CN114202304A

CN114202304A - Intelligent monitoring processing method and system for power grid faults

Info

Publication number: CN114202304A
Application number: CN202111477630.4A
Authority: CN
Inventors: 陈方之; 廖华; 申晓杰; 钟文明; 钟仁浩; 董羊城; 奉钰力; 仲卫; 陆飞
Original assignee: NARI Nanjing Control System Co Ltd; Nanning Monitoring Center of Extra High Voltage Power Transmission Co
Current assignee: NARI Nanjing Control System Co Ltd; Nanning Monitoring Center of Extra High Voltage Power Transmission Co
Priority date: 2021-12-06
Filing date: 2021-12-06
Publication date: 2022-03-18

Abstract

The invention provides a power grid fault intelligent monitoring processing method and system, and relates to the technical field of power grid monitoring. The method comprises the following steps: acquiring real-time monitoring signals of equipment, matching the monitoring signals with an event mapping relation model, and generating abnormal signal events; obtaining an operation monitoring relativity evaluation result of the equipment, matching the evaluation result with an event mapping relation model, and generating a relativity evaluation event; receiving an alarm event and an operation event, and generating task items based on a task model library; performing task treatment on task items; associating the equipment ledger, the three remote data, the alarm event and the task handling information, and establishing an equipment lifetime resume library; and generating an eventing rule expert library and a task handling expert library based on the equipment lifetime resume library. The method has the advantages that the alarm monitoring is converted into the event monitoring by constructing a mapping relation model of the monitoring signals and the events, and the conversion from the passive monitoring to the active monitoring is realized. Greatly alleviate personnel's supervision screen pressure, promote the efficiency of handling.

Description

Intelligent monitoring processing method and system for power grid faults

Technical Field

The disclosure relates to the technical field of power grid monitoring, in particular to a power grid fault intelligent monitoring processing method and system.

Background

Nowadays, the scale of power enterprises is continuously getting bigger and bigger, the interconnection degree of power systems is also getting higher and higher, and the system is gradually evolving to a system with a large amount of data and information calculation convergence. The western and east electricity transmission, the three gorges grid-connected power generation and the smart grid interconnection project in China form a nationwide interconnected smart grid with large-scale large units, ultrahigh voltage and large power grids. With the advance of the integrated work of power grid regulation and control, the operation mode of the monitoring center is more and more popular in the ultra-high voltage power grid, and the scale of the monitoring center is larger and larger. The number of monitoring stations is increased rapidly from the original number to dozens of monitoring stations, and the monitoring stations are important stations for transmitting the alternating current to the west and the east. Along with the expansion of the monitoring range, the frequency and complexity of fault tripping of the equipment are higher and higher, so that higher requirements are provided for the efficiency of handling accidents by operators on duty running in the monitoring center.

At present, when a monitoring center attendant carries out accident handling, information collection and reporting are mainly carried out by telephone contact, analysis and judgment of faults and determination of a handling scheme are mainly carried out by the experience of the attendant, isolation operation of fault equipment and power restoration operation of intact equipment still need to be carried out manually, the informatization level is low, and the requirement of rapidly handling the power grid faults under a new situation is obviously not met. There are several outstanding problems: firstly, the existing monitoring master station system is inconvenient to rapidly collect and intensively display fault information. The monitoring system has a plurality of access points, and the information uploading quantity is large and dispersed when the accident tripping occurs, so that a person on duty is inconvenient to quickly collect the key information required by fault processing. The existing monitoring system has an imperfect fault information centralized display function, but simply collects main information of the monitoring system during tripping, has too much irrelevant information and cannot accurately judge the fault property. Secondly, the intelligent level of fault analysis and processing is not high. The current monitoring system has incomplete fault intelligent analysis function, and can not determine a recovery path according to an analysis conclusion and provide a processing reference scheme. The existing monitoring system intelligent alarm module can analyze simple faults, but cannot analyze and judge multi-element simultaneous faults, conversion faults, switch failures, dead zone faults, protection refusal and the like, cannot judge which equipment does not have power restoration conditions and give reasons, and even has the condition of missed judgment on some uncomplicated faults. And thirdly, failure processing lacks an information platform for unified command, and the failure processing efficiency is low. After the fault occurs, the monitoring center attendant needs to inform the controlled station attendant one by one through the telephone, and the monitoring center attendant not in the attendant room cannot automatically send a short message or automatically make a call. The on-site primary and secondary equipment inspection information needs to be reported by the on-duty personnel of the controlled station through a telephone, and the on-duty personnel of the monitoring center manually records and verifies, so that the efficiency is low. When reporting fault information to the dispatching, the monitoring center needs to manually input the fault information on the dispatching information interaction system through telephone contact and manual operation, the information quantity of the reporting and inputting system is large, and the fault information needs to be repeated and checked.

