CN111143447A - Power grid weak link dynamic monitoring and early warning decision system and method - Google Patents
Power grid weak link dynamic monitoring and early warning decision system and method Download PDFInfo
- Publication number
- CN111143447A CN111143447A CN201911191417.XA CN201911191417A CN111143447A CN 111143447 A CN111143447 A CN 111143447A CN 201911191417 A CN201911191417 A CN 201911191417A CN 111143447 A CN111143447 A CN 111143447A
- Authority
- CN
- China
- Prior art keywords
- power grid
- early warning
- data
- weak link
- establishing
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000012544 monitoring process Methods 0.000 title claims abstract description 21
- 238000003745 diagnosis Methods 0.000 claims abstract description 34
- 238000012545 processing Methods 0.000 claims abstract description 29
- 230000004927 fusion Effects 0.000 claims abstract description 22
- 230000008569 process Effects 0.000 claims abstract description 12
- 238000013135 deep learning Methods 0.000 claims abstract description 11
- 230000008447 perception Effects 0.000 claims abstract description 9
- 238000013473 artificial intelligence Methods 0.000 claims abstract description 8
- 238000004364 calculation method Methods 0.000 claims description 24
- 238000004458 analytical method Methods 0.000 claims description 6
- 238000005516 engineering process Methods 0.000 claims description 6
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 239000000523 sample Substances 0.000 claims description 3
- 238000013499 data model Methods 0.000 claims description 2
- 230000005611 electricity Effects 0.000 claims description 2
- 230000007246 mechanism Effects 0.000 abstract description 8
- 238000013500 data storage Methods 0.000 abstract description 2
- 238000007726 management method Methods 0.000 description 10
- 238000010586 diagram Methods 0.000 description 7
- 230000008901 benefit Effects 0.000 description 6
- 238000010276 construction Methods 0.000 description 5
- 238000011161 development Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000010801 machine learning Methods 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 238000000605 extraction Methods 0.000 description 2
- 238000009472 formulation Methods 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000001537 neural effect Effects 0.000 description 2
- 230000008439 repair process Effects 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000013075 data extraction Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/254—Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/252—Integrating or interfacing systems involving database management systems between a Database Management System and a front-end application
-
- 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
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- 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
- G06Q10/20—Administration of product repair or maintenance
-
- 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The embodiment of the invention discloses a power grid weak link dynamic monitoring and early warning decision system and a method, wherein the power grid weak link dynamic monitoring and early warning decision system comprises a power grid information perception module, a data acquisition module, a data storage module and a data fusion module, wherein the power grid information perception module is used for acquiring multi-source data and performing metadata storage and data fusion; the power grid state diagnosis module is used for establishing a power grid diagnosis expert knowledge base, defining power grid weak link indexes from multiple dimensions, calculating index values of the power grid weak links in real time and pushing a problem list; the weak link early warning module is used for establishing an early warning model, adopting artificial intelligence or a deep learning algorithm to predict and early warn to form an early warning list; and the intelligent auxiliary decision module provides a solution for the problem list and the early warning list and collects a processing process and a processing result. The invention continuously improves the accuracy of early warning and the reliability of early warning results, provides a closed-loop working mechanism for a problem list and an early warning list, and integrally manages the power grid problem.
Description
Technical Field
The invention relates to the technical field of power grid data processing, in particular to a dynamic monitoring and early warning decision system and method for a weak link of a power grid.
Background
The weak links of the power grid, such as real-time investigation, accurate diagnosis and high-efficiency treatment, are the basis of high-quality development of the power grid. The power grid and user basic data required by power grid development diagnosis and analysis are large in scale, wide in related field, multiple in participating departments and quick in updating change, a traditional method relies on a manual mode to check massive power grid data, weak links of the power grid are screened one by one from the data, the problems of difficulty in sensing, positioning, treatment and the like of the weak links exist, the requirements of lean management and accurate investment of the power grid are difficult to adapt, and effective business decisions such as integration, processing and data sharing of information and chemical tools and planning project arrangement support are needed.
The dynamic monitoring and early warning of the weak link of the power grid mainly has single content, such as reliability, safety, power supply quality and the like, cannot completely cover information required by power grid planning, and is difficult to comprehensively guide actual power grid construction. In the aspect of power grid construction and development, some power grid weak link index systems are available for reference, but are mainly determined according to power grid data which can be directly obtained at present and are limited by factors such as data transmission barriers, and therefore early warning reliability is low.
