CN116089782A - Multi-guide matched power distribution network refined line loss data system and application thereof - Google Patents
Multi-guide matched power distribution network refined line loss data system and application thereof Download PDFInfo
- Publication number
- CN116089782A CN116089782A CN202211474721.7A CN202211474721A CN116089782A CN 116089782 A CN116089782 A CN 116089782A CN 202211474721 A CN202211474721 A CN 202211474721A CN 116089782 A CN116089782 A CN 116089782A
- Authority
- CN
- China
- Prior art keywords
- data
- distribution network
- power distribution
- matrix
- 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
- 239000011159 matrix material Substances 0.000 claims abstract description 101
- 238000000034 method Methods 0.000 claims abstract description 44
- 238000012545 processing Methods 0.000 claims abstract description 36
- 230000005540 biological transmission Effects 0.000 claims abstract description 8
- 239000013598 vector Substances 0.000 claims description 46
- 230000008569 process Effects 0.000 claims description 21
- 230000002159 abnormal effect Effects 0.000 claims description 18
- 238000010276 construction Methods 0.000 claims description 12
- 238000005070 sampling Methods 0.000 claims description 7
- 238000011835 investigation Methods 0.000 claims description 6
- 238000011161 development Methods 0.000 claims description 4
- 238000005065 mining Methods 0.000 claims description 4
- 230000008014 freezing Effects 0.000 claims description 3
- 238000007710 freezing Methods 0.000 claims description 3
- 230000014509 gene expression Effects 0.000 claims description 3
- 238000003672 processing method Methods 0.000 abstract description 7
- 238000011160 research Methods 0.000 description 9
- 238000004458 analytical method Methods 0.000 description 7
- 230000008859 change Effects 0.000 description 6
- 230000004044 response Effects 0.000 description 5
- 230000005856 abnormality Effects 0.000 description 4
- 238000012512 characterization method Methods 0.000 description 4
- 238000004891 communication Methods 0.000 description 4
- 238000013075 data extraction Methods 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
- 238000012935 Averaging Methods 0.000 description 2
- 238000011217 control strategy Methods 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000013499 data model Methods 0.000 description 2
- 230000007812 deficiency Effects 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 241000208125 Nicotiana Species 0.000 description 1
- 235000002637 Nicotiana tabacum Nutrition 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000029087 digestion Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011065 in-situ storage Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- 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
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- 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)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Mathematical Physics (AREA)
- Pure & Applied Mathematics (AREA)
- Computational Mathematics (AREA)
- Health & Medical Sciences (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Data Mining & Analysis (AREA)
- Entrepreneurship & Innovation (AREA)
- Quality & Reliability (AREA)
- Software Systems (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Algebra (AREA)
- Operations Research (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Educational Administration (AREA)
- Databases & Information Systems (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to the technical field of intelligent power grids, in particular to a power distribution network line loss database system based on multi-direction matching and a power distribution network refined line loss space-time comprehensive processing method based on the data system; the power distribution network refined line loss data system based on data pattern model multi-direction matching comprises a space-direction covariance matrix correlation data subsystem; the data system also includes a time-oriented variation determinant and correlation data subsystem, and/or an extended data subsystem having data array configuration compatibility and data port transmission compatibility with the two subsystems. The data system can be used for carrying out in-process index early warning on the line loss data event of the power grid, and realizing in-process, real-time, single-change and index fine processing on the line loss data of the power distribution network.
Description
Technical Field
The invention relates to the technical field of intelligent power grids, in particular to a comprehensive power distribution network line loss database system based on multi-guide matching and power distribution network refined line loss space-time comprehensive processing based on the data system.
Background
The power distribution network in China is gradually huge in scale, power equipment is gradually increased, and the line loss of electric energy is generated in various links of power transmission, power transformation, power distribution and electricity selling and serves as an important comprehensive economic index of an electric power enterprise, so that the line loss rate can reflect the economical efficiency of the electric power enterprise and the profitability of the enterprise. By continuously collecting and intelligently analyzing the line loss data, the reference of fault points and fault probability can be provided.
The overall line loss rate of the national grid company is at a medium level worldwide. Under the condition that the power supply, the electricity sales and the power load of the national power grid company are greatly increased, various powerful measures are taken to strive to reduce the power grid loss and improve the operation efficiency, so that the power grid line loss management level is obviously improved. However, when the power distribution network is greatly developed, the loss of the power transmission line of the power grid is continuously increased, and the research of analyzing and positioning the line loss of the power distribution network is particularly important. Meanwhile, the informatization requirement of the intelligent power grid puts forward more refined requirements on the power grid line loss optimization.
At present, scientific research institutions such as electric power science research institute combined with Hubei province power grid combination and Wuhan university develop a multi-dimensional fine data analysis method for distribution network line loss based on line topology and data covariance analysis, fine speech analysis and processing are carried out on the line and topology of distribution network line loss data in space dimension, data tracing and investigation of line loss abnormal events can be realized in a data model and subsequent practical application, and the whole working system almost realizes complete automation, data and intelligence and shows good application and popularization prospects. However, the bureau of the power grid node data is still at a primary level, for example, operations such as averaging short-time data into daily data and the like lose real-time and process data information of power distribution network work, and for example, construction of a data system and guidance of a data processing method are used for post abnormal node investigation, so that abnormal alarm in a situation cannot be realized. For this reason, we have conducted special research and development in association with the related scientific research units of the university of Lanzhou and the like, and the invention has been created.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a power distribution network refined line loss data system based on data pattern model multi-guide matching and application thereof, which can be applied to in-process index early warning of power grid line loss data events and has good expansion compatibility.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows.
