CN114792200A - Scheduling accident plan generating and checking method based on expert knowledge base - Google Patents
Scheduling accident plan generating and checking method based on expert knowledge base Download PDFInfo
- Publication number
- CN114792200A CN114792200A CN202210426355.1A CN202210426355A CN114792200A CN 114792200 A CN114792200 A CN 114792200A CN 202210426355 A CN202210426355 A CN 202210426355A CN 114792200 A CN114792200 A CN 114792200A
- Authority
- CN
- China
- Prior art keywords
- plan
- power grid
- accident
- expert knowledge
- regulation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000004458 analytical method Methods 0.000 claims abstract description 23
- 238000007726 management method Methods 0.000 claims description 13
- 230000005540 biological transmission Effects 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 4
- 239000003990 capacitor Substances 0.000 claims description 4
- 238000003032 molecular docking Methods 0.000 claims description 4
- 230000009467 reduction Effects 0.000 claims description 3
- 238000012795 verification Methods 0.000 claims 3
- 238000012916 structural analysis Methods 0.000 claims 1
- 230000001276 controlling effect Effects 0.000 abstract description 3
- 230000001105 regulatory effect Effects 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 230000000875 corresponding effect Effects 0.000 description 4
- 238000010276 construction Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000003058 natural language processing Methods 0.000 description 3
- 238000013473 artificial intelligence Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000004804 winding Methods 0.000 description 2
- 230000002596 correlated effect Effects 0.000 description 1
- 238000013075 data extraction Methods 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000013213 extrapolation Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000001681 protective effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Software Systems (AREA)
- Marketing (AREA)
- Health & Medical Sciences (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Computing Systems (AREA)
- Data Mining & Analysis (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Primary Health Care (AREA)
- Evolutionary Computation (AREA)
- Mathematical Physics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Artificial Intelligence (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to a scheduling accident plan generating and checking method based on an expert knowledge base, which comprises the following steps of S1, acquiring a plant station model and a power grid topology model through a regulation and control cloud platform; s2, modeling the operation mode of the regulation and control according to the regulation and control rule; s3, constructing a scheduling accident intelligent disposal expert knowledge rule base through a machine self-learning method based on a historical plan; s4, automatically generating an accident disposal plan according to the station information by calling an expert knowledge rule base; and S5, checking the accident handling plan generated in the step S4 based on the current power grid operation mode. The method and the system have the advantages that the accident plan is generated more quickly, accurately and completely, the handling and analysis capability of the regulating and controlling personnel on the transformer substation plan is improved, the accuracy of the accident handling of the regulating and controlling personnel plan is improved, and the safe and stable operation of a power grid is guaranteed.
Description
Technical Field
The invention belongs to the technical field of power system automation, and particularly relates to a scheduling accident plan generating and checking method based on an expert knowledge base.
Background
In an electric power system, an extra-high voltage alternating current and direct current power grid is developed into an interconnected power grid system with a large scale and a complex structure, and the operating characteristics of the power grid are increasingly complex. With the rapid development of new energy, the uncertainty of the power flow of the power grid increases, and a plurality of challenges are faced in the regulation and control operation. The power grid operation trend puts higher requirements on the regulation and control operators in time for the quick response of the power grid accidents and the proper treatment of the power grid equipment faults, and the decision and operation pressure faced by the regulation and control operators is increased gradually.
Therefore, the method for generating and checking the plan can provide a power grid fault handling strategy quickly and shorten the fault handling time.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a scheduling accident plan generating and checking method based on an expert knowledge base, which can quickly provide a power grid fault handling strategy and shorten the fault handling time.
In order to achieve the purpose, the technical scheme of the invention comprises the following steps:
s1, acquiring a plant station model and a power grid topology model through a regulation and control cloud platform;
s2, modeling a power grid operation knowledge rule base according to regulation and control regulation related regulations on a regulation and control operation mode;
s3, constructing a scheduling accident intelligent disposal expert knowledge rule base by a machine self-learning method based on the historical plan and combining the power grid operation knowledge rule base in the step S2;
s4, automatically generating an accident handling plan according to the station information by calling an expert knowledge rule base;
and S5, checking the accident handling plan generated in the step S4 based on the current power grid operation mode.
Further, in step S1, the plant model is obtained by the data docking service, and includes voltage class, plant name, line, bus, breaker, disconnecting link, grounding disconnecting link, capacitor, and reactor plant information. Further, the power grid topology model refers to power grid operation topology association obtained according to the station information and the power transmission line connection relation.
