A power field operation safety management method and system

Abstract

The application discloses a power field operation safety management method and system, and relates to electrical engineering: the power operation space is divided into grids to generate a space index matrix, an initial risk data table containing grid numbers and basic arc-over voltage probability is constructed according to device position coordinates and voltage level V parameters; the initial risk data table is queried according to device voltage data, and an arc-over voltage risk probability is calculated through a probability fusion algorithm; the diffusion risk value of each grid is calculated through a space propagation algorithm according to the arc-over voltage risk probability; the diffusion risks of each arc-over voltage point are spatially accumulated through matrix superposition operation according to the diffusion risk value, and a global risk distribution matrix is obtained; and the risk level is divided according to the global risk distribution matrix and local discharge characteristic data. In view of the fact that the risk is not dynamically updated according to the space propagation of arc discharge, the application provides accurate graded early warning and dynamic safety distance guidance for operation personnel.

Description

Technical Field

[0001] This application relates to the field of electrical engineering, and in particular to a method and system for safety management of power field operations. Background Technology

[0002] With the continuous expansion of power systems and the ongoing upgrading of voltage levels, the operation, maintenance, and repair of power equipment are becoming increasingly frequent. When performing live-line work around or near high-voltage power equipment, workers face a serious risk of arc discharge. Arc discharge is a high-temperature, high-energy gas discharge phenomenon, with temperatures reaching thousands of degrees Celsius, capable of causing severe personal injury and equipment damage in a very short time. Therefore, accurately assessing the arc discharge risk distribution in the work area is of great significance for ensuring the safety of power workers.

[0003] Traditional electrical work safety management primarily relies on fixed safety distance regulations and simple voltage level divisions to determine safe work zones. This method has the following technical drawbacks:

[0004] First, existing technologies typically simplify arc discharge risk to a point source risk, considering only the probability of arc ignition at the location of the equipment itself, while ignoring the propagation characteristics of the arc in space. In reality, once an arc forms, it extends along the direction of maximum electric field strength, and its danger zone is much larger than the arc ignition point itself. This simplification leads to significant biases in the assessment of risk distribution in the workspace.

[0005] Secondly, existing risk assessment methods are mostly static, calculating risks based on equipment rated parameters and standard operating conditions. However, power equipment may experience anomalies such as partial discharge and insulation degradation during actual operation, which can significantly alter the arcing probability in local areas. Current technologies lack a mechanism for dynamically updating risks based on the real-time status of the equipment, failing to reflect changes in risk distribution in a timely manner.

[0006] Furthermore, the spatial propagation of electric arc discharge exhibits a clear directionality, primarily developing along the direction of the electric field gradient. Existing technologies, when calculating the spatial distribution of risk, typically assume that the risk propagates isotropically in space, which contradicts the physical characteristics of electric arc discharge, leading to inaccurate identification of high-risk areas.

[0007] Furthermore, when abnormal signs such as partial discharge are detected, existing systems often can only issue simple alarm signals, and cannot quantitatively assess the impact of the abnormality on the risk distribution of the surrounding space, nor can they provide workers with accurate recommendations for adjusting safe distances. Summary of the Invention

[0008] To address the lack of dynamic risk updates based on the spatial propagation of electric arc discharge, this application provides a method and system for safety management of power field operations. By constructing a global risk distribution matrix that considers the spatial propagation characteristics of electric arc and the directionality of the electric field, the risk level of electric arc discharge at each location in the work space is accurately assessed. When partial discharge is detected, incremental updates of the risk distribution are triggered, providing workers with accurate graded early warnings and dynamic safety distance guidance.

[0009] One aspect of this application provides a method for safety management of power field operations, comprising: S1, dividing the power operation space into grids to generate a spatial index matrix, and constructing an initial risk data table containing grid numbers and basic arc initiation probabilities based on equipment location coordinates and voltage level V parameters; S2, acquiring equipment voltage data, leakage current data, and partial discharge characteristic data; S3, querying the initial risk data table based on the equipment voltage data, and calculating the arc initiation risk probability using a probability fusion algorithm. S4, based on the probability of arc initiation risk S5. Based on the diffusion risk values, the diffusion risks of each arc initiation point are spatially accumulated through matrix superposition to obtain a global risk distribution matrix. S6. Based on the global risk distribution matrix and partial discharge characteristic data, the danger levels are classified, and an early warning dataset containing grid location, risk level, and safety distance is output. When a partial discharge pulse is detected, the arc initiation risk probability of the corresponding area is recalculated. This triggers an incremental update of the global risk distribution matrix.

[0010] Further, S1, construct an initial risk data table containing grid numbers and basic arc initiation probabilities, including: obtaining the three-dimensional boundary parameters of the power operation space, dividing the grid according to the preset grid size, assigning a unique three-dimensional index code (i, j, k) to each grid, and generating an M×N×P spatial index matrix; obtaining the location coordinates and voltage level V of the live equipment, determining the grid number of each equipment through coordinate mapping, and establishing a mapping relationship table between equipment and grids; calculating the Euclidean distance from the grid center of each table to the nearest live equipment. The system queries a preset safety distance threshold based on the voltage level V to generate an air gap distance d dataset; and calculates the basic arc initiation probability for each grid based on the voltage level V and the air gap distance d. ; The grid number, air gap distance, and base arc initiation probability are used. Combine the data to construct an initial risk data table, which includes: grid number (i, j, k), equipment number, voltage level V, air gap distance d, and basic arc initiation probability P_base;

[0011] Among them, voltage level V refers to the rated operating voltage of equipment or lines in the power system, expressed in effective value (kV); the higher the voltage level, the greater the electric field strength, the larger the gap distance required for air breakdown, and the higher the risk of arcing.

[0012] Electrical equipment in a power system that is in a powered-on state includes, but is not limited to: busbars, circuit breakers, disconnect switches; transformers, capacitors, reactors; overhead conductors, cable terminals; and GIS (Gas Insulated Switchgear) equipment.

[0013] Air gap distance d, the shortest straight-line distance from the center point of the grid to the surface of the nearest live equipment, in meters (m); base arc initiation probability. Under standard atmospheric conditions, the probability of arc discharge occurring at grid position (i, j, k) due to insufficient air gap;

[0014] Furthermore, based on the voltage level V and the air gap distance d, the basic arc initiation probability of each grid is calculated. ,include: Calculate the basic arc initiation probability for each grid cell, where, The arc-starting coefficient, Voltage index Rated voltage, This is the critical discharge distance;

[0015] Furthermore, S3 calculates the arc initiation risk probability using a probabilistic fusion algorithm. ,include: ,in, Based on the basic arc initiation probability, This is a voltage correction function. This is a leakage current correction function;

[0016] Furthermore, S4 calculates the diffusion risk value of each grid using a spatial propagation algorithm, including: the arc initiation risk probability. Grids exceeding a preset threshold are used as arc initiation sources, constructing an arc initiation source dataset Ω; the voltage level V corresponding to the arc initiation source (m, n, p) is queried from the initial risk data table, and the corresponding characteristic arc length is obtained from a preset arc propagation characteristic lookup table based on the voltage level V. Calculate the risk contribution value of each arc-initiating source (m, n, p) to the target grid (i, j, k); sum up the risk contribution values ​​of all arc-initiating sources (m, n, p) in the arc-initiating source dataset Ω to obtain the total diffusion risk value of the target grid (i, j, k). ;

[0017] Furthermore, ,in, Let be the arc initiation risk probability of the arc source (m, n, p), and d be the spatial distance from the arc source (m, n, p) to the target grid (i, j, k). The characteristic arc length corresponding to the arc source voltage level, This is a spatial decay function, indicating that the risk decreases exponentially with increasing distance.

[0018] Furthermore, the risk contribution value of each arc source (m, n, p) to the target grid (i, j, k) is calculated, including: extracting the spatial location information and risk probability data of the arc source from the arc source dataset Ω, and establishing the arc source attribute data structure; generating inter-grid distance data based on the three-dimensional coordinate parameters of the arc source and the target grid; querying the corresponding arc propagation characteristic parameters according to the voltage level V of the arc source, and calculating the spatial attenuation coefficient through the distance attenuation model; and weighting the risk probability of the arc source with the spatial attenuation coefficient to generate the corresponding risk propagation contribution value of the arc source to the target grid.

[0019] Furthermore, in S5, the diffusion risk at each starting point is spatially accumulated through matrix superposition operations to obtain a global risk distribution matrix, including: based on the diffusion risk value. Construct a diffusion risk matrix; extract the basic arc initiation probability of each grid from the initial risk data table. Based on the voltage level parameters of associated equipment, the electric field intensity vector at each grid location is calculated according to the voltage distribution, generating a spatial electric field distribution dataset. Based on the electric field distribution dataset, the electric field gradient vector for each grid is calculated. The diffusion risk value is then calculated based on the electric field gradient vector. Perform directional weighting adjustments to obtain the directional weighted risk value; based on the directional weighted risk value and the basic arc initiation probability... Perform risk superposition calculations to construct a global risk distribution matrix that includes directional features.

[0020] Furthermore, in step S6, based on the global risk distribution matrix and partial discharge characteristic data, the hazard levels are classified, including: reading the risk value data of each grid from the global risk distribution matrix, classifying the risk levels according to the preset risk threshold range, and generating a grid risk level mapping table; acquiring partial discharge characteristic data collected by the partial discharge monitoring equipment, including discharge pulse amplitude, discharge frequency, and discharge phase distribution, to determine the grid locations where partial discharge exists; when a partial discharge is detected, querying the initial risk data table based on the grid location of the discharge to obtain the basic arc initiation probability of the corresponding grid location and the grids in the neighboring area. Based on partial discharge characteristic data, the basic arc initiation probability of the corresponding region is determined. Make corrections, return to step S3, and update the arc initiation risk probability of the local area. Based on the updated arc initiation risk probability Execute the spatial propagation algorithm in step S4 to calculate the diffusion risk value of the affected area. Perform the matrix overlay operation of step S5 on the affected area to perform a local incremental update of the global risk distribution matrix; calculate the safe distance corresponding to each risk level based on the updated global risk distribution matrix and the equipment voltage level V, and generate an early warning dataset containing grid three-dimensional index coding, risk level identification and safe distance value.

[0021] Another aspect of this application provides a power field operation safety management system, comprising: a grid division module for dividing the power operation space into grids and generating a spatial index matrix, and constructing an initial risk data table containing grid numbers and basic arc initiation probabilities based on equipment location coordinates and voltage level V parameters; and a risk calculation module for collecting equipment voltage data, leakage current data, and partial discharge characteristic data; querying the initial risk data table based on the equipment voltage data, and calculating the arc initiation risk probability through a probability fusion algorithm. The risk propagation module is based on the probability of arc initiation risk. The system calculates the diffusion risk value of each grid using a spatial propagation algorithm, and then spatially accumulates the diffusion risk of each arc initiation point through matrix superposition operations based on the risk diffusion value to obtain a global risk distribution matrix. The risk warning module classifies the danger level based on the global risk distribution matrix and partial discharge characteristic data, and outputs a warning dataset containing grid location, risk level, and safety distance. When a partial discharge pulse is detected, the risk calculation module is triggered to recalculate the arc initiation risk probability of the corresponding area. This triggers the risk propagation module to perform an incremental update of the global risk distribution matrix.

[0022] Compared to existing technologies, the advantages of this application are:

[0023] Traditional power operation safety management confines risks to a fixed safety distance around equipment, neglecting the propagation and diffusion characteristics of arc discharge in space. This solution modifies the relationship between voltage level V and characteristic arc length. Correlation, using an exponential decay function This system accurately characterizes the spatial propagation patterns of electric arc risks and uses directional weighting based on the electric field gradient vector to realistically reflect the physical characteristics of the arc's preferential development along the electric field direction. In particular, when partial discharge is detected, the system can quickly calculate the spatial diffusion range of the risk based on the discharge location and adjust the global risk distribution matrix in real time through an incremental update mechanism. This dynamic risk assessment based on physical propagation mechanisms provides a scientific basis for precise safety management in complex power operation environments. Attached Figure Description

[0024] This application will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0025] Figure 1 This is an exemplary flowchart illustrating a method for safety management of power field operations according to some embodiments of this application;

[0026] Figure 2 This is an exemplary flowchart illustrating the construction of an initial risk data table according to some embodiments of this application;

[0027] Figure 3 This is an exemplary flowchart illustrating the calculation of the total diffusion risk value according to some embodiments of this application;

[0028] Figure 4 This is an exemplary flowchart illustrating the construction of a global risk distribution matrix according to some embodiments of this application. Detailed Implementation

[0029] The methods and systems provided in the embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0030] like Figure 1 As shown, the power operation space is divided into grids to generate a spatial index matrix. Based on the equipment location coordinates and voltage level V parameter, an initial risk data table containing grid numbers and basic arc initiation probabilities is constructed. Equipment voltage data, leakage current data, and partial discharge characteristic data are obtained. Based on the equipment voltage data, the initial risk data table is queried, and the arc initiation risk probability is calculated using a probability fusion algorithm. Based on the probability of arc initiation risk The diffusion risk value of each grid is calculated using a spatial propagation algorithm. Based on the diffusion risk value, the diffusion risk of each arc initiation point is spatially accumulated through matrix superposition to obtain a global risk distribution matrix. Based on the global risk distribution matrix and partial discharge characteristic data, hazard levels are classified, and an early warning dataset containing grid location, risk level, and safe distance is output. When a partial discharge pulse is detected, the arc initiation risk probability of the corresponding area is recalculated. This triggers an incremental update of the global risk distribution matrix.

[0031] Specifically, S1, obtain the three-dimensional boundary parameters of the power operation space, divide the grid according to the preset grid size, assign a unique three-dimensional index code (i, j, k) to each grid, and generate an M×N×P spatial index matrix;

[0032] Obtain the location coordinates and voltage level V of the live equipment, determine the grid number of each equipment through coordinate mapping, and establish a mapping table between equipment and grid;

[0033] Calculate the Euclidean distance from the grid center of each table to the nearest powered device. And based on the voltage level V, query the preset safety distance threshold to generate the air gap distance d dataset;

[0034] Calculate the basic arc initiation probability for each grid based on the voltage level V and the air gap distance d. , Calculate the basic arc initiation probability for each grid cell, where, This is the arc initiation coefficient. It is an empirical coefficient reflecting the ease with which air insulation arcs can be initiated under specific environmental conditions, with a value range of 0.01 to 0.05. The voltage index describes the nonlinear relationship between the arc initiation probability and voltage, with a value range of 2.0 to 3.5. Rated voltage, This is the critical discharge distance. , Distance coefficient (under standard atmospheric conditions) ), H represents altitude (m).

[0035] like Figure 2 As shown, the grid number, air gap distance, and base arc initiation probability are considered. Combine the data to construct an initial risk data table, which includes: grid number (i, j, k), equipment number, voltage level V, air gap distance d, and basic arc initiation probability. The initial risk data table for this embodiment is detailed in Table 1.

[0036] Table 1 Initial Risk Data Table

[0037] (i, j, k) Equipment Number Equipment type V(kV) phase d(m) (10,15,5) T1-HV-A Transformer bushing 220 A 15.2 (12,16,5) T1-HV-B Transformer bushing 220 B 12.5 (15,18,6) BUS-A1 busbar 220 A 8.3 (18,20,7) ISO-A2 disconnect switch 220 A 5.5 (20,22,8) CT-A3 Current transformer 220 A 3.2 (25,25,9) CB-A1 breaker 220 A 2.1 (30,28,10) T2-MV Transformer medium voltage side 110 A 6.8 (35,30,11) CAP-B1 capacitor bank 35 B 4.2

[0038] In particular, traditional power operation safety management uses fixed safety distance standards, simply dividing the space into safe and dangerous zones. This binary division ignores the gradual nature of arc discharge risk. This solution uses a formula... It accurately describes the spatial distribution of the arc initiation probability: power function term This reflects the nonlinear excitation effect of voltage on arc initiation, consistent with the physical characteristic that the probability of air breakdown increases sharply under high voltage; exponential decay term It reflects the rapid decay of electric field intensity with distance, which is consistent with the spatial locality of electric arc discharge; discretizing the continuous space into a three-dimensional grid and assigning probability values ​​makes each spatial location have a clear risk quantification index.

[0039] S2, Equipment Voltage Data: Voltage transformers (PTs) are installed at each live equipment location. The transformation ratio is selected according to the equipment voltage level (e.g., a 10000:100 transformation ratio is used for 10kV equipment). A data acquisition unit is connected to the secondary side of the transformer, using a 16-bit ADC for analog-to-digital conversion, with a sampling rate set to 5kHz. The acquired voltage signal is then digitally filtered (using a 50Hz power frequency bandpass filter) to calculate the effective value. Data is transmitted to the central processing system in real time via the Modbus TCP protocol, with a transmission cycle of 100ms.

[0040] To acquire leakage current data, a high-precision current sensor (range 0-100mA, accuracy 0.1%) is installed at the end screen of the equipment insulator string or bushing. Shielded twisted-pair cable is used to transmit weak current signals to avoid electromagnetic interference. The signal is amplified 1000 times by a preamplifier and then sampled by an ADC at a sampling rate of 1kHz. The effective value of leakage current I_leak and phase angle are calculated in real time, with a data update frequency of 10Hz.

[0041] To acquire partial discharge characteristic data, a partial discharge detection array consisting of an ultra-high frequency (UHF) sensor and a pulse current sensor is deployed. The UHF sensor operates at a frequency of 300-1500MHz and detects the electromagnetic wave signal generated by partial discharge. The pulse current sensor is coupled through a high-frequency current transformer with a bandwidth of 100kHz-30MHz. The signal passes through a hardware trigger circuit, and data acquisition is initiated when the amplitude exceeds three times the noise level. Characteristic parameters such as discharge pulse amplitude Q (pC), pulse repetition rate n (times / second), and phase distribution φ are extracted.

[0042] In S3, based on the equipment voltage data, the initial risk data table is queried, and the arc initiation risk probability P_arc is calculated using a probability fusion algorithm, specifically including:

[0043] S31, Real-time device voltage data obtained from S2 and leakage current data Establish a data stream buffer with timestamp index;

[0044] S32, Query the initial risk data table based on the equipment number and extract the basic arc initiation probability of the corresponding grid. and rated voltage ;

[0045] S33, Calculate the voltage correction function ,in, The voltage sensitivity coefficient has a value range of 2.0 to 3.5; for a 10kV system... 35kV system .

[0046] when hour, This indicates that overvoltage increases the risk of arcing.

[0047] S34, Calculate the leakage current correction function ,in, This is the current influence coefficient, with a value range of 0.5~1.2 for 10kV systems. 110kV system ; Leakage current threshold, 10kV porcelain insulator 110kV porcelain insulators .

[0048] S35, Perform probability fusion calculation The calculation results are stored in an arc initiation risk probability data table. The data structure includes: grid number (i, j, k), timestamp, and real-time voltage. Leakage current Arc initiation risk probability .

[0049] In particular, traditional methods calculate fixed risk values ​​based solely on equipment rated parameters and spatial distance, failing to reflect the impact of changes in equipment operating conditions on arc initiation risk. This solution introduces a voltage correction function. and leakage current correction function This system captures two key arc-initiating factors in real time: overvoltage directly increases the probability of air gap breakdown; and increased leakage current indicates insulation performance degradation, lowering the arc-initiating threshold. This dynamic correction mechanism improves the arc-initiating probability. It can reflect changes in equipment status in real time, and automatically raise the risk level of the corresponding area when overvoltage or insulation deterioration is detected, providing more accurate safety warnings for operators.

[0050] like Figure 3 As shown, in S4, based on the arc initiation risk probability... The diffusion risk value of each grid is calculated using a spatial propagation algorithm, specifically including:

[0051] S41, Read the arc initiation risk probability calculated by S3 Filter to meet The conditional mesh serves as a potential arc initiation source, where, Generate an arc-initiating source mesh set based on the preset arc initiation probability threshold. ;in, Threshold value range: 0.001~0.01; General assignment: Live-line work: .

[0052] S42, for any target grid (i, j, k) in the spatial index matrix, traverse each arc source (m, n, p) in the arc source grid set Ω and calculate the Euclidean distance between the two grids. ;

[0053] S43, Query the voltage level V corresponding to the arc source (m, n, p) from the initial risk data table, and obtain the corresponding characteristic arc length from the preset lookup table according to the voltage level V. Among them, the 10kV system: 35kV system: 110kV system: 220kV system: The preset lookup table for arc propagation characteristics is detailed in Table 2.

[0054] Table 2 Preset Arc Propagation Characteristics Lookup Table

[0055] Voltage level V (kV) Characteristic arc length (m) Arc temperature T (K) Propagation speed v (m / s) Energy density E (MJ / m3) 10 0.5 6000 50 10 35 1.2 8000 80 25 110 3.5 10000 120 60 220 6 12000 150 100 500 12 15000 200 200

[0056] S44, Calculate the risk contribution of each arc initiation source to the target mesh using the spatial attenuation function: ;

[0057] S45, sum the risk contribution values ​​of all arc-initiating sources in the arc-initiating source mesh set Ω to obtain the total diffusion risk value of the target mesh (i, j, k): ,in, Let be the arc initiation risk probability of the arc source (m, n, p), and d be the spatial distance from the arc source (m, n, p) to the target grid (i, j, k). The characteristic arc length corresponding to the arc source voltage level, This is a spatial decay function, indicating that the risk decreases exponentially with increasing distance.

[0058] S46, after traversing all grids to complete the diffusion risk value calculation, The data is stored in a diffusion risk matrix with dimensions M×N×P, and a spatial index structure is built for this matrix to support subsequent fast query and update operations.

[0059] In particular, traditional safety assessments assume that risk sources are isolated, neglecting the reality that multiple weak points exist simultaneously in complex power systems. However, arc discharge has a significant clustering effect—when multiple high-risk points exist in a system, these potential arc sources will form mutually influential risk fields. Discharge at one point will reduce the insulation strength of the surrounding space, promoting the development of discharge at other locations.

[0060] This application uses threshold filtering. Identify all potential hazards, rather than focusing on just the most dangerous single location; each arcing source creates an ionization path that reduces air insulation strength, and the effects of multiple sources accumulate in space, making the actual risk in some areas far higher than the assessment results of a single source; arcing sources of different voltage levels have different... The values ​​form influence ranges of varying sizes, truly reflecting the complexity of mixed voltage environments. This multi-source superposition model enables the system to identify risk hotspots that traditional methods overlook, especially in complex operating environments with dense equipment and multiple voltage levels, revealing the true risk distribution to operators.

[0061] like Figure 4 As shown, S51 reads the total diffusion risk value of each grid generated in S4. Construct a diffusion risk matrix with dimensions M×N×P;

[0062] S52, Extract the basic arc initiation probability of each grid from the initial risk data table. Given the voltage level V, calculate the electric field intensity vector for each grid. Generate electric field distribution data;

[0063] S53, Risk superposition calculation based on electric field directionality:

[0064] Calculate the electric field gradient direction vector for each grid cell. , The risk increases in the direction of the electric field. The risk is reduced when the electric field is reversed.

[0065] Risk of spread Introducing directional weighting factors Where θ is the angle between the risk propagation direction and the electric field gradient direction, and ε is the basic weight. This ensures that there is a basic risk propagation even when the direction is perpendicular to the electric field; it reflects the statistical characteristics of the random walk of the electric arc and air breakdown.

[0066] Risk superposition due to execution direction weighting: .

[0067] S54 organizes the direction-weighted global risk value data into a global risk distribution matrix. Each matrix element reflects the overall risk level taking into account the directionality of the electric field;

[0068] S55 establishes a multi-level index structure for the global risk distribution matrix, adds an electric field intensity level index, and supports combined queries by region, risk level, and electric field characteristics.

[0069] Specifically, electric arc discharge does not spread uniformly in space, but preferentially propagates along the direction of maximum electric field strength. This is because: air molecules in strong electric field regions are more easily ionized, forming conductive channels; the streamer at the arc tip is driven by the electric field force and grows along the direction of the electric field lines; and the direction of the electric field gradient represents the path of fastest potential drop, which is the dominant channel for energy release. By introducing electric field gradient information and a directional weighting factor, risk assessment can accurately reflect the physical law of the arc's preferential development along electric field lines. The weighting factor is largest when the risk propagation direction is consistent with the electric field gradient direction (θ=0); and smallest when perpendicular to the electric field direction. This directional weighting mechanism significantly improves the accuracy of predicting hazardous areas around high-voltage equipment, providing more reliable safety protection guidance for workers.

[0070] Where θ = 0° (along the direction of the electric field): It has the highest weight. (Oblique propagation): The weight is halved; (Vertical electric field): It has the smallest weight.

[0071] S6 classifies hazard levels based on the global risk distribution matrix and partial discharge characteristic data, and outputs a warning dataset containing grid location, risk level, and safe distance. Specifically, when a partial discharge pulse is detected, the arc initiation risk probability Parc for the corresponding area is recalculated, and an incremental update of the global risk distribution matrix is ​​triggered, including:

[0072] S61, traverse each grid element in the global risk distribution matrix and extract the risk value. The risk value is compared with the preset four-level risk threshold. When comparing, The time stamp is marked as the security level. The time was marked as low risk level. The time was marked as medium risk. The time was marked as high risk level. The time stamp is marked as extremely high risk level, generating a mapping data table containing grid indexes and risk level identifiers;

[0073] Specifically, : 0.005~0.015, security level threshold; : 0.025~0.075, low-risk threshold; : 0.075~0.225, medium-risk threshold; : 0.175~0.525, high-risk threshold.

[0074] S62 receives the data stream from the partial discharge monitoring system and parses the data packets to obtain the discharge pulse amplitude. Number of discharges per unit time and discharge phase angle The sensor position calibration algorithm converts the coordinates of the monitoring points into corresponding grid indices. ;

[0075] S63, establish a partial discharge event triggering mechanism, when When the preset threshold is exceeded, the risk update process is triggered: using the discharge grid. Centered on the preset radius Determine the set of grids that need to be updated. Batch query from the initial risk data table The original data records of each grid within the system; for the 10kV system: 35kV system: 110kV system: 220kV system: .

[0076] S64, Constructing the discharge characteristic correction factor: Adjusting the discharge amplitude... Normalization process yields the amplitude factor Discharge frequency Convert to frequency factor Calculate the concentration factor based on the phase distribution. , , Standard deviation of phase distribution; comprehensive correction factor ,right The basic arc initiation probability of each inner grid is corrected by multiplication. ;

[0077] S65, the revised As input parameters, the S3 processing flow is invoked to recalculate the arc initiation risk probability of the local region, and the updated probability is then applied. Values ​​are written to a temporary cache; updated values ​​are read from the temporary cache. Perform spatial propagation calculations using S4 to generate a new set of diffusion risk values ​​for the local region;

[0078] S66, create an incremental update index table to record the location of the affected grid, perform the risk superposition operation of S5 only on the grids in the index table, replace the calculation result with the value at the corresponding position in the original global risk distribution matrix, and complete the local update of the matrix;

[0079] S67. Scan the updated global risk distribution matrix. For each risk level grid, based on the voltage level V of the nearest device in that grid, query the safety distance calculation table to obtain the reference distance. Calculate the actual safe distance The grid index (i, j, k), risk level coding, and safety distance are combined. The data is assembled into structured data records and written in batches to the early warning dataset.

[0080] In particular, partial discharge can occur at insulation defects in equipment, which is an important sign of impending complete breakdown. The discharge amplitude is significant. Larger diameter → more severe insulation defects; discharge frequency Higher concentration indicates faster degradation; The higher the value, the more dangerous the defect type. Traditional risk assessments are based on one-time calculations using equipment rated parameters and spatial location, failing to detect the dynamic degradation of the equipment's insulation condition. This application uses partial discharge as an early warning signal of insulation degradation, triggering real-time local updates to the risk distribution.

[0081] The foregoing illustrative description of the present application and its embodiments is not restrictive and can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. The accompanying drawings are only one embodiment of the present application, and the actual structure is not limited thereto. Therefore, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the present application, such designs should fall within the scope of protection of this application. Furthermore, the word "comprising" does not exclude other elements or steps, and the word "a" preceding an element does not exclude the inclusion of "a plurality" of that element. Terms such as "first," "second," etc., are used to indicate names and do not indicate any specific order.

Claims

1. A method for safety management of power field operations, characterized in that, include: S1. Divide the power operation space into grids to generate a spatial index matrix. Based on the equipment location coordinates and voltage level V parameter, construct an initial risk data table containing grid number and basic arc initiation probability. S2, acquire equipment voltage data, leakage current data, and partial discharge characteristic data; S3. Based on the equipment voltage data, query the initial risk data table and calculate the arc initiation risk probability using a probability fusion algorithm. ; S4, based on the arc initiation risk probability The diffusion risk value of each grid is calculated using a spatial propagation algorithm; S5. Based on the diffusion risk value, the diffusion risk of each starting point is spatially accumulated through matrix superposition operation to obtain the global risk distribution matrix. S6, based on the global risk distribution matrix and partial discharge characteristic data, classifies the hazard levels and outputs a warning dataset containing grid location, risk level, and safe distance; among these, when a partial discharge pulse is detected, the arc initiation risk probability of the corresponding area is recalculated. This triggers an incremental update of the global risk distribution matrix; S1, Construct an initial risk data table containing grid numbers and basic arc initiation probabilities, including: Obtain the three-dimensional boundary parameters of the power operation space, divide the grid according to the preset grid size, assign a unique three-dimensional index code (i, j, k) to each grid, and generate an M×N×P spatial index matrix; Obtain the location coordinates and voltage level V of the live equipment, determine the grid number of each equipment through coordinate mapping, and establish a mapping table between equipment and grid; Calculate the Euclidean distance from the grid center of each table to the nearest powered device. And based on the voltage level V, query the preset safety distance threshold to generate the air gap distance d dataset; Calculate the basic arc initiation probability for each grid based on the voltage level V and the air gap distance d. ; The grid number, air gap distance d, and basic arc initiation probability are used. Combine the data to construct an initial risk data table, which includes: grid number (i, j, k), equipment number, voltage level V, air gap distance d, and basic arc initiation probability. ; S4, based on the arc initiation risk probability The diffusion risk value of each grid is calculated using a spatial propagation algorithm, including: The probability of arc initiation Grids exceeding a preset threshold are used as arc initiation sources, and an arc initiation source dataset Ω is constructed. The voltage level V corresponding to the arc source (m, n, p) is retrieved from the initial risk data table. Based on the voltage level V, the corresponding characteristic arc length is obtained from the preset arc propagation characteristic lookup table. ; Calculate the risk contribution value of each arc source (m, n, p) to the target mesh (i, j, k); The total diffusion risk value of the target grid (i, j, k) is obtained by summing the risk contribution values ​​of all arc-initiating sources (m, n, p) in the arc-initiating source dataset Ω. ; Calculate the basic arc initiation probability for each grid based on the voltage level V and the air gap distance d. ,include: Calculate the basic arc initiation probability for each grid cell: ,in, The arc-starting coefficient, Voltage index Rated voltage, This is the critical discharge distance; S3. Based on the equipment voltage data, query the initial risk data table and calculate the arc initiation risk probability using a probability fusion algorithm. ,include: ,in, Based on the basic arc initiation probability, This is a voltage correction function. This is the leakage current correction function.

2. The method for safety management of power field operations according to claim 1, characterized in that: ,in, Let be the arc initiation risk probability of the arc source (m, n, p), and d be the spatial distance from the arc source (m, n, p) to the target grid (i, j, k). The characteristic arc length corresponding to the arc source voltage level, This is a spatial decay function, indicating that the risk decreases exponentially with increasing distance.

3. The method for safety management of power field operations according to claim 1, characterized in that: Calculate the risk contribution value of each arc initiation source (m, n, p) to the target mesh (i, j, k), including: Spatial location information and risk probability data of arc initiation sources are extracted from the arc initiation source dataset Ω to establish an arc initiation source attribute data structure. Based on the three-dimensional coordinate parameters of the arc source and the target mesh, generate inter-mesh distance data; Based on the voltage level V of the arc initiation source, the corresponding arc propagation characteristic parameters are queried, and the spatial attenuation coefficient is calculated using the distance attenuation model. The risk probability of the arc-initiating source is weighted by the spatial attenuation coefficient to generate the risk propagation contribution value of the corresponding arc-initiating source to the target grid.

4. The method for safety management of power field operations according to claim 3, characterized in that: S5 yields the global risk distribution matrix, including: Obtain diffusion risk value ; Extract the basic arc initiation probability of each grid from the initial risk data table. Based on the voltage level parameters of the associated equipment, the electric field intensity vector at each grid location is calculated according to the voltage distribution, and a spatial electric field distribution dataset is generated. Based on the electric field distribution dataset, calculate the electric field gradient vector for each grid. Based on the diffusion risk value of the electric field gradient vector A directional weighted adjustment is performed to obtain the directional weighted risk value; Based on direction-weighted risk value and basic arc initiation probability Perform risk superposition calculations to construct a global risk distribution matrix that includes directional features.

5. The method for safety management of power field operations according to claim 4, characterized in that: S6, based on the global risk distribution matrix and partial discharge characteristic data, classifies the hazard levels, including: The risk value data of each grid is read from the global risk distribution matrix, and the risk level is divided according to the preset risk threshold range to generate a grid risk level mapping table. Acquire partial discharge characteristic data collected by partial discharge monitoring equipment, including discharge pulse amplitude, discharge frequency and discharge phase distribution, and determine the grid locations where partial discharge exists; When partial discharge is detected, the initial risk data table is queried based on the grid location of the discharge to obtain the basic arc initiation probability of the corresponding grid location and the grids in the neighborhood. ; Based on partial discharge characteristic data, the fundamental arc initiation probability of the corresponding region is determined. Make corrections, return to step S3, and update the arc initiation risk probability of the local area. ; Based on the updated arc initiation risk probability Execute the spatial propagation algorithm in step S4 to calculate the diffusion risk value of the affected area. ; Perform the matrix overlay operation of step S5 on the affected area to perform a local incremental update of the global risk distribution matrix; Based on the updated global risk distribution matrix, the safe distance corresponding to each risk level is calculated in combination with the equipment voltage level V, and an early warning dataset containing grid three-dimensional index coding, risk level identification and safe distance value is generated.

6. A power field operation safety management system, characterized in that, include: The grid division module divides the power operation space into grids and generates a spatial index matrix. Based on the equipment location coordinates and voltage level V parameter, it constructs an initial risk data table containing grid numbers and basic arc initiation probabilities. The risk calculation module collects equipment voltage data, leakage current data, and partial discharge characteristic data. Based on the equipment voltage data, the initial risk data table is consulted, and the probability of arc initiation risk is calculated using a probability fusion algorithm. ; The risk propagation module is based on the probability of arc initiation risk. The diffusion risk value of each grid is calculated by the spatial propagation algorithm, and the diffusion risk of each arc point is spatially accumulated by matrix superposition operation based on the risk diffusion value to obtain the global risk distribution matrix. The risk warning module classifies danger levels based on the global risk distribution matrix and partial discharge characteristic data, and outputs a warning dataset containing grid location, risk level, and safety distance. Specifically, when a partial discharge pulse is detected, the risk calculation module is triggered to recalculate the arc initiation risk probability of the corresponding area. This triggers the risk propagation module to incrementally update the global risk distribution matrix; S1, Construct an initial risk data table containing grid numbers and basic arc initiation probabilities, including: Obtain the three-dimensional boundary parameters of the power operation space, divide the grid according to the preset grid size, assign a unique three-dimensional index code (i, j, k) to each grid, and generate an M×N×P spatial index matrix; Obtain the location coordinates and voltage level V of the live equipment, determine the grid number of each equipment through coordinate mapping, and establish a mapping table between equipment and grid; Calculate the Euclidean distance from the grid center of each table to the nearest powered device. And based on the voltage level V, query the preset safety distance threshold to generate the air gap distance d dataset; Calculate the basic arc initiation probability for each grid based on the voltage level V and the air gap distance d. ; The grid number, air gap distance d, and basic arc initiation probability are used. Combine the data to construct an initial risk data table, which includes: grid number (i, j, k), equipment number, voltage level V, air gap distance d, and basic arc initiation probability. ; S4, based on the arc initiation risk probability The diffusion risk value of each grid is calculated using a spatial propagation algorithm, including: The probability of arc initiation Grids exceeding a preset threshold are used as arc initiation sources, and an arc initiation source dataset Ω is constructed. The voltage level V corresponding to the arc source (m, n, p) is retrieved from the initial risk data table. Based on the voltage level V, the corresponding characteristic arc length is obtained from the preset arc propagation characteristic lookup table. ; Calculate the risk contribution value of each arc source (m, n, p) to the target mesh (i, j, k); The total diffusion risk value of the target grid (i, j, k) is obtained by summing the risk contribution values ​​of all arc-initiating sources (m, n, p) in the arc-initiating source dataset Ω. ; Calculate the basic arc initiation probability for each grid based on the voltage level V and the air gap distance d. ,include: Calculate the basic arc initiation probability for each grid cell: ,in, The arc-starting coefficient, Voltage index Rated voltage, This is the critical discharge distance; S3. Based on the equipment voltage data, query the initial risk data table and calculate the arc initiation risk probability using a probability fusion algorithm. ,include: ,in, Based on the basic arc initiation probability, This is a voltage correction function. This is the leakage current correction function.

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