CN113342993B - Power failure map generation method - Google Patents
Power failure map generation method Download PDFInfo
- Publication number
- CN113342993B CN113342993B CN202110751541.8A CN202110751541A CN113342993B CN 113342993 B CN113342993 B CN 113342993B CN 202110751541 A CN202110751541 A CN 202110751541A CN 113342993 B CN113342993 B CN 113342993B
- Authority
- CN
- China
- Prior art keywords
- sampling
- point
- sampling time
- time point
- power failure
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 13
- 238000005070 sampling Methods 0.000 claims abstract description 82
- 239000013598 vector Substances 0.000 claims abstract description 42
- 239000011159 matrix material Substances 0.000 claims description 21
- 238000004364 calculation method Methods 0.000 claims description 4
- 230000007547 defect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
-
- 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)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Water Supply & Treatment (AREA)
- Marketing (AREA)
- Life Sciences & Earth Sciences (AREA)
- Public Health (AREA)
- Computational Linguistics (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Animal Behavior & Ethology (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Testing Electric Properties And Detecting Electric Faults (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
A power failure map generation method relates to the technical field of power systems, and takes power failure history recording data comprising phase current recording data and phase voltage recording data as samples, selects sampling time points from the samples according to time sequence, converts the relevant data into vectors, then utilizes the vectors to construct a gradient image as a power failure map, and can utilize the power failure map to detect failure types when power failure occurs. The method provided by the invention is suitable for detecting the power faults in the power system.
Description
Technical Field
The present invention relates to a technology of an electric power system, and in particular, to a technology of an electric power failure map generation method.
Background
The power failure recording system is a system for recording power failure data (phase current, phase voltage, etc. when a power failure occurs), and by analyzing the power failure data, the failure data characteristics can be found, and thus the cause of the failure can be determined.
At present, a neural network is adopted to analyze power fault data, and fault characteristics are required to be extracted by the analysis method so as to match and classify fault types, but the existing algorithm for extracting the fault characteristics is complex, and has the defects of low calculation speed and difficult detection of new fault types.
Disclosure of Invention
Aiming at the defects in the prior art, the technical problem to be solved by the invention is to provide a power failure map generation method capable of rapidly detecting the failure type when a power failure occurs.
In order to solve the technical problems, the power failure map generating method provided by the invention is characterized by comprising the following specific steps:
1) Acquiring power failure history recording data containing phase current recording data and phase voltage recording data;
2) Dividing phase current recording data in the power failure history recording data into R phase current wave bands, wherein the duration of each phase current wave band is a cycle, and the starting point of each phase current wave band falls on an abscissa axis, and the cycle refers to a complete sine waveform of power frequency current;
3) Taking L sampling points on each phase current wave band, defining a time point corresponding to each sampling point as a sampling time point, and forming each sampling time point into a matrix G of R rows and L columns;
in the matrix G, the sampling time points of the same row are orderly arranged from left to right according to the time sequence from first to last, and the time sequence of the sampling time point of the upper row is earlier than the time sequence of the sampling time point of the lower row;
each sampling time point has three values, wherein the three values are respectively a phase current per unit value I, a phase voltage per unit value U and a phase angle difference a of the phase current and the phase voltage of the sampling time point, and the three values are obtained from phase current recording data and phase voltage recording data in the power failure history recording data;
4) Setting a sampling point vector for each sampling time point, and calculating the vector amplitude and vector angle of the sampling point vector of each sampling time point, wherein the calculating method comprises the following steps:
for each sampling point, constructing a triangle formed by three sides of S1, S2 and S3 for the sampling time point, taking the value of the per unit value I of the phase current of the sampling time point as the side length of S1, taking the value of the per unit value U of the phase voltage of the sampling time point as the side length of S2, and taking the phase angle difference alpha of the phase current and the phase voltage of the sampling time point as the included angle between S1 and S2; then calculating the length of S3, setting the included angle beta between S1 and S3 as the vector amplitude of the sampling point vector of the sampling time point, and setting the included angle beta between S1 and S3 as the vector angle of the sampling point vector of the sampling time point;
5) The sampling point vectors of all the sampling time points form a matrix M of R rows and L columns, and the arrangement mode of the sampling point vectors in the matrix M is consistent with the arrangement mode of the sampling time points of all the sampling point vectors in the matrix G;
6) Constructing a gradient image P consisting of R rows and L columns of pixel points, and calculating a gradient value and a gradient direction value of each pixel point in the gradient image P, wherein a calculation formula is as follows:
θ(x,y)＝int(8×Mβ(x,y)/2π)
wherein H (x, y) is a gradient value of a pixel point of an xth row and a yth column in the gradient image P, θ (x, y) is a gradient direction value of a pixel point of an xth row and a yth column in the gradient image P, ml (x, y) is a vector amplitude of an element of an xth row and a yth column in the matrix M, mbeta (x, y) is a vector angle of an element of an xth row and a yth column in the matrix M, and int () is a rounding function;
7) The gradient image P is used as a power failure map, and the type of failure is detected by using the power failure map when a power failure occurs.
