Risk assessment method, device, equipment and medium for power system containing controllable phase shifter in extreme weather

Abstract

The invention relates to the technical field of electric power system safety analysis, in particular to a risk assessment method, device and equipment for an electric power system containing a controllable phase shifter in extreme weather and a medium. The method comprises the following steps: establishing a fault probability model of an overhead line and a phase shifter, considering direct and indirect fault probabilities of the phase shifter caused by ponding and equipment aging under a rainstorm condition, constructing a comprehensive fault probability model, and generating a power grid fault scene based on Latin hypercube sampling; line overload, node voltage out-of-limit and load loss risk indexes are obtained by combining load flow calculation, subjective and objective weights are fused by adopting a game theory comprehensive weighting method, a multi-dimensional comprehensive risk assessment index is formed, the power grid operation risk in extreme weather can be quantified, data support is provided for power grid dispatching decisions containing controllable phase shifters, and the method is suitable for large-scale popularization and application. And the robustness and the power supply reliability of the system in a disaster scene are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system safety analysis, and particularly relates to a risk assessment method, device, equipment and medium for a power system containing a controllable phase shifter under extreme weather. BACKGROUND

[0002] With the intensification of global climate change, the frequency and intensity of extreme weather events such as typhoon, heavy rain, extreme heat and no wind, and cold wave have increased significantly, posing a serious challenge to the safe and stable operation of the power system. Extreme weather not only directly affects the functional integrity of power equipment, but also may cause a chain of failures leading to large-scale power outages, causing significant losses to social and economic activities and residents' lives. As the core of power transmission, the failure rate of overhead lines increases significantly under extreme weather. In the extreme weather of typhoon, strong winds cause conductor dancing or tower overturning, leading to overhead line failure; in the extreme weather of heavy rain, the rain load on the overhead line is too heavy, which may cause line breakage or short circuit; in the extreme weather of extreme heat and no wind, the temperature rise of the conductor leads to an increase in sag, increasing the risk of short circuit and overload; in the extreme weather of cold wave, the superimposed effect of icing and wind load may cause mechanical failure of the line, and the water depth in the installation area of the controllable phase shifter directly affects its normal operation under extreme heavy rain.

[0003] Traditional power system risk assessment methods mainly rely on historical failure data and statistical models to estimate system risk through average failure rate or simple probability distribution. However, existing researches usually regard the phase shifter as a constant reliability device, ignoring the influence of factors such as rainwater dynamic accumulation and device aging on its failure probability, resulting in deviation in risk assessment. Moreover, traditional methods focus on a single weather condition and lack analysis of the comprehensive effect of multiple extreme weather, making it difficult to capture the dynamic changes in device failure rate under extreme weather.

[0004] The information disclosed in this BACKGROUND section is only for the purpose of enhancing the understanding of the background of the present application and should not be taken as admitting that the information constitutes prior art known to those of ordinary skill in the art. SUMMARY

[0005] The present application provides a risk assessment method, device, equipment and medium for a power system containing a controllable phase shifter under extreme weather, effectively solving the problems in the background art.

[0006] In order to achieve the above purpose, the technical solution adopted by the present application is as follows: a risk assessment method for a power system containing a controllable phase shifter under extreme weather, comprising the following steps:

[0007] Determine the type of extreme weather using grid operation data, the operation data including wind and solar output curves and load curves, and the type of extreme weather including typhoon, extreme heat and no wind, heavy rain and cold wave;

[0008] establish an overhead line fault probability model and a controllable phase shifter comprehensive fault probability model under the corresponding extreme weather type;

[0009] generate a fault scenario set of the power system based on the overhead line fault probability model and the controllable phase shifter comprehensive fault probability model by using a Latin hypercube sampling technology;

[0010] perform power flow calculation on each of the fault scenario sets to obtain power grid operation parameters and calculate device-level risk indicators and system-level risk indicators;

[0011] weight and fuse the device-level risk indicators and the system-level risk indicators based on a game theory comprehensive weighting method to calculate a comprehensive risk indicator and complete power grid risk assessment.

[0012] Further, the determination of the extreme weather type based on the power grid operation data comprises:

[0013] statistically analyze wind power output curves, photovoltaic output curves and load curves, extract typical daily average, fluctuation rate and change rate characteristic indicators, and match the characteristic indicators with characteristic templates of various extreme weather types, wherein the characteristics of various extreme weather types include:

[0014] typhoon type: high wind power output fluctuation rate, low photovoltaic output, and normal load level;

[0015] extreme heat and no wind type: low wind power output, high photovoltaic output, and high load level;

[0016] cold wave type: obvious upward trend of wind power output, low photovoltaic output, and high load level;

[0017] heavy rain type: high wind power output, low photovoltaic output, and normal or high load level;

[0018] determine the extreme weather type of the current power grid according to the matching result.

[0019] Further, the controllable phase shifter comprehensive fault probability model comprises a direct fault probability model and an indirect fault probability model:

[0020] The direct fault probability model is used to represent the direct fault probability of the device caused by factors such as water depth, waterlogging prevention height and cable joint height under extreme heavy rain conditions;

[0021] The indirect fault probability model is used to represent the indirect fault probability considering factors such as device service life and baseline failure rate;

[0022] The comprehensive failure probability model of the controllable phase shifter is a weighted fusion result of the direct failure probability model and the indirect failure probability model.

[0023] Further, the direct failure probability model of the controllable phase shifter comprises:

[0024]

[0025] In the formula, is the direct failure probability of the phase shifter under extreme rainstorm conditions, is the failure rate of the phase shifter at time t under extreme rainstorm conditions, d(t) is the water accumulation degree of the TCPST installation area at time t, h d is the TCPST design flood height, h l is the cable joint ground height of the on-site high-voltage switch cabinet, ζ and γ are attenuation coefficient and damping coefficient respectively, T is the evaluation time length, and Δt is the time step.

[0026] Further, the indirect failure probability model of the controllable phase shifter comprises:

[0027]

[0028] In the formula, is the indirect failure probability of the phase shifter under extreme rainstorm conditions, represents the indirect failure probability function of the phase shifter, β and η are shape parameter and proportion parameter of the equipment respectively, and t is the service life of the equipment.

[0029] Further, the comprehensive failure probability model of the controllable phase shifter comprises:

[0030]

[0031] In the formula, R T is the comprehensive failure probability of the controllable phase shifter, is the direct failure probability of the phase shifter under extreme rainstorm conditions, is the indirect failure probability of the phase shifter under extreme rainstorm conditions.

