CN114418204A - Method and system for analyzing and planning terminal deployment of power transmission network perception layer - Google Patents
Method and system for analyzing and planning terminal deployment of power transmission network perception layer Download PDFInfo
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Abstract
The invention relates to a method and a system for analyzing and planning terminal deployment of a power transmission network perception layer, which are characterized by comprising the following steps: performing risk assessment on the power grid equipment in the region to be planned, and determining the total risk of the power grid equipment; establishing a perception layer optimization model according to the total risk of the power grid equipment in the region to be planned; the constructed sensing layer optimization model is decomposed and optimized in a collaborative mode, and a sensing layer deployment scheme of the area to be planned is determined.
Description
Technical Field
The invention relates to the field of electrical engineering, in particular to a method and a system for analyzing and planning terminal deployment of a sensing layer of a power transmission network.
Background
The power transmission network perception layer is an important component of the power sensing network, is responsible for comprehensively perceiving a terminal and a data state, has the outstanding characteristics of multiple points, wide area, various types, outstanding risk problems, complex scene, huge investment, operation and maintenance problems and the like, reasonably plans the perception layer network, effectively evaluates the examination construction project of the perception layer, promotes the ordered construction of the perception layer, realizes the convenient operation and maintenance of the perception layer, and is a difficult problem which needs to be solved urgently in the construction of the current perception layer. However, current research on the deployment planning of a sensing layer network lacks a complete system.
At present, the terminal deployment of the perception layer is biased to manual experience, although matched standards have been issued for online monitoring of power transmission lines, online monitoring of substation power grid equipment, distribution automation terminals and the like, no standard and effective system has been formed on the planning and construction level, and certain blindness is achieved. The deployment of the perception layer is related to multidimensional factors such as application requirements, technical maturity, power grid operation risks, economic benefit values, operation and maintenance management and communication availability. Therefore, a terminal deployment analysis method capable of effectively guiding the construction of the perception layer for different application scenarios is needed.
Disclosure of Invention
In view of the above problems, the present invention aims to provide a method and a system for analyzing and planning the deployment of a sensing layer terminal of a power transmission network, which can effectively guide the construction of the sensing layer for different application scenarios.
In order to achieve the purpose, the invention adopts the following technical scheme: in a first aspect, a method for analyzing and planning terminal deployment of a power transmission network sensing layer is provided, which includes:
performing risk assessment on the power grid equipment in the region to be planned, and determining the total risk of the power grid equipment;
establishing a perception layer optimization model according to the total risk of the power grid equipment in the region to be planned;
and carrying out decomposition cooperative optimization on the constructed sensing layer optimization model, and determining a sensing layer deployment scheme of the to-be-planned area.
Further, the risk assessment of the power grid equipment in the area to be planned to determine the total risk of the power grid equipment includes:
determining the fault rate of the power grid equipment according to the state evaluation result and the fault frequency data of the power grid equipment in the region to be planned;
determining the loss risk of the power grid equipment according to the fault rate of the power grid equipment and the corresponding overhaul cost;
determining load loss risk according to the failure rate of the power grid equipment, the total load loss caused by equipment failure and the loss cost of unit electric energy;
and determining the total risk of the power grid equipment according to the power grid equipment loss risk and the load loss risk.
Further, establishing a perception layer optimization model according to the total risk of the power grid equipment in the region to be planned, including:
determining an objective function of a perception layer optimization model;
determining a constraint condition of a perception layer optimization model;
and determining the weight of each objective function in the perception layer optimization model by adopting an analytic hierarchy process.
