CN111509847A - Intelligent detection system and method for power grid unit state - Google Patents
Intelligent detection system and method for power grid unit state Download PDFInfo
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- CN111509847A CN111509847A CN202010306411.9A CN202010306411A CN111509847A CN 111509847 A CN111509847 A CN 111509847A CN 202010306411 A CN202010306411 A CN 202010306411A CN 111509847 A CN111509847 A CN 111509847A
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- 238000010977 unit operation Methods 0.000 claims abstract description 9
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for AC mains or AC distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
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- 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
- Y04S10/12—Monitoring or controlling equipment for energy generation units, e.g. distributed energy generation [DER] or load-side generation
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- 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
- Y04S10/52—Outage or fault management, e.g. fault detection or location
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Abstract
The invention relates to the technical field of power monitoring, and provides an intelligent detection system and method for the state of a power grid unit, wherein the intelligent detection system for the state of the power grid unit comprises a sensor unit, a data acquisition and analysis module, a field server and a remote diagnosis center, wherein the sensor unit monitors unit operation parameters and uploads monitoring signals to the data acquisition and analysis module through relatively independent signal channels; the invention relates to an intelligent detection system taking a remote diagnosis center as a core, which acquires important operation parameters of each part through a sensor unit consisting of a plurality of sensors, and monitors, identifies and judges potential faults of main components of a unit by matching with a signal algorithm library and a fault judgment case library as supports of fault diagnosis.
Description
Technical Field
The invention relates to the technical field of power monitoring, in particular to an intelligent detection system for the state of a power grid unit and a detection method.
Background
Due to the limitation of geographical conditions and wind energy resources, wind power plants are generally far away from cities and headquarters of wind power companies, distances among wind power plants belonging to the wind power companies can be far away, units are located at the tops of towers, and therefore, maintenance is difficult (such as personnel and equipment entering and the like), at present, planned maintenance and after-repair modes are mostly adopted for maintenance of the wind power plants, the maintenance is difficult to comprehensively and timely know the operation condition of the equipment, maintenance work is usually long-lasting, and loss is great.
How to effectively monitor and analyze the states of each wind turbine makes the safe, reliable and economic operation of the whole wind power plant become important. How to enable managers and equipment maintenance personnel far away from the wind power plant to know the operating conditions of the wind power plants more conveniently and quickly, provide technical support in time, realize remote data communication among the wind power plants, ensure the overall management operation and maintenance of the wind power plants, and obtain the online technical service and diagnosis of experts becomes an increasingly urgent need of the wind power plant.
Disclosure of Invention
Aiming at the defects of the prior art, one of the purposes of the invention is to provide an intelligent detection system for the state of a power grid unit, which can realize the networking and intelligent monitoring of the remote online monitoring of a wind generating set, and the purpose of the invention is realized by the following technical scheme:
the intelligent detection system for the state of the power grid unit comprises a sensor unit, a data acquisition and analysis module, a field server and a remote diagnosis center, wherein the sensor unit monitors unit operation parameters and uploads monitoring signals to the data acquisition and analysis module through relatively independent signal channels, the data acquisition and analysis module processes and analyzes the unit operation parameters and uploads the unit operation parameters to the remote diagnosis center through the field server, and the remote diagnosis center analyzes and judges abnormal detection points.
Particularly, the sensor unit mainly comprises a vibration acceleration sensor, a temperature sensor, an oil on-line monitoring sensor and a strain sensor, wherein different signal channels are established according to different monitoring points.
Particularly, the on-site server is used for setting parameters of the data acquisition and analysis module and storing monitoring data, and the remote diagnosis center Internet is in real-time data communication with the on-site server.
Particularly, the remote diagnosis center mainly comprises a front server, an application server and a database server, wherein a data processing module and a diagnosis analysis module are arranged in the front server, the application server is mainly used for counting and processing data and reports, and a signal algorithm library and a fault judgment case library are stored in the database server.
