A distribution network resilience improvement strategy based on environmental data prediction

Abstract

The present invention relates to a distribution network resilience enhancement strategy based on environmental data prediction. Comprehensive environmental data prediction and analysis are performed on the distribution network operating conditions, and the vulnerable links of the distribution network are predicted to enhance the early warning capability of disaster risks; a distribution network vulnerability prediction model based on environmental data prediction is established to improve the level of failure rate prediction; a distribution network fault carrying capacity assessment method based on environmental data is proposed; a distribution network fault carrying capacity assessment method is proposed; the change in system performance is used to reflect the impact of ice disasters on the resilience of the distribution network, and a distribution network resilience assessment model is established; the purpose is to implement planning and scheduling measures to enhance the resilience of the distribution network in the disaster defense stage and the post-disaster recovery stage, respectively, and then propose a distribution network resilience enhancement strategy under environmental data prediction. The present invention significantly improves the resilience of the distribution network, from active defense before the fault occurs to fault recovery after the disaster, and achieves an all-round improvement of the resilience of the distribution network during the disaster process and the post-disaster process through the coordination of the two means.

Description

Technical Field

[0001] The present invention belongs to the technical field of operation control of power systems, and in particular relates to a distribution network resilience enhancement strategy based on environmental data prediction. Background Art

[0002] As a key link that directly serves users, the normal operation of the distribution network under extreme weather conditions is of great significance to ensuring people's production and life, resisting disasters and accidents, and promoting social development. Therefore, the disaster response capacity of the distribution network has received widespread attention. On this basis, the concept of resilience is introduced in the distribution network to evaluate the ability of the distribution network to reduce the losses caused by faults under extreme weather conditions and restore to normal power supply as soon as possible. In order to avoid the occurrence of large-scale power outages caused by extreme weather disasters, it is impossible to fundamentally solve the stability and economic problems of the power system under extreme weather disasters. New resources and methods must be explored. At present, there have been some studies on the assessment of power grid resilience. Francis and Bekera introduced a resilience assessment framework for engineering systems. Yodo and Wang proposed a method to measure the resilience index using the performance function triangle, and the resilience triangle was used to calculate the performance loss and recovery time. But in general, these works are still in the exploratory stage. The research lacks a complete optimization and screening process for fault scenarios, which is not conducive to the evaluation of the resilience recovery strategy of the distribution network.

[0003] In response to a series of problems in the actual operation of the power grid under extreme weather disasters in Northeast China, by analyzing the principle of distribution network resilience recovery in the face of extreme events, based on environmental data prediction, enhancing the physical strength of the network, improving the grid operation capacity and manual maintenance, and ultimately improving the disaster emergency defense system, effectively resisting the impact of the external environment, and achieving active resilience recovery. Enhance the ability of the distribution network to resist extreme weather events and further improve the resilience of the distribution network. Summary of the invention

[0004] In view of the shortcomings of the above-mentioned prior art, the present invention provides a distribution network resilience improvement strategy based on environmental data prediction. The purpose of the present invention is to significantly improve the resilience of the distribution network based on environmental data prediction, from active defense before a fault occurs to fault recovery after a disaster, and to achieve an all-round improvement of the resilience of the distribution network during and after a disaster through the coordination of the two means.

[0005] The technical solution adopted by the present invention to achieve the above-mentioned purpose is:

[0006] A distribution network resilience improvement strategy based on environmental data prediction includes the following steps:

[0007] Step 1. Multi-source environmental data element perception, dynamic extraction and knowledge mapping technology of knowledge elements for distribution network fault recovery based on heterogeneous data such as distribution network operation data and fault evolution. Comprehensive environmental data prediction of internal and external operating conditions of the distribution network can more accurately analyze and predict the vulnerable links of the distribution network, thereby significantly improving the early warning capability of disaster risks;

[0008] Step 2. Establish a distribution network vulnerability prediction model based on environmental data prediction to improve the failure rate prediction level;

[0009] Step 3. Propose a distribution network fault carrying capacity assessment method based on environmental data, and propose a distribution network fault carrying capacity assessment method based on meteorological, time and other environmental data;

[0010] Step 4. Use the changes in system performance to reflect the impact of ice disasters on the resilience of the distribution network. According to the change process of the system performance curve, the entire ice disaster process is divided into three stages, namely, the pre-disaster warning stage, the disaster defense stage, and the post-disaster repair stage, and a distribution network resilience assessment model is established;

[0011] Step 5. The proposed distribution network resilience assessment model aims to implement planning and scheduling measures to improve the resilience of the distribution network in the disaster prevention stage and the post-disaster recovery stage respectively, and then propose a distribution network resilience improvement strategy based on environmental data prediction.

[0012] The present invention has the following beneficial effects and advantages:

[0013] The present invention considers the perception of multi-source environmental data elements, and dynamically extracts and maps knowledge elements of distribution network fault recovery based on heterogeneous data such as distribution network operation data and fault evolution. Comprehensive environmental data prediction of the internal and external operating conditions of the distribution network can more accurately analyze and predict the vulnerable links of the distribution network, thereby significantly improving the early warning capability of disaster risks; establish a distribution network vulnerability prediction model based on environmental data prediction to improve the level of fault rate prediction; propose a distribution network fault carrying capacity evaluation method based on environmental data, and propose a distribution network fault carrying capacity evaluation method based on meteorological, time and other environmental data; use the change of system performance to reflect the impact of ice disasters on the resilience of the distribution network, and divide the entire disaster process into three stages according to the change process of the system performance curve, namely, the pre-disaster warning stage, the disaster defense stage and the post-disaster repair stage, and establish a distribution network resilience evaluation model; the proposed distribution network resilience evaluation model aims to implement planning and scheduling measures to improve the resilience of the distribution network in the two periods of the disaster defense stage and the post-disaster recovery stage, and then propose a distribution network resilience improvement strategy under environmental data prediction.

[0014] The present invention can significantly improve the resilience of the distribution network based on environmental data prediction, from active defense before a fault occurs to fault recovery after a disaster. Through the coordination of the two means, the resilience of the distribution network can be comprehensively improved during and after a disaster. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0016] Figure 1 It is the fault scenario screening process at a certain moment of the present invention;

[0017] Figure 2 It is the whole process screening process of disasters of the present invention;

[0018] Figure 3 is the performance curve of the power distribution system under extreme events in the present invention;

[0019] Figure 4 It is an example diagram of the power curve of island operation in the present invention;

[0020] Figure 5 It is a flow chart of the resilience enhancement defense strategy of the present invention;

[0021] Figure 6 This is the distribution network resilience assessment model of the present invention. DETAILED DESCRIPTION

[0022] In order to more clearly understand the above-mentioned objectives, features and advantages of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0023] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0024] Refer to the following Figure 1-Figure 6 The technical solutions of some embodiments of the present invention are described.

[0025] Example 1

[0026] The present invention is a distribution network resilience improvement strategy based on environmental data prediction, comprising the following steps:

[0027] Step 1: Multi-source environmental data element perception, dynamic extraction of knowledge elements and knowledge mapping technology for distribution network fault recovery based on heterogeneous data such as distribution network operation data and fault evolution. Comprehensive environmental data prediction of internal and external operating conditions of the distribution network can more accurately analyze and predict the vulnerable links of the distribution network, thereby significantly improving the early warning capabilities of disaster risks:

[0028] Step (1) divides the environmental data prediction into network topology, power supply equipment and network defense resources, which are closely related to the improvement of distribution network resilience.

[0029] Among them, environmental data prediction meets the following conditions:

[0030] First, propose network topology situation prediction: This indicator reflects the importance of the line segment in the topology, and the line weighted betweenness is used to characterize the importance of the line segment. Suppose the number of power sources in the region is c1, the number of loads on the kth line is c2, and the total load in the region is c3, define Z k represents the impedance of line segment k, N st represents the number of optimal paths between power source s and load t, N st (k) represents the number of times the optimal path from source s to load t passes through line segment k. Line k contains weighted betweenness Describes the network topology and is used to quantify the contribution of a line segment to the propagation of power flow in the entire network. The larger the value, the greater the hub role of the line segment.

[0031]

[0032] In the formula: c1——the number of power sources in the area;

[0033] k——is the kth line;

[0034] c2——the load quantity on the kth line;

[0035] c3——total load in the area;

[0036] Z k ——represents the impedance of line section k;

[0037] N st ——Indicates the number of optimal paths between power source s and load t;

[0038] s——indicates power supply;

[0039] t——indicates load;

[0040] N st (k)——indicates the number of times the optimal path from source s to load t passes through line segment k;

[0041] ——Indicates the weighted betweenness of line k.

[0042] Second, propose a power supply stability situation forecast: This indicator reflects the impact of power supply stability on the operation of the line section. The power supply equipment of the distribution network includes conventional power supply equipment and various distributed energy equipment, etc. The impact of the external environment on the operation of the two is different, as analyzed below.

[0043] (1) Daily wear and tear of power supply equipment

[0044] Affected by the depreciation rate, the loss of conventional power components such as transformers becomes more obvious as the years of use increase, and the power supply risk also increases accordingly. The depreciation failure rate of power components is described by the Weibull function. If the equipment maintenance interval during the depreciation period is Δt, the failure rate after maintenance is l i Reduced to μ of the basic failure rate i times, the equipment failure rate is l z for:

[0045]

[0046] Where: Δt——represents the equipment maintenance interval during the wear period;

[0047] τ 2i ——is the scale parameter;

[0048] l i ——Indicates the failure rate before maintenance;

[0049] μ i ——is a multiple;

[0050] l z ——Indicates the equipment damage failure rate;

[0051] β 2i ——is a shape parameter, which represents the impact of the i-th maintenance on the basic failure rate and the speed of component aging.

[0052] (2) Distributed DG equipment considering environmental impact

[0053] Special weather has a great impact on the output of distributed power sources. Light intensity, illuminated area, ambient temperature and other factors have a strong correlation with photovoltaic output. Taking wind power and photovoltaic as examples, the time series output model of wind distributed DG is as follows:

[0054]

[0055] Where: P r —— is the rated power of the fan;

[0056] v r —— is the rated wind speed;

[0057] v q —— is the cut-in wind speed;

[0058] v t ——To cut out wind speed.

[0059] The photovoltaic power generation output model is as follows:

[0060]

[0061] Where: A——is the number of photovoltaic modules;

[0062] S——area of ​​a single photovoltaic panel;

[0063] ——is the radiation intensity of the battery surface;

[0064] ——is the photoelectric conversion efficiency under standard test conditions;

[0065] T——Ambient temperature, unit is ℃.

[0066] Then the distributed DG power supply index of line k is:

[0067]

[0068] (3) When the energy storage system is used as the main power source, its output model is as follows:

[0069]

[0070] Where: ——is the remaining capacity of the energy storage device at node t at the end of cycle t+1;

[0071] ——represent the charging and discharging power of the energy storage device in cycle t respectively;

[0072] η ch , η dis ——is the charging and discharging efficiency;

[0073] ——Indicates the maximum capacity of the energy storage device;

[0074] ——is the maximum charging and discharging power of node j in the energy storage device.

[0075] D——represents a 0-1 variable;

[0076] ——Indicates the charging and discharging status at time t;

[0077] T – is the duration;

[0078] N T ——is the total number of time intervals;

[0079] N bus ——is the number of nodes in the active distribution network.

[0080] The power supply index of the distributed energy storage system of line k is:

[0081]

[0082] In order to distinguish the impact of the power supply status of different power supply equipment on the operation of the line section, the active contribution coefficient ε of the transformer, wind power photovoltaic equipment and energy storage device to the line section can be obtained by power flow tracking. m.g , ε m.dg , ε m.ess Assume that the number of transformers, wind power and photovoltaic equipment and distributed energy storage devices on line k are a and 1,k 、a 2,k 、a 3,k The number of transformers, wind power and photovoltaic equipment and distributed energy storage devices in the distribution network area are b1, b2 and b3 respectively. The standard test conditions (1000W / m 2 , 25℃) as the benchmark for converting the actual wind power output, the risk status index of the power supply equipment in the line section can be expressed as:

[0083]

[0084] In the formula: υ1, υ2, υ3——represent weights.

