CN114169631A - Oil field power load management and control system based on data analysis - Google Patents
Oil field power load management and control system based on data analysis Download PDFInfo
- Publication number
- CN114169631A CN114169631A CN202111530994.4A CN202111530994A CN114169631A CN 114169631 A CN114169631 A CN 114169631A CN 202111530994 A CN202111530994 A CN 202111530994A CN 114169631 A CN114169631 A CN 114169631A
- Authority
- CN
- China
- Prior art keywords
- data
- signal
- abnormal
- prediction
- value
- 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
- 238000007405 data analysis Methods 0.000 title claims abstract description 13
- 230000002159 abnormal effect Effects 0.000 claims abstract description 157
- 238000004458 analytical method Methods 0.000 claims abstract description 47
- 238000012545 processing Methods 0.000 claims abstract description 42
- 238000000034 method Methods 0.000 claims abstract description 13
- 230000005856 abnormality Effects 0.000 claims abstract description 6
- 230000007613 environmental effect Effects 0.000 claims description 22
- 238000007726 management method Methods 0.000 claims description 19
- 238000010586 diagram Methods 0.000 claims description 10
- 239000011159 matrix material Substances 0.000 claims description 6
- 239000013598 vector Substances 0.000 claims description 6
- 230000002349 favourable effect Effects 0.000 claims description 5
- 101000878595 Arabidopsis thaliana Squalene synthase 1 Proteins 0.000 claims description 3
- 101000713575 Homo sapiens Tubulin beta-3 chain Proteins 0.000 claims description 3
- 102100036790 Tubulin beta-3 chain Human genes 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 3
- 230000006641 stabilisation Effects 0.000 claims description 3
- 238000011105 stabilization Methods 0.000 claims description 3
- 241000274582 Pycnanthus angolensis Species 0.000 claims description 2
- 230000005684 electric field Effects 0.000 claims description 2
- 239000011087 paperboard Substances 0.000 claims description 2
- 238000001514 detection method Methods 0.000 abstract description 4
- 230000004927 fusion Effects 0.000 abstract description 4
- 238000012937 correction Methods 0.000 abstract description 2
- 238000006467 substitution reaction Methods 0.000 description 5
- 238000010606 normalization Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000012795 verification Methods 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
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
-
- 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
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2474—Sequence data queries, e.g. querying versioned data
-
- 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
- G06F16/29—Geographical information databases
-
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
-
- 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
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Databases & Information Systems (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Data Mining & Analysis (AREA)
- Entrepreneurship & Innovation (AREA)
- Quality & Reliability (AREA)
- General Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Primary Health Care (AREA)
- Operations Research (AREA)
- General Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Water Supply & Treatment (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mathematical Physics (AREA)
- Probability & Statistics with Applications (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Public Health (AREA)
- Fuzzy Systems (AREA)
- Agronomy & Crop Science (AREA)
- Animal Husbandry (AREA)
- Marine Sciences & Fisheries (AREA)
- Mining & Mineral Resources (AREA)
- Remote Sensing (AREA)
- Testing And Monitoring For Control Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses the technical field of oilfield power load prediction, which is used for solving the problems that in the existing mode aiming at oilfield power load prediction, correction analysis on abnormal values in load data is omitted, so that the accuracy and precision of oilfield power load prediction results are difficult to ensure, and the management and control work of load prediction on an oilfield power distribution network is greatly influenced, and particularly discloses an oilfield power load management and control system based on data analysis, which comprises a data acquisition unit, an abnormality recognition unit, a repair modeling unit, a data rating unit, an early warning feedback unit and a display terminal; according to the method, the box-line graph method is selected to complete detection and analysis of the abnormal values of the load sequence data, symbolic calibration, formulaic processing and graph comparison are utilized to complete restoration of the abnormal values, and then fusion analysis is conducted on various data signals, so that the accuracy and precision of prediction of the electric load of the oil field are improved, and management and control of the power distribution network of the oil field are promoted.
Description
Technical Field
The invention relates to the technical field of oilfield power load prediction, in particular to an oilfield power load management and control system based on data analysis.
Background
The power load, also called as the power load, is represented as the sum of electric power taken by the power equipment of an electric energy user to a power system at a certain moment, so that the characteristic of power load data of an oil field distribution network is accurately mastered, the accurate prediction of the power load of the oil field is facilitated, and the accurate prediction of the power load is the basis for ensuring the safe operation of the oil field;
however, in the existing mode for predicting the power load of the oil field, the influence of various abnormal values contained in mass load data on the load prediction result is often ignored, so that the accuracy and precision of the power load prediction result of the oil field are difficult to ensure, and the management and control work of the load prediction of the power distribution network of the oil field is greatly influenced; in order to solve the above-mentioned drawbacks, a technical solution is now provided.
Disclosure of Invention
The invention aims to solve the problems that the correction analysis of abnormal values in load data is neglected in the existing mode aiming at the oil field power load prediction, so the accuracy and precision of the oil field power load prediction result are difficult to ensure, and the management and control work of the load prediction of an oil field power distribution network is greatly influenced, the detection analysis of the abnormal values of the oil field power load sequence data is completed by selecting a box line graph method, the recovery of the abnormal values is completed by utilizing symbolic calibration, formulated processing and graph comparison, various data signals are subjected to fusion analysis by adopting the modes of normalization processing, substitution analysis and matrix cross calibration, the accuracy and precision of the oil field power load prediction are improved, the management and control of the oil field power distribution network are promoted, and the economy and safety of the oil field load are improved, and provides an oil field power load management and control system based on data analysis.
