CN110994598B - Multi-target power grid fault recovery method and device - Google Patents
Multi-target power grid fault recovery method and device Download PDFInfo
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Abstract
The invention discloses a multi-target power grid fault recovery method based on a krill swarm algorithm, which comprises the following steps of: acquiring parameters of each node in a power grid; when the parameters of each node in the power grid meet the constraint conditions of power grid recovery, establishing an evaluation function for factors influencing the power grid fault recovery result; and constructing a multi-objective optimization model based on a krill swarm algorithm by using the evaluation function, solving the model, acquiring the recovery sequence of the power grid nodes, reducing the fault recovery loss and improving the power service quality.
Description
Technical Field
The application relates to the field of electricity, in particular to a multi-target power grid fault recovery method and a multi-target power grid fault recovery device.
Background
After a power grid fault occurs, fault recovery is an indispensable step, but after fault recovery, the quality of recovery effect is related, and how to determine the initial recovery sequence of node loads, reduce fault recovery loss and improve power service quality is a problem to be solved urgently.
Disclosure of Invention
The application provides a multi-target power grid fault recovery method which can reduce fault recovery loss and improve power service quality.
The application provides a multi-target power grid fault recovery method, which comprises the following steps:
acquiring parameters of each node in a power grid;
when the parameters of each node in the power grid meet the constraint conditions of power grid recovery, establishing an evaluation function for factors influencing the power grid fault recovery result;
and constructing a multi-objective optimization model based on a krill swarm algorithm by using the evaluation function, solving the model, and acquiring a recovery sequence of the power grid nodes.
Preferably, the parameter of each node in the power grid includes at least one of the following:
starting power corresponding to the node;
capacity corresponding to the node load;
the minimum value of the system frequency of the corresponding node in the recovery process;
the number of automatic switches and manual switches in the node load recovery is corresponding;
corresponding to the number of importance nodes within the node.
Preferably, when the parameters of each node in the power grid satisfy the constraint condition of power grid recovery, an evaluation function is established for factors influencing the power grid fault recovery result, and the evaluation function includes:
when the parameters of each node in the power grid meet the load constraint and the operation constraint conditions for power grid recovery;
and establishing a corresponding load recovery capacity evaluation function, a load importance evaluation function and a switch operation cost evaluation function for the load recovery capacity factor, the load importance factor and the switch operation cost factor which influence the power grid fault recovery result.
Preferably, the load constraints and operating constraints include:
the load constraint is a system frequency constraint, which is expressed as:
F min ＞F p,min
wherein, F min For the frequency minimum caused by the load during the recovery, F p,min The minimum value allowed by the system frequency;
the operating constraint is that the power of the node during start-up should be limited to within the normal operating range:
P i,min ＜P i ＜P i,max
in the formula, P i Is the power of node i, P i,min And P i,max Which are respectively the minimum value and the maximum value allowed by the power in the normal starting process of the node i.
Preferably, the load recovery capability evaluation function is:
in the formula, c i Represents the operating condition of the load i, c i When 1, the load works normally; c. C i At 0, the load is waiting to recover; l is i Represents the capacity of the load i; n is 1 The load set to be recovered at the current time step;
the load importance evaluation function is as follows:
k l ＝1-k m -k h
in the formula, k h Representing the number of first-stage loads of the node, k m Representing the number of second-stage loads of the node, k l Representing the number of the third-stage loads of the node;
the evaluation function of the switching operation cost is as follows:
E S ＝(k A N A +k M N M )
k A ,k M weight coefficients, N, for automatic and manual switches, respectively A ,N M The number of the automatic switches and the number of the manual switches in the fault recovery process are respectively, and the number of the switches corresponding to each load is different.
Preferably, the method for constructing a multi-objective optimization model based on a krill swarm algorithm by using the evaluation function, solving the model and obtaining a recovery sequence of the power grid nodes includes:
and constructing a multi-objective optimization model based on the krill mass algorithm by using the evaluation function as follows:
minE(S)＝min(-E i ,-E p ,E S )
wherein E (S) is an objective function showing recovery efficiency, E i To restore the load-bearing Capacity evaluation function, E p As a function of load importance evaluation, E S Evaluating a function for the switch operation cost;
and solving the model, wherein each output solution corresponds to the recovery sequence of a group of power grid nodes.
This application provides a multi-target electric wire netting fault recovery device simultaneously, includes:
the parameter acquisition unit is used for acquiring parameters of each node in the power grid;
the evaluation function establishing unit is used for establishing an evaluation function for factors influencing the power grid fault recovery result on the basis that the parameters of each node in the power grid meet the constraint condition of power grid recovery;
and the recovery sequence determining unit is used for constructing a multi-objective optimization model based on a krill swarm algorithm by using the evaluation function, solving the model and acquiring the recovery sequence of the power grid nodes.
Preferably, the evaluation function establishing unit includes:
the constraint condition subunit is used for meeting the load constraint and the operation constraint condition of power grid restoration when the parameters of each node in the power grid;
and the evaluation function establishing subunit is used for establishing a corresponding load recovery capability evaluation function, a load importance evaluation function and a switch operation cost evaluation function for the load recovery capability factor, the load importance factor and the switch operation cost factor which influence the power grid fault recovery result.
Preferably, the restoration order determination unit includes:
and the model construction subunit is used for constructing a multi-objective optimization model based on the krill swarm algorithm by using the evaluation function, and comprises the following steps:
min E(S)＝min(-E i ,-E p ,E S )
wherein E (S) is an objective function, representing the recovery efficiency, E i To restore the load-bearing capacity evaluation function, E p As a function of load importance evaluation, E S Evaluating a function for the switch operation cost;
and the solving subunit is used for solving the model, and each output solution corresponds to the recovery sequence of a group of power grid nodes.
The application provides a multi-target power grid fault recovery method, which comprises the steps of constructing a multi-target optimization model based on a krill swarm algorithm by using an evaluation function, solving the model, obtaining a recovery sequence of power grid nodes, reducing fault recovery loss and improving power service quality.
Drawings
FIG. 1 is a schematic flow chart diagram illustrating a multi-objective grid fault recovery method provided by the present application;
fig. 2 is a flow chart of a krill mass algorithm to which the present application relates;
FIG. 3 is a schematic diagram of section IEEE39 to which the present application relates;
FIG. 4 is a solution set spatial distribution diagram of the krill population algorithm according to the present application
Fig. 5 is a schematic diagram of a multi-target grid fault recovery device provided by the present application.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is capable of implementation in many different ways than those herein set forth and of similar import by those skilled in the art without departing from the spirit and scope of this application, and thus this application is not limited to the specific implementations disclosed below.
Fig. 1 is a schematic flow chart of a multi-target power grid fault recovery method provided by the present application, and the method provided by the present application is described in detail below with reference to fig. 1.
And S101, acquiring parameters of each node in the power grid.
And acquiring relevant parameters of each node in the power grid, wherein the relevant parameters comprise at least one of the following tables, and providing a basis for the next steps. In the method, a parameter table shown in the following table needs to be established for each node:
| node point | P i (MW) | L i (MW) | F min (HZ) | N A ,N M | k h ,k m ,k l |
| xx | xx | xx | xx | x,x | x,x,x |
In the above table, the parameters corresponding to different nodes are different, P i Starting power corresponding to the node; l is i Capacity corresponding to the node load; f min The minimum value of the system frequency of the corresponding node in the recovery process; n is a radical of hydrogen A ,N M The number of automatic switches and manual switches in the node load recovery is corresponding; k is a radical of h ,k m ,k l Corresponding to the number of importance nodes within the node.
And S102, when the parameters of each node in the power grid meet the constraint condition of power grid recovery, establishing an evaluation function for factors influencing the power grid fault recovery result.
When the parameters of each node in the power grid meet the load constraint and the operation constraint conditions for power grid recovery; and establishing corresponding load recovery capacity evaluation functions, load importance evaluation functions and switching operation cost evaluation functions for the load recovery capacity factors, the load importance factors and the switching operation cost factors which influence the grid fault recovery result.
In the recovery process of the power system, the system can be recovered to a normal state through recovery control under a stable state only if certain constraint conditions are required to be met. There are two main types of constraints in the power system, one is load constraint and the other is operation constraint. Load constraints are reflected in the conditions that need to be met in load operation. Operational constraints are reflected in the need for the operational parameters of the system to be within a reasonable range. The main constraint condition of load recovery is that the steady-state frequency must be kept within an allowable range, if the load recovery amount is small, the recovery time will be long, and if the load recovery amount is too large, the frequency drop amplitude may be too large, the system may fail again, so the system frequency cannot be lower than a certain limit value. The system frequency constraint is expressed as:
F min ＞F p,min
wherein, F min For the minimum value of the frequency caused by the load during the recovery (corresponding to the minimum value of the system frequency of the node during the recovery), F p,min The minimum value allowed by the system frequency represents that the frequency cannot be lower than the minimum value allowed by the system in the process of frequency reduction caused by the load.
From the obtained F min And P i The value of (2) should satisfy both of these two types of constraints, and then the evaluation function can be calculated. In the method, the load recovery capacity, the load importance and the switching operation cost are considered at the same time, and an evaluation function is established for the three factors influencing the grid fault recovery result respectively so as to evaluate the final recovery effect.
1) Load recovery capability evaluation function
The recovery efficiency is represented by whether the most important load points can be recovered most quickly by the power system, and the recovery sequence should be determined according to the priority of the loads to be recovered, namely, the load points with higher priority should be recovered preferentially. According to the requirement of load priority, the loads in the power network system are divided into three stages, the first stage of loads is related to the vitality of the power system, and the importance degrees of the second stage and the third stage are decreased. The load recovery capability evaluation function is:
in the formula, c i Represents the operating condition of the load i, c i When 1, the load works normally; c. C i At 0, the load is waiting to recover; l is i Represents the capacity of the load i; n is 1 The load set to be recovered at the current time step.
2) Load importance evaluation function
In the fault recovery, whether the normal operation of the power system can be quickly recovered is an important index, the fault recovery time is mainly related to the starting time of the generator set, and by taking the starting of the power supply as a time starting point, the recovery importance evaluation function is as follows:
k l ＝1-k m -k h
in the formula, k h Representing the number of first-level loads of the node, k m Representing the number of second-stage loads of the node, k l The number of the third-level loads representing the node is given weights of 5, 2 and 1 based on the importance of the third-level loads.
3) Switch operation cost evaluation function
The switch operation cost is an important index influencing a fault recovery scheme, the total operable times of the switch equipment are limited, the use of the switch equipment needs to be reduced as much as possible to ensure the long-time safe operation of the power system, and meanwhile, the switch equipment is more suitable for the fault recovery schemeThe recovery speed of the fault can be accelerated by using a small number of switches. Corresponding weights are given to different switch types according to experience due to different corresponding costs of the manual switch and the automatic switch, and a switch operation cost evaluation function E S Is expressed as follows:
E S ＝(k A N A +k M N M )
k A ,k M weight coefficients, N, for automatic and manual switches, respectively A ,N M The number of the automatic switches and the number of the manual switches in the fault recovery process are respectively, and the number of the switches corresponding to each load is different. According to the scheduling experience, k A Taken as 1, k M Taken as 15.
And S103, constructing a multi-objective optimization model based on a krill swarm algorithm by using the evaluation function, solving the model, and acquiring a recovery sequence of the power grid nodes.
In order to achieve a good fault recovery effect, only one of the aspects (one evaluation function) cannot be considered, and the three aspects (three evaluation functions) are considered simultaneously. By establishing a comprehensive objective function, the fault recovery effect can be more intuitively reflected.
The failure recovery is a multi-time-step recovery process, only the initial stage of the failure recovery is considered in the design, and the selection of the initial stage recovery scheme is crucial to the whole recovery process. A large power generation amount is required to be provided as much as possible at the initial recovery stage, so that the subsequent load recovery is facilitated; at the same time, the load with high importance needs to be recovered preferentially so as to prevent the key load from generating problems; there is also a need to reduce the use of switches to improve the reliability of the recovery scheme. According to different requirements on benefit type and cost type indexes, the system is integrated into an objective function E (S), the recovery efficiency of the system can be effectively embodied, and a multi-objective optimization model is as follows:
min E(S)＝min(-E i ,-E p ,E S )
wherein E (S) is an objective function, representing the recovery efficiency, E i To restore the evaluation function of the load capacity, E p To the degree of importance of the loadEvaluation function of E S Is an evaluation function of the operating cost of the switch. E i The larger the signal is, the stronger the recovery capability of the system is; e p The larger the node is, the greater the importance degree of the node is; e S The smaller the cost generated by the switching operation is; for E (S) as a whole, it is desirable to satisfy the requirements of the three objectives as simultaneously as possible.
In order to obtain a solution which minimizes the objective function, the krill mass algorithm is optimized for the fault recovery aid decision problem, and the objective function involved in the fault recovery problem is solved. Krill movement is considered to be a multi-objective process involving three targets, with the aim of increasing population density, finding food to sustain life, and random movement.
(1) Individual exercise induced by other krill
The krill i induced movements by other krills is defined as N i new ：
Wherein N is max Represents the maximum induction speed and is constant; n is a radical of i old Representing the original induced motion of the current individual i; omega n Representing the inertia weight of the induced motion of the individual i, and the value range is [0,1 ]]；α i Represents the direction of the individual i induced by other krill, and is expressed by the formula:
(2) Foraging movement
The movement of krill i induced by foraging movement is defined as F i ：
F i ＝V f β i +ω f F i,old
β i Represents the directional vector, beta, of individual krill foraging i ＝β i,food +β i,best Wherein beta is i,food Is the direction vector of the food source, beta i,best A direction vector for globally optimal krill; v f Is the speed of foraging; omega f Is inertia weight value with a value range of [0,1]；F i,old The position change generated by the previous foraging motion of the ith krill individual; f i The location of the current i-th individual krill is changed.
(3) Random diffusion
The random diffusion induced movement of krill i is defined as D i ：
D i ＝D max δ
D max Maximum diffusion velocity for individual krill; delta is a random diffusion direction vector with a value range of [ -1, +1 [)]；D i Is the position change caused by random diffusion.
In the natural system, the fitness of each krill should be a combination of the krill individual to food distance and the distance of the krill individual to the location where the krill mass density is highest. An effective search is carried out in a krill group algorithm according to a Lagrange model, the density of krill and the attraction of food are main factors for guiding the movement of individual krill, the movement speed of the ith krill is S (i), and the expression is as follows:
x in the formula i Represents the location of the ith krill; d i Representing the random diffusion movement speed of the ith krill; f i A movement velocity representative of foraging movement; n is a radical of i Representing the movement speed of the ith krill induced by other krill. For three velocities affecting krill movementAnd establishing a corresponding motion function. The factors causing the krill movement can make the krill change the position of the krill towards the direction with the minimum fitness, so that the solution moves towards the optimal solution direction, and the optimization algorithm is effective.
The new positions of krill are:
X i (k)＝X i (k-1)+(D i +F i +N i )
and continuously updating the position of each krill under the comprehensive influence of the three factors until the current optimal krill position conforms to the corresponding solution or the maximum iteration number is reached. In order to improve the convergence performance of the krill group algorithm in the application scene, the whole algorithm is divided into two groups for optimization, a main search group and two auxiliary search groups are set, the main search group corresponds to the load recovery capability and is also the most important index for evaluating fault recovery, and the auxiliary search groups correspond to the recovery speed and the switching cost. In the searching process, the krill in the main and auxiliary populations are exchanged and replaced, so that the main population is prevented from falling into local optimum, and the reasonability of an optimum solution set is ensured. The solving steps are as follows, and the flow chart is as shown in figure 2:
1) initializing main and auxiliary shrimp groups: reading in the power grid data after the fault, and determining the load needing to be recovered and the corresponding priority of the load; the number of the two types of krill populations is initialized, and the population quantity, the maximum iteration times, the maximum induction speed, the foraging speed and the diffusion speed are set;
2) calculating the fitness of each krill in the main population, and storing the position of the krill with the highest fitness in the main population and the fitness: calculating the fitness of each krill in the main population, selecting a position with lower fitness as an initial population position, and selecting a position with highest fitness as a global optimal krill of the main population;
3) calculating the fitness of each krill in the secondary population, and storing the position of the krill with the highest fitness in the secondary population and the fitness: calculating the fitness of each krill in the secondary population, selecting a position with lower fitness as an initial population position, and selecting a position with highest fitness as a global optimal krill of the secondary population;
4) updating individual historical positions and matched fitness of each krill in the main population and the auxiliary population;
5) updating the position and the fitness of the krill with the highest fitness, and exchanging the krills in the main and auxiliary populations according to the proportion: sorting the krill in the two populations according to the fitness, performing cross substitution on the first 19.1% of the krill in the two sub-populations and the last 38.2% of the krill in the main population, and returning to continue iteration until an iteration condition is met or convergence is reached;
6) judging whether an iteration condition is met or convergence is achieved: verifying whether constraint conditions are met or not, and calculating the value of each evaluation function;
7) taking out part of population at the front edge of Pareto for reservation;
and outputting the reserved optimal population, and converting the reserved optimal population into an output solution, wherein the output solution is the node recovery sequence.
Generally, the output solution of the multi-objective optimization model based on the krill swarm algorithm is multiple, and each output solution corresponds to the recovery sequence of a group of power grid nodes.
In order to verify the effectiveness of the multi-target grid fault recovery method provided by the application, an IEEE39 node system shown in fig. 3 is taken as an example for verification. Assuming that a set on node 31 is used as a black start power supply, the installed capacity is 600MW, the power factor is 0.9, and the start parameters of each node are shown in table 1:
TABLE 1 important parameters of IEEE39 nodes
Solving the recovery scheme of the power system in the first time step by using a krill group algorithm added with the main and auxiliary search groups, wherein the foraging speed V is f Take the constant 0.02 (ms) -1 ) Random maximum diffusion velocity D max Is taken to be 0.005 (ms) -1 ) The krill group size is 50, the number of iterations is 50, and the inertial weight ω is f Is selected as 1, F p,min Was found to be 10 (HZ). The result is collected after 30 iterationsConverging, the number of solutions at the Pareto frontier is 4, and the Pareto solution set space for recovering the first time step is shown in fig. 4. The Pareto solution set to the problem is shown in table 2.
TABLE 2 recovery scheme at first time step
The present application also provides a multi-target grid fault recovery apparatus 200, as shown in fig. 5, including:
a parameter obtaining unit 210, configured to obtain parameters of each node in the power grid;
the evaluation function establishing unit 220 is configured to establish an evaluation function for factors affecting a power grid fault recovery result when the parameters of each node in the power grid satisfy a constraint condition for power grid recovery;
and the recovery sequence determining unit 230 is configured to construct a multi-objective optimization model based on a krill swarm algorithm by using the evaluation function, solve the model, and obtain a recovery sequence of the power grid nodes.
An evaluation function establishing unit comprising: the constraint condition subunit is used for meeting the load constraint and the operation constraint condition of power grid restoration when the parameters of each node in the power grid;
and the evaluation function establishing subunit is used for establishing a corresponding load recovery capability evaluation function, a load importance evaluation function and a switch operation cost evaluation function for the load recovery capability factor, the load importance factor and the switch operation cost factor which influence the power grid fault recovery result.
A recovery order determination unit comprising:
and the model construction subunit is used for constructing a multi-objective optimization model based on the krill swarm algorithm by using the evaluation function, and comprises the following steps:
min E(S)＝min(-E i ,-E p ,E S )
wherein E (S) is an objective function, representing the recovery efficiency, E i To restore the load-bearing capacity evaluation function, E p As a function of the importance of the load, E S Evaluating a function for the switch operation cost;
and the solving subunit is used for solving the model, and each output solution corresponds to the recovery sequence of a group of power grid nodes.
The application provides a multi-target power grid fault recovery method, which comprises the steps of constructing a multi-target optimization model based on a krill swarm algorithm by using an evaluation function, solving the model, obtaining a recovery sequence of power grid nodes, reducing fault recovery loss and improving power service quality.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the invention.
Claims (5)
1. A multi-target grid fault recovery method is characterized by comprising the following steps:
acquiring parameters of each node in a power grid;
when the parameters of each node in the power grid meet the constraint conditions of power grid recovery, an evaluation function is established for the factors influencing the power grid fault recovery result, and the evaluation function comprises the following steps: when the parameters of each node in the power grid meet the load constraint and the operation constraint conditions for power grid recovery; establishing a corresponding load recovery capacity evaluation function, a load importance evaluation function and a switch operation cost evaluation function for load recovery capacity factors, load importance factors and switch operation cost factors which influence the power grid fault recovery result;
the method comprises the following steps of constructing a multi-objective optimization model based on a krill swarm algorithm by using the evaluation function, solving the model, and obtaining a recovery sequence of power grid nodes, wherein the recovery sequence comprises the following steps:
and constructing a multi-objective optimization model based on the krill swarm algorithm by using the evaluation function as follows:
minE(S)＝min(-E i ,-E p ,E S )
wherein E (S) is an objective function, representing the recovery efficiency, E i To restore the load-bearing capacity evaluation function, E p As a function of load importance evaluation, E S Evaluating a function for the switch operation cost;
and solving the model, wherein each output solution corresponds to the recovery sequence of a group of power grid nodes.
2. The method of claim 1, wherein the parameters of the nodes in the grid comprise at least one of:
starting power corresponding to the node;
capacity corresponding to the node load;
the minimum value of the system frequency of the corresponding node in the recovery process;
the number of automatic switches and manual switches in the node load recovery is corresponding;
corresponding to the number of importance nodes within the node.
3. The method of claim 1, wherein the load constraints and operating constraints comprise:
the load constraint is a system frequency constraint, which is expressed as:
F min ＞F p,min
wherein, F min For load-induced frequency minima during recovery, F p,min The minimum value allowed by the system frequency;
the operating constraint is that the power of the node during start-up should be limited to within the normal operating range:
P i,min ＜P i ＜P i,max
in the formula, P i Is the power of node i, P i,min And P i,max Respectively, the minimum value and the maximum value allowed by the power in the normal starting process of the node i.
4. The method of claim 1, wherein the load recovery capability evaluation function is:
in the formula, c i Represents the operating condition of the load i, c i When 1, the load works normally; c. C i At 0, the load is waiting to recover; l is i Represents the capacity of the load i; n is l The load set to be recovered under the current time step;
the load importance evaluation function is as follows:
k l ＝1-k m -k h
in the formula, k h Representing the number of first-level loads of the node, k m Representing the number of second-stage loads of the node, k l Representing the number of the third-stage loads of the node;
the evaluation function of the switching operation cost is as follows:
E S ＝(k A N A +k M N M )
k A ,k M weight coefficients, N, for automatic and manual switches, respectively A ,N M The number of the automatic switches and the number of the manual switches in the fault recovery process are respectively, and the number of the switches corresponding to each load is different.
5. A multi-objective grid fault recovery device, comprising:
the parameter acquisition unit is used for acquiring parameters of each node in the power grid;
the evaluation function establishing unit is used for establishing an evaluation function for factors influencing the power grid fault recovery result when the parameters of each node in the power grid meet the constraint condition of power grid recovery, and the evaluation function establishing unit comprises the following steps:
the constraint condition subunit is used for meeting the load constraint and the operation constraint condition of power grid restoration when the parameters of each node in the power grid;
the evaluation function establishing subunit is used for establishing a corresponding load recovery capability evaluation function, a load importance evaluation function and a switch operation cost evaluation function for the load recovery capability factor, the load importance factor and the switch operation cost factor which influence the power grid fault recovery result;
the recovery sequence determining unit is used for constructing a multi-objective optimization model based on a krill swarm algorithm by using the evaluation function, solving the model and acquiring the recovery sequence of the power grid nodes, and comprises the following steps:
and the model construction subunit is used for constructing a multi-objective optimization model based on the krill swarm algorithm by using the evaluation function, and comprises the following steps:
minE(S)＝min(-E i ,-E p ,E S )
wherein E (S) is an objective function showing recovery efficiency, E i To restore the load-bearing capacity evaluation function, E p As a function of load importance evaluation, E S Evaluating a function for the switch operation cost;
and the solving subunit is used for solving the model, and each output solution corresponds to the recovery sequence of a group of power grid nodes.
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