CN117852790A - Flood risk response and equipment evacuation method and system based on neural network - Google Patents
Flood risk response and equipment evacuation method and system based on neural network Download PDFInfo
- Publication number
- CN117852790A CN117852790A CN202311591545.XA CN202311591545A CN117852790A CN 117852790 A CN117852790 A CN 117852790A CN 202311591545 A CN202311591545 A CN 202311591545A CN 117852790 A CN117852790 A CN 117852790A
- Authority
- CN
- China
- Prior art keywords
- evacuation
- cost
- equipment
- constraint
- representing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 31
- 230000004044 response Effects 0.000 title claims abstract description 28
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 25
- 238000003062 neural network model Methods 0.000 claims abstract description 12
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 12
- 230000006870 function Effects 0.000 claims description 15
- 238000004590 computer program Methods 0.000 claims description 11
- 238000012502 risk assessment Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 5
- 101150067055 minC gene Proteins 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 abstract description 2
- 238000013468 resource allocation Methods 0.000 abstract description 2
- 238000007726 management method Methods 0.000 description 4
- 238000004088 simulation Methods 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 108010014173 Factor X Proteins 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000011084 recovery Methods 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 229910021389 graphene Inorganic materials 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Abstract
The invention discloses a flood risk response and equipment evacuation method and system based on a neural network, comprising the following steps: the water condition forecasting system collects data and performs pretreatment. And establishing a neural network model, calculating a flood risk index, and calculating the evacuation cost of the power distribution network equipment according to the index. And establishing a model for minimizing the evacuation cost and a model for minimizing the cost of the evacuation equipment component, and solving the two models to obtain a final scheme. The flood risk response and equipment evacuation method and system based on the neural network provided by the invention have the advantage that the efficiency and effectiveness of the flood risk response and the power grid equipment evacuation are obviously improved. By establishing the minimum evacuation cost model, the total cost of the evacuation operation can be effectively reduced on the premise of ensuring the stability of the power grid, and unnecessary economic loss is avoided. In addition, the invention optimizes the resource allocation and evacuation strategy and improves the safety and environmental protection level of the evacuation operation.
Description
Technical Field
The invention relates to the technical field of flood risk management and power grid equipment evacuation, in particular to a method and a system for flood risk response and equipment evacuation based on a neural network.
Background
With the increase of global climate change and extreme weather events, flood disasters frequently occur, and serious threats are caused to the power supply system. When flood comes, damage to distribution network facilities can lead to large-scale power interruption, and social stability and people's life are affected. Traditional flood emergency management methods are usually focused on pre-event risk assessment and post-event disaster recovery, and lack real-time dynamic risk assessment and response mechanisms. In addition, the prior art often lacks accurate cost control and risk assessment in evacuation decision making, resulting in wasted resources and potential safety hazards.
Traditional methods often rely on decentralized systems and manual analysis in terms of data collection and processing, and are inefficient and unable to effectively predict and address rapidly changing flood risks. In addition, the evacuation decision is often based on experience or a simplified model, and comprehensive consideration on dynamic changes of a complex power grid system is lacking, so that the evacuation scheme cannot be optimized, the cost is increased, and long-term stability and recovery capability of the power grid can be affected.
Disclosure of Invention
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: according to the invention, through integrating the neural network model and the optimization algorithm, the accuracy and timeliness of flood risk assessment are improved, and meanwhile, the evacuation strategy of the power grid equipment is optimized. This not only improves the efficiency of coping with flood disasters, but also ensures optimization of evacuation decisions in terms of cost and safety.
In order to solve the technical problems, the invention provides the following technical scheme: a neural network-based flood risk response and equipment evacuation method, comprising: the water condition forecasting system collects data and performs pretreatment.
And establishing a neural network model, calculating a flood risk index, and calculating the evacuation cost of the power distribution network equipment according to the index.
And establishing a model for minimizing the evacuation cost and a model for minimizing the cost of the evacuation equipment component, and solving the two models to obtain a final scheme.
As a preferable scheme of the flood risk response and equipment evacuation method based on the neural network, the invention comprises the following steps: the water condition forecasting system collects data, and preprocessing comprises collecting hydrological data and meteorological data related to flood, including water level, flow velocity, rainfall and flow.
And (3) carrying out standardized processing on the data:
where X' represents raw data, μ represents the average value, and σ represents the standard deviation.
As a preferable scheme of the flood risk response and equipment evacuation method based on the neural network, the invention comprises the following steps: the neural network model establishment comprises an input layer, a hidden layer and an output layer.
The input layer has n input nodes, each node corresponding to a flood related data point:
(x' 1 ,x' 2 ,...,x' n )
wherein x' n Representing the nth data node.
Inputting the data points into the hidden layer for weight calculation:
wherein W is ij Representing the weight of the input layer to the hidden layer, x' i Represents the ith data point, b j Representing the bias of the hidden layer, n represents the total number of data points.
Calculating flood risk coefficients at the output layer:
wherein σ () represents a sigmoid function, W i Weight, W, representing the ith data point jo Representing the weights of the hidden layer to the output layer, b o Representing the bias of the output layer.
As a preferable scheme of the flood risk response and equipment evacuation method based on the neural network, the invention comprises the following steps: the calculating the power distribution network equipment evacuation cost comprises:
wherein a represents an evacuation basis cost coefficient, b represents an evacuation variation cost coefficient, P i Representing other factors affecting cost, α represents the weighting coefficient of the additional factor.
As a preferable scheme of the flood risk response and equipment evacuation method based on the neural network, the invention comprises the following steps: the building of the minimum evacuation cost model includes,
and if the equipment evacuation cost of the power distribution network is smaller than the cost required by equipment re-purchase, establishing a minimum evacuation cost model, wherein an objective function is to minimize the equipment evacuation cost, and constructing constraint conditions including time constraint, transportation capacity constraint, equipment safety constraint, power grid stability constraint and important facility priority protection constraint.
If the equipment evacuation cost of the power distribution network is greater than or equal to the cost required for re-purchasing equipment, the cost of minimizing the parts of the equipment to be evacuated is established, and the objective function is to minimize the total cost of splitting and evacuating the most valuable parts of the equipment, wherein the constraint conditions comprise part value constraint, time constraint, transport capacity constraint, equipment part safety constraint and power grid stability constraint.
As a preferable scheme of the flood risk response and equipment evacuation method based on the neural network, the invention comprises the following steps: the set-up minimum evacuation cost model objective function is expressed as:
minC＝aX 2 +bX
where a represents an evacuation basis cost factor, b represents an evacuation variable cost factor, and X represents a scale factor of evacuation.
The time constraint is expressed as:
wherein A represents a time influence coefficient, B represents a time influence coefficient, T max Indicating an acceptable maximum evacuation time.
The transport capacity constraint is expressed as:
wherein P represents a transport capacity base coefficient, Q represents a transport capacity increment coefficient, S represents a transport weight influence coefficient, V max Representing maximum transport capacity, W max Indicating the maximum weight transported.
The device security constraints are expressed as:
wherein U represents the device security base coefficient, V represents the device additional security measures and risk management coefficients, S min Representing the lowest score of the safety criteria.
The grid stability constraint is expressed as:
wherein Y represents a power grid stability basic coefficient, Z represents a power grid stability additional influence coefficient, and P critical A critical score representing grid stability.
Important facility priority protection constraints are expressed as:
β·X 3 +γ·log(X+1)≤R max
wherein beta represents an important facility priority coefficient, R max Representing an acceptable maximum importance score. As a preferable scheme of the flood risk response and equipment evacuation method based on the neural network, the invention comprises the following steps: the set-up minimum evacuation device component cost objective function is expressed as:
wherein a is i Representing a basic cost coefficient associated with splitting the ith component, b i Representing a coefficient of variation cost associated with splitting an ith component for evacuation, x i Represents the resolution scale factor of the ith component.
The component value constraint is expressed as:
wherein V is i Representing the value coefficient, x of the ith component i Represents the resolution scale factor of the ith component, V min Representing the minimum total value of the split components.
The time constraint is expressed as:
the transport capacity constraint is expressed as:
the equipment component security constraints are expressed as:
the grid stability constraint is expressed as:
the two models are solved, a final scheme is obtained by constructing a model in Matlab, calling a commercial solver Gurobi to solve, and finally obtaining two conditions:
if the equipment is directly evacuated, solving a minimum evacuation cost model, and obtaining total evacuation cost and evacuation time, evacuation required resources and risk assessment on the power grid.