[0032] Further, the Latin hypercube sampling technology is adopted to generate a fault scene set of the power system based on the overhead line failure probability model and the comprehensive failure probability model of the controllable phase shifter, and the steps comprise:

[0033] According to the overhead line failure probability model, a plurality of subintervals are divided in the failure probability range, and a sample is randomly extracted in each subinterval to generate an initial Latin hypercube sample;

[0034] According to the comprehensive failure probability model of the controllable phase shifter, a failure probability range is divided into a plurality of subintervals, and a sample is randomly extracted in each subinterval to generate a controllable phase shifter failure state sample;

[0035] The Latin hypercube sample and the controllable phase shifter failure state sample are randomly arranged, combined with each failure scenario of the power system, to generate a failure scenario set containing overhead line and controllable phase shifter states, forming a matrix composed of 0 and 1, representing the device state under each failure scenario, wherein 0 represents normal operation and 1 represents failure occurrence;

[0036] For each failure scenario, combined with grid operation data and extreme weather conditions, the failure probability of the power system under the failure scenario is calculated.

[0037] Further, the device-level risk indicator includes an overhead line overload risk indicator and a phase shifter failure risk;

[0038] The model of the overhead line overload risk indicator includes:

[0039]

[0040] In the formula, S ev (w Li,j ) is the severity of overload of line i in the i th sampling, R L is the line overload risk value, Pr(L i ) is the probability of overload of line i, w Li,j is the value of the transmission capacity of line i exceeding the set upper limit in the i th sampling, which is 0 when less than the upper limit of the load rate;

[0041] The model of the phase shifter failure risk is a comprehensive failure probability model of the controllable phase shifter, which is obtained by weighted fusion of the direct failure probability based on the rainwater accumulation condition and the indirect failure probability based on the service life of the device, including:

[0042]

[0043] In the formula, R T (t) is the comprehensive failure probability of the controllable phase shifter, is the direct failure probability of the phase shifter under the extreme rainstorm condition, is the indirect failure probability of the phase shifter under the extreme rainstorm condition.

[0044] Further, the system-level risk indicator includes a node loss-of-load risk indicator and a node voltage out-of-line risk indicator; the model of the node loss-of-load risk indicator includes:

[0045]

[0046] wherein R LL is the node loss-of-load risk value; P i,j is the loss-of-load amount of node j at the i-th sampling; represents the state of line i at time t; N is the number of sub-intervals into which the sampling interval is divided;

[0047] The model of the node voltage overreach risk indicator comprises:

[0048]

[0049] wherein S ev (U i,j ) is the overvoltage severity of node j at the i-th sampling, R V is the node voltage overreach risk value, P(U i,j ) is the probability of voltage overreach of node j at the i-th sampling, U i,j represents the voltage of node j at the i-th sampling, and are the upper and lower limits of the voltage amplitude of node i, respectively, U N is the rated voltage.

[0050] Further, the model of the comprehensive risk indicator comprises:

[0051]

[0052] wherein μ1 to μ4 are the weights of the respective risk values, is the normalized overhead line overload risk indicator, is the normalized controllable phase shifter comprehensive failure probability, is the normalized node loss-of-load risk indicator, is the normalized node voltage overreach risk indicator.

[0053] The application also comprises a risk assessment device for a power system with controllable phase shifters under extreme weather, which uses the method as described above, comprising:

[0054] a weather type determination module for determining the type of extreme weather by using grid operation data, wherein the operation data comprises wind and light output curves and load curves, and the type of extreme weather comprises typhoon, extreme heat and no wind, heavy rain and cold wave;

[0055] a failure probability model establishment module for establishing an overhead line failure probability model and a controllable phase shifter comprehensive failure probability model under corresponding extreme weather based on the type of extreme weather;

[0056] a Latin hypercube sampling module for generating a failure scenario set of the power system based on the overhead line failure probability model and the controllable phase shifter comprehensive failure probability model by using a Latin hypercube sampling technique.

[0057] a power flow calculation module configured to perform power flow calculation on each of the sets of fault scenarios, obtain power grid operation parameters, and calculate device-level risk indicators and system-level risk indicators;

[0058] a weighted fusion module configured to perform weighted fusion on the device-level risk indicators and the system-level risk indicators based on a game theory comprehensive weighting method, calculate a comprehensive risk indicator, and complete power grid risk assessment.

[0059] The application also includes a computer 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 as described above when executing the computer program.

[0060] The application also includes a storage medium having a computer program stored thereon, wherein the computer program is executable on a processor to implement the method as described above.

[0061] The application has the following beneficial effects:

[0062] By differentiating the damage mechanisms of power grid equipment under extreme weather such as typhoon, heavy rain, extreme heat and no wind, and cold wave, a fault probability model of overhead lines and phase shifters is established, including a line fracture probability based on wind speed segmentation under a typhoon scenario, a vector sum model of rain load and line self weight under a heavy rain scenario, a conductor thermal stress failure model under an extreme heat and no wind scenario, and a combined action model of ice load and wind load under a cold wave scenario. Meanwhile, direct and indirect fault probabilities of phase shifters caused by water accumulation and equipment aging under heavy rain conditions are considered, and a comprehensive fault probability model is constructed. Based on Latin hypercube sampling, power grid fault scenarios are generated, line overload, node voltage out-of-limit, and loss-of-load risk indicators are obtained through power flow calculation, and multi-dimensional comprehensive risk assessment indicators are formed by using a game theory comprehensive weighting method to fuse subjective and objective weights. This method can quantify the operation risk of power grids under extreme weather, provide data support for power grid dispatching decisions of power grids containing controllable phase shifters, and improve the robustness and power supply reliability of the system under disaster scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, brief descriptions will be given below to the drawings needed to be used in the embodiments or prior art descriptions. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0064] Figure 1 a risk assessment method for power systems containing controllable phase shifters under extreme weather;

[0065] Figure 2A flow chart of a risk assessment method for a power system containing controllable phase shifters under extreme weather;

[0066] Figure 3 A typical day scene graph of wind and light load under various extreme weather;

[0067] Figure 4 A structural schematic diagram of a risk assessment device for a power system containing controllable phase shifters under extreme weather;

[0068] Figure 5 A structural schematic diagram of a computer device. DETAILED DESCRIPTION

[0069] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all.