Further, in step S2, the modeling is performed by modeling a grid operation knowledge rule base for scheduling management and switching operation management.
Further, in step S3, the expert knowledge rule base is a plan generation model created by learning the historical plan through machine self-learning, developing structured analysis according to the content of the scheduled accident plan, and establishing the historical plan.
And further, according to the constructed operation principle and regulation and control rule, a scheduling accident intelligent disposal expert rule base is constructed by combining with a plan model.
Furthermore, the intelligent disposal expert knowledge rule base comprises two aspects, namely, the structured analysis is carried out on the content of the predetermined plan according to the historical predetermined plan, and the content comprises the conditions of the transformer substation, the fault conditions, accident disposal key points and attention points; and secondly, combining a scheduling operation principle, a regulation and control rule and a scheduling accident intelligent disposal expert rule base, establishing a power grid operation characteristic knowledge base, and selecting the most reasonable mode to operate on power grid fault treatment by multi-dimensional consideration.
Further, in step S4, the accident handling plan is a plant station accident handling plan automatically generated by obtaining the power grid operation topology association of the power grid topology model according to step S1 and invoking the scheduling accident intelligent handling expert rule base constructed in step S3 based on the plant station information.
Further, in step S5, based on the given power grid operation mode and plan, power grid operation state analysis and calculation are performed, the content of the generated disposal plan is evaluated, and the operation mode and the influence after the fault are checked.
Further, the check of step S5 includes the following steps,
(1) the method comprises the following steps that pre-arranged plan checking data are evaluated according to the adaptability of an initial operation mode of a power grid, the operation mode and the influence after a fault are checked, fault handling measures are checked, and a checking result is output;
(2) analyzing the characteristics of the generator and the load regulation, and outputting a checking result;
(3) checking the strategies of low-frequency or low-voltage load reduction, high-frequency generator tripping and out-of-step disconnection safety devices;
(4) and checking the power flow out-of-limit caused by the power imbalance, and outputting a checking result.
The invention has the following positive effects:
the invention provides a scheduling accident plan generating and checking method based on an expert knowledge base by utilizing a power grid model and scheduling operation specifications, wherein a transformer substation model is associated with scheduling operation rules, the transformer substation accident plan is automatically generated and checked according to a transformer substation accident handling plan standardization rule base, the comprehensive management level and the emergency handling capacity for handling emergencies are improved, and an auxiliary strategy is provided for subsequent fault handling.
Drawings
FIG. 1 is a self-learning construction method of a power grid operation knowledge rule base.
FIG. 2 is a method for generating a rule base of expert knowledge for intelligent handling of scheduling accidents.
Detailed Description
The technical solutions in the embodiments of the present application will be described clearly and completely with reference to fig. 1-2, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the application, its application, or uses. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The invention provides a method for generating and checking a scheduling accident plan based on an expert knowledge base, which comprises the following steps,
and S1, acquiring a plant station and power grid topological model through a regulation cloud platform.
The regulation and control cloud platform is suitable for the integrated operation characteristics of a power grid, is guided by the needs of power grid operation and regulation and control management services, and gradually forms a regulation and control technology support system of resource virtualization, data standardization and application service by relying on IT technologies such as cloud computing, big data and mobile internet. The regulation and control cloud is used as a bottom technical foundation of the power production control business, provides basic equipment service, operation environment support, model data service and the like, provides support for the construction of a new generation of a scheduling control system analysis decision center, and simultaneously bears various applications of the regulation and control center and all departments. The model data cloud platform completes collection of standard graph models of all levels of power grids according to the structured design of the power dispatching general data object, and forms a full-voltage level power grid model of 35kV or above and a corresponding graph of the full-voltage level power grid model.
In step S1, the plant model is a plant model that obtains the regulation cloud platform through a data docking service, and the plant model includes plant information such as voltage class, plant name, line, bus, breaker, disconnecting link, grounding disconnecting link, capacitor, and reactor.
The power grid topological model refers to power grid operation topological correlation obtained according to the connection relation between the plant station information and the power transmission line. In step S1, data synchronization from the cloud data to the remote database is realized through data synchronization, so as to realize data extraction and synchronous update.
The PaaS in-layer model data cloud platform comprises metadata management, dictionary management and model data management functions, and the model data mainly comprises public model data, primary equipment model data, protective equipment model data, automatic equipment model data and the like, and corresponding topology and graphic data. And by regulating and controlling model data and graphic data in the cloud platform, topology analysis of the corresponding plant is completed, and the generation of plant accident plans is realized.
And acquiring and storing the required model data in a database of a source data terminal through data docking service, and managing and storing the whole network station by classification. The following categories were classified by type, including voltage class, substation, line, bus, transformer winding, breaker, disconnector, earthing switch, capacitor and reactor information, as shown in tables 1-7.
TABLE 1 Transformer substation information
TABLE 2 line information
TABLE 3 bus information
TABLE 4 Transformer information
TABLE 5 Transformer winding information
TABLE 6 Circuit breaker information
TABLE 7 Voltage class TABLE
| NUM | CODE | NAME |
| Serial number | Voltage encoding | Voltage class |
| 1 | 1003 | 500kV |
| 2 | 1005 | 220kV |
| 3 | 1006 | 110kV |
| 4 | 1008 | 35kV |
And according to the equipment classification in the plant station model, all models take the plant station as a main node, the physical connection relation of the power grid equipment wiring diagram computing equipment is stored in a database according to topological association, and simultaneously, graphs such as the power grid wiring diagram and the tidal current diagram are stored in a CIM/G file format.
In step S1, the power grid operation topological correlation established according to the plant station and the power transmission line means that for 500kV and 220kV substation and power plant models, each node device is correlated according to the power grid device connection relationship and according to the node number and the node device correlation relationship, so as to realize the power grid operation topological relation call.
In step S2, the modeling is to model scheduling management and switching operation management. In step S2, the modeling is to model the regulation operation mode, and according to the regulation rule-related regulations, the scheduling management and the switching operation management. And S3, constructing a scheduling accident intelligent disposal expert knowledge rule base through a machine self-learning method based on the historical plan.
In step S3, the expert knowledge rule base is a pre-arranged plan generation model that is created by learning a historical pre-arranged plan through machine self-learning, and performing structured analysis according to the content of a scheduling accident pre-arranged plan. And according to the constructed operation principle and regulation and control rule, constructing an intelligent scheduling accident disposal expert rule base by combining a plan model.
The intelligent disposal expert knowledge rule base comprises two aspects, namely, the structured analysis is carried out on the content of the plan according to the historical plan, and the content of the plan comprises transformer substation conditions, fault conditions, accident disposal key points and cautionary matters; and secondly, combining a scheduling operation principle, regulation and control rules and a scheduling accident intelligent disposal expert rule base to establish a power grid operation characteristic knowledge base, and selecting the most reasonable mode to operate the power grid fault treatment by considering the multiple dimensions.
In step S3, the expert knowledge rule base for intelligent handling of scheduling accidents refers to an accident handling plan standardization rule base that is created by structurally splitting the substation profile, the fault analysis, the accident handling key points, and the notice content according to the contents of a scheduling accident plan, where the structural information of the plan is shown in table 8.
Table 8 plan structured information
In step S3, according to various wiring modes and operation modes of the transformer substation, in combination with specific conditions of the power grid, structured analysis is performed on relevant regulations of operation principles and regulations, and a power grid operation rule knowledge base under a normal operation mode, a bus operation mode and a special operation mode is established according to influence factors of a power grid operation management process.
In step S3, based on artificial intelligence natural language processing, the historical substation fault handling plan is intelligently analyzed to form a scheduling accident plan handling expert knowledge rule base. The expert knowledge rule base is a pre-arranged plan generation model which is established by learning a historical pre-arranged plan through machine self-learning, and performing structured analysis on the content of the pre-arranged plan according to the content of a scheduling accident pre-arranged plan. And constructing an intelligent scheduling accident disposal expert rule base by combining a plan model according to the constructed operation principle model and the procedure regulation model. The disposal expert knowledge rule base comprises two aspects, namely, the structured analysis is carried out on the content of the predetermined plan according to the historical predetermined plan, and the structured analysis comprises the conditions of the transformer substation, the fault analysis, the accident handling key points and the attention items; and secondly, combining a scheduling operation theory principle, regulation and control rules with a scheduling accident intelligent disposal expert rule base to establish a power grid operation characteristic knowledge base, and selecting the most reasonable mode to operate the power grid fault treatment by considering multiple dimensions.
The specific description is as follows:
in step S3, the implementation of the intelligent scheduling accident disposal expert database includes two aspects:
(1) according to the structural characteristics of the power grid, structured analysis is carried out on the content of the pre-arranged plan according to the historical pre-arranged plan, and the content of the pre-arranged plan mainly comprises four parts, namely the condition of a transformer substation, fault analysis, accident handling key points and attention points. And analyzing the change condition of the state parameters of the weak points of the power system after the equipment of the power system breaks down by accident result analysis. Fault handling measures are listed for phase-shutdown unit output adjustment, node voltage control, load switching, line operation, fault elimination and the like in the power system according to accident handling requirements. Meanwhile, based on the operation mode of the power grid, the method combines automatic accident judgment and realizes the chart extrapolation and association of the power grid tide flow chart. And establishing a pre-arranged plan feature point structure knowledge base by intelligently analyzing and learning the four parts of knowledge points.
(2) The method comprises the steps of combining a dispatching operation theory principle and regulation related regulations with a plan characteristic structure knowledge base, establishing a power grid operation characteristic knowledge base, considering power grid faults in multiple dimensions, selecting the most reasonable mode for operation, extracting, filtering and correlating operation data and regulation schemes, and establishing an intelligent scheduling accident disposal expert knowledge base.
A self-learning construction method of a power grid operation knowledge rule base is shown in figure 1, a regulation and control center issues various operation principles and regulation requirements, and learning machine models of various expert knowledge bases are established by combining the characteristics of power services, so that the analysis result adapts to changes in aspects such as a power grid wiring mode, an operation mode and load distribution. And summarizing the operation principle and regulation rules through a knowledge base learning machine, performing structured analysis on the covered knowledge points according to the learning result, checking whether all rules and regulation contents are contained, and completing the self-adaptive learning of the model through continuous perfection of a knowledge base.
A method for generating a rule base of scheduling accident intelligent handling experts is disclosed, as shown in FIG. 2, historical fault handling process information is extracted, equipment information is analyzed through a natural language processing technology, and relevant feature points of a plan are extracted to construct a knowledge base. And analyzing the equipment information by combining the power grid fault information, generating various schemes through a knowledge base, extracting the optimal scheme and establishing a scheduling accident plan disposal knowledge base.
And S4, automatically generating an accident handling plan according to the station information by calling an expert knowledge rule base.
In step S4, the accident handling plan is a plant station accident handling plan automatically generated by obtaining the power grid operation topology association of the power grid topology model according to the plant station information in step S1, and calling the scheduling accident intelligent handling expert rule base constructed in step S3.
The transformer substation accident plan is based on transformer substation power grid model data, an expert knowledge rule base is built according to relevant regulation regulations and step S3, and the transformer substation accident plan is automatically generated.
The transformer substation accident plan generating method comprises the steps of simulating and generating the operation condition and the trend condition of a power grid after a fault according to the operation mode of the power grid based on the constructed scheduling accident intelligent disposal expert knowledge base, realizing the operation analysis of the power grid after the fault occurs, and generating a corresponding scheme and a disposal step for the disposal of the power grid after the fault according to the scheduling accident plan disposal knowledge base.
And S5, checking the accident handling plan generated in the step S4 based on the current power grid operation mode.
In step S5, based on the given power grid operation mode and plan, power grid operation state analysis and calculation are performed, the content of the generated disposal plan is evaluated, and the operation mode and the influence after the fault are checked.
The check of step S5 includes the following steps,
(1) the method comprises the following steps that pre-arranged plan checking data are evaluated according to the adaptability of an initial operation mode of a power grid, the operation mode and the influence after a fault are checked, fault handling measures are checked, and a checking result is output;
(2) analyzing the characteristics of the generator and the load regulation, and outputting a checking result;
(3) checking the strategies of low-frequency or low-voltage load reduction, high-frequency generator tripping and out-of-step disconnection safety devices;
(4) and checking the load flow calculation caused by the unbalanced power, and outputting a checking result.
In summary, the present invention is briefly summarized as follows:
and (5) when a certain substation is completely stopped, associating the substation with a power grid operation knowledge rule base and a scheduling accident intelligent disposal expert knowledge base according to the power grid model and topology through steps S1, S2 and S3, and analyzing the whole fault process. According to step S4, a scheduled accident plan is given, and a standardized handling opinion can be given to the accident handling process. And step S5, checking the provided scheduling accident plan for normativity and accuracy, providing field technicians after checking is finished, and finishing accident disposal according to the given plan.
According to the method, the fault of the transformer substation is judged by utilizing the model data of the regulation cloud platform and the power grid operation data, the generation of the scheduling accident plan is realized according to the model, the topology and the power grid operation mode and the scheduling accident handling rule base, the accident handling scheme can be more effectively and timely provided, the fault handling speed is increased, and a powerful basis is provided for quickly and accurately removing the power grid fault.
A power grid operation knowledge rule base is established by using an artificial intelligence technology, power grid operation basis rules and standard elements are analyzed, changes in aspects such as a power grid wiring mode, an operation mode and load distribution are combined, and power grid operation risk points and existing problems can be solved according to operation according to safe and effective strategies. When a fault occurs in the operation process of the power grid, the power grid fault operation steps and the logical operation method after the fault of the transformer substation occurs are provided, the fault processing time is shortened through the normative operation, and the operation level of the power grid is improved.
An intelligent scheduling accident disposal expert knowledge base is used for analyzing post-fault disposal measures of power system equipment by using a natural language processing technology on the basis of a transformer substation accident disposal plan as a knowledge source and fault operation information, a power grid state and expert experience, and is used for constructing a disposal decision base for disposing power grid faults by combining weak point state parameter change conditions of a power system and specific listed fault disposal measures such as phase shutdown group output regulation, node voltage control, load switching, line operation, fault removal and the like in the power system.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A scheduling accident plan generating and checking method based on an expert knowledge base is characterized in that: which comprises the following steps of,
s1, acquiring a plant station model and a power grid topology model through a regulation and control cloud platform;
s2, modeling a power grid operation knowledge rule base according to regulation and control rule relevant regulations on a regulation and control operation mode;
s3, constructing a scheduling accident intelligent disposal expert knowledge rule base by a machine self-learning method based on the historical plan and combining the power grid operation knowledge rule base in the step S2;
s4, automatically generating an accident handling plan according to the plant station model and the power grid topological model obtained in the step S1 by calling an expert knowledge rule base;
and S5, checking the accident handling plan generated in the step S4 based on the current power grid operation mode.
2. The expert knowledge base-based scheduling accident scenario generation and verification method according to claim 1, wherein:
in step S1, the plant model is a plant model of the regulation and control cloud platform obtained through the data docking service, and includes voltage class, plant name, line, bus, breaker, disconnecting link, grounding disconnecting link, capacitor, and reactor plant information.
3. The expert knowledge base-based scheduling accident scenario generation and correction method of claim 1, wherein: in step S1, the power grid topology model refers to power grid operation topology association obtained according to the station information and the power transmission line connection relationship.
4. The expert knowledge base-based scheduling accident scenario generation and correction method of claim 1, wherein:
in step S2, the modeling is to model a schedule management procedure and a switching operation management procedure.
5. The expert knowledge base-based scheduling accident scenario generation and verification method according to claim 1, wherein:
in step S3, the expert knowledge rule base is to learn a historical plan by machine self-learning, develop structural analysis according to the contents of a scheduled accident plan, and establish a plan generation model.
6. The expert knowledge base-based scheduling accident scenario generation and correction method of claim 5, wherein: and according to the constructed operation principle and regulation and control rules, constructing an intelligent scheduling accident disposal expert rule base by combining with a plan model.
7. The expert knowledge base-based scheduling accident scenario generation and correction method of claim 6, wherein: the intelligent disposal expert knowledge rule base comprises two aspects, namely, the structured analysis is carried out on the content of the plan according to the historical plan, and the content of the plan comprises transformer substation conditions, fault conditions, accident disposal key points and cautionary matters; and secondly, combining a scheduling operation principle, a regulation and control rule and a scheduling accident intelligent disposal expert rule base, establishing a power grid operation characteristic knowledge base, and selecting the most reasonable mode to operate on power grid fault treatment by multi-dimensional consideration.
8. The expert knowledge base-based scheduling accident scenario generation and correction method of claim 1, wherein:
in step S4, the accident disposal plan is based on the plant station information, the power grid operation topology association of the power grid topology model is obtained according to step S1, the scheduling accident intelligent disposal expert rule base constructed in step S3 is called, and the plant station accident disposal plan is automatically generated.
9. The expert knowledge base-based scheduling accident scenario generation and correction method of claim 1, wherein:
in step S5, based on the power grid operation mode, the power grid operation state analysis and calculation is performed, the content of the generated disposal plan is evaluated, and the operation mode and the influence after the fault are checked.
10. The expert knowledge base-based scheduling accident scenario generation and verification method according to claim 1, wherein:
the check of step S5 includes the following steps,
(1) the method comprises the following steps that pre-arranged plan checking data are evaluated according to the adaptability of an initial operation mode of a power grid, the operation mode and the influence after a fault are checked, fault handling measures are checked, and a checking result is output;
(2) analyzing the characteristics of the generator and the load regulation, and outputting a checking result;
(3) checking the strategies of low-frequency or low-voltage load reduction, high-frequency generator tripping and out-of-step disconnection safety devices;
(4) and checking the power flow out-of-limit caused by the power imbalance, and outputting a checking result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210426355.1A CN114792200A (en) | 2022-04-22 | 2022-04-22 | Scheduling accident plan generating and checking method based on expert knowledge base |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210426355.1A CN114792200A (en) | 2022-04-22 | 2022-04-22 | Scheduling accident plan generating and checking method based on expert knowledge base |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114792200A true CN114792200A (en) | 2022-07-26 |
Family
ID=82461325
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210426355.1A Pending CN114792200A (en) | 2022-04-22 | 2022-04-22 | Scheduling accident plan generating and checking method based on expert knowledge base |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114792200A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116611967A (en) * | 2023-06-01 | 2023-08-18 | 广州中幼信息科技有限公司 | Kindergarten operation planning strategy generation method and system based on artificial intelligence |
-
2022
- 2022-04-22 CN CN202210426355.1A patent/CN114792200A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116611967A (en) * | 2023-06-01 | 2023-08-18 | 广州中幼信息科技有限公司 | Kindergarten operation planning strategy generation method and system based on artificial intelligence |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101236575B (en) | Black start assistant decision making support/training system and its method | |
| CN108390371B (en) | Power grid system protection control strategy modeling method in online analysis | |
| CN103150426A (en) | Unified modeling method for regulation and control integrated simulation of primary grid equipment and secondary grid equipment | |
| CN111049266B (en) | Intelligent second-level power restoration method and system for regulation and control service | |
| CN104463696A (en) | Power grid operating risk recognition and prevention method and system | |
| CN104835078B (en) | Consider that the trend of prepared auto restart action determines method in static security analysis system | |
| CN112383044B (en) | Power grid model comparison method and device based on hierarchical topological structure | |
| CN104835376A (en) | Dispatcher training simulation system with automatic switching device of standby power supply automatic safety device operation simulating function | |
| CN112787398A (en) | Intelligent power distribution network exception handling method and system | |
| CN104407577A (en) | Intelligent check anti-maloperation method based on real-time trend | |
| CN104835077A (en) | Static safety analysis system with dispatcher accident prediction supporting function | |
| CN114792200A (en) | Scheduling accident plan generating and checking method based on expert knowledge base | |
| CN115390827A (en) | Power grid digital twin body construction method and platform for supporting debugging operation | |
| CN103106561B (en) | Power system load recovers aid decision-making system and decision making method thereof | |
| CN104809659A (en) | Intelligent advanced application system applicable to local dispatching system | |
| CN112560227B (en) | Simulation rehearsal method for power grid dispatching day-ahead plan | |
| CN110705001A (en) | Equipment-based monitoring information objectification modeling method for power system | |
| CN112421621B (en) | Integrated intelligent distribution network operation monitoring cloud system based on 5G Internet of things | |
| CN110489729B (en) | Automatic conversion method and system for D5000-matpower grid multi-disaster coupling cascading failure model | |
| CN102880988B (en) | Transformer station model introduction method and device based on topology comparison | |
| CN104836329A (en) | Local-level dispatch supporting system capable of improving analyzing and processing capability of dispatcher during accident generation | |
| CN104898460A (en) | Online static safety analysis system suitable for local dispatch system | |
| CN106651113A (en) | Dispatcher operation risk evaluation method based on advanced N-1 analysis | |
| CN111509703A (en) | Power distribution network loop closing and power adjusting operation decision method and system | |
| CN112865117B (en) | Reactive emergency control method, device and system after new energy collection direct current blocking |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |