CN114167314A - Transformer online monitoring device and fault detection method - Google Patents
Transformer online monitoring device and fault detection method Download PDFInfo
- Publication number
- CN114167314A CN114167314A CN202111186843.1A CN202111186843A CN114167314A CN 114167314 A CN114167314 A CN 114167314A CN 202111186843 A CN202111186843 A CN 202111186843A CN 114167314 A CN114167314 A CN 114167314A
- Authority
- CN
- China
- Prior art keywords
- data
- transformer
- state
- fault
- processing unit
- 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.)
- Pending
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 83
- 238000012806 monitoring device Methods 0.000 title claims abstract description 31
- 238000012545 processing Methods 0.000 claims abstract description 67
- 230000002159 abnormal effect Effects 0.000 claims abstract description 48
- 238000004891 communication Methods 0.000 claims abstract description 29
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 7
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical group [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims abstract description 4
- 238000006243 chemical reaction Methods 0.000 claims description 17
- 238000007689 inspection Methods 0.000 abstract description 5
- 239000000463 material Substances 0.000 abstract description 4
- 238000000034 method Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000004804 winding Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- QVFWZNCVPCJQOP-UHFFFAOYSA-N chloralodol Chemical compound CC(O)(C)CC(C)OC(O)C(Cl)(Cl)Cl QVFWZNCVPCJQOP-UHFFFAOYSA-N 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- 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
- G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
- G01R31/62—Testing of transformers
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Testing Relating To Insulation (AREA)
Abstract
The invention provides a transformer on-line monitoring device and a fault detection method, wherein a data processing unit of the transformer on-line monitoring device is respectively and electrically connected with a state detection unit and a communication unit; the state detection unit detects state data of the transformer and sends the state data to the data processing unit, wherein the state data comprises at least one of abnormal discharge data of the transformer, dissolved gas data of transformer oil, micro-water content data and iron core grounding current data; the data processing unit receives the state data, identifies abnormal data of the transformer, determines the fault probability of the transformer through the abnormal data, and sends fault early warning information to the communication unit according to the fault probability: and the communication unit receives the fault early warning information and sends the fault early warning information to the control platform. The invention does not need manual inspection, improves the speed and efficiency of fault discovery, reduces the consumption of manpower and material resources, can predict the fault and reduces the probability of fault occurrence.
Description
Technical Field
The invention relates to the field of transformer fault detection, in particular to an online transformer monitoring device and a fault detection method.
Background
For large transformers, it is generally necessary to operate in a closed and long time. In the long-time operation process, whether the transformer is normal or not is related to the safety of the whole unit. The existing transformer is in the operation process, an operator cannot observe the transformer normally through naked eyes, the transformer needs to be opened for inspection, and a large amount of manpower is consumed in the process.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides the transformer online monitoring device and the fault detection method, the state data of the transformer are collected through the state detection unit, the abnormal data in the state data are identified through the data processing unit, the fault probability of the transformer is obtained according to the abnormal data, online fault early warning is carried out based on the probability, manual inspection is not needed, the fault finding speed and efficiency are improved, the manpower and material resource consumption is reduced, the fault can be predicted, and the fault occurrence probability is reduced.
In order to solve the above problems, the present invention adopts a technical solution as follows: the transformer on-line monitoring device comprises: the device comprises a state detection unit, a data processing unit and a communication unit, wherein the data processing unit is electrically connected with the state detection unit and the communication unit respectively; the state detection unit detects state data of the transformer and sends the state data to the data processing unit, wherein the state data comprises at least one of abnormal discharge data of the transformer, dissolved gas data of transformer oil, micro-water content data and iron core grounding current data; the data processing unit receives the state data, identifies abnormal data of the transformer, determines the fault probability of the transformer according to the abnormal data, and sends fault early warning information to the communication unit according to the fault probability: and the communication unit receives the fault early warning information and sends the fault early warning information to a control platform.
Further, the state detection unit comprises a state detection sensor, the state detection sensor comprises an ultrasonic sensor, a UHF sensor, a transformer oil gas detector, a micro water sensor and a grounding sensor, and the state detection unit acquires state data of the transformer through the state detection sensor.
Furthermore, the state detection unit further comprises a signal processing module and an analog-to-digital conversion module, wherein the signal processing module is respectively connected with the state detection sensor and the analog-to-digital conversion module, amplifies data collected by the state detection sensor and transmits the amplified data to the analog-to-digital conversion module, the analog-to-digital conversion module is connected with the data processing unit, and received data are converted into digital signals and transmitted to the data processing unit.
Further, the step of detecting the state data of the transformer by the state detection unit and sending the state data to the data processing unit further includes: the data processing unit acquires state data type information to be acquired according to pre-stored state data detection information or a received instruction, and sends a state detection instruction including the state data type information to the state detection unit to acquire state data corresponding to the state data type information.
Furthermore, the transformer on-line monitoring device also comprises a power supply unit, and the power supply unit is electrically connected with the state detection unit, the data processing unit and the communication unit.
Further, the step of receiving the state data by the data processing unit and identifying abnormal data of the transformer specifically includes: standardizing the state data, establishing a sliding window according to the standardized state data, clustering the data in the sliding window, and identifying abnormal data in the state data according to the result of clustering.
Further, the step of receiving the state data by the data processing unit and identifying abnormal data of the transformer specifically includes: and the data processing unit compares the state data with pre-stored normal data and identifies abnormal data of the transformer according to a comparison result.
Further, the step of determining the fault probability of the transformer through the abnormal data specifically includes: and acquiring state data classification corresponding to the abnormal data, determining the fault probability of different components of the transformer according to the state data classification, and determining the fault probability of the transformer according to the fault probability of the different components.
Furthermore, the transformer on-line monitoring device further comprises an early warning unit, the early warning unit comprises an early warning indicator light and emergency power-off equipment, and the data processing unit is connected with the early warning unit and controls the early warning indicator light and the emergency power-off equipment to work correspondingly according to the early warning information.
Based on the same inventive concept, the invention also provides a transformer fault detection method, the transformer oil gas detection method is applied to the transformer online monitoring device, and the transformer fault detection method comprises the following steps: s101: detecting state data of the transformer through a state detection unit, and controlling the state detection unit to send the state data to a data processing unit; s102: controlling the data processing unit to receive the state data, identifying abnormal data of the transformer, determining the fault probability of the transformer through the abnormal data, and enabling the data processing unit to send fault early warning information to the communication unit according to the fault probability; s103: and the control communication unit receives the fault early warning information and then sends the fault early warning information to a control platform.
Compared with the prior art, the invention has the beneficial effects that: the state detection unit is used for acquiring the state data of the transformer, the data processing unit is used for identifying abnormal data in the state data, the probability of the transformer with faults is obtained according to the abnormal data, online fault early warning is carried out based on the probability, manual inspection is not needed, the speed and efficiency of fault finding are improved, manpower and material resource consumption is reduced, faults can be predicted, and the probability of fault occurrence is reduced.
Drawings
FIG. 1 is a structural diagram of an embodiment of an online monitoring device for a transformer according to the present invention;
FIG. 2 is a flow chart of an embodiment of a transformer oil and gas detection method of the present invention.
Detailed Description
The following description of the embodiments of the present application is provided by way of specific examples, and other advantages and effects of the present application will be readily apparent to those skilled in the art from the disclosure herein. The present application is capable of other and different embodiments and its several details are capable of modifications and/or changes in various respects, all without departing from the spirit of the present application. It should be noted that the various embodiments of the present disclosure, described and illustrated in the figures herein generally, may be combined with each other without conflict, and that the structural components or functional modules therein may be arranged and designed in a variety of different configurations. Thus, the following detailed description of the embodiments of the present disclosure, presented in the figures, is not intended to limit the scope of the claimed disclosure, but is merely representative of selected embodiments of the disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
The terminology used in the description of the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1, in which fig. 1 is a structural diagram of an embodiment of an online transformer monitoring device according to the present invention. The transformer on-line monitoring device of the invention is explained in detail with reference to the attached figure 1.
