CN114154879A - Power grid operation risk assessment method and device, electronic equipment and readable storage medium - Google Patents
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Abstract
The invention discloses a power grid operation risk assessment method and device, electronic equipment and a readable storage medium. The method comprises the following steps: determining the risk degree of the internal equipment through the equipment state data of the power grid equipment; determining an external environment risk degree through operating environment data of the power grid equipment; determining a risk correction factor; and determining the operation risk degree of the power grid according to the risk correction coefficient, the risk degree of the internal equipment and the risk degree of the external environment so as to evaluate the operation risk of the power grid. The power grid operation risk assessment method, the power grid operation risk assessment device, the electronic equipment and the computer readable storage medium can enable power grid monitoring to be more intelligent and tighter, and enable power grid operation to be safer and more reliable.
Description
Technical Field
The disclosure relates to a power grid operation risk assessment method and device and an electronic device readable storage medium.
Background
In the last 80 th century, power grid monitoring mainly relies on manual monitoring in substations, which is 1.0 time of power grid monitoring. In the middle and later 90 s, power transmission and transformation equipment can be monitored at the background of a transformer substation by means of an automatic system, and power grid monitoring is advanced to 2.0 times. After the three sets and five sets, massive power grid equipment operation data are collected to a power regulation and control center to realize regulation and control integration, which is 3.0 times of power grid monitoring, but at present, a power grid system has massive power grid information data, but an effective mining means is lacked, and the value of the data cannot be fully utilized.
Therefore, a new power grid operation risk assessment method, device, electronic equipment and computer readable storage medium are needed.
The above information disclosed in this background section is only for enhancement of understanding of the background of the disclosure and therefore it may contain information that does not constitute prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of this, the present disclosure provides a method and an apparatus for evaluating a risk of a power grid operation, an electronic device, and a computer-readable storage medium, which enable the power grid monitoring to be more intelligent and tighter, and the power grid operation to be safer and more reliable.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to an aspect of the present disclosure, a method for assessing an operation risk of a power grid is provided, the method including: determining the risk degree of the internal equipment through the equipment state data of the power grid equipment; determining an external environment risk degree through operating environment data of the power grid equipment; determining a risk correction coefficient according to historical internal risk degree data, historical external environment risk degree and historical power grid operation related data; and determining the operation risk degree of the power grid according to the risk correction coefficient, the risk degree of the internal equipment and the risk degree of the external environment so as to evaluate the operation risk of the power grid.
In an exemplary embodiment of the present disclosure, determining the internal device risk level through the device status data of the power grid device includes: obtaining usage log data of the power grid equipment; acquiring overhaul log data of the power grid equipment; acquiring fault log data of the power grid equipment; and performing data cleaning processing on the use log data, the overhaul log data and the fault log data to acquire the equipment state data.
In an exemplary embodiment of the present disclosure, determining the internal device risk level through the device status data of the power grid device further includes: inputting the cleaned log data into a service life model of the equipment, and determining a service life risk degree; inputting the maintenance log data after cleaning into a maintenance work model, and determining the maintenance risk degree; inputting the cleaned fault log data into a device fault model, and determining a fault risk degree; and determining the risk degree of the internal equipment according to the life risk degree, the overhaul risk degree and the fault risk degree.
In an exemplary embodiment of the present disclosure, determining the external environment risk degree through the operating environment data of the power grid device includes: acquiring temperature and humidity recording data of the power grid equipment; acquiring power grid load data of the power grid equipment; and performing data cleaning processing on the temperature and humidity recording data and the power grid load data to acquire the operating environment data.
In an exemplary embodiment of the present disclosure, determining the internal device risk level through the device status data of the power grid device further includes: inputting the temperature and humidity record data after cleaning into a temperature and humidity model, and determining the temperature and humidity risk degree; inputting the cleaned power grid load data into a maximum load model, and determining a load risk degree; and determining the risk degree of the internal equipment according to the temperature and humidity risk degree and the load risk degree.
In an exemplary embodiment of the present disclosure, determining a grid operation risk according to the risk correction coefficient, the internal device risk, and the external environment risk, so as to perform grid operation risk assessment includes:
wherein K is the operation risk degree of the power grid, delta and tau are the risk correction coefficients, and IiSaid internal device risk, O, for the ith deviceiThe outer ring of the i-th deviceEnvironmental risk degree.
According to an aspect of the present disclosure, a power grid operation risk assessment apparatus is provided, the apparatus including: the internal risk module is used for determining the risk degree of the internal equipment according to the equipment state data of the power grid equipment; the external risk module is used for determining the external environment risk degree through the operating environment data of the power grid equipment; a risk coefficient module for determining a risk correction coefficient; and the risk evaluation module is used for determining the power grid operation risk degree according to the risk correction coefficient, the internal equipment risk degree and the external environment risk degree so as to evaluate the power grid operation risk.
According to an aspect of the present disclosure, an electronic device is provided, the electronic device including: one or more processors; storage means for storing one or more programs; when executed by one or more processors, cause the one or more processors to implement a method as above.
According to an aspect of the disclosure, a computer-readable storage medium is proposed, on which a computer program is stored, which program, when being executed by a processor, carries out the method as above.
According to the power grid operation risk assessment method, the power grid operation risk assessment device, the electronic equipment and the computer readable storage medium, the power grid can be monitored more intelligently and more tightly, and the power grid can operate more safely and more reliably.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The above and other objects, features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are merely some embodiments of the present disclosure, and other drawings may be derived from those drawings by those of ordinary skill in the art without inventive effort.
Fig. 1 is a system block diagram illustrating a method and an apparatus for risk assessment of grid operation according to an exemplary embodiment.
Fig. 2 is a flow chart illustrating a method for assessing risk of grid operation according to an exemplary embodiment.
Fig. 3 is a schematic diagram illustrating a method for assessing risk of grid operation according to an exemplary embodiment.
Fig. 4 is a schematic diagram illustrating a grid operational risk assessment method according to another exemplary embodiment.
Fig. 5 is a block diagram illustrating a grid operation risk assessment device according to an exemplary embodiment.
FIG. 6 is a block diagram illustrating an electronic device in accordance with an example embodiment.
FIG. 7 is a schematic diagram illustrating a computer-readable storage medium according to an example embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as 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 concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one element from another. Thus, a first component discussed below may be termed a second component without departing from the teachings of the disclosed concept. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It is to be understood by those skilled in the art that the drawings are merely schematic representations of exemplary embodiments, and that the blocks or processes shown in the drawings are not necessarily required to practice the present disclosure and are, therefore, not intended to limit the scope of the present disclosure.
Fig. 1 is a system block diagram illustrating a method, an apparatus, an electronic device and a computer-readable storage medium for evaluating risk of grid operation according to an exemplary embodiment.
As shown in fig. 1, the system architecture may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a shopping application, a web browser application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a server that provides various services, and performs processing such as analysis of device status data acquired by the terminal devices 101, 102, and 103. The server 105 may perform processing such as analysis on the received data and feed back the processing result to the terminal device.
The server 105 may determine the internal device risk level, for example, from device status data of the grid devices; the server 105 may determine the external environmental risk, for example, from the operating environment data of the grid devices; the server 105 may, for example, determine a risk correction factor; the server 105 may determine the grid operation risk degree according to the risk correction coefficient, the internal equipment risk degree and the external environment risk degree, for example, to perform grid operation risk assessment.
The server 105 may be a physical server, or may be composed of a plurality of servers, for example, it should be noted that the grid operation risk assessment method provided by the embodiment of the present disclosure may be executed by the server 105, and accordingly, the grid operation risk assessment apparatus may be disposed in the server 105. Whereas the data requesting side providing data to the user is typically located in the terminal equipment 101, 102, 103.
Fig. 2 is a flow chart illustrating a method for assessing risk of grid operation according to an exemplary embodiment. The power grid operation risk assessment method at least comprises the steps S202 to S208.
As shown in fig. 2, in S202, the internal device risk is determined from the device status data of the grid device.
In one embodiment, determining the internal device risk level from the device status data of the grid device comprises: obtaining usage log data of the power grid equipment; acquiring overhaul log data of the power grid equipment; acquiring fault log data of the power grid equipment; and performing data cleaning processing on the use log data, the overhaul log data and the fault log data to acquire the equipment state data.
In one embodiment, determining the internal device risk level from the device status data of the grid device further comprises: inputting the cleaned log data into a service life model of the equipment, and determining a service life risk degree; inputting the maintenance log data after cleaning into a maintenance work model, and determining the maintenance risk degree; inputting the cleaned fault log data into a device fault model, and determining a fault risk degree; and determining the risk degree of the internal equipment according to the life risk degree, the overhaul risk degree and the fault risk degree.
The data volume of the equipment state data of the power grid equipment is huge, and nearly 150 state information volumes exist in a switch interval of a transformer substation; one transformer has 230 state information quantities; about 2500 state information quantities exist in one transformer substation, and nearly 8 ten thousand pieces of data are sent every day; taking the Hangzhou power grid as an example, the Hangzhou power grid has 361 substations, and the data is sent up to 2 thousand, 8 and more than ten thousand every day. The 2016 Hangzhou company regulation and control center receives over 100 hundred million pieces of data, and the data are printed into books and are superposed, and the height of the data can be higher than that of a shoulder pearl peak.
In the process of processing the massive information data, firstly, the transmission of all data is encrypted and isolated so as to meet the requirement of the information security of the power grid.
In S204, the external environment risk is determined from the operating environment data of the grid device.
In one embodiment, determining the external environmental risk level from the operating environment data of the grid device includes: acquiring temperature and humidity recording data of the power grid equipment; acquiring power grid load data of the power grid equipment; and performing data cleaning processing on the temperature and humidity recording data and the power grid load data to acquire the operating environment data.
In one embodiment, determining the internal device risk level from the device status data of the grid device further comprises: inputting the temperature and humidity record data after cleaning into a temperature and humidity model, and determining the temperature and humidity risk degree; inputting the cleaned power grid load data into a maximum load model, and determining a load risk degree; and determining the risk degree of the internal equipment according to the temperature and humidity risk degree and the load risk degree.
In S206, a risk correction factor is determined.
In one embodiment, determining the risk modification factor includes: and determining the risk correction coefficient according to historical internal risk degree data, historical external environment risk degree and historical power grid operation related data.
And in S208, determining the operation risk degree of the power grid according to the risk correction coefficient, the risk degree of the internal equipment and the risk degree of the external environment so as to evaluate the operation risk of the power grid.
In one embodiment, determining the grid operation risk according to the risk correction coefficient, the internal equipment risk and the external environment risk to perform grid operation risk assessment includes:
wherein K is the operation risk degree of the power grid, delta and tau are the risk correction coefficients, and IiSaid internal device risk, O, for the ith deviceiThe external environmental risk for the ith device.
According to the power grid operation risk assessment method, effective information is optimized and integrated by cleaning data; and finally, building a model from space, time, type and equipment multi-dimension by using a decision tree and an artificial neural network algorithm based on a distributed computing technology, and deeply excavating to realize the functions of pre-judging equipment hidden dangers in advance, assisting in exception handling in the process, evaluating power grid risks afterwards and the like.
For example, the system analyzes uploaded information, year-round synchronization defect records, environmental weather and other data, utilizes a box-and-whisker diagram in statistics to mine monitoring data, and when 2016, 6, month and 2 days, the system prompts that potential hazards possibly exist in 110kV time variable voltage change, the probability of the potential hazards of a secondary circuit is predicted to be the maximum, and the insulation of a station internal pressure variable secondary terminal is reduced due to high humidity in plum rain season after being detected by an on-site inspection of an overhaul unit. If the voltage-variable secondary circuit is not in time, measures are taken to develop the defect of the voltage-variable secondary circuit. The early warning of the hidden danger of the equipment is realized by analyzing the uploaded information and the historical records.
The factors of the power grid operation risk are divided into two categories, a model is built in the power grid operation risk evaluation model, the influence of temperature and humidity and power grid load is analyzed outside through data such as equipment service life, maintenance work and defect records, the model correction of re-relevance and accuracy is utilized, the power grid risk degree concept is creatively provided, and the post-evaluation of the power grid operation risk is realized. For example, during the preparation period of a certain peak meeting, the risk degree of the XX power grid in the past 24 hours is evaluated every day, the overall rising trend of the risk degree index of the XX power grid in 3-6 months is found, measures such as equipment shortage elimination, centralized overhaul, mode adjustment and the like are carried out in a targeted manner according to the evaluation result, the risk degree index of the XX power grid is remarkably reduced through two-month regulation, the minimum value is reached before the peak meeting starts, and a great contribution is made to the peak meeting power conservation task.
In addition, aiming at the conditions that the bus voltage and the reactive power of each transformer substation are out of limit and the like in different time periods, the system analyzes the regulation effect of each device in the power grid by collecting historical operation information of reactive voltage control, provides different AVC control strategies, provides a real-time optimal scheme for reactive voltage control for partial transformer substations, reduces the reactive voltage regulation times by 32.5%, and provides an auxiliary decision for disposal of a monitor in the event of abnormal power grid conditions.
It should be clearly understood that this disclosure describes how to make and use particular examples, but the principles of this disclosure are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
Fig. 3 is a schematic diagram illustrating a method for assessing risk of grid operation according to an exemplary embodiment. The method for assessing the risk of operating the power grid shown in fig. 3 is further described in detail with reference to fig. 2.
Determining the power grid operation risk according to the risk correction coefficient, the internal equipment risk and the external environment risk, so as to perform power grid operation risk assessment, wherein the method comprises the following steps:
wherein K is the operation risk degree of the power grid, delta and tau are the risk correction coefficients, and IiSaid internal device risk, O, for the ith deviceiThe external environmental risk for the ith device.
Firstly, inputting the cleaned log data into a service life model of the equipment to determine a service life risk degree; inputting the maintenance log data after cleaning into a maintenance work model, and determining the maintenance risk degree; inputting the cleaned fault log data into a device fault model, and determining a fault risk degree; and determining the risk degree of the internal equipment according to the life risk degree, the overhaul risk degree and the fault risk degree. And (4) comprehensively producing the internal risk degree according to the result calculated by the model.
Then, inputting the temperature and humidity recording data after cleaning into a temperature and humidity model to determine the temperature and humidity risk degree; inputting the cleaned power grid load data into a maximum load model, and determining a load risk degree; and determining the risk degree of the internal equipment according to the temperature and humidity risk degree and the load risk degree. And comprehensively calculating the result calculated by the model to obtain the external risk degree.
Different risk degrees and characteristics under different seasons and environments can be comprehensively regulated and controlled through risk correction coefficients. For example, in a rainy season, the temperature and humidity coefficient in the risk correction coefficient may be set to be larger, and for example, in a severe winter season, due to a sudden increase in power consumption, the coefficient response of the maximum load correlation model in the risk correction coefficient may be set to be larger.
Fig. 4 is a schematic diagram illustrating a grid operational risk assessment method according to another exemplary embodiment. Fig. 4 exemplarily illustrates a dynamic risk assessment result, which is performed periodically by human setting, and when the risk is high, risk warning information may be generated for the staff to process.
And for example, standardized hidden danger response measures and templated hidden danger early warning contact lists are established, a closed-loop management process from discovery, early warning to treatment of equipment hidden dangers is established, risk pre-control and closed-loop treatment of the hidden dangers are practically performed, various organization measures, technical measures and safety measures for ensuring safety are ensured, and seamless connection of equipment hidden danger early warning work among all departments is really realized.
For example, a review system of the hidden danger warning work can be constructed. And reporting the hidden trouble cases found in the period by the regulation and control institutions of various cities in the end of each quarter, and intensively reviewing the reported cases by professional monitoring experts of the province and the province, thereby determining the properties of the cases. In addition, in order to promote the intensive development of the hidden danger investigation work of centralized monitoring operation, improve the hidden danger investigation breadth and depth, save and regulate the centralized organization of hidden danger case exchange meetings, typical hidden danger cases are selected from the hidden dangers confirmed by the companies in various regions through expert centralized review, and are published in the whole province scope after the optimization is completed, the companies in various regions are arranged to investigate whether similar hidden dangers exist, and a solid foundation is laid for the situation that the safe production of the whole province power grid system is kept stable continuously.
Those skilled in the art will appreciate that all or part of the steps implementing the above embodiments are implemented as computer programs executed by a CPU. When executed by the CPU, performs the functions defined by the above-described methods provided by the present disclosure. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic or optical disk, or the like.
Furthermore, it should be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.
Fig. 5 is a block diagram illustrating a grid operation risk assessment device according to an exemplary embodiment. Wherein, the risk assessment device includes: an internal risk module 502, an external risk module 504, a risk factor module 506, and a risk assessment module 508.
The internal risk module 502 is configured to determine an internal device risk degree according to device state data of the power grid device; the method comprises the following steps: obtaining usage log data of the power grid equipment; acquiring overhaul log data of the power grid equipment; acquiring fault log data of the power grid equipment; and performing data cleaning processing on the use log data, the overhaul log data and the fault log data to acquire the equipment state data.
The external risk module 504 is configured to determine an external environment risk degree according to the operating environment data of the power grid device; the method comprises the following steps: acquiring temperature and humidity recording data of the power grid equipment; acquiring power grid load data of the power grid equipment; and performing data cleaning processing on the temperature and humidity recording data and the power grid load data to acquire the operating environment data.
The risk evaluation module 508 is configured to determine a power grid operation risk degree according to the risk correction coefficient, the internal device risk degree, and the external environment risk degree, so as to perform power grid operation risk evaluation. The method comprises the following steps:
wherein K is the operation risk degree of the power grid, delta and tau are the risk correction coefficients, and IiSaid internal device risk, O, for the ith deviceiAs described for the ith deviceDegree of risk of the external environment.
According to the power grid operation risk assessment device, effective information is optimized and integrated by cleaning data; and finally, building a model from space, time, type and equipment multi-dimension by using a decision tree and an artificial neural network algorithm based on a distributed computing technology, and deeply excavating to realize the functions of pre-judging equipment hidden dangers in advance, assisting in exception handling in the process, evaluating power grid risks afterwards and the like.
FIG. 6 is a block diagram illustrating an electronic device in accordance with an example embodiment.
An electronic device 200 according to this embodiment of the present disclosure is described below with reference to fig. 6. The electronic device 200 shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 6, the electronic device 200 is embodied in the form of a general purpose computing device. The components of the electronic device 200 may include, but are not limited to: at least one processing unit 210, at least one memory unit 220, a bus 230 connecting different system components (including the memory unit 220 and the processing unit 210), a display unit 240, and the like.
Wherein the storage unit stores program code executable by the processing unit 210 to cause the processing unit 210 to perform the steps according to various exemplary embodiments of the present disclosure described in the above-mentioned electronic prescription flow processing method section of the present specification. For example, the processing unit 210 may perform the steps as shown in fig. 2.
The storage unit 220 may include readable storage media in the form of volatile memory units, such as a random access memory unit (RAM)2201 and/or a cache memory unit 2202, and may further include a read only memory unit (ROM) 2203.
The storage unit 220 may also include a program/utility 2204 having a set (at least one) of program modules 2205, such program modules 2205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 200 may also communicate with one or more external devices 300 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 200, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 200 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 250. Also, the electronic device 200 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 260. The network adapter 260 may communicate with other modules of the electronic device 200 via the bus 230. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiments of the present disclosure.
Fig. 7 schematically illustrates a readable storage medium in an exemplary embodiment of the disclosure.
Referring to fig. 7, a program product 400 for implementing the above method according to an embodiment of the present disclosure is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable storage media. The readable storage medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The readable storage medium may include a propagated data signal with readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable storage medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The readable storage medium carries one or more programs which, when executed by a device, cause the computer-readable storage medium to perform the functions of: determining the risk degree of the internal equipment through the equipment state data of the power grid equipment; determining an external environment risk degree through operating environment data of the power grid equipment; determining a risk correction factor; and determining the operation risk degree of the power grid according to the risk correction coefficient, the risk degree of the internal equipment and the risk degree of the external environment so as to evaluate the operation risk of the power grid.
Those skilled in the art will appreciate that the modules described above may be distributed in the apparatus according to the description of the embodiments, or may be modified accordingly in one or more apparatuses unique from the embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Exemplary embodiments of the present disclosure are specifically illustrated and described above. It is to be understood that the present disclosure is not limited to the precise arrangements, instrumentalities, or instrumentalities described herein; on the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
In addition, the structures, the proportions, the sizes, and the like shown in the drawings of the present specification are only used for matching with the contents disclosed in the specification, so as to be understood and read by those skilled in the art, and are not used for limiting the limit conditions which the present disclosure can implement, so that the present disclosure has no technical essence, and any modification of the structures, the change of the proportion relation, or the adjustment of the sizes, should still fall within the scope which the technical contents disclosed in the present disclosure can cover without affecting the technical effects which the present disclosure can produce and the purposes which can be achieved. In addition, the terms "above", "first", "second" and "a" as used in the present specification are for the sake of clarity only, and are not intended to limit the scope of the present disclosure, and changes or modifications of the relative relationship may be made without substantial changes in the technical content.
Claims (9)
1. A power grid operation risk assessment method is characterized by comprising the following steps:
determining the risk degree of the internal equipment through the equipment state data of the power grid equipment;
determining an external environment risk degree through operating environment data of the power grid equipment;
determining a risk correction coefficient according to historical internal risk degree data, historical external environment risk degree and historical power grid operation related data; and
and determining the operation risk degree of the power grid according to the risk correction coefficient, the risk degree of the internal equipment and the risk degree of the external environment so as to evaluate the operation risk of the power grid.
2. The method of claim 1, wherein determining an internal device risk level from device status data for a grid device comprises:
acquiring use log data, overhaul log data and fault log data of the power grid equipment;
and performing data cleaning processing on the use log data, the overhaul log data and the fault log data to acquire the equipment state data.
3. The method of claim 2, wherein determining an internal device risk level from device status data of a grid device further comprises:
inputting the cleaned log data into a service life model of the equipment, and determining a service life risk degree;
inputting the maintenance log data after cleaning into a maintenance work model, and determining the maintenance risk degree;
inputting the cleaned fault log data into a device fault model, and determining a fault risk degree; and
and determining the risk degree of the internal equipment according to the life risk degree, the overhaul risk degree and the fault risk degree.
4. The method of claim 1, wherein determining the external environmental risk level from the operating environment data of the power grid device comprises:
acquiring temperature and humidity recording data of the power grid equipment;
acquiring power grid load data of the power grid equipment; and
and performing data cleaning processing on the temperature and humidity recorded data and the power grid load data to acquire the operating environment data.
5. The method of claim 4, wherein determining an internal device risk level from device status data of a grid device further comprises:
inputting the temperature and humidity record data after cleaning into a temperature and humidity model, and determining the temperature and humidity risk degree; inputting the cleaned power grid load data into a maximum load model, and determining a load risk degree; and
and determining the risk degree of the internal equipment according to the temperature and humidity risk degree and the load risk degree.
6. The method of claim 1, wherein determining the grid operation risk according to the risk correction coefficient, the internal equipment risk and the external environment risk for grid operation risk assessment comprises:
wherein K is the operation risk degree of the power grid, delta and tau are the risk correction coefficients, and IiSaid internal device risk, O, for the ith deviceiThe external environmental risk for the ith device.
7. An electrical grid operational risk assessment device, comprising:
the internal risk module is used for determining the risk degree of the internal equipment according to the equipment state data of the power grid equipment;
the external risk module is used for determining the external environment risk degree through the operating environment data of the power grid equipment;
a risk coefficient module for determining a risk correction coefficient; and
and the risk evaluation module is used for determining the power grid operation risk degree according to the risk correction coefficient, the internal equipment risk degree and the external environment risk degree so as to evaluate the power grid operation risk.
8. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.
9. Readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method according to any one of claims 1-6.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115760074A (en) * | 2022-12-07 | 2023-03-07 | 中国南方电网有限责任公司超高压输电公司广州局 | Power equipment operation and maintenance method and device, computer equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115760074A (en) * | 2022-12-07 | 2023-03-07 | 中国南方电网有限责任公司超高压输电公司广州局 | Power equipment operation and maintenance method and device, computer equipment and storage medium |
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