Disclosure of Invention
The embodiment of the invention provides a dynamic monitoring and early warning decision system and method for weak links of a power grid, and aims to solve the problems that in the prior art, the reference basis of power grid early warning is single, and the reliability of early warning results is low.
In order to solve the technical problem, the embodiment of the invention discloses the following technical scheme:
the invention provides a power grid weak link dynamic monitoring and early warning decision system in a first aspect, which comprises:
the power grid information perception module is accessed to the power business system in an ETL or service calling mode to acquire business system data, acquires power consumption related information through a web crawler to form a power grid planning comprehensive database, establishes a related relation of multi-source data in the database, and performs data fusion and power grid equipment modeling;
the power grid state diagnosis module is used for establishing a power grid diagnosis expert knowledge base, defining power grid weak link indexes from multiple dimensions, calculating the index values of the power grid weak links in real time through a multi-algorithm engine based on the power grid planning comprehensive database, and pushing a problem list;
the weak link early warning module is used for establishing a parameter file and an operation file based on the power grid planning comprehensive database, establishing an early warning model by combining influence factors, and predicting early warning by adopting artificial intelligence or a deep learning algorithm to form an early warning list;
and the intelligent auxiliary decision module provides a solution for the problem list and the early warning list, sends a processing work order to a worker, and collects a manual processing progress and a processing result.
Further, the grid information awareness module comprises:
the model matching calculation unit is used for establishing a data model including the characteristic attributes of the multi-source data and calculating the matching degree between the models through traversal comparison of the attribute information in the models;
and the multi-source data fusion unit is used for automatically associating according to the calculation condition of the matching degree, so that the automatic integrated fusion of the multi-source data is realized.
The second aspect of the invention provides a dynamic monitoring and early warning decision method for weak links of a power grid, which comprises the following steps:
accessing an electric power service system in an ETL or service calling mode, acquiring service system data, acquiring power consumption correlation information through a web crawler, forming a power grid planning comprehensive database, establishing a correlation relation of multi-source data in the database, and performing data fusion and power grid equipment modeling;
establishing a power grid diagnosis expert knowledge base, defining power grid weak link indexes from multiple dimensions, calculating the index values of the power grid weak links in real time through a multi-algorithm engine based on the power grid planning comprehensive database, and pushing a problem list;
establishing a parameter file and an operation file based on the power grid planning comprehensive database, establishing an early warning model by combining influence factors, and predicting early warning by adopting artificial intelligence or a deep learning algorithm to form an early warning list;
and for the problem list and the early warning list, a solution is provided, a processing work order is sent to a worker, and a manual processing progress and a processing result are collected.
Further, the power service system comprises a PMS system, a GIS system, an EMS system, a power supply service command system and a power utilization information acquisition system; the electricity consumption related information comprises economic information, population information, energy information and government planning information.
Further, the specific process of establishing the incidence relation of the multi-source data in the database, performing data fusion and modeling of the power grid equipment comprises the following steps:
establishing a model containing a data logic relation, a topological structure, spatial information and characteristic attributes of the power business system;
traversing and comparing the attribute information in the models, and calculating the matching degree between the models;
carrying out model association according to the matching degree condition to realize integrated fusion of data;
and for the data which cannot be fused, manually processing and fusing according to the voltage level of the data and/or the area of the equipment.
Further, the calculation of the matching degree between the models is sequentially carried out in a root node corresponding mode, a logic model traversing mode, a directed graph traversing mode, a characteristic attribute identification mode, a space coordinate conversion mode and an image identification mode.
Further, the algorithm engine comprises topology identification, N-1 calculation, load analysis, capacity-to-load ratio calculation, power supply range identification and operation mode identification.
Further, the index value of the power grid weak link is calculated in real time through a multi-algorithm engine based on the power grid planning comprehensive database, and the specific process of pushing the problem list is as follows:
defining an index calculation formula according to a power grid weak link index judgment standard, and setting an index threshold;
by utilizing a distributed parallel computing and data probe technology, based on data in a power grid planning comprehensive database, an algorithm engine is used for calculating index scores in real time according to a dimension diagnosis system and a calculation rule, dynamically monitoring the running state of power grid equipment, and pushing a problem list including equipment problems, cause problems, index scores, spatial positions and problem urgency degrees.
Further, the specific process of predicting and early warning by using the deep learning algorithm is as follows:
setting an empirical value of a model or parameter to be predicted;
predicting actual values generated in a set historical time period one by one, comparing the fitting degree of a predicted result and an actual occurrence result, and adjusting each parameter according to set precision;
and performing problem early warning by using the adjustment result.
