CN114942398A - Transformer fault prediction method for monitoring dissolved gas in transformer oil - Google Patents
Transformer fault prediction method for monitoring dissolved gas in transformer oil Download PDFInfo
- Publication number
- CN114942398A CN114942398A CN202210299870.8A CN202210299870A CN114942398A CN 114942398 A CN114942398 A CN 114942398A CN 202210299870 A CN202210299870 A CN 202210299870A CN 114942398 A CN114942398 A CN 114942398A
- Authority
- CN
- China
- Prior art keywords
- transformer
- monitoring
- dissolved gas
- alarm
- transformer oil
- 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
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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/26—Oils; viscous liquids; paints; inks
- G01N33/28—Oils, i.e. hydrocarbon liquids
- G01N33/2835—Oils, i.e. hydrocarbon liquids specific substances contained in the oil or fuel
- G01N33/2841—Oils, i.e. hydrocarbon liquids specific substances contained in the oil or fuel gas in oil, e.g. hydrogen in insulating oil
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
- G08B25/01—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium
- G08B25/08—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium using communication transmission lines
Landscapes
- Chemical & Material Sciences (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Power Engineering (AREA)
- Medicinal Chemistry (AREA)
- General Chemical & Material Sciences (AREA)
- Oil, Petroleum & Natural Gas (AREA)
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Food Science & Technology (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Housings And Mounting Of Transformers (AREA)
Abstract
The invention discloses a transformer fault prediction method for monitoring dissolved gas in transformer oil, which comprises the following steps: establishing model data in the master control system according to the relevant standard of dissolved gas in the transformer oil; the transformer is provided with a real-time monitoring system, and the monitoring system acquires the concentration value of dissolved gas in transformer oil in real time; the monitoring system collects real-time data of each monitoring terminal and performs comparison and verification; the main control system obtains a concentration value of dissolved gas in the transformer oil, and compares the concentration value with the established model data for judgment; the master control system sends out corresponding alarms according to the abnormal state model data; the alarm system comprises a local alarm and a remote alarm; the method comprises the following steps that a person on duty related to the transformer wears remote terminal equipment, and the remote terminal equipment accesses a cloud server to know real-time information of the transformer; the two types of model data of the main control system enable the alarm system to pertinently send out different alarms, and the transformer maintenance tool is favorably and pertinently prepared in advance.
Description
Technical Field
The invention relates to the technical field of transformer fault prediction, in particular to a transformer fault prediction method for monitoring dissolved gas in transformer oil.
Background
The transformer is used as a main power transformation device in a power grid and has a very important position in the power grid, so the on-line monitoring technology is an important component in the state maintenance technology, and the on-line monitoring of the dissolved gas in the oil is used as a monitoring method with high comprehensive sensitivity, so that the on-line monitoring method is quickly applied and popularized and also becomes an effective means for maintaining and evaluating the transformer;
at present, the fault prediction of a transformer generally cannot specifically judge the fault type of the transformer, so that a transformer maintenance tool cannot be prepared in advance in a targeted manner, and therefore a transformer fault prediction method for monitoring dissolved gas in transformer oil is provided.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a transformer fault prediction method for monitoring dissolved gas in transformer oil, wherein multiple terminals of a monitoring system verify each other to ensure the data accuracy, so that the accuracy of online data prediction of the dissolved gas in the transformer oil is improved, two types of model data of a main control system enable an alarm system to give different alarms in a targeted manner, so that the fault prejudgment and maintenance measures are more accurate and reliable, each alarm corresponds to one fault type, fault information is roughly obtained according to the alarm information, and the method is favorable for preparing a transformer maintenance tool in a targeted manner in advance.
In order to solve the technical problems, the invention provides the following technical scheme: .