The power distribution network refined line loss data system based on data pattern model multi-direction matching comprises a space-direction covariance matrix correlation data subsystem; the data system also includes a time-oriented variation determinant and correlation data subsystem, and/or an extended data subsystem having data array configuration compatibility and data port transmission compatibility with the two subsystems.
As a preferable technical scheme of the invention, the main body of the space-oriented covariance matrix correlation data subsystem points to post-oriented datamation tracing and investigation and is used for the wiring and topological fine processing of the line loss data of the power distribution network;
as a preferable technical scheme of the invention, the time-oriented variation determinant and correlation data subsystem main body is directed to the data index mining and early warning of in-process oriented data, and is used for the process, real-time, single-variant quantization and index fine processing of the line loss data of the power distribution network
As a preferable technical scheme of the invention, the expansion data subsystem performs subsequent development and data loading on demand by taking functions as guidance on the basis of data configuration and data port compatibility.
As a preferred embodiment of the invention, the data configuration of the time-oriented mutation determinant and the correlation data subsystem is set as a sum of the following data modules according to the logic process of the data processing:
the zero-order basic acquisition data module stores data information of the power distribution network line nodes, and the data module sets a metadata parameter k, wherein the value of the k corresponds to the number of data bits of the acquired data information of the power distribution network line nodes, so that the selected data information of the power distribution network line nodes in the zero-order basic acquisition data module is stored in a data form of a k-dimensional vector;
the first-order single-quadrant level difference data module is provided with a binary data parameter value (t-i), first, the data module constructs level difference data between two groups of k-dimensional vectors with the adjacency of i in a zero-order basic acquisition data module based on the data parameter value t, wherein the parameter value t corresponds to the sampling frequency reciprocal of the power distribution network line node data information in the zero-order basic acquisition data module, and the parameter value i corresponds to the interval numerical value between two groups of k-dimensional vectors in the zero-order basic acquisition data module for constructing the level difference data; next, the data module performs absolute value processing on the level difference vector constructed according to the binary data parameter (t-i), which corresponds to the level difference vector in all k dimensions being 2 k A first quadrant of the spatial coordinate system, constituting the necessary data phenotype and the simplified data phenotype for the subsequent determinant and data processing;
the data module constructs the level difference vector in the first-order single-quadrant level difference data module into a level difference matrix; for a first-order single-quadrant level difference database with equivalent (t-i) parameter value marks constructed in a first-order single-quadrant level difference data module, the database actually comprises (t-i ') group k-dimensional vector data, wherein the numerical value of (t-i ') is directly calibrated by specific selection of a binary data parameter value (t-i), the binary data parameter value (t-i) is sequentially constructed into a second-order level difference matrix according to the sequence of the t parameter value, and the matrix is correspondingly [ (t-i ') x k ] order matrix;
the second-order level difference variation determinant and the data module, and the data construction process of the data module is as follows:
first, in the second-order difference matrix data block, [ (t-i')=k]The correlation of the time matrix determinant on the running process data of the power distribution network is used for constructing a transposed matrix of a second level difference matrix, and the data configuration of the second level difference matrix E is fixed to be [ (t-i') ×k)]Order, then its transposed matrix E T The data configuration of (C) is fixed as [ kX (t-i')]A step;
second, two sets of independent vector inner product square sum matrices are constructed: (1) the second order difference matrix E is multiplied by the transposed matrix E T I.e.
Obtaining a square sum matrix [ EB [(t-i′)×(t-i′)] ]The method comprises the steps of carrying out a first treatment on the surface of the (2) Second order level difference matrix E T Multiplying by its transposed matrix E, i.e.)>
Obtaining a square sum matrix [ EA ] [k×k] ]；
Third, the two groups of vector inner product square sum row matrixes are both in a square matrix data configuration, namely a matrix [ EB ] [(t-i′)×(t-i′)] ]Matrix [ EA ] [k×k] ]The Schmitt orthogonalization data process can correspond determinant of two square matrixes to the volume of a multidimensional body surrounded by a k-dimensional vector group or a t-i 'dimensional vector group in a linear dimensional space, the k-dimensional vector group is directly corresponding to originally called or collected node operation data of the power distribution network, the node operation data has higher fidelity representation on the operation state of the power distribution network compared with the t-i' vector, and then the matrix [ EA ] is obtained [k×k] ]Is obtained as a main index, and [ EB [(t-i′)×(t-i′)] ]The determinant value of (2) is obtained and then is shown as an auxiliary index.
In the zero-order basic acquisition data module, the acquisition path of the node data information of the power distribution network line comprises a calling path, an acquisition path and other paths; directly calling the data of the conventional acquisition of the power grid system; for necessary data which are not acquired or data with insufficient acquisition density of a power grid system, additional acquisition is carried out by adding metering devices on each branch, each section and each contact node of a distribution network line according to the need, the added metering devices are limited to foreign or domestic main stream manufacturer sources which accord with international/national standards, and all metering devices are networked based on a wireless public data network;
as a preferred technical scheme of the present invention, in the zero-order basic data acquisition module, the power distribution network line node data information includes: electric energy freezing data, voltage curve data, current curve data, forward active electric energy indicating value curve data, electric energy power curve data, power factor curve data, electric energy meter time out-of-tolerance data, terminal power-off/power-on data, electric energy meter switch operation data and other data; any one or any combination.
As a preferable technical scheme of the invention, in the first-order single-quadrant level difference data module, different binary data parameter values (t-i) can acquire different first-order single-quadrant level difference databases; the parameter t corresponds to the acquisition frequency setting of the data acquisition hardware in the zero-order basic acquisition data module, and the variation degree is also based on the acquisition frequency settable range of the acquisition hardware; the parameter i is artificially set, and the maximum variation range is (1-t) d /t)，t d Setting the time of day, the time of month or the time of year as required; in the first-order single-quadrant level difference data module, the databases which are in the same structure but different from each other can be marked directly according to the parameter value (t-i);
as a preferred technical scheme of the invention, the data capacity in the second-order level difference matrix data module is correspondingly (t-i '), the maximum value of (t-i ') is determined by the product of the respective maximum values of t and i, the actual value of (t-i ') is manually selected, and generally, after the value of t based on acquisition hardware is determined, the value range of i is limited to (1-5), and the smaller the value means the higher the fineness of the whole data system; if (i=1, 2) is adopted to construct the main data processing system with highest precision, and (i=3, 4, 5) is adopted as the auxiliary data processing system, at the moment, (t-i') =5; or under the condition of massive basic acquisition data, directly adopting (i=1) to construct a high-precision data processing system, wherein (t-i') =1.
As a preferable technical scheme of the invention, the main index and the auxiliary index are respectively and independently compared with a set threshold value, and when any index is higher than the threshold value, the possibility of abnormality of the corresponding node of the power distribution network is judged;
as a preferable technical scheme of the invention, the main index and the auxiliary index are respectively and independently compared with a set threshold value, and when all the indexes are higher than the threshold value, the possibility of abnormality of the corresponding node of the power distribution network is judged;
as a preferable technical scheme of the invention, the product of the main index and the auxiliary index is compared with a set threshold value, and when the product index is higher than the threshold value, the possibility of abnormality of the corresponding node of the power distribution network is judged.
As a preferable technical scheme of the invention, the threshold is manually specified based on specific data types and numerical expressions of the power distribution network line node data information selected in the zero-order basic acquisition data module;
as a preferable technical scheme of the invention, the threshold is constructed based on all second-order level difference matrixes and variation determinant values thereof in the data system, all the variation determinant values are constructed into a numerical value set, the obtained numerical value set is cut according to percentages, and cut points of the set are used as comparison threshold.
The beneficial effects of adopting above-mentioned technical scheme to produce lie in:
the time-oriented variation determinant square and correlation data subsystem constructed by the invention can be applied to the mining and early warning of data indexes oriented in the past, and is used for the procedural, real-time, single-variation quantification and index fine processing of the line loss data of the power distribution network; meanwhile, the refined data processing system constructed by the invention can also meet the requirement of more follow-up intelligent power grid data functions, so that foundation is laid for the on-demand development of the follow-up functions based on the compatibility of data configuration.
The invention has the core technical bottleneck that after a second-order difference matrix is constructed, the linear correlation of each k-dimensional vector in the matrix can be used as an index concerned by people to characterize the abnormal probability of grid node operation, but the linear correlation of the k-dimensional vector in the matrix can be calculated mathematically (absolute value) through the determinant of the matrix only when the matrix is the matrix, and the second-order difference matrix is not the matrix in practical application, so that the determinant value of the second-order difference matrix cannot be calculated. Therefore, the invention obtains the variance square matrix EA and EB capable of maintaining data relativity by carrying out inner integration operation (the main diagonal is represented as the square sum of each k-dimensional vector in the matrix) on the second-order difference matrix and the transposed matrix thereof, the maintenance of the data relativity is basically based on the equivalence of the square matrix determinant value and the multidimensional volume enclosed by the k-dimensional vector in the second-order difference matrix after Schmitt orthogonalization, the variance processing of non-square matrix data is realized by the data construction and data processing process, and finally, the numerical index capable of representing the abnormal operation probability of the power distribution network node is obtained.
Detailed Description
In the following description of embodiments, for purposes of explanation and not limitation, specific details are set forth, such as particular system architectures, techniques, etc. in order to provide a thorough understanding of the embodiments of the application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail. It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It should also be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]". In addition, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance. Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
Embodiment 1, acquisition of Power distribution network line node data information
Theoretically, the data of the conventional collection of the existing power grid system is directly called; however, in practice, the data call usually has the problems of acquisition density, irregular data form and the like of the original data, and especially has the problem of acquisition density; the solution is as follows: for the acquisition density, the acquisition density is adjusted by coordinating with a data original acquisition unit, or the data precision requirement is properly reduced, the data precision requirement of the whole data system of the research is set to be matched with the actual data acquisition density, or a data calling path is changed into a data acquisition path; for the problem of nonstandard data form, the coding conversion is only needed according to the data format requirement of the data system.
Thus, construction of a data acquisition path is often necessary; the necessary data which is not acquired or the data which is not acquired in the acquisition density is additionally acquired by adding metering devices on each branch, section and contact node of the distribution network line according to the need, the additionally installed data acquisition and metering devices are limited to foreign or domestic main stream manufacturer sources which accord with international/national standards, such as eastern Weiston electric Limited company of the tobacco table in Shandong province, and all metering devices are networked based on a wireless public data network.
The method is basically consistent with a data covariance analysis system developed by Hubei electric college and university of Wuhan in terms of selection of line node data information, and comprises electric energy freezing data, voltage curve data, current curve data, forward active electric energy indicating value curve data, electric energy power curve data, power factor curve data, electric energy meter time out-of-tolerance data, terminal power-off/power-on data, electric energy meter switch operation data and other data; the latter three are necessary or can only be replaced by other information with equivalent data titers; the rest data can be appropriately increased or decreased and replaced according to analysis requirements. And acquiring the related information with highest precision based on the hardware attribute of the calling channel and the data acquisition terminal in terms of data acquisition density.
Embodiment 2, spatially directed covariance matrix correlation database System and data processing method
The method for analyzing the multi-dimensional fine data of the distribution network line loss based on the line topology and the data covariance analysis is developed by the electric power science institute in combination with the power grid in Hubei province, the scientific research units of the university of Wuhan and the like, the fine speech analysis and the processing are carried out on the line and the topology of the line loss data of the distribution network in the space dimension, the datamation tracing and the investigation of the line loss abnormal event can be realized in the data model and the subsequent practical application, the whole working system almost realizes complete automation, datamation and intelligence, and good application and popularization prospects are presented.
The present study embeds this technology for compatibility. However, the research result is still in a primary level for the bureau of the power grid node data, for example, the operations of averaging short-time data into daily data and the like can lead to the loss of real-time and process data information of the power distribution network work, and for example, the construction of a data system and the guidance of a data processing method are used for the subsequent abnormal node investigation, so that the abnormal alarm in the event can not be realized. For this purpose, a database system and a data processing method based on a variation determinant and correlation are developed, and are systematically combined with the above-mentioned spatially-oriented covariance matrix correlation database system and data processing method.
Example 3 database System of time-oriented variation determinant Party and correlation and corresponding data processing method-core technical overview
The time-oriented variation determinant and correlation data system main body point to the data index mining and early warning of the in-process oriented data, and is used for the process, real-time, single-variation and index fine processing of the line loss data of the power distribution network. The core of constructing the database system and the corresponding data processing method is that:
(1) and the time-oriented line loss related data extraction of the original power distribution network node data is realized through the construction of the data structure in the database and the construction of the logic and operation relation among the databases.
(2) And (3) carrying out characterization of the exportable quantized data on the extracted time-oriented line loss related data through construction of a data processing model. In the aspect of time-oriented line loss related data extraction, linear correlation analysis is carried out on the level difference data (the level difference data is shown as a level difference vector when the power distribution network node data is constructed as a vector) obtained after the original source power distribution network node data is cut based on a time function (corresponding to the acquisition density), so that a linear data array for representing the abnormal statistical probability of power grid operation can be obtained; the characterization of the output quantized data is performed on the time-oriented line loss related data, and since the advanced data array of the non-square matrix cannot calculate the determinant value for characterizing the linear correlation, the extracted time-oriented line loss related data is usually in a non-square matrix configuration mxn and m is not equal to n (which is caused by the non-stationarity of the original collected data of the power distribution network nodes and the unequal cutting density of the time function during the data processing, unless the accuracy of the data processing system is reduced and many data analysis functions are abandoned, the extracted time-oriented line loss related data is difficult to adjust into square matrix), so that a characterization method for the output quantized data applicable to any mxn time-oriented line loss related data array needs to be constructed.
Embodiment 4 time-oriented line loss-related data extraction of Power distribution network node data
Zero order basis acquisition database group. And constructing zero-order basic acquisition data based on the data which are called or acquired and normalized by the data format in the embodiment 1, setting a metadata parameter k for the database group, wherein the value of k corresponds to the number of data bits of the acquired power distribution network line node data information, and the selected power distribution network line node data information in the zero-order basic acquisition database group is stored in a data form of a k-dimensional vector.
The method comprises the steps that a first-order single-quadrant level difference database group firstly, level difference data between two groups of k-dimensional vectors with the adjacency of i in a zero-order basic acquisition database group are built on the basis of a data parameter t, wherein the parameter t corresponds to the sampling frequency reciprocal of power distribution network line node data information in the zero-order basic acquisition database group, and the parameter i corresponds to an interval numerical value between two groups of k-dimensional vectors in the zero-order basic acquisition database group for building the level difference data; secondly, the database group carries out absolute value processing on the level difference vector constructed according to the binary data parameter value (t-i), which corresponds to the level difference vector with all the k dimensions being 2 k The first quadrant of the spatial coordinate system constitutes the necessary data phenotype and simplification of the subsequent determinant and data processingData phenotype. In the first-order single-quadrant level difference database group, different binary data parameter values (t-i) can acquire different first-order single-quadrant level difference databases; the parameter t corresponds to the acquisition frequency setting of the data acquisition hardware in the zero-order basic acquisition database group, and the variation degree of the parameter t is also based on the acquisition frequency settable range of the acquisition hardware; the parameter i is artificially set, and the maximum variation range is (1-t) d /t)，t d Setting the time of day, the time of month or the time of year as required; within the first order single quadrant level difference database set, these homogeneous but distinct databases may be marked directly in accordance with the parameter (t-i).
A second-order level difference matrix database group, which constructs level difference vectors in the first-order single-quadrant level difference database group as level difference matrices; for a first-order single-quadrant level difference database with equivalent (t-i) parameter value marks constructed in a first-order single-quadrant level difference database group, the first-order single-quadrant level difference database actually comprises (t-i ') group k-dimensional vector data, wherein the numerical value of (t-i ') is directly calibrated by specific selection of a binary data parameter value (t-i), and the numerical value is sequentially constructed into a second-order difference matrix according to the sequence of the t parameter value, and the matrix is correspondingly [ (t-i ')xk ] order matrix. The data capacity in the second-order level difference matrix database group corresponds to (t-i '), the maximum value of (t-i ') is determined by the product of the respective maximum values of t and i, the actual value of (t-i ') is manually selected, and generally, after the value of t based on acquisition hardware is determined, the value range of i is defined as (1-5), and the smaller value means the higher the fineness of the whole data system; if (i=1, 2) is adopted to construct the main data processing system with highest precision, the (i=3, 4, 50) is adopted as an auxiliary data processing system, and (t-i ') =5 is adopted, or under the condition of acquiring data on a mass basis, the (i=1) is directly adopted to construct the high-precision data processing system, and at the moment (t-i') =1 is adopted.
Example 5 characterization of exportable quantized data of time-oriented line loss related data
In the second-order-difference variation determinant and the database group, the order of one or more groups of data matrices constructed in the second-order-difference-matrix database group is [ (t-i ') ×k ], when [ (t-i') =k ], the determinant of the matrix can be directly solved, as the second-order-difference matrix is obtained by carrying out two-step difference solving on k-dimensional vectors with interval i, the rank of the obtained second-order-difference matrix is 1, the determinant of the matrix is 0, and actually measured data enable the rank of the second-order-difference matrix to be larger than the number of rows or columns of the matrix, the determinant of the matrix is not zero, the absolute value of the matrix determinant is a univariate value directly related to the abnormal data of the distribution network node, the absolute value of the value corresponds to the abnormal statistical probability of the distribution network, and, based on the construction process of the second-order-difference matrix, the univariate value has real-time attribute representing the running process of the distribution network, and under the condition that the hardware sampling frequency is high enough and the data interval is set small enough, namely, the univariate value can be plotted under the condition that the data of the univariate value is small enough; because of the evaluable limitation of matrix determinant, [ (t-i ') =k ] is an inevitable data requirement, which constitutes a great limitation to the application of the data system, k is determined by the data call channel or the data acquisition hardware, and the value of (t-i ') is based on the sampling frequency and the step interval, and in order to satisfy [ (t-i ') =k ], the specific sampling frequency and step interval must be manually selected, so that a great amount of basic data is manually discarded, greatly reducing the titer of the data processing of the system and causing the waste of the data value.
To this end, a second-order difference variation determinant is constructed, and [ (t-i')=k) is replaced by the sum of squares thereof, i.e., the square sum of the second-order difference variation determinant]The matrix determinant is used for characterizing the node data abnormality of the power distribution network; the data construction process comprises the following steps: in the first step, [ (t-i')=k]The correlation of the time matrix determinant on the running process data of the power distribution network is used for constructing a transposed matrix of a second level difference matrix, and the data configuration of the second level difference matrix E is fixed to be [ (t-i') ×k)]Order, then its transposed matrix E T The data configuration of (C) is fixed as [ kX (t-i')]A step; second, two sets of independent vector inner product square sum matrices are constructed: (1) the second order difference matrix E is multiplied by the transposed matrix E T I.e.
Obtaining a prescriptionMatrix array]EB [(t-i′)×(t-i′)] ]The method comprises the steps of carrying out a first treatment on the surface of the (2) Second order level difference matrix E T Multiplying by its transposed matrix E, i.e.
Obtaining a square sum matrix [ EA ] [k×k] ]The method comprises the steps of carrying out a first treatment on the surface of the Third, based on the two groups of vector inner product square sum row matrixes, the matrix data configuration is presented, namely the matrix [ EB ] [(t-i′)×(t-i′)] ]Matrix [ EA ] [k×k] ]Based on the fact that the determinant of two square matrixes can be corresponding to the volume of a multidimensional body surrounded by a k-dimensional vector group or a t-i 'dimensional vector group in a linear dimensional space by a Schmidt orthogonalization data process, the matrix [ EA ] is obtained based on the fact that the k-dimensional vector group directly corresponds to originally called or acquired power distribution network node operation data, and the representation of the power distribution network operation state has higher fidelity than the t-i' vector [k×k] ]Is obtained as a main index, and [ EB [(t-i′)×(t-i′)] ]The determinant value of (2) is obtained and then is shown as an auxiliary index.
Example 6, construction of applicability of subsequent related indicators
For the procedural power grid abnormal probability risk early warning, the output data higher than the threshold value is used as the power grid real-time state with possibly larger risk to carry out system alarm and log record; the threshold value is manually specified based on specific data types and numerical expressions of the node data information of the power distribution network line selected in the zero-order basic acquisition database group; or constructing the threshold based on all second-order level difference matrixes and variation determinant values thereof in the data system, constructing all variation determinant values into a numerical value set, cutting the obtained numerical value set according to percentages, and taking cut points of the set as comparison threshold. For the threshold comparison process, the following data strategies have the optional applicability: (1) the indexes and the auxiliary indexes are respectively independent and compared with a set threshold value, and when any index is higher than the threshold value, the abnormal possibility of the corresponding node of the power distribution network is judged; (2) compared with the set threshold value, the main index and the auxiliary index are respectively independent, and when all the indexes are higher than the threshold value, the abnormal possibility of the corresponding node of the power distribution network is judged; (3) comparing the product of the main index and the auxiliary index with a set threshold value, and judging that the corresponding node of the power distribution network is abnormal when the product index is higher than the threshold value.
Example 7 development of subsequent application demand and Functions
The refined data processing system and the data application system have good expansion compatibility, can be developed based on data array configuration compatibility and data port transmission compatibility, can meet the data function requirements of more follow-up smart power grids, and can be developed and loaded on demand based on data configuration and data port compatibility by taking functions as guidance.
Example 8 preliminary study of the subsequent related line loss Compensation technique
A branch project of the research group also combines the actual conditions of areas such as Gansu Linxia and the like to research a line loss hybrid reactive compensation method, adopts an in-situ random compensation mode in a main body, and hopes that the power factor reaches the national standard through reactive compensation, thereby reducing network loss. The reactive compensation and loss reduction method is researched mainly from the aspects of upgrading and reconstruction of a reactive compensation device and reactive optimization operation of a power distribution network system. Specifically, the reactive power compensation device is upgraded and reformed, a local existing reactive power compensation device is mainly combined with a distributed power supply DG, a DG+SVG mixed reactive power compensation topological structure is provided, a layered coordination control strategy is designed, so that DG and SVG are in coordinated operation, the local consumption of DG and reactive power compensation of a distribution network are realized, the electric energy quality is improved, and the distribution network loss is reduced.
In the topology structure, the hybrid reactive compensation topology structure adopted in the branch project mainly comprises DG, SVG, intelligent phase change switch and three-phase TSC units which are connected in parallel. In the application of a power system, the main function of parallel compensation is to improve the operation characteristics of the power grid by controlling and adjusting the reactive power injected into the power grid, so as to achieve the purposes of improving the system stability, the transmission capacity, the electric energy quality and the like. That is, from the system side, the outputs of all parallel reactive compensation devices are varied with the aim of maintaining or controlling certain parameters of the grid, which means that the basic external control structure determining the functional behaviour of the reactive compensator and the required reference inputs are intrinsically related and vary independently of the form of the reactive compensation generator. Based on this, the intelligent phase change switch, the TSC, the DG and the SVG, and the hybrid reactive compensation formed by some combination of them, can be studied as a whole to investigate the differences of the general control method and the control effect thereof for achieving various control purposes. Hybrid reactive compensation emphasizes coordination, the main purpose being to enable a static compensator with accurate U-I characteristics and fast response to react in time to the dynamic part of the compensation, while other slow compensation devices are employed to cope with the steady state reactive demand in the compensation. Another purpose of coordination is to minimize static losses of the compensation system and the grid. In an actual system, the HSVG structure and the coordination control strategy often depend on the compensation requirement, and the reactive power supporting capability of the surrounding power supply is considered, so that the aims of reducing investment and loss and meeting the compensation requirement are fulfilled as much as possible during design. The coordination control of the hybrid reactive compensation in the project refers to the method that four devices or functions are combined and then are realized on the same device, namely coordination control or unified control among different control channels of the same device in the power system.
In the system principle, for the hybrid reactive compensation system provided by the branch project, when the power system operates in a three-phase unbalanced state, the sampling unit collects current signals on the bus and each branch through the current transformer, the current signals are transmitted to the main controller through the GPRS communication circuit, and the main controller performs three-phase unbalanced analysis by calculating and comparing with a set value so as to judge whether the system is in the three-phase unbalanced state. If the system is in a three-phase unbalanced state, a current value to be converted is calculated, then a corresponding switching signal is sent to a trigger circuit module of a corresponding intelligent phase change switch through a GPRS communication circuit, the trigger circuit module is triggered to work, unbalanced current is transferred from a phase with large current to a phase with small current, if the system can reach the three-phase balanced state only by putting in the intelligent phase change switch, SVG is not required to be put in, and at the moment, the output of the SVG is zero, and the loss is small. If the intelligent phase change switch is put into the system still in the three-phase unbalanced state, the main controller sends switching signals to the SVG through the GPRS communication circuit, so that the intelligent phase change switch is further adjusted, and finally the three-phase balanced state is achieved. The mixed reactive compensation system provided by the branch project aims at the situation of reactive deficiency: when the reactive power is insufficient in the operation of the power system, the TSC unit and the DG equipment installed on the transmission line can be used for compensating, if the system power factor can be compensated only by inputting the TSC unit and the DG equipment, so that the system power factor is not lower than a set target value, SVG does not need to be input, and the output of the SVG is zero at the moment, and the loss is small. If the power factor of the system is still lower than the set value after the TSC unit and the DG are put into the system, the main controller sends switching signals to the SVG through the GPRS communication circuit, so that further compensation is further performed, and the power factor of the system is ensured not to be lower than the set value all the time. The hybrid reactive power compensation system not only can compensate the reactive power deficiency of the power system, but also can solve the problem of three-phase unbalance in the system operation, and improves the utilization rate of DG by connecting the DG into the hybrid reactive power compensation system, thereby solving the problem of on-site digestion.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (9)
1. The power distribution network refined line loss data system based on data pattern model multi-direction matching comprises a space-direction covariance matrix correlation data subsystem; the method is characterized in that: the data system also includes a time-oriented variation determinant and correlation data subsystem, and/or an extended data subsystem having data array configuration compatibility and data port transmission compatibility with the two subsystems.
2. The refined line loss data system of the power distribution network based on data pattern-model multi-guide matching as claimed in claim 1, wherein:
the main body of the space-oriented covariance matrix correlation data subsystem points to post-oriented datamation tracing and investigation and is used for the wiring and topological fine processing of the line loss data of the power distribution network;
the time-oriented variation determinant and correlation data subsystem main body point to the data index mining and early warning of in-process oriented data, and are used for the procedural, real-time, single-variant and index fine processing of the line loss data of the power distribution network;
the expansion data subsystem performs subsequent on-demand development and data loading by taking functions as guidance on the basis of data configuration and data port compatibility.
3. The refined line loss data system of the power distribution network based on data pattern-model multi-guide matching as claimed in claim 1, wherein: according to the logic process of data processing, the data configuration of the time-oriented variation determinant side and the correlation data subsystem is set as a combination of the following data modules:
the zero-order basic acquisition data module stores data information of the power distribution network line nodes, and the data module sets a metadata parameter k, wherein the value of the k corresponds to the number of data bits of the acquired data information of the power distribution network line nodes, so that the selected data information of the power distribution network line nodes in the zero-order basic acquisition data module is stored in a data form of a k-dimensional vector;
the first-order single-quadrant level difference data module is provided with a binary data parameter value (t-i), first, the data module constructs level difference data between two groups of k-dimensional vectors with the adjacency of i in a zero-order basic acquisition data module based on the data parameter value t, wherein the parameter value t corresponds to the sampling frequency reciprocal of the power distribution network line node data information in the zero-order basic acquisition data module, and the parameter value i corresponds to the interval numerical value between two groups of k-dimensional vectors in the zero-order basic acquisition data module for constructing the level difference data; next, the data module performs absolute value processing on the level difference vector constructed according to the binary data parameter (t-i), which corresponds to the level difference vector in all k dimensions being 2 k A first quadrant of the spatial coordinate system, constituting the necessary data phenotype and the simplified data phenotype for the subsequent determinant and data processing;
the data module constructs the level difference vector in the first-order single-quadrant level difference data module into a level difference matrix; for a first-order single-quadrant level difference database with equivalent (t-i) parameter value marks constructed in a first-order single-quadrant level difference data module, the database actually comprises (t-i ') group k-dimensional vector data, wherein the numerical value of (t-i ') is directly calibrated by specific selection of a binary data parameter value (t-i), the binary data parameter value (t-i) is sequentially constructed into a second-order level difference matrix according to the sequence of the t parameter value, and the matrix is correspondingly [ (t-i ') x k ] order matrix;
the second-order level difference variation determinant and the data module, and the data construction process of the data module is as follows:
first, in the second-order difference matrix data block, [ (t-i')=k]The correlation of the time matrix determinant on the running process data of the power distribution network is used for constructing a transposed matrix of a second level difference matrix, and the data configuration of the second level difference matrix E is fixed to be [ (t-i') ×k)]Order, then its transposed matrix E T The data configuration of (C) is fixed as [ kX (t-i')]A step;
second, two sets of independent vector inner product square sum matrices are constructed: (1) the second order difference matrix E is multiplied by the transposed matrix E T I.e.
Obtaining a square sum matrix [ EB [(t-i′)×(t-i′)] ]The method comprises the steps of carrying out a first treatment on the surface of the (2) Second order level difference matrix E T Multiplying by its transposed matrix E, i.e.)>
Obtaining a square sum matrix [ EA ] [k×k] ]；
Third, the two groups of vector inner product square sum row matrixes are both in a square matrix data configuration, namely a matrix [ EB ] [(t-i′)×(t-i′)] ]Matrix [ EA ] [k×k] ]The Schmitt orthogonalization data process can correspond determinant of two square matrixes to the volume of a multidimensional body surrounded by a k-dimensional vector group or a t-i 'dimensional vector group in a linear dimensional space, the k-dimensional vector group is directly corresponding to originally called or collected power distribution network node operation data, the representation of higher fidelity is provided for the power distribution network operation state compared with the t-i' vector, and then a matrix [ EA ] is obtained [k×k] ]The determinant value of (b) is taken as the absolute value of the matrix, and the matrix [ EB ] is obtained [(t-i′)×(t-i′)] ]And takes the absolute value of the determinant value as an auxiliary index.
4. The refined line loss data system of the power distribution network based on data pattern-model multi-guide matching as claimed in claim 1, wherein: in the zero-order basic data acquisition module, the acquisition path of the power distribution network line node data information comprises a calling path, an acquisition path and other paths; directly calling the data of the conventional acquisition of the power grid system; the necessary data which are not collected by the power grid system or the data with insufficient collection density are additionally collected by adding metering devices on each branch, each section and each contact node of the distribution network line according to the requirement, the added metering devices are limited to foreign or domestic main stream manufacturer sources which accord with international/national standards, and all metering devices are networked based on a wireless public data network.
5. The refined line loss data system of the power distribution network based on data pattern-model multi-guide matching as claimed in claim 1, wherein: in the zero-order basic acquisition data module, the distribution network line node data information comprises: electric energy freezing data, voltage curve data, current curve data, forward active electric energy indicating value curve data, electric energy power curve data, power factor curve data, electric energy meter time out-of-tolerance data, terminal power-off/power-on data, electric energy meter switch operation data and other data; any one or any combination.
6. The refined line loss data system of the power distribution network based on data pattern-model multi-guide matching as claimed in claim 1, wherein: in the first-order single-quadrant level difference data module, different binary data parameter values (t-i) can acquire different first-order single-quadrant level difference databases; the parameter t corresponds to the acquisition frequency setting of the data acquisition hardware in the zero-order basic acquisition data module, and the variation degree is also based on the acquisition frequency settable range of the acquisition hardware; the parameter i is artificially set, and the maximum variation range is (1-t) d /t)，t d Setting the time of day, the time of month or the time of year as required; within the first order single quadrant level difference data module, these homogenous but distinct databases may be marked directly in accordance with the parameter (t-i).
7. The refined line loss data system of the power distribution network based on data pattern-model multi-guide matching as claimed in claim 1, wherein: the data capacity in the second-order level difference matrix data module corresponds to (t-i '), the maximum value of (t-i ') is determined by the product of the respective maximum values of t and i, the actual value of (t-i ') is manually selected, and generally, after the value of t based on acquisition hardware is determined, the value range of i is defined as (1-5), and the smaller value means the higher the fineness of the whole data system; if (i=1, 2) is adopted to construct the main data processing system with highest precision, and (i=3, 4, 5) is adopted as the auxiliary data processing system, at the moment, (t-i') =5; or under the condition of massive basic acquisition data, directly adopting (i=1) to construct a high-precision data processing system, wherein (t-i') =1.
8. The refined line loss data system of the power distribution network based on data pattern-model multi-guide matching as claimed in claim 1, wherein:
comparing the main index and the auxiliary index with a set threshold value respectively and independently, and judging that the corresponding node of the power distribution network has abnormal possibility when any index is higher than the threshold value;
or comparing the main index and the auxiliary index with a set threshold value respectively and independently, and judging that the corresponding node of the power distribution network has abnormal possibility when all the indexes are higher than the threshold value;
or comparing the product of the main index and the auxiliary index with a set threshold value, and judging that the abnormal possibility exists in the corresponding node of the power distribution network when the product index is higher than the threshold value.
9. The refined line loss data system of the power distribution network based on data pattern-model multi-guide matching as claimed in claim 1, wherein:
the threshold value is manually specified based on specific data types and numerical expressions of the power distribution network line node data information selected in the zero-order basic acquisition data module;
or the threshold is constructed based on all second order difference matrixes and variation determinant values thereof in the data system, all variation determinant values are constructed into a numerical value set, the obtained numerical value set is cut according to percentages, and cut points of the set are used as comparison threshold.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211474721.7A CN116089782B (en) | 2022-11-23 | 2022-11-23 | Multi-guide matched power distribution network refined line loss data system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211474721.7A CN116089782B (en) | 2022-11-23 | 2022-11-23 | Multi-guide matched power distribution network refined line loss data system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116089782A true CN116089782A (en) | 2023-05-09 |
| CN116089782B CN116089782B (en) | 2023-11-21 |
Family
ID=86207052
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211474721.7A Active CN116089782B (en) | 2022-11-23 | 2022-11-23 | Multi-guide matched power distribution network refined line loss data system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116089782B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112632028A (en) * | 2020-12-04 | 2021-04-09 | 中牟县职业中等专业学校 | Industrial production element optimization method based on multi-dimensional matrix outer product database configuration |
| AU2021106109A4 (en) * | 2021-08-20 | 2021-10-28 | Guangxi University | Evaluation index screening strategy for lean management of power system line loss under big data environment |
| CN113592236A (en) * | 2021-07-01 | 2021-11-02 | 中国电力科学研究院有限公司 | Evaluation method and system for elastic power distribution network index system |
| CN114065875A (en) * | 2021-12-02 | 2022-02-18 | 中国石油大学胜利学院 | Power grid fault identification system based on big data |
| CN114638728A (en) * | 2022-03-25 | 2022-06-17 | 国网河北省电力有限公司 | Big data-based real-time operation monitoring method for power service center |
| CN114911774A (en) * | 2022-05-27 | 2022-08-16 | 国网河北省电力有限公司营销服务中心 | User-oriented power grid service type database and application thereof |
| WO2022227948A1 (en) * | 2021-04-26 | 2022-11-03 | 南京大学 | Coherent and polarization fading suppression method for synchronous monitoring of loss and vibration |
-
2022
- 2022-11-23 CN CN202211474721.7A patent/CN116089782B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112632028A (en) * | 2020-12-04 | 2021-04-09 | 中牟县职业中等专业学校 | Industrial production element optimization method based on multi-dimensional matrix outer product database configuration |
| WO2022227948A1 (en) * | 2021-04-26 | 2022-11-03 | 南京大学 | Coherent and polarization fading suppression method for synchronous monitoring of loss and vibration |
| CN113592236A (en) * | 2021-07-01 | 2021-11-02 | 中国电力科学研究院有限公司 | Evaluation method and system for elastic power distribution network index system |
| AU2021106109A4 (en) * | 2021-08-20 | 2021-10-28 | Guangxi University | Evaluation index screening strategy for lean management of power system line loss under big data environment |
| CN114065875A (en) * | 2021-12-02 | 2022-02-18 | 中国石油大学胜利学院 | Power grid fault identification system based on big data |
| CN114638728A (en) * | 2022-03-25 | 2022-06-17 | 国网河北省电力有限公司 | Big data-based real-time operation monitoring method for power service center |
| CN114911774A (en) * | 2022-05-27 | 2022-08-16 | 国网河北省电力有限公司营销服务中心 | User-oriented power grid service type database and application thereof |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116089782B (en) | 2023-11-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108573350B (en) | Distribution network line loss synchronization calculation and multidimensional analysis method | |
| CN107871202A (en) | A kind of system adequacy evaluation method of meter and multi-voltage grade | |
| Barchi et al. | A photovoltaics-aided interlaced extended Kalman filter for distribution systems state estimation | |
| CN115684809A (en) | User side power consumption test system based on virtual power plant | |
| CN104299051A (en) | Network system used for technical and economic analysis and comment of power grid project | |
| CN113991856B (en) | Micro-energy network multi-adaptability mu PMU optimal point distribution method | |
| CN116089782B (en) | Multi-guide matched power distribution network refined line loss data system | |
| CN114911774A (en) | User-oriented power grid service type database and application thereof | |
| Pan et al. | Study on short-term load forecasting of distributed power system based on wavelet theory | |
| CN111900733A (en) | Backbone net rack optimization method and system based on power supply and demand matching contour line model | |
| CN106992516A (en) | The method that probability air extract is obtained with Density Estimator is simulated based on quasi-Monte Carlo | |
| CN111553080A (en) | Closed-loop identification method for load dynamic equivalent non-mechanism model parameters of power distribution station area | |
| CN109840646A (en) | Voltage monitoring method based on big data processing | |
| CN110544985A (en) | Network source coordination data communication system | |
| Zhou et al. | Calculation method of the line loss rate in low-voltage transformer district based on PCA and k-means clustering and support vector machine | |
| CN112749489B (en) | Power grid model generation method based on splicing technology | |
| CN115526475A (en) | Method for improving voltage quality through multi-stream fusion of intelligent power distribution network | |
| CN111709612B (en) | Power distribution network state estimation method considering mining history data | |
| CN104868489B (en) | A kind of power network energy-storage system is exerted oneself control method and device | |
| CN114139744A (en) | Abnormal photovoltaic group string branch identification method and device, electronic equipment and storage medium | |
| Tianhua et al. | A novel load estimation method in distribution network | |
| CN112149057A (en) | Method for calculating technical line loss based on load curve | |
| CN111767622A (en) | Equivalent method for power system | |
| Gu et al. | Research on fuzzy comprehensive forecasting of large user power unproductive load based on demand response | |
| CN116596199B (en) | Superposition analysis method and system for line loss influence factors of power distribution network |
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 |