If the most valuable part of the splitting equipment is removed, solving a model for minimizing the cost of the parts of the removing equipment, and obtaining an optimal scheme for splitting and removing, and carrying out total cost, time and required resources for removing and risk assessment on the power grid.
A neural network-based flood risk response and equipment evacuation system, characterized in that: comprising the steps of (a) a step of,
and the data acquisition module is used for acquiring data by the water condition forecasting system and preprocessing the data.
And the evacuation cost calculation module is used for establishing a neural network model, calculating a flood risk index and calculating the evacuation cost of the power distribution network equipment according to the index.
And establishing a minimum evacuation cost model module, establishing a minimum evacuation cost model, and outputting a final scheme.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method as described above when the processor executes the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor realizes the steps of the method as described above.
The invention has the beneficial effects that: by introducing an advanced neural network model and an optimization algorithm, the efficiency and effectiveness of flood risk response and power grid equipment evacuation are remarkably improved. Firstly, a neural network is utilized to analyze a large amount of real-time data, so that flood risk is rapidly and accurately estimated, and timeliness of emergency response is ensured. Secondly, by establishing a minimum evacuation cost model, the total cost of evacuation operation can be effectively reduced on the premise of ensuring the stability of the power grid, and unnecessary economic loss is avoided. In addition, the invention optimizes the resource allocation and evacuation strategy and improves the safety and environmental protection level of the evacuation operation.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
fig. 1 is a flowchart of a flood risk response and equipment evacuation method and system based on a neural network according to a first embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Example 1
Referring to fig. 1, for one embodiment of the present invention, there is provided a flood risk response and equipment evacuation method based on a neural network, including:
s1: the water condition forecasting system collects data and performs pretreatment.
And acquiring hydrological data and meteorological data related to flood, including water level, flow rate, rainfall and flow rate.
And (3) carrying out standardized processing on the data:
where X represents raw data, μ represents an average value, and σ represents a standard deviation.
S2: and establishing a neural network model, calculating a flood risk index, and calculating the evacuation cost of the power distribution network equipment according to the index.
The neural network model is built to include an input layer, a hidden layer and an output layer.
The input layer has n input nodes, each node corresponding to a flood related data point:
(x' 1 ,x' 2 ,...,x' n )
wherein x' n Representing the nth data node.
Inputting the data points into the hidden layer for weight calculation:
wherein W is ij Representing the weight of the input layer to the hidden layer, x' i Represents the ith data point, b j Representing the bias of the hidden layer, n represents the total number of data points.
Calculating flood risk coefficients at the output layer:
wherein σ () represents a sigmoid function, W i Weight, W, representing the ith data point jo Representing the weights of the hidden layer to the output layer, b o Representing the bias of the output layer.
Calculating the power distribution network equipment evacuation cost includes:
wherein a represents an evacuation basis cost coefficient, b represents an evacuation variation cost coefficient, P i Representing other factors affecting cost, α represents the weighting coefficient of the additional factor.
It should be noted that a, b, P i Is a parameter obtained by analyzing historical data, and can be changed empirically in practical application.
S3: and establishing a model for minimizing the evacuation cost and a model for minimizing the cost of the evacuation equipment component, and solving the two models to obtain a final scheme.
If the equipment evacuation cost of the power distribution network is smaller than the cost required by equipment re-purchase, a minimum evacuation cost model is built, the objective function is to minimize the equipment evacuation cost, and the construction constraint conditions comprise time constraint, transportation capacity constraint, equipment safety constraint, power grid stability constraint and important facility priority protection constraint.
If the equipment evacuation cost of the power distribution network is greater than or equal to the cost required for re-purchasing equipment, the minimum equipment part evacuation cost is established, and the objective function is to minimize the total cost of splitting and evacuating the most valuable parts of the equipment, wherein the constraint conditions comprise part value constraint, time constraint, transportation capability constraint, equipment part safety constraint and power grid stability constraint.
It should be noted that the time constraint is to avoid situations where evacuation has not been completed after a flood has occurred, the transportation capacity constraint is to avoid exceeding the maximum transportation capacity, the equipment safety constraint and the equipment component safety constraint are to select equipment that can be evacuated safely for evacuation, and the grid stability constraint is to ensure that some equipment or equipment components are evacuated, but that the stability of the whole distribution line is not greatly affected.
The minimum evacuation cost model objective function is formulated as:
minC＝aX 2 +bX
where a represents an evacuation basis cost factor, b represents an evacuation variable cost factor, and X represents a scale factor of evacuation.
The time constraint is expressed as:
wherein A represents a time influence coefficient, B represents a time influence coefficient, T max Indicating an acceptable maximum evacuation time.
Note that A, B is a parameter obtained by analyzing historical data, and may be changed empirically in practical applications.
The transport capacity constraint is expressed as:
wherein P represents a transport capacity base coefficient, and Q represents a transport capacityDelta coefficient, S represents transport weight influence coefficient, V max Representing maximum transport capacity, W max Indicating the maximum weight transported.
Note that P, Q, S is a parameter obtained by analyzing historical data, and may be changed empirically in practical applications.
The device security constraints are expressed as:
wherein U represents the device security base coefficient, V represents the device additional security measures and risk management coefficients, S min Representing the lowest score of the safety criteria.
Note that U, V is a parameter obtained by analyzing historical data, and may be changed empirically in practical applications.
The grid stability constraint is expressed as:
wherein Y represents a power grid stability basic coefficient, Z represents a power grid stability additional influence coefficient, and P critical A critical score representing grid stability.
Note that Y, Z is a parameter obtained by analyzing historical data, and may be changed empirically in practical applications.
Important facility priority protection constraints are expressed as:
β·X 3 +γ·log(X+1)≤R max
wherein β represents an important facility priority coefficient, R as shown in Table 1 max Representing an acceptable maximum importance score.
TABLE 1 important facility priority coefficient
The set-up minimum evacuation device component cost objective function is expressed as:
wherein a is i Representing a basic cost coefficient associated with splitting the ith component, b i Representing a coefficient of variation cost associated with splitting an ith component for evacuation, x i Represents the resolution scale factor of the ith component.
It should be noted that a i 、b i Is a parameter obtained by analyzing historical data, and can be changed empirically in practical application.
The component value constraint is expressed as:
wherein V is i Representing the value coefficient of the ith component, x as shown in Table 2 i Represents the resolution scale factor of the ith component, V min Representing the minimum total value of the split components.
TABLE 2 component value coefficient
The time constraint is expressed as:
the transport capacity constraint is expressed as:
the equipment component security constraints are expressed as:
the grid stability constraint is expressed as:
solving the two models to obtain a final scheme, wherein the final scheme comprises the steps of constructing a model in Matlab, calling a commercial solver Gurobi to solve, and finally obtaining two conditions:
if the equipment is directly evacuated, solving a minimum evacuation cost model, and obtaining total evacuation cost and evacuation time, evacuation required resources and risk assessment on the power grid.
If the most valuable part of the splitting equipment is removed, solving a model for minimizing the cost of the parts of the removing equipment, and obtaining an optimal scheme for splitting and removing, and carrying out total cost, time and required resources for removing and risk assessment on the power grid.
It should be noted that the overall scale factor X and the resolution scale factor X of the ith component in both models i The quantitative index is used for representing the evacuation scale of the evacuation or separation part, is obtained based on historical data analysis and related personnel evaluation, and can be changed as required in practical application.
In the above embodiment, the flood risk response and equipment evacuation system based on the neural network is further included, specifically:
and the data acquisition module is used for acquiring data by the water condition forecasting system and preprocessing the data.
And the evacuation cost calculation module is used for establishing a neural network model, calculating a flood risk index and calculating the evacuation cost of the power distribution network equipment according to the index.
And establishing a minimum evacuation cost model module, establishing a minimum evacuation cost model, and outputting a final scheme.
The computer device may be a server. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing data cluster data of the power monitoring system. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile memory may include Read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high density embedded nonvolatile memory, resistive random access memory (ReRAM), magnetic random access memory (MagnetoresistiveRandomAccessMemory, MRAM), ferroelectric memory (FerroelectricRandomAccessMemory, FRAM), phase change memory (PhaseChangeMemory, PCM), graphene memory, and the like. Volatile memory may include random access memory (RandomAccessMemory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can take many forms, such as static random access memory (StaticRandomAccessMemory, SRAM) or dynamic random access memory (DynamicRandomAccessMemory, DRAM), among others. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
Example 2
An embodiment of the invention provides a flood risk response and equipment evacuation method and system based on a neural network, and in order to verify the beneficial effects of the invention, scientific demonstration is carried out through simulation experiments.
Creating a simulation environment in MATLAB, inputting the model of the present invention into the simulation environment, and using the Gurobi solver, the results of the evacuation scheme are shown in table 3.
TABLE 3 simulation experiment results of evacuation protocol
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. A neural network-based flood risk response and equipment evacuation method, comprising:
the water condition forecasting system collects data and performs pretreatment;
building a neural network model, calculating a flood risk index, and calculating the evacuation cost of the power distribution network equipment according to the index;
and establishing a model for minimizing the evacuation cost and a model for minimizing the cost of the evacuation equipment component, and solving the two models to obtain a final scheme.
2. The neural network-based flood risk response and device evacuation method of claim 1, wherein: the water condition forecasting system collects data, and preprocessing comprises collecting hydrological data and meteorological data related to flood, including water level, flow rate, rainfall and flow;
and (3) carrying out standardized processing on the data:
where X' represents raw data, μ represents the average value, and σ represents the standard deviation.
3. The neural network-based flood risk response and device evacuation method of claim 2, wherein: the neural network model establishment comprises an input layer, a hidden layer and an output layer;
the input layer has n input nodes, each node corresponding to a flood related data point:
(x' 1 ,x' 2 ,...,x' n )
wherein x' n Representing an nth data node;
inputting the data points into the hidden layer for weight calculation:
wherein W is ij Representing an input layerWeights to hidden layer, x' i Represents the ith data point, b j Representing the bias of the hidden layer, n representing the total number of data points;
calculating flood risk coefficients at the output layer:
wherein σ () represents a sigmoid function, W i Weight, W, representing the ith data point jo Representing the weights of the hidden layer to the output layer, b o Representing the bias of the output layer.
4. A neural network-based flood risk response and device evacuation method as claimed in claim 3, wherein: the calculating the power distribution network equipment evacuation cost comprises:
wherein a represents an evacuation basis cost coefficient, b represents an evacuation variation cost coefficient, P i Representing other factors affecting cost, α represents the weighting coefficient of the additional factor.
5. The neural network-based flood risk response and device evacuation method of claim 4, wherein: the building of the minimum evacuation cost model includes,
if the equipment evacuation cost of the power distribution network is smaller than the cost required by equipment re-purchase, a minimum evacuation cost model is built, an objective function is to minimize the equipment evacuation cost, and constraint conditions are built, wherein the constraint conditions comprise time constraint, transport capacity constraint, equipment safety constraint, power grid stability constraint and important facility priority protection constraint;
if the equipment evacuation cost of the power distribution network is greater than or equal to the cost required for re-purchasing equipment, the cost of minimizing the parts of the equipment to be evacuated is established, and the objective function is to minimize the total cost of splitting and evacuating the most valuable parts of the equipment, wherein the constraint conditions comprise part value constraint, time constraint, transport capacity constraint, equipment part safety constraint and power grid stability constraint.
6. The neural network-based flood risk response and device evacuation method of claim 5, wherein: the set-up minimum evacuation cost model objective function is expressed as:
minC＝aX 2 +bX
wherein a represents an evacuation basis cost coefficient, b represents an evacuation variable cost coefficient, and X represents a scale factor of evacuation;
the time constraint is expressed as:
wherein A represents a time influence coefficient, B represents a time influence coefficient, T max Indicating an acceptable maximum evacuation time;
the transport capacity constraint is expressed as:
wherein P represents a transport capacity base coefficient, Q represents a transport capacity increment coefficient, S represents a transport weight influence coefficient, V max Representing maximum transport capacity, W max Representing the maximum weight of the transportation;
the device security constraints are expressed as:
wherein U representsThe device security base coefficient, V represents the device additional security measures and risk management coefficient, S min A lowest score representing a security criterion;
the grid stability constraint is expressed as:
wherein Y represents a power grid stability basic coefficient, Z represents a power grid stability additional influence coefficient, and P critical A critical score representing grid stability;
important facility priority protection constraints are expressed as:
β·X 3 +γ·log(X+1)≤R max
wherein beta represents an important facility priority coefficient, R max Representing an acceptable maximum importance score.
7. The neural network-based flood risk response and device evacuation method of claim 6, wherein: the set-up minimum evacuation device component cost objective function is expressed as:
wherein a is i Representing a basic cost coefficient associated with splitting the ith component, b i Representing a coefficient of variation cost associated with splitting an ith component for evacuation, x i Representing a resolution scale factor of the ith component;
the component value constraint is expressed as:
wherein V is i Representing the value coefficient, x of the ith component i Represents the resolution scale factor of the ith component, V min Representing the split partsIs a minimum total value of (2);
the time constraint is expressed as:
the transport capacity constraint is expressed as:
the equipment component security constraints are expressed as:
the grid stability constraint is expressed as:
the two models are solved, a final scheme is obtained by constructing a model in Matlab, calling a commercial solver Gurobi to solve, and finally obtaining two conditions:
if the equipment is directly evacuated, solving a minimum evacuation cost model to obtain total evacuation cost and evacuation time, resources required by evacuation and risk assessment on a power grid;
if the most valuable part of the splitting equipment is removed, solving a model for minimizing the cost of the parts of the removing equipment, and obtaining an optimal scheme for splitting and removing, and carrying out total cost, time and required resources for removing and risk assessment on the power grid.
8. An oil immersed power transformer dynamic load capacity management system employing the method of any one of claims 1-7, wherein:
the data acquisition module is used for acquiring data by the water condition measuring and reporting system and preprocessing the data;
the evacuation cost calculation module is used for establishing a neural network model, calculating a flood risk index and calculating the evacuation cost of the power distribution network equipment according to the index;
and establishing a minimum evacuation cost model module, establishing a minimum evacuation cost model, and outputting a final scheme.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311591545.XA CN117852790A (en) | 2023-11-24 | 2023-11-24 | Flood risk response and equipment evacuation method and system based on neural network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311591545.XA CN117852790A (en) | 2023-11-24 | 2023-11-24 | Flood risk response and equipment evacuation method and system based on neural network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117852790A true CN117852790A (en) | 2024-04-09 |
Family
ID=90539097
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311591545.XA Pending CN117852790A (en) | 2023-11-24 | 2023-11-24 | Flood risk response and equipment evacuation method and system based on neural network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117852790A (en) |
-
2023
- 2023-11-24 CN CN202311591545.XA patent/CN117852790A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108446293A (en) | A method of based on urban multi-source isomeric data structure city portrait | |
| CN112287018B (en) | 10kV pole tower damage risk assessment method and system under typhoon disaster | |
| CN114860833B (en) | Data center station and data processing method applied to digital twin hydraulic engineering | |
| CN110807550A (en) | Distribution transformer overload identification early warning method based on neural network and terminal equipment | |
| CN111552813A (en) | Power knowledge graph construction method based on power grid full-service data | |
| CN115730749B (en) | Power dispatching risk early warning method and device based on fusion power data | |
| CN106383916A (en) | Data processing method based on predictive maintenance of industrial equipment | |
| CN114462722B (en) | New energy power generation light-weight high-precision cloud prediction system, method and device | |
| CN114493052A (en) | Multi-model fusion self-adaptive new energy power prediction method and system | |
| CN116523140A (en) | Method and device for detecting electricity theft, electronic equipment and storage medium | |
| CN110807508B (en) | Bus peak load prediction method considering complex weather influence | |
| CN114266602A (en) | Deep learning electricity price prediction method and device for multi-source data fusion of power internet of things | |
| Linghu et al. | An intelligent multi-agent approach for flood disaster forecasting utilizing case based reasoning | |
| CN117852790A (en) | Flood risk response and equipment evacuation method and system based on neural network | |
| CN111177278A (en) | Grid user short-term load prediction real-time processing tool | |
| CN116826961A (en) | Intelligent power grid dispatching and operation and maintenance system, method and storage medium | |
| CN115456247A (en) | Route planning method and device, computer equipment and readable storage medium | |
| CN115481767A (en) | Operation data processing method and device for power distribution network maintenance and computer equipment | |
| CN113344290B (en) | Method for correcting sub-season rainfall weather forecast based on U-Net network | |
| CN111161121B (en) | Method and system for determining abrupt change of response of river water to river basin land utilization composition | |
| CN114254806A (en) | Power distribution network heavy overload early warning method and device, computer equipment and storage medium | |
| Cheng et al. | A novel rainfall forecast model based on the integrated non-linear attribute selection method and support vector regression | |
| CN113779178A (en) | Data storage method and device based on knowledge graph | |
| Zhang et al. | Prediction study and application of wind power development based on filtering error threshold | |
| CN116342077B (en) | New energy power transfer learning prediction method suitable for data-missing station |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |