Power grid maintenance future state dynamic generation and optimization scheduling system

Abstract

The invention discloses a power grid overhaul future state dynamic generation and optimal scheduling system, which belongs to the technical field of power grid overhaul, and comprises a multi-time plan input module, an overhaul mode adjustment module and a dynamic adjustment power grid overhaul plan based on ground state data. The safety analysis module is used for verifying power grid stability, performing static safety analysis and calculating power grid load flow distribution; the iteration module is used for dynamically correcting the maintenance plan according to the risk result data and generating a final scheme; the rehearsal verification module is used for simulating the power grid operation state under the complex working conditions of new equipment commissioning and maintenance; multi-source data of production plans, maintenance plans, load prediction and the like in different time dimensions are integrated, a ground state maintenance mode, a future power grid change section and a measurement data time sequence section are generated, and detailed ground state data are constructed; and comprehensive, accurate and dynamically updated data support is provided for subsequent links such as power grid risk assessment, maintenance strategy formulation and safety analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid maintenance, and more specifically, to a future state dynamic generation and optimization scheduling system for power grid maintenance. Background Art

[0002] With the development of society, the scale of power grids continues to expand, and the types of power equipment are becoming more and more diverse and widely distributed. The safe and stable operation of power grids is becoming more and more important to social production and life. However, power equipment is prone to failures due to manufacturing defects, installation problems, insulation aging, etc. in long-term operation. Traditional power grid maintenance is mostly based on fixed cycles or relies on manual inspections to passively respond to failures, which is difficult to adapt to the high standards and strict requirements of modern power grids for safe and reliable power supply. In recent years, although there have been technical assistance such as sensors and information systems, the amount of data has exploded. Traditional methods have many shortcomings in data fusion, analysis and risk assessment, and it is difficult to meet the needs of refined and intelligent operation and maintenance of power grids. The risk assessment is not accurate and comprehensive enough. Existing methods are mostly based on single-dimensional indicators, such as the current status of equipment or simple load forecasts, ignoring the comprehensive consideration of multi-source data, resulting in inaccurate risk assessment results and unable to fully reflect the actual risk status of the power grid. On the other hand, the maintenance plan lacks flexibility. The maintenance plan based on a fixed time window or empirical rules is difficult to adjust dynamically according to real-time risks. In addition, the multi-objective optimization capability is insufficient. Existing technologies focus on single resource optimization, such as only focusing on labor costs or spare parts inventory, without comprehensively considering multiple factors such as maintenance time and risk level, resulting in unreasonable resource allocation and inefficiency. Furthermore, the rehearsal verification function is weak, and the simulation of complex working conditions is insufficient, making it difficult to identify potential hidden dangers in advance, especially in extreme scenarios, which can easily lead to more serious consequences due to the spread of faults. Finally, data fusion and analysis are difficult. Different business systems are independently developed and constructed, which makes data fusion difficult. In addition, the quality of data collected by early sensors is uneven, and there are measurement errors, data anomalies, loss or duplication, which affects the effective use of data and analysis results. Summary of the invention

[0003] In order to solve the above problems, the present invention provides a future state dynamic generation and optimization scheduling system for power grid maintenance.

[0004] The present invention provides a future state dynamic generation and optimization scheduling system for power grid maintenance, including a multi-time plan input module for integrating production plans, maintenance plans and load forecasts in different time dimensions, generating base state maintenance methods, future power grid change sections and measurement data time series sections, and obtaining base state data; The maintenance mode adjustment module dynamically adjusts the maintenance plan of the power grid based on the baseline data; Safety analysis module, used to verify grid stability, perform static safety analysis, and calculate grid power flow distribution; A risk discrimination module, which is used to generate a future state under a single maintenance work schedule, generate a future state power grid section, evaluate the risk of the maintenance schedule, discriminate the time-series power grid risk brought by the maintenance power outage, and obtain risk result data; An iteration module, which is used to dynamically correct the maintenance plan according to the risk result data and generate a final plan; A preview verification module, which is used to simulate the power grid operation state under the commissioning of new equipment and complex maintenance conditions.

[0005] Preferably, the multi-time plan input module includes a commissioning plan unit, a maintenance plan unit, and a load forecasting unit; The commissioning plan unit is used to formulate commissioning plans for different stages, and then form a future power grid change section according to the formulated commissioning plan; The maintenance plan unit is used to formulate maintenance plans for different stages, and then conduct a basic state maintenance mode analysis to form a planned basic state section, which specifically includes parameter data under the condition of power grid equipment maintenance; The load forecasting unit is used to formulate load thresholds for different stages, and then conduct power generation forecasting and low-voltage bus forecasting to form a future measurement data time-series section.

[0006] Preferably, the specific working steps of the maintenance mode adjustment module are as follows: Obtain the basic state data, construct a spatio-temporally sensitive maintenance index to determine the priority maintenance order of equipment; Substitute the collected data into the spatio-temporally sensitive maintenance index to calculate the spatio-temporally sensitive maintenance index value of each equipment.

[0007] Sort the equipment according to the magnitude of the spatio-temporally sensitive maintenance index value. The larger the spatio-temporally sensitive maintenance index value, the higher the priority maintenance level of the equipment, and the maintenance should be arranged first to determine the priority maintenance order of the equipment.

[0008] Preferably, the specific working steps of the maintenance mode adjustment module further include the following: For spatio-temporal coordination conflict resolution, resolve maintenance conflicts, which specifically includes two constraint conditions: spatial coupling constraint and time elasticity constraint. The spatial coupling constraint identifies conflicts by considering the geographical proximity coefficient between equipment and the electrical coupling degree and geographical topology; the time elasticity constraint determines the time range of the maintenance window based on the elasticity coefficient generated by meteorological forecasting.

[0009] Preferably, the specific working steps of the maintenance mode adjustment module further include the following; Dynamically adapt elastic resources, construct a multi-objective resource optimization model, and maximize the sum of the maintenance time multiplied by the resource reduction coefficient, and at the same time minimize the sum of the product of the labor cost and the square of the distance between the equipment geographical coordinates and the location of the team station as the objective function.

[0010] Preferably, the specific steps for the safety analysis module to perform static safety analysis are as follows: Static safety analysis aims to verify the stability of the power grid under a single equipment failure (N-1) after the adjustment of the maintenance mode. First, N-1 fault enumeration is carried out, that is, all maintenance equipment and associated lines are traversed, the power grid topology after their outage is simulated, and a new adjacency matrix is generated by removing the corresponding equipment. Then, stability verification is carried out, including voltage stability verification and line overload test. Finally, probabilistic risk assessment is carried out, and the system instability probability is calculated by combining the equipment failure rate. Due to the problem of combinatorial explosion in large-scale power grid scenarios, Monte Carlo sampling or heuristic pruning optimization can be used for calculation, and a list of risk equipment and corresponding over-limit indicators are output.

[0011] Preferably, the safety analysis module is also used for power flow calculation of the power grid, and the specific steps are as follows: Generating the future state under a single maintenance work time sequence aims to construct a sequence of future states of the power grid with time tags to support dynamic risk analysis; First, time-section slicing is carried out, the maintenance plan is decomposed according to time slices, a topology change sequence is generated, considering the outage / resumption operations of the maintenance equipment and the time-sequence changes of load forecasting; Then, a state prediction model is used to predict the parameters of the future state; Finally, these section snapshots are stored in a time-sequence database for quick backtracking and comparison, and a set of future-state power grid models with time stamps is output.

[0012] Preferably, the specific steps of the risk discrimination module are as follows: The goal of time-sequence power grid risk discrimination brought by maintenance power outage is to quantify the risk levels at different time points during the maintenance process and identify the chain of cascading failures; First, a time-sequence risk chain model is built to construct a Bayesian network to depict the causal relationship among equipment failures, load transfer, and protection actions, where node A is the initial maintenance power outage event, and B and C are subsequent affected equipment or load changes; Then, risk indicators are calculated, such as the probability of load loss, which is statistically calculated by judging the relationship between the load and available power in each time period; Then, a risk heat map is generated, integrating factors such as overload rate, voltage deviation, and new energy fluctuations, and the risk values at each location and time are obtained by adding them according to weights; Then, based on graph theory algorithms, the critical path is identified to determine the maintenance path with the largest impact range.

[0013] Preferably, the specific steps for the iteration module to dynamically modify the maintenance plan according to the risk result data and generate the final plan are as follows: Starting from the initial maintenance plan, safety analysis and risk discrimination are carried out in sequence. If the risk discrimination result meets the standard, the maintenance plan is executed. If the risk discrimination result does not meet the standard, it enters the dynamic adjustment link to revise the maintenance plan, and then safety analysis and risk discrimination are carried out again until the result meets the standard.

[0014] Preferably, the specific working steps of the rehearsal verification module are as follows: Generate multiple extreme scenarios, specifically including sudden drops in new energy output and typhoon passing through, calculate the weights of each scenario, considering the scenario deviation degree and correlation coefficient; Based on the grouping matrix, divide the equipment activity queue, corresponding to the set of operable equipment for a specific time window.

[0015] Beneficial effects: By integrating multi-source data such as production plans, maintenance plans, and load forecasts in different time dimensions, generate the base-state maintenance mode, future power grid change sections, and measurement data time-series sections, and construct detailed base-state data, providing comprehensive, accurate, and dynamically updated data support for subsequent power grid risk assessment, maintenance strategy formulation, and safety analysis, etc., ensuring that each analysis module can work based on a unified and complete data foundation, thereby improving the scientificity and accuracy of the entire power grid operation and maintenance decision-making; Most of the existing technologies focus on the optimization of a single goal in the allocation of power grid maintenance resources, such as only focusing on the optimization of labor costs or spare parts inventory, lacking effective methods for multi-goal comprehensive optimization. And this step can achieve the dynamic adaptation and comprehensive optimization of resources by establishing a multi-goal resource optimization model, considering multiple factors such as maintenance time, labor costs, spare parts demand index, and risk level, and is more comprehensive and advanced compared with others; Traditional power grid maintenance resource allocation methods often have difficulty in achieving multi-goal optimization of resources, and cannot well balance multiple factors such as maintenance time, labor costs, and spare parts demand, resulting in problems such as unreasonable resource allocation and low efficiency. This step solves the problem of insufficient optimization of maintenance resources existing in the existing technology and realizes the dynamic adaptation and comprehensive optimization of resources. Brief Description of the Drawings

[0016] Figure 1 is the flowchart of the present invention. Detailed Embodiments

[0017] As Figure 1 shown: A future-state dynamic generation and optimal scheduling system for power grid maintenance includes a multi-time plan input module, which is used to integrate production plans, maintenance plans, and load forecasts in different time dimensions, generate the base-state maintenance mode, future power grid change sections, and measurement data time-series sections, and obtain the base-state data; It should be noted that the initial topology and operating state of the future power grid are formed through base state analysis (such as equipment status and load forecasting), providing a data basis for subsequent adjustments; The maintenance mode adjustment module dynamically adjusts the power grid maintenance plan based on the base state data; It should be noted that the adjusted maintenance plan is generated by combining the "base section of the maintenance plan" and the "time series section of future measurement data"; The security analysis module is used to verify the power grid stability, conduct static security analysis, and calculate the power grid power flow distribution; It should be noted that the power grid after the adjustment of the maintenance mode is ensured to meet the safe operation constraints; The risk discrimination module is used to generate the future state under the single maintenance work time series, generate the future state power grid section, evaluate the maintenance time series risk, discriminate the time series power grid risk brought by the maintenance power outage, and obtain the risk result data; It should be noted that the future state section with time series labels is generated to quantify the risk level; The iteration module is used to dynamically correct the maintenance plan according to the risk result data and generate the final plan; It should be noted that dynamic optimization is achieved through multiple "iterations", and finally the medium / long-term / short-term / ultra-short-term maintenance plans are output; The preview verification module is used to simulate the power grid operation state under the complex working conditions of new equipment commissioning and maintenance.

[0018] It should be noted that the power grid operation preview based on the future state section covers extreme scenarios; It should also be noted that the risk discrimination result triggers the adjustment of the maintenance mode, from the future state generation and risk discrimination module to the maintenance mode adjustment module; After problems are found in the preview verification, it returns to iterative optimization, from the preview verification module to the iterative optimization module; The "time series section of future measurement data" generated by the data preparation module provides input for the adjustment of the maintenance mode; The power flow calculation result of the security analysis module is directly used for future state generation and risk discrimination; For the high-risk time periods identified by the risk discrimination module, the maintenance resources are reallocated through the iterative optimization module; The simulation result of the preview verification module is fed back to the data preparation module to update the base section; The medium- and long-term plan provides a framework constraint for the short-term / ultra-short-term, and the ultra-short-term real-time data corrects the medium- and long-term forecast.

[0019] As an optional embodiment: the multi-time plan input module includes a commissioning plan unit, a maintenance plan unit, and a load forecasting unit; It should be noted that the following different stages all include medium-term, short-term, and ultra-short-term. In this embodiment, the medium-term is 6 to 18 months, the short-term is 1 to 6 months, and the ultra-short-term is real-time data in hours; Establish an incremental model library containing commissioning / withdrawal plans, and store the commissioning time, topological connection relationship, and parameter data of grid equipment according to medium-term (6 - 18 months), short-term (1 - 6 months), and ultra-short-term (hourly level); Adopt an incremental storage method, and each version only stores the changed data relative to the previous version (such as new substations, line withdrawals), reducing storage redundancy; Extract the model data of medium-term / short-term commissioned equipment (such as the node numbers and connection relationships of new substations), and automatically splice them with the current real-time model through graph model node numbers (such as type+co_no+fac_node) to form full model data; Mark the overhauled equipment as out-of-service status, and set the corresponding switch disconnection logic in the model; Convert medium-term / short-term load thresholds and power generation prediction data into bus load prediction files (such as E-format files), and associate them with the corresponding nodes of the full model; The commissioning plan unit is used to formulate commissioning plans at different stages, and then form future grid change sections according to the formulated commissioning plans; It should be noted that for newly added equipment such as wind farms and transformers, the shortest grid connection path is determined through in-depth topological search: Search for the connection switch path to the energized bus, and close the shortest path switch to achieve automatic grid connection; The newly added wind farm automatically closes the tie switch and connects to the grid by searching for the 220kV bus line path of the neighboring substation; Use the MapReduce framework to decompose the future section generation task into multi-period parallel calculations: Each period independently executes plan data decomposition (such as power generation plan allocation), power flow calculation, outputs section results, summarizes the section data of each period, and generates a time-series section file; The overhaul plan unit is used to formulate overhaul plans at different stages, and then conduct base-state overhaul mode analysis to form a planned base-state section, specifically including parameter data in the case of grid equipment overhaul; It should be noted that obtain the current state information of the equipment from the equipment status monitoring system or historical maintenance records, such as the operating duration, fault history, performance indicators, etc. of the equipment, to determine whether the equipment is in a normal, abnormal, or serious state; Collect the real-time operation data of the grid from the grid real-time monitoring system (such as the SCADA system), including key parameters such as bus voltage, unit output, and line power flow; Inject the collected real-time grid operation data into the whole network model as the initial operation mode to ensure that the model can accurately reflect the current actual operation state of the grid; After determining the maintenance strategy and equipment outage status, call a professional power flow calculation engine (such as BPA / PSASP, etc.) to perform the base state power flow calculation on the whole network model injected with real-time data; Obtain the key parameters of voltage distribution and power distribution of the grid under maintenance through power flow calculation to form a planned base state section; The load forecasting unit is used to formulate load thresholds at different stages, and then perform power generation forecasting and low-voltage bus forecasting to form a future measurement data time series section.

[0020] It should be noted that the ARIMA or LSTM model is used to predict the annual / monthly load, and the accuracy is improved by superimposing a meteorological correction factor (such as the temperature cumulative effect coefficient); Collect SCADA measurement data in real time, dynamically correct the predicted value through Kalman filtering, and generate a minute-level load time series curve; Compare the deviation between the predicted data and the actual measurement. If it exceeds the threshold (such as the error > 5%), trigger an early warning and adjust the parameters of the prediction model; Output a standardized time series section file (such as CIM / E format), including time series data such as bus load and new energy output.

[0021] As an optional embodiment: The specific working steps of the maintenance mode adjustment module are as follows: Obtain the base state data, construct a spatio-temporal sensitive maintenance index to determine the priority maintenance order of equipment; Substitute the collected data into the spatio-temporal sensitive maintenance index to calculate the spatio-temporal sensitive maintenance index value of each equipment.

[0022] Sort the equipment according to the size of the spatio-temporal sensitive maintenance index value. The larger the spatio-temporal sensitive maintenance index value, the higher the priority maintenance level of the equipment, and the maintenance should be arranged first to determine the priority maintenance order of the equipment.

[0023] Comprehensively consider multiple-dimensional states, and at the same time combine the relationship between the continuous operation duration of the equipment and the average life threshold of similar equipment, and give priority to dealing with the equipment with the highest risk and located on the critical path to avoid cascading failures; specifically include multiple-dimensional states such as the real-time health degree of the equipment, the power flow sensitivity factor, and the fault propagation probability; It should be noted that in this embodiment, the specific calculation formula of the spatio-temporal sensitive maintenance index is as follows: ; Where is the real-time health of the device, which is obtained by averaging and weighting the comparison between the power grid device parameter data and the corresponding standard values, comprehensively reflecting the current health status of the device; is the power flow sensitivity factor, and the calculation formula is , the rated capacity divided by the number of overloads reflects the overload tolerance of the device, and the node betweenness reflects the importance of the node in the network, comprehensively measuring the sensitivity of the device in the power flow distribution; is the fault propagation probability, which is generated based on the Bayesian network to simulate the N-2 cascading fault scenario, that is, considering the probability of fault propagation when two devices fail simultaneously; is the continuous operation duration of the device, is the average life threshold of similar devices, used to measure the relative relationship between the device operation time and the average life; is the preset time decay coefficient, which takes the value of 0.876 in this embodiment; It should be noted that the prior art usually adopts a static weight allocation mode to evaluate the device status and determine the maintenance priority. However, this step breaks through this traditional mode, introduces dynamic weights, and combines factors such as the continuous operation duration of the device and the average life threshold of similar devices to more scientifically reflect the spatio-temporal sensitivity risk of the device, making the determination of the maintenance priority more reasonable and dynamic; By constructing a spatio-temporally sensitive maintenance index (TSMI), comprehensively considering the multi-dimensional status of the device, realizing the spatio-temporal correlation modeling of the device risk, and dynamically determining the maintenance priority of the device; this helps to more accurately identify high-risk devices, reasonably arrange maintenance resources, improve the efficiency and pertinence of power grid maintenance, and ensure the safe and stable operation of the power grid; The traditional method for determining the maintenance priority often based on a single-dimensional status index is difficult to comprehensively and accurately reflect the actual risk status of the device. This step solves the problems of incomplete and inaccurate device risk assessment and inability to dynamically adapt to spatio-temporal changes in the prior art.

[0024] As an optional embodiment: The specific working steps of the maintenance method adjustment module further include the following: is the spatio-temporal coordination conflict resolution, solving the maintenance conflict, specifically including two constraint conditions: spatial coupling constraint and time elasticity constraint. The spatial coupling constraint identifies conflicts by considering the geographical proximity coefficient between devices and the electrical coupling degree and geographical topology; the time elasticity constraint determines the time range of the maintenance window based on the elasticity coefficient generated by weather prediction. It should be noted that, and the parameters will be adjusted during high-risk periods to compress the window to achieve multi-dimensional collaborative optimization of the power grid physical constraints and external environmental factors; It should also be noted that the specific expression of the spatial coupling constraint is: i, j ∈ Ecouple); Among them is the geographical proximity coefficient of the device and which is composed of the passing time of the maintenance vehicle and the safety isolation duration; couple is a set of devices identified jointly through electrical coupling degree and geographical topology, ensuring that devices adjacent in space and closely electrically connected do not conflict in the maintenance time arrangement; The time flexibility constraint expression is: ; Among them represents the maintenance time window of device i; Among them is the maintenance window flexibility coefficient generated based on meteorological predictions (typhoons, rain, snow, etc.), used to measure the impact of meteorological conditions on the maintenance time window; is a dynamically adjustable parameter, which decreases by 30% during high-risk periods to compress the window and ensure that the maintenance is carried out within a relatively safe time range; According to the expression, by adjusting and values, the maintenance time window of each device can be dynamically determined; When considering the maintenance time window in the prior art, the dynamic impact of external environmental factors such as meteorology is often not fully considered, and usually a fixed time window or only simple empirical rules are used for adjustment. After introducing the time flexibility constraint expression, it is possible to more accurately adjust the maintenance time window according to meteorological conditions, enhancing the flexibility and adaptability of the maintenance plan, and effectively reducing the maintenance risk and delay risk caused by external factors such as meteorology; In the prior art, simple heuristic algorithms or empirical rules are usually used in resolving power grid maintenance conflicts, and the physical constraints of the power grid and external environmental factors are not considered comprehensively and systematically enough. In this step, a mixed-integer spatio-temporal programming model is used, taking into account a variety of complex constraint conditions, and precisely solved through mathematical programming methods, so as to obtain a better maintenance plan, avoiding the limitations and one-sidedness of traditional methods; When dealing with power grid maintenance plans in the prior art, it is often difficult to fully consider the spatial coupling relationship between devices and the impact of external environmental factors on maintenance time, resulting in ineffective resolution of maintenance conflicts. This step specifically addresses these problems, and through comprehensive consideration of multi-factors such as geographical proximity coefficient, electrical coupling degree, and meteorological prediction, more accurate resolution of maintenance conflicts is achieved.

[0025] As an optional embodiment, the specific working steps of the maintenance method adjustment module further include the following: Flexible resource dynamic adaptation, constructing a multi-objective resource optimization model, maximizing the sum of the maintenance time multiplied by the resource reduction coefficient, and minimizing the sum of the product of the labor cost and the square of the distance between the equipment geographical coordinates and the team station location as the objective function. It should be noted that in the constraint conditions, factors such as spare part demand index, dynamic inventory, and risk level are considered to support the real-time linkage optimization of human resource scheduling, spare part supply chain, and risk level; It should also be noted that the objective function of the multi-objective resource optimization model is: Determine the maintenance time of each device, which is negatively correlated with the personnel skill level, that is, the higher the personnel skill level (from LV1 to LV3, the skill weights are 1.0 to 0.6), the shorter the maintenance time. Then, multiply the T maintenance of each device by the corresponding resource reduction coefficient (R reduction), and finally add up the products of all devices to get a total. The resource reduction coefficient reflects the degree of resource consumption that can be reduced through optimizing resource allocation. The larger this value, the higher the resource utilization efficiency; Minimize the sum of the product of the labor cost and the square of the distance between the equipment geographical coordinates and the team station location: Determine the geographical coordinates of each device and the station location of each maintenance team, calculate the square of the distance between device i and team j, and then multiply it by the corresponding labor cost. Finally, add up the products of all device-team combinations to get another total. The labor cost represents the labor cost required to arrange the team to perform maintenance on the device. The farther the distance, the higher the labor cost usually is; Combining the above two parts, the objective function is to make the first total as large as possible and the second total as small as possible under the premise of meeting the constraint conditions, so as to achieve efficient utilization of resources and effective control of costs; Calculate the sum of the product of the spare part demand index of all devices and the corresponding variables, and this sum must be less than or equal to the dynamic inventory multiplied by the risk level. Among them, the spare part demand index integrates factors such as supplier delivery cycle and fault urgency, reflecting the urgency and importance of the spare parts required by the device; the dynamic inventory is the current available spare part inventory; the risk level is determined according to factors such as the operation status of the power grid and the external environment, and is used to measure the risk degree faced by the current maintenance work. This constraint ensures that while meeting the maintenance requirements, the spare part inventory is within a reasonable range, avoiding inventory backlog or shortage, and at the same time considering the impact of risk factors on the inventory; Most of the existing technologies focus on the optimization of single objectives in the allocation of power grid maintenance resources. For example, they only focus on the optimization of labor costs or spare part inventories, lacking effective methods for multi-objective comprehensive optimization. This step, by establishing a multi-objective resource optimization model and considering various factors such as maintenance time, labor costs, spare part demand index, and risk level, can achieve the dynamic adaptation and comprehensive optimization of resources, which is more comprehensive and advanced compared with others; Traditional power grid maintenance resource allocation methods often have difficulty in achieving multi-objective optimization of resources and cannot well balance various factors such as maintenance time, labor costs, and spare part requirements, resulting in problems such as unreasonable resource allocation and low efficiency. This step solves the problem of insufficient optimization of maintenance resources in the existing technology and realizes the dynamic adaptation and comprehensive optimization of resources.

[0026] As an optional embodiment: The specific steps for the safety analysis module to perform static safety analysis are as follows: Static safety analysis aims to verify the stability of the power grid under single equipment failure (N-1) after the adjustment of the maintenance method. First, perform N-1 fault enumeration, that is, traverse all maintenance equipment and associated lines, simulate the power grid topology after their outage, and generate a new adjacency matrix by removing the corresponding equipment; Then carry out stability verification, including voltage stability verification and line overload test. Finally, perform probabilistic risk assessment, calculate the system instability probability in combination with equipment failure rates. Due to the problem of combinatorial explosion in large-scale power grid scenarios, Monte Carlo sampling or heuristic pruning optimization can be used for calculation, and a list of risk equipment and corresponding over-limit indicators are output.

[0027] It should be noted that the specific steps of the adjacency matrix generation formula are as follows: ; Among them, represents the adjacency matrix after simulating the outage of the equipment, the adjacency matrix of the original power grid, represents the removed faulty equipment. The specific steps for this formula to describe the topological structure change of the power grid after the outage of equipment failure and calculate the system instability probability are as follows: ; Among them is the threshold for calculating the thermal stability limit; For large-scale power grid scenarios with combinatorial explosion, Monte Carlo sampling or heuristic pruning optimization is used for calculation; Among them, is the system instability probability, is the failure rate of equipment , is the indicator function. When the power flow of line is exceed its thermal stability limit When Take 1, otherwise 0. This formula is used to evaluate the possibility of system instability caused by equipment failure.

[0028] As an optional embodiment: The safety analysis module is also used for power grid power flow calculation, and the specific steps are as follows: Generating the future state under a single maintenance work time sequence aims to construct a time-tagged power grid future state sequence to support dynamic risk analysis; First, perform time-section slicing, decompose the maintenance plan by time slices, generate a topology change sequence, considering the outage / resumption operations of the maintenance equipment and the time-sequence changes of load forecasting; Then, use the state prediction model to predict the future state parameters; it should be noted that, for example, the fluctuations in new energy output, these models are trained based on historical data and relevant influencing factors to predict the power parameters at future moments; Finally, use the time-sequence database to store these section snapshots for quick backtracking and comparison, and output a set of future state power grid models with time stamps.

[0029] It should be noted that the topology change sequence formula is as follows: ; Where is the power grid topology at future time t, which synthesizes the basic state and various changes; is the basic topology of the power grid, which is the initial state when the power grid operates normally and comes from the original planning design and operation data of the power grid; is the change amount caused by the outage or resumption operation of the maintenance equipment at time t, which is determined according to the maintenance plan; is the change amount of load forecasting at time t. In this embodiment, it can be obtained by the load forecasting model based on historical load data and relevant influencing factors; The state prediction model can be a Markov prediction model. In this embodiment, it can be satisfied by the following specific formula: ; is the predicted wind power output at future time t, which is the result output by the model and reflects the future state estimation of wind power; is the prediction model, such as LSTM or Transformer, and these models make predictions by learning the patterns in historical data; is the actual value or predicted value of the wind power output at the previous time t−1, providing continuous information in the time series for the model. Meteorological data are meteorological factors affecting wind power output, such as wind speed, wind direction, etc. These data are sourced from meteorological observations or forecasts and are important external variables affecting new energy output.

[0030] As an optional embodiment: The specific steps of the risk discrimination module are as follows: The goal of time - series power grid risk discrimination brought about by maintenance power outages is to quantify the risk levels at different time points during the maintenance process and identify cascading fault chains; First, conduct time - series risk chain modeling and construct a Bayesian network to depict the causal relationships among equipment failures, load transfers, and protection actions. Among them, node A is the initial maintenance power outage event, and B and C are subsequent affected equipment or load changes; Then calculate risk indicators, such as the loss - of - load probability, which is statistically calculated by judging the relationship between the load and available power in each time period; Next, generate a risk heat map, comprehensively considering factors such as the overload rate, voltage deviation, and new energy fluctuations, and obtain the risk values at each location and time by adding them according to weights; Then, based on graph - theory algorithms, identify the critical path and determine the maintenance path with the largest impact range.

[0031] It should be noted that a Bayesian network is constructed to depict the causal relationships among equipment failures, load transfers, and protection actions in the power grid, forming a causal inference network of equipment failure - load transfer - protection action.

[0032] Determine the nodes, where node A is the initial maintenance power outage event. The probability P(A) of the initial maintenance power outage event occurring is obtained by analyzing historical statistical data such as the maintenance plan arrangement and the average failure rate of equipment during maintenance.

[0033] Determine the subsequent nodes B, C, etc. Analyze the conditional probability P(B|A) of the subsequent affected equipment or load mutation event B occurring under the condition that the initial maintenance power outage event A occurs. This needs to be determined based on the electrical connection relationships among equipment, the action logic of protection devices, and the load transfer paths and other power grid operation characteristics and historical fault data statistical laws.

[0034] Similarly, under the condition that event B occurs, determine the conditional probability P(C|B) of event C occurring, and so on. The determination of each subsequent conditional probability depends on the power grid structure, operation mode, and reliability parameters of equipment, etc. Calculate the probability of the entire interlocking fault occurring, thereby quantifying the likelihood of a series of subsequent faults triggered by the initial maintenance power outage event; Calculate the loss - of - load probability (LOLP). First, determine the total number of time periods T divided within the entire maintenance time period, for example, divided at intervals such as hours or 15 minutes.

[0035] For each time period t, obtain the load power, which is derived from the load forecasting model combined with the load change trend during the maintenance period.

[0036] Meanwhile, to determine the available power generation, factors such as the impact of the maintenance schedule on the output of generating units and the transmission limitations of the power grid need to be considered.

[0037] Using the indicator function, at the t-th time period, if the load power exceeds the available power generation, the function value is 1, indicating a load shedding situation in this time period; otherwise, it is 0. Sum up the indicator function values of each time period and then divide by the total number of time periods T to obtain the loss of load probability LOLP, that is, to measure the probability that the power grid cannot meet the load demand during the maintenance period, reflecting the deficiency degree of the power grid power supply reliability.

[0038] As an optional embodiment: The specific steps for the iterative module to dynamically modify the maintenance plan according to the risk result data and generate the final plan are as follows: Starting from the initial maintenance plan, conduct safety analysis and risk discrimination in sequence. If the risk discrimination result meets the standard, execute this maintenance plan. If the risk discrimination result does not meet the standard, enter the dynamic adjustment link to modify the maintenance plan, and then conduct safety analysis and risk discrimination again until the result meets the standard.

[0039] As an optional embodiment: The specific working steps of the preview verification module are as follows: Generate multiple extreme scenarios, specifically including a sharp drop in new energy output and a typhoon passing by, calculate the weights of each scenario, considering the scenario deviation degree and the correlation coefficient; Based on the grouping matrix, divide the equipment activity queue, corresponding to the set of operable equipment for a specific time window.

[0040] The above is only the preferred implementation mode of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art in this technical field, several improvements and refinements made without departing from the principle of the present invention should also be regarded as within the protection scope of this template.

Claims

1. A future state dynamic generation and optimization scheduling system for power grid maintenance, characterized in that: It includes a multi-time plan input module, which is used to integrate the production plan, maintenance plan and load forecast of different time dimensions, generate the base state maintenance mode, future power grid change section and measurement data time series section, and obtain the base state data; The maintenance mode adjustment module dynamically adjusts the maintenance plan of the power grid based on the baseline data; Safety analysis module, used to verify grid stability, perform static safety analysis, and calculate grid power flow distribution; The risk identification module is used to generate the future state under the single maintenance work sequence, generate the future state power grid section, evaluate the maintenance sequence risk, identify the sequence power grid risk caused by the maintenance power outage, and obtain the risk result data; Iteration module, used to dynamically modify the maintenance plan according to the risk result data and generate the final plan; The rehearsal verification module is used to simulate the operation status of the power grid under complex working conditions such as the commissioning and maintenance of new equipment.

2. A power grid maintenance future state dynamic generation and optimization scheduling system according to claim 1, characterized in that: The multi-time plan input module includes a production plan unit, a maintenance plan unit and a load forecasting unit; The commissioning planning unit is used to formulate commissioning plans at different stages, and then form the future power grid change section according to the formulated commissioning plans; The maintenance planning unit is used to formulate maintenance plans at different stages, and then analyze the base state maintenance mode to form a planned base state section, which specifically includes parameter data under the maintenance of power grid equipment; The load forecasting unit is used to formulate load thresholds at different stages, and then perform power generation forecasts and low-voltage bus forecasts to form a time series section of future measurement data.

3. A power grid maintenance future state dynamic generation and optimization scheduling system according to claim 2, characterized in that: The specific working steps of the maintenance mode adjustment module are as follows: Obtain the base state data and construct a time-space sensitive maintenance index to determine the priority maintenance sequence of the equipment; Substitute the collected data into the time-space sensitive maintenance index to calculate the time-space sensitive maintenance index value of each device; The equipment is sorted according to the size of the time-space sensitive maintenance index value. The larger the time-space sensitive maintenance index value, the higher the equipment's maintenance priority level, and maintenance should be arranged first to determine the equipment's maintenance priority order.

4. A power grid maintenance future state dynamic generation and optimization scheduling system according to claim 3, characterized in that: The specific working steps of the maintenance mode adjustment module also include the following: In order to resolve the conflicts in time-space coordination, maintenance conflicts are solved, which specifically include two constraints: spatial coupling constraint and temporal elasticity constraint. The spatial coupling constraint identifies conflicts by considering the geographical proximity coefficient between equipment, electrical coupling degree and geographical topology; the temporal elasticity constraint determines the time range of the maintenance window based on the elasticity coefficient generated by meteorological forecast.

5. A power grid maintenance future state dynamic generation and optimization scheduling system according to claim 3, characterized in that: The specific working steps of the maintenance mode adjustment module also include the following: Flexible resources are dynamically adapted to build a multi-objective resource optimization model, maximizing the sum of maintenance time multiplied by the resource reduction coefficient, while minimizing the sum of labor costs and the product of the square of the distance between the equipment's geographical coordinates and the team's station location as the objective function.

6. A power grid maintenance future state dynamic generation and optimization scheduling system according to claim 1, characterized in that: The specific steps of the security analysis module for performing static security analysis are as follows: Static safety analysis aims to verify the stability of the power grid under a single device failure (N-1) after the maintenance mode is adjusted. First, N-1 fault enumeration is performed, that is, all maintenance equipment and associated lines are traversed, the power grid topology after its shutdown is simulated, and a new adjacency matrix is ​​generated by removing the corresponding equipment; Next, stability verification is carried out, including voltage stability verification and line overload test. Finally, probabilistic risk assessment is carried out, and the probability of system instability is calculated in combination with the equipment failure rate. Due to the combinatorial explosion problem in large-scale power grid scenarios, Monte Carlo sampling or heuristic pruning optimization calculation can be used to output a list of risky equipment and corresponding out-of-limit indicators.

7. A power grid maintenance future state dynamic generation and optimization scheduling system according to claim 6, characterized in that: The safety analysis module is also used to perform power grid flow calculation, and the specific steps are as follows: The purpose of generating the future state under a single maintenance work sequence is to construct a future state sequence of the power grid with time labels to support dynamic risk analysis; First, the time series section is sliced ​​to decompose the maintenance plan into time slices to generate a topology change sequence, taking into account the shutdown / restore operation of the maintenance equipment and the time series changes of load forecast; Then, the state prediction model is used to predict future state parameters; Finally, a time series database is used to store these cross-section snapshots for quick backtracking and comparison, and a collection of future-state power grid models with timestamps is output.

8. A power grid maintenance future state dynamic generation and optimization scheduling system according to claim 7, characterized in that: The specific steps of the risk identification module are as follows: The goal of sequential grid risk identification caused by maintenance outages is to quantify the risk level at different time points during the maintenance process and identify cascading failure chains; First, the temporal risk chain modeling is carried out and a Bayesian network is constructed to characterize the causal relationship between equipment failure, load transfer, and protection action. Node A is the initial maintenance power outage event, and node B and C are the subsequent affected equipment or load changes. Then, the risk indicators, such as the probability of load loss, are calculated by judging the relationship between the load and the available power in each period; Then, a risk heat map is generated, which integrates overload rate, voltage deviation, and new energy fluctuation factors, and adds them according to the weights to obtain the risk value of each location and time; Then, based on the graph theory algorithm, the critical path is identified and the maintenance path with the largest impact range is determined.

9. A future state dynamic generation and optimization scheduling system for power grid maintenance according to claim 8, characterized in that: The specific steps of the iteration module to dynamically modify the maintenance plan according to the risk result data and generate the final plan are as follows: Starting from the initial maintenance plan, safety analysis and risk identification are carried out in sequence. If the risk identification results meet the standards, the maintenance plan is executed. If the risk identification results do not meet the standards, the dynamic adjustment phase is entered to revise the maintenance plan, and then safety analysis and risk identification are carried out again until the results meet the standards.

10. A power grid maintenance future state dynamic generation and optimization scheduling system according to claim 9, characterized in that: The specific working steps of the preview verification module are as follows: Generate a variety of extreme scenarios, including a sudden drop in renewable energy output and a typhoon passing through, and calculate the weight of each scenario, taking into account the scenario deviation and correlation coefficient; The device activity queue is divided based on the grouping matrix, corresponding to the set of operable devices in a specific time window.

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