CN115481767A - Operation data processing method and device for power distribution network maintenance and computer equipment - Google Patents
Operation data processing method and device for power distribution network maintenance and computer equipment Download PDFInfo
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Abstract
The application relates to a power distribution network maintenance-oriented operation data processing method and device, computer equipment, storage medium and computer program product. The method comprises the following steps: acquiring an equipment fault information determination model and an equipment overhaul information determination model corresponding to the power distribution network, and performing fuzzification processing respectively to obtain an equipment fault risk membership information determination model and an equipment overhaul risk membership information determination model; acquiring historical operation data of the power distribution network, respectively inputting the historical operation data to an equipment fault risk membership information determination model and an equipment overhaul risk membership information determination model, respectively obtaining equipment fault risk membership information and equipment overhaul risk membership information, and generating equipment risk membership vectors; and adjusting the equipment risk membership vector according to the equipment fault risk coefficient and the equipment overhaul risk coefficient of the power distribution network to obtain the operation risk information of the power distribution network. By adopting the method, the operation data of the power distribution network can be processed more accurately.
Description
Technical Field
The present application relates to the field of power system maintenance technologies, and in particular, to a method and an apparatus for processing operation data for power distribution network maintenance, a computer device, a storage medium, and a computer program product.
Background
The power system of the power distribution network has a complex structure, and the operation of the power system is often influenced by extreme natural conditions such as typhoons and earthquakes and the use habits of users, so that the power system needs to be frequently overhauled to maintain efficient operation. With the development of information technology, more and more power system troubleshooting technologies are developed to ensure the normal operation of the power system.
At present, when the fault of the power system is overhauled, the operation risk of the power system is often required to be evaluated, and a basis is provided for the fault overhaul of the power system. However, when the traditional technology evaluates the operation risk of the power system, only the fault risk of each device in the power system is considered, and the overhaul risk of each device is not considered, i.e. the overhaul data of each device is ignored, so that the operation data processing of the power distribution network is not accurate enough, a more effective overhaul basis cannot be provided, and the overhaul effect is not good.
Therefore, the traditional technology has the problem that the operation data processing of the power distribution network is not accurate enough.
Disclosure of Invention
In view of the foregoing, it is necessary to provide an operation data processing method, an apparatus, a computer device, a computer readable storage medium, and a computer program product for power distribution network service.
A power distribution network maintenance-oriented operation data processing method is characterized by comprising the following steps:
acquiring an equipment fault information determining model and an equipment maintenance information determining model corresponding to the power distribution network; the equipment fault information determination model is used for determining fault information of each equipment in the power distribution network; the equipment maintenance information determining model is used for determining maintenance information of various equipment of the power distribution network;
fuzzification processing is respectively carried out on the equipment fault information determination model and the equipment overhaul information determination model to obtain an equipment fault risk membership information determination model and an equipment overhaul risk membership information determination model;
acquiring historical operation data of the power distribution network, inputting the historical operation data to an equipment fault risk membership information determination model to obtain equipment fault risk membership information corresponding to the power distribution network, and inputting the historical operation data to an equipment maintenance risk membership information determination model to obtain equipment maintenance risk membership information corresponding to the power distribution network;
generating an equipment risk membership vector according to the equipment fault risk membership information and the equipment maintenance risk membership information; the equipment risk membership vector is used for representing the membership degree of equipment risks corresponding to the power distribution network to the operation risks of the power distribution network;
and adjusting the equipment risk membership vector according to the equipment fault risk coefficient and the equipment overhaul risk coefficient of the power distribution network to obtain the operation risk information of the power distribution network.
In one embodiment, the method for adjusting the equipment risk membership vector according to the equipment failure risk coefficient and the equipment overhaul risk coefficient of the power distribution network to obtain the operation risk information of the power distribution network comprises the following steps:
generating an equipment operation risk weight vector (k, m) of the power distribution network according to the equipment fault risk coefficient k and the equipment maintenance risk coefficient m of the power distribution network;
adjusting the equipment risk membership vector according to the equipment operation risk weight vector (k, m) to obtain operation risk information of the power distribution network, wherein the operation risk information is expressed as
R s ＝(kR f-L +mR M-L )/2
Wherein R is s Is a risk indicator of the distribution network, (R) f-L ,R M-L ) And (4) the equipment risk membership vector.
In one embodiment, the equipment failure information determination model is represented as:
wherein R is f For the value of the risk of equipment failure, lambda, of the distribution network i Failure rate of equipment i in power distribution network, with units of (times/year), P i Representing blackout load, T, during a fault in a device i of a distribution network i Expected blackout time, C, representing a fault of a device i of an electric distribution network i (t) expected outage loss per capacity for a failure of equipment I, I, of a power distribution network max Number of devices, T, of distribution network max Is the maximum number of cycles of the distribution network.