Further, the objective function of the perception layer optimization model comprises a cost function, a profit function and a risk function; the cost function comprises investment cost and operation and maintenance cost, and the investment cost is as follows:
wherein, F11The investment cost; if the sensor is deployed at the location candidate point i, x i1, otherwise x i0; NI is the total number of the candidate points deployed on the sensing layer; c. CiThe construction cost of deploying the sensor for the candidate point i deployed on the sensing layer is obtained;
the operation and maintenance cost is as follows:
wherein, F12The operation and maintenance cost; a. theurThe number of the sensing layer power grid equipment is 0-1, if the communication connection is established between the u sensing layer power grid equipment and the r sensing layer power grid equipment in the region to be planned, the number of the sensing layer power grid equipment is 1, otherwise, the number of the sensing layer power grid equipment is 0; rurThe communication distance between the power grid equipment of the u sensing layer and the power grid equipment of the r sensing layer is obtained; m is the total number of the power grid equipment of the sensing layer; vtCommunication cost per unit distance of year t; c is the inherent cost of communication between two sensing layer power grid devices; t is the total planned commissioning age of the perception layer; c. CitThe operation and maintenance cost of deploying the sensors for the candidate point i for the sensing layer of the t year;
the revenue function is:
wherein, F21The support degree of the power grid service by the deployment scheme of the sensing layer is represented as a gain function, and the larger the value of the support degree, the more the deployment scheme can support the power grid service; y isiqA business-position candidate point incidence matrix is obtained; NQ is the total number of the power grid services;
the risk function is:
wherein, F3The larger the value of the risk function is, the more likely the sensing layer deployment scheme is to monitor the fault state of the power grid equipment, and the smaller the value of the risk function is, the more likely the sensing layer deployment scheme is to monitor the fault of the power grid equipment; rbIs the total risk of the grid device b; NB is the total number of the power grid equipment in the area to be planned; sibAnd the value of the ith row and the b th column in the power grid equipment-sensor terminal correlation matrix is obtained.
Further, the constraint conditions of the perception layer optimization model comprise cost constraint and communication constraint;
the cost constraint comprises a construction cost constraint and an operation cost constraint:
wherein, CkAt the maximum construction cost allowed, CbThe maximum operation and maintenance cost allowed;
the communication constraints are:
0≤Qv≤Qmax
wherein Q ismaxFor the sensing layer is provided withPreparing the maximum total amount of data which can be stored every day; qvThe total amount of information received by the power grid equipment of the v perception layer every day; j. the design is a squaremaxThe maximum allowable number of connections in a certain communication mode.
Further, determining the weight of each objective function in the perception layer optimization model by adopting an analytic hierarchy process includes:
normalizing each objective function value;
and determining the weight of each normalized objective function value by adopting an analytic hierarchy process.
Further, the determining the weight of each objective function value after normalization by using an analytic hierarchy process includes:
establishing a hierarchical structure of an evaluation system, wherein the hierarchical structure comprises a target layer and an index layer, the target layer is a total target constructed by a perception layer, and the index layer comprises a cost function, a profit function and a risk function;
comparing the importance of the index layers pairwise, and establishing a pairwise comparison judgment matrix;
and calculating the weight of each element in the pairwise comparison judgment matrix by adopting a root method, and further obtaining the weight of the cost function, the income function and the risk function.
Further, the decomposing cooperative optimization of the constructed sensing layer optimization model to determine the sensing layer deployment scheme of the to-be-planned area includes:
dividing a constructed sensing layer optimization model into a planning layer model and an operation layer model;
calling a solver to solve the planning layer model, outputting an estimation value of a target function of the perception layer deployment scheme and the operation layer model obtained by the solver, and sending the estimation value of the target function of the perception layer deployment scheme and the operation layer model to the operation layer model;
thirdly, the operation layer model carries out operation simulation according to the deployment scheme of the sensing layer, judges the estimation value of the objective function of the operation layer model and feeds back the judgment result to the planning layer model in the form of constraint conditions;
and solving the model added with the constraint condition by the planning layer model according to the judgment result of the operation layer model to obtain the corrected sensing layer deployment scheme and the estimation value of the objective function of the operation layer model, sending the estimation value to the operation layer model, entering the step III until the operation layer model does not provide a feedback judgment result for the planning layer model any more, iteratively converging, and outputting the final sensing layer deployment scheme.
