Long-process simulation deduction continuous section construction method and system

Abstract

The invention discloses a long-process simulation deduction continuous section construction method and system, and belongs to the technical field of power grid simulation. The method comprises the steps that the total duration of long-process simulation deduction is subdivided into a plurality of calculation time points at equal intervals, and then a power grid structure, regulation and control operation and meteorological prediction data in the total duration of long-process simulation deduction are obtained; the transient fault probability and the disaster-caused fault probability of the whole-network equipment are calculated respectively, and then a whole-network equipment fault probability model is constructed; calculating whole-network probabilistic power flow distribution according to a whole-network equipment fault probability model by taking the whole-network equipment transient fault probability and the disaster-caused fault probability as input, and determining the running state of the whole-network equipment according to the whole-network probabilistic power flow distribution; performing real-time load flow calculation to obtain a simulation deduction calculation section of a calculation time point; and finally obtaining a long-process simulation deduction continuous section sequence in the simulation time period. According to the method, a power grid operator can better formulate an operation and maintenance strategy, resource configuration is optimized, and the safety and stability of the power grid are improved.

Description

Technical Field

[0001] The invention proposes a method and system for constructing a long-process simulation-based continuous section, belonging to the technical field of power grid simulation. Background Art

[0002] Extreme external weather events, such as typhoons, heavy rains, snowstorms, lightning, hail, sandstorms, etc., pose a serious threat to the safe and stable operation of the power system. These weather phenomena not only affect the equipment components of the power system, but may also cause power supply interruptions and grid instability. The formation of extreme weather is often accompanied by strong meteorological changes, such as a sharp drop in air pressure and a sudden change in temperature. When extreme weather strikes, strong winds may cause transmission lines to collapse and trees to fall on power facilities. Heavy rains may cause floods to wash away power equipment or cause water to enter underground facilities. Snow and hail may damage the mechanical structure of power equipment. Single or multiple equipment failures caused by extreme weather may cause power system power flow redistribution. This redistribution may cause other normal equipment components to bear loads beyond the design standards, resulting in mass failures.

[0003] When extreme external weather such as typhoons, lightning, ice cover, and heavy rain occur, they will have a continuous and random impact on the power system: first, it will affect the output of new energy sources such as wind power and photovoltaics, and their violent fluctuations may cause grid stability problems. At the same time, large fluctuations in new energy sources may cause serious power balance problems; second, it will cause equipment component failures, and the flow transfer caused by single or multiple equipment failures may further cause group and chain failures. Considering the probability and impact range of power grid equipment failures under extreme weather such as typhoons, it is of great significance to construct a simulation section of power grid failures and operating status in a continuous time process to provide a zero-cost trial-and-error environment for dispatching and operating personnel to simulate low-probability events such as extreme weather.

[0004] The patent application entitled "Time-series flow simulation method, device, equipment and medium for power grid control operation simulation" discloses a time-series flow simulation method, device, equipment and medium for power grid control operation simulation; the method includes: obtaining model data of multiple sources and multiple time periods of the power grid, performing power grid model and data fusion, multi-source data correction and low-voltage side flow data allocation; storing a power grid model data snapshot, n power grid equipment initial state snapshots and equipment operation state snapshots corresponding to the simulation operation at each moment; calling the stored power grid model data snapshot and n power grid equipment state snapshots to advance the simulation clock to perform power grid control operation simulation time-series flow simulation. The present invention solves the problems of unreasonable future state multi-source and multi-time period data and incoherent provincial data, improves the data storage and loading speed of time-series flow calculation, realizes rapid switching of simulation states at each moment and accurate inheritance of operation events, and supports rapid simulation of various scenarios of power grid control operation simulation over a long time scale.

[0005] The patent application named "Fault Event Probability Estimation Method, Device, Computer Equipment and Storage Medium" is based on the Gaussian process regression algorithm. According to the initial sample set of the system to be tested, the initial mean and initial covariance matrix of the initial sample set and the posterior probability density distribution of the fault event are determined; the initial confidence interval is determined according to the initial mean and the initial covariance matrix, and according to the initial confidence interval, an augmented sample is generated using a genetic algorithm, and the augmented sample is added to the initial sample set to obtain a target sample set; the target mean and target covariance matrix of the target sample set are calculated, and the target mean is used as the initial mean, and the target covariance matrix is ​​used as the initial covariance matrix, and the above steps are repeated until the loop stop condition is reached. The fault event probability estimation method provided in the present application does not require a large amount of manpower and material resources to obtain the initial sample set, and can achieve accurate estimation of the probability of a fault event.

[0006] When using the semi-invariant method to calculate the probability flow distribution of the power grid, the results depend largely on the accuracy and representativeness of the input data. If there is a deviation or incompleteness in the input data, the calculation results may be inaccurate. When using the control system model and operation data for probability flow analysis, the input data needs to be re-preprocessed and analyzed and verified, otherwise it is easy to cause inaccurate probability distribution results. Summary of the invention

[0007] In order to solve the problems existing in the prior art, the present invention proposes a method and system for constructing a long-process simulation and deduction continuous section considering the probabilistic failure of equipment. The method helps power grid operators to better formulate operation and maintenance strategies, optimize resource allocation, and improve the safety and stability of the power grid.

[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is: In a first aspect, the present invention provides a method for constructing a continuous section by simulation deduction of a long process considering probabilistic equipment failures, comprising: The total duration of the long-process simulation is divided into multiple calculation time points at equal intervals, so as to obtain the grid structure, control operation and meteorological forecast data within the total duration of the long-process simulation; Conduct simulation deduction at the first calculation time point, specifically including: based on the grid structure, control operation and meteorological forecast data, respectively calculate the probability of instantaneous failure of equipment in the entire network and the probability of failure due to disasters, take the instantaneous failure of equipment and the failure due to disasters as random variables, and establish a probability model of equipment failure in the entire network based on a discrete probability model; Taking the instantaneous failure probability and the failure probability due to disaster of the whole network equipment as input, the probability power flow distribution of the whole network is calculated according to the probability power flow distribution of the whole network equipment, and the operation status of the whole network equipment is determined according to the probability power flow distribution of the whole network; According to the operation status of the whole network equipment, real-time power flow calculation is performed to obtain the power flow results of the whole network, and the whole network model information and the power flow results of the whole network are stored and modeled to obtain the simulation deduction calculation section at the current calculation time point; Perform simulation deduction at the next calculation time point, calculate the simulation deduction calculation section at the next calculation time point, and iterate the calculation in a loop until the simulation deduction calculation sections of all calculation time points within the total duration of the long process simulation deduction are obtained, and the long process simulation deduction continuous section sequence is summarized.

[0009] As a further improvement of the method of the present invention, the total duration of the long process simulation deduction is subdivided into multiple calculation time points at equal intervals, including: According to the total simulation time and calculation interval requirements, the whole process is divided into T 1 , T 2 ,…,T m There are a total of m simulation calculation time points, each calculation time point corresponds to a simulation calculation section, forming T 1 To T m Long-term simulation of continuous section sequence S:

[0010] in, express T l The simulation and deduction calculation section at each moment stores the power grid model information, operation data and key external environment information.

[0011] As a further improvement of the method of the present invention, the acquisition of the grid structure, regulation and operation and meteorological forecast data within the total duration of the long process simulation deduction includes: Obtaining T from the regulatory system 1 To T m The grid structure, control operation and meteorological forecast data within the time period are analyzed to analyze the changes in the topological connection relationship of the grid, build a full-grid model and power flow distribution diagram, and set T according to the meteorological forecast data. 1 To T m The damage situation of the local power grid during the time period simulation; Determine the distance between each device and the disaster center based on the distribution of local power grid equipment , , ; in, Indicates that the device is T l The distance from the disaster center at a given moment; n represents the total number of devices in the entire network.

[0012] As a further improvement of the method of the present invention, the calculation method of the instantaneous failure probability of the whole network equipment is: Considering the randomness and uncertainty of power grid equipment failure, the Markov process is used to calculate the probability of failure of equipment i in T l The instantaneous failure probability at time is, , ; in, w i For equipment i The historical failure rate of the equipment is calculated as follows: historical failure duration of the equipment / total operation duration of the equipment*100%.

[0013] As a further improvement of the method of the present invention, the calculation method of the probability of failure due to disaster is: Analyze the characteristics of extreme weather, simulate the range changes of disasters caused by extreme weather, and calculate the probability of equipment failure due to disasters based on the Weibull distribution equation as follows: , ; in, r i is the failure ratio parameter due to disasters; when the disaster weather is a typhoon, r i The parameter is set to 8~10; when the disaster weather is thunder and lightning, r i The parameter is set to 14~18; when the disaster weather is ice-covered, r i The parameter is set to 3~5.

[0014] As a further improvement of the method of the present invention, the method of establishing a network-wide equipment failure probability model based on a discrete probability model includes: The instantaneous failure probability and disaster failure probability of the entire network equipment are used as random variables to establish the failure probability model of the entire network equipment , the calculation formula is as follows:

[0015] in, .

[0016] As a further improvement of the method of the present invention, the instantaneous failure probability and the failure probability due to disaster of the whole network equipment are used as input, the probability power flow distribution of the whole network is calculated according to the probability power flow distribution of the whole network equipment, and the operation state of the whole network equipment is determined according to the probability power flow distribution of the whole network, including: According to each device T lThe discrete probability model at the time is sampled to obtain the faulty equipment; the semi-invariant method is used to calculate the probability flow and obtain the probability distribution of branch flow and node voltage f(x) ; Computing device i T l The expected value of the branch flow or node voltage at a certain moment is:

[0017] According to the probability flow calculation results, the operating status of the entire network equipment is determined; T l The operating status at this moment is: ; ; in, Indicates the rated power or voltage amplitude upper limit of device i.

[0018] As a further improvement of the method of the present invention, the real-time power flow calculation is performed according to the operating status of the whole network equipment to obtain the whole network power flow result, the whole network model information and the whole network power flow result are stored and modeled, and the simulation deduction calculation section at the current calculation time point is obtained, including: Considering the operation status of the whole network model equipment, the topology analysis of the power grid diagram is re-performed; the whole network base state flow calculation is re-performed based on the PQ decomposition method to obtain the whole network flow results; the whole network model information and flow results are stored and modeled to obtain T l Simulation and calculation section of time .

[0019] As a further improvement of the method of the present invention, the cycle is repeated to obtain a long process simulation deduction continuous section sequence within the simulation period, including: Will T l The simulation calculation section at the moment is used as T l+1 Input the data required for simulation and analysis at all times, repeat the cycle, and get T 1 To T m The long process simulation of the time period is used to deduce the continuous section sequence S.

[0020] In a second aspect, the present invention provides a long process simulation and deduction continuous section construction system considering equipment probabilistic failure, comprising: A subdivision acquisition module is used to subdivide the total duration of the long-process simulation into multiple calculation time points at equal intervals, and then obtain the grid structure, control operation and meteorological forecast data within the total duration of the long-process simulation; The probability calculation module is used to perform simulation deduction at the first calculation time point, specifically including: based on the grid structure, control operation and meteorological forecast data, respectively calculating the probability of instantaneous failure of equipment in the entire network and the probability of failure due to disasters, taking the instantaneous failure of equipment and the failure due to disasters as random variables, and establishing a probability model of equipment failure in the entire network based on a discrete probability model; The state determination module is used to calculate the probability flow distribution of the whole network according to the probability model of the whole network equipment failure, and determine the operation state of the whole network equipment according to the probability flow distribution of the whole network; The section calculation module is used to perform real-time power flow calculation according to the operating status of the whole network equipment, obtain the power flow results of the whole network, store and model the whole network model information and the whole network power flow results, and obtain the simulation deduction calculation section at the current calculation time point; The loop iteration module is used to perform simulation deduction at the next calculation time point, calculate the simulation deduction calculation section at the next calculation time point, and loop iteratively calculate until the simulation deduction calculation sections of all calculation time points within the total duration of the long process simulation deduction are obtained, and the long process simulation deduction continuous section sequence is summarized.

[0021] As a further improvement of the system of the present invention, in the subdivision acquisition module, the total duration of the long process simulation deduction is subdivided into multiple calculation time points at equal intervals, including: According to the total simulation time and calculation interval requirements, the whole process is divided into T 1 , T 2 ,…,T m There are a total of m simulation calculation time points, each calculation time point corresponds to a simulation calculation section, forming T 1 To T m Long-term simulation of continuous section sequence S:

[0022] in, express T l The simulation and deduction calculation section at each moment stores the power grid model information, operation data and key external environment information.

[0023] As a further improvement of the system of the present invention, in the subdivision acquisition module, the grid structure, regulation and operation and meteorological forecast data within the total duration of the long process simulation are acquired, including: Obtaining T from the regulatory system 1 To T m The grid structure, control operation and meteorological forecast data within the time period are analyzed to analyze the changes in the topological connection relationship of the grid, build a full-grid model and power flow distribution diagram, and set T according to the meteorological forecast data. 1 To Tm The damage situation of the local power grid during the time period simulation; Determine the distance between each device and the disaster center based on the distribution of local power grid equipment , , ; in, Indicates that the device is T l The distance from the disaster center at a given moment; n represents the total number of devices in the entire network.

[0024] As a further improvement of the system of the present invention, the calculation method of the instantaneous failure probability of the whole network equipment is: Considering the randomness and uncertainty of power grid equipment failure, the Markov process is used to calculate the probability of failure of equipment i in T l The instantaneous failure probability at time is, , ; in, w i For equipment i The historical failure rate of the equipment is calculated as follows: historical failure duration of the equipment / total operation duration of the equipment*100%.

[0025] As a further improvement of the system of the present invention, the calculation method of the probability of failure due to disaster is: Analyze the characteristics of extreme weather, simulate the range changes of disasters caused by extreme weather, and calculate the probability of equipment failure due to disasters based on the Weibull distribution equation as follows: , ; in, r i is the failure ratio parameter due to disasters; when the disaster weather is a typhoon, r i The parameter is set to 8~10; when the disaster weather is thunder and lightning, r i The parameter is set to 14~18; when the disaster weather is ice-covered, r i The parameter is set to 3~5.

[0026] As a further improvement of the system of the present invention, in the probability calculation module, a network-wide equipment failure probability model is constructed, including: The instantaneous failure probability and disaster failure probability of the entire network equipment are used as random variables to establish the failure probability model of the entire network equipment , the calculation formula is as follows:

[0027] in, .

[0028] As a further improvement of the system of the present invention, in the state determination module, the instantaneous failure probability of the whole network equipment and the failure probability due to disaster are used as inputs, the probability flow distribution of the whole network is calculated according to the probability model of the whole network equipment failure, and the operation state of the whole network equipment is determined according to the probability flow distribution of the whole network, including: According to each device T l The discrete probability model at the time is sampled to obtain the faulty equipment; the semi-invariant method is used to calculate the probability flow and obtain the probability distribution of branch flow and node voltage f(x) ; Computing device i T l The expected value of the branch flow or node voltage at a certain moment is:

[0029] According to the probability flow calculation results, the operating status of the entire network equipment is determined; T l The operating status at this moment is: ; ; in, Indicates the rated power or voltage amplitude upper limit of device i.

[0030] As a further improvement of the system of the present invention, in the section calculation module, according to the operating status of the whole network equipment, real-time power flow calculation is performed to obtain the whole network power flow result, the whole network model information and the whole network power flow result are stored and modeled, and the simulation deduction calculation section at the current calculation time point is obtained, including: Considering the operation status of the whole network model equipment, the topology analysis of the power grid diagram is re-performed; the whole network base state flow calculation is re-performed based on the PQ decomposition method to obtain the whole network flow results; the whole network model information and flow results are stored and modeled to obtain T l Simulation and calculation section of time .

[0031] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for constructing a continuous section through long-process simulation and deduction taking into account probabilistic failures of the equipment when executing the computer program.

[0032] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for constructing a continuous section by long-process simulation and deduction taking into account probabilistic equipment failures.

[0033] In a fifth aspect, the present invention provides a computer program product, which includes computer instructions, and the computer instructions instruct a computer to execute the method for constructing a continuous section by long-process simulation and deduction considering probabilistic equipment failures.

[0034] The beneficial effects of the present invention compared with the prior art are as follows: The method of the present invention first divides the long-process simulation deduction into multiple simulation time points, constructs the instantaneous failure probability and disaster failure probability of the whole network equipment through Markov process analysis and Weber equation optimization, and then uses this as the random variable of the probability flow calculation to calculate the probability flow distribution and expected fault equipment of the whole network, and finally recalculates the flow to obtain the simulation deduction calculation section. Based on the historical statistical data of the equipment and the characteristics of extreme weather changes, a whole network equipment failure probability model that comprehensively considers the instantaneous failure of the equipment and the failure due to disasters is proposed. The Markov process analysis and Weber equation optimization are used to fully consider the inevitability and future randomness of equipment failures, and the long-process state analysis and future state distribution prediction of the power grid equipment can be realized. The simulation deduction section calculation simulation effect is better. According to the size of the disaster characteristics, different disaster failure ratio coefficients can be flexibly set, and the disaster weight of the equipment can be dynamically adjusted; at the same time, the present invention fully considers the probability and impact range of power grid equipment failures under extreme weather such as typhoons, constructs a continuous time process power grid failure and operation state simulation deduction section, and provides a zero-cost trial and error environment for the simulation of dispatching and operating personnel under low-probability events such as extreme weather. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the embodiments of the present invention or the drawings of related technical solutions in the prior art are introduced below. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0036] Figure 1 A flow chart of a method for constructing a continuous section by simulating and deducing a length test process provided by the present invention; Figure 2 A schematic diagram of a method for constructing a continuous section by simulation of a long process taking into account probabilistic equipment failures provided by an embodiment of the present invention; Figure 3A long process simulation and deduction continuous section construction device considering equipment probabilistic failure is provided by the present invention; Figure 4 A schematic diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0037] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limitations of the present invention. For the step numbers in the following embodiments, they are only provided for the convenience of explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0038] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0039] Explanation of relevant terms Markov process: is a mathematical model used to describe the evolution of the state of a system over time. Its core feature is memorylessness, that is, the future state transition depends only on the current state and has nothing to do with the past state history. This property is called the Markov property. In a Markov process, the state of the system at any given moment is determined by its state at the previous moment, and has nothing to do with all previous states. Markov processes are widely used in practical applications, including physical systems, chemical processes, ecological models, economic forecasting, financial analysis, weather forecasting, genetics, information theory, language models, and computer science. For example, in the financial field, Markov processes can be used to simulate changes in stock prices; in ecology, it can be used to predict changes in the distribution of species.

[0040] Weibull distribution: It is a continuous-time random process that describes random fluctuations. When the shape value is equal to 1, the Weibull distribution is exponentially decreasing. This shape of the Weibull distribution is suitable for random failures and multi-cause failures, and can be used to model the service life of products. Application scenarios mainly include industrial manufacturing, research on the relationship between production processes and transportation time, extreme value theory, weather prediction, reliability and failure analysis, and distribution modeling of clutter signals received by radar systems. In the wireless communication technology with good fit, the Weibull attenuation model has a better fit for attenuation channel modeling than the exponential attenuation channel model. Repeated claims, predicted technological changes, and wind speed in quantitative life insurance models are used to describe the distribution of wind speed because the shape of the curve matches the actual situation.

[0041] Probabilistic power flow calculation: is a power system analysis method that takes into account the impact of uncertain factors in the system on the operating state of the power system. These uncertain factors may include load fluctuations, randomness of renewable energy generation (such as wind and solar energy), equipment failures, etc. Compared with traditional deterministic power flow calculation, probabilistic power flow calculation can provide a probabilistic description of the system operating state, thereby more comprehensively evaluating the stability and reliability of the system.

[0042] PQ decomposition method: It is an effective power system analysis tool, also known as active and reactive power decomposition method, which is a method used to simplify power flow calculation in power system analysis. In the power system, the power of a node usually consists of two parts: active power (P) and reactive power (Q). The PQ decomposition method simplifies the power flow calculation process by decomposing the power demand of the node into active and reactive parts and processing them separately.

[0043] The first object of the present invention is to provide a method for constructing a continuous section by simulation deduction of a long process considering the probability failure of equipment, comprising: S1, subdividing the total duration of the long-process simulation into multiple calculation time points at equal intervals, and then obtaining the grid structure, control operation and meteorological forecast data within the total duration of the long-process simulation; S2, performing simulation deduction at the first calculation time point, specifically including: calculating the instantaneous failure probability and the failure probability due to disaster of the whole network equipment respectively based on the grid structure, regulation and operation and meteorological forecast data, taking the instantaneous failure of equipment and the failure due to disaster as random variables, and establishing the whole network equipment failure probability model based on the discrete probability model; S3, taking the instantaneous failure probability and the failure probability due to disaster of the whole network equipment as input, calculates the probability power flow distribution of the whole network according to the probability power flow distribution of the whole network equipment, and determines the operation status of the whole network equipment according to the probability power flow distribution of the whole network; S4, according to the operation status of the whole network equipment, real-time power flow calculation is performed to obtain the power flow results of the whole network, and the whole network model information and the power flow results of the whole network are stored and modeled to obtain the simulation deduction calculation section at the current calculation time point; S5 is used to perform simulation deduction at the next calculation time point, calculate the simulation deduction calculation section at the next calculation time point, and iterate the calculation in a loop until the simulation deduction calculation sections of all calculation time points within the total duration of the long process simulation deduction are obtained, and the long process simulation deduction continuous section sequence is summarized.

[0044] The present invention is a method for constructing a long-process simulation deduction continuous section considering the probability of equipment failure. The method first divides the long-process simulation deduction into multiple simulation time points, and constructs the instantaneous failure probability and the probability of failure due to disaster of the whole network equipment through Markov process analysis and Weber equation optimization, and then uses this as a random variable for the probability flow calculation to calculate the probability flow distribution of the whole network and the expected failure equipment, and finally recalculates the flow to obtain the simulation deduction calculation section. Repeating the cycle, a long-process simulation deduction continuous section sequence is obtained.

[0045] The principle of time segmentation and data acquisition of the present invention is as follows: first, the total duration of the long process simulation is equally divided into multiple calculation time points to form a continuous section sequence. Then, at each calculation time point, the grid structure, control operation and meteorological forecast data within the total duration of the long process simulation are obtained. These data are the basis for subsequent calculations.

[0046] The equipment failure probability model is constructed based on the acquired grid structure, control operation and meteorological forecast data, using Markov process analysis and Weber equation optimization to calculate the instantaneous failure probability and disaster failure probability of the entire network equipment respectively. The instantaneous failure probability reflects the possibility of equipment failure under normal operating conditions, while the disaster failure probability takes into account the impact of external factors such as extreme weather on equipment failure.

[0047] Furthermore, the probability flow calculation and equipment status determination is to calculate the probability flow distribution of the entire network based on the probability model of equipment failure in the entire network. By counting the expected over-limit equipment, it is determined which equipment may fail, and the operating status of the equipment in the entire network is determined accordingly.

[0048] Then, according to the operation status of the whole network equipment, the real-time power flow calculation is performed to obtain the simulation deduction calculation section at the current calculation time point. The above steps are executed repeatedly until the total duration of the simulation deduction is completed, thereby obtaining a long-process simulation deduction continuous section sequence within the simulation period.

[0049] Therefore, the present invention not only considers the instantaneous failure probability of the equipment, but also introduces the probability of failure due to disasters, thereby more comprehensively reflecting the possibility of equipment failure. This makes the simulation results closer to the actual power grid operation and improves the accuracy and reliability of the simulation. By using the historical statistical data of the equipment and the characteristics of extreme weather changes, the present invention can more accurately predict the probability and distribution of equipment failures. This helps power grid operators to better understand the operating status and future development trends of power grid equipment. Markov process analysis can capture the transition law of equipment failure status, while Weber equation optimization can optimize the calculation process of failure probability. According to the size of disaster characteristics, the present invention can flexibly set different disaster failure ratio coefficients, thereby dynamically adjusting the weight of equipment affected by disasters. This makes the simulation results more in line with the actual situation and improves the flexibility and adaptability of the simulation. The present invention can realize the long-term state analysis and future state distribution prediction of power grid equipment, and provide more comprehensive and in-depth power grid operation status information for power grid operators. This helps power grid operators to better formulate operation and maintenance strategies, optimize resource allocation, and improve the safety and stability of the power grid.

[0050] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0051] like Figure 2 As shown, the method for constructing a continuous section by simulation deduction of a long process considering the probability failure of equipment proposed by the present invention includes: First, according to the total simulation time and calculation interval requirements, the long-process simulation is divided into multiple calculation time points, and the grid structure, control operation and meteorological forecast data within the simulation period are obtained from the control system as basic data input.

[0052] Secondly, considering the uncertainty of power grid equipment failure and the characteristic impact of extreme weather on power grid equipment in the affected area, the probability of instantaneous failure of equipment in the entire network and the probability of failure due to disasters are calculated respectively, and a probability model of failure of equipment in the entire network is constructed.

[0053] Then, the probability power flow distribution of the whole network is obtained by taking this as the random variable for probability power flow calculation, and then the expected over-limit devices are counted as the probability fault devices.

[0054] Finally, considering equipment failures and model topology changes, the power flow is recalculated to obtain the simulation section at the calculation time. Repeating the cycle, a long-process simulation section sequence is obtained within the simulation period.

[0055] Combination Figure 2 , the specific technical solutions of the present invention are as follows: (1) Dividing simulation calculation time points According to the total simulation time and calculation interval requirements, the whole process is divided into equal intervals (T 1 , T 2 , …, T m ) There are a total of m simulation and deduction calculation time points, each calculation time point corresponds to a simulation and deduction calculation section, forming T 1 to T m The long process simulation of the time period is used to deduce the continuous section sequence S.

[0056]

[0057] in, express T l The simulation and calculation section at each moment stores the power grid model information, operation data and key external environment information, which can be used as T l Complete input data for power grid flow calculation at all times.

[0058] (2) Obtaining grid structure, regulation and operation, and meteorological data Obtained from the control system T 1 to T m The grid structure, control operation and meteorological forecast data within the time period are analyzed, the changes in the topological connection relationship of the grid are analyzed, the whole network model and the power flow distribution map are constructed, and T is set according to the meteorological forecast data such as typhoon path, lightning distribution, and ice cover. 1 To T m The disaster situation of the local power grid during the time period simulation.

[0059] Determine the distance between each device and the disaster center based on the distribution of local power grid equipment ,

[0060] in, Indicates that the device is T l The distance from the disaster center at a given moment; n represents the total number of devices in the entire network.

[0061] (3) Calculate the probability of equipment instantaneous failure Considering the randomness and uncertainty of power grid equipment failures, assuming that a device has only two operating states: in operation (1) and out of operation (0), the Markov process is used to calculate the failure rate of device i in T l The instantaneous failure probability at time is:

[0062] in, w i For equipment i The historical failure rate of the equipment is calculated by dividing the historical failure time of the equipment by the total operation time of the equipment × 100%.

[0063] (4) Calculate the probability of equipment failure due to disasters When extreme weather occurs, it will have characteristic impacts on the power grid equipment in the affected area, such as the concentration of fault distribution caused by typhoon movement, the range of fault distribution caused by icing, and the randomness of lightning strike points.

[0064] By analyzing the characteristics of extreme weather and simulating the range changes of disasters caused by extreme weather, a formula for calculating the probability of equipment failure due to disasters based on the Weibull distribution equation is proposed:

[0065] in, r i is the failure ratio parameter due to disasters.

[0066] about r i Settings: When the disaster weather is a typhoon, the probability of equipment failure is the highest in the typhoon center. As the distance from the typhoon center increases, the probability of equipment failure decreases exponentially. r i The parameter should be set to 8~10; When the disaster weather is thunder and lightning, the equipment failure in the thunder and lightning area is more random. Through Monte Carlo simulation, the uncertainty factors of the probability of equipment failure in the thunder and lightning area are r i The parameter should be set to 14~18; When the disaster weather is ice-covered, the equipment in the area has a higher probability of failure. r i The parameter should be set to 3~5.

[0067] (5) Calculate the probability flow distribution of the entire network The uncertainty factors such as equipment instantaneous failure and disaster failure are taken as random variables to establish the probability model of equipment failure in the whole network. , the calculation formula is as follows:

[0068] According to each device T l The discrete probability model at the time is sampled to obtain the faulty equipment; the semi-invariant method is used to calculate the probability flow and obtain the probability distribution of branch flow and node voltage f(x) .

[0069] Computing device iT l The expected value of the branch flow or node voltage at time is,

[0070] (6) Update the operating status of all network devices According to the probability power flow calculation results, the operating status of the entire network equipment is determined. T l The running status at the moment is:

[0071] in, Indicates the rated power or voltage amplitude upper limit of device i.

[0072] (7) Output T The calculation result of the power flow of the whole network at time i Considering the operation status of the equipment in the whole network model, the topology analysis of the power grid diagram is re-performed; the base state power flow calculation of the whole network is re-performed based on the PQ decomposition method to obtain the power flow results of the whole network. Thus, the whole network model information and power flow results are stored and modeled to obtain T l Simulation and calculation section of time .

[0073] (8) Output long process simulation and deduction of continuous cross-section sequence Will T l The simulation calculation section at the moment is used as T l+1 Input the data required for the simulation analysis at each moment, and repeat steps (3) to (7), thus obtaining T 1 To T m The long process simulation of the time period is used to deduce the continuous section sequence S.

[0074] As an example, based on the historical statistical data of equipment and the characteristics of extreme weather changes, the present invention proposes a network-wide equipment failure probability model that comprehensively considers equipment instantaneous failures and disaster-related failures. It uses Markov process analysis and Weber equation optimization, fully considers the inevitability and future randomness of equipment failures, and can achieve long-term state analysis and future state distribution prediction of power grid equipment. The simulation section calculation obtained is better. In addition, according to the size of the disaster characteristics, different disaster-related failure ratio coefficients can be flexibly set to dynamically adjust the equipment disaster weight.

[0075] like Figure 3 As shown, the third object of the present invention is to provide a long process simulation and deduction continuous section construction system considering equipment probabilistic failure, comprising: The subdivision acquisition module 100 is used to subdivide the total duration of the long process simulation deduction into multiple calculation time points at equal intervals, and then obtain the grid structure, control operation and meteorological forecast data within the total duration of the long process simulation deduction; The probability calculation module 200 is used to perform simulation deduction at the first calculation time point, specifically including: based on the grid structure, control operation and meteorological forecast data, respectively calculating the probability of instantaneous failure of the whole network equipment and the probability of failure due to disasters, taking the instantaneous failure of the equipment and the failure due to disasters as random variables, and establishing a probability model of equipment failure in the whole network based on a discrete probability model; The state determination module 300 is used to calculate the probability flow distribution of the whole network according to the probability model of the whole network equipment failure based on the instantaneous failure probability and the failure probability due to disaster of the whole network equipment as input, and determine the operation state of the whole network equipment according to the probability flow distribution of the whole network; The section calculation module 400 is used to perform real-time power flow calculation according to the operation status of the whole network equipment, obtain the whole network power flow result, store and model the whole network model information and the whole network power flow result, and obtain the simulation deduction calculation section at the current calculation time point; The loop iteration module 500 is used to perform simulation deduction at the next calculation time point, calculate the simulation deduction calculation section at the next calculation time point, and loop iteratively calculate until the simulation deduction calculation sections of all calculation time points within the total duration of the long process simulation deduction are obtained, and the long process simulation deduction continuous section sequence is summarized.

[0076] The long process simulation and deduction continuous section construction system considering the probabilistic failure of equipment of the present invention is based on the long process simulation and deduction continuous section construction method considering the probabilistic failure of equipment.

[0077] like Figure 4 As shown, the third purpose of the embodiment of the present invention is to provide an electronic device, including a memory 701, a processor 702, and a computer program stored in the memory 701 and executable on the processor, wherein the processor implements the above-mentioned method for constructing a continuous section by simulation deduction of a long process considering the probability failure of the equipment when executing the computer program. It also includes a communication interface 703 and a bus 704.

[0078] The fourth objective of an embodiment of the present invention is to provide a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned method for constructing a continuous section by long-process simulation and deduction considering the probabilistic failure of equipment.

[0079] The fifth objective of an embodiment of the present invention is to provide a computer program product, which includes computer instructions, and the computer instructions instruct the computer to execute the above-mentioned method for constructing continuous sections by long-process simulation and deduction considering probabilistic equipment failures.

[0080] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0081] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0082] The present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0083] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0084] Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for constructing a continuous section by simulation of a long process considering equipment probabilistic failure, characterized in that: include: The total duration of the long process simulation is divided into multiple calculation time points at equal intervals; Obtain the grid structure, regulation and operation, and meteorological forecast data within the total duration of the long-term simulation; Conduct simulation deduction at the first calculation time point, specifically including: based on the grid structure, control operation and meteorological forecast data, respectively calculate the probability of instantaneous failure of equipment in the entire network and the probability of failure due to disasters, take the instantaneous failure of equipment and the failure due to disasters as random variables, and establish a probability model of equipment failure in the entire network based on a discrete probability model; Taking the instantaneous failure probability and the failure probability due to disaster of the whole network equipment as input, the probability power flow distribution of the whole network is calculated according to the probability power flow distribution of the whole network equipment, and the operation status of the whole network equipment is determined according to the probability power flow distribution of the whole network; According to the operation status of the whole network equipment, real-time power flow calculation is performed to obtain the power flow results of the whole network, and the whole network model information and the power flow results of the whole network are stored and modeled to obtain the simulation deduction calculation section at the current calculation time point; Perform simulation deduction at the next calculation time point, calculate the simulation deduction calculation section at the next calculation time point, and iterate the calculation in a loop until the simulation deduction calculation sections of all calculation time points within the total duration of the long process simulation deduction are obtained, and the long process simulation deduction continuous section sequence is summarized.

2. The method for constructing a continuous section by simulation of a long process considering equipment probabilistic failure according to claim 1 is characterized in that: The total duration of the long process simulation is divided into multiple calculation time points at equal intervals, including: According to the total simulation time and calculation interval requirements, the whole process is divided into equal intervals of T1, T2, ..., T m There are a total of m simulation time points, each of which corresponds to a simulation calculation section, which eventually constitutes the calculation section from T1 to T m Long-term simulation of continuous section sequence S: in, express T l Simulation and calculation section at each moment.

3. The method for constructing a continuous section by simulation of a long process considering equipment probabilistic failure according to claim 1 is characterized in that: The acquisition of the grid structure, regulation and operation, and meteorological forecast data within the total duration of the long-process simulation includes: Get T1 to T from the control system m The grid structure, control operation and meteorological forecast data within the time period are analyzed to analyze the changes in the topological connection relationship of the grid, build a full-grid model and power flow distribution diagram, and set T1 to T according to the meteorological forecast data. m The damage situation of the local power grid during the time period simulation; Determine the distance between each device and the disaster center based on the distribution of local power grid equipment , ， ； in, Indicates that the device is T l The distance from the disaster center at a given moment; n represents the total number of devices in the entire network.

4. The method for constructing a continuous section by simulation of a long process considering equipment probabilistic failure according to claim 1 is characterized in that: The calculation method of the instantaneous failure probability of the whole network equipment is: Considering the randomness and uncertainty of power grid equipment failure, the device is calculated based on the Markov process. i exist T l The instantaneous failure probability at time is: ， ； in, w i For equipment i The historical failure rate of the equipment is calculated as follows: historical failure duration of the equipment / total operation duration of the equipment × 100%.

5. The method for constructing a continuous section by simulation of a long process considering equipment probabilistic failure according to claim 1, characterized in that: The calculation method of the probability of failure due to disaster is: Analyze the characteristics of extreme weather, simulate the range changes of disasters caused by extreme weather, and calculate the probability of equipment failure due to disasters based on the Weibull distribution equation as follows: ， ； in, r i is the failure ratio parameter due to disasters; when the disaster weather is a typhoon, r i The parameter is set to 8~10; when the disaster weather is thunder and lightning, r i The parameter is set to 14~18; when the disaster weather is ice-covered, r i The parameter is set to 3~5.

6. The method for constructing a continuous section by simulation and deduction of a long process considering equipment probabilistic failure according to claim 1, characterized in that: The method of establishing a network-wide equipment failure probability model based on a discrete probability model includes: The instantaneous failure probability and disaster failure probability of the entire network equipment are used as random variables to establish the failure probability model of the entire network equipment , the calculation method is: in, .

7. The method for constructing a continuous section by simulation of a long process considering equipment probabilistic failure according to claim 1, characterized in that: The method takes the instantaneous failure probability and the failure probability due to disaster of the whole network equipment as input, calculates the probability flow distribution of the whole network according to the failure probability model of the whole network equipment, and determines the operation status of the whole network equipment according to the probability flow distribution of the whole network, including: According to each device T l The discrete probability model at the moment is sampled to obtain the faulty equipment; the instantaneous fault probability of the whole network equipment and the probability of fault due to disaster are used as input, and the semi-invariant method is used to calculate the probability flow to obtain the probability distribution of branch flow and node voltage. f(x) ; Computing device i T l The expected value of the branch flow or node voltage at a certain moment is: According to the probability power flow calculation results, the operating status of the entire network equipment is determined; then the device i T l The operating status at this moment is: ； ； in, Indicates the rated power or voltage amplitude upper limit of device i.

8. The method for constructing a continuous section by simulation of a long process considering equipment probabilistic failure according to claim 1, characterized in that: According to the operation status of the whole network equipment, real-time power flow calculation is performed to obtain the whole network power flow result, the whole network model information and the whole network power flow result are stored and modeled, and the simulation deduction calculation section at the current calculation time point is obtained, including: Considering the operation status of the whole network model equipment, the topology analysis of the power grid diagram is re-performed; based on the topology analysis, the base state power flow of the whole network is re-calculated based on the PQ decomposition method to obtain the power flow results of the whole network; the whole network model information and the whole network power flow results are stored and modeled to obtain T l Simulation and calculation section of time .

9. A long process simulation and deduction continuous section construction system considering equipment probabilistic failure, characterized in that: include: A subdivision acquisition module is used to subdivide the total duration of the long-process simulation into multiple calculation time points at equal intervals, and then obtain the grid structure, control operation and meteorological forecast data within the total duration of the long-process simulation; The probability calculation module is used to perform simulation deduction at the first calculation time point, specifically including: based on the grid structure, control operation and meteorological forecast data, respectively calculating the probability of instantaneous failure of equipment in the entire network and the probability of failure due to disasters, taking the instantaneous failure of equipment and the failure due to disasters as random variables, and establishing a probability model of equipment failure in the entire network based on a discrete probability model; The state determination module is used to calculate the probability flow distribution of the whole network according to the probability model of the whole network equipment failure, and determine the operation state of the whole network equipment according to the probability flow distribution of the whole network; The section calculation module is used to perform real-time power flow calculation according to the operating status of the whole network equipment, obtain the power flow results of the whole network, store and model the whole network model information and the whole network power flow results, and obtain the simulation deduction calculation section at the current calculation time point; The loop iteration module is used to perform simulation deduction at the next calculation time point, calculate the simulation deduction calculation section at the next calculation time point, and loop iteratively calculate until the simulation deduction calculation sections of all calculation time points within the total duration of the long process simulation deduction are obtained, and the long process simulation deduction continuous section sequence is summarized.

10. The long process simulation and deduction continuous section construction system considering equipment probabilistic failure according to claim 9 is characterized in that: In the subdivision acquisition module, the total duration of the long process simulation is subdivided into multiple calculation time points at equal intervals, including: According to the total simulation time and calculation interval requirements, the whole process is divided into equal intervals of T1, T2, ..., T m There are a total of m simulation time points, each of which corresponds to a simulation calculation section, which eventually constitutes the calculation section from T1 to T m Long-term simulation of continuous section sequence S: in, express T l The simulation and deduction calculation section at each moment stores the power grid model information, operation data and key external environment information.

11. The long process simulation and deduction continuous section construction system considering equipment probabilistic failure according to claim 9 is characterized in that: In the subdivision acquisition module, the grid structure, control operation and meteorological forecast data within the total duration of the long process simulation are obtained, including: Get T1 to T from the control system m The grid structure, control operation and meteorological forecast data within the time period are analyzed to analyze the changes in the topological connection relationship of the grid, build a full-grid model and power flow distribution diagram, and set T1 to T according to the meteorological forecast data. m The damage situation of the local power grid during the time period simulation; Determine the distance between each device and the disaster center based on the distribution of local power grid equipment , ， ； in, Indicates that the device is T l The distance from the disaster center at a given moment; n represents the total number of devices in the entire network.

12. The long process simulation and deduction continuous section construction system considering equipment probabilistic failure according to claim 9 is characterized in that: The calculation method of the instantaneous failure probability of the whole network equipment is: Considering the randomness and uncertainty of power grid equipment failure, the Markov process is used to calculate the probability of failure of equipment i in T l The instantaneous failure probability at time is, ， ； in, w i For equipment i The historical failure rate of the equipment is calculated as follows: historical failure duration of the equipment / total operation duration of the equipment × 100%.

13. The long process simulation and deduction continuous section construction system considering equipment probabilistic failure according to claim 9 is characterized in that: The calculation method of the probability of failure due to disaster is: Analyze the characteristics of extreme weather, simulate the range changes of disasters caused by extreme weather, and calculate the probability of equipment failure due to disasters based on the Weibull distribution equation as follows: ， ； in, r i is the failure ratio parameter due to disasters; when the disaster weather is a typhoon, r i The parameter is set to 8~10; when the disaster weather is thunder and lightning, r i The parameter is set to 14~18; when the disaster weather is ice-covered, r i The parameter is set to 3~5.

14. The long process simulation and deduction continuous section construction system considering equipment probabilistic failure according to claim 9, characterized in that: In the probability calculation module, a network-wide equipment failure probability model is constructed, including: The instantaneous failure probability and disaster failure probability of the entire network equipment are used as random variables to establish the failure probability model of the entire network equipment , the calculation formula is as follows: in, .

15. The long process simulation and deduction continuous section construction system considering equipment probabilistic failure according to claim 9, characterized in that: In the state determination module, the instantaneous failure probability and the failure probability due to disaster of the whole network equipment are used as inputs, the probability flow distribution of the whole network is calculated according to the probability model of the whole network equipment failure, and the operation state of the whole network equipment is determined according to the probability flow distribution of the whole network, including: According to each device T l The discrete probability model at the moment is sampled to obtain the faulty equipment; the instantaneous fault probability of the whole network equipment and the probability of fault due to disaster are used as input, and the semi-invariant method is used to calculate the probability flow to obtain the probability distribution of branch flow and node voltage. f(x) ; Computing device i T l The expected value of the branch flow or node voltage at a certain moment is: According to the probability flow calculation results, the operating status of the entire network equipment is determined; T l The operating status at this moment is: ； ； in, Indicates the rated power or voltage amplitude upper limit of device i.

16. The long process simulation and deduction continuous section construction system considering equipment probabilistic failure according to claim 9, characterized in that: In the cross-section calculation module, according to the operation status of the whole network equipment, real-time power flow calculation is performed to obtain the whole network power flow result, the whole network model information and the whole network power flow result are stored and modeled, and the simulation deduction calculation cross-section at the current calculation time point is obtained, including: Considering the operation status of the whole network model equipment, the topology analysis of the power grid diagram is re-performed; based on the topology analysis, the base state power flow of the whole network is re-calculated based on the PQ decomposition method to obtain the power flow results of the whole network; the whole network model information and the whole network power flow results are stored and modeled to obtain T l Simulation and calculation section of time .

17. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for constructing a continuous section by simulation and deduction of a long process taking into account probabilistic failures of equipment as described in any one of claims 1 to 9 is implemented.

18. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for constructing a continuous section by simulation and deduction of a long process considering probabilistic equipment failures as described in any one of claims 1 to 9 is implemented.

19. A computer program product, comprising computer instructions, characterized in that: The computer instructions instruct the computer to execute the method for constructing a continuous section by simulation and deduction of a long process taking into account probabilistic equipment failures as described in any one of claims 1-9.

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