In this embodiment, the online monitoring device for the transformer includes: the data processing unit is electrically connected with the state detection unit and the communication unit respectively; the state detection unit detects state data of the transformer and sends the state data to the data processing unit, wherein the state data comprises at least one of abnormal discharge data of the transformer, dissolved gas data of transformer oil, micro-water content data and iron core grounding current data; the data processing unit receives the state data, identifies abnormal data of the transformer, determines the fault probability of the transformer through the abnormal data, and sends fault early warning information to the communication unit according to the fault probability: and the communication unit receives the fault early warning information and sends the fault early warning information to the control platform.
In this embodiment, the status data may also include winding voltage current detection data, winding temperature data, transformer internal humidity data, transformer oil temperature data, and other data that can be used to detect the status of the transformer.
In this embodiment, the state detection unit includes a state detection sensor, the state detection sensor includes an ultrasonic sensor, a UHF sensor, a transformer oil gas detector, a micro water sensor, and a ground sensor, and the state detection unit acquires the state data of the transformer through the state detection sensor. The installation position of the state detection sensor on the transformer and the data detection mode are the prior art, and are not described in detail herein.
In a preferred embodiment, in order to save electric energy, the status detection unit further includes a power management module, where the power management module is connected to the status detection sensors and controls the corresponding status detection sensors to be turned on according to instructions of the data processing unit, and turns off the status detection sensors corresponding to the high status data when the status data does not need to be acquired.
In other embodiments, the data processing unit may also be an edge server or other computing device capable of receiving the state data, identifying abnormal data according to the state data, and acquiring the fault probability of the transformer.
In the present embodiment, the communication unit is any one of bluetooth, LORA, WiFi, ZigBee, 2G, 3G, 4G, and other devices capable of wirelessly transmitting data. In other embodiments, the communication unit may transmit data by way of power carrier communication as well as other wired communication.
In this embodiment, the fault warning information transmitted by the communication unit includes identification information of the transformer, a fault probability, acquired abnormal data, and a time corresponding to the abnormal data.
In this embodiment, the state detection unit further includes a signal processing module and an analog-to-digital conversion module, the signal processing module is respectively connected to the state detection sensor and the analog-to-digital conversion module, amplifies data collected by the state detection sensor and transmits the amplified data to the analog-to-digital conversion module, the analog-to-digital conversion module is connected to the data processing unit, and the received data is converted into digital signals and transmitted to the data processing unit. The signal processing module comprises an integral amplifying circuit and a filter circuit, wherein the integral amplifying circuit amplifies signals input by the state detection sensor and outputs signals meeting the input requirements of the analog-to-digital conversion module; the filter circuit can control the frequency of the signal amplified by the integral amplifying circuit within 50Hz working frequency, filter high-frequency signals and reduce noise interference.
In other embodiments, the state detection unit further includes a protocol conversion module, the protocol conversion module is connected to the analog-to-digital conversion module, and the protocol conversion module converts the state data including the plurality of protocols output by the different state detection sensors into the state data of the same data protocol.
In this embodiment, the step of detecting the state data of the transformer by the state detecting unit and sending the state data to the data processing unit further includes: the data processing unit acquires the type information of the state data to be acquired according to the pre-stored state data detection information or the received instruction, and sends the state detection instruction including the type information of the state data to the state detection unit to acquire the state data corresponding to the type information of the state data.
In this embodiment, the on-line transformer monitoring device further includes a power supply unit electrically connected to the state detection unit, the data processing unit and the communication unit to supply power to the state detection unit, the data processing unit and the communication unit. The power supply unit further comprises a battery, and long-term operation of the transformer on-line monitoring device is achieved through a battery energy storage mode, and influence of external power supply interruption is avoided.
In one embodiment, the data processing unit receives the status data, and the step of identifying abnormal data of the transformer specifically includes: standardizing the state data, establishing a sliding window according to the standardized state data, clustering the data in the sliding window, and identifying abnormal data in the state data according to the clustering result.
Wherein the normalized state data is obtained by means of a pre-processing: the absolute deviation of the mean value is obtained from the time series of the state data and the mean value of the state data, and the normalized state data is obtained from the absolute deviation.
Clustering data of the sliding window corresponding to a certain period of time to obtain a plurality of clustering centers, calculating the sum of distances from each data of the sliding window to the clustering centers, and further calculating the average distance from all data in the sliding window to the clustering centers according to the sum of the distances. And calculating the average distance from all the data in the sliding window of the adjacent time period to the cluster center in the same way, calculating the difference between the average distances of the time period and the adjacent time period, and judging whether the difference between the average distances is greater than a preset threshold value. And if so, determining that the data corresponding to the time interval is abnormal data.
In another embodiment, the data processing unit receives the status data, and the step of identifying abnormal data of the transformer specifically includes: and the data processing unit compares the state data with the pre-stored normal data and identifies the abnormal data of the transformer according to the comparison result. If the time corresponding to the data beyond the range of the normal data is continuous and the length of the formed time period is greater than the preset time, the data is determined to be abnormal data.
In this embodiment, the step of determining the fault probability of the transformer through the abnormal data specifically includes: and obtaining state data classification corresponding to the abnormal data, determining the fault probability of different parts of the transformer according to the state data classification, and determining the fault probability of the transformer according to the fault probability of the different parts. And determining the fault probability of different parts of the transformer according to the association probability of the state data classification and the different parts of the transformer, wherein the higher the association probability is, the higher the fault probability is. The data processing unit prestores processing priorities and corresponding weights of different parts, and the fault probability of the transformer is obtained according to the processing priorities and the weights.
In this embodiment, the on-line monitoring device for the transformer further comprises an early warning unit, the early warning unit comprises an early warning indicator lamp and an emergency power-off device, and the data processing unit is connected with the early warning unit and controls the early warning indicator lamp and the emergency power-off device to work correspondingly according to early warning information. The data processing unit controls the early warning indicator lamp to emit light with different colors according to state data classification corresponding to the abnormal data and the fault probability of the transformer, and controls the emergency power-off equipment to close the transformer on-line monitoring device or the transformer when the fault probability is larger than an emergency power-off threshold or the fault of a relay controlling the transformer to be powered off is determined.
Has the advantages that: according to the transformer online monitoring device, the state data of the transformer are collected through the state detection unit, the abnormal data in the state data are identified through the data processing unit, the probability of the fault of the transformer is obtained according to the abnormal data, online fault early warning is carried out based on the probability, manual inspection is not needed, the speed and the efficiency of fault finding are improved, the consumption of manpower and material resources is reduced, the fault can be predicted, and the probability of the fault occurrence is reduced.
Based on the same inventive concept, the invention further provides a transformer oil gas detection method, please refer to fig. 2, fig. 2 is a flowchart of an embodiment of the transformer oil gas detection method of the invention, and the gas detection method of the invention is explained with reference to fig. 2.
In this embodiment, the transformer oil-gas detection method is applied to the transformer online monitoring device, and includes:
s101: and detecting the state data of the transformer through a state detection unit, and controlling the state detection unit to send the state data to a data processing unit.
S102: and the control data processing unit receives the state data, identifies abnormal data of the transformer, determines the fault probability of the transformer through the abnormal data, and enables the data processing unit to send fault early warning information to the communication unit according to the fault probability.
S103: and the control communication unit receives the fault early warning information and then sends the fault early warning information to the control platform.
The above embodiments are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereby, and any insubstantial changes and substitutions made by those skilled in the art based on the present invention are within the protection scope of the present invention.
