A distribution network fault modeling method under extreme weather conditions

Abstract

The present invention discloses a method for modeling distribution network failures under extreme weather conditions, comprising: 1. constructing a distribution network component reliability model under extreme weather conditions based on a load effect-component strength function; 2. constructing a distribution network component damage model under secondary disasters using a fuzzy mathematical method; and 3. integrating the distribution network reliability model under wind and rain loads with the distribution network component damage model under secondary disasters to obtain a distribution network failure rate model that comprehensively considers the impact of extreme wind and rain loads and secondary disasters, thereby quantifying the impact of extreme weather on the distribution network. The present invention comprehensively considers the impact of wind and rain loads and secondary disasters on the distribution network, combines disaster mechanisms with geographical and meteorological factors, constructs a distribution network reliability model based on wind and rain loads and a distribution network component damage model under secondary disasters, and integrates these models to obtain a distribution network failure rate model. This model accurately quantifies the adverse effects of extreme weather on distribution network lines, providing a basis for evaluation, analysis, and planning and scheduling of distribution networks under extreme weather conditions.

Description

Technical Field

[0001] The present invention relates to the field of distribution network power system planning, and in particular to a distribution network fault modeling method under extreme weather conditions. Background Art

[0002] With the rapid development of society and the economy, the current power system has become a large-scale grid with a high proportion of renewable energy access. However, as global climate change intensifies, extreme weather events such as rainstorms, hurricanes, and freezing temperatures are becoming more frequent and more severe. The resulting series of large-scale power outages not only cause irreparable damage to the power system but can also result in massive property losses and even significant casualties. As a critical link in the power system, the distribution network, with its vast number of lines and widespread distribution, directly supports user electricity demand. With the frequent occurrence of extreme weather, the distribution network faces even more stringent power supply reliability requirements.

[0003] Extreme weather events such as hurricanes, heavy rains, and snowstorms have a low probability of occurrence, but when they do occur, they can cause irreparable damage to power grids. These events are characterized by wide-scale, prolonged, and severe losses. Quantifying the adverse impacts of extreme natural disasters on distribution networks is crucial for disaster prevention. Natural disasters are complex, and the lack of historical databases makes direct modeling difficult. Current research primarily focuses on analyzing and modeling the failure mechanisms of a single disaster. Traditional assessments of safe and reliable operation of distribution networks are overly limited, failing to account for the multiple failures triggered by extreme natural disasters. Furthermore, different types of disasters can transform and trigger each other, with a single disaster often evolving into multiple secondary disasters and ultimately into serious power outages, whose adverse impacts are even more significant. For these "low-probability, high-loss" extreme weather events, there is an urgent need to assess the resilience of distribution networks and research methods to improve their resilience. This can assist decision-making during disaster prevention and recovery, effectively mitigate the risk of power outages for users, and provide theoretical and technical references for the prevention and mitigation of distribution networks. Therefore, it is crucial to study the distribution network fault modeling under various types of natural disasters caused by extreme weather, so as to provide a basis for further evaluation, analysis, planning and scheduling of distribution networks under extreme weather. Summary of the Invention

[0004] In order to overcome the deficiencies in the above-mentioned prior art, the present invention proposes a distribution network fault modeling method under extreme weather conditions, in order to quantify the adverse effects of extreme weather on the failure rate of distribution network lines, thereby providing a basis for the evaluation, analysis, planning and scheduling of distribution networks under extreme weather conditions.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] The present invention provides a method for modeling distribution network faults under extreme weather conditions, which is characterized by the following steps:

[0007] Step 1: Comprehensively consider the impact of wind and rain load impact on the distribution network, and combine it with the disaster mechanism and geographical meteorological factors to construct a distribution network reliability model based on wind and rain load;

[0008] Step 1.1: Construct a wind load model; determine the wind speed value of the study point based on the positional relationship between the typhoon center and the study point;

[0009] Step 1.1.1: Use the Batts model to simulate the wind speed and direction of the typhoon wind field, and use formula (1) to obtain the maximum gradient wind speed V gx (t):

[0010]

[0011] In formula (1), K is the empirical coefficient; ΔP is the central pressure difference; f is the Coriolis force parameter of the Earth's rotation; R max is the maximum wind speed radius;

[0012] Step 1.1.2: Use formula (2) to obtain the average maximum wind speed V at the maximum wind speed radius. M :

[0013] V M =0.865V gx +0.5V T (2)

[0014] In formula (2), V T is the overall moving speed of the typhoon;

[0015] Step 1.1.3: Use formula (3) to obtain the average wind speed V at the distance r between the distribution line and the typhoon center in the typhoon wind field:

[0016]

[0017] In formula (3), x is a parameter related to the radial intensity attenuation of the typhoon;

[0018] Step 1.1.4: Use equations (4) and (5) to obtain the wind load F acting on the conductor. wl and the wind load F on the tower wg :