In one embodiment, the equipment overhaul information determination model is represented as:
wherein R is M For the equipment overhaul risk value, P, of the distribution network Mi Indicating the blackout load, T, in the process of servicing a device i of a distribution network Mi Indicating negative power failure in the process of overhauling power distribution networkLotus, C Mi (t) represents the expected blackout loss per unit capacity during the overhaul of a distribution network, equipment i, C MR And (t) represents the resource cost required to be input by the maintenance equipment i of the power distribution network, se (t) represents a maintenance input cost coefficient of the power distribution network, se (t) =2 when the maintenance period is summer, se (t) =1.5 when the maintenance period is winter, and Se (t) =1 when the maintenance period is the rest of the seasons.
In one embodiment, the equipment fault risk membership information determination model is represented as:
wherein R is f-max Representing the maximum value of the equipment fault risk of the power distribution network, and the expression is as follows:
R f-max ＝3E y *C y /K，
wherein E is y Indicating the average annual power shortage load of the distribution network, C y The average annual electricity price of the power distribution network is represented, and K represents the average annual power failure times of the power distribution network;
wherein R is f-min The minimum value, which represents the risk of failure of the distribution network, is set to 0.
In one embodiment, the equipment overhaul risk membership information determination model is represented as:
wherein R is M-max The maximum value of the equipment maintenance risk of the power distribution network is represented by the expression:
R M-max ＝1.5E y *C y /K
wherein: e y Indicating the average annual power shortage load of the distribution network, C y The average annual electricity price of the power distribution network is represented, and K represents the average annual power failure times of the power distribution network;
wherein R is M-min The minimum value representing the overhaul risk of the distribution network is set to 0.
The utility model provides an operation data processing apparatus towards distribution network maintenance, its characterized in that, the device includes:
the acquisition module is used for acquiring an equipment fault information determination model and an equipment maintenance information determination model corresponding to the power distribution network; the equipment fault information determination model is used for determining fault information of each equipment in the power distribution network; the equipment maintenance information determining model is used for determining maintenance information of various equipment of the power distribution network;
the fuzzy module is used for respectively carrying out fuzzification processing on the equipment fault information determination model and the equipment overhaul information determination model to obtain an equipment fault risk membership information determination model and an equipment overhaul risk membership information determination model;
the input module is used for acquiring historical operation data of the power distribution network, inputting the historical operation data to the equipment fault risk membership information determination model to obtain equipment fault risk membership information corresponding to the power distribution network, and inputting the historical operation data to the equipment maintenance risk membership information determination model to obtain equipment maintenance risk membership information corresponding to the power distribution network;
the generating module is used for generating equipment risk membership vectors according to the equipment fault risk membership information and the equipment maintenance risk membership information; the equipment risk membership vector is used for representing the membership degree of equipment risks corresponding to the power distribution network to the operation risks of the power distribution network;
and the adjusting module is used for adjusting the equipment risk membership vector according to the equipment fault risk coefficient and the equipment overhaul risk coefficient of the power distribution network to obtain the operation risk information of the power distribution network.
A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the above-mentioned method when executing the computer program.
A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program realizes the steps of the above-mentioned method when being executed by a processor.
A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the above-mentioned method when executed by a processor.
According to the operation data processing method and device, the computer equipment, the storage medium and the computer program product for power distribution network maintenance, the equipment fault information determination model and the equipment maintenance information determination model corresponding to the power distribution network are obtained; the equipment fault information determination model is used for determining fault information of each equipment in the power distribution network; the equipment maintenance information determining model is used for determining maintenance information of various equipment of the power distribution network; performing fuzzification processing on the equipment fault information determination model and the equipment maintenance information determination model respectively to obtain an equipment fault risk membership information determination model and an equipment maintenance risk membership information determination model; acquiring historical operation data of the power distribution network, inputting the historical operation data to the equipment fault risk membership information determination model to obtain equipment fault risk membership information corresponding to the power distribution network, and inputting the historical operation data to the equipment maintenance risk membership information determination model to obtain equipment maintenance risk membership information corresponding to the power distribution network; generating equipment risk membership vectors according to the equipment fault risk membership information and the equipment maintenance risk membership information; the equipment risk membership vector is used for representing the membership degree of equipment risks corresponding to the power distribution network to the operation risks of the power distribution network; finally, adjusting the equipment risk membership vector according to the equipment fault risk coefficient and the equipment overhaul risk coefficient of the power distribution network to obtain operation risk information of the power distribution network; therefore, the operation data of the power distribution network can be processed, the equipment fault risk and the equipment maintenance risk which affect the operation risk of the power distribution network are comprehensively considered, the functional relation between the equipment fault risk and the equipment maintenance risk to the operation risk of the power distribution network is determined, the evaluation value of the operation risk of the power distribution network is given, the quantitative evaluation of the operation risk of the power distribution network is realized, on the basis, the corresponding equipment fault risk coefficient and the corresponding equipment maintenance risk coefficient can be determined according to the importance degree of the actual equipment fault risk condition and the actual equipment maintenance risk condition to the operation risk of the power distribution network, the equipment risk membership vector is flexibly adjusted, more accurate processing is carried out on the operation data of the power distribution network, the operation risk of the power distribution network is more reasonably predicted, and more accurate maintenance basis can be provided for the maintenance plan of the power distribution network.