The effect provided in the summary of the invention is only the effect of the embodiment, not all the effects of the invention, and one of the above technical solutions has the following advantages or beneficial effects:
1. the method comprises the steps of providing a multi-dimensional power grid weak link index through acquisition and fusion of multi-source data, calculating an index value of a current state in real time, pushing a problem list, adopting a deep learning algorithm to predict early warning, continuously improving accuracy of the early warning and reliability of an early warning result, providing a closed-loop working mechanism for the problem list and the early warning list, and overall management of power grid problems.
2. The power grid information perception module fuses internal and external multi-source data, diagnosis data are automatically acquired from manual collection, data resources accumulated in the construction of the power internet of things are established, high fusion of information such as power grids, customers and government planning is achieved, internal and external information of the power grids is updated in real time, and a data basis is provided for diagnosis and treatment of weak links.
3. The power grid current situation diagnosis module constructs a 24-dimensional diagnosis system, weak links are changed into intelligent identification from manual searching, abnormal characteristics of the weak links of the power grid are deeply analyzed, a power grid diagnosis nerve sensing system is created, the weak links of the power grid are automatically judged, the power grid state is scanned, diagnosed and evaluated station by station, line by line and station by station, problems in the power grid are captured in real time, and a problem list is intelligently generated.
4. The intelligent assistant decision module establishes an overall process on-line management mechanism, a solution is changed from manual formulation into an intelligent decision, an intelligent alternative solution is provided, a professional and cooperative overall process on-line management mechanism is established, scheme optimization and project automatic sequencing are achieved, and the accuracy of power grid investment is improved.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a schematic diagram of the system of the present invention;
FIG. 2 is a schematic flow diagram of the process of the present invention;
FIG. 3 is an architecture diagram of the present invention for grid information sensing and data fusion;
FIG. 4 is a schematic diagram of the present state of the grid diagnostic of the present invention;
FIG. 5 is an architecture diagram of the 24-dimensional grid diagnostic neural sensing system of the present invention;
FIG. 6 is a schematic diagram of early warning of weak links of the power grid according to the present invention;
FIG. 7 is a schematic diagram of the intelligent aid decision phase of the present invention.
Detailed Description
In order to clearly explain the technical features of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
As shown in fig. 1, the power grid weak link dynamic monitoring and early warning decision-making system of the present invention includes a power grid information sensing module, a power grid state diagnosis module, a weak link early warning module, and an intelligent assistant decision-making module.
The power grid state diagnosis module establishes a power grid diagnosis expert knowledge base, defines power grid weak link indexes from multiple dimensions, calculates the index values of the power grid weak links in real time through a multi-algorithm engine based on a power grid planning comprehensive database, and pushes a problem list; the weak link early warning module establishes a parameter file and an operation file based on the power grid planning comprehensive database, establishes an early warning model by combining influence factors, and adopts artificial intelligence or a deep learning algorithm to predict early warning to form an early warning list; the intelligent assistant decision module provides a solution for the problem list and the early warning list, sends a processing work order to the manual work, and collects the manual processing progress and the processing result.
The power grid information perception comprises functional units such as multi-source data access, metadata storage, equipment unified modeling, model matching degree calculation, multi-remote data fusion and the like; the power grid current situation diagnosis comprises a power grid diagnosis expert knowledge base, a 24-dimension diagnosis system, a problem list, weak link positioning and other functional units; the weak link early warning comprises a power grid planning comprehensive database, an early warning model, a machine learning calculation, an early warning list and other functional units; the intelligent aid decision making comprises the functional units of diagnosis conclusion, solution recommendation, professional collaborative optimization, project progress on-line management, closed-loop feedback and the like.
As shown in fig. 2, the dynamic monitoring and early warning decision method for the weak link of the power grid of the invention comprises the following steps:
s1, accessing the power business system through ETL or service calling to obtain business system data, obtaining power consumption correlation information through a web crawler to form a power grid planning comprehensive database, establishing a correlation relation of multi-source data in the database, and performing data fusion and power grid equipment modeling;
s2, establishing a power grid diagnosis expert knowledge base, defining power grid weak link indexes from multiple dimensions, calculating the index values of the power grid weak links in real time through a multi-algorithm engine based on the power grid planning comprehensive database, and pushing a problem list;
s3, establishing a parameter file and an operation file based on the power grid planning comprehensive database, establishing an early warning model by combining influence factors, and predicting and early warning by adopting artificial intelligence or a deep learning algorithm to form an early warning list;
and S4, for the problem list and the early warning list, proposing a solution, sending a processing work order to a worker, and collecting a manual processing progress and a processing result.