The purpose of the invention can be realized by the following technical scheme:
an oil field power load management and control system based on data analysis comprises a data acquisition unit, an abnormality identification unit, a repair modeling unit, a data rating unit, an early warning feedback unit and a display terminal;
the data acquisition unit is used for acquiring power grid data information in the oil field power load in unit time period and sending the power grid data information to the abnormality identification unit;
the data acquisition unit is also used for acquiring multi-index associated data information in the oil field power load in unit time period and sending the multi-index associated data information to the data rating unit;
the abnormal recognition unit analyzes and processes the received power grid data information in unit time period by using box line graph parameters, generates a signal with large abnormal data interference, a signal with basic abnormal data interference and a signal with small abnormal data interference according to the box line graph parameters, sends the signal with basic abnormal data interference to the data rating unit, and sends the signal with large abnormal data interference and the signal with small abnormal data interference to the repair modeling unit;
the restoration modeling unit is used for receiving a signal with large abnormal data interference and a signal with small abnormal data interference, calling an abnormal data sample according to the abnormal data interference to restore the abnormal data sample, generating a prediction feasible signal and a prediction infeasible signal according to the abnormal data sample, and sending the prediction feasible signal and the prediction infeasible signal to the data rating unit;
the data rating unit performs load prediction performance rating analysis processing on the received basic abnormal-free data interference signal, the predicted feasible signal and the predicted infeasible signal, generates a more-accurate-prediction signal, a general-accurate-prediction signal and a fuzzy-prediction signal according to the load prediction performance rating analysis processing, and sends the more-accurate-prediction signal, the general-accurate-prediction signal and the fuzzy-prediction signal to the early warning feedback unit;
the early warning feedback unit is used for carrying out early warning feedback analysis processing on the received prediction accurate signal, the prediction general accurate signal and the prediction fuzzy signal, generating a high-grade prediction signal, a middle-grade prediction signal and a low-grade prediction signal according to the early warning feedback analysis processing, and sending the high-grade prediction signal, the middle-grade prediction signal and the low-grade prediction signal to a display terminal of the oil field control equipment for analysis and explanation.
Further, the power grid data information is used for representing the power utilization load condition of the power grid in the power load management of the oil field, and the power grid data information comprises an active power load sequence and a reactive power load sequence, wherein the active power load sequence is used for representing electric power sequence data required for keeping normal operation of oilfield equipment, namely electric power sequence data for converting electric energy into other forms of energy, and the reactive power load sequence is used for representing electric power sequence data for establishing and maintaining a magnetic field in the electric equipment in the power grid working at the oil field;
the multi-index associated data information is used for representing various index factor information influencing power load change in an oil field power grid, and comprises an environmental data index and a spatial data index, wherein the environmental data index is used for representing the ratio of abnormal environmental parameters to normal environmental parameters in a unit time period, and the spatial data index is used for representing the utilization rate of the power grid in a planning space in the unit time period.
Further, the specific operation steps of the boxplot parameter analysis processing are as follows:
s1: obtaining useful power and useless power in the power grid data information of a unit time period, respectively and randomly extracting a group of data items of useful power load sequences or a group of data items of useless power load sequences, and calibrating the data items into data items
I = { useful power group, useless power group }, n is expressed as a set of useful power load sequences or a set of useless power load sequences
The total number of (2) is represented by time as abscissa and power data as ordinate, and
converting into a rectangular box body form in the box line graph;
s2: according to the formula
And
respectively find the upper quartile
And lower quartile
Respectively drawing an upper quartile line and a lower quartile line in a box diagram square box body in a straight line drawing mode;
s3: according to the formula
To find the median
Drawing a median line in a box diagram square box body in a straight line drawing mode;
s4: according to the formula
And
respectively find the maximum value
And minimum value
Drawing an upper limit boundary and a lower limit boundary in a boxboard square box body in a straight line drawing mode;
s5: calibrating the data values exceeding the upper limit boundary and the lower limit boundary as abnormal points, displaying the abnormal points in a box chart square box body by red delta, and calibrating the abnormal points as abnormal points
；
S6: for abnormal points existing in the box line graph
The number of the abnormal points is summed up to generate the numerical value of the abnormal point
And counting the number of abnormal points
To rated threshold value
Performing comparison analysis, and determining the number of abnormal points
Greater than a rated threshold value
When the maximum value is larger than the maximum value, generating a signal with larger abnormal data interference, and when the maximum value is larger than the maximum value, generating a numerical value at an abnormal point
Less than nominal threshold
When the minimum value is less than the predetermined value, generating a substantially abnormal data interference free signal, when the number of abnormal points is less than the predetermined value
At rated threshold
When the abnormal data interference is less than the preset threshold, an abnormal data interference less signal is generated.
