CN114048815A - Power grid operation information sensing system and sensing method based on plant side - Google Patents
Power grid operation information sensing system and sensing method based on plant side Download PDFInfo
- Publication number
- CN114048815A CN114048815A CN202111337079.3A CN202111337079A CN114048815A CN 114048815 A CN114048815 A CN 114048815A CN 202111337079 A CN202111337079 A CN 202111337079A CN 114048815 A CN114048815 A CN 114048815A
- Authority
- CN
- China
- Prior art keywords
- operation information
- matrix
- sequence
- power grid
- plant
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000012545 processing Methods 0.000 claims abstract description 71
- 238000003745 diagnosis Methods 0.000 claims abstract description 61
- 230000003993 interaction Effects 0.000 claims abstract description 8
- 230000008447 perception Effects 0.000 claims abstract description 7
- 239000011159 matrix material Substances 0.000 claims description 89
- 239000013598 vector Substances 0.000 claims description 55
- 238000010606 normalization Methods 0.000 claims description 25
- 230000009467 reduction Effects 0.000 claims description 14
- 230000008569 process Effects 0.000 claims description 13
- 238000005336 cracking Methods 0.000 claims description 6
- 238000000354 decomposition reaction Methods 0.000 claims description 6
- 238000005259 measurement Methods 0.000 claims description 5
- 230000001105 regulatory effect Effects 0.000 claims description 4
- 238000013024 troubleshooting Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 5
- 238000012423 maintenance Methods 0.000 description 5
- 238000000513 principal component analysis Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 239000000463 material Substances 0.000 description 4
- 230000004075 alteration Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000010223 real-time analysis Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Business, Economics & Management (AREA)
- Mathematical Physics (AREA)
- Economics (AREA)
- Mathematical Analysis (AREA)
- Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Computational Mathematics (AREA)
- Databases & Information Systems (AREA)
- Public Health (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Algebra (AREA)
- Evolutionary Computation (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computing Systems (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention provides a power grid operation information sensing system and a sensing method based on a plant side, wherein the system comprises the following components: the equipment access layer is used for accessing the intelligent equipment of the power grid; the data acquisition layer is used for acquiring the operation information of the intelligent power grid equipment; the data processing layer is used for carrying out standardization processing on the operation information, and the standardization processing comprises the following steps: data unification and data identification; the application layer is used for inputting the operation information subjected to the standardized processing into the fault cause diagnosis model so as to obtain a fault cause diagnosis result according to the fault cause diagnosis model; and the external interaction layer is used for uploading the diagnosis result of the fault cause to the cooperation platform on the plant station side and the cooperation App on the mobile terminal. According to the method, the collected operation information of the intelligent power grid equipment is subjected to standardization processing and then is input into the relevant diagnosis model for fault recognition, so that the accuracy and the efficiency of perception of the diagnosis model can be improved, and further the treatment efficiency of the power grid fault can be improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a power grid operation information sensing system based on a plant side and a power grid operation information sensing method based on the plant side.
Background
At present, when a power grid at a plant side fails, not only operation and inspection personnel need to check on site, but also other professionals need to re-check if detailed conditions of automatic equipment failure and secondary equipment failure are needed. The troubleshooting mode prolongs the elimination time, slows down the fault processing flow, consumes a large amount of labor cost, resource cost and the like.
In the related art, although there is a technology for performing power grid fault diagnosis by using a fault diagnosis model, it is considered that the fault diagnosis model has low efficiency and low accuracy due to a large amount and a large variety of power grid operation information, and thus the handling efficiency of the power grid fault is affected.
Disclosure of Invention
In order to solve the technical problems, the invention provides a sensing system based on power grid operation information of a plant side, which inputs collected operation information of power grid intelligent equipment into a relevant diagnosis model for fault recognition after standardized processing, so that the sensing accuracy and high efficiency of the diagnosis model can be improved, operation and maintenance personnel can quickly and accurately acquire power grid faults and quickly perform fault troubleshooting and processing, and further the handling efficiency of the power grid faults can be improved.
The second purpose of the invention is to provide a sensing method based on the power grid operation information of the plant side.