[0085] 3. Propose a risk resistance situation prediction: This indicator reflects the defense capability of the power grid against risks inside and outside, involving all aspects of human, material and financial resources. Assume that the defense resources of a certain line are u. Considering the decreasing marginal benefit, the defense risk situation of this line section is:

[0086]

[0087]

[0088] Where: U t ——Indicates the real-time available defense resources and their level in the distribution network at time t;

[0089] ——Indicates the total amount of defense resources and their horizontal quantity available in real time in the distribution network;

[0090] s——indicates the power protection level of users on the line, which can be set to 4 levels from high to low according to the importance, 1≥s1>s2>s3>s4>0.

[0091] 4. Propose a prediction of the power flow distribution: In an ice disaster environment, failures such as line breakage, tower collapse, or dancing flashover will cause the redistribution of line power flow, and the transfer of power flow may cause some components in the system to exit operation due to overload or serious deviation from normal working conditions. In the power distribution system, the line failure rate and line power flow have the following relationship:

[0092]

[0093] Where: l is the line flow size;

[0094] L nor ——It is the rated transmission power of the line;

[0095] L max ——It is the line transmission limit;

[0096] ——It is the historical statistical average value of line failure probability.

[0097] The mathematical model shows that if the active power does not reach the rated capacity, the line operates normally and the possibility of line failure is stabilized at a very small probability value; if the active power is between the rated capacity and the maximum capacity, the failure probability shows a linear growth trend; if the active power exceeds the limit of line transmission, the failure rate is 1.

[0098] 5. Propose a prediction of the proneness of cascading failures: The cascading failures of distribution lines in an ice disaster environment have to take into account the impact of power flow changes. Under certain conditions, the power flow disturbance of the line will cause some lines in the system to be in a self-organized critical state. According to the theory of complex systems, the self-organized critical state of the power system is a key factor in the occurrence of cascading failures.

[0099] The constraint criteria for determining whether a line is a critical line are as follows:

[0100]

[0101] In the formula: K——represents the critical coefficient of the line;

[0102] L i,max ——is the rated transmission power of line i;

[0103] L i ——The actual delivered power of transmission line i;

[0104] P li ——It is the active power flow of the branch line;

[0105] P li,max ——Rated active power transmission capacity of the branch;

[0106] I——represents the set consisting of all lines and transformers;

[0107] ε——critical reference coefficient, the empirical value is generally 1.05.

[0108] Assume that the total number of lines in the region is N, and define the cascading failure propagation degree of the kth critical line as:

[0109]

[0110]

[0111] Where: N Count ——is a counting function;

[0112] ——Indicates the correlation between line segment k and line segment y. If k fails, y fails, which means correlation, f(k,y)=1, otherwise f(k,y)=0;

[0113] ——Indicates the number of all line segments related to line segment k.

[0114] Define the average failure rate predicted by environmental data as R = n2 / n a2 , where n2 is the number of disaster failures in the line section, n a2 is the total number of disaster failures in area a, then the proneness of cascading failures in section k considering power flow disturbance is composed of the average failure rate and the failure propagation degree:

[0115]

[0116] Step (2) uses the logarithmic synthesis method and introduces the mean square error to jointly describe the internal operation status of the distribution network. When the mean square error is greater than the critical value σ0, the smaller index value can be ignored and the remaining index value is taken for calculation, that is:

[0117]

[0118]

[0119] Where: when ρ≤ρ0, N=5; when ρ>ρ0, N<5, and the definition is When θ i represents the indicator weight, ∑θ i =1. is the indicator value of multi-source environmental data,

[0120] Step 2: Establish a distribution network vulnerability prediction model based on environmental data prediction to improve the level of failure rate prediction:

[0121] The environmental data prediction method is used to analyze the internal and external operating status of the distribution network under natural environment, define the concepts of network topology status prediction, power supply equipment status prediction, defense resource status prediction, power flow distribution status prediction and cascading fault propagation status prediction, and jointly establish a distribution network vulnerability prediction model based on environmental data prediction to improve the level of fault rate prediction. Comprehensively considering the two parts, the vulnerability prediction model of the active distribution network under disaster conditions based on environmental data prediction can be obtained as follows:

[0122]

[0123] Where: ——It is the indicator value of multi-source environmental data;

[0124] ——Indicates the failure rate;

[0125] F(k,i,t)——represents the vulnerability of node i of the k-th line at time t.

[0126] Step 3: Propose a distribution network fault carrying capacity assessment method based on environmental data, and propose a distribution network fault carrying capacity assessment method based on meteorological, time and other environmental data:

[0127] Step 3.1. The distribution network interconnection line efficiency analysis and carrying capacity assessment method based on environmental data analyzes the distribution network carrying capacity and the ability to safely transfer loads from the perspective of scenario occurrence probability.

[0128] The system vulnerability vector is used to calculate the number and probability of failures in a random scenario at a certain moment. Combined with the time-varying system vulnerability F(k,i,t), the probability of occurrence of the fault scenario and its corresponding number of faults can be calculated.

[0129]

[0130]

[0131]

[0132] u hi =F h F i

[0133] u hij =F h F i F j

[0134] u hijk =F h F i F j Fk ...

[0136] Where: h ——represents the probability of occurrence of a scenario in which only component h fails in the distribution network of the region;

[0137] ψ hi ——Indicates the probability of occurrence of a scenario where only component h and component i fail in the distribution network of the region;

[0138] ψ hij ——Indicates the probability of occurrence of a scenario where only component h, component i, and component j fail in the distribution network of the region;

[0139] u h ——represents the probability of failure of component h;

[0140] u hi ——represents the probability of simultaneous failure of component h and component i;

[0141] u hij ——represents the probability of the scenario where components h, i, and j fail at the same time;

[0142] u hijk ——Indicates the probability of simultaneous failure of components h, i, j, and k;

[0143] F is the weakness rate of the corresponding overhead line component.

[0144] Define the set of scenarios consisting of the same number of faults as the fault number scenario set, and use Ω t,m Indicates that Where m is the number of faulty lines. From the perspective of probability distribution, the random distribution law of the number of faults and the corresponding fault probability is described. The probability distribution model of the number of faults is:

[0145]

[0146]

[0147] In the formula, P(s i ) represents the scenario s with m number of faults i The probability of occurrence, the confidence interval can be obtained by To calculate, use the principles of probability statistics to calculate the reasonable confidence interval of the number of failures:

[0148]

[0149] m∈[a,b]

[0150] In the formula, is the cumulative probability. 1-α represents the confidence level of the number of failures, which is used to describe the probability that the number of failures falls within this interval. Keep all the scenarios that belong to the confidence interval .in, This step achieves the preliminary screening of the scene set.

[0151] Step 3.2. Analyze the distribution network's carrying capacity and ability to safely transfer loads from the perspective of information entropy.

[0152] The confidence interval of the number of failures can observe the credibility of a certain type of scenario, but it cannot make a judgment on a specific scenario. Information entropy can analyze the rationality of any scenario from the perspective of uncertainty. Therefore, the principle of information entropy is used to screen again from the transition scenario set. Information entropy is used to describe the uncertainty of the information source. C.E. Shannon borrowed this concept from thermodynamics and used mathematical language to explain the relationship between the probability of random events and information redundancy. From this, the information entropy value H (s) in a random scenario can be calculated. i ).

[0153]

[0154] In the formula, is the weak rate of line k at time t, is the state of line k at time t. When the number of faults m is constant, the smaller the line weakness rate, the smaller the probability of the fault scenario, and the larger the information entropy value, indicating that the uncertainty of the occurrence of this scenario is smaller, and the scenario simulation effect is more consistent with the typical fault scenario, and vice versa. The discrete point set can be transformed into Fitting logarithmic curve, the mathematical expression is:

[0155]

[0156] f t,m :H(s i )=-KlnP(s i )-L,K>0,L>0

[0157] Among them, K and L are coefficients. It can be seen that the fitting function strictly follows monotonically decreasing, and a reasonable scenario must ensure that the scenario occurrence probability and information entropy value are within a certain range, that is,

[0158] P min ≤P≤P max

[0159] H min ≤H≤H max

[0160] Where P min and P maxrepresents the scene probability threshold, H min and H max Represents the upper and lower limits of information entropy. The value range is determined by the probability distribution of scene coordinates. If most of the scene coordinate values ​​fall within a certain area, the area enclosed by the constraint is taken as the typical scene area, and the coordinates belonging to this area [P(s i ),H(s i )] The corresponding scenes constitute a typical scene set, using Therefore, the typical scenario at time t is Solvable:

[0161]

[0162]

[0163] In the formula, is The largest integer.

[0164] The above process can only make predictions based on the system operation status at a certain moment of the disaster, and cannot be applied to the entire time scale. Therefore, the prediction results have obvious defects and lack persuasiveness. In order to realize the fault scenario screening of the entire disaster process, it is necessary to determine the initial moment of the scenario prediction and the end moment of the disaster. This article stipulates that if the ice thickness R of a certain line is greater than 1mm, the fault scenario prediction and screening process will be started. Then set a suitable step size, solve the results at each moment, and finally find their union as the final prediction result, and use s GTFS Since the global defense plan is unique, the selected scenario should include as many lines as possible, which means that as many defense measures as possible can be taken on these high-risk lines:

[0165]

[0166] Fault scenario screening process Figure 1 and 2 shown.

[0167] Step 4: Use the changes in system performance to reflect the impact of ice disasters on the resilience of the distribution network. According to the change process of the system performance curve, the entire ice disaster process is divided into three stages, namely, the pre-disaster warning stage, the disaster defense stage, and the post-disaster repair stage, and a distribution network resilience assessment model is established.

[0168] Step 4.1: System Function Curve Figure 3 It describes the response of the distribution network to a specific disaster, so the index defined by this can only describe the resilience of the distribution network under this disaster. l It is a lower performance change curve, Q n is the performance curve under normal conditions, Q hIt is a higher performance change curve. s represents the initial moment of the disaster, and t0 represents the disturbance moment, at which time the weakness rate begins to increase gradually and the system performance begins to decrease. r Indicates the actual fault occurrence time, t m Indicates the time when the system state is stable, t n Indicates the time when the disaster ends, t e Indicates the time when the repair is completed. N Indicates the normal performance value of the system, Q(t r )、Q(t m )、Q(t n ) are the performance at the moment of failure, system stability, and the end of the disaster, respectively.

[0169] The system performance function model is shown as follows:

[0170]

[0171] Where: q k (t)——indicates the load level of the node;

[0172] F k (t)——indicates the weakness rate of the distribution line;

[0173] ρ(t)——represents the recovery factor;

[0174] Represents the response speed factor.

[0175] The recovery factor refers to the percentage of the system that can be recovered through operation, and the calculation formula is:

[0176]

[0177] Where: ΔQ(t)——the change value of load per unit time;

[0178] Q r (t)——The load that can be increased through operation. This paper considers that Q r (t)=0 holds.

[0179] The response speed factor is related to the recovery response speed and the duration of manual repair. The calculation formula is:

[0180]

[0181] Where: ——Indicates the response time;

[0182] ——represents the remaining time to repair line k;

[0183] D1——represents the set of lines that have not experienced any faults, which includes lines in normal operation, lines protected by the defense plan, and unprotected power outage lines;

[0184] D2——represents the set of lines where faults occur, where D1∪D2=D.

[0185] Step 4.2: Establish a calculation model for distribution network resilience evaluation indicators that takes economic factors into consideration. It can comprehensively measure the effectiveness of resilience improvement strategies from three aspects: robustness, adequacy, and rapidity. The expression is:

[0186]

[0187] Where REERT is the distribution network resilience index. The larger the REERT value, the better the improvement effect. x is the probability of the scene occurring, and ι is the coefficient.

[0188] Step 5: The proposed distribution network resilience assessment model aims to implement planning and scheduling measures to improve the resilience of the distribution network in the disaster prevention stage and the post-disaster recovery stage respectively, and then propose a distribution network resilience improvement strategy based on environmental data prediction.

[0189] Among them, the strategy steps include:

[0190] Step 1. Use weather forecasts to estimate the severity and duration of the disaster, predict possible faults during the disaster, and activate reserve resources to put the distribution network in an active defense state. Reserve resources include backup interconnection lines, distributed generation (DGs), energy storage systems (ESS), and backup power supplies. DGs and ESS can greatly promote the development of distribution networks with built-in flexibility and are key means to improve resilience; load transfer also plays an important role in the active defense process due to its simple operation, fast response speed, and low cost.

[0191] Step 1.1: During the entire defense cycle, the output power and load power of the DG will fluctuate over time, so the operating state of the ESS will also change. Figure 4 Two island operation modes A and B are shown.

[0192] The specific process of establishing an island is as follows:

[0193] (1) Before performing island division, the estimated power outage area is determined based on the fault scenario prediction results.

[0194] (2) Find the maximum power supply range of the DER (Distributed Energy Resources) system in all branchless power supply paths, find the connected area containing DES in the power outage area, narrow the search space and improve the search efficiency.

[0195] (3) The nodes are divided into layers. The load nodes directly connected to the DER are the first layer, the load points directly connected to the first layer nodes are the second layer nodes, and so on. The O of each load point is calculated. i L i The value of each layer node is O i L i Sort in descending order.

[0196] (4) Starting from DER, visit all first-layer load points and find the load points that make O i L i The node with the largest sum is stored in the island load node set, and the next layer of nodes is visited to find the load nodes connected to the island load node set. Under the premise of satisfying the constraints, the load nodes are connected with the island load node set. i L i The load node with larger value replaces the previous layer O i L i For load nodes with smaller values, the above method is used until the last layer of nodes is searched, and the initial island division scheme can be obtained. Repeat steps (2) to (4) to obtain the island scheme for all areas.

[0197] (5) In the obtained single island division scheme, if two or more islands contain the same load point, it means that the island areas overlap. According to the principle of minimizing the number of islands in the island division principle, it is necessary to merge the islands with overlapping load nodes and output the final island scheme.

[0198] In addition, it should be noted that the method for checking whether the island meets the constraint conditions in step (4) is as follows: first, determine whether the total load connected to the island exceeds the power and capacity of the DER, that is, whether the island balance constraint is satisfied; then verify its connectivity constraint; finally, perform power flow calculation on the island corresponding to the scheme to obtain the voltage of each node and the power of each branch, and determine whether the node voltage constraint and branch power constraint are satisfied.

[0199] Step 1.2: After the island division scheme is determined, for the power-off nodes that cannot be included in the island, the network is reconstructed according to the heuristic algorithm to transfer the remaining load to the area with normal power supply. The objective function is as follows:

[0200]

[0201] Where: α1, α2 and α3 are the equivalent penalty coefficients for power outages of level 1, level 2 and level 3 loads, respectively, and can be set according to actual needs, where α1>α2>α3. 1,u , L 2,v and L 3,w They are the first, second and third level loads that are not powered.

[0202] The heuristic rule gives priority to the interconnecting switches close to the power-off area and with large capacity margin for power supply. A power flow calculation is performed for each closed interconnecting switch to verify whether the current state meets the constraints. If so, the power supply restoration is completed; if not, the search starts from the end of the line and the corresponding section switches are disconnected until the constraints are met. Count other power-off loads, and continue to perform switching operations until all loads are transferred or there are no interconnecting switches that can be closed. The load transfer is completed and the final active defense power supply plan is output. The active defense optimization model can be derived by combining the island division process and the load transfer process. The active defense optimization strategy process based on fault scenario prediction is as follows: Figure 5 shown.

[0203] It should be noted that a power restoration plan should be generated immediately after a real fault occurs. If there are omissions in the active defense plan, corresponding adjustments and corrections should be made according to the power restoration plan to form a new defense plan.

[0204] Step 2. Comprehensively consider the emergency repair materials, geographical location and maintenance personnel allocation plan, and mobilize emergency repair resources to restore the faulty area after the fault occurs.

[0205] Step 2.1: After the disaster is over, start repairing the actual faulty lines. Figure 3 (t n ,t e ) is the fault repair process. The status of faulty equipment in the disaster area is divided into two types: repairable and unrepairable. Unrepairable components are replaced. Repairable components are repaired by emergency repair personnel and priority is given to areas with high power outages. At the same time, emergency generators can also maintain power supply for some important loads. The economic loss model of distribution network failure is constructed by comprehensively considering the efficiency discount coefficient and the travel cost coefficient, and referring to the concept of expected energy not served (EENS):

[0206]

[0207] Where: a ij ——represents the repair efficiency of the i-th team in repairing the j-th fault;

[0208] b ij ——represents the travel cost coefficient of the i-th squad to the j-th fault;

[0209] ——represents the time taken by the emergency team with a repair efficiency of 1 to repair fault j;

[0210] ——represents the driving time of emergency team i to the farthest fault point;

[0211] m, n——represent the number of maintenance teams and the number of faults, respectively;

[0212] N j ——is the set of power-off load nodes caused by the jth fault;

[0213] O k ——Indicates the load amount determined by the importance of the user, which is the power of the load;

[0214] L k ——The load included in the island.

[0215] Step 2.2: Considering typical fault scenarios, the resilience improvement and assessment of the ice-disaster distribution network specifically includes three steps: vulnerability prediction, fault scenario selection, and comparison and verification. The flowchart is as follows: Figure 5 shown.

[0216] The first step is to predict vulnerability and record data; the second step uses the proposed method to select typical failure scenarios; the third step is to implement disaster defense plans and post-disaster failure recovery, and use resilience assessment models to analyze the effectiveness of resilience improvement strategies. The specific steps are:

[0217] (1) Input meteorological data and network data, and combine them with ice disaster simulation parameters to predict the time-varying weakness rate of the distribution system. Meteorological data includes meteorological parameters at each time, such as wind speed; network data includes various electrical parameters of the line;

[0218] (2) Update the defense plan according to the defense plan until the disaster is over. Obtain the manual maintenance plan based on the actual fault situation, output the system function curve and enter step (3);

[0219] (3) Analyze the differences between the predicted results and the actual fault conditions, and compare the resilience improvement effects of the existing and proposed strategies using the proposed resilience assessment model.

Claims

1. A distribution network resilience improvement strategy based on environmental data prediction, characterized by: The following steps are involved: Step 1. Multi-source environmental data element perception: Dynamic extraction and knowledge mapping technology of knowledge elements for distribution network fault recovery based on distribution network operation data and fault evolution heterogeneous data, and comprehensive environmental data prediction of internal and external operating conditions of the distribution network, which can more accurately analyze and predict the vulnerable links of the distribution network, thereby significantly improving the early warning capability of disaster risks; Step 2. Establish a distribution network vulnerability prediction model based on environmental data prediction to improve the failure rate prediction level; Step 3. Propose a distribution network fault carrying capacity assessment method based on environmental data, wherein the environmental data includes weather and time; Step 4. Use the changes in system performance to reflect the impact of ice disasters on the resilience of the distribution network. According to the change process of the system performance curve, the entire ice disaster process is divided into three stages, namely, the pre-disaster warning stage, the disaster defense stage, and the post-disaster repair stage, and a distribution network resilience assessment model is established; Step 5. The proposed distribution network resilience assessment model aims to implement planning and dispatching measures to improve the resilience of the distribution network in the disaster prevention stage and the post-disaster recovery stage, and then propose a distribution network resilience improvement strategy based on environmental data prediction; The step 1 comprises: Step (1) divide the environmental data prediction into network topology situation prediction, power supply stability situation prediction, risk resistance situation prediction, power flow distribution situation prediction and cascading failure prone situation prediction, which are closely related to the improvement of distribution network resilience; Among them, environmental data prediction meets the following conditions: First, we propose a network topology situation prediction. The network topology situation index reflects the importance of the line segment in the topology. We use the line weighted betweenness to characterize the importance of the line segment. Let the number of power sources in the region be c1, the number of loads on the kth line be c2, and the total load in the region be c3. Define Z k represents the impedance of line segment k, N st represents the number of optimal paths between power source s and load t, N st (k) represents the number of times the optimal path from source s to load t passes through line segment k. Line k contains weighted betweenness Describes the network topology and is used to quantify the contribution of a line segment to the propagation of power flow in the entire network. The larger the value, the greater the hub role of the line segment. In the formula: c1——is the number of power sources in the area; k——is the kth line; c2——the load quantity on the kth line; c3——total load in the area; Z k ——represents the impedance of line section k; N st ——Indicates the number of optimal paths between power source s and load t; s——indicates power supply; t——indicates load; N st (k)——indicates the number of times the optimal path from source s to load t passes through line segment k; ——Indicates the weighted betweenness of line k; Second, the power supply stability situation prediction is proposed. The power supply stability situation index reflects the impact of power supply stability on the operation of the line section. The power supply equipment of the distribution network includes conventional power supply equipment and various distributed energy equipment. The impact of the external environment on the operation of the two is different. The following analysis is as follows: (1) Daily wear and tear of power supply equipment, Affected by the depreciation rate, most conventional power components of transformers lose more and more with the increase of investment years, and the power supply risk also increases accordingly. The depreciation failure rate of power components is described by Weibull function. If the equipment maintenance interval during the depreciation period is Δt, the failure rate after maintenance is given by Reduced to μ of the basic failure rate i times, the equipment failure rate for: Where: Δt——represents the equipment maintenance interval during the wear period; τ 2i ——is the scale parameter; ——Indicates the failure rate before maintenance; μ i ——is a multiple; ——Indicates the equipment damage failure rate; β 2i ——is a shape parameter, which indicates the impact of the i-th maintenance on the basic failure rate and the speed of component aging; (2) Distributed DG equipment considering environmental impact, Special weather has a great impact on the output of distributed power sources. Light intensity, illuminated area, and ambient temperature factors have a strong correlation with photovoltaic output. Taking wind power and photovoltaic as examples, the time series output model of wind distributed DG is as follows: Where: P r —— is the rated power of the fan; v r —— is the rated wind speed; v q —— is the cut-in wind speed; v t ——To cut out wind speed; The photovoltaic power generation output model is as follows: Where: A——is the number of photovoltaic modules; S——area of ​​a single photovoltaic panel; ——is the radiation intensity of the battery surface; ——is the photoelectric conversion efficiency under standard test conditions; T——Ambient temperature, in °C; Then the distributed DG power supply index of line k is: (3) When the energy storage system is used as the main power source, its output model is as follows: Where: ——is the remaining capacity of the energy storage device at node t at the end of cycle t+1; ——represent the charging and discharging power of the energy storage device in cycle t respectively; η ch , η dis ——is the charging and discharging efficiency; ——Indicates the maximum capacity of the energy storage device; ——is the maximum charging and discharging power of node j in the energy storage device; D——represents a 0-1 variable; ——Indicates the charging and discharging status at time t; T – is the duration; N T ——is the total number of time intervals; N bus ——is the number of nodes in the active distribution network; The power supply index of the distributed energy storage system of line k is: In order to distinguish the impact of the power supply status of different power supply equipment on the operation of the line section, the active contribution coefficient ε of the transformer, wind power photovoltaic equipment and energy storage device to the line section is obtained by power flow tracking. m.g , ε m.dg , ε m.ess , assuming that the number of transformers, wind power and photovoltaic equipment and distributed energy storage devices on line k are a 1,k 、a 2,k 、a 3,k The number of transformers, wind power and photovoltaic equipment and distributed energy storage devices in the distribution network area are b1, b2, and b3, respectively. 2 The actual wind power output is converted based on the wind power and photovoltaic output under the standard test conditions of 25℃. The risk status index of the power supply equipment in the line section is expressed as: In the formula: υ1, υ2, υ3——represent weights; Third, the risk resistance situation prediction is proposed. The risk resistance situation index reflects the defense capability of the power grid to resist risks inside and outside, involving human, material and financial resources. Assuming that the defense resources of a certain line are u, considering the decreasing marginal benefit, the defense risk situation of the line section is: Where: U t ——Indicates the defense resources and their level available in the distribution network at time t; ——Indicates the total amount of defense resources and their horizontal quantity available in real time in the distribution network; s——indicates the power protection level of users on the line, and is set to 4 levels from high to low according to the importance, 1≥s1>s2>s3>s4>0; Fourth, the prediction of power flow distribution is proposed. In the ice disaster environment, the failure of line breaking, tower collapse or dancing flashover will cause the redistribution of line power flow. The transfer of power flow will cause some components in the system to stop operating due to overload or serious deviation from normal working conditions. In the distribution system, the line failure rate and line power flow have the following relationship: Where: l is the line flow size; L nor ——It is the rated transmission power of the line; L max ——It is the line transmission limit; ——It is the historical statistical average value of line failure probability; The mathematical model of the relationship between line failure rate and line power flow shows that if the active power does not reach the rated capacity, the line operates normally and the possibility of line failure is stabilized at a very small probability value; if the active power is between the rated capacity and the maximum capacity, the failure probability shows a linear growth trend; if the active power exceeds the limit of line transmission, the failure rate is 1; 5. The prediction of the proneness of cascading failures is proposed. The cascading failures of distribution lines under ice disasters should take into account the influence of power flow changes. Under certain conditions, the power flow disturbance of the line will cause some lines in the system to be in a self-organized critical state. According to the theory of complex systems, the self-organized critical state of the power system is the key factor for the occurrence of cascading failures. The constraint criteria for determining whether a line is a critical line are as follows: In the formula: K——represents the critical coefficient of the line; L i,max ——is the rated transmission power of line i; L i ——The actual delivered power of transmission line i; P li ——It is the active power flow of the branch line; P li,max ——Rated active power transmission capacity of the branch; I——represents the set consisting of all lines and transformers; ε——critical reference coefficient, generally the empirical value is 1.05; Assume that the total number of lines in the region is N, and define the cascading failure propagation degree of the kth critical line as: Where: N Count ——is a counting function; ——Indicates the correlation between line segment k and line segment y. If k fails, y fails, which means correlation, f(k,y)=1, otherwise f(k,y)=0; ——represents the number of all line segments related to line segment k; Define the average failure rate predicted by environmental data as R = n2 / n a2 , where n2 is the number of disaster failures in the line section, n a2 is the total number of disaster failures in area a, then the proneness of cascading failures in section k considering power flow disturbance is composed of the average failure rate and the failure propagation degree: Step (2) uses the logarithmic synthesis method and introduces the mean square error to jointly describe the internal operation status of the distribution network. When the mean square error is greater than the critical value σ0, the smaller index value is ignored and the remaining index value is calculated, that is: Where: when ρ≤ρ0, N=5; when ρ>ρ0, N<5, and the definition is When θ i represents the indicator weight, ∑θ i =1, is the indicator value of multi-source environmental data, 2. A distribution network resilience improvement strategy based on environmental data prediction according to claim 1, characterized in that: The establishment of a distribution network vulnerability prediction model based on environmental data prediction in step 2 improves the level of failure rate prediction, which is: The environmental data prediction method is used to analyze the internal and external operating status of the distribution network under the natural environment, and the concepts of network topology situation prediction, power supply stability situation prediction, risk resistance situation prediction, power flow distribution situation prediction and cascading failure prone situation prediction are defined. A distribution network vulnerability prediction model based on environmental data prediction is jointly established to improve the level of failure rate prediction; considering the two parts comprehensively, the vulnerability prediction model of the active distribution network under disaster conditions based on environmental data prediction is obtained as follows: Where: ——It is the indicator value of multi-source environmental data; ——Indicates the failure rate; F(k,i,t)——represents the vulnerability of node i of the k-th line at time t.

3. A distribution network resilience improvement strategy based on environmental data prediction according to claim 1, characterized in that: The method for evaluating the fault carrying capacity of a distribution network based on environmental data described in step 3 includes:

1. The efficiency analysis and carrying capacity assessment method of the distribution network tie line based on environmental data, analyzing the carrying capacity of the distribution network and the ability to safely transfer loads from the perspective of the probability of occurrence of the scenario; The system vulnerability vector is used to calculate the number and probability of failures in a random scenario at a certain moment. Combined with the time-varying system vulnerability F(k,i,t), the probability of occurrence of the fault scenario and the corresponding number of faults are calculated. u hi =F h F i u hij =F h F i F j u hijk =F h F i F j F k ... Where: h ——represents the probability of occurrence of a scenario in which only component h fails in the distribution network of the selected area; ψ hi ——Indicates the probability of occurrence of a scenario in which only component h and component i fail in the distribution network of the selected area; ψ hij ——Indicates the probability of occurrence of a scenario in which only component h, component i, and component j fail in the distribution network of the selected area; u h ——represents the probability of failure of component h; u hi ——represents the probability of simultaneous failure of component h and component i; u hij ——represents the probability of the scenario where components h, i, and j fail at the same time; u hijk ——Indicates the probability of simultaneous failure of components h, i, j, and k; F is the weakness rate of the corresponding overhead line element; Define the set of scenarios consisting of the same number of faults as the fault number scenario set, and use Ω t,m Indicates that Among them, m is the number of faulty lines. From the perspective of probability distribution, the random distribution law of the number of faults and the corresponding fault probability is described. The probability distribution model of the number of faults is: In the formula, P(s i ) represents the scenario s with m number of faults i The probability of occurrence, the confidence interval is To calculate, use the principles of probability statistics to calculate the reasonable confidence interval of the number of failures: m∈[a,b] In the formula, is the cumulative probability, 1-α represents the confidence level of the number of faults, which is used to describe the probability that the number of faults falls within this interval, and all scenario sets belonging to the confidence interval are retained. in, This step achieves the initial screening of the scene set; Second, analyze the distribution network's carrying capacity and ability to safely transfer loads from the perspective of information entropy. The confidence interval of the number of failures can observe the credibility of a certain type of scenario, but it cannot make a judgment on a specific scenario. The information entropy can analyze the rationality of any scenario from the perspective of uncertainty. Therefore, the information entropy principle is used to screen again from the transition scenario set. Information entropy is used to describe the uncertainty of the information source. C.E. Shannon borrowed this concept from thermodynamics and used mathematical language to explain the relationship between the probability of random events and information redundancy. In this way, the information entropy value H (s) in a random scenario can be calculated. i ), In the formula, is the weak rate of line k at time t, is the state of line k at time t. When the number of faults m is constant, the smaller the line weakness rate is, the smaller the probability of the fault scenario is, and the larger the information entropy value is, indicating that the uncertainty of the occurrence of this scenario is smaller, and the scenario simulation effect is more consistent with the typical fault scenario. Vice versa, with the help of mathematical fitting methods, the discrete point set Fitting logarithmic curve, the mathematical expression is: f t,m :H(s i )=-KlnP(s i )-L,K>0,L>0 Among them, K and L are coefficients. It can be seen that the fitting function strictly follows monotonically decreasing. A reasonable scenario must ensure that the scenario occurrence probability and information entropy value are within a certain range, that is, P min ≤P≤P max H min ≤H≤H max Where P min and P max represents the scene probability threshold, H min and H max Represents the upper and lower limits of information entropy. The value range is determined by the probability distribution of scene coordinates. If most of the scene coordinate values ​​fall within a certain area, the area enclosed by the constraints is taken as the typical scene area, and the coordinates belonging to this area [P(s i ),H(s i )] The corresponding scenes constitute a typical scene set, using Therefore, the typical scenario at time t is Solution: In the formula, is The largest smallest integer; The above process can only make predictions based on the system operation status at a certain moment of the disaster, and cannot be applied to the entire time scale. In order to realize the fault scenario screening of the entire disaster process, it is necessary to determine the initial moment of scenario prediction and the end moment of the disaster. Therefore, it is stipulated that if the ice thickness R of a certain line is greater than 1mm, the fault scenario prediction and screening process will be started, and then the appropriate step size will be set to solve the results at each moment, and finally their union will be obtained as the final prediction result, and s GTFS It indicates that, since the global defense plan is unique, the selected scenario should include as many lines as possible, and as many defense measures as possible should be taken on these high-risk lines:

4. The distribution network resilience improvement strategy based on environmental data prediction according to claim 1 is characterized by: In step 4, the change of system performance is used to reflect the impact of ice disasters on the resilience of the distribution network. According to the change process of the system performance curve, the whole process of ice disasters is divided into three stages, namely, the pre-disaster warning stage, the disaster defense stage and the post-disaster repair stage, and a distribution network resilience assessment model is established; including: First, the system function curve describes the response of the distribution network to a specific disaster. Therefore, the indicators defined by this curve can only describe the resilience of the distribution network under this disaster. l It is a lower performance change curve, Q n is the performance curve under normal conditions, Q h is a higher performance change curve, t s represents the initial moment of the disaster, t0 represents the disturbance moment, at which the weakness rate begins to increase gradually and the system performance begins to decrease, t r Indicates the actual fault occurrence time, t m Indicates the time when the system state is stable, t n Indicates the time when the disaster ends, t e Indicates the time when the repair is completed, Q N Indicates the normal performance value of the system, Q(t r )、Q(t m )、Q(t n ) are the performance at the time of failure, system stability, and the end of the disaster, respectively. The system performance function model is shown as follows: Where: q k (t)——indicates the load level of the node; F k (t)——indicates the weakness rate of the distribution line; ρ(t)——represents the recovery factor; represents the response speed factor; The recovery factor refers to the percentage of the system that can be recovered through operation, and the calculation formula is: Where: ΔQ(t)——the change value of load per unit time; Q r (t)——The load that can be lifted by operation, only when the system is not dispatched and operated Q r (t) = 0 holds; The response speed factor is related to the recovery response speed and the duration of manual repair. The calculation formula is: Where: ——Indicates the response time; ——represents the remaining time to repair line k; D1——represents the set of lines that have not experienced any faults, which includes lines in normal operation, lines protected by the defense plan, and unprotected power outage lines; D2——represents the set of faulty lines, where D1∪D2=D; Second, establish a calculation model for distribution network resilience evaluation indicators that takes economic factors into consideration, and comprehensively measure the effectiveness of resilience improvement strategies from three aspects: robustness, adequacy, and rapidity. The expression is: Where REERT is the distribution network resilience index. The larger the REERT value, the better the improvement effect. x is the probability of the scene occurring, and ι is the coefficient.

5. The distribution network resilience improvement strategy based on environmental data prediction according to claim 1 is characterized by: The distribution network resilience assessment model proposed in step 5 aims to implement planning and dispatching measures to improve the resilience of the distribution network in the disaster prevention stage and the post-disaster recovery stage, and then propose a distribution network resilience improvement strategy based on environmental data prediction; Among them, the strategy steps include: Step a. Use weather forecasts to estimate the severity and duration of the disaster, predict possible faults during the disaster, and activate reserve resources to put the distribution network in an active defense state. Reserve resources include backup interconnection lines, distributed generation (DGs), energy storage systems (ESS), and backup power sources. DGs and ESS can promote the development of distribution networks with built-in flexibility and are key means to improve resilience. Load transfer also plays an important role in the active defense process due to its advantages of simple operation, fast response speed, and low cost. Step b. Comprehensively consider the emergency repair materials, geographical location and maintenance personnel allocation plan, and mobilize emergency repair resources to restore the faulty area after the fault occurs.

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