Drawings
FIG. 1 is a diagram of an application environment of a method for processing operational data for power distribution network maintenance in one embodiment;
FIG. 2 is a schematic flow chart illustrating a method for processing operational data for power distribution network maintenance in one embodiment;
FIG. 3 is a schematic flow chart of a method for processing operation data for power distribution network overhaul in another embodiment;
FIG. 4 is a block diagram of an embodiment of an operational data processing apparatus for distribution network service;
FIG. 5 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The operation data processing method for power distribution network overhaul can be applied to the application environment shown in fig. 1. Wherein the terminal 102 may communicate with the server 104 over a network. The server 104 acquires an equipment fault information determination model and an equipment maintenance information determination model corresponding to the power distribution network; the equipment fault information determination model is used for determining fault information of each equipment in the power distribution network; the equipment maintenance information determining model is used for determining maintenance information of various equipment of the power distribution network; the server 104 respectively fuzzifies the equipment fault information determination model and the equipment overhaul information determination model to obtain an equipment fault risk membership information determination model and an equipment overhaul risk membership information determination model; the server 104 acquires historical operation data of the power distribution network, inputs the historical operation data into the equipment fault risk membership information determination model to obtain equipment fault risk membership information corresponding to the power distribution network, and inputs the historical operation data into the equipment maintenance risk membership information determination model to obtain equipment maintenance risk membership information corresponding to the power distribution network; the server 104 generates an equipment risk membership vector according to the equipment fault risk membership information and the equipment overhaul risk membership information; the equipment risk membership vector is used for representing the membership degree of equipment risks corresponding to the power distribution network to the operation risks of the power distribution network; the server 104 adjusts the equipment risk membership vector according to the equipment failure risk coefficient and the equipment overhaul risk coefficient of the power distribution network, and obtains operation risk information of the power distribution network. The server 104 sends the operational risk information to the terminal 102. In practical applications, the terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices.
In one embodiment, as shown in fig. 2, there is provided an operation data processing method for power distribution network overhaul, which is described by taking the method as an example for being applied to a server, and includes the following steps:
step S202, acquiring an equipment fault information determination model and an equipment maintenance information determination model corresponding to the power distribution network; the equipment fault information determination model is used for determining fault information of each equipment in the power distribution network; the equipment maintenance information determining model is used for determining maintenance information of various equipment of the power distribution network.
The fault information may be related parameter information when each device in the power distribution network fails. For example, the failure information may be a failure rate of each device, a blackout load of each device during a failure, an expected blackout time when each device fails, an expected blackout loss per unit capacity of each device failure, a total number of devices, and a maximum period of investigation.
The overhaul information may be related parameter information when each device in the power distribution network is overhauled. For example, the overhaul information may be blackout load of each overhaul device in the overhaul process, blackout time of the power distribution network, expected blackout loss per unit capacity of each overhaul device in the overhaul process, resource cost required to be invested by each overhaul device in the overhaul process, and overhaul investment cost coefficient in the overhaul process.
The equipment fault information determination model can be a mathematical model which is constructed according to fault information of each equipment in the power distribution network and can be used for measuring the equipment fault risk of the power distribution network. For example, the equipment failure information determination model may be a computational model for calculating economic losses of the power distribution network that may be caused by the risk of equipment failure during the research period.
The equipment overhaul information determination model can be a mathematical model which is constructed according to overhaul information of each piece of equipment in the power distribution network and can be used for measuring equipment overhaul risks of the power distribution network. For example, the equipment servicing information determination model may be a computational model for calculating economic losses of the power distribution grid that may be caused by equipment servicing risks during a research period.
In the specific implementation, the server determines the fault information and the overhaul information of each device in the power distribution network by acquiring a device fault information determination model and a device overhaul information determination model corresponding to the power distribution network.
For example, the server obtains an economic loss value of the power distribution network, which may be caused by the equipment failure risk, and an economic loss value of the power distribution network, which may be caused by the equipment maintenance risk, by obtaining a calculation formula of economic loss of the power distribution network, which may be caused by the equipment failure risk, and a calculation formula of economic loss, which may be caused by the equipment maintenance risk, i.e., the server obtains corresponding failure information and maintenance information in the power distribution network.
Step S204, the equipment fault information determination model and the equipment maintenance information determination model are fuzzified respectively to obtain an equipment fault risk membership information determination model and an equipment maintenance risk membership information determination model.
The equipment fault risk membership information determination model can be a mathematical model which is obtained after the equipment fault information determination model is fuzzified and can be used for measuring the trueness degree of a fuzzy set of operation risk information of the power distribution network to which the equipment fault risk information belongs. For example, the equipment fault risk membership information determination model may be used to fuzzify equipment fault risk information, and measure the true degree of the equipment fault risk information on the operation risk information of the power distribution network by using the fuzzified equipment fault risk information.
The equipment overhaul risk membership information determination model can be a mathematical model which is obtained after the equipment overhaul information determination model is fuzzified and can be used for measuring the trueness degree of a fuzzy set of operation risk information of the power distribution network to which the equipment overhaul risk information belongs. For example, the equipment overhaul risk membership information determination model may be used to fuzzify the equipment overhaul risk information, and measure the true degree of the equipment overhaul risk information to the operation risk information of the power distribution network by using the fuzzified equipment overhaul risk information.
In the specific implementation, the server fuzzifies the equipment fault information determination model corresponding to the power distribution network so as to establish a membership model corresponding to the equipment fault information determination model and generate a corresponding equipment fault risk membership information determination model, and the server fuzzifies the equipment overhaul information determination model corresponding to the power distribution network so as to establish a membership model corresponding to the equipment overhaul information determination model and generate a corresponding equipment overhaul risk membership information determination model.
For example, after the server determines the equipment fault information, a proper fuzzy distribution is selected, the server fuzzifies the equipment fault risk information based on the fuzzy distribution, so that the server establishes a proper membership function, meanwhile, after the server determines the equipment overhaul information, a proper fuzzy distribution is selected, the server fuzzifies the equipment overhaul risk information based on the fuzzy distribution, and therefore the server establishes a proper membership function.
Step S206, historical operation data of the power distribution network are obtained, the historical operation data are input into the equipment fault risk membership information determining model, equipment fault risk membership information corresponding to the power distribution network is obtained, and the historical operation data are input into the equipment maintenance risk membership information determining model, and equipment maintenance risk membership information corresponding to the power distribution network is obtained.
The equipment fault risk membership information may be truth information for measuring a fuzzy set of equipment fault risk information of the power distribution network, wherein the equipment fault risk membership information is operation risk information of the power distribution network.
The equipment overhaul risk membership information can be truth degree information used for measuring that the equipment overhaul risk information of the power distribution network belongs to a fuzzy set of operation risk information of the power distribution network.
In the specific implementation, the server obtains historical operation data of the power distribution network, namely operation information of the power distribution network in the past years, the server processes the historical operation data to obtain the sorted data information, the server inputs the data information to the equipment fault risk membership information determination model, the server outputs the equipment fault risk membership information through the equipment fault risk membership information determination model, meanwhile, the server inputs the data information to the equipment maintenance risk membership information determination model, and the server outputs the equipment maintenance risk membership information through the equipment fault risk membership information determination model.
For example, the server acquires running information of the power distribution network for a plurality of years, processes the running information of the power distribution network for a plurality of years to obtain sorted data information, substitutes the data information into an equipment fault risk membership function, outputs the membership degree of the equipment fault risk to the running risk of the power distribution network through the equipment fault risk membership function, substitutes the data information into an equipment maintenance risk membership function, and outputs the membership degree of the equipment maintenance risk to the running risk of the power distribution network through the equipment maintenance risk membership function.
Step S208, generating equipment risk membership vectors according to the equipment fault risk membership information and the equipment maintenance risk membership information; the equipment risk membership vector is used for representing the membership degree of the equipment risk corresponding to the power distribution network to the operation risk of the power distribution network.
In the specific implementation, the server determines the membership degree of the equipment failure risk and the equipment overhaul risk to the operation risk of the power distribution network according to the equipment failure risk membership information and the equipment overhaul risk membership information, and then generates an equipment risk membership vector according to the membership degree of the equipment failure risk and the equipment overhaul risk to the operation risk of the power distribution network.
And step S210, adjusting the equipment risk membership vector according to the equipment fault risk coefficient and the equipment overhaul risk coefficient of the power distribution network to obtain operation risk information of the power distribution network.
The equipment fault risk coefficient may be a coefficient for measuring the importance degree of the equipment fault risk to the operation risk of the power distribution network.
The equipment overhaul risk coefficient may be a coefficient for measuring the importance degree of the equipment overhaul risk to the operation risk of the power distribution network.
In the specific implementation, the server determines an equipment failure risk coefficient and an equipment overhaul risk coefficient of the power distribution network according to actual conditions and relevant literature data, and then adjusts equipment failure risk membership information and equipment overhaul risk membership information according to the equipment failure risk coefficient and the equipment overhaul risk coefficient to obtain operation risk information of the power distribution network.
For example, the server determines the equipment fault risk coefficient of the power distribution network as k and the equipment fault risk of the power distribution network as l according to the actual conditions and relevant literature data, and then the server assigns the equipment fault risk coefficient k and the equipment maintenance risk coefficient l to equipment fault risk membership information R f-L And equipment maintenance risk membership information R M-L Adjusting to obtain operation risk information R of the power distribution network s ＝(kR f-L +lR M-L )/2。
In the operation data processing method facing the power distribution network maintenance, an equipment fault information determination model and an equipment maintenance information determination model corresponding to the power distribution network are obtained; the equipment fault information determination model is used for determining fault information of each equipment in the power distribution network; the equipment maintenance information determining model is used for determining maintenance information of various equipment of the power distribution network; performing fuzzification processing on the equipment fault information determination model and the equipment maintenance information determination model respectively to obtain an equipment fault risk membership information determination model and an equipment maintenance risk membership information determination model; acquiring historical operation data of the power distribution network, inputting the historical operation data to the equipment fault risk membership information determination model to obtain equipment fault risk membership information corresponding to the power distribution network, and inputting the historical operation data to the equipment maintenance risk membership information determination model to obtain equipment maintenance risk membership information corresponding to the power distribution network; generating equipment risk membership vectors according to the equipment fault risk membership information and the equipment maintenance risk membership information; the equipment risk membership vector is used for representing the membership degree of equipment risks corresponding to the power distribution network to the operation risks of the power distribution network; finally, adjusting the equipment risk membership vector according to the equipment fault risk coefficient and the equipment overhaul risk coefficient of the power distribution network to obtain operation risk information of the power distribution network; therefore, the operation data of the power distribution network can be processed, the equipment fault risk and the equipment maintenance risk which affect the operation risk of the power distribution network are comprehensively considered, the functional relation between the equipment fault risk and the equipment maintenance risk to the operation risk of the power distribution network is determined, the evaluation value of the operation risk of the power distribution network is given, the quantitative evaluation of the operation risk of the power distribution network is realized, on the basis, the corresponding equipment fault risk coefficient and the corresponding equipment maintenance risk coefficient can be determined according to the importance degree of the actual equipment fault risk condition and the actual equipment maintenance risk condition to the operation risk of the power distribution network, the equipment risk membership vector is flexibly adjusted, more accurate processing is carried out on the operation data of the power distribution network, the operation risk of the power distribution network is more reasonably predicted, and more accurate maintenance basis can be provided for the maintenance plan of the power distribution network.
In another embodiment, the adjusting the equipment risk membership vector according to the equipment failure risk coefficient and the equipment overhaul risk coefficient of the power distribution network to obtain the operation risk information of the power distribution network comprises: generating an equipment operation risk weight vector (k, m) of the power distribution network according to the equipment fault risk coefficient k and the equipment overhaul risk coefficient m of the power distribution network; adjusting the equipment risk membership vector according to the equipment operation risk weight vector (k, m) to obtain operation risk information of the power distribution network, wherein the operation risk information is expressed as
R s ＝(kR f-L +mR M-L )/2
Wherein R is s Is a risk indicator of the distribution network, (R) f-L ,R M-L ) And (4) the equipment risk membership vector.
According to the technical scheme, the corresponding equipment fault risk coefficient and the equipment overhaul risk coefficient are determined according to the importance degree of the actual equipment fault risk condition and the actual equipment overhaul risk condition to the operation risk of the power distribution network, the equipment risk membership vector can be flexibly adjusted, and the operation risk of the power distribution network can be more accurately evaluated.
In another embodiment, the equipment failure information determination model is represented as:
wherein R is f For the value of the risk of equipment failure, lambda, of the distribution network i Failure rate of equipment i in power distribution network, with units of (times/year), P i Indicating the blackout load, T, during a fault in a device i of a power distribution network i Expected blackout time, C, representing a fault of a device i of an electric distribution network i (t) represents the expected loss of blackout per unit capacity for a fault in equipment I of the distribution network, I max Number of devices, T, of distribution network max Is the maximum number of cycles of the distribution network.
In another embodiment, the equipment servicing information determination model is represented as:
wherein R is M For the equipment overhaul risk value, P, of the distribution network Mi Indicating the blackout load, T, in the process of servicing equipment i of the distribution network Mi Indicating the blackout load in the overhaul of the distribution network, C Mi (t) represents the expected blackout loss per unit capacity during the overhaul of a distribution network, equipment i, C MR And (t) represents the resource cost required to be input by the maintenance equipment i of the power distribution network, se (t) represents a maintenance input cost coefficient of the power distribution network, se (t) =2 when the maintenance period is summer, se (t) =1.5 when the maintenance period is winter, and Se (t) =1 when the maintenance period is the rest of the seasons.
In another embodiment, the equipment failure risk membership information determination model is represented as:
wherein R is f-max Representing the maximum value of the equipment fault risk of the power distribution network, and the expression is as follows:
R f-max ＝3E y *C y /K
wherein E is y Indicating the average annual power shortage load of the distribution network, C y The average annual electricity price of the power distribution network is represented, and K represents the average annual power failure times of the power distribution network;
wherein R is f-min The minimum value, which represents the risk of failure of the distribution network, is set to 0.
In another embodiment, the equipment overhaul risk membership information determination model is represented as:
wherein R is M-max The maximum value of the equipment maintenance risk of the power distribution network is represented by the expression:
R M-max ＝1.5E y *C y /K
wherein: e y Indicating the average annual power shortage load of the distribution network, C y The average annual power price of the power distribution network is represented, and K represents the average annual power failure times of the power distribution network;
wherein R is M-min The minimum value representing the overhaul risk of the distribution network is set to 0.
In another embodiment, as shown in fig. 3, an operation data processing method for power distribution network overhaul is provided, which is described by taking the method as an example for being applied to a server, and includes the following steps:
step S302, acquiring an equipment fault information determination model and an equipment maintenance information determination model corresponding to the power distribution network; the equipment fault information determination model is used for determining fault information of each equipment in the power distribution network; the equipment maintenance information determining model is used for determining maintenance information of various equipment of the power distribution network.
Step S304, the equipment fault information determination model and the equipment overhaul information determination model are fuzzified respectively to obtain an equipment fault risk membership information determination model and an equipment overhaul risk membership information determination model.
Step S306, historical operation data of the power distribution network are obtained, the historical operation data are input into the equipment fault risk membership information determining model, equipment fault risk membership information corresponding to the power distribution network is obtained, and the historical operation data are input into the equipment maintenance risk membership information determining model, and equipment maintenance risk membership information corresponding to the power distribution network is obtained.
Step S308, generating equipment risk membership vectors according to the equipment fault risk membership information and the equipment maintenance risk membership information; the equipment risk membership vector is used for representing the membership degree of the equipment risk corresponding to the power distribution network to the operation risk of the power distribution network.
And S310, generating an equipment operation risk weight vector (k, m) of the power distribution network according to the equipment fault risk coefficient k and the equipment overhaul risk coefficient m of the power distribution network.
Step S312, adjusting the equipment risk membership vector according to the equipment operation risk weight vector (k, m) to obtain operation risk information of the power distribution network, wherein the operation risk information is expressed as
R s ＝(kR f-L +mR M-L )/2
Wherein R is s Is a risk indicator of the distribution network, (R) f-L ,R M-L ) And (4) the equipment risk membership vector.
It should be noted that, the specific limitations of the above steps may refer to the above specific limitations of an operation data processing method for power distribution network overhaul.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a part of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the application also provides an operation data processing device for power distribution network overhaul, which is used for realizing the operation data processing method for power distribution network overhaul. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the method, so the specific limitations in the following one or more embodiments of the operation data processing device for power distribution network overhaul provided herein can be referred to the limitations on the operation data processing method for power distribution network overhaul provided above, and are not described herein again.
In one embodiment, as shown in fig. 4, there is provided an operation data processing apparatus for power distribution network service, including:
an obtaining module 402, configured to obtain an equipment fault information determination model and an equipment overhaul information determination model corresponding to a power distribution network; the equipment fault information determination model is used for determining fault information of each equipment in the power distribution network; the equipment maintenance information determining model is used for determining maintenance information of various equipment of the power distribution network;
a fuzzy module 404, configured to perform fuzzification processing on the equipment fault information determination model and the equipment overhaul information determination model respectively to obtain an equipment fault risk membership information determination model and an equipment overhaul risk membership information determination model;
the input module 406 is used for acquiring historical operation data of the power distribution network, inputting the historical operation data to the equipment fault risk membership information determination model to obtain equipment fault risk membership information corresponding to the power distribution network, and inputting the historical operation data to the equipment maintenance risk membership information determination model to obtain equipment maintenance risk membership information corresponding to the power distribution network;
the generating module 408 is configured to generate an equipment risk membership vector according to the equipment fault risk membership information and the equipment overhaul risk membership information; the equipment risk membership vector is used for representing the membership degree of equipment risks corresponding to the power distribution network to the operation risks of the power distribution network;
and the adjusting module 410 is configured to adjust the equipment risk membership vector according to the equipment failure risk coefficient and the equipment overhaul risk coefficient of the power distribution network, so as to obtain operation risk information of the power distribution network.
In one embodiment, the equipment risk membership vector is adjusted according to an equipment failure risk coefficient and an equipment overhaul risk coefficient of the power distribution network to obtain operation risk information of the power distribution network, and the adjusting module 410 is specifically configured to generate an equipment operation risk weight vector (k, m) of the power distribution network according to an equipment failure risk coefficient k and an equipment overhaul risk coefficient m of the power distribution network; adjusting the equipment risk membership vector according to the equipment operation risk weight vector (k, m) to obtain operation risk information of the power distribution network, wherein the operation risk information is expressed as
R s ＝(kR f-L +mR M-L )/2
Wherein R is s Is a risk indicator of the distribution network, (R) f-L ,R M-L ) And (4) the equipment risk membership vector.
In one embodiment, the obtaining module 302, the device failure information determination model is represented as:
wherein R is f For the value of the risk of equipment failure, lambda, of the distribution network i Failure rate of equipment i in distribution network, with units of (times/year), P i Indicating the blackout load, T, during a fault in a device i of a power distribution network i Expected blackout time, C, representing a fault of a device i of an electric distribution network i (t) represents the expected loss of blackout per unit capacity for a fault in equipment I of the distribution network, I max Number of devices, T, of distribution network max Is the maximum number of cycles of the distribution network.
In one embodiment, the obtaining module 402, the equipment overhaul information determination model, is represented as:
wherein R is M For the equipment overhaul risk value, P, of the distribution network Mi Indicating the blackout load, T, in the process of servicing equipment i of the distribution network Mi Indicating the load of the distribution network during maintenance, C Mi (t) represents the expected blackout loss per unit capacity during the overhaul of a distribution network, equipment i, C MR And (t) represents the resource cost required to be input by the maintenance equipment i of the power distribution network, se (t) represents a maintenance input cost coefficient of the power distribution network, se (t) =2 when the maintenance period is summer, se (t) =1.5 when the maintenance period is winter, and Se (t) =1 when the maintenance period is the rest of the seasons.
In one embodiment, the fuzzy module 404, the equipment failure risk membership information determination model is represented as:
wherein R is f-max Representing the maximum value of the equipment fault risk of the power distribution network, and the expression is as follows:
R f-max ＝3E y *C y /K
wherein E is y Indicating the average annual power shortage load of the distribution network, C y The average annual power price of the power distribution network is represented, and K represents the average annual power failure times of the power distribution network;
wherein R is f-min The minimum value, which represents the risk of failure of the distribution network, is set to 0.
In one embodiment, the fuzzy module 404, the equipment overhaul risk membership information determination model, is represented as:
wherein R is M-max The maximum value of the equipment maintenance risk of the power distribution network is represented by the expression:
R M-max ＝1.5E y *C y /K
wherein: e y Indicating the average annual power shortage load of the distribution network, C y The average annual power price of the power distribution network is represented, and K represents the average annual power failure times of the power distribution network;
wherein R is M-min The minimum value representing the overhaul risk of the distribution network is set to 0.
All or part of each module in the operation data processing device for the power distribution network overhaul can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, and a network interface connected by a system bus. 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, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer equipment is used for storing operation data processing data for power distribution network overhaul. The network 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 for processing operational data for overhaul of a power distribution network.
It will be appreciated by those skilled in the art that the configuration shown in fig. 5 is a block diagram of only a portion of the configuration associated with the present application, and is not intended to limit the computing device to which the present application may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, a computer arrangement is provided, comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of the above-described method of operational data processing for overhaul of an electric power distribution network. Here, the steps of an operation data processing method for power distribution network overhaul may be steps in an operation data processing method for power distribution network overhaul according to the foregoing embodiments.
In an embodiment, a computer-readable storage medium is provided, in which a computer program is stored, which, when being executed by a processor, causes the processor to carry out the steps of the above-mentioned operational data processing method for power distribution network service. Here, the steps of the operation data processing method for power distribution network overhaul may be steps in an operation data processing method for power distribution network overhaul according to the foregoing embodiments.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, causes the processor to perform the steps of a method of operational data processing for power distribution network service oriented as described above. Here, the steps of the operation data processing method for power distribution network overhaul may be steps in an operation data processing method for power distribution network overhaul according to the foregoing embodiments.
It should be noted that, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include a Read-Only Memory (ROM), a magnetic tape, a floppy disk, a flash Memory, an optical Memory, a high-density embedded nonvolatile Memory, a resistive Random Access Memory (ReRAM), a Magnetic Random Access Memory (MRAM), a Ferroelectric Random Access Memory (FRAM), a Phase Change Memory (PCM), a graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases involved in the embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the various embodiments provided herein may be, without limitation, general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, or the like.
All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (10)
1. An operation data processing method for power distribution network overhaul is characterized by comprising the following steps:
acquiring an equipment fault information determination model and an equipment maintenance information determination model corresponding to the power distribution network; the equipment fault information determination model is used for determining fault information of each equipment in the power distribution network; the equipment maintenance information determining model is used for determining maintenance information of various equipment of the power distribution network;
fuzzification processing is respectively carried out on the equipment fault information determination model and the equipment overhaul information determination model to obtain an equipment fault risk membership information determination model and an equipment overhaul risk membership information determination model;
acquiring historical operation data of the power distribution network, inputting the historical operation data to the equipment fault risk membership information determination model to obtain equipment fault risk membership information corresponding to the power distribution network, and inputting the historical operation data to the equipment maintenance risk membership information determination model to obtain equipment maintenance risk membership information corresponding to the power distribution network;
generating equipment risk membership vectors according to the equipment fault risk membership information and the equipment overhaul risk membership information; the equipment risk membership vector is used for representing the membership degree of equipment risks corresponding to the power distribution network to the operation risks of the power distribution network;
and adjusting the equipment risk membership vector according to the equipment fault risk coefficient and the equipment overhaul risk coefficient of the power distribution network to obtain the running risk information of the power distribution network.
2. The method according to claim 1, wherein the adjusting the equipment risk membership vector according to the equipment failure risk coefficient and the equipment overhaul risk coefficient of the power distribution network to obtain the operation risk information of the power distribution network comprises:
generating an equipment operation risk weight vector (k, m) of the power distribution network according to the equipment fault risk coefficient k and the equipment overhaul risk coefficient m of the power distribution network;
adjusting the equipment risk membership vector according to the equipment operation risk weight vector (k, m) to obtain operation risk information of the power distribution network, wherein the operation risk information is expressed as
R s ＝(kR f-L +mR M-L )/2
Wherein R is s (R) is a risk indicator of the distribution network f-L ,R M-L ) And the equipment risk membership vector is used.
3. The method of claim 1, wherein the equipment failure information determination model is represented as:
wherein R is f Is an equipment failure risk value, lambda, of the distribution network i Is the failure rate of the equipment i of the power distribution network, and has the unit of (times/year), P i Representing the blackout load, T, during a fault of a device i of said distribution network i Expected blackout time, C, representing a fault of a device i of the power distribution network i (t) represents the expected loss of blackout per capacity for a failure of equipment I, of said distribution network max Is the number of devices of the distribution network, T max Is the maximum number of cycles of the distribution network.
4. The method of claim 1, wherein the equipment overhaul information determination model is represented as:
wherein R is M Is the equipment overhaul risk value, P, of the distribution network Mi Representing the blackout load, T, in the process of servicing a device i of the distribution network Mi Representing the blackout load in the overhaul of the distribution network, C Mi (t) represents the expected blackout loss per capacity during the overhaul of the distribution network, equipment i, C MR And (t) represents the resource cost required to be input by the maintenance equipment i of the power distribution network, se (t) represents a maintenance input cost coefficient of the power distribution network, se (t) =2 when the maintenance period is summer, se (t) =1.5 when the maintenance period is winter, and Se (t) =1 when the maintenance period is the rest of the seasons.
5. The method of claim 1, wherein the equipment failure risk membership information determination model is represented as:
wherein R is f-max Representing a maximum value of the equipment fault risk of the power distribution network, wherein the expression is as follows:
R f-max ＝3E y *C y /K
wherein E is y Representing the average annual power shortage load, C, of said distribution network y Expressing the average annual electricity price of the power distribution network, and K expressing the average annual power failure times of the power distribution network;
wherein R is f-min A minimum value representing a risk of failure of the distribution network is set to 0.
6. The method of claim 1, wherein the equipment overhaul risk membership information determination model is represented as:
wherein R is M-max Maximum value representing the risk of equipment overhaul of said distribution network, expressionThe formula is as follows:
R M-max ＝1.5E y *C y /K
wherein: e y Representing the average annual power shortage load, C, of said distribution network y Expressing the average annual electricity price of the power distribution network, and K expressing the average annual power failure times of the power distribution network;
wherein R is M-min And setting the minimum value of the maintenance risk representing the power distribution network to be 0.
7. An operational data processing device for power distribution network maintenance, the device comprising:
the acquisition module is used for acquiring an equipment fault information determination model and an equipment maintenance information determination model corresponding to the power distribution network; the equipment fault information determination model is used for determining fault information of each equipment in the power distribution network; the equipment maintenance information determining model is used for determining maintenance information of various equipment of the power distribution network;
the fuzzy module is used for respectively carrying out fuzzification processing on the equipment fault information determination model and the equipment overhaul information determination model to obtain an equipment fault risk membership information determination model and an equipment overhaul risk membership information determination model;
the input module is used for acquiring historical operating data of the power distribution network, inputting the historical operating data to the equipment fault risk membership information determination model to obtain equipment fault risk membership information corresponding to the power distribution network, and inputting the historical operating data to the equipment maintenance risk membership information determination model to obtain equipment maintenance risk membership information corresponding to the power distribution network;
the generating module is used for generating equipment risk membership vectors according to the equipment fault risk membership information and the equipment maintenance risk membership information; the equipment risk membership vector is used for representing the membership degree of equipment risks corresponding to the power distribution network to the operation risks of the power distribution network;
and the adjusting module is used for adjusting the equipment risk membership vector according to the equipment fault risk coefficient and the equipment overhaul risk coefficient of the power distribution network to obtain the operation risk information of the power distribution network.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 6.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
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| CN116843324A (en) * | 2023-09-01 | 2023-10-03 | 国网山东省电力公司东平县供电公司 | Distribution network operation and maintenance system, method, equipment and medium based on artificial intelligence |
| CN116843324B (en) * | 2023-09-01 | 2024-02-02 | 国网山东省电力公司东平县供电公司 | Distribution network operation and maintenance system, method, equipment and medium based on artificial intelligence |
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