According to the power failure map generation method provided by the invention, the power failure history recording data is taken as a sample, the phase current recording data and the phase voltage recording data are converted into vectors, a gradient image is constructed, the constructed gradient image is taken as a power failure map, the power failure map is utilized to simulate the data characteristic classification when the power failure occurs, and the failure type can be rapidly detected according to the power failure map when the power failure occurs.
Detailed Description
The technical scheme of the present invention is further described in detail below with reference to specific embodiments, but the present embodiment is not intended to limit the present invention, and all similar structures and similar variations using the present invention should be included in the scope of the present invention, where the numbers represent the relationships of the same, and the english letters in the present invention distinguish the cases.
The power failure map generation method provided by the embodiment of the invention is characterized by comprising the following specific steps of:
1) Acquiring power failure history recording data containing phase current recording data and phase voltage recording data;
the power failure history wave recording data can be obtained through the existing power failure wave recording system, the phase current wave recording data and the phase voltage wave recording data are waveform data expressed by two-dimensional rectangular coordinates, the abscissa axes of the phase current wave recording data and the phase voltage wave recording data are time axes, the ordinate axis of the phase current wave recording data is a phase current value axis, and the ordinate axis of the phase voltage wave recording data is a phase voltage value axis;
2) Dividing phase current recording data in the power failure history recording data into R phase current wave bands, wherein the duration of each phase current wave band is a cycle, and the starting point of each phase current wave band falls on an abscissa axis, and the cycle refers to a complete sine waveform of power frequency current;
3) Taking L sampling points on each phase current wave band, defining a time point corresponding to each sampling point as a sampling time point, and forming each sampling time point into a matrix G of R rows and L columns;
in the matrix G, the sampling time points of the same row are orderly arranged from left to right according to the time sequence from first to last, and the time sequence of the sampling time point of the upper row is earlier than the time sequence of the sampling time point of the lower row;
each sampling time point has three values, wherein the three values are respectively a phase current per unit value I, a phase voltage per unit value U and a phase angle difference a of the phase current and the phase voltage of the sampling time point, and the three values are obtained from phase current recording data and phase voltage recording data in the power failure history recording data;
4) Setting a sampling point vector for each sampling time point, and calculating the vector amplitude and vector angle of the sampling point vector of each sampling time point, wherein the calculating method comprises the following steps:
for each sampling point, constructing a triangle formed by three sides of S1, S2 and S3 for the sampling time point, taking the value of the per unit value I of the phase current of the sampling time point as the side length of S1, taking the value of the per unit value U of the phase voltage of the sampling time point as the side length of S2, and taking the phase angle difference alpha of the phase current and the phase voltage of the sampling time point as the included angle between S1 and S2; then calculating the length of S3, setting the included angle beta between S1 and S3 as the vector amplitude of the sampling point vector of the sampling time point, and setting the included angle beta between S1 and S3 as the vector angle of the sampling point vector of the sampling time point;
5) The sampling point vectors of all the sampling time points form a matrix M of R rows and L columns, and the arrangement mode of the sampling point vectors in the matrix M is consistent with the arrangement mode of the sampling time points of all the sampling point vectors in the matrix G;
6) Constructing a gradient image P consisting of R rows and L columns of pixel points, and calculating a gradient value and a gradient direction value of each pixel point in the gradient image P, wherein a calculation formula is as follows:
θ(x,y)＝int(8×Mβ(x,y)/2π)
wherein H (x, y) is a gradient value of a pixel point of an xth row and a yth column in the gradient image P, θ (x, y) is a gradient direction value of a pixel point of an xth row and a yth column in the gradient image P, ml (x, y) is a vector amplitude of an element of an xth row and a yth column in the matrix M, mbeta (x, y) is a vector angle of an element of an xth row and a yth column in the matrix M, and int () is a rounding function;
7) The gradient image P is used as a power failure map, and the type of failure is detected by using the power failure map when a power failure occurs.
Claims (1)
1. The power failure map generation method is characterized by comprising the following specific steps of:
1) Acquiring power failure history recording data containing phase current recording data and phase voltage recording data;
2) Dividing phase current recording data in the power failure history recording data into R phase current wave bands, wherein the duration of each phase current wave band is a cycle, and the starting point of each phase current wave band falls on an abscissa axis, and the cycle refers to a complete sine waveform of power frequency current;
3) Taking L sampling points on each phase current wave band, defining a time point corresponding to each sampling point as a sampling time point, and forming each sampling time point into a matrix G of R rows and L columns;
in the matrix G, the sampling time points of the same row are orderly arranged from left to right according to the time sequence from first to last, and the time sequence of the sampling time point of the upper row is earlier than the time sequence of the sampling time point of the lower row;
each sampling time point has three values, wherein the three values are respectively a phase current per unit value I, a phase voltage per unit value U and a phase angle difference a of the phase current and the phase voltage of the sampling time point, and the three values are obtained from phase current recording data and phase voltage recording data in the power failure history recording data;
4) Setting a sampling point vector for each sampling time point, and calculating the vector amplitude and vector angle of the sampling point vector of each sampling time point, wherein the calculating method comprises the following steps:
for each sampling point, constructing a triangle formed by three sides of S1, S2 and S3 for the sampling time point, taking the value of the per unit value I of the phase current of the sampling time point as the side length of S1, taking the value of the per unit value U of the phase voltage of the sampling time point as the side length of S2, and taking the phase angle difference alpha of the phase current and the phase voltage of the sampling time point as the included angle between S1 and S2; then calculating the length of S3, setting the included angle beta between S1 and S3 as the vector amplitude of the sampling point vector of the sampling time point, and setting the included angle beta between S1 and S3 as the vector angle of the sampling point vector of the sampling time point;
5) The sampling point vectors of all the sampling time points form a matrix M of R rows and L columns, and the arrangement mode of the sampling point vectors in the matrix M is consistent with the arrangement mode of the sampling time points of all the sampling point vectors in the matrix G;
6) Constructing a gradient image P consisting of R rows and L columns of pixel points, and calculating a gradient value and a gradient direction value of each pixel point in the gradient image P, wherein a calculation formula is as follows:
θ(x,y)＝int(8×Mβ(x,y)/2π)
wherein H (x, y) is a gradient value of a pixel point of an xth row and a yth column in the gradient image P, θ (x, y) is a gradient direction value of a pixel point of an xth row and a yth column in the gradient image P, ml (x, y) is a vector amplitude of an element of an xth row and a yth column in the matrix M, mbeta (x, y) is a vector angle of an element of an xth row and a yth column in the matrix M, and int () is a rounding function;
7) The gradient image P is used as a power failure map, and the type of failure is detected by using the power failure map when a power failure occurs.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110751541.8A CN113342993B (en) | 2021-07-02 | 2021-07-02 | Power failure map generation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110751541.8A CN113342993B (en) | 2021-07-02 | 2021-07-02 | Power failure map generation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113342993A CN113342993A (en) | 2021-09-03 |
| CN113342993B true CN113342993B (en) | 2023-10-03 |
Family
ID=77482319
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110751541.8A Active CN113342993B (en) | 2021-07-02 | 2021-07-02 | Power failure map generation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113342993B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104698343A (en) * | 2015-03-26 | 2015-06-10 | 广东电网有限责任公司电力调度控制中心 | Method and system for judging power grid faults based on historical recording data |
| CN110108964A (en) * | 2019-05-23 | 2019-08-09 | 上海申瑞继保电气有限公司 | Electric power supervisory control object outages recorder data processing method based on Internet of Things |
| CN111737496A (en) * | 2020-06-29 | 2020-10-02 | 东北电力大学 | Power equipment fault knowledge map construction method |
| CN112269901A (en) * | 2020-09-14 | 2021-01-26 | 合肥中科类脑智能技术有限公司 | Fault distinguishing and reasoning method based on knowledge graph |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140278162A1 (en) * | 2013-03-15 | 2014-09-18 | Echelon Corporation | Detecting and locating power outages via low voltage grid mapping |
-
2021
- 2021-07-02 CN CN202110751541.8A patent/CN113342993B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104698343A (en) * | 2015-03-26 | 2015-06-10 | 广东电网有限责任公司电力调度控制中心 | Method and system for judging power grid faults based on historical recording data |
| CN110108964A (en) * | 2019-05-23 | 2019-08-09 | 上海申瑞继保电气有限公司 | Electric power supervisory control object outages recorder data processing method based on Internet of Things |
| CN111737496A (en) * | 2020-06-29 | 2020-10-02 | 东北电力大学 | Power equipment fault knowledge map construction method |
| CN112269901A (en) * | 2020-09-14 | 2021-01-26 | 合肥中科类脑智能技术有限公司 | Fault distinguishing and reasoning method based on knowledge graph |
Non-Patent Citations (1)
| Title |
|---|
| 一起500 kV变电站35 kV电容器组故障分析及防范措施;唐武等;《电网技术》(第191期);第28-31页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113342993A (en) | 2021-09-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Yu et al. | Dilated residual networks | |
| CN103455797A (en) | Detection and tracking method of moving small target in aerial shot video | |
| CN102737243A (en) | Method and device for acquiring descriptive information of multiple images and image matching method | |
| CN113468760B (en) | Motor weak fault detection method and system based on dictionary learning | |
| CN105321254A (en) | Equipment, method and device for detecting thickness of measured object | |
| CN103700091A (en) | Image significance object detection method based on multiscale low-rank decomposition and with sensitive structural information | |
| CN111768022A (en) | Equipment detection method and device for coal machine production equipment | |
| CN117272143A (en) | Power transmission line fault identification method and device based on gram angle field and residual error network | |
| CN113420837A (en) | Fault diagnosis method based on multi-source compressed sensing | |
| CN110729938A (en) | Asynchronous motor rotor broken bar fault identification method based on machine vision | |
| CN115481657A (en) | Wind generating set communication slip ring fault diagnosis method based on electric signals | |
| CN113342993B (en) | Power failure map generation method | |
| CN105761207B (en) | Image Super-resolution Reconstruction method based on the insertion of maximum linear block neighborhood | |
| CN106707118A (en) | Method and device for identifying partial discharge pattern | |
| Imran et al. | Image-based automatic energy meter reading using deep learning | |
| CN106204538A (en) | A kind of image partition method and system | |
| Li et al. | Insulator infrared image segmentation algorithm based on dynamic mask and box annotation | |
| CN116704241A (en) | Full-channel 3D convolutional neural network hyperspectral remote sensing image classification method | |
| Mitiche et al. | Naive bayes multi-label classification approach for high-voltage condition monitoring | |
| CN115239952A (en) | Method for reading industrial table based on YOLOV5 and U2-Net framework assistance | |
| Wang et al. | The prediction of membrane protein types with NPE | |
| CN109060824A (en) | Strain-pulse based on DIC technology responds covariance damnification recognition method | |
| CN115695590A (en) | CAN signal analysis method, equipment and storage medium | |
| CN115236272A (en) | Gas sensor fault diagnosis method and device under multi-working condition and storage medium | |
| CN115249319A (en) | Method for detecting sun dark stripes in full-sun-surface image |
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 |