The technical scheme adopted by the invention is as follows:
an embodiment of the first aspect of the present invention provides a sensing system based on power grid operation information at a plant side, including: the device access layer is used for accessing the intelligent power grid device; the data acquisition layer is used for acquiring the operation information of the power grid intelligent device, and the operation information comprises: at least one of voltage, current, power, temperature; the data processing layer is used for carrying out standardization processing on the operation information, and the standardization processing comprises the following steps: data unification and data identification; the application layer is used for inputting the operation information subjected to the standardization processing into a fault cause diagnosis model so as to obtain a fault cause diagnosis result according to the fault cause diagnosis model; and the external interaction layer is used for uploading the fault cause diagnosis result to a cooperation platform at the plant station side and a cooperation App (Application) at the mobile terminal.
The above proposed data synchronization method of the present invention may further have the following additional technical features:
according to one embodiment of the invention, the grid smart device comprises: at least one of a protection device, a measurement and control device and an intelligent auxiliary system.
According to an embodiment of the present invention, the data processing layer is specifically configured to: converting the operation information into a matrix D, wherein the matrix D is an n-column m-row matrix, and n and m are positive integers; taking column vectors in the matrix D to form a first sequence L; randomly selecting a column vector a in the sequence LiFor reference, the rest n-1 column vectors form a distance matrix O through Euclidean distancesg( g 1, 2.., n-1) and according to the distance matrix OgDeduction to generate distance loss matrix PiWherein g ═ 1,2,., n-1), i ═ 1,2,., m; selecting the distance loss matrix PiThe shortest path in (1) forms a second sequence Q; dynamically Warping the second sequence Q by using a DTW (Dynamic Time Warping) algorithm to obtain a planning vector R; root of herbaceous plantAnd acquiring the operation information after the standardization processing according to the planning vector R.
According to an embodiment of the invention, the data processing layer is further configured to: distance adjustment is performed on the planning vector R using the following formula to form a third sequence S,
where f (x) is an element in the third sequence S, x is each element in the planning vector R, and μ and δ are the mean and variance of the elements in the planning vector R, respectively.
According to an embodiment of the present invention, the data processing layer is specifically configured to: the data recognition was achieved using multidimensional cracking and PCA (Principal Component Analysis).
According to an embodiment of the invention, the data processing layer is further configured to: carrying out normalization operation on the third sequence S to obtain a normalization matrix U; calculating the covariance of the normalization matrix U to obtain a covariance matrix C; performing singular value decomposition on the covariance matrix C to obtain a dimension reduction matrix G; and selecting a pre-set column key vector of the dimension reduction matrix G to form the operation information after the standardization processing.
The embodiment of the second aspect of the invention provides a method for sensing power grid operation information based on a plant side, which comprises the following steps: accessing to power grid intelligent equipment; gather the running information of electric wire netting smart machine, the running information includes: at least one of voltage, current, power, temperature; the operation information is standardized, and the standardization process comprises the following steps: data unification and data identification; inputting a fault cause diagnosis model according to the operation information subjected to the standardization processing so as to obtain a fault cause diagnosis result according to the fault cause diagnosis model; and uploading the fault cause diagnosis result to a cooperative platform on the plant station side and a cooperative App on the mobile terminal.
The sensing method based on the power grid operation information at the plant side provided by the invention also has the following additional technical characteristics:
according to an embodiment of the present invention, the normalization process specifically includes: converting the operation information into a matrix D, wherein the matrix D is an n-column m-row matrix, and n and m are positive integers; taking column vectors in the matrix D to form a first sequence L; randomly selecting a column vector a in the sequence LiFor reference, the rest n-1 column vectors form a distance matrix O through Euclidean distancesg( g 1, 2.., n-1) and according to the distance matrix OgDeduction to generate distance loss matrix PiWherein g ═ 1,2,., n-1), i ═ 1,2,., m; selecting the distance loss matrix PiThe shortest path in (1) forms a second sequence Q; dynamically regulating the second sequence Q by using a DTW algorithm to obtain a planning vector R; and acquiring the operation information after the standardization processing according to the planning vector R.