[0070] Embodiment 1:

[0071] As shown in the figure: a risk assessment method for a power system containing controllable phase shifters under extreme weather, comprising the following steps: Figure 1

[0072] S10: determining the type of extreme weather by using grid operation data, the operation data including wind and light output curves and load curves, and the type of extreme weather including typhoon, extreme heat and no wind, rainstorm and cold wave;

[0073] S20: based on the type of extreme weather, establishing an overhead line fault probability model and a controllable phase shifter comprehensive fault probability model under corresponding extreme weather;

[0074] S30: using Latin hypercube sampling technology, generating a fault scene set of the power system based on the overhead line fault probability model and the controllable phase shifter comprehensive fault probability model;

[0075] S40: performing power flow calculation on each fault scene set to obtain grid operation parameters, and calculating device-level risk indicators and system-level risk indicators;

[0076] S50: weighting and fusing the device-level risk indicators and the system-level risk indicators based on the game theory comprehensive weighting method to calculate the comprehensive risk indicators, and completing the grid risk assessment.

[0077] ​The method establishes fault probability models of overhead lines and phase shifters according to the differentiated damage mechanisms of extreme weather such as typhoons, heavy rain, extremely hot and windless weather and cold waves on power grid equipment, including line fracture probability based on wind speed segmentation in a typhoon scenario, a vector sum model of rain load and line self weight in a heavy rain scenario, a conductor thermal stress failure model in an extremely hot and windless scenario, and a combined action model of ice load and wind load in a cold wave scenario. Meanwhile, direct and indirect fault probabilities of phase shifters caused by water accumulation and equipment aging under heavy rain conditions are considered, and a comprehensive fault probability model is constructed. Based on Latin hypercube sampling, power grid fault scenarios are generated, line overload, node voltage out-of-limit and loss of load risk indicators are obtained through power flow calculation, and a multi-dimensional comprehensive risk assessment index is formed by using a game theory comprehensive weighting method to integrate subjective and objective weights. The method can quantify the operation risk of the power grid under extreme weather conditions, provide data support for dispatching decisions of the power grid containing controllable phase shifters, and improve the robustness and power supply reliability of the system in disaster scenarios.

[0078] The type of extreme weather is determined by using power grid operation data (wind and solar power output curves, load curves), which can dynamically identify various typical extreme weather conditions including typhoons, extremely hot and windless weather, heavy rain and cold waves; helps to realize scenario-specific modeling, improve the real adaptability and accuracy of the evaluation method; combines the fault probability models of overhead lines and controllable phase shifters, so that the reliability of multiple key components in the power grid is considered; effectively reflects the different effects of extreme weather on different equipment in a complex power system, improves the comprehensiveness and authenticity of fault scenario modeling; compared with traditional Monte Carlo sampling, Latin hypercube sampling has higher sampling efficiency and uniformity; can generate a more representative fault scenario set under a limited number of samples, improve the statistical stability and reliability of risk simulation; extracts device-level indicators and system-level indicators during the risk assessment process, realizes multi-dimensional risk expression; helps to find specific weak links and guide precise operation and reinforcement strategies; the game theory comprehensive weighting method avoids the setting of artificial subjective weights, more objectively and reasonably integrates multiple risk indicators; improves the reliability of the final comprehensive risk indicator, has stronger practical value and decision reference ability.

[0079] In the determination of the type of extreme weather based on meteorological data and power grid operation data in the application, extreme weather refers to a small probability event that presents a supply-demand imbalance on both sides of source and load under specific meteorological or climatic conditions. Such scenarios are usually affected by multiple uncertain factors such as extreme weather, climate, social economy, etc., leading to energy supply shortage, severe fluctuations or sustained high / low output of new energy.

[0080] As a preferred embodiment of the above embodiment, in step S10, determining the type of extreme weather by using power grid operation data includes:

[0081] The wind power output curve, the photovoltaic output curve and the load curve are statistically analyzed, and the typical daily average, fluctuation rate and change rate characteristic indexes are extracted. The characteristic indexes are matched with the characteristic templates of various extreme weather types. The characteristics of various extreme weather types include:

[0082] Typhoon type: large wind power output fluctuation rate, low photovoltaic output, and normal load level;

[0083] Extreme heat and no wind type: low wind power output, high photovoltaic output, and high load level;

[0084] Cold wave type: obvious upward trend of wind power output, low photovoltaic output, and high load level;

[0085] Rainstorm type: high wind power output, low photovoltaic output, and normal or high load level;

[0086] According to the matching result, it is judged that the current power grid is in which type of extreme weather.

[0087] Through the clear description of the typhoon, extreme heat and no wind, cold wave and rainstorm and other extreme weather types, a clear weather classification basis is provided for the risk assessment of the power system. Accurate weather type determination can ensure that the risk assessment is more targeted and reduce the power system failure caused by meteorological factors.

[0088] In the present embodiment, in step S20, the overhead line fault probability model includes:

[0089] The line fault rate model under the typhoon scenario is represented as a piecewise function, and the input is the ratio of the actual wind speed to the line wind resistance design wind speed:

[0090]

[0091] Where v is the current actual wind speed; V is the maximum wind resistance wind speed design value of the overhead transmission line.

[0092] The line outage probability model under the rainstorm scenario calculates whether the vector sum of the rain load and the line self weight exceeds the bearing capacity threshold. The rain load of the overhead line is represented as:

[0093]

[0094] Where d is the raindrop diameter; the number of raindrops with diameter d per unit volume n = n0e -Λd Λd 3 ; the slope factor Λ = 4.1η -0.21 ; η is the rainfall; b is the contact width of the line and the raindrop; V s is the speed of the raindrop when it contacts the transmission line.

[0095] The outage probability model of overhead transmission lines under the action of rain load alone is expressed as:

[0096]

[0097] where f Wmax (r) is the strength of the transmission line; is the angle between the direction of raindrop impact on the overhead line and the direction of the self-weight of the line, W rg is the synthetic vector of the rain load W r and the self-weight W g of the overhead line at time t.

[0098] The line failure probability model under the scenario of extremely hot and no wind is expressed as:

[0099]

[0100] where α and β are the aging coefficients of the conductor material; I max is the upper limit of the line current-carrying capacity; and ΔT is the deviation of the ambient temperature from the design reference.

[0101] The line failure rate model under the scenario of cold wave is determined according to the combined action of ice load and wind load and the upper limit of the line design load, where the ice load and the wind load represent the additional mechanical loads on the overhead line caused by ice and wind in cold weather, respectively:

[0102] W i = 0.9πgδ(d+δ)×10 -3 ;

[0103]

[0104] where W i and W w represent the ice load and the wind load on the overhead line in the extreme weather of cold wave, respectively; δ is the ice thickness; g is the acceleration of gravity; d is the outer diameter of the transmission line; α is the wind pressure unevenness coefficient of the electric wire; μ z is the body shape coefficient of the electric wire; μ sc is the overhead line category coefficient; d is the outer diameter of the transmission line; l H is the distance between the towers; and v is the wind speed. is the angle between the wind direction and the overhead line, and the comprehensive load W

[0105] The failure rate of the overhead line under the extreme scenario of cold wave is:

[0106]

[0107] where P iwThe failure rate of the line under ice wind load; γ i and γ w are the upper limit values of the rain load and wind load for overhead line design, respectively.

[0108] As a preferred embodiment of the above, in step S20, the comprehensive failure probability model of the controllable phase shifter includes a direct failure probability model and an indirect failure probability model:

[0109] The direct failure probability model is used to characterize the direct failure probability of the equipment caused by factors such as water depth, flood prevention height, and cable joint height above ground under extreme rainstorm conditions,

[0110] The indirect failure probability model is used to characterize the indirect failure probability considering factors such as equipment service life and reference failure rate,

[0111] The comprehensive failure probability model of the controllable phase shifter is the weighted fusion result of the above direct failure probability model and indirect failure probability model.

[0112] The direct failure probability and the indirect failure probability are weighted and fused, which can more comprehensively reflect the failure risk of the equipment under different conditions. Through weighted fusion, this model can consider both external environment (such as rainstorm) and aging of the equipment itself, thereby forming more accurate failure prediction and effectively improving the robustness of the power system under extreme weather conditions.

[0113] In this embodiment, in step S21, the direct failure probability model of the controllable phase shifter includes:

[0114]

[0115] In the formula, is the direct failure probability of the phase shifter under extreme rainstorm conditions, is the failure rate of the phase shifter at time t under extreme rainstorm conditions, d(t) is the water depth in the TCPST installation area at time t, h d is the TCPST design flood prevention height, h l is the height of the cable joint of the indoor high-voltage switch cabinet above ground, ζ and γ are the attenuation coefficient and damping coefficient, respectively, T is the evaluation period, and Δt is the time step.

[0116] By establishing the direct failure probability model of the controllable phase shifter, the influence of factors such as water depth, flood prevention design height, and equipment installation position on the failure of the phase shifter under extreme rainstorm conditions can be accurately reflected, which helps to identify which equipment is more prone to failure under extreme weather conditions such as rainstorm, so as to take necessary protection measures in advance and ensure the stability of the power system.

[0117] In step S22, the indirect failure probability model of the controllable phase shifter includes:

[0118]

[0119] wherein, is the direct failure probability of the phase shifter under extreme rainstorm conditions, represents the phase shifter indirect failure probability function, β and η are the shape parameter and scale parameter of the device, respectively, and t is the service life of the device.

[0120] By adopting the indirect failure probability model, especially considering the shape parameter, scale parameter and service life of the device, the failure probability of the controllable phase shifter under extreme rainstorm conditions can be accurately quantified, the aging and wear effects of the device over time can be captured, and their impact on device failure can be included in the risk assessment, thereby providing more accurate failure prediction.

[0121] In the embodiment, in step S23, the comprehensive failure probability model of the controllable phase shifter includes:

[0122]

[0123] wherein, R T (t) is the comprehensive failure probability of the controllable phase shifter, is the direct failure probability of the phase shifter under extreme rainstorm conditions, is the indirect failure probability of the phase shifter under extreme rainstorm conditions.

[0124] By combining the direct failure probability and the indirect failure probability of the phase shifter to form the comprehensive failure probability model of the controllable phase shifter, the failure risk of the phase shifter under extreme rainstorm conditions can be comprehensively evaluated. The direct failure is usually related to the sudden rainstorm factor, while the indirect failure is related to the factors such as device aging, long-term effects caused by water accumulation, etc. By comprehensively evaluating the two, the overall risk of the device can be more accurately evaluated.

[0125] As a preferred embodiment of the above, in step S30, the Latin hypercube sampling technique is adopted to generate a set of fault scenarios of the power system based on the overhead line failure probability model and the comprehensive failure probability model of the controllable phase shifter, and the step includes:

[0126] S31: According to the overhead line failure probability model, divide the failure probability range into multiple subintervals, and randomly extract a sample in each subinterval to generate an initial Latin hypercube sample;

[0127] S32: According to the comprehensive failure probability model of the controllable phase shifter, divide the failure probability range into multiple subintervals, and randomly extract a sample in each subinterval to generate a controllable phase shifter failure state sample;

[0128] S33: Randomly arrange the Latin hypercube samples and controllable phase shifter fault state samples, combine each fault scenario of the power system, generate a fault scenario set containing overhead line and controllable phase shifter state, form a matrix composed of 0 and 1, representing the device state under each fault scenario, wherein 0 represents normal operation and 1 represents fault occurrence;

[0129] S34: For each fault scenario, combine the power grid operation data and extreme weather conditions to calculate the fault probability of the power system under the fault scenario, and further verify the rationality and accuracy of the fault scenario.

[0130] According to the fault probability, the sampling interval is divided into N subintervals, and a sample is randomly selected from each subinterval. The initial Latin hypercube sample is generated by randomly arranging the sample, and the device state is determined by comparing the device fault probability to generate a fault scenario set containing line state set and controllable phase shifter state set , which is a matrix composed of 0 and 1, representing fault and normal operation respectively.

[0131] In this embodiment, in step S40, the device-level risk indicators include overhead line overload risk indicators and phase shifter failure risk indicators.

[0132] The model of the overhead line overload risk indicator includes:

[0133]

[0134] In the formula, S ev (w Li,j ) is the severity of line i overload in the i-th sampling, R L is the line overload risk value, Pr(L i ) is the probability of line i overload, w Li,j is the value of line i transmission capacity exceeding the set upper limit, which is 0 when less than the upper limit of load rate.

[0135] The phase shifter failure risk indicator selects the above controllable phase shifter comprehensive failure probability as the failure risk indicator of the phase shifter under heavy rain extreme weather, which is obtained by weighted fusion of direct failure probability based on heavy rain water accumulation condition and indirect failure probability based on device service life, including:

[0136]

[0137] In the formula, R T (t) is the comprehensive failure probability of the controllable phase shifter, is the direct failure probability of the phase shifter under extreme rain conditions, is the indirect failure probability of the phase shifter under extreme rain conditions.

[0138] By comparing the load power of the overhead line with the maximum transmission capacity, an overload risk model is established, which can accurately calculate the severity of the overload of the overhead line under different load conditions, ensure that the power system can monitor and respond to possible load abnormalities in real time, prevent system overload from causing failures or shutdowns, and improve the stability and safety of the power system.

[0139] In step S40, the system-level risk indicators include node loss-of-load risk indicators and node voltage out-of-limit risk indicators.

[0140] The model of the node loss-of-load risk indicator includes:

[0141]

[0142] wherein R LL is the node loss-of-load risk value; P i,j is the loss-of-load amount of node j at the i-th sampling; represents the state of line i at time t; N is the number of subintervals into which the sampling interval is divided.

[0143] The model of the node voltage out-of-limit risk indicator includes:

[0144]

[0145] wherein S ev (U i,j ) is the voltage overvoltage severity of node j at the i-th sampling, R V is the node voltage out-of-limit risk value, P(U i,j ) is the probability of voltage out-of-limit of node j at the i-th sampling, U i,j represents the voltage of node j at the i-th sampling, and are the upper and lower limits of the voltage amplitude of node i (generally 105% and 93% of the rated voltage), U N is the rated voltage.

[0146] By evaluating the loss-of-load risk based on the ratio of the node loss-of-load amount to the maximum load capacity, an effective method for quantifying the probability of node loss-of-load is provided, which can identify nodes with load exceeding the maximum load capacity in a timely manner by monitoring the load conditions of nodes in real time, so as to take appropriate scheduling or protection measures to ensure the stability and continuous power supply capability of the power system.

[0147] In step S40, the Newton-Raphson method is used to solve the steady-state operating parameters of the power system under each fault scenario, including the node voltage amplitude, phase angle, and line flow distribution.

[0148] In this embodiment, in step S50, the device-level risk indicators and system-level risk indicators are weighted and fused based on the game theory comprehensive weighting method to calculate the comprehensive risk indicator. The steps include:

[0149] The subjective weights W1 and objective weights W2 of each risk indicator were obtained based on literature using the analytic hierarchy process (AHP) and entropy weight method, respectively.

[0150] To minimize the bias in the overall weights, the L2 norm is used to measure the bias, and an optimization objective is established.

[0151] Based on the differential property of matrices, it can be transformed into:

[0152] In the formula, β1 and β2 are the comprehensive weighting coefficients; β i These are the weighting coefficients; W T This is the transpose of the basic weight vector of the risk indicators;

[0153] The comprehensive weight W = β1W1 + β2W2 can then be obtained by linear combination.

[0154] In this embodiment, in step S50, the model for the comprehensive risk index includes:

[0155]

[0156] In the formula, μ1 to μ4 are the weights (i.e., W) of each risk value. This is a normalized overload risk indicator for overhead power lines. The normalized overall failure probability of the controllable phase shifter. This is a normalized indicator of node load loss risk. This is a normalized indicator of the risk of node voltage exceeding the limit.

[0157] By integrating factors such as overhead line overload risk, controllable phase shifter integrated failure probability, node unload risk, and node voltage overrun risk into a single model, a comprehensive risk assessment framework can be provided. Multi-factor comprehensive assessment helps to fully identify potential risks to the power system under extreme weather conditions and avoids system instability caused by neglecting a single risk factor.

[0158] Example 2:

[0159] Figure 2 The flowchart illustrates a risk assessment method for a power system with a controllable phase shifter under extreme weather conditions, including the following steps:

[0160] Step 1: initialize the sampling number n, collect meteorological data and power grid operation data, including offshore wind power daily output curve, photovoltaic daily output curve and daily load curve.

[0161] Step 2: determine the current extreme weather type according to the meteorological data and wind and light load output curve.

[0162] Step 3: for the current extreme weather type, establish fault modeling of overhead line and controllable phase shifter, and calculate the fault probability of overhead line and controllable phase shifter at each time.

[0163] Step 4: simulate the power grid operation state according to the Latin hypercube sampling to obtain the fault set S line , S TCPST .

[0164] Step 5: the power flow calculation obtains the power grid operation parameters, and calculates the power grid risk indicators R L , R T , R LL and R V .

[0165] Step 6: calculate the comprehensive weight coefficient, and solve the comprehensive risk index R S of the power grid.

[0166] Example 3:

[0167] Figure 3 It is a typical day scene graph of wind and light load under various extreme weather in the application, and the extreme scene refers to a small probability event that presents supply and demand imbalance on both sides of source and load under specific meteorological or climatic conditions. This kind of scene is usually affected by multiple uncertain factors such as extreme weather, climate, society and economy, resulting in energy supply shortage, new energy fluctuation or continuous high / low output.

[0168] Taking a coastal area in Jiangsu as an example, by consulting the historical extreme disaster events, the potential extreme weather with significant characteristics, easy to cause source and load imbalance and equipment failure is extracted: typhoon, extreme heat and no wind, cold wave and heavy rain. When these extreme weather occurs, the new energy output and load level has significant time sequence characteristics, as shown in Figure 3 .

[0169] Under the extreme weather of typhoon: affected by typhoon, wind speed changes dramatically and wind direction adjusts frequently, wind power output will appear obvious climbing. Heavy rain and thick cloud cover result in a significant decrease in effective solar radiation, and photovoltaic system output capacity decreases. Household and industrial electricity remains stable, and load level is not affected by weather and fluctuates greatly.

[0170] Extreme hot weather without wind: In high temperature, the atmospheric convection is weakened, the wind speed is insufficient or unstable, and the fan runs in a low power state or stops. Extreme hot weather is usually accompanied by clear and cloudless weather, and the atmospheric radiation increases, which makes the output of the photovoltaic system significantly increase. Although high temperature may slightly reduce the efficiency of photovoltaic panels, the overall output remains at a high level. In extreme hot weather, people use a large amount of air conditioning and other refrigeration equipment to cope with the high temperature environment, resulting in a significant increase in electricity demand, and the load level is higher than that in normal state.

[0171] Extreme weather of cold wave: when the cold wave comes, the low temperature has an adverse effect on the lubrication and aerodynamic efficiency of the fan, and there is usually a feature of cutting the machine. The change of solar radiation angle and cloud distribution under cold wave conditions may cause insufficient light of photovoltaic modules, and the low temperature environment may cause the surface of the module to frost or freeze, thereby reducing the output. In order to cope with the low temperature environment, residents and industrial users generally increase the use of heating and heating equipment, resulting in an increase in load level.

[0172] Extreme weather of heavy rain: heavy rain is usually accompanied by local strong wind or gust, and part of the fan can enter high power running state in a short time, and the overall output of the wind power plant has fluctuation characteristics. Heavy rain and thick clouds can seriously block solar radiation, resulting in a sharp drop in photovoltaic system output; in addition, the erosion and increased humidity of rainwater may also affect the efficiency of photovoltaic power generation. In heavy rain weather, although the local power grid maintenance and protection measures are appropriate, the load remains normal, but due to local industrial or emergency dispatching reasons, the load of some areas may increase slightly.

[0173] The application also includes a risk assessment device for a power system containing controllable phase shifters under extreme weather, using the method as described above, as shown in the figure, comprising: Figure 4

[0174] A weather type determination module is used to determine the type of extreme weather by using grid operation data, the operation data including wind and light output curves and load curves, and the type of extreme weather including typhoon, extreme hot weather without wind, heavy rain and cold wave;

[0175] A fault probability model establishment module is used to establish an overhead line fault probability model and a controllable phase shifter comprehensive fault probability model under corresponding extreme weather based on the type of extreme weather;

[0176] A Latin hypercube sampling module is used to generate a fault scenario set of the power system based on the overhead line fault probability model and the controllable phase shifter comprehensive fault probability model by using the Latin hypercube sampling technology;

[0177] A power flow calculation module is used to perform power flow calculation on each fault scenario set, obtain grid operation parameters, and calculate device-level risk indicators and system-level risk indicators;

[0178] ​The weighted fusion module is configured to perform weighted fusion on the device-level risk index and the system-level risk index based on a game theory comprehensive weighting method, calculate a comprehensive risk index, and complete power grid risk assessment.

[0179] As a preferred embodiment of the above, in the weather type determination module, determining the extreme weather type using power grid operation data comprises:

[0180] The weather type determination module is configured to perform statistical analysis on the wind power output curve, the photovoltaic output curve and the load curve respectively, extract typical daily mean, fluctuation rate and change rate characteristic indexes, and match the characteristic indexes with characteristic templates of various extreme weather types, wherein the characteristics of various extreme weather types include:

[0181] Typhoon type: large wind power output fluctuation rate, low photovoltaic output, normal load level;

[0182] Extremely hot and windless type: low wind power output, high photovoltaic output, high load level;

[0183] Cold wave type: obvious upward trend of wind power output, low photovoltaic output, high load level;

[0184] Heavy rain type: high wind power output, low photovoltaic output, normal or high load level;

[0185] Determine the extreme weather type of the current power grid according to the matching result.

[0186] In this embodiment, in the fault probability model establishment module, the controllable phase shifter comprehensive fault probability model includes a direct fault probability model and an indirect fault probability model:

[0187] The direct fault probability model is used to represent the direct fault probability of the device caused by the factors of water depth, waterlogging prevention height and cable joint height under extreme heavy rain conditions;

[0188] The indirect fault probability model is used to represent the indirect fault probability considering the factors of device service life and reference failure rate;

[0189] The controllable phase shifter comprehensive fault probability model is a weighted fusion result of the direct fault probability model and the indirect fault probability model.

[0190] The direct fault probability model of the controllable phase shifter includes:

[0191]

[0192]

[0193] In the formula, is the direct fault probability of the phase shifter under extreme heavy rain conditions, is the failure rate of the phase shifter at time t, d(t) is the waterlogging degree of the TCPST installation area at time t, h d is the anti-flooding height of the TCPST l is the cable joint height of the high-voltage switch cabinet in the station, ζ and γ are the attenuation coefficient and damping coefficient respectively, T is the evaluation length, and Δt is the time step.

[0194] wherein the indirect failure probability model of the controllable phase shifter comprises:

[0195]

[0196] wherein, is the indirect failure probability of the phase shifter under extreme rainstorm conditions, represents the indirect failure probability function of the phase shifter, β and η are the shape parameter and the scale parameter of the device respectively, and t is the service life of the device.

[0197] In this embodiment, the comprehensive failure probability model of the controllable phase shifter comprises:

[0198]

[0199] wherein, R T is the comprehensive failure probability of the controllable phase shifter, is the direct failure probability of the phase shifter under extreme rainstorm conditions, is the indirect failure probability of the phase shifter under extreme rainstorm conditions.

[0200] As a preferred embodiment of the above, in the Latin hypercube sampling module, for generating a fault scenario set of the power system based on the overhead line failure probability model and the comprehensive failure probability model of the controllable phase shifter by using the Latin hypercube sampling technology, the steps comprise:

[0201] According to the overhead line failure probability model, the failure probability range is divided into multiple subintervals, and a sample is randomly extracted in each subinterval to generate an initial Latin hypercube sample;

[0202] According to the comprehensive failure probability model of the controllable phase shifter, the failure probability range is divided into multiple subintervals, and a sample is randomly extracted in each subinterval to generate a controllable phase shifter failure state sample;

[0203] The Latin hypercube sample and the controllable phase shifter failure state sample are randomly arranged, combined with each fault scenario of the power system, to generate a fault scenario set containing the states of the overhead line and the controllable phase shifter, forming a matrix composed of 0 and 1, indicating the device state under each fault scenario, wherein 0 indicates normal operation and 1 indicates fault occurrence;

[0204] For each fault scenario, the fault probability of the power system under the fault scenario is calculated in combination with the power grid operation data and extreme weather conditions.

[0205] The system-level risk assessment module is further included for calculating the node load loss risk index and the node voltage out-of-limit risk index.

[0206] The model of the overhead line overload risk index comprises:

[0207]

[0208] In the formula, S ev (w Li,j ) is the severity of the overload of the line i in the ith sampling, R L is the line overload risk value, Pr(L i ) is the probability of the overload of the line i, w Li,j is the value of the transmission capacity of the line i exceeding the set upper limit, and is 0 when less than the upper limit of the load rate;

[0209] The model of the phase shifter failure risk is a comprehensive failure probability model of the controllable phase shifter, which is obtained by weighted fusion of the direct failure probability based on the rainwater accumulation condition and the indirect failure probability based on the service life of the equipment, and comprises:

[0210]

[0211] In the formula, R T (t) is the comprehensive failure probability of the controllable phase shifter, is the direct failure probability of the phase shifter under the extreme rainstorm condition, is the indirect failure probability of the phase shifter under the extreme rainstorm condition.

[0212] The system-level risk assessment module is further included for calculating the node load loss risk index and the node voltage out-of-limit risk index,

[0213] The model of the node load loss risk index comprises:

[0214]

[0215] In the formula, R LL is the node load loss risk value; P i,j is the load loss amount of the node j in the ith sampling; represents the state of the line i at the time t; and N is the number of subintervals into which the sampling interval is divided.

[0216] The model of the node voltage out-of-limit risk index comprises:

[0217]

[0218] In the formula, S ev (U i,j ) is the overvoltage severity of the i-th sampling node j, R V is the node voltage overline risk value, P(U i,j ) is the probability of the i-th sampling node j voltage overline, U i,j represents the voltage of the i-th sampling node j, and are the upper and lower limits of the voltage amplitude of node i, U N is the rated voltage.

[0219] As a preferred embodiment of the above, in the weighted fusion module, the game theory comprehensive weighting method is used to weight and fuse the device-level and system-level risk indicators, and the fusion model is:

[0220]

[0221] In the formula, μ1 to μ4 are the weights of the respective risk values, is the normalized overhead line overload risk index, is the normalized controllable phase shifter comprehensive failure probability, is the normalized node loss load risk index, is the normalized node voltage overline risk index.

[0222] Please refer to Figure 5 The computer device provided by the embodiment of the present application. The computer device 400 provided by the embodiment of the present application comprises a processor 410 and a memory 420, the memory 420 stores a computer program executable by the processor 410, and the computer program is executed by the processor 410 to perform the method as above.

[0223] The embodiment of the present application further provides a storage medium 430, and the storage medium 430 stores a computer program, and the computer program is executed by the processor 410 to perform the method as above.

[0224] The storage medium 430 can be implemented by any type of volatile or nonvolatile storage devices or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read only memory (EPROM), programmable read only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic or optical disk.

[0225] In the description of the present application, the terms "first", "second", "third", etc. are used only to describe purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. The meaning of "plurality" is two or more, unless otherwise explicitly specified and limited.

[0226] In the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connecting", "fixing" and the like should be understood in a broad sense, for example, can be fixed connection, can also be detachable connection, or integral; can be mechanical connection, can also be electrical connection; can be directly connected, can also be indirectly connected through an intermediate medium, can be the internal communication of two elements or the interaction relationship between two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0227] In the description of the present application, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present application, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine the different embodiments or examples described in the present application and the features of different embodiments or examples without contradiction.

[0228] Any processes or methods described in the flowcharts or otherwise described herein can be understood as representing modules, segments, or portions of code that include one or more executable instructions for implementing specific logic functions or steps, and alternative implementations are within the scope of the preferred embodiments of the application that are directed towards the functions performed by the steps, in which the order of performance can be changed, functions can be performed in parallel, or in reverse order, as appropriate, and appropriate combinations of them, as would be understood by one of ordinary skill in the art.

[0229] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a list of executable instructions for implementing the logic function, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor- containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions, or a combination of both. In the context of this specification, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electronic connection having one or more wires (electronic devices), a portable computer diskette (magnetic device), a random-access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, by optically scanning the paper or other suitable medium, then electronically converted into a form that is suitable for use by the instruction execution system, apparatus, or device, and for storage in computer memory.

[0230] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, known in the art, or combinations thereof, can be used: discrete logic circuitry having logic gates for implementing logic functions upon data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and so forth.

[0231] Those skilled in the art can understand that all or part of the steps of the method carried out by the above-mentioned embodiments can be instructed by a program to complete the relevant hardware, and the program can be stored in a computer readable storage medium. When the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0232] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above-mentioned embodiments are exemplary and cannot be understood as limiting the present application. Those skilled in the art can make changes, modifications, replacements and variations to the above-mentioned embodiments within the scope of the present application.

Claims

1. A risk assessment method for a power system containing a controllable phase shifter under extreme weather conditions, characterized in that, Includes the following steps: Extreme weather types are determined by using power grid operation data, which includes wind and solar power output curves and load curves. The extreme weather types include typhoons, extreme heat with no wind, heavy rain, and cold waves. Based on the aforementioned extreme weather types, establish corresponding overhead line fault probability models and controllable phase shifter integrated fault probability models under extreme weather conditions; Using Latin hypercube sampling technology, a set of power system fault scenarios is generated based on the overhead line fault probability model and the controllable phase shifter integrated fault probability model. For each set of fault scenarios, power flow calculation is performed to obtain power grid operating parameters, and equipment-level risk indicators and system-level risk indicators are calculated. The device-level risk indicators and system-level risk indicators are weighted and fused based on the game theory-based comprehensive weighting method to calculate the comprehensive risk indicator and complete the power grid risk assessment.

2. The risk assessment method for power systems with controllable phase shifters under extreme weather conditions according to claim 1, characterized in that, The method of using power grid operation data to determine extreme weather types includes: Statistical analysis was performed on the wind power output curve, photovoltaic power output curve, and load curve to extract typical daily average values, volatility, and rate of change characteristic indicators. These characteristic indicators were then matched with characteristic templates for various extreme weather types, including the following features: Typhoon type: large fluctuation in wind power output, low photovoltaic power output, and normal load level; Extremely hot and windless conditions: low wind power output, high photovoltaic power output, and high load level; Cold wave type: Wind power output shows a significant upward trend, photovoltaic power output is low, and load level is high; Rainstorm type: High wind power output, low solar power output, normal or high load level; The matching results determine the type of extreme weather the power grid is currently experiencing.

3. The risk assessment method for power systems with controllable phase shifters under extreme weather conditions according to claim 1, characterized in that, The comprehensive failure probability model of the controllable phase shifter includes a direct failure probability model and an indirect failure probability model: The direct failure probability model is used to characterize the probability of direct equipment failure caused by factors such as water depth, flood control height, and cable joint elevation under extreme rainstorm conditions. The indirect failure probability model is used to characterize the indirect failure probability considering factors such as the service life of the equipment and the baseline failure rate. The comprehensive failure probability model of the controllable phase shifter is a weighted fusion result of the above-mentioned direct failure probability model and indirect failure probability model.

4. The risk assessment method for power systems with controllable phase shifters under extreme weather conditions according to claim 3, characterized in that, The direct failure probability model of the controllable phase shifter includes: In the formula, The probability of direct failure of the phase shifter under extreme rainstorm conditions. Let d(t) be the phase shifter failure rate at time t under extreme rainstorm conditions, d(t) be the water accumulation level in the area where the TCPST is installed at time t, and h be the phase shifter failure rate at time t. d The design flood protection height for TCPST is h. l The elevation of the cable joint of the high-voltage switchgear in the station relative to the ground is given by ζ and γ, respectively, where ζ and γ are the attenuation coefficient and damping coefficient, T is the evaluation duration, and Δt is the time step.

5. The risk assessment method for power systems with controllable phase shifters under extreme weather conditions according to claim 3, characterized in that, The indirect failure probability model of the controllable phase shifter includes: In the formula, The probability of indirect phase shifter failure under extreme rainstorm conditions. Let represent the indirect failure probability function of the phase shifter, where β and η are the shape parameter and proportional parameter of the equipment, respectively, and t is the service life of the equipment.

6. The risk assessment method for power systems with controllable phase shifters under extreme weather conditions according to claim 3, characterized in that, The comprehensive failure probability model of the controllable phase shifter includes: In the formula, R T (t) represents the overall failure probability of the controllable phase shifter. The probability of direct failure of the phase shifter under extreme rainstorm conditions. This represents the probability of indirect phase shifter failure under extreme rainstorm conditions.

7. The risk assessment method for power systems with controllable phase shifters under extreme weather conditions according to claim 1, characterized in that, Using Latin hypercube sampling technology, a set of power system fault scenarios is generated based on the overhead line fault probability model and the controllable phase shifter integrated fault probability model. The steps include: Based on the overhead line fault probability model, the fault probability range is divided into multiple sub-intervals, and a sample is randomly selected from each sub-interval to generate an initial Latin hypercube sample. Based on the comprehensive fault probability model of the controllable phase shifter, the fault probability range is divided into multiple sub-intervals, and a sample is randomly selected from each sub-interval to generate a controllable phase shifter fault state sample. The Latin hypercube samples and the controllable phase shifter fault state samples are randomly arranged and combined with various fault scenarios of the power system to generate a fault scenario set containing the states of overhead lines and controllable phase shifters, forming a matrix composed of 0 and 1 to represent the equipment state under each fault scenario, where 0 represents normal operation and 1 represents the occurrence of a fault. For each fault scenario, the probability of power system failure under the fault scenario is calculated by combining power grid operation data and extreme weather conditions.

8. The risk assessment method for power systems with controllable phase shifters under extreme weather conditions according to claim 1, characterized in that, The equipment-level risk indicators include overhead line overload risk indicators and phase shifter failure risk; The model for the overhead line overload risk index includes: In the formula, S ev (w Li,j R represents the severity of the overload occurring on line i in the i-th sampling. L Pr(L) represents the overload risk value of the line. i Let w be the probability of line i becoming overloaded. Li,j This is the value at which the transmission capacity of line i in the i-th sampling exceeds the set upper limit; it is 0 when it is less than the upper limit of the load rate. The model for the failure risk of the phase shifter is a comprehensive failure probability model for controllable phase shifters, which is obtained by weighted fusion of the direct failure probability based on rainstorm and water accumulation conditions and the indirect failure probability based on the service life of the equipment, including: In the formula, R T (t) represents the overall failure probability of the controllable phase shifter. The probability of direct failure of the phase shifter under extreme rainstorm conditions. This represents the probability of indirect phase shifter failure under extreme rainstorm conditions.

9. The risk assessment method for power systems with controllable phase shifters under extreme weather conditions according to claim 1, characterized in that, The system-level risk indicators include node load failure risk indicators and node voltage overrun risk indicators; The model for the node load loss risk index includes: Among them, R LL P represents the node's risk of load loss. i,j Let $\frac{i}{j}$ be the load loss of node $j$ during the $i$-th sampling. This represents the state of line i at time t; N represents the number of sub-intervals into which the sampling interval is divided. The model for the node voltage over-limit risk index includes: In the formula, S ev (U i,j R represents the severity of overvoltage at sampling node j in the i-th sampling period. V P(U) represents the risk value for node voltage crossing. i,j U represents the probability that the voltage at sampling node j crosses the line during the i-th sampling. i,j This represents the voltage at the i-th sampling node j. and These are the upper and lower limits of the voltage amplitude at node i, respectively, U N This is the rated voltage.

10. The risk assessment method for power systems with controllable phase shifters under extreme weather conditions according to claim 1, characterized in that, The model for the comprehensive risk index includes: In the formula, μ1 to μ4 are the weights of each risk value. This is a normalized overload risk indicator for overhead power lines. The normalized overall failure probability of the controllable phase shifter. This is a normalized indicator of node load loss risk. This is a normalized indicator of the risk of node voltage exceeding the limit.

11. A risk assessment device for a power system with a controllable phase shifter under extreme weather conditions, characterized in that, Using the method as described in any one of claims 1 to 10, comprising: The weather type determination module is used to determine the type of extreme weather using power grid operation data, including wind and solar power output curves and load curves. The extreme weather types include typhoons, extreme heat with no wind, rainstorms, and cold waves. The fault probability model establishment module is used to establish a fault probability model for overhead lines and a comprehensive fault probability model for controllable phase shifters under the corresponding extreme weather conditions based on the extreme weather type. The Latin hypercube sampling module is used to generate a set of power system fault scenarios based on the overhead line fault probability model and the controllable phase shifter integrated fault probability model using Latin hypercube sampling technology. The power flow calculation module is used to perform power flow calculations on each of the aforementioned fault scenario sets, obtain power grid operating parameters, and calculate equipment-level risk indicators and system-level risk indicators. The weighted fusion module is used to perform weighted fusion of the equipment-level risk indicators and system-level risk indicators based on the game theory comprehensive weighting method, calculate the comprehensive risk indicator, and complete the power grid risk assessment.

12. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-10.

13. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-10.

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