Further, the specific operation steps of the abnormal data sample repairing process are as follows:
SS 1: receiving abnormal constantAccording to the signal with larger interference and the signal with smaller abnormal data interference, the abnormal value sample data of the unit time period is called and calibrated as
And performing authenticity constraint processing on the product according to a formula
Determining a loss value of authenticity of the abnormal value sample data
Wherein, in the step (A),
representing the noise vector input data values in the WGAN model,
representing data values generated by a generator in the WGAN model,
the output of the discriminator in the WGAN model for discriminating the generated data is represented;
SS 2: sample data of acquired and abnormal value
Measuring the most similar sample data, and performing context constraint processing on the sample data according to a formula
To find out the similarity loss constraint value
Wherein, in the step (A),
is a multiplication operation of the elements of the matrix,
the sample data that is an abnormal value is,
similar original sample data;
SS 3: generating a final optimization objective according to steps SS1 and SS2
Wherein, in the step (A),
representing the clutter distribution relationship between the real data,
representing the distribution of noise vectors from between the real data, and performing data reconstruction processing according to the formula
Obtaining a final restored and reconstructed data sample, wherein the final restored and reconstructed data sample consists of a part corresponding to an abnormal value in the generated sample and an available part in the original sample item;
SS 4: and comparing and analyzing the final restored and reconstructed data sample image and the initial original data sample image, if the variation trends of the final restored and reconstructed data sample curve and the initial original data sample curve are basically consistent, generating a prediction feasible signal, otherwise, generating a prediction infeasible signal.
Further, the specific operation steps of the load prediction performance rating analysis processing are as follows:
step 1: receiving a basic abnormal data interference-free signal, a prediction feasible signal and a prediction infeasible signal, calling an environmental data index and a spatial data index in multi-index associated data information according to the basic abnormal data interference-free signal, and calibrating the environmental data index into a standard value
Calibrating the space data indexIs composed of
According to the formula
To find out the associated pre-measured value
Wherein, in the step (A),
are respectively environmental data indexes
And spatial data index
A weight factor coefficient of, and
，
to correct the coefficient, and
the assignment is 1.2613;
step 2: the obtained correlation pre-measurement value
Substituting the corresponding preset threshold value
If the predicted value is correlated
At a preset threshold
When it is within, a load stabilization signal is generated, if a pre-measured value is correlated
At a preset threshold
Otherwise, generating a load fluctuation signal;
step 3: respectively calibrating a basic abnormal data interference-free signal, a prediction feasible signal and a prediction infeasible signal as Z-1, Z-1+ and Z-2, respectively calibrating a load stable signal and a load fluctuation signal as F-1 and F-2, and performing cross analysis on the signals;
step 4: when obtaining
Then generating a more accurate predicted signal, when acquired
Then generating a prediction blur signal, when obtained
Then a predicted generally accurate signal is generated.
Further, the specific operation steps of the early warning feedback analysis processing are as follows:
when a more accurate prediction signal is received, a high-grade prediction signal is generated, a text word is used for 'the basic data is favorable for accurately predicting the condition of the oil field power load', when a general accurate prediction signal is received, a middle-grade prediction signal is generated, a text word is used for 'the basic data is favorable for predicting the accuracy of the condition of the oil field power load', when a fuzzy prediction signal is received, a low-grade prediction signal is generated, and a text word is used for 'the basic data is unfavorable for accurately predicting the condition of the oil field power load'.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, the detection and analysis of the abnormal value of the oil field power load sequence data are completed by selecting a box line graph method, and the abnormal value in the power grid data information is further subjected to signal calibration in a data summation and substitution analysis mode, so that the abnormal value condition existing in the power load sequence is determined, a foundation is further laid for the power load prediction accuracy, and the accuracy of the oil field power load result prediction is promoted;
2. according to the method, the abnormal values in the oil field power load sequence data are repaired through symbolic calibration, formula processing and graph comparison, so that the effectiveness and reliability of the load data are further improved while the abnormal values in the power load sequence are repaired;
3. according to the invention, the collected two types of data are subjected to fusion analysis by means of normalization processing, substitution analysis and matrix cross calibration of multi-index associated data information, so that the accuracy and precision of prediction of the power load of the oil field are improved, meanwhile, the management and control of the power distribution network of the oil field are promoted, and the economy and safety of the load of the oil field are improved.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a general block diagram of the system of the present invention;
fig. 2 is a box diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
as shown in fig. 1, an oil field power load management and control system based on data analysis includes a data acquisition unit, an anomaly identification unit, a repair modeling unit, a data rating unit, an early warning feedback unit, and a display terminal;
the data acquisition unit is used for acquiring power grid data information in the oil field power load in a unit time period and sending the power grid data information to the abnormality identification unit, wherein the power grid data information is used for representing the power utilization load condition of a power grid in the oil field power load management, and the power grid data information comprises an active power load sequence and a reactive power load sequence, the active power load sequence is used for representing electric power sequence data required for keeping normal operation of oil field equipment, namely converting electric energy into electric power sequence data of other forms of energy, the reactive power load sequence is used for representing the exchange of an electric field and a magnetic field in the power grid working in the oil field, and is used for establishing and maintaining the electric power sequence data of the magnetic field in the electric equipment;