A transformer fault prediction method for monitoring dissolved gas in transformer oil comprises the following steps:
s1, establishing model data in the main control system according to the relevant standards of dissolved gas in the transformer oil;
s1-1, normal state model data, the main control system demarcating the threshold value of the dissolved gas in the transformer oil according to the relevant standard;
s1-2, according to the abnormal state model data, the main control system establishes a trigger condition for each abnormal state according to the relevant standard and establishes a corresponding control command for the trigger condition;
s2, arranging a real-time monitoring system for the transformer, and acquiring the concentration value of dissolved gas in the transformer oil in real time by the monitoring system;
s2-1, at least two sets of monitoring terminals in the monitoring system are designed, and data measured by each monitoring terminal are mutually verification data;
s3, the monitoring system collects real-time data of each monitoring terminal and performs comparison and verification;
s3-1, if the data of each monitoring terminal are at the same level, judging that the monitoring terminals are normal, and uploading the data to a master control system;
s3-2, if the data of each monitoring terminal are different and have large differences, judging that the monitoring terminal is abnormal, sending out a warning by the monitoring system, calling a worker to process in time, and ensuring the accuracy and stability of the monitoring system;
s4, the main control system obtains the concentration value of the dissolved gas in the transformer oil, and compares the concentration value with the established model data for judgment;
s4-1, if the concentration value of the dissolved gas in the obtained transformer oil is within a safety threshold, judging that the transformer is normal;
s4-2, if the concentration value of the dissolved gas in the obtained transformer oil exceeds a safety threshold value, judging that the transformer is abnormal;
s5, the master control system sends out corresponding alarm according to the abnormal state model data;
s5-1, when the transformer has a fault, the content of some corresponding gas of different faults can be rapidly increased, otherwise, if the content of some gas is rapidly increased, the transformer has a corresponding fault;
s5-2, each fault corresponds to an independent alarm, the main control system controls the alarm system to send out corresponding alarms according to the specific fault, namely, the fault information is roughly obtained according to the alarm information;
s6, the alarm system comprises a local alarm and a remote alarm;
s6-1, the local alarm is that the transformer sends out photoelectric warning or voice broadcast warning locally on site to prompt site staff to process in time;
s6-2, the remote alarm is that the main control system uploads the judgment result to a cloud server according to the Internet, and the cloud server gives out an alarm for the off-site personnel;
s7, the relevant person on duty of the transformer wears the remote terminal device, and the remote terminal device accesses the cloud server to know the real-time information of the transformer.
Preferably, the monitoring system in step S3 filters invalid data, wherein the invalid data is the values of hydrogen, methane, ethane, ethylene, carbon monoxide, carbon dioxide and total hydrocarbons, and some types of gases are continuously zero or negative.
Compared with the prior art, the invention can achieve the following beneficial effects:
the data accuracy is guaranteed through mutual verification of multiple terminals of the monitoring system, the accuracy of online data prediction of dissolved gas in transformer oil is improved, the alarm system can give different alarms in a targeted mode through two kinds of model data of the main control system, fault pre-judgment and maintenance measures are more accurate and reliable, each alarm corresponds to one fault type, fault information is obtained roughly according to the alarm information, a transformer maintenance tool can be prepared in a targeted mode in advance, the reliability of maintenance and use of the transformer is guaranteed, and meanwhile the service life of the transformer is prolonged.
Detailed Description
The present invention will be further described with reference to specific embodiments for the purpose of facilitating an understanding of technical means, characteristics of creation, objectives and functions realized by the present invention, but the following embodiments are only preferred embodiments of the present invention, and are not intended to be exhaustive. Based on the embodiments in the implementation, other embodiments obtained by those skilled in the art without any creative efforts belong to the protection scope of the present invention. The experimental procedures in the following examples were carried out in a conventional manner unless otherwise specified, and materials, reagents and the like used in the following examples were commercially available unless otherwise specified.
Examples
The invention provides a transformer fault prediction method for monitoring dissolved gas in transformer oil, which comprises the following steps: firstly, establishing model data in a main control system according to the relevant standard of dissolved gas in transformer oil, wherein the model data comprises normal state model data and abnormal state model data, the normal state model data is used for the main control system to define the threshold of the dissolved gas in the transformer oil according to the relevant standard, the abnormal state model data is used for the main control system to establish a trigger condition for each abnormal state according to the relevant standard, and establishing a corresponding control command for the trigger condition, then arranging a real-time monitoring system in the transformer, acquiring the concentration value of the dissolved gas in the transformer oil in real time by the monitoring system, designing at least two sets of monitoring terminals in the monitoring system, measuring the data by each monitoring terminal to be mutually verified data, summarizing the real-time data of each monitoring terminal by the monitoring system, comparing and verifying, if the data of each monitoring terminal is at the same level, judging that the monitoring terminals are normal, and uploading the data to the main control system, and the main control system acquires a concentration value of dissolved gas in the transformer oil, compares the concentration value with the established model data, and judges that the transformer is normal if the acquired concentration value of the dissolved gas in the transformer oil is within a safety threshold.
Firstly, model data are established in a main control system according to the relevant standard of dissolved gas in transformer oil, the model data comprise normal state model data and abnormal state model data, a real-time monitoring system is arranged in the transformer, the monitoring system acquires the concentration value of the dissolved gas in the transformer oil in real time, the monitoring system collects the real-time data of each monitoring terminal and performs comparison and verification, the monitoring system filters invalid data, the invalid data are hydrogen, methane, ethane, ethylene, carbon monoxide, carbon dioxide and total hydrocarbon, the value of certain gas is continuously zero or negative, if the difference of the data of each monitoring terminal is huge, the monitoring terminal is judged to be abnormal, the monitoring system sends out an alarm, a worker is called to process the data in time, and the accuracy and the stability of the monitoring system are ensured.
Firstly, establishing model data in a main control system according to the relevant standard of dissolved gas in transformer oil, wherein the model data comprises normal state model data and abnormal state model data, arranging a real-time monitoring system in the transformer, acquiring the concentration value of the dissolved gas in the transformer oil in real time by the monitoring system, summarizing the real-time data of each monitoring terminal by the monitoring system, carrying out comparison and verification, uploading the data to the main control system, judging that the transformer is abnormal if the acquired concentration value of the dissolved gas in the transformer oil exceeds a safety threshold value, when a fault occurs in the transformer, the corresponding gas contents of different faults can be rapidly increased, otherwise, if the corresponding gas contents are rapidly increased, indicating that the corresponding fault occurs in the transformer, each fault corresponds to an independent alarm, and controlling the alarm system to send out corresponding alarms according to specific faults by the main control system, the method comprises the steps that fault information is obtained roughly according to alarm information, local alarm is that photoelectric warning or voice broadcasting warning is sent out on the local site of the transformer, field workers are prompted to process timely, remote alarm is that a main control system uploads a judgment result to a cloud server according to the Internet, the cloud server sends out warning for off-site workers, remote terminal equipment is worn by workers on duty related to the transformer, and the remote terminal equipment accesses the cloud server to know real-time information of the transformer.
The data accuracy is guaranteed through mutual verification of multiple terminals of the monitoring system, the accuracy of online data prediction of dissolved gas in transformer oil is improved, the alarm system can give different alarms in a targeted mode through two kinds of model data of the main control system, fault pre-judgment and maintenance measures are more accurate and reliable, each alarm corresponds to one fault type, fault information is obtained roughly according to the alarm information, a transformer maintenance tool can be prepared in a targeted mode in advance, the reliability of maintenance and use of the transformer is guaranteed, and meanwhile the service life of the transformer is prolonged.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. A transformer fault prediction method for monitoring dissolved gas in transformer oil is characterized by comprising the following steps:
s1, establishing model data in the main control system according to the relevant standards of dissolved gas in the transformer oil;
s2, arranging a real-time monitoring system for the transformer, and acquiring the concentration value of dissolved gas in the transformer oil in real time by the monitoring system;
s3, the monitoring system collects real-time data of each monitoring terminal and performs comparison and verification;
s4, the main control system obtains the concentration value of the dissolved gas in the transformer oil, and compares the concentration value with the established model data for judgment;
s5, the master control system sends out corresponding alarm according to the abnormal state model data;
s6, the alarm system comprises a local alarm and a remote alarm;
s7, the transformer-related person on duty wears the remote terminal device, and the remote terminal device accesses the cloud server to know the real-time information of the transformer.
2. The transformer fault prediction method for monitoring the dissolved gas in the transformer oil according to claim 1, characterized in that: the model data in the step S1 includes:
s1-1, normal state model data, the main control system demarcating the threshold value of the dissolved gas in the transformer oil according to the relevant standard;
s1-2, abnormal state model data, the master control system establishes a trigger condition for each abnormal state according to the relevant standard, and establishes a corresponding control command for the trigger condition.
3. The transformer fault prediction method for monitoring the dissolved gas in the transformer oil according to claim 1, characterized in that: at least two sets of monitoring terminals of the monitoring system in the step S2 are designed, and the data measured by each monitoring terminal are mutually verification data.
4. The transformer fault prediction method for monitoring the dissolved gas in the transformer oil according to claim 1, characterized in that: the step of comparing and verifying in the step of S3 includes:
s3-1, if the data of each monitoring terminal are at the same level, judging that the monitoring terminals are normal, and uploading the data to a main control system;
s3-2, if the data of each monitoring terminal are different and have large differences, the monitoring terminal is judged to be abnormal, the monitoring system sends out a warning, a worker is called to process the warning in time, and the accuracy and the stability of the monitoring system are guaranteed.
5. The transformer fault prediction method for monitoring the dissolved gas in the transformer oil according to claim 1, characterized in that: the comparison and determination in the step S4 includes:
s4-1, if the concentration value of the dissolved gas in the obtained transformer oil is within a safety threshold, judging that the transformer is normal;
s4-2, if the concentration value of the dissolved gas in the obtained transformer oil exceeds a safety threshold value, judging that the transformer is abnormal.
6. The transformer fault prediction method for monitoring the dissolved gas in the transformer oil according to claim 1, characterized in that: the logic in the step S5 is:
s5-1, when the transformer has a fault, the content of some corresponding gas of different faults can be rapidly increased, otherwise, if the content of some gas is rapidly increased, the transformer has a corresponding fault;
and S5-2, each fault corresponds to an independent alarm, and the master control system controls the alarm system to send out the corresponding alarm according to the specific fault, namely roughly acquiring fault information according to the alarm information.
7. The transformer fault prediction method for monitoring the dissolved gas in the transformer oil according to claim 1, characterized in that: the alarm in the step S6 includes:
s6-1, the local alarm is that the transformer sends out photoelectric warning or voice broadcast warning locally on site to prompt site staff to process in time;
s6-2, the remote alarm is that the main control system uploads the judgment result to a cloud server according to the Internet, and the cloud server gives out an alarm for the off-site personnel.
8. The transformer fault prediction method for monitoring the dissolved gas in the transformer oil according to claim 1, characterized in that: in the step S3, the monitoring system filters invalid data, where the invalid data is six gases of hydrogen, methane, ethane, ethylene, carbon monoxide, and carbon dioxide, and some gas value of the total hydrocarbons is continuously zero or negative.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210299870.8A CN114942398A (en) | 2022-03-25 | 2022-03-25 | Transformer fault prediction method for monitoring dissolved gas in transformer oil |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210299870.8A CN114942398A (en) | 2022-03-25 | 2022-03-25 | Transformer fault prediction method for monitoring dissolved gas in transformer oil |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114942398A true CN114942398A (en) | 2022-08-26 |
Family
ID=82905830
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210299870.8A Pending CN114942398A (en) | 2022-03-25 | 2022-03-25 | Transformer fault prediction method for monitoring dissolved gas in transformer oil |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114942398A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117110587A (en) * | 2023-10-25 | 2023-11-24 | 国网四川省电力公司超高压分公司 | Method and system for on-line monitoring abnormality alarm of dissolved gas in oil |
-
2022
- 2022-03-25 CN CN202210299870.8A patent/CN114942398A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117110587A (en) * | 2023-10-25 | 2023-11-24 | 国网四川省电力公司超高压分公司 | Method and system for on-line monitoring abnormality alarm of dissolved gas in oil |
| CN117110587B (en) * | 2023-10-25 | 2024-01-23 | 国网四川省电力公司超高压分公司 | Method and system for on-line monitoring abnormality alarm of dissolved gas in oil |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107657375A (en) | A kind of method for electric network fault judgement, verification and fault incidence analysis | |
| CN102324067A (en) | Electrical secondary equipment state evaluation method and system thereof | |
| CN114942398A (en) | Transformer fault prediction method for monitoring dissolved gas in transformer oil | |
| CN110516941A (en) | One kind producing hidden danger method and device based on big data analysis enterprise | |
| CN109212364A (en) | A kind of shunt capacitor group initial failure method for early warning based on dispersion mutation | |
| CN116342999A (en) | Open space fire smoke detection and identification method for ultrahigh voltage transformer substation | |
| CN105913126A (en) | Transformer station intelligent alarm model method for big data and cloud environment | |
| CN117409526A (en) | Electrical fire extremely early warning and monitoring system and fire extinguishing method | |
| CN111626555A (en) | Fire-fighting equipment operation warning and state evaluation system and device | |
| CN116665401A (en) | Accident prevention alarm system for chemical production | |
| CN116973498A (en) | Transformer bushing safety monitoring system based on artificial intelligence | |
| CN111509839A (en) | Trip event model analysis method based on alarm signal | |
| CN116247820A (en) | Autonomous operation and maintenance management system | |
| CN106451760A (en) | Real-time telemetry information monitoring method for 500kV transformer station centralized control system | |
| CN115514099A (en) | Electric power utilization safety inspection system and method | |
| CN114384192A (en) | Method for eliminating false alarm of oil chromatography monitoring device | |
| CN112345630A (en) | Device and method for detecting lead sealing state of large-thickness nonmetal coating layer accessory | |
| CN116246404A (en) | Distributed optical fiber fire disaster identification method and system based on underground comprehensive pipe rack | |
| Gao et al. | Study on Intelligent Management System of Electrical Safety | |
| CN113904312B (en) | Method and device for breaking link and eliminating defect of 220kV bus differential protection networking optical fiber of intelligent station | |
| CN114779122B (en) | Method and device for detecting state of arm vehicle based on historical data in live-wire work | |
| CN218567503U (en) | Diagnostic system of distribution network system fault type | |
| CN115860154B (en) | Processing method and system for infrared temperature measurement detection operation of power distribution network | |
| CN213545522U (en) | Cable temperature sensing early warning system | |
| CN117252556B (en) | Safety assessment method and system for hydrogen station |
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 |