In particular, the diagnostic and analysis module mainly includes a process control section and a failure analysis control section for transmitting necessary diagnostic contents to the process control section.
The second purpose of the invention is realized by the following technical scheme:
the intelligent detection method for the state of the power grid unit comprises the following operation steps: detecting and acquiring running state data of each monitoring department of the unit through a sensor unit; the data acquisition and analysis module processes and identifies the running state data and uploads an identification result to the front-end server through the field server; the fault analysis control part grabs the abnormal detection points, identifies the fault types, and generates relative diagnosis contents according to the fault types and the fault judgment case base; the process control part receives the diagnosis content, and calls a corresponding signal processing model and an intelligent fuzzy judgment model according to the diagnosis content to carry out reasoning and analysis; and obtaining a fault diagnosis conclusion of the abnormal detection point.
Particularly, the fault diagnosis conclusion can automatically generate a fault diagnosis report, and the fault judgment case library can be expanded and corrected.
In particular, the diagnosis content may be specified by a user or automatically generated by the failure analysis control section based on the failure determination case library.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, through an intelligent detection system taking a remote diagnosis center as a core, important operation parameters of each part are obtained through a sensor unit consisting of a plurality of sensors, a signal algorithm library and a fault judgment case library are matched as supports for fault diagnosis to monitor, identify and judge potential faults of main components of a unit, and powerful data support and guidance are provided in cooperation with field fault investigation and maintenance, so that fault hidden danger can be eliminated in time; the wind generating set is managed in an omnibearing, remote and scientific mode, the running state of the set is in a controllable and re-controllable state, and the intelligent management mode of few people and unattended operation of a wind power plant is favorably realized.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the present invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings, in which:
FIG. 1 is a schematic diagram of an intelligent detection system according to the present invention;
fig. 2 is a schematic flow chart of the intelligent detection method of the present invention.
Detailed Description
Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the preferred embodiments are illustrative of the invention only and are not limiting upon the scope of the invention.
As shown in fig. 1, the present embodiment provides an intelligent detection system for a state of a power grid unit, which includes a sensor unit, a data acquisition and analysis module, a field server and a remote diagnosis center, wherein the field server is used for setting parameters of the data acquisition and analysis module and storing monitoring data, and the remote diagnosis center performs real-time data communication with the field server via the Internet.
The sensor unit monitors the unit operation parameters, monitoring signals are uploaded to the data acquisition and analysis module through relatively independent signal channels, the data acquisition and analysis module processes and analyzes the unit operation parameters, the unit operation parameters are uploaded to the remote diagnosis center through the field server, and the remote diagnosis center analyzes and judges abnormal detection points.
The sensor unit mainly comprises a vibration acceleration sensor, a temperature sensor, an oil on-line monitoring sensor and a strain sensor, different signal channels are established according to different monitoring points, and the independent signal channels are convenient for a system to detect the generation position of an abnormal point and are beneficial to determining the fault position.
The vibration acceleration sensor adopts oil low frequency and oil high frequency, the vibration signal can reflect mechanical fault characteristics, and the change of the mechanical state can be reflected through the vibration signal. Accurate inspection and fault diagnosis can be carried out on the rotating assembly of the fan, such as rotor unbalance, oil film oscillation, rotating shaft bending and the like. The oil on-line monitoring sensor detects the performance of lubricating oil and hydraulic oil in the equipment and grasps the lubricating information and the wear information of parts during the operation of the equipment. The oil monitoring comprises oil quality inspection, scrap iron inspection and the like. The temperature of the temperature sensor is generally used for fault diagnosis of electronic and electrical components, and for a wind generating set, equipment such as a generator, a gear box, a frequency converter and the like needs to be monitored.
The remote diagnosis center mainly comprises a front server, an application server and a database server, wherein a data processing module and a diagnosis analysis module are arranged in the front server, and the application server is mainly used for counting and processing data and reports; the application server can also generate and display a normal toggle range trend graph of each operation parameter, and send out an alarm when exceeding a normal fluctuation range; and monitoring personnel can conveniently know the information in time.
The database server is stored with a signal algorithm library and a fault judgment case library. The content of the fault judgment case library mainly comprises the following steps: the system comprises professional knowledge, fault symptoms, fault reasons and analysis of each part, processing means, a fault analysis map, fault diagnosis case column query and the like, and a fault judgment case library can be expanded and corrected. The signal algorithm library mainly stores a signal processing model and an intelligent fuzzy judgment model for diagnosing different signal objects
The diagnostic analysis module mainly comprises a process control part and a fault analysis control part, wherein the fault analysis control part is used for sending required diagnostic contents to the process control part.
As shown in fig. 2, this embodiment further provides an intelligent detection method for the state of a power grid unit, which includes the following steps:
and S1, detecting and acquiring the running state data of each monitoring department of the unit through the sensor unit.
And S2, the data acquisition and analysis module processes and identifies the running state data and uploads the identification result to the front-end server through the field server.
S3, the fault analysis control part grabs the abnormal detection points, identifies the fault types and generates relative diagnosis contents according to the fault types and the fault judgment case base; the diagnosis content can be specified by a user or automatically generated by a fault analysis control part according to a fault judgment case library, and the stability of the operation of the diagnosis process is greatly improved by an external specified design.
And S4, the process control part receives the diagnosis content, and calls the corresponding signal processing model and the intelligent fuzzy judgment model according to the diagnosis content to carry out reasoning and analysis.
S5, obtaining the fault diagnosis conclusion of the abnormal detection point; the fault diagnosis conclusion can automatically generate a fault diagnosis report.
When the alarm of the abnormal point is obtained, monitoring personnel at a diagnosis center need to track the diagnosis and analysis of the abnormal point data, and on the other hand, on-site maintenance personnel are informed to check and check whether the operation of each instrument is normal.
According to the intelligent detection system taking the remote diagnosis center as the core, important operation parameters of each part are obtained through a sensor unit consisting of a plurality of sensors, a signal algorithm library and a fault judgment case library are matched as supports for fault diagnosis, potential faults of main parts of a unit are monitored, identified and judged, and powerful data support and guidance are provided in cooperation with field fault investigation and maintenance, so that potential faults are eliminated in time.
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. For example, 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, and the program may be stored in a computer readable storage medium, and when 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.
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 do not necessarily 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.
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 in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (8)
1. The utility model provides an intelligent detection system of electric wire netting unit state which characterized in that: the system comprises a sensor unit, a data acquisition and analysis module, a field server and a remote diagnosis center, wherein the sensor unit monitors unit operation parameters and uploads monitoring signals to the data acquisition and analysis module through relatively independent signal channels, the data acquisition and analysis module processes and analyzes the unit operation parameters and uploads the parameters to the remote diagnosis center through the field server, and the remote diagnosis center analyzes and judges abnormal detection points.
2. The system of claim 1, wherein the system comprises: the sensor unit mainly comprises a vibration acceleration sensor, a temperature sensor, an oil on-line monitoring sensor and a strain sensor, wherein different sensors are provided with different signal channels according to different monitoring points.
3. The system of claim 1, wherein the system comprises: the on-site server is used for setting parameters of the data acquisition and analysis module and storing monitoring data, and the remote diagnosis center Internet is in real-time data communication with the on-site server.
4. The system of claim 1, wherein the system comprises: the remote diagnosis center mainly comprises a front-end server, an application server and a database server, wherein a data processing module and a diagnosis analysis module are arranged in the front-end server, the application server is used for counting and processing data and reports, and a signal algorithm library and a fault judgment case library are stored in the database server.
5. The system of claim 1, wherein the system comprises: the diagnostic analysis module mainly comprises a process control part and a fault analysis control part, wherein the fault analysis control part is used for sending required diagnostic contents to the process control part.
6. An intelligent detection method for the state of a power grid unit is characterized in that: the method comprises the following operation steps:
step S1: detecting and acquiring running state data of each monitoring department of the unit through a sensor unit;
step S2: the data acquisition and analysis module processes and identifies the running state data and uploads an identification result to the front-end server through the field server;
step S3: the fault analysis control part grabs the abnormal detection points, identifies the fault types, and generates relative diagnosis contents according to the fault types and the fault judgment case base;
step S4: the process control part receives the diagnosis content, and calls a corresponding signal processing model and an intelligent fuzzy judgment model according to the diagnosis content to carry out reasoning and analysis;
step S5: and obtaining a fault diagnosis conclusion of the abnormal detection point.
7. The intelligent detection method for the state of the power grid unit as claimed in claim 6, wherein the fault diagnosis conclusion can automatically generate a fault diagnosis report, and the fault judgment case library can be expanded and corrected.
8. The intelligent detection method for the state of the power grid unit as claimed in claim 6, wherein the diagnosis content is automatically generated according to a fault judgment case library through user specification or a fault analysis control part.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111968356A (en) * | 2020-08-11 | 2020-11-20 | 重庆电子工程职业学院 | Intelligent building energy consumption monitoring system and method |
| CN112484767A (en) * | 2020-11-20 | 2021-03-12 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | Automatic fault diagnosis method and device for icing equipment |
| CN112526196A (en) * | 2020-11-30 | 2021-03-19 | 扬州鹏为软件有限公司 | Operation detection method of intelligent equipment |
| CN113190693A (en) * | 2021-05-31 | 2021-07-30 | 冯凯 | Data processing method and device of energy storage system, electronic equipment and storage medium |
| CN113420784A (en) * | 2021-05-28 | 2021-09-21 | 国网河北省电力有限公司营销服务中心 | Fault diagnosis system for intelligent electric meter |
| CN114325155A (en) * | 2021-11-19 | 2022-04-12 | 国网湖南省电力有限公司 | Fault detection system for transformer |
| CN114487361A (en) * | 2022-02-22 | 2022-05-13 | 白进东 | Oil on-line monitoring system |
| CN114691401A (en) * | 2020-12-31 | 2022-07-01 | 三一汽车制造有限公司 | Monitoring system and method for work machine, work machine and electronic device |
| CN114966402A (en) * | 2022-07-28 | 2022-08-30 | 山东翔讯科技有限公司 | Fault diagnosis system for switched reluctance motor |
| CN115422209A (en) * | 2022-11-07 | 2022-12-02 | 东方电气风电股份有限公司 | Wind power case data processing system and method |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109269801A (en) * | 2018-09-27 | 2019-01-25 | 中北大学 | A kind of gearbox fault monitoring device for wind-powered electricity generation |
-
2020
- 2020-04-17 CN CN202010306411.9A patent/CN111509847A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109269801A (en) * | 2018-09-27 | 2019-01-25 | 中北大学 | A kind of gearbox fault monitoring device for wind-powered electricity generation |
Non-Patent Citations (5)
| Title |
|---|
| 季文韬，等: "基于案例模糊的无人机故障诊断系统", vol. 20, no. 1, pages 32 - 35 * |
| 张彪，等: "基于协整分析的风电机组状态监测方法", 机械设计与研究辑, vol. 36, no. 1, 29 February 2020 (2020-02-29), pages 185 - 189 * |
| 金晓航，等: "风力发电机组故障诊断与预测技术研究综述", 仪器仪表学报, vol. 38, no. 5, 31 May 2017 (2017-05-31), pages 1041 - 1053 * |
| 马成龙，等: "基于服务导向网络架构的风电场故障诊断支撑系统平台", vol. 35, no. 4, pages 77 - 81 * |
| 龙泉，等: "状态监测与故障诊断在风电机组上的应用", 现代电力, vol. 25, no. 6, 31 December 2008 (2008-12-31), pages 55 - 59 * |
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