It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.

Disclosure of Invention

The present disclosure is directed to a method and a system for intelligently monitoring and processing a power grid fault, which are used to overcome, at least to some extent, the problem that a smart power grid dispatching system needs to provide new technical support due to the limitations and disadvantages of the related art.

According to a first aspect of the present disclosure, a method for intelligently monitoring and processing a power grid fault is provided, which includes:

acquiring real-time monitoring signals of equipment, matching preset monitoring signals with an event mapping relation model, and generating abnormal signal events;

obtaining an operation monitoring relativity evaluation result of the equipment, matching a preset evaluation result with an event mapping relation model, and generating a relativity evaluation event;

receiving an alarm event and an operation event, and generating task items based on a task model library, wherein the alarm event comprises an abnormal signal event and a relativity evaluation event, and the operation event comprises remote operation, information acceptance and defect flow;

task handling is carried out on task items, wherein the task handling comprises task allocation, task processing, task tracking and task archiving;

associating the equipment ledger, the three remote data, the alarm event and the task handling information, and establishing an equipment lifetime resume library;

and generating an eventing rule expert library and a task handling expert library based on the equipment lifetime resume library.

In an exemplary embodiment of the present disclosure, a monitoring signal and event mapping relation model is constructed according to historical fault events and/or an eventing rule expert library of equipment; the abnormal signal event comprises a monitoring signal name, an event type and an event grade.

In an exemplary embodiment of the disclosure, quantitative scoring is performed on operation monitoring relativity evaluation results of equipment, different types of relativity evaluation events are set according to different score intervals, and an evaluation result and event mapping relation model is constructed; wherein the relative evaluation event comprises: device type, rating score, and event type.

In an exemplary embodiment of the present disclosure, the task model library includes an emergency task configuration module and a planned task configuration module, wherein the emergency task configuration module is invoked to generate an emergency task item according to an alarm event and an operation event; and calling a plan task configuration module according to a preset timing calling plan so as to generate plan task items.

In an exemplary embodiment of the present disclosure, the task assignment process includes:

acquiring the existing task amount of an on-value monitor, and analyzing to obtain the bearable workload of the on-value monitor;

generating a task allocation plan according to the received task items and the bearable workload of the on-value monitor;

and pushing the task allocation plan to a shift interface for automatic allocation or manual allocation.

In an exemplary embodiment of the present disclosure, the generation steps of the eventing rules expert base are as follows:

generating a standardized association model of primary equipment and secondary equipment according to a name naming rule of a monitoring signal of the power grid equipment and monitoring alarm information accessed by a current system;

acquiring a typical fault event case, analyzing necessary conditions of fault occurrence based on a standardized association model, and designing and constructing an event characteristic rule base and an event rule base;

and generating an event rule expert library according to the alarm information, the event characteristic rule library and the event rule library.

In an exemplary embodiment of the present disclosure, the eventing rules expert library is configured based on an event definition tool, wherein the event definition tool includes an alarm object, a base object, a feature definition, and a rule definition.

In an exemplary embodiment of the disclosure, the task disposal expert database is constructed according to an analysis process of historical fault events, a disposal scheme, an event type, an event range and a device power restoration possession.

According to a second aspect of the present disclosure, there is provided a power grid fault intelligent monitoring processing system, including:

the abnormal signal event generating module is used for acquiring a real-time monitoring signal of the equipment, matching a preset monitoring signal and event mapping relation model and generating an abnormal signal event;

the relativity evaluation event generation module is used for acquiring an operation monitoring relativity evaluation result of the equipment, matching a preset evaluation result with the event mapping relation model and generating a relativity evaluation event;

the task item generation module is used for receiving alarm events and operation events and generating task items based on a task model library, wherein the alarm events comprise abnormal signal events and relativity evaluation events, and the operation events comprise remote operation, information acceptance and defect flows;

the task processing module is used for performing task processing on task items, wherein the task processing comprises task allocation, task processing, task tracking and task archiving;

the history label library generating module is used for associating the equipment machine account, the three-remote data, the alarm event and the task handling information and establishing an equipment lifetime history library;

and the expert base generation module is used for generating an eventing rule expert base and a task handling expert base based on the equipment lifetime resume base.

According to a third aspect of the present disclosure, there is provided an electronic device comprising:

a processor; and

a memory for storing executable instructions of the processor;

wherein the processor is configured to execute the grid fault intelligent monitoring processing method as described above via execution of the executable instructions.

According to the embodiment of the invention, the monitoring signal and event mapping relation model, the evaluation result and event mapping relation model are constructed and matched with the monitoring signal and the operation relativity evaluation of equipment to generate the abnormal signal event and the relativity evaluation event, and the alarm monitoring is converted into the event monitoring, so that the conversion from passive monitoring to active monitoring is realized, the monitoring screen pressure of personnel is greatly reduced, and the disposal efficiency is improved. And creating an event service-driven management mode, generating task items based on a task model library, realizing the flow and convenient control of a service process, and improving the efficiency of distributing and disposing tasks. In addition, an eventing rule expert library and a task handling expert library are generated by establishing an equipment lifetime record library, so that accurate auxiliary decisions are provided for each business of the power grid.

The system integrates a power grid model and operation data, considers the space-time coupling relation of information generation, carries out evened merging and fusion on discrete monitoring signals expressing the operation characteristics of the power grid, generates specific task items, and realizes that the monitoring processing of the power grid is changed from passive response depending on an alarm information text to active discovery based on an evened technology.

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.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.

Fig. 1 schematically illustrates a flow chart of a power grid fault intelligent monitoring processing method in an embodiment of the present disclosure;

FIG. 2 schematically illustrates a block diagram of the interaction layer 100 of FIG. 1;

FIG. 3 schematically illustrates a task item presentation interface for an on-duty monitor;

FIG. 4 schematically illustrates a timeline representation of a device lifetime history repository;

FIG. 5 schematically illustrates a schematic diagram of a grid fault intelligent monitoring processing system;

fig. 6 schematically shows a block diagram of an electronic device in an exemplary 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 examples 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 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 the like. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.

Further, the drawings are merely schematic illustrations of the present disclosure, in which the same reference numerals denote the same or similar parts, and thus, a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. 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 devices and/or microcontroller devices.

The following detailed description of exemplary embodiments of the disclosure refers to the accompanying drawings.

Fig. 1 schematically shows a flowchart of a grid fault intelligent monitoring processing method in a first embodiment of the present disclosure.

Referring to fig. 1, a method for intelligently monitoring and processing a power grid fault includes:

s110, collecting real-time monitoring signals of the equipment, matching preset monitoring signals with an event mapping relation model, and generating abnormal signal events;

s120, obtaining an operation monitoring relativity evaluation result of the equipment, matching a preset evaluation result with an event mapping relation model, and generating a relativity evaluation event;

s130, receiving an alarm event and an operation event, and generating a task item based on a task model library, wherein the alarm event comprises an abnormal signal event and a relativity evaluation event, and the operation event comprises a remote operation, an information acceptance and a defect process;

s140, task treatment is carried out on the task items, wherein the task treatment comprises task allocation, task processing, task tracking and task archiving;

s150, associating the equipment ledger, the three-remote data, the alarm event and the task handling information, and establishing an equipment lifetime resume library;

and S160, generating an eventing rule expert library and a task handling expert library based on the equipment lifetime resume library.

In a disclosed embodiment, in step S110, a mapping relation model between the monitoring signal and the event is constructed according to a historical failure event and/or an event rule expert database of the device; the abnormal signal event comprises a monitoring signal name, an event type and an event grade. Through monitoring the mapping relation model between the signal and the event, the bill of lading

An example of the matching of the monitoring signals and events is given as shown in table 1 below.

TABLE 1

In one disclosed embodiment, a single monitoring signal or a plurality of monitoring signals are converted into abnormal signal events through a monitoring signal and event mapping relation model. Further, the generation of an exception signal event follows the following rules: (1) the matching priority of the combined signal is greater than that of the single signal; (2) some combined signal match passes, generating an anomalous signal event for the combined signal map, and none of the anomalous signal events for the individual signal maps within the combined signal. As shown in fig. 2, the combined signal M generates an event C, the combined signal N generates an event D, and neither event a nor event B is generated. By the matching rule, the generation of invalid events is greatly reduced.

In a disclosed embodiment, in step S110, a model of mapping between the monitoring signals and the events of the single signal is established manually or in a mechanical manner. Further preferably, the monitoring signal and event mapping relation model is generated by machine training or by combining human intervention with machine training. The machine training mode is generated by a convolutional neural network or the like.

In one specific embodiment, the generation step of the monitoring signal and event mapping relation model is as follows:

constructing a hidden Markov model, taking an abnormal signal event as a hidden state, and taking a monitoring signal as an observation sequence;

observation sequence V ═ V₁,v₂,…,v_MM is the number of observable states, and the observation event at the time t is O_tHidden state S ═ q₁,q₂,q₃,…,q_NN is the number of hidden states, and the state at time t is q_tModel parameters λ ═ (pi, a, B), where a ═ α_ijIs the state transition probability matrix, B ═ B_j(k) Is the observed probability matrix for state j, pi ═ pi_iAnd (4) initializing state distribution, and performing parameter optimization by using a Baum-Welch theory, wherein the process is to bring an observation sequence and a model initial parameter lambda (pi, A and B) into the following parameter reestimation formula:

in the formula: xi_t(i, j) represents that given O and λ, the state at time t is S_iAnd the state at the time t +1 becomes S_jProbability of (a), γ_t(i) Indicating that the state at time t is S given O and lambda_iThe probability of (d);

obtaining a more desirable set of parameters by parameter re-estimation

In the general case of the above-mentioned,

the time loop is terminated, and the maximum likelihood solution of the hidden Markov model can be obtained after multiple times of carrying in reestimation;

based on the trained hidden Markov model, decoding the hidden state by using a real-time monitoring signal through a Viterbi algorithm, and taking the hidden state of the observation vector corresponding to the maximum probability as a current event.

It can be understood that the monitoring signal and event mapping relation model constructed by using the hidden markov model is only one of the machine training modes, and other modes can be adopted for training.

In a disclosed embodiment, in step S120, quantitatively scoring an operation monitoring relativity evaluation result of the device, setting different types of relativity evaluation events according to different score intervals, and constructing an evaluation result and event mapping relationship model; wherein the relative evaluation event comprises: device type, rating score, and event type. For example, in one embodiment, the operational monitoring score set for the device S ═ S (S) is established₁*q₁+s₂*q₂+…s_i*q_i…+s_n*q_n)，s_iIndicating the i-th state parameter score, q, of the device_iRepresenting the impact weight of the ith state parameter of the device.

Specifically, in one embodiment, the event types of the relativity assessment events include, but are not limited to, key monitoring, out-of-surveillance control, equipment crisis. Through the relative evaluation of the equipment operation condition, the operation monitoring of the equipment is converted into an event, and the treatment efficiency is further improved.

In a disclosed embodiment, in step S130, various events are accessed through the event receiving server, and the events are matched with the task model library to generate a task. Specifically, the task model library comprises an emergency task configuration module and a planned task configuration module, wherein the emergency task configuration module is called according to an alarm event and an operation event to generate an emergency task item; and calling a plan task configuration module according to a preset timing calling plan so as to generate plan task items. The emergency task event may be, for example, an alarm event processing task, a remote operation processing task, a duty transfer processing task, or the like. The scheduled event may be, for example, a shift task, a substation patrol task, a video patrol task, a spring check/autumn check task, a peak-to-head check task, etc.

It should be noted that the task model library may be set manually.

The task processing comprises task allocation, task processing, task tracking and task archiving, and in one embodiment, after the task event is generated, the task event is allocated to different on-duty monitors in a duty manager interface, and the task processing is carried out in different on-duty monitor interfaces. During task processing, aid decisions may be provided by a task handling expert library.

Further, in one disclosed embodiment, in step S140, the task assignment process includes:

and S141, acquiring the existing task amount of the on-value monitor, and analyzing to obtain the bearable workload of the on-value monitor. Specifically, the existing task amount can be quantized into the estimated task completion time, so that the bearable workload of the on-value monitor can be measured.

And S142, generating a task allocation plan according to the received task items and the bearable workload of the on-value monitor.

And S143, pushing the task allocation plan to a shift interface for automatic allocation or manual allocation.

Through the process, the dynamic allocation of the task items is realized, and the optimal configuration of the task allocation is realized. FIG. 3 illustrates a task item presentation interface for a value monitor.

In one disclosed embodiment, in step S150, a device lifetime history repository is generated according to the commissioning timeline of the device, as shown in fig. 4. The device history tag library can be searched for event records, task data, task processing conditions, and the like of the device. Further, the lifetime history repository is managed by creating a tag, such as an event tag, an apparatus operation record tag, a task operation record tag, and a task processing record tag.

It should be noted that, in this embodiment, the three-remote data is specifically remote signaling, remote sensing, and remote control data of the device.

In one disclosed embodiment, in step S160, the event rule expert database and the task handling expert database may be generated by performing big data analysis on data in the lifetime history database of the device and using a self-learning algorithm or the like.

Specifically, in one disclosed embodiment, the task handling expert database is constructed according to an analysis process of historical fault events, a handling scheme, event types, event ranges and equipment power restoration conditions. For example, a task processing expert library is formed by extracting task processing records in the equipment lifetime record label library and analyzing and processing data in the task processing records, so that processing suggestions are provided for task processing of monitoring personnel, and decision making of the monitoring personnel is assisted.

Specifically, in one embodiment, the generation step of the eventing rule expert database is as follows:

and S161, generating a standardized association model of the primary equipment and the secondary equipment according to the name rule of the monitoring signal of the power grid equipment and the monitoring alarm information accessed by the current system. Specifically, the normalized association model of the primary device and the secondary device is a standardized model structure with a hierarchy of typical intervals, typical devices, basic information objects and the like, and is used for mapping information such as remote signaling, remote measurement, remote control and the like.

Due to the fact that alarm information of power grid monitoring services is various in types and different in names, the remote signaling, remote measuring and remote control information is not standard, for example, information description and function explanation of equipment manufacturers are different, description wording of designers and installation and debugging is different, and information requirements of early equipment are different. Through the steps, the standardized storage of the alarm information is realized in a mapping and importing mode, and a basis is provided for subsequent eventing.

And S162, acquiring a typical fault event case, analyzing necessary conditions of fault occurrence based on the normalized correlation model of the primary equipment and the secondary equipment, and designing and constructing an event characteristic rule base and an event rule base. Analyzing the logic relation among isolated events through a typical fault event case, constructing the incidence relation among objects based on a monitoring information basic object, analyzing the necessary conditions of fault occurrence according to different fault eventing results, and establishing an event characteristic rule base and an event rule base based on multi-dimensional design of equipment type, voltage level, event type, signal type and the like, thereby establishing the corresponding relation among alarm information, event characteristics and events. The event characteristics are formed by alarm information according to a logic rule relation, such as signal action, signal instantaneous action, signal frequency action, telemetering quantity out-of-limit, measuring value failure, fault tripping, switch reclosing, switch failure action, spare power automatic switching action, topological mode, low pressure, insulation fault, alternating current and direct current power failure and the like. The plurality of information combinations describe characteristics, the plurality of characteristics describe events, and the event and event combinations describe integrated events. Event rules are used to describe requirements and non-requirements for event decisions.

The event rule operator is an important component of the event feature rule and the event rule, and as shown in the following table 2, the rule operator mainly includes:

TABLE 2

Expression dictionary	Description of the invention
		"@"	Rule base object
"->"	In sequence
		"\|"	OR operation
"&"	And operation
		"＝"	Assignment of value
"()"	Collection
		"！"	NOT operation
">"	Measure greater than
		"<"	Measure less than

For example, a regular expression may be described as:

(@1＝1&(@3＝0|@4<1.1))|((@2＝0->@2＝1->@2＝0)&@3＝0)

and S163, generating an event rule expert library according to the alarm information, the event characteristic rule library and the event rule library. And generating a monitoring signal and event mapping model and performing iterative optimization through the generated eventing rule expert library. And after the monitoring information isolated event occurs, acquiring a corresponding basic object through the mapping relation, identifying the possibly occurring event characteristics, and acquiring an event result when the event related characteristics are met.

Further, in one embodiment, the eventing rules expert library is configured based on an event definition tool, wherein the event definition tool comprises an alarm object, a base object, a feature definition, and a rule definition.

Fig. 5 schematically illustrates a schematic diagram of a grid fault intelligent monitoring processing system 500 of the present disclosure.

Referring to fig. 5, a system 500 for intelligently monitoring and processing grid faults includes:

an abnormal signal event generating module 510, configured to collect a real-time monitoring signal of the device, match a preset monitoring signal and event mapping relationship model, and generate an abnormal signal event;

a relativity evaluation event generating module 520, configured to collect an operation monitoring relativity evaluation result of the device, match a preset evaluation result with the event mapping relationship model, and generate a relativity evaluation event;

the task item generating module 530 is configured to receive an alarm event and an operation event, and generate a task item based on a task model library, where the alarm event includes an abnormal signal event and a relativity evaluation event, and the operation event includes a remote operation, an information acceptance and a defect flow;

the task handling module 540 is configured to perform task handling on task items, where the task handling includes task allocation, task processing, task tracking, and task archiving;

the device lifetime history library generating module 550 is configured to associate the device ledger, the three-remote data, the alarm event, and the task handling information, and establish a device lifetime history library;

and the expert database generation module 560 is used for generating an eventing rule expert database and a task handling expert database based on the equipment lifetime resume database.

According to the embodiment of the disclosure, the abnormal signal event generation module and the relativity evaluation event generation module are constructed, alarm monitoring is converted into event monitoring, conversion from passive monitoring to active monitoring is realized, the screen monitoring pressure of personnel is greatly reduced, and the disposal efficiency is improved. And creating an event service-driven management mode, generating task items based on a task model library, realizing the flow and convenient control of a service process, and improving the efficiency of distributing and disposing tasks. In addition, an eventing rule expert library and a task handling expert library are generated by establishing an equipment lifetime record library, so that accurate auxiliary decisions are provided for each business of the power grid. .

Since each function of the power grid fault intelligent monitoring processing system has been described in detail in the corresponding method embodiment, the details of the disclosure are not repeated herein.

It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.

In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.

An electronic device 300 according to this embodiment of the invention is described below with reference to fig. 6. The electronic device 300 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 invention.

As shown in fig. 6, electronic device 300 is embodied in the form of a general purpose computing device. The components of electronic device 300 may include, but are not limited to: a memory 320, and a processor 310 coupled to the memory 320, the processor 310 configured to execute the new energy automatic control strategy analysis method 100 described above based on instructions stored in the memory 320. Data is transferred between the memory 320 and the processor 310 via the bus 330.

Wherein the memory 320 stores program code that may be executed by the processor 310 to cause said processor 310 to perform the steps according to various exemplary embodiments of the present invention as described in the above section "exemplary method" of the present specification. For example, the processor 310 may perform steps S110 to S160 as shown in fig. 1.

The memory 320 may include readable media in the form of volatile storage units, such as a random access memory unit (RAM)3201 and/or a cache memory unit 3202, and may further include a read only memory unit (ROM) 3203.

The memory 320 may also include a program/utility 3204 having a set (at least one) of program modules 3205, such program modules 3205 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.

Bus 330 may be one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 300 may also communicate with one or more external devices 400 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 300, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 300 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 350. Also, the electronic device 300 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 360. As shown, network adapter 360 communicates with the other modules of electronic device 300 via bus 330. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with electronic device 300, 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, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above section "exemplary methods" of the present description, when said program product is run on the terminal device.

The program product for implementing the above method according to an embodiment of the present invention may employ a portable compact disc read only memory (CD-ROM) and include program codes, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present 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 media. The readable 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.

A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, 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 signal medium may also be any readable 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 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 aspects of the present invention 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).

Furthermore, the above-described figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the invention, 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.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice in the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims

1. A power grid fault intelligent monitoring processing method is characterized by comprising the following steps:

2. The intelligent power grid fault monitoring and processing method according to claim 1, wherein a monitoring signal and event mapping relation model is constructed according to historical fault events and/or an eventing rule expert library of equipment; the abnormal signal event comprises a monitoring signal name, an event type and an event grade.

3. The intelligent monitoring and processing method for the power grid faults is characterized in that quantitative scoring is carried out on operation monitoring relativity evaluation results of equipment, different types of relativity evaluation events are set according to different score intervals, and an evaluation result and event mapping relation model is constructed; wherein the relative evaluation event comprises: device type, rating score, and event type.

4. The intelligent monitoring and processing method for the power grid faults as claimed in claim 1, wherein the task model library comprises an emergency task configuration module and a planned task configuration module, wherein the emergency task configuration module is called according to the alarm event and the operation event to generate emergency task items; and calling a plan task configuration module according to a preset timing calling plan so as to generate plan task items.

5. The intelligent monitoring and processing method for the power grid faults as claimed in claim 1, wherein the task allocation process comprises:

6. The intelligent monitoring and processing method for the power grid faults as claimed in claim 1, wherein the generation steps of the eventing rule expert database are as follows:

obtaining typical fault event cases, analyzing necessary conditions of fault occurrence based on a normalized correlation model, designing and constructing an event characteristic rule base and an event rule base,

7. The intelligent monitoring and processing method for the power grid faults as claimed in claim 6, wherein the event rule expert database is configured based on an event definition tool, wherein the event definition tool comprises an alarm object, a base object, a feature definition and a rule definition.

8. The intelligent power grid fault monitoring and processing method according to claim 1, wherein the task disposal expert database is constructed according to an analysis process, a disposal scheme, an event type, an event range and a device power restoration condition of a historical fault event.

9. An intelligent monitoring and processing system for power grid faults is characterized by comprising:

10. An electronic device, comprising:

a processor; and

a memory for storing executable instructions of the processor;

the processor is configured to execute the intelligent power grid fault monitoring processing method according to any one of claims 1 to 8 through execution of the executable instructions.