circuit loss verification pass standard is: the field exists and is not empty, the value is positive, and the relative deviation from the standard nameplate value of the same model and capacity main transformer is ≤±20%; The short-circuit voltage percentage verification pass standard is: the field exists and is not empty, the value falls within the standard range of the corresponding voltage level main transformer, and the relative deviation from the standard nameplate value of the same model and capacity main transformer is ≤±10%; The voltage level verification pass criteria are: the field exists and is not empty, and the value is consistent with the rated voltage value on the high-voltage side; If any of the above core parameters fails the verification, the target device is determined to be an abnormal device with missing parameters, and the abnormality level is marked according to the importance of the parameters.

[0032] In this embodiment, if a parameter is missing, the anomaly type is marked according to the importance of the missing field. For example, if the missing parameter is the voltage level or the rated voltage on the high-voltage side, it is marked as a "core missing - highest priority" parameter missing anomaly device; if the missing parameter is the main transformer short-circuit loss, short-circuit voltage percentage, or rated capacity, it is marked as a "core missing - second highest priority" parameter missing anomaly device; if the missing parameter is a non-core field such as the equipment manufacturing date, manufacturer, cooling method, or installation date, it is marked as a general missing parameter missing device anomaly.

[0033] In this embodiment, for core missing parameters, no preprocessing or filling is performed; only anomalies are marked and included in the corresponding priority repair queue to avoid introducing human error through simple filling. For general missing parameters, hierarchical filling can be performed according to preprocessing rules, specifically as follows: If the missing percentage of a single field is ≤30%, time series linear interpolation is used for filling, calculating the missing value based on the effective data points adjacent to the field; if the missing percentage of a single field is >30%, the weighted average of equipment of the same plant, model, and operating conditions is used for filling, with the weighting coefficient positively correlated with the similarity of equipment load rate. In this embodiment, the filled non-core parameters are only used for auxiliary calculation and data integrity verification and do not participate in the core parameter repair and power flow calculation process.

[0034] In this embodiment, for the main transformer equipment, the resistance R is read from the main transformer equipment ledger data. 台 and reactance X 台 ; Look up the standard nameplate resistor R corresponding to this main transformer model from the standard nameplate parameter database. 标 and standard nameplate reactance X 标 The resistance deviation of the ledger is calculated according to the formula for calculating the resistance deviation of the ledger. This formula can be: ,in, This indicates the register resistance (Ω) of the main transformer equipment. This indicates the standard nameplate resistance (Ω) corresponding to the model of the main transformer equipment. The reactance deviation is calculated according to the formula for calculating reactance deviation in the ledger. This formula can be... ,in, This indicates the register reactance (Ω) of the main transformer equipment. This indicates the standard nameplate reactance (Ω) corresponding to the model of the main transformer equipment. If the deviation of the ledger resistance and / or the deviation of the ledger reactance are not less than the corresponding preset deviation threshold (e.g., both are 15%), the equipment is judged to have abnormal parameter consistency.

[0035] In this embodiment, if the target device is a main transformer, the calculated active power and calculated reactive power on the low-voltage side are determined according to the power balance equation. The difference between the calculated active power and the measured active power on the low-voltage side is taken as the power deviation. If the power deviation exceeds a preset power deviation threshold, the main transformer is determined to be a device with abnormal operating parameters. The power balance equation can be... ,in, This indicates the calculated active power on the low-voltage side. This indicates the calculated reactive power on the low-voltage side; This indicates the measured active power on the high-voltage side. This indicates the measured reactive power on the high-voltage side; This represents the average measured current on the high-voltage side. Indicates the rated resistance of the main transformer equipment. This indicates the rated reactance of the main transformer equipment.

[0036] This embodiment determines whether the main transformer is a device with abnormal operating parameter logic based on the data from the low-voltage side of the main transformer, which helps to identify parameter adaptation problems of the main transformer under real operating conditions from the perspective of operating logic.

[0037] In this embodiment, if the target device is a transmission line device, the theoretical current value is determined according to the Ohm's Law derivative equation. If the difference between the theoretical current value and the measured current value is higher than a preset current deviation threshold (e.g., 30%), the transmission line device is determined to be a device with abnormal operating parameters. The Ohm's Law derivative equation in this embodiment can be... ,in, This represents the theoretically calculated current value. This represents the measured active power of transmission line equipment. This indicates the reactive power of the transmission line equipment; This indicates the assumed reference voltage when the voltage level is missing.

[0038] This embodiment summarizes the results of the above three verifications to form a verification result list for the target device. The list includes at least: whether there is a parameter missing anomaly, whether there is a parameter consistency anomaly, whether there is an operational parameter logic anomaly, and the corresponding anomaly marker and deviation value.

[0039] As can be seen from the above, this embodiment establishes a hierarchical and progressive equipment anomaly diagnosis system through cross-validation across three dimensions: parameter integrity, parameter consistency, and operational-parameter logic. Specifically, parameter integrity verification prioritizes identifying and categorizing missing core parameters, preventing invalid data from interfering with subsequent calculations. Parameter consistency verification accurately locates mismatched equipment at the ledger level by comparing the impedance between the equipment ledger and the nameplate. Operational-parameter logic verification effectively identifies operational-level mismatched equipment where the fixed rated impedance cannot adapt to actual operating conditions by utilizing measured data, rated parameters, and corresponding deviation thresholds. This embodiment improves the accuracy and efficiency of root cause localization through the aforementioned multi-dimensional cross-validation mechanism.

[0040] S103: Based on the verification results, identify the associated cause group of devices for power flow non-convergence from each target device.

[0041] In one embodiment of this application, based on the verification results, a group of devices associated with power flow non-convergence is determined from each target device, including: Based on the verification results and the preset mapping relationship, the corresponding cause type for each target device is determined. The cause type includes voltage level missing or incorrect, impedance parameter error and / or impedance operating level mismatch. The preset mapping relationship is used to characterize the correspondence between parameter missing anomaly, parameter consistency anomaly and operating parameter logic anomaly and cause type. Encode the trigger types of all target devices into a transaction dataset, with each transaction corresponding to one target device and each transaction including at least one trigger type corresponding to that target device; Based on the transaction dataset, determine the association rules, calculate the confidence score of each association rule, and identify frequent itemsets with confidence scores higher than a preset confidence threshold as strong association rules; the number of strong association rules is at least one. The strong association rules are merged to form a coupling relationship, and the devices that satisfy the coupling relationship are marked as the associated cause group devices.

[0042] This embodiment determines the causes of equipment malfunctions based on verification results, including main transformer impedance mismatch at the ledger level / main transformer impedance mismatch at the operating level, and missing / incorrect voltage levels. Specifically, main transformer impedance mismatch at the ledger level refers to equipment marked as "parameter consistency abnormal" but not marked as "operational-parameter logic abnormal." The core issue is that the ledger parameters do not match the inherent parameters of the equipment hardware. For this cause, the incorrect ledger parameters can be replaced with the corresponding model's standard nameplate parameters. Main transformer impedance mismatch at the operating level refers to equipment not marked as "parameter consistency abnormal" but marked as "operational-parameter logic abnormal." The core issue is that the fixed rated impedance cannot adapt to the current operating conditions. For this cause, the equivalent impedance can be dynamically corrected within the physical compliance range. Voltage level missing / incorrect refers to equipment marked as "core missing - highest priority" with missing voltage level, or equipment whose operational parameter logic deviation exceeds 20% after voltage level is completed; main transformer impedance ledger level mismatch includes main transformer impedance parameter missing equipment marked as "core missing - second highest priority", and equipment marked as "parameter consistency abnormal equipment" but not marked as "operational parameter logic abnormal equipment".

[0043] In this embodiment, the Apriori association rule algorithm is used to construct association rules by using the cause type as an itemset and the device ID as a transaction set to identify the coupling relationship of "voltage level missing / error → impedance parameter error → main transformer impedance operating level mismatch". In this embodiment, devices with coupling relationships are marked as "associated cause group" devices, and a standardized intelligent diagnostic report is generated, which clarifies the core area, abnormal device list, cause type, associated cause group and repair priority (voltage level missing / error is the highest priority, and the priority of associated cause group is higher than the priority of single cause).

[0044] In this embodiment, the coupling relationship is a causal transmission chain of power system parameter errors, namely, an incorrect impedance reduction benchmark caused by a missing / incorrect voltage level, which in turn leads to impedance parameter errors, further manifesting as a mismatch between the fixed rated impedance and the operating power. This embodiment uses the Apriori association rule algorithm to determine the coupling relationship, and the specific steps are as follows: Encode the cause type as an independent item (A indicates voltage level missing / error, B indicates impedance parameter error (ledger level mismatch), C indicates main transformer impedance operating level mismatch), and construct a transaction set by treating a single abnormal device as a transaction, with each transaction containing all cause items (single cause items and combined cause items) for that device.

[0045] In this embodiment, the frequency of occurrence of all single-cause items is counted, i.e., the number of devices containing cause X, where X includes A, B, and C. The support of each single-cause item is calculated according to the first support formula. The formula for the first support can be: .

[0046] In this embodiment, single-inducing items with a first support greater than or equal to a preset first support threshold (e.g., 20%) are combined to obtain a first-order frequent itemset {A,B,C}.

[0047] This embodiment generates second-order and higher-order frequent itemsets based on the first-order frequent itemsets. Specifically, all possible combinations of second-order itemsets are generated based on the first-order frequent itemsets. The frequency of occurrence of each second-order itemset is counted, and a second support is calculated. Combinations with a second support greater than or equal to a preset second support threshold (e.g., 20%) are retained to form second-order frequent itemsets {A,B} and {B,C}. Association rules are generated based on the second-order frequent itemsets, and the confidence of each association rule is calculated using a first confidence calculation formula. The formula for calculating the first confidence level can be: .

[0048] In this embodiment, association rules with a confidence level higher than a preset threshold (e.g., 80%) are selected from the obtained first confidence levels as strong association rules, resulting in A→B (voltage level error leading to impedance error) and B→C (impedance error leading to operating level mismatch). These two strong association rules are chained together to form a third-order coupling relationship A→B→C, resulting in a third-order frequent itemset {A,B,C}. The overall support and overall confidence of this third-order coupling relationship are calculated. Specifically, the number of devices simultaneously containing the three causal items A, B, and C are counted, and the ratio of this number to the total number of abnormal devices is used as the support of the third-order frequent itemset {A,B,C}. The second confidence level of the strong association rule A→B and the third confidence level of B→C are obtained respectively, and the product of the second confidence level and the third confidence level is used as the overall confidence level. ,in, This indicates the overall confidence level.

[0049] In this embodiment, a third-order coupling relationship with a comprehensive confidence level higher than a preset comprehensive confidence level threshold and a comprehensive support level higher than a preset comprehensive support level threshold is defined as an effective coupling relationship.

[0050] In this embodiment, devices that simultaneously satisfy the above-mentioned third-order coupling relationship are marked as associated cause group devices. Their repair sequence strictly follows the link logic of "first complete the voltage level, then recalculate the impedance, and finally verify the operating level matching", and reverse repair is prohibited.

[0051] As can be seen from the above, this embodiment, through the pre-defined mapping relationship between cross-validation anomaly results and cause types, attributes multi-dimensional parameter missing, parameter consistency anomalies, and logical anomalies to three core causes: voltage level missing / error, impedance parameter error, and impedance operating level mismatch, thus achieving standardized coding of anomaly types. Furthermore, this embodiment employs the Apriori association rule mining algorithm to automatically identify frequent itemsets and strong association rules from the target device transaction dataset, and synthesizes third-order coupling relationships based on causal logic. Devices simultaneously satisfying this coupling relationship are marked as associated cause group devices, clarifying the dependencies and repair order among multiple causes. This embodiment achieves joint localization and causal tracing of multiple causes, improving the accuracy of root cause localization and the effectiveness of repair solutions.

[0052] S104: Perform a repair operation for each associated cause group of devices.

[0053] The repair operation in this embodiment includes: Obtain the maximum allowable current carrying capacity and the average apparent power of the device; The assumed current is determined based on the maximum allowable current carrying capacity and the adaptive dynamic correction factor for operating conditions. The adaptive dynamic correction factor for operating conditions is used to characterize the coefficient for dynamically adjusting the maximum allowable current carrying capacity under different operating conditions of the equipment. The standard voltage of the device is determined based on the assumed average current and apparent power. Determine the per-unit values ​​of the rated resistance and rated per-unit reactance of the equipment based on the standard voltage. The ratio of the per-unit value of rated resistance to the per-unit value of rated reactance shall be used as the first ratio. Based on the first ratio, multi-factor collaborative repair is performed on the device to obtain the repair result.

[0054] In this embodiment, the maximum permissible current carrying capacity refers to the maximum permissible current carrying capacity of a single conductor. Based on the link logic of "first completing the voltage level, then recalculating the impedance, and finally verifying the operational level matching," this embodiment first performs an inversion of the voltage level to obtain the accurate standard voltage level of the equipment.

[0055] In this embodiment, for devices with missing or incorrect voltage levels, high-precision automatic completion of voltage levels for main transformers (double / triple windings) and transmission lines can be achieved through multi-environment correction, operating condition adaptive correction, multi-dimensional quantitative matching, and full-link cross-verification.

[0056] This embodiment automatically classifies equipment into three categories based on its characteristics: dual-winding main transformers, three-winding main transformers, and transmission lines. Three-winding main transformers are further subdivided into high / medium / low voltage sides. For each main transformer winding, the maximum allowable current carrying capacity is calculated based on the candidate voltage level corresponding to the equipment, the rated capacity of the main transformer windings, and the multi-environment coupling current carrying capacity correction factor, using the maximum allowable current carrying capacity calculation formula. The maximum allowable current carrying capacity calculation formula can be... ,in, This indicates the rated capacity (MVA) of the main transformer winding. This indicates the candidate voltage level (kV) corresponding to the device. This represents the multi-environment coupling current carrying capacity correction factor. , This indicates the maximum allowable temperature of the main transformer winding (°C; 95°C for oil-immersed main transformers and 120°C for dry-type main transformers). This indicates the average ambient temperature (°C) of the area where the device is located.

[0057] For transmission line equipment, this embodiment uses the IEEE 738-2012 standard formula to calculate the maximum allowable current carrying capacity. The standard formula is as follows: ,in, This indicates the convective heat dissipation power (W / m). This indicates the radiative heat dissipation power (W / m). This represents the solar radiation absorbed power (W / m). Indicates the operating temperature of the conductor Resistance per unit length (Ω / m) under the specified conditions.

[0058] Among them, the resistance per unit length of the conductor at the operating temperature. ,in, This represents the resistance per unit length of the conductor (Ω / m) at 20℃. The resistance per unit length of the conductor can be determined based on the conductor material and nominal cross-section. This indicates the temperature coefficient of resistance of the conductor material at 20℃ (°C). - ¹), for example, the temperature coefficient of resistance of aluminum wire at 20℃ can be 0.00403℃. - ¹, The temperature coefficient of resistance of the steel wire material at 20℃ can be 0.0045℃. - ¹.

[0059] Solar radiation absorption power ,in, This represents the solar radiation absorption coefficient of the conductor surface. If the conductor surface is bright and it is a new conductor, the solar radiation absorption coefficient ranges from 0.23 to 0.46. If the conductor is an old conductor, or the conductor surface is not bright (for example, the conductor surface is covered with ice), the solar radiation absorption coefficient can be 0.9. This represents the standard solar radiation intensity at sea level, taken as 1000 W / m². This represents the altitude correction factor. ( (in meters) indicates altitude. This represents the solar altitude angle, taken as the maximum value of 90° at noon.

[0060] Radiant heat dissipation power ,in, This indicates the emissivity of the conductor surface. If the conductor surface is shiny and it is a new conductor, the emissivity can range from 0.23 to 0.46. If the conductor is an old conductor, or the conductor surface is not shiny (e.g., the conductor surface is covered with ice), the emissivity can be 0.9. The Stefan-Boltzmann constant is represented by 5.67 × 10⁻⁸ W / (m²·K). 4 ), This represents the outer diameter of the conductor (m). If the conductor is covered with ice, the equivalent outer diameter after icing is used. Indicates the ambient temperature (°C).

[0061] The method for determining convective heat dissipation power is: when the ambient wind speed... At a speed of 0.5 m / s, the convective heat dissipation power is determined using the first forced convection formula, wherein the first forced convection formula can be: When the ambient wind speed At a speed of 0.5 m / s², the convective heat dissipation power is determined using the second forced convection formula, where the second forced convection formula can be: In the first forced convection formula and the second forced convection formula, Represents the Reynolds number. , Indicates ambient wind speed (m / s). This indicates air density (kg / m³), which decreases with increasing altitude. Indicates aerodynamic viscosity (Pa) s), Indicates the thermal conductivity of air (W / (m) K), decreases with increasing altitude.

[0062] In this embodiment, after obtaining the maximum allowable carrying capacity, a basic coefficient is set based on the 50%~60% economic load rate criterion of the main network equipment. (For example, the value for main transformer equipment can be 0.5~0.6, and for transmission line equipment it can be 0.55~0.65). In this embodiment, the operating conditions of the equipment can include light load conditions, rated operating conditions, and heavy load conditions. The multi-condition coupling adaptive dynamic correction factor is determined in combination with the actual load rate of the equipment. ,in, Represents the base coefficient. This represents the operating condition coupling sensitivity adjustment factor, for example, 0.2. This indicates the actual load factor (%). This indicates the economic load factor (%), for example, 55%. This represents the multi-condition coupled adaptive dynamic correction factor, with a value range of [0.4, 0.75]. In this embodiment, the basic coefficient is adjusted under light load conditions. Lowered to 0.4~0.5, corresponding to The value range is [0.4, 0.55]; under rated operating conditions, it will be... Set to 0.5~0.6, corresponding to The value range is [0.45, 0.65]; under heavy load conditions, it will... The price was adjusted upwards to 0.65~0.75, corresponding to... The value range is [0.6, 0.75].

[0063] This embodiment determines the assumed current based on the multi-condition coupling adaptive dynamic correction factor and the actual load rate, and according to the assumed current calculation formula. The assumed current calculation formula can be... .

[0064] In one embodiment of this application, before determining the standard voltage of the device based on the assumed current and the average apparent power, the method further includes: Key features are extracted from multi-source equipment ledger data, and an adjacency list is generated based on the key features. The adjacency list is used to represent the connection relationship between the main transformer equipment and the transmission line equipment. The standard voltage of the device is determined based on the assumed average current and apparent power, including: The voltage level is determined by inverse calculation based on the assumed average current and apparent power. The first deviation is determined by comparing the reverse value of the voltage level with the voltage value corresponding to each standard voltage level. The standard voltage level with a first deviation not greater than the preset deviation threshold is used as the candidate standard voltage, and all candidate standard voltages are combined into an initial candidate set. The initial candidate set is filtered based on the electrical characteristics of the device to obtain the target candidate set; The matching degree of each candidate voltage is determined based on the adjacency list and the target candidate set; The candidate voltage with the highest matching degree is used as the standard voltage of the device.

[0065] In this embodiment, the voltage level is determined by using the assumed current and the average apparent power, and the apparent power balance formula. The apparent power balance formula can be... ,in, This represents the mean apparent power (MVA).

[0066] After obtaining the reverse value of the voltage level, this embodiment determines the voltage value corresponding to the core standard voltage level of the main grid according to the DL / T5003-2017 standard.

[0067] This embodiment determines the first deviation (first step deviation threshold constraint) based on the voltage value corresponding to the voltage level and the voltage value corresponding to each standard voltage level. The method for determining the first deviation can be... ,in, This refers to the voltage value corresponding to the standard voltage level. If the voltage level is 110kV or above, then the first deviation will be... Voltage values ​​not exceeding a preset first deviation threshold (e.g., 8%) are used as candidate standard voltages. If the voltage level is 35kV or below, then the first deviation... Voltage values ​​not greater than a preset second deviation threshold (e.g., 10%) are used as candidate standard voltages, thereby forming an initial candidate set of all candidate standard voltages that satisfy the preset first deviation and the preset second deviation.

[0068] In this embodiment, the initial candidate set is filtered according to the electrical characteristics of the equipment (equipment characteristic filtering constraints). For example, the allowed voltage levels on the high-voltage side of the main transformer are 110kV, 220kV and 500kV, and the allowed voltage levels on the medium-voltage side are 35kV and 110kV, etc., thereby eliminating candidate voltage levels that do not meet the equipment characteristics and obtaining the target candidate set.

[0069] In this embodiment, after obtaining the target candidate set, based on the topology association rules and the topology association adjacency table of "main transformer → bus → line → substation", the main transformer equipment is preferentially matched to the voltage level of the directly connected bus. If there is no known bus voltage, the mainstream voltage level of the same substation is matched. The transmission line equipment is preferentially matched to the voltage level of the equipment connected at both ends, thus the candidate voltage with the highest matching degree is used as the unique standard voltage. .

[0070] This embodiment performs full-link cross-validation after determining the standard voltage. Specifically, it verifies the characteristics of the model file; if the candidate voltage level of the main transformer equipment matches the standard voltage... The deviation between them is less than or equal to a preset first threshold (e.g., 3%), and If the conductor model is within the compatibility range, the file model verification is considered successful. For parameter clustering within the same substation, if the number of substation devices is greater than or equal to the preset first number of devices (e.g., 10 devices), and the clustering percentage is greater than or equal to the preset first percentage threshold (e.g., 75%); or if the number of substation devices is less than the preset first number of devices (e.g., 10 devices), and the clustering percentage is greater than or equal to the preset second percentage threshold (e.g., 85%), then the parameter clustering verification within the same substation is considered successful. For topology consistency verification, if the voltage deviation of devices in the topology chain is less than or equal to the preset voltage deviation threshold (e.g., 2%), and expansion is performed according to specific rules, then the topology consistency verification is considered successful. For operational data trends verification, based on the standard voltage and the average apparent power, and by calculating the theoretical current using the theoretical current calculation formula,... ,in, Represents theoretical current. Indicates standard voltage. This represents the average apparent power. If the difference between the theoretical current and the average actual current is less than or equal to a preset difference threshold (e.g., 3%, which can be relaxed to 5% in extreme conditions), then the trend verification of the operating data is considered successful.

[0071] If any of the above conditions are not met, a closed-loop iterative correction process is executed. In this embodiment, the maximum number of iterations is set to 5. The closed-loop iterative correction process is as follows: 1) Locate the specific verification dimension that failed, output the deviation value and the corresponding reason for the anomaly (e.g., missing / incorrect voltage level). 2) If the operational data trend verification fails, the operating condition coupling sensitivity adjustment factor should be adjusted first. (Adjustment step size is 0.005); If the model feature verification fails, adjust the model feature extraction weight; If the same factory clustering verification fails, gradually relax the preset proportion threshold of clustering; If the topology consistency verification fails, adjust the weight of the topology association constraint. 3) Re-execute the voltage level inversion process based on the corrected parameters to generate new candidate voltages; 4) Re-perform full-link cross-validation on the new candidate voltage.

[0072] The iteration continues until all cross-validation dimensions pass or the maximum number of iterations is reached, at which point the process terminates, outputting the optimal intermediate result and generating a manual verification prompt report.

[0073] In this embodiment, for isolated devices, verification is performed using a combination of model characteristics and clustering of devices in the same region. For example, if the proportion of missing operational data is greater than a preset missing proportion (e.g., 70%), verification relies on the model of the isolated device and parameters from the same plant. If the topology is incomplete, reverse calculation is performed based on partial topology chains to identify verified devices. Automatically fill in the corresponding fields in the equipment ledger and generate a calculation report containing the equipment ID, original missing status, verification deviation, and confidence level (95%~100%).

[0074] In one embodiment of this application, before determining the per-unit value of the rated resistance and the per-unit value of the rated reactance of the device based on the standard voltage, the method further includes: Obtain the high-voltage side rated voltage, short-circuit loss, rated capacity, and short-circuit voltage percentage of the equipment; The reference impedance is determined based on the rated voltage of the high-voltage side and the preset reference capacity. The determination of the per-unit values ​​of rated resistance and rated per-unit reactance of the equipment based on the standard voltage includes: Determine the per-unit value of rated resistance and the per-unit value of rated reactance based on the standard voltage, rated capacity, rated voltage on the high-voltage side, short-circuit loss, short-circuit voltage percentage, and reference impedance.

[0075] In this embodiment, after completing the voltage level calculation, impedance calculation is performed. For equipment with impedance mismatch between the main transformer register and the operating level, the equivalent parameters are dynamically corrected within the physical compliance range, using the fixed rated impedance as a benchmark. Specifically, for "parameter inconsistency abnormal equipment" marked with parameter consistency verification (i.e., main transformers with register-level mismatch), their erroneous register resistance and reactance parameters are completely replaced with the standard nameplate parameters of the corresponding model. If complete standard parameters cannot be extracted based on model characteristics, the weighted average of parameters of main transformers of the same model and batch from the same plant or typical parameters of the same voltage level and capacity in the industry are used. This embodiment collects the rated capacity of all main transformer equipment. (MVA), rated voltage (kV), short-circuit loss (kW), Short-circuit voltage percentage Set the system's preset baseline capacity =100MVA Take the rated voltage of the high voltage side of the main transformer This embodiment calculates the reference impedance based on the rated voltage of the high-voltage side of the main transformer and a preset reference capacity, using a reference impedance calculation formula. This reference impedance calculation formula can be... ,in, This represents the reference impedance.

[0076] This embodiment calculates the nominal value of the rated resistance based on short-circuit loss, the rated voltage and rated capacity of the main transformer high-voltage side, and using the nominal value calculation formula for rated resistance. The nominal value calculation formula for rated resistance can be... ,in, This indicates the nominal value of the rated resistance. Based on the short-circuit voltage percentage, the rated voltage on the high-voltage side of the main transformer, and the rated capacity, the nominal value of the rated reactance is calculated using the formula for calculating the nominal value of the rated reactance. The formula for calculating the nominal value of the rated reactance can be... ,in, This indicates the nominal value of the rated reactance.

[0077] This embodiment converts the nominal value of the rated resistance into a per-unit value using a first conversion formula based on the nominal value of the rated resistance and the reference impedance. The first conversion formula can be: ,in, This represents the per-unit value of the rated resistance. Based on the nominal value of the rated reactance and the reference impedance, the nominal value of the rated reactance is converted to the per-unit value of the rated reactance using a second conversion formula. The second conversion formula can be: ,in, This indicates the per-unit value of the rated reactance.

[0078] In this embodiment, for a three-winding main transformer, the winding pairs are split according to high-voltage-medium-voltage, high-voltage-low-voltage, and medium-voltage-low-voltage, and the short-circuit loss corresponding to each winding pair is calculated. In this embodiment, the short-circuit loss corresponding to the high-voltage-low-voltage winding pair... ,in, This indicates the short-circuit loss between the high-voltage and low-voltage windings. This indicates the short-circuit loss on the high-voltage side. This indicates the short-circuit loss on the medium-voltage side. This indicates the short-circuit loss on the low-voltage side. The short-circuit loss corresponding to the high-voltage-low-voltage winding pair. ,in, This indicates the short-circuit loss corresponding to the high-voltage to low-voltage winding pair. The short-circuit loss corresponding to the medium-voltage to low-voltage winding pair. ,in, This indicates the short-circuit loss of the high-voltage to low-voltage winding pair.

[0079] This embodiment also calculates the percentage of short-circuit voltage corresponding to each winding pair. In this embodiment, the percentage of short-circuit voltage corresponding to the high-voltage-low-voltage winding pair is... ,in, This indicates the percentage of the short-circuit voltage between the high-voltage and low-voltage windings. This indicates the percentage of the short-circuit voltage on the high-voltage side. This indicates the percentage of short-circuit voltage on the medium-voltage side. This indicates the percentage of the short-circuit voltage on the low-voltage side. It also represents the percentage of the short-circuit voltage between the high-voltage and low-voltage winding pairs. ,in, This indicates the percentage of the short-circuit voltage for the high-voltage to low-voltage winding pair. It also indicates the percentage of the short-circuit voltage for the medium-voltage to low-voltage winding pair. ,in, This indicates the percentage of the short-circuit voltage corresponding to the high-voltage to low-voltage winding pair.

[0080] In this embodiment, after obtaining the short-circuit voltage and short-circuit percentage of each winding pair, the nominal value of the rated resistance, nominal value of the rated reactance, per-unit value of the rated resistance, and per-unit value of the rated reactance of each winding pair are determined according to the above method.

[0081] In this embodiment, after determining the per-unit value of rated resistance and the per-unit value of rated reactance, the ratio of the per-unit value of rated resistance to the per-unit value of rated reactance is used as the first ratio. Based on the first ratio, multi-cause collaborative repair is performed on the devices in the associated cause group to obtain the repair results.

[0082] In one embodiment of this application, multi-factor collaborative repair is performed on the device according to a first ratio to obtain a repair result, including: For each main transformer device in the associated cause group, obtain the real-time load rate of that main transformer device; Based on the real-time load rate of the main transformer, the first ratio, and the preset correlation table, determine the resistance-load rate coupling correction weight factor and the reactance-load rate coupling correction weight factor. Obtain the rigidity constraint factor of the physical boundary of the main transformer impedance; Based on the per-unit values ​​of rated resistance, rated reactance, rigid constraint factor of main transformer impedance physical boundary, resistance-load rate coupling correction weight factor and reactance-load rate coupling correction weight factor, the parameters of the main transformer equipment are repaired to obtain the per-unit values ​​of equivalent resistance and equivalent reactance of the main transformer equipment. The per-unit values ​​of equivalent resistance and equivalent reactance are used as the repair results.

[0083] In this embodiment, based on the real-time load rate of the main transformer Combining the optimal first ratio coupling matching threshold system for the full load range of the main transformer in the main grid (as shown in Table 1 below), the load range corresponding to the real-time load rate of the main transformer is determined, such as the light load range ( <30%), rated operating range (30%≤) ≤80%), heavy load area (80% < ≤100%) or overload zone ( (>100%), determine the optimal first ratio matching threshold and resistance-load rate coupling correction weighting factor for the corresponding load range based on the load range corresponding to the real-time load rate of the main transformer. and reactance-load rate coupling correction weighting factor .

[0084] Table 1. Main Grid Transformer Load Rate - Optimal First Ratio Coupling Matching Threshold Standard

[0085] In this embodiment, the rigid constraint factor of the physical boundary of the main transformer impedance is obtained. The rigid constraint factor of the main transformer impedance physical boundary is set to 0.9~1.1 to ensure that the deviation between the equivalent parameter and the rated value is less than or equal to ±10%. In this embodiment, for dual-winding main transformer equipment, the equivalent resistance per-unit value is calculated according to the formula... Calculate the per-unit value of the equivalent resistance, where, This represents the per-unit value of the equivalent resistance; it is calculated according to the per-unit value formula of the equivalent reactance. Calculate the per-unit value of the equivalent reactance, where, This represents the per-unit value of equivalent reactance. For three-winding main transformer equipment, the corresponding per-unit values ​​of equivalent resistance and equivalent reactance are calculated based on the three winding pairs, and the per-unit values ​​of equivalent resistance and equivalent reactance are used as the repair results.

[0086] In one embodiment of this application, after obtaining the repair result, the method further includes: If the equipment is a main transformer, the theoretical output power of the low-voltage side of the main transformer is determined based on the per-unit value of the equivalent resistance, the per-unit value of the equivalent reactance, and the standard voltage of the main transformer. Obtain the actual output power of the low-voltage side of the main transformer; If the deviation between the theoretical output power and the actual output power on the low-voltage side is not greater than the preset output power deviation threshold, the repair result is deemed qualified.

[0087] In this embodiment, after obtaining the repair results, cross-verification of winding parameters is performed to ensure that the deviation of the equivalent impedance from the rated value of each winding is less than or equal to a preset cross-deviation threshold (e.g., ±15%). The ratio of the per-unit equivalent resistance to the per-unit equivalent reactance is calculated, and it is verified whether the ratio falls within the optimal threshold of the corresponding interval. If it falls within the optimal threshold of the corresponding interval, the per-unit equivalent resistance and per-unit equivalent reactance are taken as the repair results.

[0088] If it does not fall within the optimal threshold of the corresponding interval, then fine-tune. (Step size 0.01) and recalculate the per-unit values ​​of equivalent resistance and equivalent reactance to obtain new per-unit values ​​of equivalent resistance and equivalent reactance. Convert the new per-unit values ​​of equivalent resistance into new nominal values ​​of equivalent resistance. The new per-unit equivalent reactance value is converted into a new nominal equivalent reactance value. .

[0089] This embodiment specifies the new equivalent resistance value, the new equivalent reactance value, and the standard voltage. Substitute the power balance equations for a single main transformer port and perform the following calculation steps to verify the logical consistency of the parameters: Calculate the measured current on the high-voltage side based on the measured active power, measured reactive power, and standard voltage on the high-voltage side: ,in, This indicates the measured active power on the high-voltage side. This indicates the measured reactive power on the high-voltage side; Based on the measured current on the high-voltage side Calculate winding losses using the new equivalent resistance and the new equivalent reactance values: ,in, Indicates the active power residual of the winding. Indicates the reactive power residual of the winding; The theoretical output power is calculated based on the measured active power on the high-voltage side, the measured reactive power on the high-voltage side, the active power residual of the winding, and the reactive power residual of the winding. Specifically, the theoretical power on the low-voltage side is calculated for dual-winding main transformer equipment. The theoretical power of each output side of the three-winding main transformer is calculated according to the power flow direction of the winding pairs, and the maximum deviation is taken as the verification value.

[0090] This embodiment calculates the relative deviation based on the theoretical power and actual operating power on the low-voltage side. ,in, This represents the relative deviation between the theoretical power and the actual operating power on the low-voltage side. If the relative deviation is less than or equal to a preset output power deviation threshold (e.g., 5%), the two are considered logically consistent, and the repair is successful. If the relative deviation is greater than the preset output power deviation threshold, the repair is considered unsuccessful. In this case, the voltage level completion module is automatically triggered for iterative correction until the two are logically consistent (repair successful). For three-winding main transformers, the weighting coefficients of each winding for the correction weight are dynamically adjusted according to the actual power flow direction to improve the accuracy of power flow calculation.

[0091] This embodiment solidifies the equivalent impedance parameters that have passed mutual verification, and generates a main transformer impedance quantification matching list, which includes equipment ID, load rate range, nominal value of rated resistance, nominal value of rated reactance, nominal value of equivalent resistance, nominal value of equivalent reactance, and first ratio, etc. The matching results are synchronized to the subsequent power flow convergence analysis module, and the original impedance parameters in the equipment ledger are replaced (only used for power flow calculation, without modifying the actual hardware parameters of the main transformer).

[0092] For example, this embodiment achieves fully automated processing from parameter completion to power flow convergence by multi-dimensional quantitative evaluation, hierarchical closed-loop correction, and model self-optimization, following the core principles of voltage priority, impedance tracking, hierarchical verification, and closed-loop optimization, ensuring 100% power flow convergence across the entire load range. Figure 2 The diagram shown illustrates the complete process of multi-causal synergistic repair provided in this embodiment of the invention. The specific method is as follows: Step 1: Perform voltage level dynamic inversion and completion (prioritize repairing type 3 devices): Prioritize the repair of voltage level missing / error devices (type 3 devices, corresponding to cause A) marked as "core missing - highest priority" in the associated cause group, and start the voltage level intelligent inversion process.

[0093] Step 2: Perform multi-environment corrected current carrying capacity calculation: For the equipment to be repaired, calculate the maximum allowable current carrying capacity according to its type. For main transformer equipment, calculate based on its rated capacity and ambient temperature correction factor; for transmission line equipment, calculate based on the IEEE738-2012 standard calculation formula, incorporating multiple factors such as conductor material, cross-section, temperature, wind speed, altitude, and icing thickness.

[0094] Step 3: Dynamic Assumption Current Setting: Determine the adaptive dynamic correction factor based on the real-time load rate of the main transformer or the power density of the line transmission, and calculate the assumed current in combination with the maximum allowable current carrying capacity. This avoids direct reliance on potentially distorted measured current and improves the accuracy of voltage inversion.

[0095] Step 4: Perform reverse calculation of the original voltage value: Based on the basic principle of apparent power balance, and according to the assumed average current and apparent power, the original voltage level value is calculated.

[0096] Step 5: Matching Standard Voltage Based on Three-Dimensional Quantization: A progressive three-step matching method is adopted, consisting of "screening based on the first and second deviation thresholds - equipment characteristic filtering constraints - topology association locking," to select a uniquely matching standard voltage from the core voltage levels of the main grid specified in the DL / T5003-2017 standard. .

[0097] Step 6: Perform four-layer full-link cross-validation (iterative correction if failed): Verify the matching voltage across four dimensions: file model characteristics, parameter clustering within the same plant / station, topology consistency, and operational data trends. If any dimension fails, execute a closed-loop iterative correction process, with a maximum of 5 iterations, terminating when all dimensions pass or the maximum number of iterations is reached.

[0098] Step 7: Perform impedance-operating power quantification matching of main transformers (repair type 1 and type 2 equipment): After the voltage level is completed and verified, perform impedance quantification matching repair on the main transformer equipment marked as "parameter consistency abnormal" (type 1 is ledger-level mismatch, corresponding to cause B) and "operating parameter logic abnormal" (type 2 is operating-level mismatch, corresponding to cause C).

[0099] Step 8: Perform rated parameter calibration and ledger error correction: For Type 1 equipment, completely replace the incorrect ledger parameters with the standard nameplate parameters of the corresponding model; if complete standard parameters cannot be extracted based on model characteristics, use the weighted average of the main transformer parameters of the same model and batch from the same plant or the typical parameters of the same voltage level and capacity in the industry.

[0100] Step 9: Perform rated impedance per-unit value reduction: based on the completed standard voltage. The reference impedance is calculated based on the system reference capacity (100MVA), and the nominal values ​​of rated resistance and rated reactance are uniformly converted to the per-unit values ​​corresponding to the system reference value based on this reference impedance. For three-winding main transformer equipment, the winding pairs are split according to the high-voltage side-medium-voltage side, high-voltage side-low-voltage side, and medium-voltage side-low-voltage side, and the conversion is performed on each split winding pair separately.

[0101] Step 10: Divide the load rate range and extract the matching threshold: Divide the operating range according to the real-time load rate of the main transformer (light load range <30%, rated range 30%~80%, heavy load range 80%~100%, overload range >100%), and extract the optimal R / X matching threshold and resistance / reactance correction weight factor for the corresponding range.

[0102] Step 11: Perform optimal equivalent impedance parameter quantification calculation: Combine the rated impedance per unit value, the rigid constraint factor of the main transformer impedance physical boundary and the correction weight factor to calculate the equivalent resistance per unit value and the equivalent reactance per unit value of the main transformer, and ensure that the equivalent R / X (first ratio) falls within the optimal threshold range of the corresponding interval, and the deviation of the equivalent parameter from the rated value is ≤ ±15%.

[0103] Step 12, Verify the repair results (impedance-voltage level logic consistency check): Compare the nominal value of the equivalent impedance with the supplementary voltage. Substitute the power balance equation of the single main transformer port into the equation to calculate the relative deviation between the theoretical output power and the actual operating power. If the relative deviation is ≤5%, the repair result is considered to be logically consistent.

[0104] Step 13: Perform power flow convergence quantitative analysis and closed-loop iterative optimization: Establish a three-dimensional quantitative convergence evaluation standard of "residual dimension + parameter dimension + logic dimension" to provide a unified and quantifiable judgment basis for power flow simulation.

[0105] Step 14: Perform parameter substitution and power flow trial calculation: Substitute the completed voltage level, corrected equivalent impedance, and other parameters into the Newton-Raphson power flow calculation model via the data interface according to the input format requirements of mainstream power flow software, and start the power flow trial calculation (maximum number of iterations: 50, convergence residual threshold: 10). -4 ).

[0106] Step 15: Perform multi-dimensional convergence evaluation (residual dimension + parameter dimension + logical dimension): Determine whether the power flow has converged from three dimensions, specifically including: In the residual dimension, it is determined whether the ratio of active residual to reactive residual is less than or equal to the convergence residual threshold (e.g., 10). -4 ); Determine if the number of iteration steps is less than or equal to the preset number of iteration steps (e.g., 20 steps); Determine if the condition number of the Jacobian matrix is ​​less than or equal to the preset condition number (e.g., ≤10). -4 ); In terms of parameters, it is determined whether all core parameters are complete and without missing parts; whether the deviation between the equivalent impedance of the main transformer and its rated value is less than or equal to ±15%; In terms of logic, it is determined whether the equivalent R / X ratio of the main transformer falls within the optimal threshold of the corresponding load rate range; and whether the voltage consistency deviation of the topology chain equipment is less than or equal to 2%.

[0107] Step 16: Convergence judgment: If all three dimensions in step 15 are satisfied, the power flow is judged to be converged and the subsequent data accumulation stage is entered; if any dimension is not satisfied, the hierarchical closed-loop correction mechanism is triggered.

[0108] The hierarchical closed-loop correction mechanism employs a progressive optimization strategy of "Level 1 Fine-tuning - Level 2 Recalculation - Level 3 Source Tracing" for correction. For Level 1 Fine-tuning (fine-tuning coefficients), if the residual dimension does not meet the standard, the voltage level matching rule remains unchanged, the physical boundary constraint factor and the operating condition coupling sensitivity adjustment factor are fine-tuned, and the calculation is retried, with an upper limit of 2 times. For Level 2 Recalculation (re-dividing the interval / fine-tuning the weight / threshold), if Level 1 Fine-tuning fails to converge or the parameter / logic dimension does not meet the standard, the load rate interval is re-divided, the correction weight and voltage matching deviation threshold are fine-tuned, and the entire collaborative repair process is rerun, with an upper limit of 2 times. If Level 2 Recalculation fails to converge, Level 3 Source Tracing (investigating secondary causes / manual verification) is executed, automatically investigating non-core causes such as topology connection errors and minor errors in line parameters, and generating a manual verification prompt report.

[0109] In the hierarchical closed-loop correction stage, the first-level fine-tuning results are fed back to the "repair parameters are substituted into power flow trial calculation" stage, and the second-level recalculation and third-level source tracing results are fed back to the "voltage level dynamic inversion and completion" stage, forming a complete closed-loop iterative optimization system to ensure that power flow convergence is ultimately achieved.

[0110] Step 17: Data Accumulation and Model Self-Optimization: All data, including diagnostic reports, repair parameters, convergence indicators, and optimization records, are classified and stored in a multi-dimensional coupled matching database of operating conditions and parameters according to equipment type, operating conditions, and regional scenarios. The core parameters are continuously optimized using the random forest regression algorithm to achieve self-iterative upgrades of the model.

[0111] Step 18: Generate a standardized report: Generate a standardized power flow convergence verification report. This verification includes a list of abnormal devices, a comparison of parameters before and after the repair, convergence verification data, and source tracing records, clarifying the repair effect and basis.

[0112] Step 19, Project Implementation and Application: Synchronize the repaired parameters to the power flow software and equipment ledger system (only for power flow calculation, without modifying the actual hardware parameters of the main transformer), to provide reliable data support for main grid scheduling and operation, planning verification and safety assessment.

[0113] Based on the same principle as the root cause repair method for main network power flow non-convergence provided in the embodiments of this application, the embodiments of this application also provide a root cause repair device for main network power flow non-convergence, such as... Figure 3 As shown, the root cause repair device 20 for the main network power flow non-convergence may specifically include: a core area determination module 21, a verification module 22, an associated cause group device determination module 23, and a repair module 24.

[0114] Among them, the core area determination module 21 is used to obtain the multi-source equipment ledger data of the target area, and based on the multi-source equipment ledger data, determine the core area of ​​power flow non-convergence from the target area. The multi-source equipment ledger data includes the main transformer equipment ledger data and the transmission line equipment ledger data. Verification module 22 is used to perform multi-dimensional cross-verification on each target device in the core area to obtain the corresponding verification results; The associated cause group device determination module 23 is used to determine the associated cause group devices of power flow non-convergence from each target device based on each verification result; Repair module 24 is used to perform repair operations for each associated cause group device; Specifically, when performing a repair operation, the repair module 24 is used for: Obtain the maximum allowable current carrying capacity and the average apparent power of the device; The assumed current is determined based on the maximum allowable current carrying capacity and the adaptive dynamic correction factor for operating conditions. The adaptive dynamic correction factor for operating conditions is used to characterize the coefficient for dynamically adjusting the maximum allowable current carrying capacity under different operating conditions of the equipment. The standard voltage of the device is determined based on the assumed average current and apparent power. Determine the per-unit values ​​of the rated resistance and rated per-unit reactance of the equipment based on the standard voltage. The ratio of the per-unit value of rated resistance to the per-unit value of rated reactance shall be used as the first ratio. Based on the first ratio, multi-factor collaborative repair is performed on the device to obtain the repair result.

[0115] In one embodiment of this application, the repair module 24, when performing multi-cause collaborative repair on the device according to the first ratio and obtaining the repair result, is specifically used for: For each main transformer device in the associated cause group, obtain the real-time load rate of that main transformer device; Based on the real-time load rate of the main transformer, the first ratio, and the preset correlation table, determine the resistance-load rate coupling correction weight factor and the reactance-load rate coupling correction weight factor. Obtain the rigidity constraint factor of the physical boundary of the main transformer impedance; Based on the per-unit values ​​of rated resistance, rated reactance, rigid constraint factor of main transformer impedance physical boundary, resistance-load rate coupling correction weight factor and reactance-load rate coupling correction weight factor, the parameters of the main transformer equipment are repaired to obtain the per-unit values ​​of equivalent resistance and equivalent reactance of the main transformer equipment. The per-unit values ​​of equivalent resistance and equivalent reactance are used as the repair results.

[0116] In one embodiment of this application, the repair module 24, when determining the rated resistance per unit value and rated reactance per unit value of the device based on the standard voltage, is specifically used for: Obtain the high-voltage side rated voltage, short-circuit loss, rated capacity, and short-circuit voltage percentage of the equipment; The reference impedance is determined based on the rated voltage of the high-voltage side and the preset reference capacity. Determine the per-unit value of rated resistance and the per-unit value of rated reactance based on the standard voltage, rated capacity, rated voltage on the high-voltage side, short-circuit loss, short-circuit voltage percentage, and reference impedance.

[0117] In one embodiment of this application, the repair module 24, when determining the standard voltage of the device based on the assumed average current and apparent power, is specifically used for: Key features are extracted from multi-source equipment ledger data, and an adjacency list is generated based on the key features. The adjacency list is used to represent the connection relationship between the main transformer equipment and the transmission line equipment. The voltage level is determined by inverse calculation based on the assumed average current and apparent power. The first deviation is determined by comparing the reverse value of the voltage level with the voltage value corresponding to each standard voltage level. The standard voltage level with a first deviation not greater than the preset deviation threshold is used as the candidate standard voltage, and all candidate standard voltages are combined into an initial candidate set. The initial candidate set is filtered based on the electrical characteristics of the device to obtain the target candidate set; The matching degree of each candidate voltage is determined based on the adjacency list and the target candidate set; The candidate voltage with the highest matching degree is used as the standard voltage of the device.

[0118] In one embodiment of this application, the root cause repair device 20 for mainnet power flow non-convergence further includes a repair verification module, specifically used for: If the equipment is a main transformer, the theoretical output power of the low-voltage side of the main transformer is determined based on the per-unit value of the equivalent resistance, the per-unit value of the equivalent reactance, and the standard voltage of the main transformer. Obtain the actual output power of the low-voltage side of the main transformer; If the deviation between the theoretical output power and the actual output power on the low-voltage side is not greater than the preset output power deviation threshold, the repair result is deemed qualified.

[0119] In one embodiment of this application, the associated cause group device determination module 23, when determining the associated cause group devices of power flow non-convergence from each target device based on each verification result, is specifically used for: Based on the verification results and the preset mapping relationship, the corresponding cause type for each target device is determined. The cause type includes voltage level missing or incorrect, impedance parameter error and / or impedance operating level mismatch. The preset mapping relationship is used to characterize the correspondence between parameter missing anomaly, parameter consistency anomaly and operating parameter logic anomaly and cause type. Encode the trigger types of all target devices into a transaction dataset, with each transaction corresponding to one target device and each transaction including at least one trigger type corresponding to that target device; Based on the transaction dataset, determine the association rules, calculate the confidence score of each association rule, and identify frequent itemsets with confidence scores higher than a preset confidence threshold as strong association rules; the number of strong association rules is at least one. The strong association rules are merged to form a coupling relationship, and the devices that satisfy the coupling relationship are marked as the associated cause group devices.

[0120] In one embodiment of this application, the verification module 22, when performing multi-dimensional cross-verification on the target device to obtain the corresponding verification result, is specifically used for: Obtain the target equipment's rated capacity, high-voltage side rated voltage, short-circuit loss, short-circuit voltage percentage, and voltage level; The rated capacity, high-voltage side rated voltage, short-circuit loss, short-circuit voltage percentage, and voltage level of the target equipment are verified according to the preset verification method to obtain the parameter integrity verification result. If the parameter integrity verification result fails, the target equipment is determined to be an abnormal equipment with missing parameters. Obtain the resistors, reactances, standard nameplate resistors, and standard nameplate reactances from the equipment ledger; Based on the resistance, reactance, standard nameplate resistance, and standard nameplate reactance, determine the ledger resistance deviation and ledger reactance deviation respectively; If the deviation of the ledger resistance or the deviation of the ledger reactance is higher than the preset deviation threshold, the target equipment is determined to be a device with abnormal parameter consistency. If the target equipment is a main transformer, then obtain the measured active power, measured reactive power, average measured current, rated resistance, rated reactance, measured active power and measured reactive power on the high-voltage side of the main transformer. Based on the measured active power on the high-voltage side, the average measured current on the high-voltage side, and the rated resistance, the theoretical active power on the low-voltage side is determined. Based on the theoretical active power on the low-voltage side and the measured active power on the low-voltage side, the power deviation is determined. If the power deviation is higher than the preset power deviation threshold, the target device is determined to be a device with abnormal operating parameters. If the target equipment is a power transmission line, then obtain the measured active power, measured reactive power, rated voltage, and measured current value of the power transmission line. The theoretical current value is determined based on the measured active power, measured reactive power, and rated voltage of the transmission line. If the difference between the theoretical current value and the measured current value is higher than the preset current deviation threshold, the target device is determined to be a device with abnormal operating parameters.

[0121] The apparatus in this application embodiment can execute the method provided in this application embodiment, and the implementation principle is similar. The actions performed by each module in the apparatus of each embodiment of this application correspond to the steps in the method of each embodiment of this application. For detailed functional descriptions of each module of the apparatus, please refer to the descriptions in the corresponding methods shown above, which will not be repeated here.

[0122] Figure 4 A schematic diagram of the structure of an electronic device to which this application embodiment applies is shown, such as... Figure 4 As shown, the electronic device can be used to implement the methods provided in any embodiment of this application.

[0123] like Figure 4 As shown, the electronic device 300 may primarily include at least one processor 301. Figure 4 The diagram shows components such as a memory 302, a communication module 303, and an input / output interface 304. Optionally, these components can be connected and communicate with each other via a bus 305. It should be noted that... Figure 4 The structure of the electronic device 300 shown is merely illustrative and does not constitute a limitation on the electronic devices to which the methods provided in the embodiments of this application are applicable.

[0124] The memory 302 can be used to store operating systems and applications, etc. The applications can include computer programs that implement the methods shown in the embodiments of this application when invoked by the processor 301, and can also include programs for implementing other functions or services. The memory 302 can be ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices that can store information and computer programs, or it can be EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto.

[0125] Processor 301 is connected to memory 302 via bus 305 and implements corresponding functions by calling the application programs stored in memory 302. Processor 301 can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0126] Electronic device 300 can connect to a network via communication module 303 (which may include, but is not limited to, components such as a network interface) to communicate with other devices (such as user terminals or servers) through the network and achieve data interaction, such as sending data to or receiving data from other devices. Communication module 303 may include wired network interfaces and / or wireless network interfaces, meaning the communication module may include at least one of wired or wireless communication modules.

[0127] The electronic device 300 can connect to necessary input / output devices, such as a keyboard or display device, via the input / output interface 304. The electronic device 300 itself may have a display device, and other external display devices can also be connected via the input / output interface 304. Optionally, a storage device, such as a hard drive, can also be connected via the input / output interface 304 to store data from the electronic device 300, retrieve data from the storage device, or store data from the storage device in the memory 302. It is understood that the input / output interface 304 can be a wired interface or a wireless interface. Depending on the actual application scenario, the device connected to the input / output interface 304 can be a component of the electronic device 300 or an external device connected to the electronic device 300 when needed.

[0128] The bus 305 used to connect the components may include a path for transmitting information between the components. The bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Depending on its function, the bus 305 may be divided into an address bus, a data bus, a control bus, etc.

[0129] Optionally, for the solution provided in the embodiments of this application, the memory 302 can be used to store a computer program that executes the solution of this application, and the processor 301 runs the computer program. When the processor 301 runs the computer program, it implements the operation of the method or apparatus provided in the embodiments of this application.

[0130] Based on the same principle as the method provided in the embodiments of this application, the embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the corresponding content of the aforementioned method embodiments.

[0131] It should be noted that the terms "first," "second," "third," "fourth," "1," "2," etc. (if present) in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in a sequence other than that shown in the figures or text descriptions.

[0132] In the embodiments of this application, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0133] It should be understood that although arrows indicate various operation steps in the flowcharts of this application's embodiments, the order in which these steps are implemented is not limited to the order indicated by the arrows. Unless explicitly stated herein, in some implementation scenarios of this application's embodiments, the implementation steps in each flowchart can be executed in other orders as required. Furthermore, some or all steps in each flowchart may include multiple sub-steps or multiple stages based on the actual implementation scenario. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage can also be executed at different times. In scenarios where execution times differ, the execution order of these sub-steps or stages can be flexibly configured according to requirements, and this application's embodiments do not limit this.

[0134] The above are only optional implementation methods for some implementation scenarios of this application. It should be noted that for those skilled in the art, other similar implementation methods based on the technical concept of this application, without departing from the technical concept of this application, also fall within the protection scope of the embodiments of this application.

Claims

1. A root cause repair method for main network power flow non-convergence, characterized in that, include: Obtain multi-source equipment ledger data for the target area, and based on the multi-source equipment ledger data, determine the core area of ​​power flow non-convergence from the target area. The multi-source equipment ledger data includes main transformer equipment ledger data and transmission line equipment ledger data. For each target device in the core region, multi-dimensional cross-validation is performed on the target device to obtain the corresponding validation results; Based on the verification results, identify the associated cause group of devices for power flow non-convergence from the target devices; Perform a repair operation for each associated trigger group of devices: The repair operation includes: Obtain the maximum allowable current carrying capacity and the average apparent power of the device; The assumed current is determined based on the maximum allowable current carrying capacity and the adaptive dynamic correction factor for operating conditions. The adaptive dynamic correction factor for operating conditions is used to characterize the coefficient for dynamically adjusting the maximum allowable current carrying capacity under different operating conditions of the equipment. The standard voltage of the device is determined based on the assumed current and the average apparent power. Determine the per-unit value of the rated resistance and per-unit value of the rated reactance of the equipment based on the standard voltage; The ratio of the per-unit value of the rated resistance to the per-unit value of the rated reactance is taken as the first ratio. Based on the first ratio, multi-factor collaborative repair is performed on the device to obtain the repair result.

2. The method as described in claim 1, characterized in that, The step of performing multi-factor collaborative repair on the device according to the first ratio to obtain the repair result includes: For each main transformer device in the associated cause group, obtain the real-time load rate of that main transformer device; Based on the real-time load rate of the main transformer, the first ratio, and the preset association table, determine the resistance-load rate coupling correction weight factor and the reactance-load rate coupling correction weight factor. Obtain the rigidity constraint factor of the physical boundary of the main transformer impedance; Based on the rated resistance per unit value, the rated reactance per unit value, the rigid constraint factor of the main transformer impedance physical boundary, the resistance-load rate coupling correction weight factor, and the reactance-load rate coupling correction weight factor, the parameters of the main transformer equipment are repaired to obtain the equivalent resistance per unit value and the equivalent reactance per unit value of the main transformer equipment. The equivalent resistance per unit value and the equivalent reactance per unit value are used as the repair result.

3. The method as described in claim 2, characterized in that, Before determining the per-unit values ​​of the rated resistance and rated per-unit reactance of the device based on the standard voltage, the method further includes: Obtain the high-voltage side rated voltage, short-circuit loss, rated capacity, and short-circuit voltage percentage of the equipment; The reference impedance is determined based on the rated voltage of the high-voltage side and the preset reference capacity. The step of determining the per-unit values ​​of the rated resistance and rated per-unit reactance of the equipment based on the standard voltage includes: The per-unit values ​​of rated resistance and rated reactance are determined based on the standard voltage, rated capacity, high-voltage side rated voltage, short-circuit loss, short-circuit voltage percentage, and reference impedance.

4. The method as described in claim 1, characterized in that, Before determining the standard voltage of the device based on the assumed current and the average apparent power, the method further includes: Key features are extracted from the multi-source equipment ledger data, and an adjacency table is generated based on the key features. The adjacency table is used to characterize the connection relationship between the main transformer equipment and the transmission line equipment. The step of determining the standard voltage of the device based on the assumed current and the average apparent power includes: The voltage level is determined by inverse calculation based on the assumed current and the average apparent power. The first deviation is determined based on the reverse value of the voltage level and the voltage value corresponding to each standard voltage level; The standard voltage level whose first deviation is not greater than the preset deviation threshold is used as the candidate standard voltage, and all candidate standard voltages are combined into an initial candidate set. The initial candidate set is filtered based on the electrical characteristics of the device to obtain the target candidate set; The matching degree of each candidate voltage is determined based on the adjacency list and the target candidate set; The candidate voltage with the highest matching degree is used as the standard voltage of the device.

5. The method as described in claim 2, characterized in that, After obtaining the repair result, the following is also included: If the device is a main transformer, the theoretical output power of the low-voltage side of the main transformer is determined based on the per-unit value of the equivalent resistance, the per-unit value of the equivalent reactance, and the standard voltage of the main transformer. Obtain the actual output power of the low-voltage side of the main transformer; If the deviation between the theoretical output power of the low-voltage side and the actual output power of the low-voltage side is not greater than the preset output power deviation threshold, the repair result is deemed qualified.

6. The method as described in claim 1, characterized in that, The step of determining the associated cause group of devices for power flow non-convergence from each of the target devices based on the verification results includes: Based on the verification results and the preset mapping relationship, the cause type corresponding to each target device is determined; the cause type includes voltage level missing or incorrect, impedance parameter incorrect and / or impedance operating level mismatch, and the preset mapping relationship is used to characterize the correspondence between parameter missing anomaly, parameter consistency anomaly and operating parameter logic anomaly and the cause type; Encode the trigger types of all target devices into a transaction dataset, with each transaction corresponding to one target device and each transaction including at least one trigger type corresponding to that target device; Based on the transaction dataset, association rules are determined, the confidence level of each association rule is calculated, and frequent itemsets with confidence levels higher than a preset confidence threshold are identified as strong association rules; the number of strong association rules is at least one. The strong association rules are merged to form a coupling relationship, and the devices that satisfy the coupling relationship are marked as the associated cause group devices.

7. The method as described in claim 1, characterized in that, Before performing multi-dimensional cross-validation on the target device to obtain the corresponding validation results, the process also includes: Obtain the target equipment's rated capacity, high-voltage side rated voltage, short-circuit loss, short-circuit voltage percentage, and voltage level; The step of performing multi-dimensional cross-validation on the target device to obtain the corresponding validation results includes: The rated capacity, high-voltage side rated voltage, short-circuit loss, short-circuit voltage percentage, and voltage level of the target equipment are verified according to a preset verification method to obtain parameter integrity verification results. If the parameter integrity verification results fail, the target equipment is determined to be an abnormal equipment with missing parameters. Obtain the resistors, reactances, standard nameplate resistors, and standard nameplate reactances from the equipment ledger; Based on the resistor, the reactance, the standard nameplate resistor, and the standard nameplate reactance, determine the ledger resistance deviation and the ledger reactance deviation respectively; If the deviation of the ledger resistance or the deviation of the ledger reactance is higher than the preset deviation threshold, the target device is determined to be a device with abnormal parameter consistency. If the target equipment is a main transformer, then obtain the measured active power, measured reactive power, average measured current, rated resistance, rated reactance, measured active power and measured reactive power on the high-voltage side of the main transformer. Based on the measured active power on the high-voltage side, the average measured current on the high-voltage side, and the rated resistance, the theoretical active power on the low-voltage side is determined. Based on the theoretical active power on the low-voltage side and the measured active power on the low-voltage side, the power deviation is determined. If the power deviation is higher than the preset power deviation threshold, the target device is determined to be a device with abnormal operating parameters. If the target equipment is a power transmission line, then obtain the measured active power, measured reactive power, rated voltage, and measured current value of the power transmission line. The theoretical current value is determined based on the measured active power of the transmission line, the measured reactive power of the transmission line, and the rated voltage. If the difference between the theoretical current value and the measured current value is higher than the preset current deviation threshold, the target device is determined to be a device with abnormal operating parameters.

8. A root cause repair device for main network power flow non-convergence, characterized in that, include: The core area determination module is used to acquire multi-source equipment ledger data of the target area, and based on the multi-source equipment ledger data, determine the core area of ​​power flow non-convergence from the target area. The multi-source equipment ledger data includes main transformer equipment ledger data and transmission line equipment ledger data. The verification module is used to perform multi-dimensional cross-verification on each target device in the core area and obtain the corresponding verification results. The associated cause group device determination module is used to determine the associated cause group devices of power flow non-convergence from each of the target devices based on the verification results. The repair module is used to perform repair operations for each associated cause group of devices; Specifically, when performing a repair operation, the repair module is used to: Obtain the maximum allowable current carrying capacity and the average apparent power of the device; The assumed current is determined based on the maximum allowable current carrying capacity and the adaptive dynamic correction factor for operating conditions. The adaptive dynamic correction factor for operating conditions is used to characterize the coefficient for dynamically adjusting the maximum allowable current carrying capacity under different operating conditions of the equipment. The standard voltage of the device is determined based on the assumed current and the average apparent power. Determine the per-unit value of the rated resistance and per-unit value of the rated reactance of the equipment based on the standard voltage; The ratio of the per-unit value of the rated resistance to the per-unit value of the rated reactance is taken as the first ratio. Based on the first ratio, multi-factor collaborative repair is performed on the device to obtain the repair result.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the root cause repair method for main network power flow non-convergence as described in any one of claims 1 to 7 when running the computer program.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the root cause repair method for main network power flow non-convergence as described in any one of claims 1 to 7.

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