According to one embodiment of the invention, the normalization process further comprises: distance adjustment is performed on the planning vector R by using the following formula to form a third sequence S,
where f (x) is an element in the third sequence S, x is each element in the planning vector R, and μ and δ are the mean and variance of the elements in the planning vector R, respectively.
According to one embodiment of the invention, the normalization process further comprises: carrying out normalization operation on the third sequence S to obtain a normalization matrix U; calculating the covariance of the normalization matrix U to obtain a covariance matrix C; performing singular value decomposition on the covariance matrix C to obtain a dimension reduction matrix G; and selecting a pre-set column key vector of the dimension reduction matrix G to form the operation information after the standardization processing.
The invention has the beneficial effects that:
according to the method, the collected operation information of the intelligent power grid equipment is subjected to standardized processing and then is input into the relevant diagnosis model for fault recognition, so that the accuracy and the high efficiency of diagnosis model perception can be improved, operation and maintenance personnel can quickly and accurately acquire the power grid fault and quickly perform fault troubleshooting and processing, and further the treatment efficiency of the power grid fault can be improved.
Drawings
FIG. 1 is a block schematic diagram of a sensing system based on plant-side grid operational information according to one embodiment of the present invention;
FIG. 2 is a schematic diagram of data unification, according to one embodiment of the present invention;
FIG. 3 is a schematic diagram of data recognition according to one embodiment of the present invention;
fig. 4 is a flowchart of a sensing method based on plant-side grid operation information according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious 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.
Fig. 1 is a block schematic diagram of a sensing system based on plant-side grid operation information according to an embodiment of the present invention, as shown in fig. 1, the system includes: the device comprises a device access layer 1, a data acquisition layer 2, a data processing layer 3, an application layer 4 and an external interaction layer 5.
The equipment access layer 1 is used for accessing to the intelligent equipment of the power grid; the data acquisition layer 2 is used for acquiring the operation information of the power grid intelligent device, and the operation information comprises: at least one of voltage, current, power, temperature; the data processing layer 3 is used for standardizing the operation information, and the standardization process comprises the following steps: data unification and data identification; the application layer 4 is used for inputting the operation information subjected to the standardization processing into the fault cause diagnosis model so as to obtain a fault cause diagnosis result according to the fault cause diagnosis model; the external interaction layer 5 is used for sending the diagnosis result of the fault cause to the cooperation platform on the plant station side and the cooperation App on the mobile terminal.
In one embodiment of the invention, a grid smart device comprises: at least one of a protection device, a measurement and control device and an intelligent auxiliary system.
Specifically, the device access layer 1 is used for accessing a power grid intelligent device, and the accessed power grid intelligent device mainly comprises a protection device, a measurement and control device, an intelligent auxiliary system and the like. Then, the data acquisition layer 2 acquires operation information of the power grid intelligent device in the access system, wherein the operation information mainly comprises voltage, current, power, temperature and the like. Then, the data processing layer 3 performs standardization processing such as data unification and data identification on the operation information, the application layer 4 inputs the operation information subjected to the standardization processing into a fault cause diagnosis model, the fault cause diagnosis model can output a fault cause diagnosis result according to the input operation information subjected to the standardization processing, and the fault cause diagnosis result can include a fault position and a fault type. And finally, the external interaction layer 5 transmits the diagnosis result of the fault cause to the cooperative platform at the main station side and the cooperative App at the mobile end through the safety three areas of the power grid, and related maintainers can quickly acquire the abnormal cause of the power grid through the cooperative platform and the cooperative App at the mobile end, so that the workload of one-to-one investigation is reduced, the information cooperation is realized, the working pressure of related disposal professionals of the power grid is reduced, and the power grid fault disposal efficiency is improved.
Therefore, the system inputs the collected operation information of the intelligent power grid equipment into the relevant diagnosis model after standardized processing for fault recognition, the accuracy and the high efficiency of diagnosis model perception can be improved, operation and maintenance personnel can quickly and accurately acquire power grid faults and quickly conduct fault troubleshooting and processing, and the handling efficiency of the power grid faults can be improved.