the data acquisition unit is further used for acquiring multi-index associated data information in the oil field power load in the unit time period and sending the multi-index associated data information to the data rating unit, wherein the multi-index associated data information is used for representing various index factor information influencing the power load change in an oil field power grid, and the multi-index associated data information comprises an environmental data index and a spatial data index, the environmental data index is used for representing the ratio of abnormal environmental parameters to normal environmental parameters in the unit time period, and it needs to be described that the larger the expression value of the environmental data index is, the smaller the factor influencing the power load change is, and the smaller the interference on the power load prediction analysis is represented;
the space data index is used for representing the utilization rate of the power grid occupying the planning space in a unit time period, and it needs to be explained that the larger the expression value of the space data index is, the smaller the factors influencing the power load change are, and the more optimized the setting of the power grid point is represented;
the abnormal recognition unit analyzes and processes the received power grid data information in unit time period by using box line graph parameters, generates a signal with large abnormal data interference, a signal with basic abnormal data interference and a signal with small abnormal data interference according to the box line graph parameters, sends the signal with basic abnormal data interference to the data rating unit, and sends the signal with large abnormal data interference and the signal with small abnormal data interference to the repair modeling unit;
the restoration modeling unit is used for receiving a signal with large abnormal data interference and a signal with small abnormal data interference, calling an abnormal data sample according to the abnormal data interference to restore the abnormal data sample, generating a prediction feasible signal and a prediction infeasible signal according to the abnormal data sample, and sending the prediction feasible signal and the prediction infeasible signal to the data rating unit;
the data rating unit carries out load prediction performance rating analysis processing on the received basic abnormal-free data interference signal, the prediction feasible signal and the prediction infeasible signal, generates a prediction accurate signal, a prediction general accurate signal and a prediction fuzzy signal according to the load prediction performance rating analysis processing, and sends the prediction accurate signal, the prediction general accurate signal and the prediction fuzzy signal to the early warning feedback unit;
the early warning feedback unit is used for carrying out early warning feedback analysis processing on the received prediction accurate signal, the prediction general accurate signal and the prediction fuzzy signal, generating a high-grade prediction signal, a middle-grade prediction signal and a low-grade prediction signal according to the early warning feedback analysis processing, and sending the high-grade prediction signal, the middle-grade prediction signal and the low-grade prediction signal to a display terminal of the oil field control equipment for analysis and explanation.
Example two:
as shown in fig. 1 and fig. 2, when the anomaly identification unit receives the grid data information of a unit time period, and performs the box diagram parameter analysis processing according to the grid data information, the specific operation steps are as follows:
s1: obtaining useful power and useless power in the power grid data information of a unit time period, respectively and randomly extracting a group of data items of useful power load sequences or a group of data items of useless power load sequences, and calibrating the data items into data items
I = { useful power group, useless power group }, n is expressed as a set of useful power load sequences or a set of useless power load sequences
The total number of (2) is represented by a time interval with time as abscissa and 24 hours as abscissa, a power interval with power data as ordinate and 10 power as ordinate, and
converting into a rectangular box body form in the box line graph;
s2: according to the formula
Respectively find the upper quartile
And lower quartile
Respectively drawing an upper quartile line and a lower quartile line in a box diagram square box body in a straight line drawing mode;
s3: according to the formula
To find the median
Drawing a median line in a box diagram square box body in a straight line drawing mode;
s4: according to the formula
Respectively find the maximum value
And minimum value
Drawing the upper limit boundary and the lower limit boundary in a box diagram square box body in a straight line drawing mode, wherein the maximum value is
Indicating the maximum upper limit value, the minimum value in the non-abnormal range
A minimum lower limit value in a non-abnormal range;
s5: calibrating the data values exceeding the upper limit boundary and the lower limit boundary as abnormal points, displaying the abnormal points in a box chart square box body by red delta, and calibrating the abnormal points as abnormal points
；
S6: for abnormal points existing in the box line graph
The number of the abnormal points is summed up to generate the numerical value of the abnormal point
And counting the number of abnormal points
To rated threshold value
Performing comparison analysis, and determining the number of abnormal points
Greater than a rated threshold value
When the maximum value is larger than the maximum value, generating a signal with larger abnormal data interference, and when the maximum value is larger than the maximum value, generating a numerical value at an abnormal point
Less than nominal threshold
When the minimum value is less than the predetermined value, generating a substantially abnormal data interference free signal, when the number of abnormal points is less than the predetermined value
At rated threshold
If so, generating a signal with less abnormal data interference;
sending a signal basically free from abnormal data interference to a data rating unit, and sending a signal with large abnormal data interference and a signal with small abnormal data interference to a repair modeling unit;
it should be noted that, as shown in fig. 2, a rectangular box represents a range of data, upper and lower vertical lines represent an upper limit of the data and a lower limit of the data, Q1 is a lower quartile, Q3 is an upper quartile, IQR is a median, a maximum value is a value greater than 1.5 times the difference of the upper quartile, a minimum value is a value less than 1.5 times the difference of the lower quartile, and an abnormal point is defined as a value less than the lower limit or greater than the upper limit;
it should be noted that, the boxplot judges the abnormal process of the data, completely depends on the actual data, does not need to assume a data distribution form, truly reflects the original change trend of the data, and the judgment standard is based on the quartile and the quartile distance, so that the abnormal value does not influence the standard, and the identification result is more accurate;
the position and the range of data distribution are reflected through the box line graph, the identification and the processing of abnormal data are realized, a foundation is provided for the prediction of the power load, and the accuracy and the authenticity of the power load prediction are promoted.
Example three:
as shown in fig. 1, when the repair modeling unit receives a signal with large abnormal data interference and a signal with small abnormal data interference, and calls an abnormal data sample to perform abnormal data sample repair processing according to the signal, the specific operation steps are as follows:
SS 1: receiving the signal with larger abnormal data interference and the signal with smaller abnormal data interference, calling the abnormal value sample data of unit time period according to the abnormal value sample data, and calibrating the abnormal value sample data into the abnormal value sample data
And to itPerforming reality constraint processing according to formula
Determining a loss value of authenticity of the abnormal value sample data
Wherein, in the step (A),
representing the noise vector input data values in the WGAN model,
representing data values generated by a generator in the WGAN model,
the output of the discriminator in the WGAN model for discriminating the generated data is represented;
SS 2: sample data of acquired and abnormal value
Measuring the most similar sample data, and performing context constraint processing on the sample data according to a formula
To find out the similarity loss constraint value
Wherein, in the step (A),
is a multiplication operation of the elements of the matrix,
the sample data that is an abnormal value is,
similar to the original sample data, it should be noted thatThe line context constraint processing is to ensure that the verification model can continuously optimize the input z, so that the abnormal value sample data has a consistent context relationship, thereby realizing the accuracy and effectiveness of repairing the abnormal value sample data and ensuring that the repaired data has the characteristic of consistency with the original authentic data;
SS 3: generating a final optimization objective according to steps SS1 and SS2
Wherein, in the step (A),
representing the clutter distribution relationship between the real data,
representing a noise vector
From the distribution between the real data, and performing data reconstruction processing according to the formula
Obtaining a final restored and reconstructed data sample, wherein the final restored and reconstructed data sample consists of a part corresponding to an abnormal value in the generated sample and an available part in the original sample item;
SS 4: and comparing and analyzing the final restored and reconstructed data sample image and the initial original data sample image, if the variation trends of the final restored and reconstructed data sample curve and the initial original data sample curve are basically consistent, generating a prediction feasible signal, otherwise, generating a prediction infeasible signal, and sending both the prediction feasible signal and the prediction infeasible signal to a data rating unit.
Example four:
as shown in fig. 1, when the data rating unit receives a substantially abnormal-free data interference signal, a predicted feasible signal and a predicted infeasible signal, and performs load prediction performance rating analysis processing according to the received signals, the specific operation steps are as follows:
step 1: receiving a basic abnormal data interference-free signal, a prediction feasible signal and a prediction infeasible signal, calling an environmental data index and a spatial data index in multi-index associated data information according to the basic abnormal data interference-free signal, and calibrating the environmental data index into a standard value
Demarcating the spatial data index as
According to the formula
To find out the associated pre-measured value
Wherein, in the step (A),
are respectively environmental data indexes
And spatial data index
A weight factor coefficient of, and
，
to correct the coefficient, and
the assignment is 1.2613;
step 2: the obtained correlation pre-measurement value
Substituting the corresponding preset threshold value
If the predicted value is correlated
At a preset threshold
When it is within, a load stabilization signal is generated, if a pre-measured value is correlated
At a preset threshold
Otherwise, generating a load fluctuation signal;
step 3: respectively calibrating a basic abnormal data interference-free signal, a prediction feasible signal and a prediction infeasible signal as Z-1, Z-1+ and Z-2, respectively calibrating a load stable signal and a load fluctuation signal as F-1 and F-2, and performing cross analysis on the signals;
step 4: when obtaining
Or
Then generating a more accurate predicted signal, when acquired
Then generating a prediction blur signal, when obtained
Generating a general accurate prediction signal, and sending a more accurate prediction signal, a general accurate prediction signal and a fuzzy prediction signal to an early warning feedback unit;
when the early warning feedback unit receives the prediction accurate signal, the general prediction accurate signal and the prediction fuzzy signal, and performs early warning feedback analysis processing according to the received prediction accurate signal, the specific operation steps are as follows:
when a more accurate prediction signal is received, generating a high-grade prediction signal, and accurately predicting the condition of the oil field power load by using a text word ' the basic data is favorable for accurate prediction of the condition of the oil field power load ', when a general accurate prediction signal is received, generating a medium-grade prediction signal, and accurately predicting the condition of the oil field power load by using the text word ' the basic data is general ', and when a fuzzy prediction signal is received, generating a low-grade prediction signal, and accurately predicting the condition of the oil field power load by using the text word ' the basic data is unfavorable for accurate prediction;
and the high-grade prediction signal, the middle-grade prediction signal and the low-grade prediction signal are all sent to a display terminal of the oilfield control equipment for analysis and explanation.
The formulas are obtained by acquiring a large amount of data and performing software simulation, and the coefficients in the formulas are set by the technicians in the field according to actual conditions;
such as the formula:
；
collecting multiple groups of sample data and setting corresponding weight factor coefficient for each group of sample data by the technicians in the field; substituting the set weight factor coefficient and the collected sample data into a formula, forming a linear equation set by any two formulas, screening the calculated coefficients and taking the mean value to obtain the coefficient
Values of 1.0121 and 0.2492, respectively;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and a corresponding weight factor coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relationship between the parameters and the quantized values is not affected.
When the method is used, the power grid data information in the power load of the oil field is collected, the box line graph method is selected to complete the detection and analysis of the abnormal value of the power load sequence data of the oil field, and the abnormal value in the power grid data information is further subjected to signal calibration in a data summation and substitution analysis mode, so that the abnormal value condition existing in the power load sequence is determined, meanwhile, a foundation is further laid for the power load prediction accuracy, and the accuracy of the oil field power load result prediction is promoted;
load sequence data is called according to the content of signal calibration, abnormal data sample repair is carried out on the load sequence data, and a judgment signal for judging whether power load prediction is carried out or not is output through symbolic calibration, formulaic processing and comparison of a curve graph, so that the effectiveness and reliability of the load data are further improved while the repair of abnormal values in the power load sequence is realized;
according to the judgment signal of the power load prediction, multi-index associated data information influencing the power load of the oil field is called, and the collected two types of data are subjected to fusion analysis by means of normalization processing, substitution analysis and matrix cross calibration, so that the accuracy and precision of the power load prediction of the oil field are improved, meanwhile, the management and control of an oil field power distribution network are promoted, and the economy and safety of the oil field load are improved.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (6)
1. An oil field power load management and control system based on data analysis is characterized by comprising a data acquisition unit, an abnormality recognition unit, a repair modeling unit, a data rating unit, an early warning feedback unit and a display terminal;
the data acquisition unit is used for acquiring power grid data information in the oil field power load in unit time period and sending the power grid data information to the abnormality identification unit;
the data acquisition unit is also used for acquiring multi-index associated data information in the oil field power load in unit time period and sending the multi-index associated data information to the data rating unit;
the abnormal recognition unit analyzes and processes the received power grid data information in unit time period by using box line graph parameters, generates a signal with large abnormal data interference, a signal with basic abnormal data interference and a signal with small abnormal data interference according to the box line graph parameters, sends the signal with basic abnormal data interference to the data rating unit, and sends the signal with large abnormal data interference and the signal with small abnormal data interference to the repair modeling unit;
the restoration modeling unit is used for receiving a signal with large abnormal data interference and a signal with small abnormal data interference, calling an abnormal data sample according to the abnormal data interference to restore the abnormal data sample, generating a prediction feasible signal and a prediction infeasible signal according to the abnormal data sample, and sending the prediction feasible signal and the prediction infeasible signal to the data rating unit;
the data rating unit performs load prediction performance rating analysis processing on the received basic abnormal-free data interference signal, the predicted feasible signal and the predicted infeasible signal, generates a more-accurate-prediction signal, a general-accurate-prediction signal and a fuzzy-prediction signal according to the load prediction performance rating analysis processing, and sends the more-accurate-prediction signal, the general-accurate-prediction signal and the fuzzy-prediction signal to the early warning feedback unit;
the early warning feedback unit is used for carrying out early warning feedback analysis processing on the received prediction accurate signal, the prediction general accurate signal and the prediction fuzzy signal, generating a high-grade prediction signal, a middle-grade prediction signal and a low-grade prediction signal according to the early warning feedback analysis processing, and sending the high-grade prediction signal, the middle-grade prediction signal and the low-grade prediction signal to a display terminal of the oil field control equipment for analysis and explanation.
2. The oilfield power load management and control system based on data analysis according to claim 1, wherein the grid data information is used for representing the power load situation of the power grid in oilfield power load management, and the grid data information includes an active power load sequence and a reactive power load sequence, wherein the active power load sequence is used for representing electric power sequence data required for keeping normal operation of oilfield equipment, namely converting electric energy into electric power sequence data of other forms of energy, and the reactive power load sequence is used for representing the exchange of electric fields and magnetic fields in circuits in the power grid in oilfield operation and is used for establishing and maintaining the electric power sequence data of the magnetic fields in the electric equipment;
the multi-index associated data information is used for representing various index factor information influencing power load change in an oil field power grid, and comprises an environmental data index and a spatial data index, wherein the environmental data index is used for representing the ratio of abnormal environmental parameters to normal environmental parameters in a unit time period, and the spatial data index is used for representing the utilization rate of the power grid in a planning space in the unit time period.
3. The oil field power load management and control system based on data analysis according to claim 1, wherein the specific operation steps of the boxplot parameter analysis processing are as follows:
s1: obtaining useful power and useless power in the power grid data information of a unit time period, respectively and randomly extracting a group of data items of useful power load sequences or a group of data items of useless power load sequences, and calibrating the data items into data items
I = { useful power group, useless power group }, n is expressed as a set of useful power load sequences or a set of useless power load sequences
The total number of (2) is represented by time as abscissa and power data as ordinate, and
is converted into a boxThe form of a rectangular box in the line drawing;
s2: according to the formula
Respectively find the upper quartile
And lower quartile
Respectively drawing an upper quartile line and a lower quartile line in a box diagram square box body in a straight line drawing mode;
s3: according to the formula
To find the median
Drawing a median line in a box diagram square box body in a straight line drawing mode;
s4: according to the formula
Respectively find the maximum value
And minimum value
Drawing an upper limit boundary and a lower limit boundary in a boxboard square box body in a straight line drawing mode;
s5: calibrating the data values exceeding the upper limit boundary and the lower limit boundary as abnormal points, displaying the abnormal points in a box chart square box body by red delta, and calibrating the abnormal points as abnormal points
；
S6: for abnormal points existing in the box line graph
The number of the abnormal points is summed up to generate the numerical value of the abnormal point
And counting the number of abnormal points
To rated threshold value
Performing comparison analysis, and determining the number of abnormal points
Greater than a rated threshold value
When the maximum value is larger than the maximum value, generating a signal with larger abnormal data interference, and when the maximum value is larger than the maximum value, generating a numerical value at an abnormal point
Less than nominal threshold
When the minimum value is less than the predetermined value, generating a substantially abnormal data interference free signal, when the number of abnormal points is less than the predetermined value
At rated threshold
When the abnormal data interference is less than the preset threshold, an abnormal data interference less signal is generated.
4. The oil field power load management and control system based on data analysis according to claim 1, wherein the specific operation steps of the abnormal data sample restoration processing are as follows:
SS 1: receiving a signal with large abnormal data interference and a signal with small abnormal data interference, calling abnormal value sample data of a unit time period according to the abnormal value sample data, calibrating the abnormal value sample data into the abnormal value sample data, performing authenticity constraint processing on the abnormal value sample data, and performing authenticity constraint processing according to a formula
Determining a loss value of authenticity of the abnormal value sample data
Wherein, in the step (A),
representing the noise vector input data values in the WGAN model,
representing data values generated by a generator in the WGAN model,
the output of the discriminator in the WGAN model for discriminating the generated data is represented;
SS 2: sample data of acquired and abnormal value
Measuring the most similar sample data, and performing context constraint processing on the sample data according to a formula
To find out the similarity loss constraint value
Wherein, in the step (A),
is a multiplication operation of the elements of the matrix,
the sample data that is an abnormal value is,
similar original sample data;
SS 3: generating a final optimization objective according to steps SS1 and SS2
Wherein, in the step (A),
representing the clutter distribution relationship between the real data,
representing a noise vector
From the distribution between the real data, and performing data reconstruction processing according to the formula
Obtaining a final restored and reconstructed data sample, wherein the final restored and reconstructed data sample consists of a part corresponding to an abnormal value in the generated sample and an available part in the original sample item;
SS 4: and comparing and analyzing the final restored and reconstructed data sample image and the initial original data sample image, if the variation trends of the final restored and reconstructed data sample curve and the initial original data sample curve are basically consistent, generating a prediction feasible signal, otherwise, generating a prediction infeasible signal.
5. The oil field power load management and control system based on data analysis according to claim 1, wherein the specific operation steps of the load prediction performance rating analysis processing are as follows:
step 1: receiving a basic abnormal data interference-free signal, a prediction feasible signal and a prediction infeasible signal, calling an environmental data index and a spatial data index in multi-index associated data information according to the basic abnormal data interference-free signal, and calibrating the environmental data index into a standard value
Demarcating the spatial data index as
According to the formula
To find out the associated pre-measured value
Wherein, in the step (A),
are respectively environmental data indexes
And spatial data index
A weight factor coefficient of, and
，
to correct the coefficient, and
the assignment is 1.2613;
step 2: the obtained correlation pre-measurement value
Substituting the corresponding preset threshold value
If the predicted value is correlated
At a preset threshold
When it is within, a load stabilization signal is generated, if a pre-measured value is correlated
At a preset threshold
Otherwise, generating a load fluctuation signal;
step 3: respectively calibrating a basic abnormal data interference-free signal, a prediction feasible signal and a prediction infeasible signal as Z-1, Z-1+ and Z-2, respectively calibrating a load stable signal and a load fluctuation signal as F-1 and F-2, and performing cross analysis on the signals;
step 4: when obtaining
Then generating a more accurate predicted signal, when acquired
Then generating a prediction blur signal, when obtained
Then a predicted generally accurate signal is generated.
6. The oil field power load management and control system based on data analysis according to claim 1, wherein the specific operation steps of the early warning feedback analysis processing are as follows:
when a more accurate prediction signal is received, a high-grade prediction signal is generated, a text word is used for 'the basic data is favorable for accurately predicting the condition of the oil field power load', when a general accurate prediction signal is received, a middle-grade prediction signal is generated, a text word is used for 'the basic data is favorable for predicting the accuracy of the condition of the oil field power load', when a fuzzy prediction signal is received, a low-grade prediction signal is generated, and a text word is used for 'the basic data is unfavorable for accurately predicting the condition of the oil field power load'.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111530994.4A CN114169631B (en) | 2021-12-15 | 2021-12-15 | Oil field power load management and control system based on data analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111530994.4A CN114169631B (en) | 2021-12-15 | 2021-12-15 | Oil field power load management and control system based on data analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114169631A true CN114169631A (en) | 2022-03-11 |
| CN114169631B CN114169631B (en) | 2022-10-25 |
Family
ID=80486575
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111530994.4A Active CN114169631B (en) | 2021-12-15 | 2021-12-15 | Oil field power load management and control system based on data analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114169631B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114817865A (en) * | 2022-06-28 | 2022-07-29 | 深圳市傲立电子有限公司 | Big data-based operation fault prediction system for radio frequency power amplifier |
| CN115062653A (en) * | 2022-06-09 | 2022-09-16 | 山东龙源电力工程有限公司 | Analysis maintenance system based on steam turbine of thermal power plant |
| CN117216490A (en) * | 2023-11-08 | 2023-12-12 | 中国铁道科学研究院集团有限公司电子计算技术研究所 | Intelligent big data acquisition system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106709816A (en) * | 2016-11-29 | 2017-05-24 | 国网陕西省电力公司电力科学研究院 | Power load abnormal data recognition and modification method based on nonparametric regression analysis |
| WO2020027207A1 (en) * | 2018-08-03 | 2020-02-06 | パナソニックＩｐマネジメント株式会社 | Abnormality detecting method, information processing device, and abnormality detecting system |
| CN110874381A (en) * | 2019-10-30 | 2020-03-10 | 西安交通大学 | User side load data abnormal value identification method based on space density clustering |
| CN111046913A (en) * | 2019-11-18 | 2020-04-21 | 杭州海兴电力科技股份有限公司 | Load abnormal value identification method |
-
2021
- 2021-12-15 CN CN202111530994.4A patent/CN114169631B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106709816A (en) * | 2016-11-29 | 2017-05-24 | 国网陕西省电力公司电力科学研究院 | Power load abnormal data recognition and modification method based on nonparametric regression analysis |
| WO2020027207A1 (en) * | 2018-08-03 | 2020-02-06 | パナソニックＩｐマネジメント株式会社 | Abnormality detecting method, information processing device, and abnormality detecting system |
| CN110874381A (en) * | 2019-10-30 | 2020-03-10 | 西安交通大学 | User side load data abnormal value identification method based on space density clustering |
| CN111046913A (en) * | 2019-11-18 | 2020-04-21 | 杭州海兴电力科技股份有限公司 | Load abnormal value identification method |
Non-Patent Citations (2)
| Title |
|---|
| 任丽娜等: "异常电力负荷数据的辨识方法研究", 《水力发电》 * |
| 赵天辉等: "基于非参数回归分析的工业负荷异常值识别与修正方法", 《电力***自动化》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115062653A (en) * | 2022-06-09 | 2022-09-16 | 山东龙源电力工程有限公司 | Analysis maintenance system based on steam turbine of thermal power plant |
| CN115062653B (en) * | 2022-06-09 | 2023-04-07 | 山东龙源电力工程有限公司 | Analysis maintenance system based on steam turbine of thermal power plant |
| CN114817865A (en) * | 2022-06-28 | 2022-07-29 | 深圳市傲立电子有限公司 | Big data-based operation fault prediction system for radio frequency power amplifier |
| CN117216490A (en) * | 2023-11-08 | 2023-12-12 | 中国铁道科学研究院集团有限公司电子计算技术研究所 | Intelligent big data acquisition system |
| CN117216490B (en) * | 2023-11-08 | 2024-01-19 | 中国铁道科学研究院集团有限公司电子计算技术研究所 | Intelligent big data acquisition system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114169631B (en) | 2022-10-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114169631B (en) | Oil field power load management and control system based on data analysis | |
| Worden et al. | Novelty detection in a changing environment: regression and interpolation approaches | |
| CN108549908B (en) | Chemical process fault detection method based on multi-sampling probability kernel principal component model | |
| CN113239132B (en) | Online out-of-tolerance identification method for voltage transformer | |
| CN116125361B (en) | Voltage transformer error evaluation method, system, electronic equipment and storage medium | |
| CN109389325B (en) | Method for evaluating state of electronic transformer of transformer substation based on wavelet neural network | |
| CN113821938B (en) | Short-term prediction method and device for metering error state of mutual inductor | |
| CN114626413A (en) | Structural modal parameter identification method and device based on random subspace algorithm | |
| CN111340367B (en) | Oil-shortage equipment state evaluation method and system based on multi-dimensional relevance data analysis | |
| CN113987294A (en) | CVT (continuously variable transmission) online fault diagnosis method based on genetic optimization GRU (generalized regression Unit) neural network | |
| CN113325357A (en) | Voltage transformer error evaluation method and system based on output time series difference | |
| CN113935535A (en) | Principal component analysis method for medium-and-long-term prediction model | |
| CN113657622A (en) | Method, device, terminal and storage medium for fusing multidimensional state data of electrical equipment | |
| CN114740303A (en) | Fault monitoring system of wireless passive high-voltage switch cabinet | |
| CN114819777A (en) | Enterprise sales business analysis and management system based on digital twin technology | |
| CN115616470B (en) | Method, system, equipment and medium for predicting metering error state of current transformer | |
| CN116957534A (en) | Method for predicting replacement number of intelligent electric meter | |
| CN112000831A (en) | Abnormal data identification optimization method based on transformer substation graph transformation | |
| CN111612149A (en) | Main network line state detection method, system and medium based on decision tree | |
| CN115951292A (en) | Electronic voltage transformer error state online evaluation method and device | |
| CN114118587B (en) | Power quality assessment method and system, equipment and storage medium of distributed photovoltaic | |
| CN115685045A (en) | Online evaluation method for voltage transformer | |
| EP2693388A1 (en) | Risk-profile generation device | |
| CN108053093A (en) | A kind of k- neighbour's method for diagnosing faults based on the conversion of average influence Value Data | |
| CN112098066A (en) | High-voltage shunt reactor fault diagnosis method and system based on gate control circulation unit |
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 | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20221012 Address after: 257000 No. two, 271 North Road, Shandong, Dongying Applicant after: Shandong Institute of petroleum and chemical engineering Address before: 257000 No. two, 271 North Road, Shandong, Dongying Applicant before: Shengli College China University of Petroleum |
|
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20230719 Address after: Room 4F401-06, Building 1, No. 800 Naxian Road, China (Shanghai) Pilot Free Trade Zone, Pudong New Area, Shanghai, 201210 Patentee after: Shanghai Bangding Smart Technology Co.,Ltd. Address before: 257000 No. two, 271 North Road, Shandong, Dongying Patentee before: Shandong Institute of petroleum and chemical engineering |