As shown in fig. 3, in step S1, the multi-source data access includes multiple service System data such as a pms (power Management System) System, a GIS (Geographic Information System or Geo-Information System) System, an EMS (Energy Management System) System, a power supply service command System, and a power consumption Information acquisition System, and power consumption related Information such as economy, population, Energy, and government planning of relevant government websites is obtained through a web crawler technology to form a power grid planning comprehensive database integrating power grid equipment accounts, equipment topology relations, equipment real-time operation Information, equipment historical operation data, equipment space Geographic Information, economy, population, Energy, and government planning.
The formed power grid planning comprehensive database comprises an equipment database, an operation database, a graph database and a planning database.
The storage of the source data includes storage of data structures and data processing. The storage of the data structure comprises structured data, unstructured data, spatial data, massive historical data and quasi-real-time data; data processing includes data extraction, data cleansing, update mechanisms, and big data storage.
Because the data of the multi-source system are relatively independent, the incidence relation is lacked, the data can not be directly used, and the fusion of the multi-source heterogeneous data is realized by the following method:
establishing a model comprising data logic relation, topological structure, spatial information, characteristic attribute and the like of each source system; the logic model comprises a tree structure, equipment types and voltage grades; the topological model comprises a directed graph structure, an electrical connection topology and a space connection topology; the feature model comprises a linear structure, feature attribute extraction and spatial attribute extraction.
Calculating the matching degree between the models through traversing and comparing the attribute information of the models; and calculating the matching degree between the models sequentially by the modes of root node correspondence, logic model traversal, directed graph traversal, characteristic attribute identification, space coordinate conversion and image identification.
Carrying out automatic association according to the matching degree condition to realize automatic integration and fusion of data; the matching results are classified into three categories, identified by A, B and C. The matching degree-A refers to the information of the automatic associated equipment and records the slight difference log of the data among the systems; the matching degree-B is that after manual reconfirmation, data matching basis and the reason of non-automatic matching are recorded through system function fusion; the matching degree-C is the attribute of missing matching data, recording the reason and informing the metadata system to carry out data repair. And distributing the data which are not fused to corresponding users in a system in a task mode according to the voltage level of the data and the area of the equipment for manual repair.
The method comprises the steps of establishing a power grid equipment model of a transformer substation, a transformer, a line and the like, wherein the model comprises equipment parameters such as capacity, length and commissioning time, indexes such as equipment N-1 passing rate, heavy overload times, heavy overload duration, capacity-load ratio and fault times, displaying power grid information in a visualized and panoramic mode by using methods such as data mining, image recognition, vector coordinate conversion and the like, comprehensively checking all indexes, and solving the problem of difficulty in accurate perception.
As shown in fig. 4 and 5, in step S2, a power grid diagnosis expert knowledge base is constructed according to power grid related standards, historical accident cases, power grid economic requirements and power grid weak link classifications, 24 key weak link indexes such as overload, multiple T-connection of lines, old equipment and multi-station series supply are defined from 5 dimensions of power supply capacity, power grid structure, equipment level, voltage quality and efficiency benefit according to the content of the expert knowledge base, an index calculation formula is defined according to the power grid weak link index judgment standards (for example, the line overload is defined as the overload when the line load rate reaches 70%, and the overload when the load rate exceeds 100%), an index threshold value is set, distributed parallel calculation and data probe technology are used for deepening inside the comprehensive database, and topology identification, N-1 calculation, load analysis, capacity-load ratio calculation, power supply range identification are performed through topology identification, N-1 calculation, load analysis, capacity-load ratio calculation, and power, The operation mode identification and other algorithm engines calculate index scores in real time according to a 24-dimensional diagnosis system and related calculation rules, dynamically monitor the operation state of the power grid equipment and push an equipment problem list in real time, wherein the problem list comprises: equipment information, problem cause, index score, spatial location, problem urgency.
The 24-dimensional power grid diagnosis neural perception system is divided into a diagnosis system of a high-voltage power distribution network and a diagnosis system of a medium-voltage power distribution network and a low-voltage power distribution network. The high voltage distribution network diagnoses power supply capacity, grid structure, equipment level and efficiency benefit respectively, and specifically comprises the aspects of power supply capacity: insufficient local power supply capacity, overload of a main transformer and overload of a line; in the aspect of the grid structure: the method comprises the following steps of single-line single-side station, double-circuit and double-radiation power supply in the same tower, multiple T connection of a single line, incapability of N-1 verification of the line, incapability of N-1 verification of a main transformer and 30-degree phase angle difference of a high-voltage side incoming line of a 35KV transformer substation; equipment level aspect: old substations and old lines; the efficiency and benefit are as follows: main transformer light load and line light load.
The medium and low voltage distribution networks respectively diagnose power supply capacity, grid structure, equipment level, efficiency benefit and voltage quality. Power supply capability: heavy overload of distribution transformer and heavy overload of line; in the aspect of the grid structure: the single radiation line and the line do not meet the N-1 check; equipment level aspect: the insulation levels of old distribution transformer, old line and 10KV overhead line are improved; the efficiency and benefit are as follows: distributing and changing light load and line light load; the voltage quality aspect includes voltage quality issues.
As shown in fig. 6, in step S3, an early warning model integrating historical data and multiple influence factors is constructed, so as to implement advanced pre-determination of the power grid operation state and timely prevention and control of potential risks. On the basis of a comprehensive database, parameter files and operation files are established station by station, line by line and station by station, an early warning model is established by combining influence factors such as a load curve, newly-added installation, power grid construction, power supply margin, user properties and meteorological information, an early warning is predicted by adopting artificial intelligence and/or a deep learning algorithm to form an early warning list, professionals pay key attention to the early warning list, priority is established, and root tracing and source tracing and accurate pre-judgment are achieved.
The early warning accuracy is gradually improved by applying a machine learning technology, for example, the equipment overload early warning is taken as an example, an empirical parameter is firstly set, the fitting degree of a calculation result and an actual occurrence result is compared aiming at the monthly prediction of historical load rate data, then, each parameter is adjusted according to the percentile precision, the early warning accuracy is gradually improved, and the problem early warning is carried out by using an adjustment result.
As shown in fig. 7, based on the diagnosis problem list and the early warning list, an online management mechanism for the whole process of power grid diagnosis and problem management is established, wherein the whole process comprises development specialties, operation and maintenance specialties, regulation specialties, infrastructure specialties and marketing specialties, and the power grid weak link is scientifically and orderly managed. For the diagnosed problems and the early warning list, a machine learning technology is utilized to provide an alternative solution, a processing work order is initiated and transmitted to relevant professionals in real time, the professionals receive the work order through a mobile terminal, the solution is optimized, the progress condition is uploaded to a system in real time, a closed-loop working mechanism of diagnosis, early warning, scheme optimization and processing feedback is established, a weak link check result is utilized to optimize an analysis model repeatedly, and the accuracy of intelligent diagnosis and decision assistance is continuously improved. The online management of project progress is through a planning phase, an exploratory phase, an investment planning phase, a project construction phase and a post-project evaluation phase. The introduction of intelligent aid decision promotes professional cooperation, plans overall formulation solution, realizes managing weak links scientifically and orderly with minimum investment. And establishing a coordinated and smooth closed-loop working mechanism of each specialty, and comprehensively treating the power grid problem by taking planning as a guide.
The foregoing is only a preferred embodiment of the present invention, and it will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the principle of the invention, and such modifications and improvements are also considered to be within the scope of the invention.
Claims (9)
1. A power grid weak link dynamic monitoring early warning decision system is characterized by comprising:
the power grid information perception module is accessed to the power business system in an ETL or service calling mode to acquire business system data, acquires power consumption related information through a web crawler to form a power grid planning comprehensive database, establishes a related relation of multi-source data in the database, and performs data fusion and power grid equipment modeling;
the power grid state diagnosis module is used for establishing a power grid diagnosis expert knowledge base, defining power grid weak link indexes from multiple dimensions, calculating the index values of the power grid weak links in real time through a multi-algorithm engine based on the power grid planning comprehensive database, and pushing a problem list;
the weak link early warning module is used for establishing a parameter file and an operation file based on the power grid planning comprehensive database, establishing an early warning model by combining influence factors, and predicting early warning by adopting artificial intelligence or a deep learning algorithm to form an early warning list;
and the intelligent auxiliary decision module provides a solution for the problem list and the early warning list, sends a processing work order to a worker, and collects a manual processing progress and a processing result.
2. The system of claim 1, wherein the grid information perception module comprises:
the model matching calculation unit is used for establishing a data model including the characteristic attributes of the multi-source data and calculating the matching degree between the models through traversal comparison of the attribute information in the models;
and the multi-source data fusion unit is used for automatically associating according to the calculation condition of the matching degree, so that the automatic integrated fusion of the multi-source data is realized.
3. A power grid weak link dynamic monitoring and early warning decision method is characterized by comprising the following steps:
accessing an electric power service system in an ETL or service calling mode, acquiring service system data, acquiring power consumption correlation information through a web crawler, forming a power grid planning comprehensive database, establishing a correlation relation of multi-source data in the database, and performing data fusion and power grid equipment modeling;
establishing a power grid diagnosis expert knowledge base, defining power grid weak link indexes from multiple dimensions, calculating the index values of the power grid weak links in real time through a multi-algorithm engine based on the power grid planning comprehensive database, and pushing a problem list;
establishing a parameter file and an operation file based on the power grid planning comprehensive database, establishing an early warning model by combining influence factors, and predicting early warning by adopting artificial intelligence or a deep learning algorithm to form an early warning list;
and for the problem list and the early warning list, a solution is provided, a processing work order is sent to a worker, and a manual processing progress and a processing result are collected.
4. The dynamic monitoring and early warning decision method for the weak link of the power grid as claimed in claim 3, wherein the power service system comprises a PMS system, a GIS system, an EMS system, a power supply service command system and a power utilization information acquisition system; the electricity consumption related information comprises economic information, population information, energy information and government planning information.
5. The dynamic monitoring and early warning decision method for the weak link of the power grid as claimed in claim 4, wherein the specific process of establishing the incidence relation of multi-source data in the database and carrying out data fusion and modeling of power grid equipment comprises the following steps:
establishing a model containing a data logic relation, a topological structure, spatial information and characteristic attributes of the power business system;
traversing and comparing the attribute information in the models, and calculating the matching degree between the models;
carrying out model association according to the matching degree condition to realize integrated fusion of data;
and for the data which cannot be fused, manually processing and fusing according to the voltage level of the data and/or the area of the equipment.
6. The dynamic monitoring and early warning decision method for the weak link of the power grid as claimed in claim 5, wherein the calculation of the matching degree between the models is performed sequentially through the modes of root node correspondence, logic model traversal, directed graph traversal, feature attribute recognition, space coordinate conversion and image recognition.
7. The dynamic monitoring and early warning decision method for the weak link of the power grid as claimed in claim 3, wherein the algorithm engine comprises topology recognition, N-1 calculation, load analysis, capacity-to-load ratio calculation, power supply range recognition and operation mode recognition.
8. The dynamic monitoring and early warning decision method for the weak link of the power grid as claimed in claim 7, wherein the method for calculating the index value of the weak link of the power grid in real time through a multi-algorithm engine based on the power grid planning comprehensive database comprises the following specific processes:
defining an index calculation formula according to a power grid weak link index judgment standard, and setting an index threshold;
by utilizing a distributed parallel computing and data probe technology, based on data in a power grid planning comprehensive database, an algorithm engine is used for calculating index scores in real time according to a dimension diagnosis system and a calculation rule, dynamically monitoring the running state of power grid equipment, and pushing a problem list including equipment problems, cause problems, index scores, spatial positions and problem urgency degrees.