It can be understood that the fault cause diagnosis model may be a neural network model, and multiple characteristic parameters may be fused in advance according to the operation information after the relevant standardization processing to implement the advance training of the fault cause diagnosis model, and the application layer 4 may implement the real-time analysis of the fault cause of the power grid by using the trained fault cause diagnosis model.
How to standardize the operation information will be described below with reference to specific embodiments.
According to an embodiment of the present invention, as shown in FIG. 2, the data processing layer 3 performs data unification through the following steps S1-S6.
And S1, converting the operation information into a matrix D, wherein the matrix D is an n-column m-row matrix, and n and m are positive integers.
Specifically, the collected operation information includes information such as voltage, current, power, temperature, etc., and each operation information (e.g., voltage) is denoted as Ti＝{T1,T2,...TkK is a positive integer, and each operation information is collected to form a matrix D, namely D is a matrix converted from single operation information,
And S2, taking the column vectors in the matrix D to form a first sequence L.
Specifically, L ═ a1,a2,...,an) Where a is the column vector in matrix D, forming a first sequence L.
S3, randomly selecting a column vector a in the sequence LiFor reference, the rest n-1 column vectors form a distance matrix O through Euclidean distancesg( g 1, 2.., n-1) according to a distance matrix OgDeduction to generate distance loss matrix PiWherein g ═ 1,2,., n-1), and i ═ 1,2,., m.
Specifically, OgFormal reference is shown below:
wherein C is an euclidean distance, i is 1,2,.. said, m, j is 1,2,. said, m, and j is not equal to i;
S4, selecting a distance loss matrix PiThe shortest path in (b) forms a second sequence Q.
Specifically, Q ═ { Q ═ Q1,Q2,...,Qm}。
And S5, dynamically warping the second sequence Q by using a DTW algorithm to obtain a planning vector R.
Specifically, Rn＝(b1,b2,...,bn) And b is the adjusted acquired data.
And S6, acquiring the operation information after the standardization processing according to the planning vector R.
According to an embodiment of the present invention, after being processed through the above-mentioned steps S1-S6, the data processing layer is further configured to: the distance adjustment is performed on the planning vector R using the following formula to form a third sequence S,
where f (x) is the element in the third sequence S, x is each element in the planning vector R, and μ and δ are the mean and variance of the elements in the planning vector R, respectively.
Therefore, after the processing, the third sequence S is used as the operation information after the standardized processing, the efficiency of the fault cause diagnosis model can be improved, and the technical defects of large calculated amount and long training time caused by overlarge feature matrix are overcome.
According to one embodiment of the present invention, the data processing layer 3 performs data recognition by the following steps: and the data identification is realized by means of multidimensional cracking, PCA and the like, and the accuracy of subsequent intelligent perception is improved.
It is understood that multidimensional cracking is to solve the problem of poor recognition in a single dimension, and mainly adopts multidimensional cracking work based on a Gaussian kernel function. The PCA needs to calculate the variance or the sum of the squares of the differences of all the features and evaluate the size of effective information to realize data dimension reduction, and related repeated redundant information can be deleted, the range can be reduced, and key feature quantities can be extracted, so that the lightweight of a fault cause diagnosis model can be realized.
According to an embodiment of the present invention, as shown in fig. 3, the data processing layer 3 specifically realizes data recognition through the following steps S11-S14:
and S11, carrying out normalization operation on the third sequence S to obtain a normalization matrix U.
S12, the covariance of the normalized matrix U is calculated to obtain a covariance matrix C.
And S13, performing singular value decomposition on the covariance matrix C to obtain a dimension reduction matrix G.
And S14, selecting a pre-set column key vector of the dimensionality reduction matrix G to form operation information after standardization processing.
Specifically, after the data processing layer 3 forms the third sequence S in the foregoing manner, the steps S11-S14 are adopted to perform the dimensionality reduction processing on the data, so that the efficiency of the subsequent algorithm can be improved.
In summary, according to the sensing system based on the power grid operation information at the plant side in the embodiment of the present invention, the access layer accesses the power grid intelligent device through the device access layer, the data acquisition layer acquires the operation information of the power grid intelligent device, and the operation information includes: at least one of voltage, electric current, power, temperature, the data processing layer carries out standardized processing to the operation information, and standardized processing includes: and data unification and data identification are carried out, the application layer inputs the operation information subjected to standardization processing into a fault cause diagnosis model, so that a fault cause diagnosis result is obtained according to the fault cause diagnosis model, and the fault cause diagnosis result is uploaded to the cooperation platform on the plant station side and the cooperation App on the mobile terminal by the outer interaction layer. Therefore, the system inputs the collected operation information of the intelligent power grid equipment into the relevant diagnosis model after standardized processing for fault recognition, the accuracy and the high efficiency of diagnosis model perception can be improved, operation and maintenance personnel can quickly and accurately acquire power grid faults and quickly conduct fault troubleshooting and processing, and the handling efficiency of the power grid faults can be improved.
Corresponding to the above sensing system based on the power grid operation information of the plant side, the invention further provides a sensing method based on the power grid operation information of the plant side, and details which are not disclosed in the method embodiment can refer to the above system embodiment, and are not described in detail in the invention.
Fig. 4 is a flowchart of a sensing method based on plant-side grid operation information according to an embodiment of the present invention, and as shown in fig. 4, the method includes the following steps:
and S10, accessing the power grid intelligent equipment.
S20, collecting operation information of the power grid intelligent device, wherein the operation information comprises: at least one of voltage, current, power, temperature.
S30, the operation information is standardized, and the standardized processing comprises the following steps: data unification and data identification.
And S40, inputting the fault cause diagnosis model according to the operation information after the standardization processing, and obtaining the fault cause diagnosis result according to the fault cause diagnosis model.
And S50, uploading the diagnosis result of the fault cause to the cooperative platform at the plant station side and the cooperative App at the mobile terminal.
According to one embodiment of the invention, a grid smart device comprises: at least one of a protection device, a measurement and control device and an intelligent auxiliary system.
According to an embodiment of the present invention, the normalization process specifically includes: converting the operation information into a matrix D, wherein the matrix D is an n-column m-row matrix, and n and m are positive integers; taking column vectors in the matrix D to form a first sequence L; randomly selecting a column vector a in a sequence LiOn the basis of the restn-1 column vectors form a distance matrix O by Euclidean distancesg( g 1, 2.., n-1) according to a distance matrix OgDeduction to generate distance loss matrix PiWherein g ═ 1,2,., n-1), i ═ 1,2,., m; selecting a distance loss matrix PiThe shortest path in (1) forms a second sequence Q; dynamically regulating the second sequence Q by using a DTW algorithm to obtain a planning vector R; and acquiring the operation information after the standardization processing according to the planning vector R.
According to one embodiment of the invention, the normalization process further comprises: the distance adjustment is performed on the planning vector R using the following formula to form a third sequence S,
where f (x) is the element in the third sequence S, x is each element in the planning vector R, and μ and δ are the mean and variance of the elements in the planning vector R, respectively.
According to an embodiment of the present invention, the data identification specifically includes: and realizing data identification by utilizing multidimensional cracking and PCA.
According to one embodiment of the invention, the normalization process further comprises: carrying out normalization operation on the third sequence S to obtain a normalization matrix U; calculating the covariance of the normalization matrix U to obtain a covariance matrix C; singular value decomposition is carried out on the covariance matrix C to obtain a dimension reduction matrix G; and selecting a pre-set column key vector of the dimension reduction matrix G to form operation information after standardization processing.
According to the sensing method of the power grid operation information based on the plant side, the power grid intelligent equipment is accessed, then the operation information of the power grid intelligent equipment is collected, and the operation information comprises the following steps: at least one of voltage, current, power and temperature, and then standardizing the operation information, wherein the standardization comprises the following steps: and data unification and data identification are carried out, the operation information after standardization processing is input into a fault cause diagnosis model, a fault cause diagnosis result is obtained according to the fault cause diagnosis model, and finally the fault cause diagnosis result is uploaded to a cooperation platform on the plant station side and a cooperation App on the mobile terminal. Therefore, the collected operation information of the intelligent power grid equipment is subjected to standardization processing and then is input into the relevant diagnosis model for fault recognition, the accuracy and the efficiency of diagnosis model perception can be improved, operation and maintenance personnel can quickly and accurately acquire power grid faults and quickly perform fault troubleshooting and processing, and the handling efficiency of the power grid faults can be improved.
In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. The meaning of "plurality" is two or more unless specifically limited otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; the two components can be directly connected with each other or indirectly connected with each other through an intermediate medium, and can be communicated with each other in the compartment or the interaction relation of the two components. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction. In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A perception system based on power grid operation information of a plant side is characterized by comprising:
the device access layer is used for accessing the intelligent power grid device;
the data acquisition layer is used for acquiring the operation information of the power grid intelligent device, and the operation information comprises: at least one of voltage, current, power, temperature;
the data processing layer is used for carrying out standardization processing on the operation information, and the standardization processing comprises the following steps: data unification and data identification;
the application layer is used for inputting the operation information subjected to the standardization processing into a fault cause diagnosis model so as to obtain a fault cause diagnosis result according to the fault cause diagnosis model;
and the external interaction layer is used for uploading the fault cause diagnosis result to a cooperation platform at the plant station side and a cooperation App at the mobile terminal.
2. The plant-side based awareness system of grid operation information according to claim 1, wherein the grid smart device comprises: at least one of a protection device, a measurement and control device and an intelligent auxiliary system.
3. The plant-side-based awareness system of grid operation information according to claim 1, wherein the data processing layer is specifically configured to:
converting the operation information into a matrix D, wherein the matrix D is an n-column m-row matrix, and n and m are positive integers;
taking column vectors in the matrix D to form a first sequence L;
randomly selecting a column vector a in the sequence LiFor reference, the rest n-1 column vectors form a distance matrix O through Euclidean distancesg(g 1, 2.., n-1) and according to the distance matrix OgDeduction to generate distance loss matrix PiWherein g ═ 1,2,., n-1), i ═ 1,2,., m;
selecting the distance loss matrix PiThe shortest path in (1) forms a second sequence Q;
dynamically regulating the second sequence Q by using a DTW algorithm to obtain a planning vector R;
and acquiring the operation information after the standardization processing according to the planning vector R.
4. The plant-side based awareness system of grid operating information of claim 3, wherein the data processing layer is further configured to:
distance adjustment is performed on the planning vector R using the following formula to form a third sequence S,
where f (x) is an element in the third sequence S, x is each element in the planning vector R, and μ and δ are the mean and variance of the elements in the planning vector R, respectively.
5. The plant-side-based awareness system of grid operation information according to claim 4, wherein the data processing layer is specifically configured to:
the data recognition is achieved using multidimensional cracking and PCA.
6. The plant-side based awareness system of grid operating information of claim 5, wherein the data processing layer is further configured to:
carrying out normalization operation on the third sequence S to obtain a normalization matrix U;
calculating the covariance of the normalization matrix U to obtain a covariance matrix C;
performing singular value decomposition on the covariance matrix C to obtain a dimension reduction matrix G;
and selecting a pre-set column key vector of the dimension reduction matrix G to form the operation information after the standardization processing.
7. A power grid operation information sensing method based on a plant side is characterized by comprising the following steps:
accessing to power grid intelligent equipment;
gather the running information of electric wire netting smart machine, the running information includes: at least one of voltage, current, power, temperature;
standardizing the operation information, wherein the standardization comprises the following steps: data unification and data identification;
inputting a fault cause diagnosis model according to the operation information subjected to the standardization processing so as to obtain a fault cause diagnosis result according to the fault cause diagnosis model;
and uploading the fault cause diagnosis result to a cooperative platform on the plant station side and a cooperative App on the mobile terminal.
8. The method for sensing the plant-side-based power grid operation information according to claim 7, wherein the standardization process specifically comprises:
converting the operation information into a matrix D, wherein the matrix D is an n-column m-row matrix, and n and m are positive integers;
taking column vectors in the matrix D to form a first sequence L;
randomly selecting a column vector a in the sequence LiFor reference, the rest n-1 column vectors form a distance matrix O through Euclidean distancesg(g 1, 2.., n-1) and according to the distance matrix OgDeduction to generate distance loss matrix PiWherein g ═ 1,2,., n-1), i ═ 1,2,., m;
selecting the distance loss matrix PiThe shortest path in (1) forms a second sequence Q;
dynamically regulating the second sequence Q by using a DTW algorithm to obtain a planning vector R;
and acquiring the operation information after the standardization processing according to the planning vector R.
9. The method for sensing plant-side based grid operation information according to claim 8, wherein the normalization process further comprises:
distance adjustment is performed on the planning vector R by adopting the following formula to form a third sequence S,
where f (x) is an element in the third sequence S, x is each element in the planning vector R, and μ and δ are the mean and variance of the elements in the planning vector R, respectively.
10. The method for sensing plant-side based grid operation information according to claim 9, wherein the normalization process further comprises:
carrying out normalization operation on the third sequence S to obtain a normalization matrix U;
calculating the covariance of the normalization matrix U to obtain a covariance matrix C;
performing singular value decomposition on the covariance matrix C to obtain a dimension reduction matrix G;
and selecting a pre-set column key vector of the dimension reduction matrix G to form the operation information after the standardization processing.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111337079.3A CN114048815B (en) | 2021-11-12 | 2021-11-12 | Power grid operation information sensing system and sensing method based on plant side |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111337079.3A CN114048815B (en) | 2021-11-12 | 2021-11-12 | Power grid operation information sensing system and sensing method based on plant side |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114048815A true CN114048815A (en) | 2022-02-15 |
| CN114048815B CN114048815B (en) | 2023-11-28 |
Family
ID=80208672
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111337079.3A Active CN114048815B (en) | 2021-11-12 | 2021-11-12 | Power grid operation information sensing system and sensing method based on plant side |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114048815B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109001596A (en) * | 2018-10-17 | 2018-12-14 | 广东电网有限责任公司 | A kind of electric network failure diagnosis and transmission line parameter identification system |
| CN109816161A (en) * | 2019-01-14 | 2019-05-28 | 中国电力科学研究院有限公司 | A kind of power distribution network operation computer-aided decision support System and its application method |
| WO2019233047A1 (en) * | 2018-06-07 | 2019-12-12 | 国电南瑞科技股份有限公司 | Power grid dispatching-based operation and maintenance method |
| CN111208384A (en) * | 2018-11-19 | 2020-05-29 | 中国电力科学研究院有限公司 | Smart power grid management system |
| CN111598376A (en) * | 2020-02-18 | 2020-08-28 | 中国电力科学研究院有限公司 | Method and system for carrying out auxiliary decision-making on power grid big data based on information driving |
| US20200358396A1 (en) * | 2019-05-06 | 2020-11-12 | Arizona Board Of Regents On Behalf Of Arizona State University | Solar array fault detection, classification, and localization using deep neural nets |
| CN111965486A (en) * | 2020-08-07 | 2020-11-20 | 国网江苏省电力有限公司电力科学研究院 | Power distribution network fault identification method and system based on intelligent data fusion analysis |
| US20210134085A1 (en) * | 2019-11-01 | 2021-05-06 | GM Global Technology Operations LLC | Systems and Methods for Diagnosing Perception Systems of Vehicles Based on Temporal Continuity of Sensor Data |
| US20210278478A1 (en) * | 2020-03-02 | 2021-09-09 | Wuhan University | Deep parallel fault diagnosis method and system for dissolved gas in transformer oil |
| CN113433419A (en) * | 2021-06-04 | 2021-09-24 | 国电南瑞南京控制***有限公司 | Intelligent alarm method and system based on polymorphic data coprocessing |
-
2021
- 2021-11-12 CN CN202111337079.3A patent/CN114048815B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019233047A1 (en) * | 2018-06-07 | 2019-12-12 | 国电南瑞科技股份有限公司 | Power grid dispatching-based operation and maintenance method |
| CN109001596A (en) * | 2018-10-17 | 2018-12-14 | 广东电网有限责任公司 | A kind of electric network failure diagnosis and transmission line parameter identification system |
| CN111208384A (en) * | 2018-11-19 | 2020-05-29 | 中国电力科学研究院有限公司 | Smart power grid management system |
| CN109816161A (en) * | 2019-01-14 | 2019-05-28 | 中国电力科学研究院有限公司 | A kind of power distribution network operation computer-aided decision support System and its application method |
| US20200358396A1 (en) * | 2019-05-06 | 2020-11-12 | Arizona Board Of Regents On Behalf Of Arizona State University | Solar array fault detection, classification, and localization using deep neural nets |
| US20210134085A1 (en) * | 2019-11-01 | 2021-05-06 | GM Global Technology Operations LLC | Systems and Methods for Diagnosing Perception Systems of Vehicles Based on Temporal Continuity of Sensor Data |
| CN111598376A (en) * | 2020-02-18 | 2020-08-28 | 中国电力科学研究院有限公司 | Method and system for carrying out auxiliary decision-making on power grid big data based on information driving |
| US20210278478A1 (en) * | 2020-03-02 | 2021-09-09 | Wuhan University | Deep parallel fault diagnosis method and system for dissolved gas in transformer oil |
| CN111965486A (en) * | 2020-08-07 | 2020-11-20 | 国网江苏省电力有限公司电力科学研究院 | Power distribution network fault identification method and system based on intelligent data fusion analysis |
| CN113433419A (en) * | 2021-06-04 | 2021-09-24 | 国电南瑞南京控制***有限公司 | Intelligent alarm method and system based on polymorphic data coprocessing |
Non-Patent Citations (1)
| Title |
|---|
| 刘士亚: "城市电网监控应急***的设计", 《电气技术》, vol. 22, no. 2, pages 11 - 16 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114048815B (en) | 2023-11-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107832855B (en) | Line loss multi-source diagnosis method and system based on correlation analysis | |
| US10729382B2 (en) | Methods and systems to predict a state of the machine using time series data of the machine | |
| DE102019103825A1 (en) | FAULT DIAGNOSIS DEVICE AND MACHINE LEARNING DEVICE | |
| US20160369777A1 (en) | System and method for detecting anomaly conditions of sensor attached devices | |
| CN116125361B (en) | Voltage transformer error evaluation method, system, electronic equipment and storage medium | |
| DE102018115155A1 (en) | Method for predicting plant data and device using it | |
| CN114255784A (en) | Substation equipment fault diagnosis method based on voiceprint recognition and related device | |
| CN111967620A (en) | Photovoltaic module diagnosis method, device, equipment and readable storage medium | |
| CN111553808B (en) | Index information display method and device for wind power plant and storage medium | |
| CN110865924A (en) | Health degree diagnosis method and health diagnosis framework for internal server of power information system | |
| JP3718765B2 (en) | Plant diagnostic equipment | |
| CN108204892B (en) | Roller set equipment fault detection method based on flexible array type pressure sensor | |
| TW202120949A (en) | Diagnostic device and diagnostic method | |
| CN117193088B (en) | Industrial equipment monitoring method and device and server | |
| CN117833468A (en) | Maintenance method for circuit breaker unit transmission control part of power distribution ring main unit in operation | |
| CN117331790A (en) | Machine room fault detection method and device for data center | |
| CN114048815A (en) | Power grid operation information sensing system and sensing method based on plant side | |
| CN116448234A (en) | Power transformer running state voiceprint monitoring method and system | |
| US11339763B2 (en) | Method for windmill farm monitoring | |
| CN114091618A (en) | Industrial equipment health state diagnosis management method and device and server | |
| EP4150422A1 (en) | System and method for determining a cause of an operating anomaly of a machine, computer program and electronically readable data storage device | |
| CN112070535A (en) | Electric vehicle price prediction method and device | |
| CN117783769B (en) | Power distribution network fault positioning method, system, equipment and storage medium based on visual platform | |
| CN117808157B (en) | Intelligent identification-based unreported outage behavior prediction analysis system | |
| CN117272844B (en) | Method and system for predicting service life of distribution board |
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 |