Self-healing capability evaluation method and device of power distribution network, terminal equipment and storage medium

Abstract

The invention discloses a self-healing capability evaluation method and device for a power distribution network, terminal equipment and a storage medium, and the method comprises the steps: evaluating the expected self-healing level of the power distribution network according to the power flow operation data of the power distribution network in a plurality of fault scenes, and then taking a plurality of scheme data items in a self-healing scheme as influence factors, thereby achieving the self-healing capability evaluation of the power distribution network. The method comprises the steps of obtaining influence factors, clustering self-healing schemes according to the influence factors, calculating a second similarity of a second factor-lacking scheme according to first similarities between a plurality of all-factor schemes and each first factor-lacking scheme, calculating a factor weight of each influence factor according to the second similarity, and finally, obtaining a self-healing result according to the factor weights and an expected self-healing level. And calculating a self-healing ability evaluation score. Therefore, the self-healing capability of the power distribution network is accurately evaluated by calculating the factor weight of each influence factor in the self-healing scheme and combining the factor weight with the overall performance level of the power distribution network.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method for evaluating the self-healing capability of a distribution network. Background Art

[0002] With the continuous growth of electricity demand and the access of more and more distributed generation (DG), the distribution system has become more complex. The self-healing capability of the distribution network refers to the ability of the distribution system to automatically detect, locate and isolate the fault and quickly restore power supply after a fault occurs.

[0003] The research on the self-healing capacity evaluation of domestic distribution networks mainly focuses on the following aspects. In terms of multi-objective optimization, researchers explore multi-objective optimization methods to balance multiple objectives of self-healing systems, such as fault isolation speed, timeliness of power restoration, equipment loss and system stability; in terms of machine learning and artificial intelligence, with the rapid development of artificial intelligence technology, researchers have used deep learning, reinforcement learning and other methods to optimize the process of fault detection, diagnosis and recovery; in terms of distributed control technology, distributed control strategies are studied to enhance the flexibility and robustness of distribution network self-healing systems and reduce dependence on centralized control. Some scholars have proposed a multi-period optimization decision-making method that coordinates multiple power sources and energy storage to quickly restore important loads. The example verified that its effect is better than the recovery strategy that only considers a single time section. Based on the power-transportation-communication network, it is proposed to improve the resilience of the distribution network under extreme events based on V2G and other means.

[0004] Although there are many methods for self-healing of distribution network faults, there is currently a lack of means to evaluate the self-healing capabilities of distribution networks. Summary of the invention

[0005] The embodiment of the present invention provides a method for evaluating the self-healing capability of a distribution network, which accurately evaluates the self-healing capability of the distribution network by calculating the factor weight of each influencing factor and combining it with the overall performance level of the distribution network.

[0006] An embodiment of the present invention provides a method for evaluating the self-healing capability of a distribution network, comprising:

[0007] Acquire the power flow operation data of the distribution network to be evaluated in several fault scenarios, and the self-healing scheme of the distribution network in each fault scenario; wherein the self-healing scheme includes several scheme data items; one of the scheme data items corresponds to an indicator or a type of component adjusted by the distribution network when performing fault self-healing;

[0008] Calculating the expected self-healing level of the distribution network and the overall performance level of the distribution network under various fault scenarios according to the power flow operation data;

[0009] The scheme data items are used as influencing factors, and the self-healing schemes are clustered according to the influencing factors to generate a full-factor clustering set and a plurality of missing-factor clustering sets; wherein the missing-factor clustering set includes: a plurality of self-healing schemes that lack a corresponding influencing factor;

[0010] According to the expected self-healing level, calculating first similarities between a plurality of full-factor solutions in the full-factor clustering set and a plurality of first-factor-deficient solutions in each of the deficient-factor clustering sets;

[0011] Constructing a collection of second factor-missing solutions for each full-factor solution; wherein each of the second factor-missing solutions is a solution generated by deleting one of the influencing factors from the corresponding full-factor solution;

[0012] Calculating, according to the first similarity, a second similarity between the full-factor solution and each second-factor-missing solution in the corresponding collection of second-factor-missing solutions;

[0013] Calculating the factor weight of each influencing factor according to the second similarity;

[0014] A self-healing capability evaluation score of the distribution network is calculated according to the factor weights and the overall performance level.

[0015] Furthermore, the obtaining of power flow operation data of the distribution network to be evaluated in several fault scenarios, and the self-healing solution of the distribution network in each fault scenario, includes:

[0016] Acquire a fault self-healing model of the distribution network; wherein the fault self-healing model is a simulation model constructed according to the distribution network and used to simulate the self-healing of the distribution network after a fault occurs;

[0017] Randomly applying a number of preset fault scenarios to the fault self-healing model, so that the fault self-healing model, under each fault scenario, takes the minimum loss load as the goal, simulates the actions of each switch element of the distribution network in each operation stage and adjusts the indicators of each item, and records the power flow operation data of the distribution network in each operation stage of each fault scenario; wherein the operation stage includes: an initial stage, a fault location stage, a fault isolation stage, and a power supply restoration stage;

[0018] According to the actions of each switch element of the distribution network at each operation stage and the indicators of each item are adjusted, a self-healing plan for each stage of the distribution network under each fault scenario is generated.

[0019] Furthermore, the power flow operation data includes: the amount of lost load, the time to isolate the fault area, the time to restore the load in the non-fault area, and the active load of each node;

[0020] The calculating the expected self-healing level of the distribution network according to the power flow operation data includes:

[0021] The expected self-healing level of the distribution network is calculated according to the following formula:

[0022]

[0023] Where R is the expected self-healing level, is the loss load of the cth fault scenario, p c is the probability of occurrence of the cth fault scenario, T ir / T rr are the time to isolate the fault area and the load recovery time in the non-fault area, ω i is the node load weight, P i load is the active load of the ith node, Ω snr A collection of fault scenarios.

[0024] Furthermore, according to the power flow operation data, the overall performance level of the distribution network under each fault scenario is calculated, including:

[0025] The overall performance level of the distribution network under various fault scenarios is calculated according to the following formula:

[0026]

[0027] Among them, R c is the overall performance level of the cth fault scenario, P c shed is the loss load of the cth fault scenario, T ir / T rr are the time to isolate the fault area and the load recovery time in the non-fault area, ω i is the node load weight, P i load is the active load of the ith node, Ω snr A collection of fault scenarios.

[0028] Furthermore, the calculating, according to the first similarity, a second similarity between the full-factor solution and each second-factor-missing solution in the corresponding set of second-factor-missing solutions includes:

[0029] Calculate, according to the first similarity, a first domain radius of each full-factor solution in the full-factor cluster set, and a second domain radius of each second missing-factor solution in the corresponding missing-factor cluster set;

[0030] Calculating the initial similarity between the full factor solution and each second missing factor solution in the corresponding second missing factor solution set according to the first domain radius and the second domain radius;

[0031] Calculate the domain similarity between the full factor cluster set and each of the missing factor cluster sets according to the first total number of all factor solutions in the full factor cluster set and the second total number of first missing factor solutions in each missing factor cluster set;

[0032] The second similarity between the full-factor solution and each second missing-factor solution in the corresponding collection of second missing-factor solutions is calculated according to the initial similarity and the domain similarity.

[0033] Furthermore, the calculating the factor weight of each influencing factor according to the second similarity includes:

[0034] Calculate the factor weight of each influencing factor according to the following formula:

[0035]

[0036]

[0037] Among them, t j is the factor weight of the jth influencing factor, X j is the solution set of several second missing factor solutions that are missing the jth influencing factor; X0 is the full factor clustering set; r is (X j ,X0) is the set similarity between the scheme set with the jth influencing factor missing and the set of all factor clustering; r ji is the second similarity between the i-th full-factor solution and the corresponding second missing-factor solution that lacks the j-th influencing factor.

[0038] Another embodiment of the present invention provides a self-healing capability evaluation device for a distribution network, comprising:

[0039] A data acquisition module, used to acquire the power flow operation data of the distribution network to be evaluated in several fault scenarios, and the self-healing solution of the distribution network in each fault scenario; wherein the self-healing solution includes several solution data items; one of the solution data items corresponds to an indicator or a type of component adjusted by the distribution network when performing fault self-healing;

[0040] A level evaluation module, used to calculate the expected self-healing level of the distribution network and the overall performance level of the distribution network under various fault scenarios according to the power flow operation data;

[0041] A scheme clustering module is used to take the scheme data items as influencing factors, and cluster the self-healing schemes according to the influencing factors to generate a full-factor clustering set and a plurality of missing-factor clustering sets; wherein the missing-factor clustering set includes: a plurality of self-healing schemes that lack a corresponding influencing factor;

[0042] A first similarity calculation module, used for calculating, according to the expected self-healing level, first similarities between a plurality of full-factor solutions in the full-factor clustering set and a plurality of first lack-factor solutions in each lack-factor clustering set;

[0043] A solution generation module is used to construct a collection of second factor-deficient solutions for each full-factor solution; wherein each of the second factor-deficient solutions is a solution generated by deleting one of the influencing factors from the corresponding full-factor solution;

[0044] A second similarity calculation module, configured to calculate, based on the first similarity, a second similarity between the full-factor solution and each second-factor-missing solution in the corresponding second-factor-missing solution collection;

[0045] A weight calculation module, used for calculating the factor weight of each influencing factor according to the second similarity;

[0046] The self-healing capability evaluation module is used to calculate the self-healing capability evaluation score of the distribution network according to the factor weights and the overall performance level.

[0047] Furthermore, the data acquisition module acquires the power flow operation data of the distribution network to be evaluated in several fault scenarios, and the self-healing solution of the distribution network in each fault scenario, including:

[0048] Acquire a fault self-healing model of the distribution network; wherein the fault self-healing model is a simulation model constructed according to the distribution network and used to simulate the self-healing of the distribution network after a fault occurs;

[0049] Randomly applying a number of preset fault scenarios to the fault self-healing model, so that the fault self-healing model, under each fault scenario, takes the minimum loss load as the goal, simulates the actions of each switch element of the distribution network in each operation stage and adjusts the indicators of each item, and records the power flow operation data of the distribution network in each operation stage of each fault scenario; wherein the operation stage includes: an initial stage, a fault location stage, a fault isolation stage, and a power supply restoration stage;

[0050] According to the actions of each switch element of the distribution network at each operation stage and the indicators of each item are adjusted, a self-healing plan for each stage of the distribution network under each fault scenario is generated.

[0051] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a method for evaluating the self-healing capability of a distribution network as described in any one of the embodiments is implemented.

[0052] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a method for evaluating the self-healing capability of a distribution network as described in any of the above embodiments.

[0053] The following beneficial effects are achieved by implementing the present invention:

[0054] The present invention discloses a method, an apparatus, a terminal device and a storage medium for evaluating the self-healing capability of a distribution network. The method evaluates the expected self-healing level of the distribution network according to the flow operation data of the distribution network in several fault scenarios, and then uses several scheme data items in the self-healing scheme as influencing factors affecting the self-healing level of the distribution network, and clusters the self-healing schemes of the distribution network in each fault scenario according to the influencing factors, and calculates the second similarity between the full-factor scheme and the second factor-deficient scheme corresponding to itself according to the first similarity between several full-factor schemes and each first factor-deficient scheme, and calculates the factor weight of each influencing factor according to the first total number of the full-factor scheme, the second total number of the first factor-deficient scheme, and the second similarity, and finally, calculates the self-healing capability evaluation score of the distribution network according to the factor weight and the expected self-healing level. It can be understood that the present invention calculates the first similarity between the full factor solution and the first missing factor solution according to the expected self-healing level, and then evaluates the second similarity between the full factor solution and the solution after each influencing factor is missing according to the first similarity. The larger the second similarity, the closer the expected self-healing level results of the full factor solution and the second missing factor solution are, which means that the influencing factor has little effect on the self-healing ability of the distribution network. Based on the entropy weight theory, the influencing factor should be assigned a smaller weight, otherwise, a larger weight should be assigned. Therefore, the present invention evaluates the contribution of each action made by the distribution network in the fault self-healing process to the fault self-healing by calculating the weight assigned to each data item of the several self-healing solutions in the distribution network, and accurately evaluates the self-healing ability of the distribution network in combination with the overall performance level of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of a method for evaluating the self-healing capability of a distribution network provided by an embodiment of the present invention.

[0056] Figure 2 It is a structural schematic diagram of a self-healing capability evaluation device for a distribution network provided by an embodiment of the present invention.

[0057] Figure 3 A schematic diagram of a distribution network topology structure is provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0060] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0061] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0062] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0063] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0064] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0065] See also Figure 1 , is a flow chart of a method for evaluating the self-healing capability of a distribution network provided by an embodiment of the present invention, comprising:

[0066] S1. Obtaining the power flow operation data of the distribution network to be evaluated in several fault scenarios, and the self-healing scheme of the distribution network in each fault scenario; wherein the self-healing scheme includes several scheme data items; one of the scheme data items corresponds to an indicator or a type of component adjusted by the distribution network when performing fault self-healing;

[0067] Preferably, obtaining the power flow operation data of the distribution network to be evaluated in several fault scenarios, and the self-healing solution of the distribution network in each fault scenario, includes:

[0068] S11, obtaining a fault self-healing model of the distribution network; wherein the fault self-healing model is a simulation model constructed according to the distribution network and used to simulate the self-healing of the distribution network after a fault occurs;

[0069] S12, randomly applying a plurality of preset fault scenarios to the fault self-healing model, so that the fault self-healing model, under each fault scenario, takes the minimum loss load as the goal, simulates the actions of each switch element of the distribution network in each operation stage and adjusts the indicators of each item, and records the power flow operation data of the distribution network in each operation stage of each fault scenario; wherein the operation stage includes: an initial stage, a fault location stage, a fault isolation stage, and a power supply restoration stage;

[0070] S13. Generate a self-healing solution for each stage of the distribution network under each fault scenario according to the actions of each switch element of the distribution network at each operation stage and the indicators of each item adjusted.

[0071] In a preferred embodiment of the present invention, the historical operation data and self-healing scheme recorded by the distribution network in the real fault scenario can be directly used. However, in order to ensure sufficient data volume, this embodiment establishes a distribution system self-healing improvement method model (fault self-healing model) based on the distribution network that coordinates the prevention process and the response and recovery process. Among them, a scenario-based random programming method is used to consider the uncertainty of faults caused by extreme events. By optimizing the system self-healing level (such as the expected loss of load, etc.), a self-healing improvement strategy that coordinates the prevention process and the response and recovery process of the distribution system under extreme events can be generated.

[0072] The distribution system self-healing improvement model that coordinates the prevention process and the response and restoration process includes five parts, namely, prevention stage constraints (initial stage), degradation stage constraints (fault location stage), fault isolation stage constraints, power supply restoration stage constraints and objective function.

[0073] In the prevention phase, active islands are formed through network reconstruction. At this time, the system needs to meet the radial network topology constraints and ensure that the needs of all loads can be met. Assume that manual switches (MS) and remote-controllable switches (RCS) are configured in pairs at both ends of the line and operate in pairs. Since the location of RCS has an important impact on the rapid fault isolation and power supply restoration of the distribution system in the post-accident stage, the optimization of the RCS location is considered in the prevention phase. RCS needs to meet the upper limit constraint, as shown in the following formula. When the RCS location in the system is determined, Know your own limits.

[0074]

[0075] Where: 0-1 constant, indicating whether line l is configured with RCS, if yes, then 1; N rcs is the total number of RCS.

[0076] The radial topology constraints of the distribution system in the prevention stage are shown in the following three equations.

[0077]

[0078]

[0079]

[0080] Where: M is a very large positive number, is a 0-1 variable, indicating the prevention stage; Ω bus Node collection; is a 0-1 variable, indicating the prevention stage; F lpre Prevent power supply restoration stage; Ω line Line collection.

[0081] The power flow and safety constraints of the distribution system in the prevention stage are shown in the following eight equations.

[0082]

[0083]

[0084]

[0085]

[0086]

[0087]

[0088]

[0089]

[0090] Among them, the first three equations are linearized DistFlow power flow equations; the fourth and fifth equations are the upper limit constraints of line capacity; the sixth equation is the upper and lower limit constraints of voltage amplitude; the seventh and eighth equations are the upper and lower limit constraints of DG active and reactive output. i load , Q i load represents the active / reactive load at node i; is the active and reactive output of DG at node i at time t; In the prevention phase, the active / reactive output of DG at node i; In the prevention phase, the active and reactive power flows of line l; Prevention / fault isolation / power restoration phase, (scenario c) voltage of node i; a l / b l Resistance / reactance of line l.

[0091] In the degradation stage, affected by the active island in the fault and prevention stages, the distribution system will form fault areas and non-fault areas. Based on the fault state transfer model, the fault state transfer constraints in the degradation stage are as follows.

[0092]

[0093] The first formula indicates that if there is a fault on the closed line, the nodes at both ends of the line will be in the fault area; the second formula indicates that the nodes at both ends of the closed line are in the fault area at the same time or not in the fault area at the same time. 0-1 variable, indicating whether node i is in the fault affected area in scenario c during the degradation / fault isolation phase. If so, it is 1; f l,c 0-1 constant, indicating whether line l is faulty in scenario c, if so, it is 1; 0-1 variable, indicating whether the line is closed during the prevention / fault isolation / power restoration phase, and 1 if so; and Determined during the fault isolation phase; Ω snr A collection of failure scenarios.

[0094] Since only faults occur in the degradation phase and the network topology is not reconstructed, the radial network topology formed by the distribution system before the accident will remain radial in the degradation phase, and there is no need to consider the radial topology constraints in this phase. In addition, since the non-fault area can still maintain the same operating conditions as in the prevention phase, there is no need to consider its power flow and safety constraints.

[0095] Based on the above analysis, the load of nodes in the non-fault area will not be affected, and the nodes in the fault area will lose all load. Accordingly, the following two equations show the load loss of nodes in the fault area and non-fault area.

[0096]

[0097]

[0098] Where: During the fault isolation phase, the active / reactive load loss power of node i in scenario c.

[0099] At the end of the degradation phase, the RCS completes the action, disconnects the line to isolate the fault, and blocks the transmission of the fault state in the system, thereby reducing the fault impact area. Similarly, based on the fault state transmission model, the area affected by the fault in the fault isolation phase can be determined. The RCS action constraints and fault state transmission constraints in the fault isolation phase are shown in the following formula.

[0100]

[0101]

[0102]

[0103] Among them, the first formula shows that in the fault isolation stage, only the line configured with RCS can be disconnected for rapid fault isolation, and the occurrence of a fault will cause the line to no longer be connected; the second formula shows that if a closed line without RCS configuration fails, the nodes at both ends of the line will be in the fault area. On the contrary, when the line is configured with RCS or the two ends of the line have been disconnected in the pre-fault stage, the nodes at both ends of the line can be isolated from the fault and belong to the non-fault area; the third formula indicates that the nodes at both ends of the closed line will belong to the same fault area or the same non-fault area.

[0104] The system operation constraints during the fault isolation phase are as follows.

[0105]

[0106]

[0107] Among them, the above formula is the linearized DisFlow power flow equation;

[0108]

[0109]

[0110] Among them, the above formula is the upper limit constraint of line capacity;

[0111]

[0112] Among them, the above formula is the upper and lower limit constraints of the voltage amplitude;

[0113]

[0114]

[0115] Among them, the above formula is the upper and lower limits of the active and reactive output of DG. When the DG in the fault area stops running, its output is 0;

[0116]

[0117]

[0118]

[0119] The above formula is the upper limit constraint of node load loss, where the nodes in the fault area will lose all load.

[0120] After the fault is isolated, the load in the non-fault area can be restored through the rapid action of the RCS in the non-fault area. At this time, it is necessary to ensure that the fault area formed in the fault isolation stage is not reconnected with the normal area in the power restoration stage, that is, the fault area cannot be expanded. Since the network is reconstructed in the non-fault area during the power restoration stage, the radial network topology constraints need to be considered in this stage.

[0121] The RCS action constraints and radial network topology constraints in the power restoration phase are shown in the following equations.

[0122]

[0123]

[0124]

[0125]

[0126] Where: In the prevention / power restoration phase, the commodity flow through line l (in scenario c). The first formula shows that only lines equipped with RCS can take quick action to quickly restore the load. At the same time, when a fault occurs on the line, the line will no longer be connected. In the fault isolation phase, it is necessary to ensure that the non-fault area will not be reconnected to the fault area. The corresponding fault state transfer constraint is shown in the last formula.

[0127]

[0128] in, and Determined in the fault isolation stage. When the nodes at both ends of the line belong to the fault area, there are Accordingly, according to the above formula, we can know This ensures that the non-faulty area will not reconnect to the faulty area.

[0129] The power flow and operation constraints of the distribution system during the power restoration phase are shown below.

[0130]

[0131]

[0132]

[0133] Among them, the above formula is the linearized DisFlow power flow equation;

[0134]

[0135]

[0136] Among them, the above formula is the upper limit constraint of line capacity;

[0137]

[0138] Among them, the above formula is the upper and lower limit constraints of the voltage amplitude;

[0139]

[0140]

[0141] Among them, the above formula is the upper and lower limit constraints of the active and reactive output of DG;

[0142]

[0143]

[0144] Among them, the above formula is the upper limit constraint of node load loss.

[0145] The impact of extreme events on the distribution system can be expressed by the missing area of ​​the system performance curve. When the total load of the system is used to represent the system performance, the missing area of ​​the system performance curve means the amount of system load loss in the corresponding time period. Therefore, in order to reduce the degree of impact on the system, the objective function shown in the following formula is used to minimize the expected amount of system load loss under extreme events.

[0146]

[0147] Among them, p c The probability of scenario c; It can be obtained by the following formula.

[0148]

[0149] Where: T f Fault occurrence time; T im MS action time; T ir / T rr The moment when RCS isolates the fault / restores the load. i Node load weight.

[0150] S2. Calculating the expected self-healing level of the distribution network and the overall performance level of the distribution network under various fault scenarios according to the power flow operation data;

[0151] Preferably, the power flow operation data includes: the amount of lost load, the time to isolate the fault area, the time to restore the load in the non-fault area, and the active load of each node;

[0152] S21, calculating the expected self-healing level of the distribution network according to the power flow operation data, comprising:

[0153] The expected self-healing level of the distribution network is calculated according to the following formula:

[0154]

[0155] Among them, R is the expected self-healing level, P c shed is the loss load of the cth fault scenario, p c is the probability of occurrence of the cth fault scenario, T ir / T rr are the time to isolate the fault area and the load recovery time in the non-fault area, ω i is the node load weight, P i load is the active load of the ith node, Ω snr A collection of fault scenarios.

[0156] Preferably, calculating the overall performance level of the distribution network under various fault scenarios according to the power flow operation data includes:

[0157] S22. Calculate the overall performance level of the distribution network under each fault scenario according to the following formula:

[0158]

[0159] Among them, R c is the overall performance level of the cth fault scenario, is the loss load of the cth fault scenario, T ir / T rr are the time to isolate the fault area and the load recovery time in the non-fault area, ω i is the node load weight, P i load is the active load of the ith node, Ω snr A collection of fault scenarios.

[0160] In a preferred embodiment of the present invention, in order to analyze the self-healing level of the system, R x,c , R c , R represent the system performance level in the degradation stage, fault isolation stage and power supply restoration stage under scenario c, the overall performance level of the system under scenario c and the expected self-healing level of the system:

[0161]

[0162] The distribution system self-healing improvement model proposed in this embodiment that coordinates the prevention process and the response and recovery process is a MILP model, which can be quickly solved by commercial solvers such as Cplex and Gurobi.

[0163] S3, taking the scheme data item as an influencing factor, and clustering the self-healing scheme according to the influencing factor to generate a full-factor clustering set and a plurality of missing-factor clustering sets; wherein the missing-factor clustering set includes: a plurality of self-healing schemes that lack a corresponding influencing factor;

[0164] In a preferred embodiment of the present invention, five scheme data items in the self-healing scheme, namely, the number of primary and secondary equipment, communication capability, grid transmittable power capacity, master station recovery strategy, and the number of faulty lines, are analyzed as influencing factors. Figure 3 Define the standard status.

[0165] The standard states defined are as follows:

[0166] (1) The number of RCS is 11 and is located at 2, 5, 6, 8, 11, 12, 18, 19, 23, 24, 25, 28, 32

[0167] (2) Communication time: the time from when the fault occurs to when the fault is sensed, and the time from when the master station issues a command to allow closing after disconnection. The sum of the two is set to 0.6 h.

[0168] (3) The maximum power allowed by the grid is set to 0.3 MW

[0169] (4) The master station recovery strategy is set to consider the load weight in all stages, that is, give priority to key nodes

[0170] (5) The number of faulty lines is 4, i.e. 4 lines are faulty

[0171] S4. Calculate, according to the expected self-healing level, first similarities between a number of full-factor solutions in the full-factor clustering set and a number of first-deficient-factor solutions in each deficient-factor clustering set;

[0172] In a preferred embodiment of the present invention, five influencing factors are first numbered (i=1-5): RCS number, communication time, maximum transmission power allowed by the grid, master station recovery strategy, and number of faulty lines. Then, the schemes using the five influencing factors in each state are clustered based on the AP clustering algorithm to obtain all clustering results. m (0≤m≤6, m∈Z); when m=6, J m represents the clustering result under all influencing factors; when m=i, it represents the clustering result under the missing i-th influencing factor. Each group of clustering influencing factors can be divided into n detailed schemes, namely J m = {Bm1 ,B m2 ,…,B mn}, each solution is independent of each other and has no coupling relationship.

[0173] In the case of all influencing factors and the absence of a certain influencing factor, the similarity matrix method is used to calculate the standard state, and the similarity matrix is:

[0174]

[0175] Where: e xy For the xth solution s x With the yth solution s y The similarity between them, that is, the first similarity, can be obtained using the cosine similarity formula, namely:

[0176]

[0177] S5, constructing a collection of second factor-deficient solutions for each full-factor solution; wherein each of the second factor-deficient solutions is a solution generated by deleting one of the influencing factors from the corresponding full-factor solution;

[0178] S6. Calculate, based on the first similarity, a second similarity between the full-factor solution and each second-factor-missing solution in the corresponding collection of second-factor-missing solutions;

[0179] Preferably, the calculating, based on the first similarity, a second similarity between the full-factor solution and each second-factor-missing solution in the corresponding set of second-factor-missing solutions comprises:

[0180] S61, calculating, according to the first similarity, a first domain radius of each full-factor solution in the full-factor cluster set, and a second domain radius of each second missing-factor solution in the corresponding missing-factor cluster set;

[0181] S62, calculating the initial similarity between the full factor solution and each second missing factor solution in the corresponding collection of second missing factor solutions according to the first domain radius and the second domain radius;

[0182] S63, calculating the domain similarity between the full factor cluster set and each of the missing factor cluster sets according to the first total number of all factor solutions in the full factor cluster set and the second total number of first missing factor solutions in each missing factor cluster set;

[0183] S64. Calculate, according to the initial similarity and the domain similarity, a second similarity between the full-factor solution and each second missing-factor solution in the corresponding collection of second missing-factor solutions.

[0184] In a preferred embodiment of the present invention, based on the idea of ​​structural similarity, the structural similarity between each strategy in the clustering results of all influencing factors and missing influencing factors is calculated. x In the case of clustering of all influencing factors, the classification and clustering B 0h In the case of missing the mth influencing factor cluster, it is classified as B ml Then, solution s x The field radius in both cases is:

[0185]

[0186]

[0187] Where: b 0h is the number of detailed strategies in the hth clustering partition under all influencing factors; b jl is the number of detailed strategies in the lth clustering partition when the jth influencing factor is missing; s 0x and jx This is the solution under all influencing factors and missing j indicators.

[0188] Then we get s x The similarity (initial similarity) between all influencing factors and the influencing factors without a certain indicator is:

[0189]

[0190] Solutions x The similarity (domain similarity) of the neighborhood under all influencing factors and the absence of a certain influencing factor is:

[0191]

[0192] Where: b1 and b2 are the number of strategies in the two cases of the set, namely the first total number and the second total number.

[0193] Then strategies x The similarity (second similarity) under comprehensive consideration of all influencing factors and influencing factors with a certain indicator missing is:

[0194] D sim,s (s 0x ,s jx )=D sim,c (s 0x ,s jx )D sim,d (s 0x ,s jx )

[0195] S7. Calculate the factor weight of each influencing factor according to the second similarity;

[0196] Preferably, calculating the factor weight of each influencing factor according to the second similarity includes:

[0197] S71. Calculate the factor weight of each influencing factor according to the following formula:

[0198]

[0199]

[0200] Among them, t j is the factor weight of the jth influencing factor, X j is the solution set of several second missing factor solutions that are missing the jth influencing factor; X0 is the full factor clustering set; r is (X j ,X0) is the set similarity between the scheme set with the jth influencing factor missing and the set of all factor clustering; r ji is the second similarity between the i-th full-factor solution and the corresponding second missing-factor solution that lacks the j-th influencing factor.

[0201] In a preferred embodiment of the present invention, similarities under comprehensive consideration in other cases can be obtained in the same way, so that a similarity matrix under comprehensive consideration is constructed as follows:

[0202]

[0203] Where: r ji is the second similarity between the i-th full-factor solution and the corresponding second missing-factor solution that lacks the j-th influencing factor.

[0204] The comprehensive similarity vector of the clustering results under all influencing factors and their own clustering results is [1,1,…,]. The relationship between all influencing factors and the influencing factors without a certain indicator can be measured using the Euclidean distance formula, and the calculated distance value is:

[0205]

[0206] Then the indicator t j The weight can be calculated by the following formula:

[0207]

[0208] Where 0≤t j ≤1,

[0209] S8. Calculate a self-healing capability evaluation score of the distribution network according to the factor weights and the overall performance level.

[0210] In a preferred embodiment of the present invention, by analyzing various fault scenarios and obtaining the weights of various indicators, a relatively accurate calculation formula for the self-healing capability can be obtained as follows:

[0211] R'=t j1 R a +t j2 R b +t j3 R c +t j4 R d +t j5 R e ;

[0212] Where: R' is the final more accurate self-healing ability, t ji is the weight of different influencing factors, R a,b,c,d,e,f They are the overall performance levels in different scenarios.

[0213] The present embodiment provides a method for evaluating the self-healing capability of a distribution network, by evaluating the expected self-healing level of the distribution network according to the flow operation data of the distribution network in several fault scenarios, and then taking several scheme data items in the self-healing scheme as influencing factors affecting the self-healing level of the distribution network, and clustering the self-healing schemes of the distribution network in each fault scenario according to the influencing factors, and calculating the second similarity between the full-factor scheme and the second factor-deficient scheme corresponding to itself according to the first similarity between several full-factor schemes and each first factor-deficient scheme, and calculating the factor weight of each influencing factor according to the first total number of the full-factor schemes, the second total number of the first factor-deficient schemes, and the second similarity, and finally, calculating the self-healing capability evaluation score of the distribution network according to the factor weight and the expected self-healing level. It can be understood that the present invention calculates the first similarity between the full factor solution and the first missing factor solution according to the expected self-healing level, and then evaluates the second similarity between the full factor solution and the solution after each influencing factor is missing according to the first similarity. The larger the second similarity, the closer the expected self-healing level results of the full factor solution and the second missing factor solution are, which means that the influencing factor has little effect on the self-healing ability of the distribution network. Based on the entropy weight theory, the influencing factor should be assigned a smaller weight, otherwise, a larger weight should be assigned. Therefore, the present invention evaluates the contribution of each action made by the distribution network in the fault self-healing process to the fault self-healing by calculating the weight assigned to each data item of the several self-healing solutions in the distribution network, and accurately evaluates the self-healing ability of the distribution network in combination with the overall performance level of the distribution network.

[0214] See also Figure 2 , is a schematic diagram of the structure of a self-healing capability evaluation device for a distribution network provided by an embodiment of the present invention, comprising:

[0215] A data acquisition module, used to acquire the power flow operation data of the distribution network to be evaluated in several fault scenarios, and the self-healing solution of the distribution network in each fault scenario; wherein the self-healing solution includes several solution data items; one of the solution data items corresponds to an indicator or a type of component adjusted by the distribution network when performing fault self-healing;

[0216] A level evaluation module, used to calculate the expected self-healing level of the distribution network and the overall performance level of the distribution network under various fault scenarios according to the power flow operation data;

[0217] A scheme clustering module is used to take the scheme data items as influencing factors, and cluster the self-healing schemes according to the influencing factors to generate a full-factor clustering set and a plurality of missing-factor clustering sets; wherein the missing-factor clustering set includes: a plurality of self-healing schemes that lack a corresponding influencing factor;

[0218] A first similarity calculation module, used for calculating, according to the expected self-healing level, first similarities between a plurality of full-factor solutions in the full-factor clustering set and a plurality of first lack-factor solutions in each lack-factor clustering set;

[0219] A solution generation module is used to construct a collection of second factor-deficient solutions for each full-factor solution; wherein each of the second factor-deficient solutions is a solution generated by deleting one of the influencing factors from the corresponding full-factor solution;

[0220] A second similarity calculation module, configured to calculate, based on the first similarity, a second similarity between the full-factor solution and each second-factor-missing solution in the corresponding second-factor-missing solution collection;

[0221] A weight calculation module, used for calculating the factor weight of each influencing factor according to the second similarity;

[0222] The self-healing capability evaluation module is used to calculate the self-healing capability evaluation score of the distribution network according to the factor weights and the overall performance level.

[0223] Preferably, the data acquisition module acquires the power flow operation data of the distribution network to be evaluated in several fault scenarios, and the self-healing solution of the distribution network in each fault scenario, including:

[0224] Acquire a fault self-healing model of the distribution network; wherein the fault self-healing model is a simulation model constructed according to the distribution network and used to simulate the self-healing of the distribution network after a fault occurs;

[0225] Randomly applying a number of preset fault scenarios to the fault self-healing model, so that the fault self-healing model, under each fault scenario, takes the minimum loss load as the goal, simulates the actions of each switch element of the distribution network in each operation stage and adjusts the indicators of each item, and records the power flow operation data of the distribution network in each operation stage of each fault scenario; wherein the operation stage includes: an initial stage, a fault location stage, a fault isolation stage, and a power supply restoration stage;

[0226] According to the actions of each switch element of the distribution network at each operation stage and the indicators of each item are adjusted, a self-healing plan for each stage of the distribution network under each fault scenario is generated.

[0227] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0228] Those skilled in the art can clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0229] Another preferred embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a method for evaluating the self-healing capability of a distribution network as described in any one of the above embodiments is implemented.

[0230] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0231] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0232] The memory can be used to store the computer program, and the processor realizes various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0233] Another preferred embodiment of the present invention provides a storage medium, the storage medium is a computer-readable storage medium, the computer program is stored in the computer-readable storage medium, and the computer program, when executed by the processor, can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0234] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for evaluating the self-healing capability of a distribution network, characterized in that: include: Acquire the power flow operation data of the distribution network to be evaluated in several fault scenarios, and the self-healing scheme of the distribution network in each fault scenario; wherein the self-healing scheme includes several scheme data items; one of the scheme data items corresponds to an indicator or a type of component adjusted by the distribution network when performing fault self-healing; Calculating the expected self-healing level of the distribution network and the overall performance level of the distribution network under various fault scenarios according to the power flow operation data; The scheme data items are used as influencing factors, and the self-healing schemes are clustered according to the influencing factors to generate a full-factor clustering set and a plurality of missing-factor clustering sets; wherein the missing-factor clustering set includes: a plurality of self-healing schemes that lack a corresponding influencing factor; According to the expected self-healing level, calculating first similarities between a plurality of full-factor solutions in the full-factor clustering set and a plurality of first-factor-deficient solutions in each of the deficient-factor clustering sets; Constructing a collection of second factor-missing solutions for each full-factor solution; wherein each of the second factor-missing solutions is a solution generated by deleting one of the influencing factors from the corresponding full-factor solution; Calculating, according to the first similarity, a second similarity between the full-factor solution and each second-factor-missing solution in the corresponding collection of second-factor-missing solutions; Calculating the factor weight of each influencing factor according to the second similarity; A self-healing capability evaluation score of the distribution network is calculated according to the factor weights and the overall performance level.

2. A method for evaluating the self-healing capability of a distribution network as claimed in claim 1, characterized in that: The obtaining of power flow operation data of the distribution network to be evaluated in several fault scenarios, and the self-healing solution of the distribution network in each fault scenario, includes: Acquire a fault self-healing model of the distribution network; wherein the fault self-healing model is a simulation model constructed according to the distribution network and used to simulate the self-healing of the distribution network after a fault occurs; Randomly applying a number of preset fault scenarios to the fault self-healing model, so that the fault self-healing model, under each fault scenario, takes the minimum loss load as the goal, simulates the actions of each switch element of the distribution network in each operation stage and adjusts the indicators of each item, and records the power flow operation data of the distribution network in each operation stage of each fault scenario; wherein the operation stage includes: an initial stage, a fault location stage, a fault isolation stage, and a power supply restoration stage; According to the actions of each switch element of the distribution network at each operation stage and the indicators of each item are adjusted, a self-healing plan for each stage of the distribution network under each fault scenario is generated.

3. A method for evaluating the self-healing capability of a distribution network as claimed in claim 2, characterized in that: The power flow operation data includes: the amount of lost load, the time to isolate the fault area, the time to restore the load in the non-fault area, and the active load of each node; The calculating the expected self-healing level of the distribution network according to the power flow operation data includes: The expected self-healing level of the distribution network is calculated according to the following formula: Among them, R is the expected self-healing level, P c shed is the loss load of the cth fault scenario, p c is the probability of occurrence of the cth fault scenario, T ir / T rr are the time to isolate the fault area and the load recovery time in the non-fault area, ω i is the node load weight, P i load is the active load of the ith node, Ω snr A collection of fault scenarios.

4. A method for evaluating the self-healing capability of a distribution network as claimed in claim 3, characterized in that: According to the power flow operation data, the overall performance level of the distribution network under each fault scenario is calculated, including: The overall performance level of the distribution network under various fault scenarios is calculated according to the following formula: Among them, R c is the overall performance level of the cth fault scenario, P c shed is the loss load of the cth fault scenario, T ir / T rr are the time to isolate the fault area and the load recovery time in the non-fault area, ω i is the node load weight, P i load is the active load of the ith node, Ω snr A collection of fault scenarios.

5. A method for evaluating the self-healing capability of a distribution network as claimed in claim 4, characterized in that: The calculating, according to the first similarity, a second similarity between the full-factor solution and each second-factor-deficient solution in the corresponding second-factor-deficient solution collection includes: Calculate, according to the first similarity, a first domain radius of each full-factor solution in the full-factor cluster set, and a second domain radius of each second missing-factor solution in the corresponding missing-factor cluster set; Calculating the initial similarity between the full factor solution and each second missing factor solution in the corresponding second missing factor solution set according to the first domain radius and the second domain radius; Calculate the domain similarity between the full factor cluster set and each of the missing factor cluster sets according to the first total number of all factor solutions in the full factor cluster set and the second total number of first missing factor solutions in each missing factor cluster set; A second similarity between the full-factor solution and each second missing-factor solution in the corresponding second missing-factor solution set is calculated according to the initial similarity and the domain similarity.

6. A method for evaluating the self-healing capability of a distribution network as claimed in claim 5, characterized in that: The calculating the factor weight of each influencing factor according to the second similarity includes: Calculate the factor weight of each influencing factor according to the following formula: Among them, t j is the factor weight of the jth influencing factor, X j is the solution set of several second missing factor solutions that are missing the jth influencing factor; X0 is the full factor clustering set; r is (X j ,X0) is the set similarity between the scheme set with the jth influencing factor missing and the set of all factor clustering; r ji is the second similarity between the i-th full-factor solution and the corresponding second missing-factor solution that lacks the j-th influencing factor.

7. A self-healing capability evaluation device for a distribution network, characterized in that: include: A data acquisition module, used to acquire the power flow operation data of the distribution network to be evaluated in several fault scenarios, and the self-healing solution of the distribution network in each fault scenario; wherein the self-healing solution includes several solution data items; one of the solution data items corresponds to an indicator or a type of component adjusted by the distribution network when performing fault self-healing; A level evaluation module, used to calculate the expected self-healing level of the distribution network and the overall performance level of the distribution network under various fault scenarios according to the power flow operation data; A scheme clustering module is used to take the scheme data items as influencing factors, and cluster the self-healing schemes according to the influencing factors to generate a full-factor clustering set and a plurality of missing-factor clustering sets; wherein the missing-factor clustering set includes: a plurality of self-healing schemes that lack a corresponding influencing factor; A first similarity calculation module, used for calculating, according to the expected self-healing level, first similarities between a plurality of full-factor solutions in the full-factor clustering set and a plurality of first lack-factor solutions in each lack-factor clustering set; A solution generation module is used to construct a collection of second factor-deficient solutions for each full-factor solution; wherein each of the second factor-deficient solutions is a solution generated by deleting one of the influencing factors from the corresponding full-factor solution; A second similarity calculation module, configured to calculate, based on the first similarity, a second similarity between the full-factor solution and each second-factor-missing solution in the corresponding second-factor-missing solution collection; A weight calculation module, used for calculating the factor weight of each influencing factor according to the second similarity; The self-healing capability evaluation module is used to calculate the self-healing capability evaluation score of the distribution network according to the factor weights and the overall performance level.

8. A self-healing capability evaluation device for a distribution network as claimed in claim 7, characterized in that: The data acquisition module acquires the power flow operation data of the distribution network to be evaluated in several fault scenarios, and the self-healing solution of the distribution network in each fault scenario, including: Acquire a fault self-healing model of the distribution network; wherein the fault self-healing model is a simulation model constructed according to the distribution network and used to simulate the self-healing of the distribution network after a fault occurs; Randomly applying a number of preset fault scenarios to the fault self-healing model, so that the fault self-healing model, under each fault scenario, takes the minimum loss load as the goal, simulates the actions of each switch element of the distribution network in each operation stage and adjusts the indicators of each item, and records the power flow operation data of the distribution network in each operation stage of each fault scenario; wherein the operation stage includes: an initial stage, a fault location stage, a fault isolation stage, and a power supply restoration stage; According to the actions of each switch element of the distribution network at each operation stage and the indicators of each item are adjusted, a self-healing plan for each stage of the distribution network under each fault scenario is generated.

9. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a method for evaluating the self-healing capability of a distribution network as described in any one of claims 1 to 6 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is executed, the device where the storage medium is located is controlled to execute a method for evaluating the self-healing capability of a distribution network as described in any one of claims 1 to 6.

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