9. The dynamic monitoring and early warning decision method for the weak link of the power grid as claimed in claim 3, wherein the specific process of adopting the deep learning algorithm to predict and early warning is as follows:
setting an empirical value of a model or parameter to be predicted;
predicting actual values generated in a set historical time period one by one, comparing the fitting degree of a predicted result and an actual occurrence result, and adjusting each parameter according to set precision;
and performing problem early warning by using the adjustment result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911191417.XA CN111143447B (en) | 2019-11-28 | 2019-11-28 | Dynamic monitoring early warning decision system and method for weak links of power grid |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911191417.XA CN111143447B (en) | 2019-11-28 | 2019-11-28 | Dynamic monitoring early warning decision system and method for weak links of power grid |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111143447A true CN111143447A (en) | 2020-05-12 |
| CN111143447B CN111143447B (en) | 2023-10-31 |
Family
ID=70517302
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911191417.XA Active CN111143447B (en) | 2019-11-28 | 2019-11-28 | Dynamic monitoring early warning decision system and method for weak links of power grid |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111143447B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111817265A (en) * | 2020-06-29 | 2020-10-23 | 北京智芯微电子科技有限公司 | Low-voltage transformer area power distribution protection method and system |
| CN112907081A (en) * | 2021-02-22 | 2021-06-04 | 国网安徽省电力有限公司合肥供电公司 | Method and system for intelligently diagnosing operation problems of agricultural distribution network |
| CN113685335A (en) * | 2021-08-27 | 2021-11-23 | 广东鑫钻节能科技股份有限公司 | Safe energy-saving air compression station with power quality monitoring system |
| CN113723869A (en) * | 2021-11-02 | 2021-11-30 | 国网北京市电力公司 | Power grid infrastructure project post-evaluation method, device, equipment and medium |
| CN114860839A (en) * | 2022-07-07 | 2022-08-05 | 中国长江三峡集团有限公司 | Power distribution network data analysis and visualization system and method |
| CN117707027A (en) * | 2024-02-05 | 2024-03-15 | 国网甘肃省电力公司白银供电公司 | Centralized control station one-key sequential control system based on multi-source data fusion |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101179195A (en) * | 2007-11-15 | 2008-05-14 | 上海交通大学 | Power distribution network planning scheme assistant decision system |
| CN102708472A (en) * | 2012-06-20 | 2012-10-03 | 江西省电力公司超高压分公司 | Intelligent processing method for warning information of combined power grid |
| CN103647276A (en) * | 2013-12-10 | 2014-03-19 | 国家电网公司 | Electric energy quality early warning system and method thereof |
| CN103679293A (en) * | 2013-12-20 | 2014-03-26 | 长园深瑞继保自动化有限公司 | Intelligent substation warning and aid decision making system |
| CN106026383A (en) * | 2016-05-16 | 2016-10-12 | 国网辽宁省电力有限公司朝阳供电公司 | Intelligent transformer station equipment state multi-system cooperative diagnosis method based on information fusion |
| CN107274115A (en) * | 2017-08-11 | 2017-10-20 | 国网江苏省电力公司电力科学研究院 | Active distribution network Situation Awareness System and method based on distributed monitoring and Multi-source Information Fusion |
| CN107402976A (en) * | 2017-07-03 | 2017-11-28 | 国网山东省电力公司经济技术研究院 | Power grid multi-source data fusion method and system based on multi-element heterogeneous model |
| CN107464062A (en) * | 2017-08-09 | 2017-12-12 | 国网山东省电力公司经济技术研究院 | A kind of power network development Evaluation of Diagnosis system |
| CN109343995A (en) * | 2018-10-25 | 2019-02-15 | 金税信息技术服务股份有限公司 | Intelligent O&M analysis system based on multi-source heterogeneous data fusion, machine learning and customer service robot |
| CN109816161A (en) * | 2019-01-14 | 2019-05-28 | 中国电力科学研究院有限公司 | A kind of power distribution network operation computer-aided decision support System and its application method |
| CN109978295A (en) * | 2017-12-28 | 2019-07-05 | 南京易司拓电力科技股份有限公司 | A kind of lean decision system of data-driven power distribution network |
-
2019
- 2019-11-28 CN CN201911191417.XA patent/CN111143447B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101179195A (en) * | 2007-11-15 | 2008-05-14 | 上海交通大学 | Power distribution network planning scheme assistant decision system |
| CN102708472A (en) * | 2012-06-20 | 2012-10-03 | 江西省电力公司超高压分公司 | Intelligent processing method for warning information of combined power grid |
| CN103647276A (en) * | 2013-12-10 | 2014-03-19 | 国家电网公司 | Electric energy quality early warning system and method thereof |
| CN103679293A (en) * | 2013-12-20 | 2014-03-26 | 长园深瑞继保自动化有限公司 | Intelligent substation warning and aid decision making system |
| CN106026383A (en) * | 2016-05-16 | 2016-10-12 | 国网辽宁省电力有限公司朝阳供电公司 | Intelligent transformer station equipment state multi-system cooperative diagnosis method based on information fusion |