In a second aspect, a system for analyzing and planning terminal deployment in a sensing layer of a power transmission network is provided, which includes:
the risk assessment module is used for performing risk assessment on the power grid equipment in the to-be-planned area and determining the total risk of the power grid equipment;
the model building module is used for building a perception layer optimization model according to the total risk of the power grid equipment in the region to be planned;
and the collaborative optimization decomposition module is used for decomposing collaborative optimization on the constructed perception layer optimization model and determining a perception layer deployment scheme of the area to be planned.
In a third aspect, a processing device is provided, and includes computer program instructions, where the computer program instructions, when executed by the processing device, are configured to implement the steps corresponding to the foregoing power transmission network awareness layer terminal deployment analysis and planning method.
In a fourth aspect, a computer-readable storage medium is provided, where computer program instructions are stored on the computer-readable storage medium, and when executed by a processor, the computer program instructions are used to implement the steps corresponding to the foregoing power transmission network awareness layer terminal deployment analysis and planning method.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. the perception layer deployment scheme obtained by the invention can balance multiple perception layer construction indexes (cost, income and risk), and limits the construction cost and the perception layer risk while maximizing the perception layer construction income.
2. The sensing layer optimization model constructed in the invention comprises a cost function, a profit function and a risk function, wherein the risk function can effectively represent the coverage degree of the sensing layer deployment scheme on the high-risk equipment of the power grid, and the key coverage target of the sensing layer is determined.
3. The method solves the constructed model by adopting a decomposition collaborative solving method based on the Benders decomposition algorithm, is quick and efficient, and has good convergence characteristics.
4. The method can effectively guide the construction of the sensing layer, avoid the phenomenon that the construction is carried out for the construction or the benefit does not reach the standard, enable the sensing layer to be better suitable for the development of the ubiquitous power Internet of things service, and save the investment cost of the sensing layer.
In conclusion, the invention can be widely applied to the field of electrical engineering.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Like reference numerals refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a schematic flow chart diagram provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of multi-objective optimization provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of a traffic-sensor correlation matrix provided by an embodiment of the invention;
FIG. 4 is a schematic diagram of a device-sensor correlation matrix provided by an embodiment of the present invention;
fig. 5 is a schematic diagram of a power transmission network aware layer deployment optimization framework provided by an embodiment of the present invention;
fig. 6 is a schematic diagram of a mathematical model for the sensing layer planning of the power transmission network according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a two-stage co-optimization method according to an embodiment of the present invention;
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
It is to be understood that the terminology used herein is for the purpose of describing particular example embodiments only, and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" may be intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises," "comprising," "including," and "having" are inclusive and therefore specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order described or illustrated, unless specifically identified as an order of performance. It should also be understood that additional or alternative steps may be used.
According to the method and the system for analyzing and planning terminal deployment of the power transmission network sensing layer, provided by the embodiment of the invention, a sensing layer optimization model comprehensively considering the risk, benefit and cost of terminal deployment of the sensing layer is established, the weight of each objective function in the sensing layer optimization model is determined by adopting a chromatographic analysis method, and finally the model is solved by adopting a decomposition cooperative algorithm, so that theoretical support and auxiliary decision are provided for planning and deploying terminal equipment of the power transmission network sensing layer. The invention can effectively guide the construction of the perception layer and reasonably deploy the perception layer network.
Example 1
As shown in fig. 1, the present embodiment provides a method for analyzing and planning terminal deployment of a power transmission network sensing layer, including the following steps:
1) and carrying out risk assessment on the power grid equipment in the region to be planned, and determining the total risk of the power grid equipment.
Specifically, the risk of the power grid equipment comprises four independent risks, namely equipment loss risk, personal environment risk, system loss risk and social risk.
More specifically, the risk assessment of the power grid equipment is performed after the state assessment of the power grid equipment, and the risk assessment is performed on the power grid equipment in an abnormal state to determine the risk possibly caused by the power grid equipment and provide a basis for the state maintenance decision. The specific process of the step is as follows:
1.1) determining the fault rate p of the power grid equipment according to the state evaluation result and the fault frequency data of the power grid equipment in the area to be planned.
1.2) determining the loss risk R of the power grid equipment according to the fault rate p of the power grid equipment and the corresponding overhaul cost1。
1.3) according to the failure rate p of the power grid equipment and the total load loss L caused by the equipment failure2And loss cost L of unit electric energy3Determining the risk of load loss R2。
1.4) risk of loss R according to the power grid equipment1And risk of load loss R2Determining the total risk R of the power grid equipment:
R＝p×L1+p×L2×L3 (1)
2) as shown in fig. 5, according to the total risk of the power grid device in the region to be planned, a perception layer optimization model is established, which includes an objective function and constraint conditions, and specifically includes:
2.1) determining an objective function of the perception layer optimization model.
A single optimization target can only optimize one aspect of the planning scheme, and the decision of the terminal deployment scheme of the sensing layer includes a plurality of variables and needs to be optimized under a plurality of objective functions, as shown in fig. 2, so that different evaluation indexes describing the performance of each aspect of the terminal deployment scheme of the sensing layer need to be integrated to obtain the objective function of the optimization model of the sensing layer. Multiobjective optimization under certain constraints, the optimization problem of two or more objectives is considered at the same time, and the objective is to find an optimal solution with the best overall efficiency in the whole optimization period. Compared with single-target optimization, multi-target optimization requires more complexity in aspects such as optimization difficulty and constraint conditions to be considered during optimization, so that the multi-target optimization requires a planning method completely different from the single-target optimization. Therefore, a multi-objective optimization solution, a linear combination of multiple objectives, can be used to minimize the objective function F:
wherein n is the number of objective functions (index number); lambda [ alpha ]jIs the weight of the jth objective function; fjIs the jth objective function.
More specifically, the objective function of the perceptual layer optimization model comprises a cost function, a revenue function, and a risk function, wherein:
2.1.1) cost function.
The equivalent investment cost of the sensing layer terminal is divided into two parts, wherein the first part is the purchase cost of the intelligent terminal with initial investment, and the purchase cost generally comprises the cost of an intelligent terminal body, the installation cost and the configuration cost of a communication device; the second part is for maintaining the required cost of intelligent terminal normal operating, and the operation and maintenance cost mainly includes the energy consumption expense and the operation and maintenance maintainer routine maintenance expense of equipment operation stage:
wherein, F11The investment cost; if the sensor is deployed at the location candidate point i, xi1, otherwise xi0; NI is the total number of the candidate points deployed on the sensing layer; c. CiAnd (4) construction cost for deploying the sensors for the candidate point i for the sensing layer.
The operation and maintenance cost of the power grid equipment of the perception layer is as follows:
wherein, F12The operation and maintenance cost; a. theurThe number of the sensing layer power grid equipment is 0-1, if the communication connection is established between the u sensing layer power grid equipment and the r sensing layer power grid equipment in the region to be planned, the number of the sensing layer power grid equipment is 1, otherwise, the number of the sensing layer power grid equipment is 0; rurFor the u perception layer power grid equipment and the r perception layer power gridCommunication distance between devices; m is the total number of the power grid equipment of the sensing layer; vtCommunication cost per unit distance of year t; c is the inherent cost of communication between two sensing layer power grid devices; t is the total planned commissioning age of the perception layer; c. CitAnd (5) deploying the operation and maintenance cost of deploying the sensors for the candidate point i for the sensing layer of the t year.
2.1.2) revenue function.
And the revenue function of the perception layer is expressed by adopting the service support degree, the service support degree is the support degree of the representation deployment scheme on the power grid service, and the larger the value of the service support degree is, the more the power grid service can be supported by the perception layer deployment scheme.
Therefore, an objective function F considering the service support degree of the intelligent terminal deployment scheme21Comprises the following steps:
wherein, F21The support degree of the power grid service by the deployment scheme of the sensing layer is represented as a gain function, and the larger the value of the support degree, the more the deployment scheme can support the power grid service; y isiqFor the traffic-location candidate point incidence matrix, as shown in FIG. 3, if a sensor deployed at a location candidate point i can support traffic q, then y iq1, otherwise, y iq0; NQ is the total number of the power grid services.
The transmission network data acquisition service comprises line temperature monitoring and dynamic capacity increase, line fault intelligent diagnosis and abnormal discharge active detection, line external insulation state perception early warning, shared iron tower safety intelligent monitoring, microclimate universe monitoring and auxiliary decision, lightning monitoring early warning and intelligent decision.
2.1.3) risk function.
2.1.3.1) establishing a grid device-sensor terminal association matrix S, as shown in fig. 4, if the sensors deployed at the position candidate points i can monitor the state of the grid device b, S ib1, otherwise S ib0, wherein SibThe value of the ith row and the b th column in the power grid equipment-sensor terminal incidence matrix S reflects the deployment candidate of the perception layerThe sensing state of the sensor deployed at the point i on the power grid equipment b can only take two values of 0 and 1, wherein 0 represents that the sensor deployed at the sensing layer deployment candidate point i cannot monitor the state of the power grid equipment b, and 1 represents that the sensor deployed at the sensing layer deployment candidate point i can monitor the state of the power grid equipment b.
2.1.3.2) one of the objectives of the deployment of the sensing layer is to monitor the state of the power grid equipment, even if the sensing equipment fails, and reduce the fault loss of the power grid equipment. Therefore, according to the established power grid equipment-sensor terminal incidence matrix S, determining a sensing layer deployment objective function F considering the risk of the power grid equipment3Comprises the following steps:
wherein, F3The larger the value of the risk function is, the more likely the sensing layer deployment scheme monitors the fault state of the power grid equipment, and the smaller the value of the risk function is, the more likely the sensing layer deployment scheme cannot monitor the fault of the power grid equipment; rbIs the total risk of the grid device b; and NB is the total number of the power grid equipment in the area to be planned.
2.2) determining the constraint conditions of the perception layer optimization model, including cost constraint and communication constraint.
2.2.1) cost constraints.
When a planning sensing layer network is deployed, the planning sensing layer network is limited by construction capital, so cost constraints are required, including construction cost constraints and operation cost constraints:
wherein, CkAt the maximum construction cost allowed, CbThe maximum allowable operation and maintenance cost.
2.2.2) communication constraints
After the sensing layer terminal collects the power grid data, the power grid data needs to be transmitted to the edge node in a wired or wireless mode, and the limiting conditions in the data transmission process are collectively referred to as data transmission constraints. For example: the wired or wireless communication has the maximum transmission distance, and the communication mode of each terminal is compatible with that of the edge computing node. In order to ensure normal data acquisition work of the sensing layer equipment, the communication distance between the sensing layer equipment must be limited within a certain range, otherwise, the communication quality is seriously influenced. And the data storage capacity of the sensing layer equipment needs to meet the following constraints:
0≤Qv≤Qmax (9)
wherein Q ismaxThe maximum total data amount which can be stored by the sensing layer equipment every day; qvAnd the total amount of information received by the power grid equipment of the v perception layer every day.
The communication modes of the sensing layer are various, but the connection quantity of each communication mode is limited, and the connection quantity of the sensing layer equipment and other equipment cannot exceed the maximum allowable connection quantity under a certain communication mode:
wherein, JmaxThe maximum allowable number of connections in a certain communication mode.
2.3) determining the weight of each objective function in the perception layer optimization model by adopting an analytic hierarchy process, which specifically comprises the following steps:
2.3.1) normalizing the respective objective function values.
Specifically, the types of all the objective functions (i.e., the cost function, the risk function, and the revenue function in the perception layer optimization model) are different, the dimensions are different, and the values are different, so that all the objective function values need to be normalized, so that all the objective function values have a uniform measurement range, and then the objective function values are evaluated to obtain reasonable evaluation objective values.
2.3.2) determining the weight of each objective function value after normalization by adopting an analytic hierarchy process:
2.3.2.1) establishing a hierarchical structure of the evaluation system, wherein the hierarchical structure comprises a target layer and an index layer, the target layer is an overall target constructed by the perception layer, and the index layer comprises a cost function, a profit function and a risk function.
2.3.2.2) comparing the importance of the index layers two by two, and establishing a two by two comparison judgment matrix A.
Specifically, the scale for comparing the importance includes five levels, which are respectively represented by 9, 7, 5, 3 and 1 as specially important, very important, more important, slightly important and the same important, and then a judgment matrix A, a is established according to the average scores scored by experts of each indexehTo determine the e-th row and h-th column elements, a, in matrix AehThe e-th index is particularly important in comparison with the h-th index, a being 9eh1 means that the e-th index is as important as the h-th index, aeh1/9 denotes that the e-th index is particularly less important than the h-th index, and that each element in the matrix a satisfies aeh＝1/aheThe relationship (2) of (c).
2.3.2.3) calculating the weight of each element in the pairwise comparison judgment matrix A by adopting a root method, and further obtaining the weight of the cost function, the gain function and the risk function:
a) and multiplying the elements in the judgment matrix A by two to obtain a new vector.
b) Each component of the new vector is raised to the power n, which may be 3.
c) Normalizing the obtained vector to obtain a weight vector W of a cost function, a risk function and a profit functionk：
Wk＝{W1,W2,W3} (12)
Wherein, WkIs the weight of the objective function; w1A weight vector that is a cost function; w2A weight vector that is a risk function; w3A weight vector that is a revenue function.
3) Decomposing and collaborative optimization is carried out on the constructed perception layer optimization model, and a perception layer deployment scheme of the to-be-planned area is determined, specifically comprising the following steps:
specifically, as shown in fig. 6, the constructed sensing layer optimization model is divided into two layers, namely a planning layer and an operating layer, the sensing layer optimization models of the planning layer and the operating layer are cooperatively solved by using a Benders decomposition algorithm, the planning layer is regarded as a main problem of the Benders decomposition algorithm, the operating layer is regarded as a sub-problem of the Benders decomposition algorithm, and as shown in fig. 7, the specific solving process is as follows:
and 3.1) calling a solver to solve a planning layer model, namely a main problem in the Benders decomposition algorithm, outputting an estimation value of a target function of the sensing layer deployment scheme and the operation layer model obtained by the solver, and sending the estimation value of the target function of the sensing layer deployment scheme and the operation layer model to the operation layer model.
Specifically, the objective function of the planning layer model includes a risk function and a revenue function; the objective function of the operation layer model is a cost function, and the constraint condition is corresponding cost constraint. The planning layer model considers the target and the constraint of the construction of the perception layer, and the time scale is 1; the operation layer model considers the target and the constraint of the operation of the perception layer, and the time scale is T.
And 3.2) the operation layer model carries out operation simulation according to the deployment scheme of the perception layer, judges the estimation value of the objective function of the operation layer model, and feeds back the judgment result to the planning layer model in the form of constraint conditions.
Specifically, the operation simulation and evaluation process comprises the following steps: solving the estimation values of the target functions of the perception layer deployment scheme and the operation layer model obtained according to the step 3.1)
The optimal value of the dual variable related to the operation layer model is obtained, and a pair is formed according to the optimal value of the dual variable
The constraint condition (Benders cut), which is the judgment result, is returned to the planning layer model to guide the further optimization of the planning layer model.
3.3) solving the model added with the constraint condition by the planning layer model according to the judgment result of the operation layer model to obtain the corrected perception layer deployment scheme and the estimation value of the objective function of the operation layer model, sending the estimation values to the operation layer model, entering the step 3.2) until the operation layer model does not feed back the judgment result to the planning layer model any more, iteratively converging, and outputting the final perception layer deployment scheme.
Example 2
The embodiment provides a system for analyzing and planning terminal deployment of a power transmission network perception layer, which comprises:
and the risk evaluation module is used for carrying out risk evaluation on the power grid equipment in the to-be-planned area and determining the total risk of the power grid equipment.
And the model building module is used for building a perception layer optimization model according to the total risk of the power grid equipment in the region to be planned.
And the collaborative optimization decomposition module is used for decomposing collaborative optimization on the constructed perception layer optimization model and determining a perception layer deployment scheme of the area to be planned.
Example 3
The present embodiment provides a processing device corresponding to the method for analyzing and planning deployment of a terminal in a power transmission network sensing layer provided in embodiment 1, where the processing device may be a processing device for a client, such as a mobile phone, a notebook computer, a tablet computer, a desktop computer, and the like, so as to execute the method in embodiment 1.
The processing equipment comprises a processor, a memory, a communication interface and a bus, wherein the processor, the memory and the communication interface are connected through the bus so as to complete mutual communication. The memory stores a computer program that can be run on the processing device, and the processing device executes the method for analyzing and planning the deployment of the power transmission network sensing layer terminal provided in this embodiment 1 when running the computer program.
In some implementations, the Memory may be a high-speed Random Access Memory (RAM), and may also include a non-volatile Memory, such as at least one disk Memory.
In other implementations, the processor may be various general-purpose processors such as a Central Processing Unit (CPU), a Digital Signal Processor (DSP), and the like, and is not limited herein.
Example 4
This embodiment provides a computer program product corresponding to the method for analyzing and planning the deployment of the terminal in the transmission network awareness layer provided in this embodiment 1, and the computer program product may include a computer-readable storage medium on which computer-readable program instructions for executing the method for analyzing and planning the deployment of the terminal in the transmission network awareness layer described in this embodiment 1 are loaded.
The computer readable storage medium may be a tangible device that retains and stores instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any combination of the foregoing.
The above embodiments are only used for illustrating the present invention, and the structure, connection mode, manufacturing process, etc. of the components may be changed, and all equivalent changes and modifications performed on the basis of the technical solution of the present invention should not be excluded from the protection scope of the present invention.
Claims (10)
1. A method for analyzing and planning terminal deployment of a power transmission network perception layer is characterized by comprising the following steps:
performing risk assessment on the power grid equipment in the region to be planned, and determining the total risk of the power grid equipment;
establishing a perception layer optimization model according to the total risk of the power grid equipment in the region to be planned;
and carrying out decomposition cooperative optimization on the constructed sensing layer optimization model, and determining a sensing layer deployment scheme of the to-be-planned area.
2. The method for analyzing and planning terminal deployment in the sensing layer of the power transmission network according to claim 1, wherein the performing risk assessment on the power grid equipment in the area to be planned and determining the total risk of the power grid equipment comprises:
determining the fault rate of the power grid equipment according to the state evaluation result and the fault frequency data of the power grid equipment in the region to be planned;
determining the loss risk of the power grid equipment according to the fault rate of the power grid equipment and the corresponding overhaul cost;
determining load loss risk according to the failure rate of the power grid equipment, the total load loss caused by equipment failure and the loss cost of unit electric energy;
and determining the total risk of the power grid equipment according to the power grid equipment loss risk and the load loss risk.
3. The method for analyzing and planning terminal deployment in the sensing layer of the power transmission network according to claim 1, wherein the establishing of the optimization model of the sensing layer according to the total risk of the power grid equipment in the area to be planned comprises:
determining an objective function of a perception layer optimization model;
determining a constraint condition of a perception layer optimization model;
and determining the weight of each objective function in the perception layer optimization model by adopting an analytic hierarchy process.
4. The method for analyzing and planning terminal deployment in the sensing layer of the power transmission network according to claim 3, wherein the objective function of the optimization model in the sensing layer comprises a cost function, a revenue function and a risk function;
the cost function comprises investment cost and operation and maintenance cost, and the investment cost is as follows:
wherein, F11The investment cost; if the sensor is deployed at the location candidate point i, xi1, otherwise xi0; NI is the total number of candidate points deployed by the sensing layer；ciThe construction cost of deploying the sensor for the candidate point i deployed on the sensing layer is obtained;
the operation and maintenance cost is as follows:
wherein G is12The operation and maintenance cost; a. theurThe number of the sensing layer power grid equipment is 0-1, if the communication connection is established between the u sensing layer power grid equipment and the r sensing layer power grid equipment in the region to be planned, the number of the sensing layer power grid equipment is 1, otherwise, the number of the sensing layer power grid equipment is 0; rurThe communication distance between the power grid equipment of the u sensing layer and the power grid equipment of the r sensing layer is obtained; m is the total number of the power grid equipment of the sensing layer; vtCommunication cost per unit distance of year t; c is the inherent cost of communication between two sensing layer power grid devices; t is the total planned commissioning age of the perception layer; c. CitThe operation and maintenance cost of deploying the sensors for the candidate point i for the sensing layer of the t year;
the revenue function is:
wherein, F21The support degree of the power grid service by the deployment scheme of the sensing layer is represented as a gain function, and the larger the value of the support degree, the more the deployment scheme can support the power grid service; y isiqA business-position candidate point incidence matrix is obtained; NQ is the total number of the power grid services;
the risk function is:
wherein, F3The larger the value of the risk function is, the more likely the sensing layer deployment scheme is to monitor the fault state of the power grid equipment, and the smaller the value of the risk function is, the more likely the sensing layer deployment scheme is to monitor the fault of the power grid equipment; rbIs the total risk of the grid device b; NB is in the region to be plannedThe total number of grid devices; sibAnd the value of the ith row and the b th column in the power grid equipment-sensor terminal correlation matrix is obtained.
5. The method for analyzing and planning terminal deployment in the sensing layer of the power transmission network according to claim 4, wherein the constraint conditions of the optimization model in the sensing layer comprise cost constraint and communication constraint;
the cost constraint comprises a construction cost constraint and an operation cost constraint:
wherein, CkAt the maximum construction cost allowed, CbThe maximum operation and maintenance cost allowed;
the communication constraints are:
0≤Qv≤Qmax
wherein Q ismaxThe maximum total data amount which can be stored by the sensing layer equipment every day; qvThe total amount of information received by the power grid equipment of the v perception layer every day; j. the design is a squaremaxThe maximum allowable number of connections in a certain communication mode.
6. The method for analyzing and planning terminal deployment of the power transmission network perception layer according to claim 3, wherein the determining the weight of each objective function in the perception layer optimization model by using an analytic hierarchy process comprises:
normalizing each objective function value;
and determining the weight of each normalized objective function value by adopting an analytic hierarchy process.
7. The method for analyzing and planning terminal deployment of the sensing layer of the power transmission network according to claim 1, wherein the decomposing cooperative optimization is performed on the constructed sensing layer optimization model to determine the sensing layer deployment scheme of the area to be planned, and the method comprises the following steps:
dividing a constructed sensing layer optimization model into a planning layer model and an operation layer model;
calling a solver to solve the planning layer model, outputting an estimation value of a target function of the perception layer deployment scheme and the operation layer model obtained by the solver, and sending the estimation value of the target function of the perception layer deployment scheme and the operation layer model to the operation layer model;
thirdly, the operation layer model carries out operation simulation according to the deployment scheme of the sensing layer, judges the estimation value of the objective function of the operation layer model and feeds back the judgment result to the planning layer model in the form of constraint conditions;
and solving the model added with the constraint condition by the planning layer model according to the judgment result of the operation layer model to obtain the corrected sensing layer deployment scheme and the estimation value of the objective function of the operation layer model, sending the estimation value to the operation layer model, entering the step III until the operation layer model does not provide a feedback judgment result for the planning layer model any more, iteratively converging, and outputting the final sensing layer deployment scheme.
8. A power transmission network perception layer terminal deployment analysis and planning system is characterized by comprising:
the risk assessment module is used for performing risk assessment on the power grid equipment in the to-be-planned area and determining the total risk of the power grid equipment;
the model building module is used for building a perception layer optimization model according to the total risk of the power grid equipment in the region to be planned;
and the collaborative optimization decomposition module is used for decomposing collaborative optimization on the constructed perception layer optimization model and determining a perception layer deployment scheme of the area to be planned.
9. A processing device comprising computer program instructions, wherein the computer program instructions, when executed by the processing device, are adapted to implement the steps corresponding to the grid awareness layer terminal deployment analysis and planning method of any of claims 1-7.
10. A computer readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, are adapted to implement the steps corresponding to the grid awareness layer terminal deployment analysis and planning method according to any of claims 1-7.
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