CN111105218A - Power distribution network operation monitoring method based on holographic image technology - Google Patents
Power distribution network operation monitoring method based on holographic image technology Download PDFInfo
- Publication number
- CN111105218A CN111105218A CN202010012052.6A CN202010012052A CN111105218A CN 111105218 A CN111105218 A CN 111105218A CN 202010012052 A CN202010012052 A CN 202010012052A CN 111105218 A CN111105218 A CN 111105218A
- Authority
- CN
- China
- Prior art keywords
- equipment
- data
- distribution network
- power distribution
- power grid
- 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
- 238000009826 distribution Methods 0.000 title claims abstract description 34
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000005516 engineering process Methods 0.000 title claims abstract description 16
- 238000012544 monitoring process Methods 0.000 title claims abstract description 14
- 238000004458 analytical method Methods 0.000 claims abstract description 20
- 238000011156 evaluation Methods 0.000 claims abstract description 16
- 238000013210 evaluation model Methods 0.000 claims abstract description 14
- 238000013439 planning Methods 0.000 claims abstract description 8
- 230000000007 visual effect Effects 0.000 claims abstract description 8
- 238000011161 development Methods 0.000 claims description 10
- 238000009434 installation Methods 0.000 claims description 5
- 238000007405 data analysis Methods 0.000 claims description 3
- 230000007547 defect Effects 0.000 claims description 3
- 230000003068 static effect Effects 0.000 claims description 3
- 238000013024 troubleshooting Methods 0.000 claims description 2
- 238000011160 research Methods 0.000 abstract description 7
- 238000003745 diagnosis Methods 0.000 abstract description 6
- 238000012423 maintenance Methods 0.000 abstract description 3
- 238000010191 image analysis Methods 0.000 abstract description 2
- 230000018109 developmental process Effects 0.000 description 9
- 230000008569 process Effects 0.000 description 9
- 238000004364 calculation method Methods 0.000 description 8
- 238000009825 accumulation Methods 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 238000004140 cleaning Methods 0.000 description 4
- 230000001186 cumulative effect Effects 0.000 description 4
- 230000032683 aging Effects 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 229910052802 copper Inorganic materials 0.000 description 1
- 239000010949 copper Substances 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000012812 general test Methods 0.000 description 1
- 238000000265 homogenisation Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 229910052742 iron Inorganic materials 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 238000012797 qualification Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 230000009466 transformation 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
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
-
- 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—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
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Operations Research (AREA)
- Health & Medical Sciences (AREA)
- Quality & Reliability (AREA)
- Public Health (AREA)
- Primary Health Care (AREA)
- Data Mining & Analysis (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to a holographic image technology-based power distribution network operation monitoring method, which comprises the steps of establishing a power distribution network equipment archive base, deepening equipment label research, developing an equipment multi-dimensional information evaluation label center, constructing a power grid main equipment holographic image evaluation model, namely an equipment image, establishing a visual power grid equipment image analysis platform, and providing analysis basis and data support for power grid diagnosis analysis, planning, equipment comprehensive evaluation, scheduling operation and maintenance and the like.
Description
Technical Field
The invention relates to the technical field of power distribution network operation, in particular to a power distribution network operation monitoring method based on a holographic image technology.
Background
At the present stage, along with the continuous improvement of the intelligent level of the power distribution network, the power distribution network generates massive machine accounts and operation data, meanwhile, the continuous maturity of a big data processing and analyzing and mining technology also prompts a power grid enterprise to obtain a plurality of practical achievements in the aspects of carrying out power grid operation state diagnosis and analysis, guiding power grid development and planning and the like by using the big data technology, and the power supply reliability and the power grid investment effect of the power grid are greatly improved.
However, the information system is lack of equipment characteristic information which can be directly applied to development decision, lacks of unified information integration analysis for supporting development decision, and lacks of equipment characteristic information application strategy for supporting lean improvement of planning. Therefore, it is necessary to build an equipment image based on the scenes of comprehensive equipment evaluation, power distribution network development diagnosis, accurate investment analysis and the like, and build a method for facilitating analysis and application of business personnel and decision-making personnel.
Disclosure of Invention
In view of the above, the present invention provides a power distribution network operation monitoring method based on a holographic image technology, which is used to build a visual power grid equipment figure analysis platform, and provide analysis basis and data support for power grid diagnosis analysis, planning plan, equipment comprehensive evaluation, scheduling operation and maintenance, and the like.
The invention is realized by adopting the following scheme: a power distribution network operation monitoring method based on holographic image technology specifically comprises the following steps:
establishing a power distribution network equipment archive library;
setting an equipment label;
carrying out multi-dimensional information evaluation on the equipment, and constructing a holographic portrait evaluation model of the main equipment of the power grid;
and constructing a visual power grid equipment portrait analysis platform by using the power grid master equipment holographic portrait evaluation model.
Further, the data in the power distribution network equipment archive library comprise attribute state data based on static ledger data, perception operation data of multiple time scales, fault overhaul data with statistical characteristics, associated data related to external economic environment, investment data related to development planning and index data.
Further, the setting of the device tag specifically includes: and (3) taking professional big data analysis technology in combination with expert experience and industrial technical guide rules as a definition rule, analyzing and calculating data of the equipment including operation monitoring data, ledger attribute data, fault defect data, installation environment data and data of the area where the equipment is located, and giving a feature label based on the main equipment of the power grid and the administrative area where the main equipment belongs.
The device tags are divided into 4 categories according to the generation mode of the tags: a field type tag, an index type tag, a statistical type tag and a model tag; device labels are classified into 3 categories according to the research dimension of the label: the device comprises a single device label, a device cluster label and a region index label.
Further, the multi-dimensional information evaluation of the equipment and the establishment of the holographic portrait evaluation model of the power grid master equipment are specifically as follows: on the basis of the constructed equipment label, label information is combined by using an information display form comprising a word cloud, a meter panel, a statistical graph and an attribute list, and a holographic equipment evaluation model is constructed from a plurality of dimensionalities evaluation comprehensive portrait images of the power grid equipment, including running information and attribute information of the equipment, environment information outside the equipment and area information to which the equipment belongs.
Preferably, the visual power grid equipment portrait analysis platform is provided with a data interface, supports various professional business personnel to acquire the overall feature information of various power grid equipment at any time, assists the management and control decision of each business, and provides guidance and correction for the development of actual work.
Compared with the prior art, the invention has the following beneficial effects: according to the method, a power distribution network equipment archive base is established, then equipment label research is deepened, an equipment multi-dimensional information evaluation label center is developed, a power grid main equipment holographic image evaluation model, namely an equipment image, is established, a visual power grid equipment image analysis platform is established, and analysis basis and data support are provided for power grid diagnosis analysis, planning, equipment comprehensive evaluation, scheduling operation and maintenance and the like.
Drawings
FIG. 1 is a schematic flow chart of a method according to an embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1, the present embodiment provides a method for monitoring operation of a power distribution network based on a holographic image technology, which specifically includes the following steps:
establishing a power distribution network equipment archive library;
setting an equipment label;
carrying out multi-dimensional information evaluation on the equipment, and constructing a holographic portrait evaluation model of the main equipment of the power grid;
and constructing a visual power grid equipment portrait analysis platform by using the power grid master equipment holographic portrait evaluation model.
In this embodiment, the data in the power distribution network equipment archive includes attribute state data based on static ledger data, sensing operation data with multiple time scales, troubleshooting data with statistical characteristics, associated data related to an external economic environment, investment data related to development planning, and index data.
Preferably, the established power distribution network equipment archive is a continuous deepening and continuous improving process, main field data of a PMS system, an OMS system, an SCADA system, marketing and other systems are extracted to form a primary power distribution network equipment archive by relying on a full-service data center, then equipment data of more systems are obtained along with the passage of time, and data distributed in a plurality of storage resources are integrated to support label generation and analysis to make detailed requirements on data content, data granularity, data dimension and data timeliness, so that the final power distribution network equipment archive with real-time data updating, close correlation among data and strong service logic is formed.
In this embodiment, the setting of the device tag specifically includes: and (3) taking professional big data analysis technology in combination with expert experience and industrial technical guide rules as a definition rule, analyzing and calculating data of the equipment including operation monitoring data, ledger attribute data, fault defect data, installation environment data and data of the area where the equipment is located, and giving a feature label based on the main equipment of the power grid and the administrative area where the main equipment belongs.
The feature tags are divided into 4 categories according to the generation mode of the tags: a field type tag, an index type tag, a statistical type tag and a model tag; feature labels are classified into 3 categories according to the research dimension of the label: the device comprises a single device label, a device cluster label and a region index label.
Among the three kinds of labels divided according to the research dimension, the single equipment label means that the label reflects the characteristic condition of single equipment; the equipment cluster label is the high-dimensional display of a single equipment label, and the single equipment label is gathered by dimensions such as voltage level, rated capacity, power supply area, management department, installation environment and the like, so that statistical values such as average value, summary value, occupation ratio and the like of the equipment in the group under the classification dimension can be obtained, and the high-dimensional label of various equipment groups can be obtained by combining statistical modes such as clustering, confidence classification and the like with expert experience; the regional index label is not generated by regional summarization of single equipment labels, and is a view of standing in a geographical region to measure the overall performance of the whole region in equipment characteristics, such as power supply reliability, power grid capacity-to-load ratio, unit investment increased power supply load, unit investment increased power supply quantity, unit load increase increased power supply quantity and the like. Next, the present embodiment takes the tags of the transmission line and the large distribution feeder, the tags of the main transformer and the distribution transformer, and the tags of the area devices constructed in the area to which the devices belong as examples.
The single equipment labels of the power transmission line and the large power distribution feeder are as follows:
the single equipment labels of the main transformer and the distribution transformer are as follows:
the area index label and the equipment cluster label constructed for the area and the characteristics of the equipment are as follows:
in four kinds of labels divided according to the production mode, a field type label refers to a single field of a certain system from which the label comes, and is generally a key field of an equipment ledger, such as the level of a power supply area where the equipment is located, the property of equipment assets, the running state of the equipment, whether rural power grids exist or not, and the like, if a plurality of systems all have the field, matching, comparison and selection are needed when the value of the label is taken; the index type label refers to an equipment index label constructed by referring to a power grid development diagnosis analysis index system and the like, such as a voltage qualification rate, a maximum load rate, a platform area low-voltage power supply radius and the like; the statistical type label is used for carrying out state analysis on equipment attributes through mathematical statistical methods such as time ratio, equipment comparison of the same type, average value comparison, change rate and the like, such as labels of load running state, average load rate, line section standard, line load classification and the like; the model label is a comprehensive characteristic situation for establishing a model analysis device, such as a single device load rate label, in the calculation process of the label, a model is required to be used for evaluating the economic load rate standard of each device, then the actual operation load of the device is compared with the economic load rate standard, label results of reasonable load, too low load and too high load are obtained, and the model label is established mainly through the following processes:
and (3) service investigation: when the copper loss and the iron loss of the equipment are equal, the energy consumption is relatively most economical when the equipment is operated. According to general test data, the theoretical economic load of the 10kV distribution transformer is about 40%, but the actual economic efficiency of the equipment is often far from the theoretical data due to different actual operating environments and aging and potential hidden dangers caused by increased operating time, and how to accurately estimate the actual economic load of each equipment is the core significance of the model. In the embodiment, through research and discussion with business experts, the detail of characteristics affecting the economic operation state of the 10kv distribution transformer main body is analyzed and determined, and field screening is performed from the feasibility point of view.
Constructing a broad table: according to the construction idea of research and discussion, various data are extracted by relying on a full-service data center, service and ledger information which may affect the economic load rate of equipment are selected as original features of modeling, 18 factor indexes are constructed, and an original factor wide table with 54 factors is established by combining with three time scales, wherein the method specifically comprises the following steps:
data cleaning: when the database stores data, most of the category information is coded and stored, a common code table of each system needs to be acquired in the cleaning process to perform corresponding decoding (for example, in a pms system, equipment operation state information of '10' indicates installation, '20' indicates operation, '30' indicates shutdown and the like), and meanwhile, partial information such as 'overhaul cost' and the like has partial loss due to data source problems, direct elimination or one-hot processing needs to be judged according to importance degree in the data cleaning process, and meanwhile, partial information can be incorporated into a model only by text-to-digital processing.
Characteristic engineering: reconstructing data by combining the selected model according to the data cleaning result to obtain the characteristics required by the model, finding out multiple collinearity among the characteristics by methods such as correlation analysis and the like, selectively eliminating partial redundant characteristics, and finally determining 31 index variables of the input model as follows:
average load of nearly 1 month; low voltage accumulation times in last 1 year; average load in the last 1 year; the low voltage accumulated time is about 1 month; maximum load of nearly 1 month; the low voltage accumulated time of the last 1 year; the number of reloading times in approximately 1 month; low voltage days continuously in approximately 1 month; the number of reloading times in the last 1 year; low voltage days continuously in last 1 year; the accumulated time of heavy load is nearly 1 month; accumulating the output electric energy in about 1 month; the accumulated time of heavy load is nearly 3 months; accumulating the output electric energy in nearly 3 months; overload times of approximately 1 month; accumulating the output electric energy in the last 1 year; overload times of approximately 3 months; the maximum output electric energy is nearly 3 months per day; overload accumulated time of nearly 1 month; the maximum output electric energy of one month in the last 1 year; overload accumulated time of nearly 1 year; accumulating line loss in about 1 month; consecutive days of reloading in approximately 1 month; accumulating line loss in the last 1 year; consecutive days of reloading in approximately 3 months; maximum monthly line loss in nearly 1 year; consecutive overload days in approximately 3 months; maximum daily loss of nearly 1 month; low voltage accumulation times in about 1 month; maximum daily loss of nearly 3 months; the low voltage accumulation times in the last 3 months.
The calculation process is as follows:
the active loss calculation formula of the transformer when the transformer is loaded with S is as follows:
ΔPi≈ΔPo+Kq*ΔQo+(ΔPk+Kq*ΔQN)*(S/SN)2
in the formula: delta PoFor no-load losses of transformers, Δ PkFor short-circuit losses of transformers, Δ QoIs the reactive loss, Delta Q, of the transformer when it is no-loadNFor the increase of reactive losses at rated load of the transformer, SNRated capacity of the transformer, KqThe reactive power economic equivalent of the transformer is taken as the industrial transformation and distribution mean value of 0.1.
Wherein:
ΔQo≈SN*Io％/100
in the formula: i iso% is the percentage value of the no-load current of the transformer in the rated current;
ΔQN≈SN*Uk％/100
in the formula: u shapek% of short-circuit voltage (impedance) of the transformerVoltage UZ) as a percentage of the nominal voltage;
the theoretical economic load calculation formula of the transformer is as follows:
the theoretical economic load factor of the transformer is derived and calculated as follows:
since the above formula is the theoretical optimal load rate, and after the device is actually put into operation, the optimal loads of different transformers at different times are significantly different due to the influence of various factors in the operation process, including the familial process characteristics of manufacturers and the like, the weight of each influence factor needs to be calculated by using a machine learning model to adjust the optimal load, and the model selected in this embodiment is an entropy model.
The calculation steps of the entropy method model are as follows:
1) constructing a data matrix: the feature width table is constructed as a data matrix suitable for the model as follows:
2) normalization processing of indexes: heterogeneous indexes are homogeneous, and measurement units of all indexes are not uniform, so before the indexes are used for calculating comprehensive indexes, the indexes are standardized, namely absolute values of the indexes are converted into relative values, and the homogenization problem of various heterogeneous index values is solved. Moreover, since the positive index and the negative index have different meanings (the higher the positive index value is, the better the negative index value is), the data normalization processing is performed by using different algorithms for the high and low indexes. The specific method comprises the following steps:
the forward direction index is as follows:
negative direction index:
then XijIs the value of the jth index of the ith body. For convenience, the normalized data is still denoted as Xij。
3) Calculating the proportion of the ith main body in the j index:
4) calculating the entropy value of the j index:
wherein k is 1/lnm, satisfies 0. ltoreq. ej≤1；
5) Calculating the information entropy redundancy:
gj＝1-ej
6) calculating difference coefficients (weight values) of the indexes:
7) calculating the comprehensive score of each index:
according to the calculation process, the screened influence factors are weighted, the aging degree of the equipment is scored, and the weight of the influence factors is as follows:
average load 0.98720606655705 for approximately 1 month; average load 0.20090539597133 in last 1 year; near 1 month maximum load 1.17954494329975; number of reloads 0.80933176543694 in the last 1 month; number of reloading 0.41158008538239 in the last 1 year; the cumulative duration 0.13380944525195 of the heavy load in the last 1 month; the cumulative duration of the last 3 months of reloading 1.14173382732746; number of overloads in approximately 1 month 0.68945865721614; number of overloads in approximately 3 months 1.39931839941803; cumulative time of overload 1.45446677652512 for the last 1 month; cumulative time of overload 0.57907598183834 in the last 1 year; consecutive days of reloading in the last 1 month 0.05600122688966; consecutive days of reloading in the last 3 months 1.33680175741245; consecutive overload days in the last 3 months 1.26831046917255; the low voltage accumulation times are-0.06364923526476 in the last 1 month; the low voltage accumulated times of nearly 3 months are-1.08449556911692; the low voltage accumulation times of the last 1 year are-0.63293542166525; the low voltage accumulated time length of about 1 month is-0.91700007313289; the low voltage accumulated time of the last 1 year is-1.72812558939482; continuous low voltage days of nearly 1 month-0.60328489013406; low voltage days-1.19652176660354 in last 1 year; the power output 0.37840348179604 is accumulated in the last 1 month; the power output is 0.37761265009779 accumulated in last 3 months; the power is output 0.59729425867891 in the last 1 year; 0.43767393766074 maximum output power in a single day in nearly 3 months; the maximum output power of a month in the last 1 year is 0.53209966342014; line loss 1.00580495461493 accumulated over approximately 1 month; line loss 1.31279197621695 accumulated in the last 1 year; maximum monthly line loss 0.83789555835840 in the last 1 year; maximum daily loss 0.44607097265706 for approximately 1 month; maximum daily loss 0.03201095650522 in approximately 3 months.
The aging coefficient calculation formula is as follows:
wherein a isiThe weight of each factor obtained by an entropy method; wherein
The normalized impact factor value (i.e., the normalized input index value).
In particular, the final coefficients are normalized coefficients
The economic load rate calculation formula of the equipment is as follows: kec*＝Kec*coef*。
Load rate evaluation target of final equipmentIn signaturing, the actual operating maximum load rate P and the economic load rate Kec will be evaluated*The ratio of (A) is defined as "reasonable load" when the ratio is between 0.9 and 1.1, and "too high load" when the ratio exceeds 1.1, and "too low load" when the ratio is below 0.9.
In this embodiment, the performing multidimensional information evaluation on the device and constructing a holographic portrait evaluation model of the power grid master device specifically includes: on the basis of the constructed equipment label, label information is combined by using an information display form comprising a word cloud, a meter panel, a statistical graph and an attribute list, and a holographic equipment evaluation model is constructed from a plurality of dimensionalities evaluation comprehensive portrait images of the power grid equipment, including running information and attribute information of the equipment, environment information outside the equipment and area information to which the equipment belongs.
Preferably, the visual power grid equipment portrait analysis platform of the embodiment is provided with a data interface, supports various professional business personnel to develop and acquire the overall feature information of various power grid equipment at any time, assists the management and control decision of each business, and provides guidance and correction for the development of actual work.
The foregoing is directed to preferred embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the protection scope of the technical solution of the present invention.
Claims (4)
1. A power distribution network operation monitoring method based on holographic image technology is characterized by comprising the following steps:
establishing a power distribution network equipment archive library;
setting an equipment label;
carrying out multi-dimensional information evaluation on the equipment, and constructing a holographic portrait evaluation model of the main equipment of the power grid;
and constructing a visual power grid equipment portrait analysis platform by using the power grid master equipment holographic portrait evaluation model.
2. The method for monitoring the operation of the power distribution network based on the holographic image technology as claimed in claim 1, wherein the data in the power distribution network equipment archive includes attribute status data based on static ledger data, multi-time scale sensing operation data, troubleshooting data with statistical characteristics, associated data related to external economic environment, investment data related to development planning, and index data.
3. The method for monitoring the operation of the power distribution network based on the holographic image technology as claimed in claim 1, wherein the setting device tag is specifically: the big data analysis technology is combined with expert experience and industrial technical guide rules to serve as a definition rule, data including operation monitoring data, ledger attribute data, fault defect data, installation environment data and data of the located region of the equipment are analyzed and calculated, and feature labels based on the main equipment of the power grid and the administrative region of the main equipment are given.
4. The method for monitoring the operation of the power distribution network based on the holographic image technology as claimed in claim 1, wherein the step of performing multi-dimensional information evaluation on the equipment and constructing the holographic image evaluation model of the main equipment of the power distribution network specifically comprises the steps of: on the basis of the constructed equipment label, label information is combined by using an information display form comprising a word cloud, a meter panel, a statistical graph and an attribute list, and a holographic equipment evaluation model is constructed from a plurality of dimensionalities evaluation comprehensive portrait images of the power grid equipment, including running information and attribute information of the equipment, environment information outside the equipment and area information to which the equipment belongs.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010012052.6A CN111105218A (en) | 2020-01-07 | 2020-01-07 | Power distribution network operation monitoring method based on holographic image technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010012052.6A CN111105218A (en) | 2020-01-07 | 2020-01-07 | Power distribution network operation monitoring method based on holographic image technology |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111105218A true CN111105218A (en) | 2020-05-05 |
Family
ID=70426054
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010012052.6A Pending CN111105218A (en) | 2020-01-07 | 2020-01-07 | Power distribution network operation monitoring method based on holographic image technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111105218A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112990647A (en) * | 2020-12-31 | 2021-06-18 | 国网湖南省电力有限公司 | Distribution network project equipment portrait index evaluation system and evaluation method |
| CN113408753A (en) * | 2021-06-07 | 2021-09-17 | 国网天津市电力公司 | Main network equipment portrait drawing device and method based on label technology |
| CN114024302A (en) * | 2021-09-24 | 2022-02-08 | 深圳市国电科技通信有限公司 | Method and device for evaluating distribution room |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180018867A1 (en) * | 2016-07-12 | 2018-01-18 | Tyco Fire & Security Gmbh | Holographic Technology Implemented Security Solution |
| CN109857831A (en) * | 2019-02-20 | 2019-06-07 | 云南电网有限责任公司信息中心 | A kind of power equipment portrait label system building method based on big data technology |
| CN110264095A (en) * | 2019-06-25 | 2019-09-20 | 昆明能讯科技有限责任公司 | A kind of Distribution Network Equipment visualization auxiliary O&M method based on GIS-Geographic Information System |
| CN110336378A (en) * | 2019-07-10 | 2019-10-15 | 云南电网有限责任公司电力科学研究院 | A kind of power distribution network operation monitoring method based on hologram technology |
-
2020
- 2020-01-07 CN CN202010012052.6A patent/CN111105218A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180018867A1 (en) * | 2016-07-12 | 2018-01-18 | Tyco Fire & Security Gmbh | Holographic Technology Implemented Security Solution |
| CN109857831A (en) * | 2019-02-20 | 2019-06-07 | 云南电网有限责任公司信息中心 | A kind of power equipment portrait label system building method based on big data technology |
| CN110264095A (en) * | 2019-06-25 | 2019-09-20 | 昆明能讯科技有限责任公司 | A kind of Distribution Network Equipment visualization auxiliary O&M method based on GIS-Geographic Information System |
| CN110336378A (en) * | 2019-07-10 | 2019-10-15 | 云南电网有限责任公司电力科学研究院 | A kind of power distribution network operation monitoring method based on hologram technology |
Non-Patent Citations (1)
| Title |
|---|
| 张珂珩等: "基于全息时标量测数据挖掘的配电网设备健康状态诊断分析", 《计算机测量与控制》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112990647A (en) * | 2020-12-31 | 2021-06-18 | 国网湖南省电力有限公司 | Distribution network project equipment portrait index evaluation system and evaluation method |
| CN113408753A (en) * | 2021-06-07 | 2021-09-17 | 国网天津市电力公司 | Main network equipment portrait drawing device and method based on label technology |
| CN114024302A (en) * | 2021-09-24 | 2022-02-08 | 深圳市国电科技通信有限公司 | Method and device for evaluating distribution room |
| CN114024302B (en) * | 2021-09-24 | 2023-10-03 | 深圳市国电科技通信有限公司 | Method and device for evaluating a region |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113159339B (en) | One-area one-index line loss management method and system based on big data | |
| CN104951866B (en) | Line loss comprehensive management benchmarking evaluation system and method for county-level power supply enterprise | |
| CN111429027B (en) | Regional power transmission network operation multidimensional analysis method based on big data | |
| CN111105218A (en) | Power distribution network operation monitoring method based on holographic image technology | |
| CN107194149B (en) | Highway electromechanical system health index evaluation method | |
| CN109816269B (en) | Power distribution network project planning method based on comprehensive benefits of power distribution units | |
| CN110705873A (en) | Novel power distribution network operation state portrait analysis method | |
| CN111628499A (en) | Method for evaluating new energy consumption capability of power distribution network considering multiple risk factors | |
| CN206312210U (en) | A kind of status assessing system of Distribution Network Equipment | |
| CN110826228B (en) | Regional power grid operation quality limit evaluation method | |
| CN110690701A (en) | Analysis method for influence factors of abnormal line loss | |
| CN113469486A (en) | Line negative loss and high loss analysis method | |
| CN112348272A (en) | Power grid investment allocation decision method and system based on comprehensive evaluation of power distribution network | |
| CN110705859A (en) | PCA-self-organizing neural network-based method for evaluating running state of medium and low voltage distribution network | |
| CN113850693A (en) | Distribution network power failure risk comprehensive assessment and early warning method based on multi-source information fusion | |
| CN111626543A (en) | Method and device for processing power related data | |
| CN114862229A (en) | Power quality evaluation method and device, computer equipment and storage medium | |
| CN111967684B (en) | Metering asset active distribution method based on big data analysis | |
| CN112785060A (en) | Lean operation and maintenance level optimization method for power distribution network | |
| CN116187640B (en) | Power distribution network planning method and device based on grid multi-attribute image system | |
| CN115409264A (en) | Power distribution network emergency repair stagnation point position optimization method based on feeder line fault prediction | |
| CN114139847A (en) | Importance evaluation method, device and equipment for intelligent power distribution network construction target | |
| CN110727912B (en) | Method for selecting differential transformation scheme of power secondary equipment | |
| CN112200458B (en) | Power distribution network planning data application method and system | |
| CN114254806A (en) | Power distribution network heavy overload early warning method and device, computer equipment and storage medium |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200505 |