| CN107402976A (en) * | 2017-07-03 | 2017-11-28 | 国网山东省电力公司经济技术研究院 | Power grid multi-source data fusion method and system based on multi-element heterogeneous model |
| CN107464062A (en) * | 2017-08-09 | 2017-12-12 | 国网山东省电力公司经济技术研究院 | A kind of power network development Evaluation of Diagnosis system |
| CN107274115A (en) * | 2017-08-11 | 2017-10-20 | 国网江苏省电力公司电力科学研究院 | Active distribution network Situation Awareness System and method based on distributed monitoring and Multi-source Information Fusion |
| CN109978295A (en) * | 2017-12-28 | 2019-07-05 | 南京易司拓电力科技股份有限公司 | A kind of lean decision system of data-driven power distribution network |
| CN109343995A (en) * | 2018-10-25 | 2019-02-15 | 金税信息技术服务股份有限公司 | Intelligent O&M analysis system based on multi-source heterogeneous data fusion, machine learning and customer service robot |
| CN109816161A (en) * | 2019-01-14 | 2019-05-28 | 中国电力科学研究院有限公司 | A kind of power distribution network operation computer-aided decision support System and its application method |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111817265A (en) * | 2020-06-29 | 2020-10-23 | 北京智芯微电子科技有限公司 | Low-voltage transformer area power distribution protection method and system |
| CN112907081A (en) * | 2021-02-22 | 2021-06-04 | 国网安徽省电力有限公司合肥供电公司 | Method and system for intelligently diagnosing operation problems of agricultural distribution network |
| CN113685335A (en) * | 2021-08-27 | 2021-11-23 | 广东鑫钻节能科技股份有限公司 | Safe energy-saving air compression station with power quality monitoring system |
| CN113685335B (en) * | 2021-08-27 | 2023-07-25 | 广东鑫钻节能科技股份有限公司 | Safety energy-saving air compression station with electric energy quality monitoring system |
| CN113723869A (en) * | 2021-11-02 | 2021-11-30 | 国网北京市电力公司 | Power grid infrastructure project post-evaluation method, device, equipment and medium |
| CN114860839A (en) * | 2022-07-07 | 2022-08-05 | 中国长江三峡集团有限公司 | Power distribution network data analysis and visualization system and method |
| CN117707027A (en) * | 2024-02-05 | 2024-03-15 | 国网甘肃省电力公司白银供电公司 | Centralized control station one-key sequential control system based on multi-source data fusion |
| CN117707027B (en) * | 2024-02-05 | 2024-04-16 | 国网甘肃省电力公司白银供电公司 | Centralized control station one-key sequential control system based on multi-source data fusion |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111143447B (en) | 2023-10-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111143447B (en) | Dynamic monitoring early warning decision system and method for weak links of power grid | |
| CN113159339B (en) | One-area one-index line loss management method and system based on big data | |
| CN110825885B (en) | Power equipment knowledge graph application system | |
| CN111126866B (en) | Comprehensive energy efficiency evaluation management system and method for AC/DC renewable energy system | |
| CN116865258B (en) | Hierarchical distributed power supply intelligent power grid construction method | |
| CN113935562A (en) | Intelligent rating and automatic early warning method for health condition of power equipment | |
| CN110826228B (en) | Regional power grid operation quality limit evaluation method | |
| CN113487447A (en) | Power distribution network load prediction and line loss cause analysis method based on big data | |
| CN105426980A (en) | Power distribution network health index assessment engineering application system | |
| CN113537415A (en) | Convertor station inspection method and device based on multi-information fusion and computer equipment | |
| CN115423009A (en) | Cloud edge coordination-oriented power equipment fault identification method and system | |
| CN116599151A (en) | Source network storage safety management method based on multi-source data | |
| CN115511656A (en) | Demand planning auxiliary decision system based on mining power grid data value | |
| CN115908046A (en) | Visual power distribution system based on airport terminal building BIM | |
| CN116933010A (en) | Load rate analysis and evaluation method and system based on multi-source data fusion and deep learning | |
| CN113420162B (en) | Equipment operation chain state monitoring method based on knowledge graph | |
| CN103617447A (en) | Evaluation system and method for intelligent substation | |
| CN113949155A (en) | Panoramic power quality monitoring system with real-time monitoring function | |
| CN107862459B (en) | Metering equipment state evaluation method and system based on big data | |
| CN117390944A (en) | Substation operation condition simulation system | |
| CN115936663A (en) | Maintenance method and device for power system | |
| CN114021869A (en) | Input-output evaluation method and system with power grid investment income maximization as target | |
| Sun et al. | Study of power grid development diagnosis system based on multi-source data analysis | |
| CN117312293B (en) | Electric power multisource heterogeneous data management and intelligent analysis method and system | |
| CN117691741A (en) | Electric power data processing system |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |