Meteorological condition-based power grid safety assessment method and system

Abstract

The invention discloses a power grid safety assessment method and system based on meteorological conditions, and relates to the technical field of power grid safety planning. Calculating the power flow distribution corresponding to each single fault by adopting a pre-trained artificial neural network to obtain the total expected insufficient power supply quantity of the power grid; analyzing the fault rate, the equipment repair speed and the load demand probability of the power grid according to historical operation data, and then establishing a risk assessment model of the power grid; and in the operation process of the power grid, inputting real-time power grid operation data into the risk assessment model to obtain a risk value for assessing the safety of the power grid. By adopting the embodiment of the invention, the operation risk and reliability of the power grid under the meteorological conditions of typhoon and heavy rainfall can be quickly and effectively evaluated, so that a power grid dispatcher is helped to take proper prevention and control measures, and the stable operation of a power system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid security planning, and in particular to a power grid security assessment method and system based on meteorological conditions. Background Art

[0002] Frequent natural disasters have had a significant impact on the normal operation of the power system. Risk assessment plays a key role in power grid dispatching and operation, helping power grid dispatching and operation personnel to fully understand and grasp the level of power grid operation risks and their changing trends.

[0003] Existing risk assessment methods often fail to fully utilize key features in the data, which may result in the selection of features that are irrelevant or redundant to the grid security assessment. This not only reduces the accuracy of model assessment, but also increases the problem of computational complexity and feature dimensionality. Summary of the invention

[0004] The technical problem to be solved by the present invention is how to improve the stability of the power system under extreme weather conditions. A power grid security assessment method and system suitable for extreme meteorological conditions are provided, which can quickly and accurately assess the risks of the power system under extreme meteorological conditions, and combine the power system flow distribution to ensure the reliability and safety of power supply.

[0005] To solve the above technical problems, an embodiment of the present invention provides a method for evaluating power grid security based on meteorological conditions, comprising:

[0006] Collecting historical operation data of the power grid; the historical operation data includes load demand, fault conditions and meteorological conditions;

[0007] Using a pre-trained artificial neural network to calculate the power flow distribution corresponding to each single fault, and obtain the total expected power shortage of the power grid;

[0008] Obtaining a failure rate of the power grid according to the failure condition and the meteorological condition;

[0009] Obtain the rated wind speed and key wind speed parameters, and combine them with the meteorological conditions to obtain the equipment repair speed;

[0010] According to the load demand, the total load of each node of the power grid at time t is expressed as a normal distribution, and the load demand probability is calculated;

[0011] Establishing a risk assessment model for the power grid according to the total expected power shortage, the failure rate, the equipment repair speed and the load demand probability;

[0012] The real-time grid operation data is input into the risk assessment model to obtain a risk value for assessing grid security.

[0013] As an improvement of the above solution, the method of using a pre-trained artificial neural network to calculate the power flow distribution corresponding to each single fault to obtain the total expected power shortage of the power grid includes:

[0014] Using a pre-trained artificial neural network to simulate a single fault of the power grid to obtain an optimal power flow under the single fault;

[0015] According to the optimal power flow, the expected power shortage amount of each node of the power grid and the number of nodes to be reduced in load are obtained;

[0016] pass Calculate the total expected power shortage of the power grid corresponding to each single fault; where G i N is the expected power shortage of node i; s is the total number of nodes in the power grid; D s To reduce the number of load nodes.

[0017] As an improvement of the above solution, obtaining the failure rate of the power grid according to the fault situation and the meteorological conditions includes:

[0018] According to the fault situation, the random probability of the fault under general meteorological conditions is modeled using HPP distribution to obtain a first fault rate of the power grid under general meteorological conditions;

[0019] Obtaining a critical wind speed from the meteorological condition, and calculating a second failure rate of the power grid under the first extreme meteorological condition in combination with the fault condition;

[0020] Obtaining the lightning ground-to-ground lightning density from the meteorological conditions, and calculating the third failure rate of the power grid under the second extreme meteorological conditions in combination with the fault conditions;

[0021] The failure rate of the power grid is obtained according to the first failure rate, the second failure rate and the third failure rate.

[0022] As an improvement of the above solution, according to the fault situation, the random probability of the fault under general meteorological conditions is modeled using HPP distribution to obtain the first fault rate of the power grid under general meteorological conditions, including:

[0023] According to the fault condition, the number of faults per kilometer of line within a preset time period is obtained;

[0024] pass Model the random probability of failure under general meteorological conditions; where C f (t) is the number of faults per kilometer of line in time t; k is the kth fault; ξ n is the first failure rate of the power grid under general meteorological conditions.