[0019]

[0020]

[0021] In formula (4) and formula (5), d0 is the outer diameter of the conductor; α is the wind pressure unevenness coefficient; β is the wind load adjustment coefficient; μ z is the wind pressure height variation coefficient; μ sc and μ′ sc are the body coefficients of conductor and tower respectively; D0 and D p h is the outer diameter of the tower tip and the pole root; r is the tower height; θ is the angle between the wind direction and the line;

[0022] Step 1.2, construct rain load model;

[0023] Step 1.2.1: Use formula (6) to construct the raindrop spectrum distribution n(D):

[0024] n(D)=n0 exp(-ΛD) (6)

[0025] In formula (6), D is the raindrop diameter; n0 is the number of raindrops per unit volume; Λ is the slope factor;

[0026] Step 1.2.2: Use equation (7) to get the final velocity V of the raindrop s :

[0027]

[0028] In formula (7), δ1, δ2, and δ3 are the diameter thresholds of three raindrops respectively;

[0029] Step 1.2.3: Use formula (8) to obtain the rain load F on the distribution line. r :

[0030]

[0031] In formula (8), n1 is the number of raindrops per unit volume; S r is the rain-exposed area of ​​the line;

[0032] Step 1.3: Analyze the load effect of distribution components;

[0033] Step 1.3.1, conductor stress calculation;

[0034] The horizontal distance between the conductor suspension points of two adjacent towers is defined as a span. Within a span, the superposition force F of the wind and rain load uniformly distributed along the conductor and the gravity load G per unit length of the conductor are obtained using equations (9) and (10), respectively:

[0035] F=F wl +F r (9)

[0036] G=mg (10)

[0037] In equations (9) and (10), m is the weight of the wire per unit length, and g is the acceleration due to gravity;

[0038] The comprehensive load H borne by the unit conductor is obtained using formula (11):

[0039]

[0040] The conductor tension at the highest suspension point is used as the conductor research point where the load acts. The comprehensive tension T of the conductor research point in the tangent direction is obtained using formula (12):

[0041]

[0042] In formula (12), is the elevation angle, i.e. the angle between the line connecting the suspension points of the towers on both sides and the horizontal plane; l1 is the distance from the conductor suspension point to the lowest point of the sag;

[0043] Using formula (13), we can get the stress σ on the conductor cross section: d :

[0044]

[0045] In formula (13), s is the calculated cross-sectional area of ​​the conductor;

[0046] Step 1.3.2, calculate tower bending moment;

[0047] Using formula (14), we can get the wind and rain load H on the conductor borne by the tower: d :

[0048] H d =Fl (14)

[0049] In formula (14), l is the average span of the distribution line;

[0050] Using formula (15), the tower bending moment M1 caused by the conductor load is obtained:

[0051]

[0052] In formula (15), H 1d,v is the comprehensive load on the vth conductor; h v is the distance difference between the vth conductor and the base of the tower; N is the number of conductors on the tower;

[0053] Assuming that the wind load on the tower and the direction of the strong wind are in the same plane, the tower bending moment M2 caused by the wind load is obtained using formula (19):

[0054] M2=F wg Z (16)

[0055] In formula (16), Z is the moment arm from the tower root to the point of action of the resultant wind pressure on the tower;

[0056] Using formula (17), we can get the bending moment M of the tower: T :

[0057] M T =M1+M2 (17)

[0058] Step 1.4: Construct a distribution network reliability model;

[0059] According to the relevant theories of structural reliability, the distribution component state Z is obtained using formula (18):

[0060] Z=RS (18)

[0061] In formula (18), R is the internal effect of the component caused by the load effect; S is the strength of the component;

[0062] When any component in the distribution network is in a failure state, that is, Z < 0, it is considered a faulty component, and the probability of the faulty component P is obtained. r ;

[0063] Using formula (19), we can get the load unreliability p of the conductor under wind load impact in extreme weather conditions: fd and the load unreliability p of the tower ft :

[0064]

[0065] In formula (19), δ d and μ d is the mean and standard deviation of the steel core aluminum stranded wire material strength; δ t and μ t is the mean and standard deviation of the concrete tower material strength; σ is the strength of the conductor; M is the strength of the tower;

[0066] Using formula (20), the failure rate p of the kth conductor of any distribution line i in the distribution network under wind and rain load is obtained: h,i,k :

[0067] p h,i,k =1-(1-p fd,i,k )(1-p fg,i,k ) (20)

[0068] In formula (20), p fd,i,k is the failure rate of the kth conductor of distribution line i; p fg,i,k is the failure rate of the kth section conductor of distribution line i on the tower;

[0069] Using formula (21), we can get the total failure rate p of distribution line i in the distribution network under the influence of extreme wind and rain: h,i :

[0070]

[0071] In formula (21), K is the total number of conductor sections of distribution line i;

[0072] Step 2: Construct a fuzzy mathematical model considering secondary disasters:

[0073] Step 2.1, fuzzy reasoning of factors causing foreign matter hanging on the wire;

[0074] Using formula (22) and formula (23), we can get the disaster-causing factors of foreign objects hanging on the line, including: geographical environment factors A y and line design parameter B y , and used as the first input of the fuzzy system:

[0075] A y =κV′sinθ (22)

[0076]

[0077] In formula (22) and formula (23), κ is the geographical environment around the line; V′ is the normalized value of the wind speed of the line in the actual environment; N is the number of conductors on the tower;

[0078] The geographical environment factor A is quantified using triangular membership function. y and line design parameter B y The domain of discourse, thus obtaining the foreign body hanging line failure rate p y fuzzy rule table;

[0079] Step 2.2, fuzzy reasoning of landslide factors;

[0080] Using equations (24) and (25), we can get the disaster factors that lead to landslide, including: disaster intensity A p and line vulnerability B p , and the second input of the fuzzy system is:

[0081]

[0082]

[0083] In formula (24) and formula (25), I′ is the normalized value of effective rainfall I; α s is the hydrogeological condition parameter, b x is the mountain slope morphological parameter; b p is the mountain slope parameter; b h is the mountain height parameter; dz is a geological parameter; ε d is the position parameter of the tower relative to the disaster body; s t is the ratio of the actual service life of the line to the designed service life; d is the tower bedrock safety parameter;

[0084] The disaster intensity A is quantified using the triangular membership function. p and line vulnerability B p The domain of discourse, thus obtaining the landslide failure rate p s fuzzy rule table;

[0085] Step 2.3, fuzzy reasoning of flood factors;

[0086] Using formula (26) and formula (25), we can get the disaster factors that lead to floods, including: disaster intensity C p and line vulnerability B p , and serves as the third input of the fuzzy system:

[0087] C p =α w β d b′ p b h I′ (26)

[0088] In formula (26), α w is the channel distribution coefficient; β d is the channel morphology parameter, b′ h is the mountain slope parameter;

[0089] The disaster intensity A is quantified using the triangular membership function. p and line vulnerability B p The domain of discourse, thus obtaining the flood failure rate p h fuzzy rule table;

[0090] Step 2.4: Calculate the distribution network line failure rate p under secondary disasters c,i ;

[0091] Step 2.4.1: The fuzzy system outputs three output quantities according to the three input quantities, and uses the center of gravity method to defuzzify the three output quantities of the fuzzy system to obtain the foreign body hanging line failure rate p y , landslide failure rate p s and flood failure rate p h ;

[0092] Step 2.4.3: Use formula (27) to obtain the failure rate p of the kth conductor of the distribution line i in the distribution network caused by secondary disasters: c,i,k :

[0093] pc,i,k =1-(1-p y,i,k )(1-p s,i,k )(1-p h,i,k ) (27)

[0094] In formula (27), p v,i,k 、p s,i,k and p h,i,k are the probability of failure of the kth section conductor of distribution line i due to foreign objects hanging on the wire, landslide and flood in secondary disasters;

[0095] Step 2.4.4: Use formula (28) to obtain the total failure rate p of distribution line i under secondary disasters c,i :

[0096]

[0097] Step 3: Construct a distribution network failure rate model;

[0098] Step 3.1, calculate the failure rate of each line in the distribution network, and equate the lines in the distribution network to a series component model, so as to use formula (29) to obtain the total failure rate p of the distribution line i at time t in the distribution network: i,t :

[0099]

[0100] In formula (29), p h,i,t represents the total failure rate of distribution line i in the distribution network under the influence of extreme wind and rain at time t, p c,i,t It represents the total failure rate of distribution line i in the power distribution under the secondary disaster at time t.

[0101] An electronic device of the present invention includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the method, and the processor is configured to execute the program stored in the memory.

[0102] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program executes the steps of the method when executed by a processor.

[0103] Compared with the prior art, the beneficial effects of the present invention are embodied in:

[0104] 1. To address the difficulty in directly modeling distribution network failures in extreme weather, the present invention calculates wind loads and rain loads separately, constructs a distribution network component reliability model under extreme weather conditions through a load effect-component strength function, quantifies the impact of wind and rain shocks on the distribution network, and reflects the failure rate of distribution lines under wind and rain load shocks in extreme weather, thereby providing early warning information to grid operators and ensuring the safe operation of the distribution system.

[0105] 2. The present invention addresses the problem that the evaluation of safe and reliable operation of distribution networks is too limited and cannot cover the multiple faults caused by extreme natural disasters. It considers the damage to distribution network components caused by secondary disasters, summarizes the causes and influencing mechanisms of secondary disasters including foreign objects hanging on wires, landslides and floods, and constructs a damage model for distribution network components under secondary disasters through fuzzy mathematics methods. It quantifies the impact of secondary disasters on distribution network components and reflects the failure rate of distribution lines under secondary disasters, thereby facilitating grid operators to take emergency measures in a timely manner.

[0106] 3. To address the problem that traditional failure probability models do not adequately consider complex mass disasters under extreme weather conditions, this invention integrates the component reliability model under wind and rain loads and the distribution network component damage model under secondary disasters to obtain a distribution network failure rate model that comprehensively considers extreme wind and rain impacts and secondary disasters. This accurately quantifies the impact of extreme weather on the distribution network, thus laying the foundation for subsequent consideration of distribution network resilience assessment and improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] Figure 1 This is a design flow chart of fuzzy control of distribution network failure rate under secondary disasters of the present invention;

[0108] Figure 2 This is the flow chart for calculating the failure rate of distribution network lines. DETAILED DESCRIPTION

[0109] In this embodiment, a distribution network failure modeling method under extreme weather conditions is proposed based on the failure mechanism of distribution network components and taking into account the types of secondary disasters. First, considering the impact of wind and rain load impact on the distribution network, the loads acting on the distribution network components due to strong winds and rainstorms are analyzed and calculated, and a distribution network component reliability model is constructed through the load effect-component strength function function; secondly, considering that secondary disasters can also damage distribution network components, the causes and influencing mechanisms of secondary disasters including foreign objects hanging on wires, landslides and floods are summarized, and a distribution network component damage model under secondary disasters is constructed through fuzzy mathematics methods; finally, the distribution network component reliability model and the distribution network component damage model are integrated to obtain a distribution network failure rate model that comprehensively considers extreme wind and rain impacts and secondary disasters, and accurately quantifies the impact of extreme weather on the distribution network. Specifically, the method is carried out in the following steps:

[0110] Step 1: Build a reliability model for distribution network components:

[0111] Fault events will cause structural damage to the distribution network directly connected to the user, thereby causing large-scale and long-term power outages of the load. Therefore, in order to quantify the impact of extreme weather on the distribution network, it is necessary to perform fault modeling on the distribution network components. Commonly used methods include fault mechanism simulation and statistical data analysis. However, the factors that cause extreme weather disasters are complex and lack sufficient historical data. The temporal and spatial correlation between extreme weather and distribution network components is also easily overlooked, making it impossible to accurately portray the specific dynamic process of the disaster. The present invention comprehensively considers the impact of wind and rain load shocks on the distribution network, combines the disaster mechanism and geographical meteorological factors, and constructs a distribution network component reliability model based on wind and rain loads.

[0112] Step 1.1: Construct a wind load model; determine the wind speed value of the study point based on the positional relationship between the typhoon center and the study point;

[0113] Step 1.1.1. In meteorology, axisymmetric circular vortices are often used to simulate typhoon wind fields. To accurately construct a reliability model for power distribution components under typhoon weather, the Batts model is used to simulate the wind speed and direction of the typhoon wind field, and the maximum gradient wind speed V is obtained using formula (1). gx (t):

[0114]

[0115] In formula (1), K is the empirical coefficient; ΔP is the central pressure difference; f is the Coriolis force parameter of the Earth's rotation; R max is the maximum wind speed radius.

[0116] Step 1.1.2: Use formula (2) to obtain the average maximum wind speed V at the maximum wind speed radius. M :

[0117] V M =0.865V gx +0.5V T (2)

[0118] In formula (2), V T The overall moving speed of the typhoon.

[0119] Step 1.1.3: Use formula (3) to obtain the average wind speed V at the distance r between the distribution line and the typhoon center in the typhoon wind field:

[0120]

[0121] In formula (3), x is a parameter related to the radial intensity attenuation of the typhoon.

[0122] Step 1.1.4: When the wind speed is too high, the overhead distribution line will be subjected to a huge impact force, which may cause line breakage and tower collapse accidents. The wind load F acting on the conductor due to the typhoon is obtained using equations (4) and (5). wl and the wind load F on the tower wg :

[0123]

[0124]

[0125] In formula (4) and formula (5), d0 is the outer diameter of the conductor; α is the wind pressure unevenness coefficient; β is the wind load adjustment coefficient; μ z is the wind pressure height variation coefficient; μ sc and μ′ sc are the body coefficients of conductor and tower respectively; D0 and D p h is the outer diameter of the tower tip and the pole root; r is the tower height; θ is the angle between the wind direction and the line.

[0126] Step 1.2: Construct a rain load model. As raindrops fall through the air, their shape is determined by their size. Generally speaking, when the raindrop radius is less than 140 μm, the shape of the raindrop can be considered a sphere. As the raindrop radius increases, it can be approximated as an ellipse or a flat-bottomed ellipsoid. When the raindrop radius is greater than 6 mm, the raindrop will break. In actual calculations, the equivalent diameter of the raindrop is often used to approximate the raindrop size. Based on a large number of observations, it is found that the raindrop spectrum distribution follows a negative exponential distribution. The raindrop spectrum distribution n(D) is constructed using formula (6):

[0127] n(D)=n0exp(-ΛD) (6)

[0128] In formula (6), D is the raindrop diameter; n0 is the number of raindrops per unit volume; Λ is the slope factor.

[0129] Step 1.2.2: Use equation (7) to get the final velocity V of the raindrop s :

[0130]

[0131] In formula (7), δ1, δ2, and δ3 are the diameter thresholds of three raindrops respectively. In this embodiment, δ1, δ2, and δ3 are 1.0 mm, 3.0 mm, and 6.0 mm respectively.

[0132] Step 1.2.3: Use formula (10) to obtain the rain load F on the distribution line. r :

[0133]

[0134] In formula (8), n1 is the number of raindrops per unit volume; S r is the rain-receiving area of ​​the line.

[0135] Step 1.3: Analyze the load effects of distribution components. In distribution line design, wind load is the primary consideration. However, under conditions of constant wind speed, as rainfall intensity increases, the impact of rain load on the line structure cannot be ignored. Treat rain load as an additional load to wind load and perform linear superposition in the actual stress analysis.

[0136] Step 1.3.1, conductor stress calculation;

[0137] The horizontal distance between the conductor suspension points of two adjacent towers is defined as a span. Within a span, the superposition force F of the wind and rain load uniformly distributed along the conductor and the gravity load G per unit length of the conductor are obtained using equations (9) and (10), respectively:

[0138] F=F wl +F r (9)

[0139] G=mg (10)

[0140] In equations (9) and (10), m is the weight of the wire per unit length, and g is the acceleration due to gravity.

[0141] The comprehensive load H borne by the unit conductor is obtained using formula (11):

[0142]

[0143] When the load on the conductor changes, the conductor suspended on the tower will shrink or expand, causing the conductor surface tension to change. In actual engineering, the relevant parameters of the conductor sag and tension at average temperature are generally known. Therefore, the conductor tension under the current load can be solved by the conductor state equation. The conductor generally breaks at the highest suspension point, that is, the highest suspension point is the point where the conductor is subjected to the maximum tension. The conductor tension at the highest suspension point is used as the conductor research point under the load, and the comprehensive tension T of the conductor research point in the tangent direction is obtained using formula (12):

[0144]

[0145] In formula (12), is the height angle, that is, the angle between the line connecting the suspension points of the towers on both sides and the horizontal plane; l1 is the distance from the suspension point of the conductor to the lowest point of the sag.

[0146] Using formula (13), we can get the stress σ on the conductor cross section: d :

[0147]

[0148] In formula (13), s is the calculated cross-sectional area of ​​the conductor.

[0149] Step 1.3.2: Calculate the bending moment of the tower. Tower collapse usually occurs at the base of the tower, mainly because the bending moment at the base of the tower is usually the largest. The loads borne by the tower include the load from the conductor, the tower's own load, and the wind load acting on the tower. In fact, wind and rain directly acting on the conductor will only generate loads perpendicular to the conductor axis. Therefore, the wind and rain load on the conductor borne by the tower is H. d is perpendicular to the conductor, and the wind and rain load H on the conductor borne by the tower is obtained using formula (14): d :

[0150] H d =Fl (14)

[0151] In formula (14), l is the average span of the distribution line.

[0152] Using formula (15), the tower bending moment M1 caused by the conductor load is obtained:

[0153]

[0154] In formula (15), H 1d,v is the comprehensive load on the vth conductor; h v is the distance difference between the vth conductor and the base of the tower; N is the number of conductors on the tower;

[0155] Assuming that the wind load on the tower and the direction of the strong wind are in the same plane, the tower bending moment M2 caused by the wind load is obtained using formula (19):

[0156] M2=F wg Z (16)

[0157] In formula (16), Z is the moment arm from the tower root to the point of action of the resultant wind pressure on the tower.

[0158] Using formula (17), we can get the bending moment M of the tower: T :

[0159] M T =M1+M2 (17)

[0160] Step 1.4: Construct a distribution network reliability model;

[0161] According to the relevant theories of structural reliability, the distribution component state Z is obtained using formula (18):

[0162] Z=RS (18)

[0163] In formula (18), R is the internal effect of the component caused by the load effect; S is the strength of the component;

[0164] When any component in the distribution network is in a failure state, that is, Z < 0, it is considered a faulty component, and the probability of the faulty component P is obtained. r .

[0165] Using formula (19), we can get the load unreliability p of the conductor under wind load impact in extreme weather conditions: fd and the load unreliability p of the tower ft :

[0166]

[0167] In formula (19), δ d and μ d is the mean and standard deviation of the steel core aluminum stranded wire material strength; δ t and μ t are the mean and standard deviation of the concrete tower material strength; σ is the strength of the conductor; and M is the strength of the tower.

[0168] Using formula (20), the failure rate p of the kth conductor of any distribution line i in the distribution network under wind and rain load is obtained: h,i,k :

[0169] p h,i,k =1-(1-p fd,i,k )(1-p fg,i,k ) (20)

[0170] In formula (20), p fd,i,k is the failure rate of the kth conductor of distribution line i; p fg,i,k is the failure rate of the k-th section conductor of distribution line i on the tower.

[0171] Using formula (21), we can get the total failure rate p of distribution line i in the distribution network under the influence of extreme wind and rain: h,i :

[0172]

[0173] In formula (21), K is the total number of conductor sections of distribution line i.

[0174] Step 2: Construct a distribution network component damage model. Based on the fuzzy control method, the factors causing secondary disasters, the strength of distribution components, the geographical geological conditions and meteorological conditions of the distribution network area are summarized as inputs, and the component failure rate is used as the output to construct a related fuzzy mathematical model. The specific design process flow chart is as follows: Figure 1 As shown:

[0175] Step 2.1, fuzzy reasoning of factors related to foreign body hanging on wire;

[0176] Using formula (22) and formula (23), we can get the disaster-causing factors of foreign objects hanging on the line, including: geographical environment factors A y and line design parameter B y , and used as the first input of the fuzzy system:

[0177] A y =κV′sinθ (22)

[0178]

[0179] In formulas (22) and (23), κ is the geographical environment around the line, which is divided into industrial areas, residential areas, mountainous areas, etc. according to the complexity of the environment, and its value ranges from 1 to 2; V' is the normalized value of the wind speed of the line in the actual environment; N is the number of conductors on the tower.

[0180] The geographical environment factor A is quantified using triangular membership function. y and line design parameter B y The domain of discourse, thus obtaining the foreign body hanging line failure rate p y The fuzzy rule table.

[0181] Step 2.2, fuzzy reasoning of landslide factors;

[0182] Using equations (24) and (25), we can get the disaster factors that lead to landslide, including: disaster intensity A p and line vulnerability B p , and the second input of the fuzzy system is:

[0183]

[0184]

[0185] In formula (24) and formula (25), I′ is the normalized value of effective rainfall I; α s is the hydrogeological condition parameter; b x is the mountain slope morphological parameter; b p is the mountain slope parameter; b h is the mountain height parameter; d z is a geological parameter; ε d is the position parameter of the tower relative to the disaster body; s t is the ratio of the actual service life of the line to the designed service life; d is the tower bedrock safety parameter;

[0186] The disaster intensity A is quantified using the triangular membership function. pand line vulnerability B p The domain of discourse, thus obtaining the landslide failure rate p s The fuzzy rule table.

[0187] Step 2.3, fuzzy reasoning of flood factors;

[0188] Using formula (26) and formula (25), we can get the disaster factors that lead to floods, including: disaster intensity C p and line vulnerability B p , and serves as the third input of the fuzzy system:

[0189] C p =α w β d b′ p b h I′ (26)

[0190] In formula (26), α w is the channel distribution coefficient; β d is the channel morphology parameter, b′ h is the mountain slope parameter;

[0191] The disaster intensity A is quantified using the triangular membership function. p and line vulnerability B p The domain of discourse, thus obtaining the flood failure rate p h The fuzzy rule table.

[0192] Step 2.4: Calculate the distribution network line failure rate p under secondary disasters c,i ;

[0193] Step 2.4.1: The fuzzy system outputs three output quantities according to the three input quantities, and uses the center of gravity method to defuzzify the three output quantities of the fuzzy system to obtain the foreign body hanging line failure rate p y , landslide failure rate p s and flood failure rate p h .

[0194] Step 2.4.2: Use formula (27) to obtain the failure rate p of the kth conductor of the distribution line i in the distribution network caused by secondary disasters: c,i,k :

[0195] p c,i,k =1-(1-p y,i,k )(1-p s,i,k )(1-p h,i,k ) (27)

[0196] In formula (27), p v,i,k 、p s,i,k and p h,i,kare the probabilities of failure of the kth section conductor of distribution line i due to foreign objects hanging on the wire, landslide and flood in secondary disasters.

[0197] Step 2.4.3: Use formula (28) to obtain the total failure rate p of distribution line i under secondary disasters: c,i :

[0198]

[0199] Step 3: Construct a distribution network failure rate model. In extreme weather conditions, multiple disasters often occur simultaneously. Under mutual influence, they may develop into complex cluster disasters, which are more powerful than a single disaster, further increasing the probability of damage to distribution network components. In order to accurately describe the failure rate of distribution network components, a distribution network failure rate model under cluster disasters is constructed. The specific flow chart is as follows: Figure 2 shown.

[0200] Step 3.1, calculate the failure rate of each line in the distribution network, and equate the lines in the distribution network to a series component model, so as to use formula (29) to obtain the total failure rate p of the distribution line i at time t in the distribution network: i,t :

[0201]

[0202] In formula (29), p h,i,t represents the total failure rate of distribution line i in the distribution network under the influence of extreme wind and rain at time t, p c,i,t It represents the total failure rate of distribution line i in the power distribution under the secondary disaster at time t.

[0203] In this embodiment, an electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above method, and the processor is configured to execute the program stored in the memory.

[0204] In this embodiment, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are executed.

Claims

1. A distribution network fault modeling method under extreme weather conditions, characterized in that: The steps are as follows: Step 1: Comprehensively consider the impact of wind and rain load impact on the distribution network, and combine it with the disaster mechanism and geographical meteorological factors to construct a distribution network reliability model based on wind and rain load; Step 1.1: Construct a wind load model; determine the wind speed value of the study point based on the positional relationship between the typhoon center and the study point; Step 1.1.1: Use the Batts model to simulate the wind speed and direction of the typhoon wind field, and use formula (1) to obtain the maximum gradient wind speed V gx (t): In formula (1), K is the empirical coefficient; ΔP is the central pressure difference; f is the Coriolis force parameter of the Earth's rotation; R max is the maximum wind speed radius; Step 1.1.2: Use formula (2) to obtain the average maximum wind speed V at the maximum wind speed radius. M : V M =0.865V gx +0.5V T (2) In formula (2), V T is the overall moving speed of the typhoon; Step 1.1.3: Use formula (3) to obtain the average wind speed V at the distance r between the distribution line and the typhoon center in the typhoon wind field: In formula (3), x is a parameter related to the radial intensity attenuation of the typhoon; Step 1.1.4: Use equations (4) and (5) to obtain the wind load F acting on the conductor. wl and the wind load F on the tower wg : In formula (4) and formula (5), d0 is the outer diameter of the conductor; α is the wind pressure unevenness coefficient; β is the wind load adjustment coefficient; μ z is the wind pressure height variation coefficient; μ sc and μ s ' c are the body coefficients of conductor and tower respectively; D0 and D p h is the outer diameter of the tower tip and the pole root; r is the tower height; θ is the angle between the wind direction and the line; Step 1.2, construct rain load model; Step 1.2.1: Use formula (6) to construct the raindrop spectrum distribution n(D): n(D)=n0 exp(-ΛD) (6) In formula (6), D is the raindrop diameter; n0 is the number of raindrops per unit volume; Λ is the slope factor; Step 1.2.2: Use equation (7) to get the final velocity V of the raindrop s : In formula (7), δ1, δ2, and δ3 are the diameter thresholds of three raindrops respectively; Step 1.2.3: Use formula (8) to obtain the rain load F on the distribution line. r : In formula (8), n1 is the number of raindrops per unit volume; S r is the rain-exposed area of ​​the line; Step 1.3: Analyze the load effect of distribution components; Step 1.3.1, conductor stress calculation; The horizontal distance between the conductor suspension points of two adjacent towers is defined as a span. Within a span, the superposition force F of the wind and rain load uniformly distributed along the conductor and the gravity load G per unit length of the conductor are obtained using equations (9) and (10), respectively: F=F wl +F r (9) G=mg (10) In equations (9) and (10), m is the weight of the wire per unit length, and g is the acceleration due to gravity; The comprehensive load H borne by the unit conductor is obtained using formula (11): The conductor tension at the highest suspension point is used as the conductor research point where the load acts. The comprehensive tension T of the conductor research point in the tangent direction is obtained using formula (12): In formula (12), is the elevation angle, i.e. the angle between the line connecting the suspension points of the towers on both sides and the horizontal plane; l1 is the distance from the conductor suspension point to the lowest point of the sag; Using formula (13), we can get the stress σ on the conductor cross section: d : In formula (13), s is the calculated cross-sectional area of ​​the conductor; Step 1.3.2, calculate tower bending moment; Using formula (14), we can get the wind and rain load H on the conductor borne by the tower: d : H d =Fl (14) In formula (14), l is the average span of the distribution line; Using formula (15), the tower bending moment M1 caused by the conductor load is obtained: In formula (15), H 1d,v is the comprehensive load on the vth conductor; h v is the distance difference between the vth conductor and the base of the tower; N is the number of conductors on the tower; Assuming that the wind load on the tower and the direction of the strong wind are in the same plane, the tower bending moment M2 caused by the wind load is obtained using formula (19): M2=F wg From (16) In formula (16), Z is the moment arm from the tower root to the point of action of the resultant wind pressure on the tower; Using formula (17), we can get the bending moment M of the tower: T : M T =M1+M2 (17) Step 1.4: Construct a distribution network reliability model; According to the relevant theories of structural reliability, the distribution component state Z is obtained using formula (18): Z=RS (18) In formula (18), R is the internal effect of the component caused by the load effect; S is the strength of the component; When any component in the distribution network is in a failure state, that is, Z < 0, it is considered a faulty component, and the probability of the faulty component P is obtained. r ; Using formula (19), we can get the load unreliability p of the conductor under wind load impact in extreme weather conditions: fd and the load unreliability p of the tower ft : In formula (19), δ d and μ d is the mean and standard deviation of the steel core aluminum stranded wire material strength; δ t and μ t is the mean and standard deviation of the concrete tower material strength; σ is the strength of the conductor; M is the strength of the tower; Using formula (20), the failure rate p of the kth conductor of any distribution line i in the distribution network under wind and rain load is obtained: h,i,k : p h,i,k =1-(1-p fd,i,k )(1-p fg,i,k ) (20) In formula (20), p fd,i,k is the failure rate of the kth conductor of distribution line i; p fg,i,k is the failure rate of the k-th section conductor of distribution line i on the tower; Using formula (21), we can get the total failure rate p of distribution line i in the distribution network under the influence of extreme wind and rain: h,i : In formula (21), K is the total number of conductor sections of distribution line i; Step 2: Construct a fuzzy mathematical model considering secondary disasters: Step 2.1, fuzzy reasoning of factors causing foreign matter hanging on the wire; Using formula (22) and formula (23), we can get the disaster-causing factors of foreign objects hanging on the line, including: geographical environment factors A y and line design parameter B y , and used as the first input of the fuzzy system: A y =κV′sinθ (22) In formula (22) and formula (23), κ is the geographical environment around the line; V′ is the normalized value of the wind speed of the line in the actual environment; N is the number of conductors on the tower; The geographical environment factor A is quantified using triangular membership function. y and line design parameter B y The domain of discourse, thus obtaining the foreign body hanging line failure rate p y Fuzzy rule table; Step 2.2, fuzzy reasoning of landslide factors; Using equations (24) and (25), we can get the disaster factors that lead to landslide, including: disaster intensity A p and line vulnerability B p , and the second input of the fuzzy system is: In formula (24) and formula (25), I′ is the normalized value of effective rainfall I; α s is the hydrogeological condition parameter, b x is the mountain slope morphological parameter; b p is the mountain slope parameter; b h is the mountain height parameter; d z is a geological parameter; ε d is the position parameter of the tower relative to the disaster body; s t is the ratio of the actual service life of the line to the designed service life; d is the tower bedrock safety parameter; The disaster intensity A is quantified using the triangular membership function. p and line vulnerability B p The domain of discourse, thus obtaining the landslide failure rate p s Fuzzy rule table; Step 2.3, fuzzy reasoning of flood factors; Using formula (26) and formula (25), we can get the disaster factors that lead to floods, including: disaster intensity C p and line vulnerability B p , and serves as the third input of the fuzzy system: C p =a w b d b′ p b h I' (26) In formula (26), α w is the channel distribution coefficient; β d is the channel morphology parameter, b′ h is the mountain slope parameter; The disaster intensity A is quantified using the triangular membership function. p and line vulnerability B p The domain of discourse, thus obtaining the flood failure rate p h Fuzzy rule table; Step 2.4: Calculate the distribution network line failure rate p under secondary disasters c,i ; Step 2.4.1: The fuzzy system outputs three output quantities according to the three input quantities, and uses the center of gravity method to defuzzify the three output quantities of the fuzzy system to obtain the foreign body hanging line failure rate p y , landslide failure rate p s and flood failure rate p h ; Step 2.4.3: Use formula (27) to obtain the failure rate p of the kth conductor of the distribution line i in the distribution network caused by secondary disasters: c,i,k : p c,i,k =1-(1-p y,i,k )(1-p s,i,k )(1-p h,i,k ) (27) In formula (27), p v,i,k 、p s,i,k and p h,i,k are the probability of failure of the kth section conductor of distribution line i due to foreign objects hanging on the wire, landslide and flood in secondary disasters; Step 2.4.4: Use formula (28) to obtain the total failure rate p of distribution line i under secondary disasters c,i : Step 3: Construct a distribution network failure rate model; Step 3.1, calculate the failure rate of each line in the distribution network, and equate the lines in the distribution network to a series component model, so as to use formula (29) to obtain the total failure rate p of the distribution line i at time t in the distribution network: i,t : In formula (29), p h,i,t represents the total failure rate of distribution line i in the distribution network under the influence of extreme wind and rain at time t, p c,i,t It represents the total failure rate of distribution line i in the power distribution under the secondary disaster at time t.

2. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store a program for supporting a processor to execute the method according to claim 1 , and the processor is configured to execute the program stored in the memory.

3. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to claim 1 are performed.

Images