Claims (10)
1. The utility model provides a transformer on-line monitoring device which characterized in that, transformer on-line monitoring device includes: the device comprises a state detection unit, a data processing unit and a communication unit, wherein the data processing unit is electrically connected with the state detection unit and the communication unit respectively;
the state detection unit detects state data of the transformer and sends the state data to the data processing unit, wherein the state data comprises at least one of abnormal discharge data of the transformer, dissolved gas data of transformer oil, micro-water content data and iron core grounding current data;
the data processing unit receives the state data, identifies abnormal data of the transformer, determines the fault probability of the transformer according to the abnormal data, and sends fault early warning information to the communication unit according to the fault probability:
and the communication unit receives the fault early warning information and sends the fault early warning information to a control platform.
2. The on-line transformer monitoring device according to claim 1, wherein the state detection unit comprises a state detection sensor, the state detection sensor comprises an ultrasonic sensor, a UHF sensor, a transformer oil gas detector, a micro water sensor and a grounding sensor, and the state detection unit acquires the state data of the transformer through the state detection sensor.
3. The transformer online monitoring device according to claim 2, wherein the state detection unit further comprises a signal processing module and an analog-to-digital conversion module, the signal processing module is respectively connected to the state detection sensor and the analog-to-digital conversion module, amplifies data collected by the state detection sensor and transmits the amplified data to the analog-to-digital conversion module, and the analog-to-digital conversion module is connected to the data processing unit, converts the received data into digital signals, and transmits the digital signals to the data processing unit.
4. The transformer on-line monitoring device according to claim 1, wherein the step of detecting the status data of the transformer by the status detecting unit and sending the status data to the data processing unit further comprises:
the data processing unit acquires state data type information to be acquired according to pre-stored state data detection information or a received instruction, and sends a state detection instruction including the state data type information to the state detection unit to acquire state data corresponding to the state data type information.
5. The on-line transformer monitoring device of claim 1, further comprising a power supply unit electrically connected to the state detection unit, the data processing unit and the communication unit.
6. The transformer online monitoring device according to claim 1, wherein the data processing unit receives the status data, and the step of identifying abnormal data of the transformer specifically comprises:
standardizing the state data, establishing a sliding window according to the standardized state data, clustering the data in the sliding window, and identifying abnormal data in the state data according to the result of clustering.
7. The transformer online monitoring device according to claim 1, wherein the data processing unit receives the status data, and the step of identifying abnormal data of the transformer specifically comprises:
and the data processing unit compares the state data with pre-stored normal data and identifies abnormal data of the transformer according to a comparison result.
8. The transformer online monitoring device according to claim 1, wherein the step of determining the fault probability of the transformer through the abnormal data specifically comprises:
and acquiring state data classification corresponding to the abnormal data, determining the fault probability of different components of the transformer according to the state data classification, and determining the fault probability of the transformer according to the fault probability of the different components.
9. The transformer online monitoring device according to claim 1, further comprising an early warning unit, wherein the early warning unit comprises an early warning indicator light and an emergency power-off device, and the data processing unit is connected to the early warning unit and controls the early warning indicator light and the emergency power-off device to work correspondingly according to the early warning information.
10. A transformer fault detection method, characterized in that the transformer oil gas detection method is applied to the transformer on-line monitoring device of any one of claims 1-9, and comprises the following steps:
s101: detecting state data of the transformer through a state detection unit, and controlling the state detection unit to send the state data to a data processing unit;
s102: controlling the data processing unit to receive the state data, identifying abnormal data of the transformer, determining the fault probability of the transformer through the abnormal data, and enabling the data processing unit to send fault early warning information to the communication unit according to the fault probability;
s103: and the control communication unit receives the fault early warning information and then sends the fault early warning information to a control platform.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111186843.1A CN114167314A (en) | 2021-10-12 | 2021-10-12 | Transformer online monitoring device and fault detection method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111186843.1A CN114167314A (en) | 2021-10-12 | 2021-10-12 | Transformer online monitoring device and fault detection method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114167314A true CN114167314A (en) | 2022-03-11 |
Family
ID=80476862
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111186843.1A Pending CN114167314A (en) | 2021-10-12 | 2021-10-12 | Transformer online monitoring device and fault detection method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114167314A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113376562A (en) * | 2021-06-08 | 2021-09-10 | 国网重庆市电力公司营销服务中心 | CVT (continuously variable transmission) verification method, device and medium based on rolling time window-FCM (fuzzy c-means) clustering |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105512474A (en) * | 2015-12-02 | 2016-04-20 | 国网山东省电力公司电力科学研究院 | Transformer state monitoring data anomaly detection method |
| CN110688624A (en) * | 2019-10-09 | 2020-01-14 | 国网宁夏电力有限公司 | Transformer fault probability calculation method based on abnormal operation state information |
| CN112527788A (en) * | 2020-12-17 | 2021-03-19 | 北京中恒博瑞数字电力科技有限公司 | Method and device for detecting and cleaning abnormal value of transformer monitoring data |
-
2021
- 2021-10-12 CN CN202111186843.1A patent/CN114167314A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105512474A (en) * | 2015-12-02 | 2016-04-20 | 国网山东省电力公司电力科学研究院 | Transformer state monitoring data anomaly detection method |
| CN110688624A (en) * | 2019-10-09 | 2020-01-14 | 国网宁夏电力有限公司 | Transformer fault probability calculation method based on abnormal operation state information |
| CN112527788A (en) * | 2020-12-17 | 2021-03-19 | 北京中恒博瑞数字电力科技有限公司 | Method and device for detecting and cleaning abnormal value of transformer monitoring data |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113376562A (en) * | 2021-06-08 | 2021-09-10 | 国网重庆市电力公司营销服务中心 | CVT (continuously variable transmission) verification method, device and medium based on rolling time window-FCM (fuzzy c-means) clustering |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105738809B (en) | A kind of motor temperature rise and vibration monitoring device and its implementation | |
| CN110672962A (en) | State monitoring system for electric equipment | |
| CN104296810A (en) | Intelligent integrated monitoring system of cable branch box | |
| CN106211450B (en) | A kind of LED mark light source intelligent control systems and its long-range control method | |
| CN114167314A (en) | Transformer online monitoring device and fault detection method | |
| KR20200136171A (en) | Elevator monitoring system for customer safety based on Internet of Things, and monitoring method thereof | |
| CN108649774A (en) | A kind of intelligent voltage adapter | |
| CN105573233A (en) | Circuit intelligence management system | |
| CN115754249A (en) | On-line monitoring system for micro-water content in transformer oil | |
| KR101576433B1 (en) | Method and apparatus for detecting an abnormally operating device comparing to other devices | |
| CN111273571A (en) | Equipment running state monitoring method, device and system, storage medium and equipment | |
| CN212413184U (en) | Dynamic monitoring gateway for speed reduction motor | |
| KR102207181B1 (en) | Device for diagnosing breakdown of switchgear using energy harvesting, diagnosing breakdown of switchgear system comprising the same and operating method thereof | |
| CN205622325U (en) | Remote intelligent power dispatching automation equipment examine monitoring system | |
| CN203704977U (en) | Terminal equipment of electric oil filling equipment oil quality on-line monitoring system | |
| CN110568249A (en) | parallel power transformer monitoring system based on circulating current detection and monitoring method thereof | |
| CN115932492A (en) | System and method for detecting fault of oiled paper capacitive bushing | |
| CN105241564A (en) | Bus temperature grading warning device | |
| CN102478616A (en) | Intelligent overhead line failure alarm with self-adaptive function | |
| CN102735352A (en) | Wireless temperature measurement real-time alarm system for high-voltage line | |
| CN113098132A (en) | Improved machine learning fault diagnosis system based on group intelligent optimization | |
| CN207424153U (en) | The power-off monitoring system of icp ms | |
| CN112540561A (en) | Wireless online monitoring system and method based on industrial Internet of things | |
| CN110926536A (en) | Cable trench detection system based on wireless signal acquisition and detection method thereof | |
| CN110855011A (en) | Photovoltaic system with composite communication photovoltaic optimizer and working method thereof |
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 |