[0025] As an improvement of the above solution, the step of obtaining the key wind speed from the meteorological condition and calculating the second failure rate of the power grid under the first extreme meteorological condition in combination with the fault condition includes:

[0026] Acquire a key wind speed from the meteorological condition, and determine it as a first extreme meteorological condition when the key wind speed is greater than a preset wind speed threshold;

[0027] By W w (t) = W c (t)+Δ w (t) Calculate the wind speed intensity within time t under the first extreme meteorological condition;

[0028] pass calculating a second failure rate of the power grid under a first extreme meteorological condition;

[0029] Among them, ξ n is the first failure rate of the power grid under general meteorological conditions; W c (t) is the critical wind speed; Δ w (t) is the preset critical wind speed random threshold; W w (t) is the wind speed intensity within time t under the first extreme meteorological condition; a w is the adjustment coefficient of the second failure rate.

[0030] As an improvement of the above solution, obtaining the lightning ground lightning density from the meteorological conditions and calculating the third failure rate of the power grid under the second extreme meteorological conditions in combination with the fault conditions includes:

[0031] The lightning ground-to-ground lightning density is obtained from the meteorological conditions, and when the lightning ground-to-ground lightning density is greater than a preset lightning ground-to-ground lightning density threshold, it is determined to be a second extreme meteorological condition; the change of the lightning ground-to-ground lightning density follows a logarithmic distribution;

[0032] By lg (C g (t))=ξ n β1C g (t) calculating a third failure rate of the power grid under a second extreme meteorological condition; wherein, ξ n is the first failure rate of the power grid under general meteorological conditions; β1 is a preset parameter; C g (t) is the density of ground-to-ground lightning.

[0033] As an improvement of the above solution, the rated wind speed and key wind speed parameters are obtained, and the equipment repair speed is obtained in combination with the meteorological conditions, including:

[0034] When the meteorological conditions are normal meteorological conditions and the wind speed intensity is not less than the preset wind speed intensity threshold, Calculate equipment repair speed;

[0035] When the meteorological condition is an extreme meteorological condition and the wind speed intensity is less than the preset wind speed intensity threshold, Calculate equipment repair speed;

[0036] When the meteorological condition is an extreme meteorological condition and the wind speed intensity is not less than the preset wind speed intensity threshold, Calculate equipment repair speed;

[0037] Among them, v n is the preset rated wind speed; η is the preset first positive parameter; W w (t) is the wind speed intensity; W c (t) is the critical wind speed; ψ is the preset second positive parameter; N g It is the preset extreme weather condition parameter.

[0038] As an improvement of the above solution, according to the load demand, the total load of each node of the power grid at time t is expressed as a normal distribution, and the load demand probability is calculated, including:

[0039] According to the load demand, the total load of each node of the power grid at time t is expressed as a normal distribution to obtain normal distribution parameters;

[0040] pass Calculate the load demand probability; where μ is the mean of the total load; σ is the standard deviation of the total load; and θ is the normal distribution parameter.

[0041] As an improvement of the above solution, the risk assessment model of the power grid is established according to the total expected power shortage, the failure rate, the equipment repair speed and the load demand probability, including:

[0042] By P g =α(E,v,G)+β(ξ) to calculate the unit output of the power grid;

[0043] By P c =γ(E,v,G)+ε(ξ) to calculate the load reduction of the power grid;

[0044] By F = minf(P g ,P c ) establishing a risk assessment model for the power grid;

[0045] Among them, E is the total expected power shortage; v is the equipment repair speed; G is the load demand probability; ξ is the failure rate; α and ε are preset positive parameters; β and γ are preset negative parameters.

[0046] The embodiment of the present invention further provides a power grid security assessment system based on meteorological conditions, comprising:

[0047] A historical operation data collection module is used to collect historical operation data of the power grid; the historical operation data includes load demand, fault conditions and meteorological conditions;

[0048] A total expected power shortage calculation module is used to calculate the power flow distribution corresponding to each single fault using a pre-trained artificial neural network to obtain the total expected power shortage of the power grid;

[0049] A failure rate calculation module, used to obtain the failure rate of the power grid according to the failure situation and the meteorological conditions;

[0050] An equipment repair speed calculation module is used to obtain the rated wind speed and key wind speed parameters, and obtain the equipment repair speed in combination with the meteorological conditions;

[0051] A load demand probability calculation module is used to express the total load of each node of the power grid at time t as a normal distribution according to the load demand, and calculate the load demand probability;

[0052] A model building module, used to build a risk assessment model of the power grid according to the total expected power shortage, the failure rate, the equipment repair speed and the load demand probability;

[0053] The safety assessment module is used to input real-time power grid operation data into the risk assessment model to obtain a risk value for assessing power grid safety.

[0054] Compared with the prior art, the present invention discloses a method and system for evaluating the safety of a power grid based on meteorological conditions, which collects the historical operation data of the power grid; the historical operation data includes load demand, fault conditions and meteorological conditions; a pre-trained artificial neural network is used to calculate the flow distribution corresponding to each single fault, and the total expected power shortage of the power grid is obtained; the fault rate of the power grid is obtained according to the fault condition and the meteorological conditions; the rated wind speed and key wind speed parameters are obtained, and the equipment repair speed is obtained in combination with the meteorological conditions; according to the load demand, the total load of each node of the power grid at time t is expressed as a normal distribution, and the load demand probability is calculated; according to the total expected power shortage, the fault rate, the equipment repair speed and the load demand probability, a risk assessment model of the power grid is established; the real-time power grid operation data is input into the risk assessment model to obtain a risk value for evaluating the safety of the power grid. By adopting the embodiment of the present invention, the operation risk and reliability of the power grid under typhoon and heavy rainfall meteorological conditions can be quickly and effectively evaluated, thereby helping the power grid dispatcher to take appropriate prevention and control measures to ensure the stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a schematic diagram of the steps of a method for evaluating power grid security based on meteorological conditions provided by an embodiment of the present invention;

[0056] Figure 2 It is a structural schematic diagram of a power grid security assessment system based on meteorological conditions provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] In the description of the specification and claims, it is to be understood that the terms first, second, etc. in the specification and claims are only used for the purpose of describing the same technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated, nor necessarily describing the order or time sequence. The terms are interchangeable where appropriate. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features.

[0059] The embodiment of the present invention provides a method for evaluating power grid security based on meteorological conditions. Figure 1 In this embodiment, the grid security assessment method based on meteorological conditions is specifically performed through steps S1 to S7:

[0060] S1. Collect historical operation data of the power grid; the historical operation data includes load demand, fault conditions and meteorological conditions.

[0061] S2. Calculate the power flow distribution corresponding to each single fault using a pre-trained artificial neural network to obtain the total expected power shortage of the power grid.

[0062] It should be noted that the expected power shortage is used as an expected value to measure the reduction in load demand of the power system due to insufficient power generation capacity or power grid restrictions in a specific time period. In the embodiment of the present invention, the total expected power shortage reflects the resilience of the power grid.

[0063] In the embodiment of the present invention, the desired calculation can be quickly achieved through the pre-trained artificial neural network, reducing the solution time.

[0064] S3. Obtaining a failure rate of the power grid according to the failure situation and the meteorological conditions.

[0065] It should be noted that the fault conditions corresponding to different meteorological conditions are different. Faults under general meteorological conditions mainly include events with low correlation with meteorological conditions, such as natural aging of equipment and equipment failure. Faults under extreme meteorological conditions involve problems that may occur under severe meteorological conditions, such as insulation medium breakdown, equipment damage caused by excessive wind speed, etc. The failure rate described in the embodiments of the present invention comprehensively considers the impact of general meteorological conditions failure mode and extreme meteorological conditions failure mode.

[0066] S4. Obtain the rated wind speed and key wind speed parameters, and obtain the equipment repair speed in combination with the meteorological conditions.

[0067] In the embodiment of the present invention, it is assumed that the emergency repair personnel can reach the fault point in time to perform the emergency repair task, the network can reach the normal operation level after the faulty line is repaired, and the fault time is extremely short and negligible compared with the repair time.

[0068] S5. According to the load demand, the total load of each node in the power grid at time t is expressed as a normal distribution, and the load demand probability is calculated.

[0069] It should be noted that load demand varies with time and space scales and is a physical quantity associated with two-dimensional characteristics of time and space. Random models can be used to explain time correlation.

[0070] S6. Establishing a risk assessment model for the power grid according to the total expected power shortage, the failure rate, the equipment repair speed and the load demand probability.

[0071] S7. Input the real-time grid operation data into the risk assessment model to obtain a risk value for assessing grid security.

[0072] The risk value can characterize the risk level of the power grid under various meteorological conditions, thereby guiding the power grid dispatcher to adopt corresponding power grid dispatch strategies for power grid dispatch. For example, the power grid dispatch strategy includes increasing spare capacity, optimizing line layout, reducing power supply pressure, etc.

[0073] In the above scheme, the total expected power shortage, failure rate, equipment repair speed and load demand probability are comprehensively considered, which can quickly and effectively evaluate the operation risk and reliability of the power grid under typhoon and heavy rainfall meteorological conditions, thereby helping power grid dispatchers to take appropriate prevention and control measures to ensure the stable operation of the power system.

[0074] As a preferred implementation, step S2, using a pre-trained artificial neural network to calculate the power flow distribution corresponding to each single fault to obtain the total expected power shortage of the power grid, includes:

[0075] Using a pre-trained artificial neural network to simulate a single fault of the power grid to obtain an optimal power flow under the single fault;

[0076] According to the optimal power flow, the expected power shortage amount of each node of the power grid and the number of nodes to be reduced in load are obtained;

[0077] pass Calculate the total expected power shortage of the power grid corresponding to each single fault; where G i N is the expected power shortage of node i; s is the total number of nodes in the power grid; D s To reduce the number of load nodes.

[0078] Exemplarily, the pre-trained artificial neural network includes an input layer, at least one hidden layer and an output layer. The input layer, the hidden layer and the output layer each include a number of artificial neurons, and each neuron is linked to the neurons of its adjacent layer through a weighted relationship. That is, in each neuron, the input is first weighted and then summed.

[0079] It should also be noted that after a node fails, the change in network power flow may cause a failure of the entire network, and the existing technology has not taken into account the problem of system power flow distribution. In an embodiment of the present invention, by performing multiple simulations within the simulation time T and solving multiple optimal power flows, each power flow assumes one type of failure. Generally speaking, the time required to solve the optimal power flow is closely related to the network structure, and the corresponding parallel solution strategy can be used to shorten the solution time. However, the optimization problem cannot be linearized and multiple solutions are required, which is more time-consuming. The embodiment of the present invention can effectively shorten the expected calculation time through the pre-trained artificial neural network, and can evaluate the reliability of the power grid under extreme weather conditions through multiple simulations and optimal power flow solutions.

[0080] As a preferred implementation, step S3, obtaining the failure rate of the power grid according to the fault condition and the meteorological condition, is specifically performed through steps S31 to S34:

[0081] S31. According to the fault situation, use HPP distribution to model the random probability of fault occurrence under general meteorological conditions to obtain a first fault rate of the power grid under general meteorological conditions.

[0082] Under normal meteorological conditions, the occurrence of power grid failures is usually not closely related to meteorological conditions, and is generally caused by natural aging of equipment or other natural failures. In an embodiment of the present invention, the random probability of failures under normal meteorological conditions is simulated by HPP (Homogeneous Poisson Process) distribution.

[0083] It should also be noted that the random probability of failures occurring under extreme weather conditions is simulated by NHPP (Non-Homogeneous Poisson Process) distribution.

[0084] S32. Obtain a critical wind speed from the meteorological condition, and calculate a second failure rate of the power grid under the first extreme meteorological condition in combination with the fault condition.

[0085] It is understandable that under typhoon conditions, the critical wind speed is inconsistent with that under general meteorological conditions. Generally speaking, the wind speed will be higher and have a more regular trend of change. By obtaining the critical wind speed and fault conditions, the second failure rate of the power grid under typhoon extreme meteorological conditions can be obtained. Subsequently, the faults under typhoon meteorological conditions can be analyzed through the second failure rate based on real-time operation data.

[0086] S33. Obtain ground-to-ground lightning density from the meteorological conditions, and calculate a third failure rate of the power grid under the second extreme meteorological conditions in combination with the fault conditions.

[0087] It is understandable that lightning has a great impact on the line, and heavy rain is usually accompanied by lightning. In the embodiment of the present invention, the intensity of lightning is represented by the lightning ground flash density, and the faults under heavy rain or thunderstorm weather conditions can be analyzed based on the third fault rate according to the real-time operation data.

[0088] S34. Obtain a failure rate of the power grid according to the first failure rate, the second failure rate, and the third failure rate.

[0089] The level of wind speed can directly or indirectly affect the stability of the equipment, and lightning activity can directly affect the insulation level of the equipment. Thunderstorms accompanied by typhoons and heavy rains can pose a great safety threat to power grid lines. In the embodiments of the present invention, the failure rate under extreme meteorological conditions of typhoons and heavy rains is mainly analyzed.

[0090] Further, in some embodiments, step S31 includes:

[0091] According to the fault condition, the number of faults per kilometer of line within a preset time period is obtained;

[0092] pass Model the random probability of failure under general meteorological conditions; where C f (t) is the number of faults per kilometer of line in time t; k is the kth fault; ξ n is the first failure rate of the power grid under general meteorological conditions.

[0093] Exemplarily, the NHPP distribution is used to represent the occurrence of extreme meteorological conditions. Specifically, Model the random probability of failure under extreme meteorological conditions; where k is the kth failure; V e t is the time-independent occurrence rate of extreme meteorological conditions; V e (t′) is the time-varying occurrence rate of extreme meteorological conditions.

[0094] Preferably, in some embodiments, step S32 includes:

[0095] Acquire a key wind speed from the meteorological condition, and determine it as a first extreme meteorological condition when the key wind speed is greater than a preset wind speed threshold;

[0096] By W w (t) = W c (t)+Δ w (t) Calculate the wind speed intensity within time t under the first extreme meteorological condition;

[0097] pass calculating a second failure rate of the power grid under a first extreme meteorological condition;

[0098] Among them, ξ n is the first failure rate of the power grid under general meteorological conditions; W c (t) is the critical wind speed; Δ w (t) is the preset critical wind speed random threshold; W w (t) is the wind speed intensity within time t under the first extreme meteorological condition; a w is the adjustment coefficient of the second failure rate.

[0099] It should be noted that by adjusting the coefficient a w , can further target wind speeds greater than the critical wind speed W c (t), the second failure rate is calculated more accurately.

[0100] As a preferred implementation, step S33 includes:

[0101] The lightning ground-to-ground lightning density is obtained from the meteorological conditions, and when the lightning ground-to-ground lightning density is greater than a preset lightning ground-to-ground lightning density threshold, it is determined to be a second extreme meteorological condition; the change of the lightning ground-to-ground lightning density follows a logarithmic distribution;

[0102] By lg (C g (t))=ξ n β1C g (t) calculating a third failure rate of the power grid under a second extreme meteorological condition; wherein, ξ n is the first failure rate of the power grid under general meteorological conditions; β1 is a preset parameter; C g(t) is the density of ground-to-ground lightning.

[0103] The ground-to-ground lightning density is the number of ground lightning strikes within a certain time unit and area, and its change follows a logarithmic distribution. The specific values ​​of the parameters of the logarithmic distribution are obtained according to historical operation data. In the embodiment of the present invention, the ground-to-ground lightning density is used to represent the lightning intensity.

[0104] Preferably, in some embodiments, step S4, obtaining the rated wind speed and key wind speed parameters, and combining the meteorological conditions to obtain the equipment repair speed, includes:

[0105] When the meteorological conditions are normal meteorological conditions and the wind speed intensity is not less than the preset wind speed intensity threshold, Calculate equipment repair speed;

[0106] When the meteorological condition is an extreme meteorological condition and the wind speed intensity is less than the preset wind speed intensity threshold, Calculate equipment repair speed;

[0107] When the meteorological condition is an extreme meteorological condition and the wind speed intensity is not less than the preset wind speed intensity threshold, Calculate equipment repair speed;

[0108] Among them, v n is the preset rated wind speed; η is the preset first positive parameter; W w (t) is the wind speed intensity; W c (t) is the critical wind speed; ψ is the preset second positive parameter; N g It is the preset extreme weather condition parameter.

[0109] It should be noted that the extreme weather condition parameters can be determined according to the specific parameters of the extreme weather conditions. For example, in the case of a typhoon, if the wind speed intensity is within the preset first threshold range, it is considered that N g =0.2; if the wind speed intensity is within the preset first threshold range, it is considered that N g =0.4.

[0110] It should also be noted that, in the embodiment of the present invention, when the meteorological conditions are normal meteorological conditions, it is considered that N g =0.

[0111] As a preferred implementation, step S5, according to the load demand, represents the total load of each node of the power grid at time t as a normal distribution, and calculates the load demand probability, including:

[0112] According to the load demand, the total load of each node of the power grid at time t is expressed as a normal distribution to obtain normal distribution parameters;

[0113] pass Calculate the load demand probability; where μ is the mean of the total load; σ is the standard deviation of the total load; and θ is the normal distribution parameter.

[0114] It should be noted that load demand varies with time and space scales and is a physical quantity associated with time and space two-dimensional characteristics. The random model of load can be used to explain time correlation. In the embodiment of the present invention, the change of average load level is mainly considered, and the influence of seasons and holidays is ignored.

[0115] As a preferred implementation, step S6, establishing a risk assessment model for the power grid according to the total expected power shortage, the failure rate, the equipment repair speed and the load demand probability, comprises:

[0116] By P g =α(E,v,G)+β(ξ) to calculate the unit output of the power grid;

[0117] By P c =γ(E,v,G)+ε(ξ) to calculate the load reduction of the power grid;

[0118] By F = minf(P g ,P c ) establishing a risk assessment model for the power grid;

[0119] Among them, E is the total expected power shortage; v is the equipment repair speed; G is the load demand probability; ξ is the failure rate; α and ε are preset positive parameters; β and γ are preset negative parameters.

[0120] It should be noted that under extreme meteorological conditions, according to the risk assessment model, when the unit output P g Increase, the grid is more likely to meet the load reduction P c , can reduce the risk of overload and failure, thereby reducing the failure rate; and when the load is reduced P c The power supply pressure of the power grid is reduced, and the failure rate can also be reduced. Those skilled in the art can perform power grid dispatching based on the above ideas.

[0121] The meteorological conditions-based power grid security assessment method provided by the embodiment of the present invention can quickly and accurately assess the risks of the power system under extreme meteorological conditions, and combine the power system flow distribution to ensure the reliability and safety of power supply.

[0122] See also Figure 2The embodiment of the present invention provides a grid security assessment system based on meteorological conditions. The grid security assessment system based on meteorological conditions includes a historical operation data acquisition module 11, a total expected power shortage calculation module 12, a failure rate calculation module 13, an equipment repair speed calculation module 14, a load demand probability calculation module 15, a model building module 16 and a security assessment module 17, wherein:

[0123] The historical operation data collection module 11 is used to collect the historical operation data of the power grid; the historical operation data includes load demand, fault conditions and meteorological conditions;

[0124] A total expected power shortage calculation module 12 is used to calculate the power flow distribution corresponding to each single fault using a pre-trained artificial neural network to obtain the total expected power shortage of the power grid;

[0125] A failure rate calculation module 13 is used to obtain the failure rate of the power grid according to the failure situation and the meteorological conditions;

[0126] The equipment repair speed calculation module 14 is used to obtain the rated wind speed and key wind speed parameters, and obtain the equipment repair speed in combination with the meteorological conditions;

[0127] A load demand probability calculation module 15 is used to express the total load of each node of the power grid at time t as a normal distribution according to the load demand, and calculate the load demand probability;

[0128] A model building module 16, used to build a risk assessment model of the power grid according to the total expected power shortage, the failure rate, the equipment repair speed and the load demand probability;

[0129] The safety assessment module 17 is used to input the real-time power grid operation data into the risk assessment model to obtain a risk value for assessing the safety of the power grid.

[0130] As a preferred implementation, the total expected power shortage amount calculation module 12 includes:

[0131] A fault simulation unit, used to simulate a single fault of the power grid using a pre-trained artificial neural network to obtain an optimal power flow under the single fault;

[0132] A node power flow solving unit, used for obtaining the expected power shortage amount and the number of load reduction nodes of each node of the power grid according to the optimal power flow;

[0133] Expected calculation unit, used to pass Calculate the total expected power shortage of the power grid corresponding to each single fault; where G i N is the expected power shortage of node i; sis the total number of nodes in the power grid; D s To reduce the number of load nodes.

[0134] As a preferred implementation, the failure rate calculation module 13 includes:

[0135] A first failure rate calculation unit is used to model the random probability of failure under general meteorological conditions using HPP distribution according to the failure situation, so as to obtain a first failure rate of the power grid under general meteorological conditions;

[0136] A second failure rate calculation unit is used to obtain a key wind speed from the meteorological condition, and calculate a second failure rate of the power grid under the first extreme meteorological condition in combination with the fault condition;

[0137] a third fault rate calculation unit, configured to obtain a lightning ground-to-ground lightning density from the meteorological conditions, and calculate a third fault rate of the power grid under a second extreme meteorological condition in combination with the fault condition;

[0138] A failure rate calculation unit is used to obtain the failure rate of the power grid according to the first failure rate, the second failure rate and the third failure rate.

[0139] Further, preferably, the first failure rate calculation unit is specifically used to:

[0140] According to the fault condition, the number of faults per kilometer of line within a preset time period is obtained;

[0141] pass Model the random probability of failure under general meteorological conditions; where C f (t) is the number of faults per kilometer of line in time t; k is the kth fault; ξ n is the first failure rate of the power grid under general meteorological conditions.

[0142] Preferably, the second failure rate calculation unit is specifically used to:

[0143] Acquire a key wind speed from the meteorological condition, and determine it as a first extreme meteorological condition when the key wind speed is greater than a preset wind speed threshold;

[0144] By W w (t) = W c (t)+Δ w (t) Calculate the wind speed intensity within time t under the first extreme meteorological condition;

[0145] pass calculating a second failure rate of the power grid under a first extreme meteorological condition;

[0146] Among them, ξn is the first failure rate of the power grid under general meteorological conditions; W c (t) is the critical wind speed; Δ w (t) is the preset critical wind speed random threshold; W w (t) is the wind speed intensity within time t under the first extreme meteorological condition; a w is the adjustment coefficient of the second failure rate.

[0147] In some preferred embodiments, the third failure rate calculation unit is specifically used to:

[0148] The lightning ground-to-ground lightning density is obtained from the meteorological conditions, and when the lightning ground-to-ground lightning density is greater than a preset lightning ground-to-ground lightning density threshold, it is determined to be a second extreme meteorological condition; the change of the lightning ground-to-ground lightning density follows a logarithmic distribution;

[0149] By lg (C g (t))=ξ n β1C g (t) calculating a third failure rate of the power grid under a second extreme meteorological condition; wherein, ξ n is the first failure rate of the power grid under general meteorological conditions; β1 is a preset parameter; C g (t) is the density of ground-to-ground lightning.

[0150] Preferably, the equipment repair speed calculation module 14 includes:

[0151] The first situation calculation unit is used for, when the meteorological condition is a general meteorological condition and the wind speed intensity is not less than a preset wind speed intensity threshold, Calculate equipment repair speed;

[0152] The second situation calculation unit is used to calculate the wind speed intensity when the meteorological condition is an extreme meteorological condition and the wind speed intensity is less than a preset wind speed intensity threshold. Calculate equipment repair speed;

[0153] The third situation calculation unit is used to calculate the wind speed intensity when the meteorological condition is an extreme meteorological condition and the wind speed intensity is not less than a preset wind speed intensity threshold. Calculate equipment repair speed;

[0154] Among them, v n is the preset rated wind speed; η is the preset first positive parameter; W w (t) is the wind speed intensity; W c (t) is the critical wind speed; ψ is the preset second positive parameter; N g It is the preset extreme weather condition parameter.

[0155] As a preferred implementation, the load demand probability calculation module 15 includes:

[0156] A normal distribution parameter calculation unit, used to express the total load of each node of the power grid at time t as a normal distribution according to the load demand, and obtain normal distribution parameters;

[0157] Load demand probability calculation unit is used to calculate the load demand probability through Calculate the load demand probability; where μ is the mean of the total load; σ is the standard deviation of the total load; and θ is the normal distribution parameter.

[0158] Preferably, the model building module 16 includes:

[0159] Unit output calculation unit, used to calculate the power of the generator through P g =α(E,v,G)+β(ξ) to calculate the unit output of the power grid;

[0160] Load reduction calculation unit, used to calculate the load through P c =γ(E,v,G)+ε(ξ) to calculate the load reduction of the power grid;

[0161] The risk assessment model building unit is used to calculate the risk of g ,P c ) establishing a risk assessment model for the power grid;

[0162] Among them, E is the total expected power shortage; v is the equipment repair speed; G is the load demand probability; ξ is the failure rate; α and ε are preset positive parameters; β and γ are preset negative parameters.

[0163] The meteorological condition-based power grid security assessment system provided by the embodiment of the present invention can quickly and accurately assess the risks of the power system under extreme meteorological conditions, and combine the power system flow distribution to ensure the reliability and safety of power supply.

[0164] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0165] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for evaluating power grid security based on meteorological conditions, characterized in that: include: Collecting historical operation data of the power grid; the historical operation data includes load demand, fault conditions and meteorological conditions; Using a pre-trained artificial neural network to calculate the power flow distribution corresponding to each single fault, and obtain the total expected power shortage of the power grid; Obtaining a failure rate of the power grid according to the failure condition and the meteorological condition; Obtain the rated wind speed and key wind speed parameters, and combine them with the meteorological conditions to obtain the equipment repair speed; According to the load demand, the total load of each node of the power grid at time t is expressed as a normal distribution, and the load demand probability is calculated; Establishing a risk assessment model for the power grid according to the total expected power shortage, the failure rate, the equipment repair speed and the load demand probability; The real-time grid operation data is input into the risk assessment model to obtain a risk value for assessing grid security.

2. A method for evaluating power grid security based on meteorological conditions according to claim 1, characterized in that: The method of using a pre-trained artificial neural network to calculate the power flow distribution corresponding to each single fault to obtain the total expected power shortage of the power grid includes: Using a pre-trained artificial neural network to simulate a single fault of the power grid to obtain an optimal power flow under the single fault; According to the optimal power flow, the expected power shortage amount of each node of the power grid and the number of nodes to be reduced in load are obtained; pass Calculate the total expected power shortage of the power grid corresponding to each single fault; where G i N is the expected power shortage of node i; s is the total number of nodes in the power grid; D s To reduce the number of load nodes.

3. A method for evaluating power grid security based on meteorological conditions as claimed in claim 1, characterized in that: The obtaining the failure rate of the power grid according to the fault condition and the meteorological condition comprises: According to the fault situation, the random probability of the fault under general meteorological conditions is modeled using HPP distribution to obtain a first fault rate of the power grid under general meteorological conditions; Obtaining a critical wind speed from the meteorological condition, and calculating a second failure rate of the power grid under the first extreme meteorological condition in combination with the fault condition; Obtaining the lightning ground-to-ground lightning density from the meteorological conditions, and calculating the third failure rate of the power grid under the second extreme meteorological conditions in combination with the fault conditions; The failure rate of the power grid is obtained according to the first failure rate, the second failure rate and the third failure rate.

4. A method for evaluating power grid security based on meteorological conditions as claimed in claim 3, characterized in that: According to the fault situation, the random probability of the fault under general meteorological conditions is modeled by using HPP distribution to obtain the first fault rate of the power grid under general meteorological conditions, including: According to the fault condition, the number of faults per kilometer of line within a preset time period is obtained; pass Model the random probability of failure under general meteorological conditions; where C f (t) is the number of faults per kilometer of line in time t; k is the kth fault; ξ n is the first failure rate of the power grid under general meteorological conditions.

5. A method for evaluating power grid security based on meteorological conditions as claimed in claim 3, characterized in that: The step of obtaining the key wind speed from the meteorological condition and calculating the second failure rate of the power grid under the first extreme meteorological condition in combination with the fault condition includes: Acquire a key wind speed from the meteorological condition, and determine it as a first extreme meteorological condition when the key wind speed is greater than a preset wind speed threshold; By W w (t) = W c (t)+Δ w (t) Calculate the wind speed intensity within time t under the first extreme meteorological condition; pass calculating a second failure rate of the power grid under a first extreme meteorological condition; Among them, ξ n is the first failure rate of the power grid under general meteorological conditions; W c (t) is the critical wind speed; Δ w (t) is the preset critical wind speed random threshold; W w (t) is the wind speed intensity within time t under the first extreme meteorological condition; a w is the adjustment coefficient of the second failure rate.

6. A method for evaluating power grid security based on meteorological conditions as claimed in claim 3, characterized in that: The obtaining of the lightning ground-to-ground lightning density from the meteorological conditions and calculating the third failure rate of the power grid under the second extreme meteorological conditions in combination with the fault condition includes: The lightning ground-to-ground lightning density is obtained from the meteorological conditions, and when the lightning ground-to-ground lightning density is greater than a preset lightning ground-to-ground lightning density threshold, it is determined to be a second extreme meteorological condition; the change of the lightning ground-to-ground lightning density follows a logarithmic distribution; By lg (C g (t))=ξ n β1C g (t) calculating a third failure rate of the power grid under a second extreme meteorological condition; wherein, ξ n is the first failure rate of the power grid under general meteorological conditions; β1 is a preset parameter; C g (t) is the density of ground-to-ground lightning.

7. A method for evaluating power grid security based on meteorological conditions according to claim 1, characterized in that: The step of obtaining the rated wind speed and the key wind speed parameters and combining the meteorological conditions to obtain the equipment repair speed includes: When the meteorological conditions are normal meteorological conditions and the wind speed intensity is not less than the preset wind speed intensity threshold, Calculate equipment repair speed; When the meteorological condition is an extreme meteorological condition and the wind speed intensity is less than the preset wind speed intensity threshold, Calculate equipment repair speed; When the meteorological condition is an extreme meteorological condition and the wind speed intensity is not less than the preset wind speed intensity threshold, Calculate equipment repair speed; Among them, v n is the preset rated wind speed; η is the preset first positive parameter; W w (t) is the wind speed intensity; W c (t) is the critical wind speed; ψ is the preset second positive parameter; N g It is the preset extreme weather condition parameter.

8. A method for evaluating power grid security based on meteorological conditions as claimed in claim 1, characterized in that: According to the load demand, the total load of each node in the power grid at time t is expressed as a normal distribution, and the load demand probability is calculated, including: According to the load demand, the total load of each node of the power grid at time t is expressed as a normal distribution to obtain normal distribution parameters; pass Calculate the load demand probability; where μ is the mean of the total load; σ is the standard deviation of the total load; and θ is the normal distribution parameter.

9. A method for evaluating power grid security based on meteorological conditions as claimed in claim 1, characterized in that: The step of establishing a risk assessment model for the power grid according to the total expected power shortage, the failure rate, the equipment repair speed and the load demand probability comprises: By P g =α(E,v,G)+β(ξ) to calculate the unit output of the power grid; By P c =γ(E,v,G)+ε(ξ) to calculate the load reduction of the power grid; By F = minf(P g ,P c ) establishing a risk assessment model for the power grid; Among them, E is the total expected power shortage; v is the equipment repair speed; G is the load demand probability; ξ is the failure rate; α and ε are preset positive parameters; β and γ are preset negative parameters.

10. A power grid security assessment system based on meteorological conditions, characterized in that: include: A historical operation data collection module is used to collect historical operation data of the power grid; the historical operation data includes load demand, fault conditions and meteorological conditions; A total expected power shortage calculation module is used to calculate the power flow distribution corresponding to each single fault using a pre-trained artificial neural network to obtain the total expected power shortage of the power grid; A failure rate calculation module, used to obtain the failure rate of the power grid according to the failure situation and the meteorological conditions; An equipment repair speed calculation module is used to obtain the rated wind speed and key wind speed parameters, and obtain the equipment repair speed in combination with the meteorological conditions; A load demand probability calculation module is used to express the total load of each node of the power grid at time t as a normal distribution according to the load demand, and calculate the load demand probability; A model building module, used to build a risk assessment model of the power grid according to the total expected power shortage, the failure rate, the equipment repair speed and the load demand probability; The safety assessment module is used to input real-time power grid operation data into the risk assessment model to obtain a risk value for assessing power grid safety.

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