CN114279573A - Internal fault inversion detection method based on surface temperature rise of lightning arrester - Google Patents
Internal fault inversion detection method based on surface temperature rise of lightning arrester Download PDFInfo
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Abstract
The invention discloses an internal fault inversion detection method based on surface temperature rise of a lightning arrester, which comprises the following steps of: step 1, establishing an MOA temperature field calculation model, setting environment and fault parameters, obtaining a temperature-fluid field of a lightning arrester, simultaneously obtaining the temperature rise of the MOA under different environmental conditions and different fault conditions, selecting MOA surface temperature characteristic position points and internal hot point temperatures, and constructing a forward modeling database; step 2, constructing an internal hot spot temperature inversion model based on MOA surface temperature; and 3, inverting the model according to the internal hot spot temperature inversion model based on the MOA surface temperature. Through the overall structure of equipment, can realize that the inside temperature of zinc oxide arrester calculates, foresee zinc oxide arrester equipment body damaged condition in advance, avoid short circuit influence system power supply and personal safety to through simulation model, big data algorithm, utilize surface temperature distribution rule direction to deduce inside temperature distribution rule, can effectively compensate infrared detection's not enough.
Description
Technical Field
The invention relates to the technical field of lightning arresters, in particular to an internal fault inversion detection method based on surface temperature rise of a lightning arrester.
Background
Whether the lightning arrester can reliably operate is an important factor influencing the safe and stable operation of the power system. Most areas in China belong to lightning strike areas, in recent years, the power failure time caused by the fault of a zinc oxide arrester is more than 100 times, and the detection significance of the fault of the zinc oxide arrester in advance is significant. The online monitoring of the state of the lightning arrester in operation is an important means for ensuring the safe operation of the lightning arrester; the infrared detection technology has the advantages of no power failure, no sampling, no contact, low cost, strong practicability and the like, and is widely applied to the aspect of fault diagnosis of power equipment. Compared with the traditional detection method, the infrared detection method has the advantages of long distance, non-contact, charged detection and the like, the infrared detection technology can convert the thermal radiation signals of the detected power equipment into electric signals, and the electric signals are converted into infrared images after processing, so that the temperature distribution and the thermal characteristics of the power equipment can be obtained, and the faults of the power equipment are inverted according to the temperature distribution and the thermal characteristics. However, the problems that manual work is excessively relied on, the field detection effect is not obvious, the detection rate of the lightning arrester is low and the like exist at present, and besides the detection operation of field operators is not standard, the problems are related to the selection and the use of equipment, the heating characteristics of the lightning arrester are not known, the theoretical research is not deep enough and the like.
When the infrared technology is used for detecting the fault arrester, the influence of environmental factors on a detection result cannot be ignored, and particularly in recent years, environmental pollution and air haze are heavy, so that the surface of the power transmission line arrester is very easy to be polluted; under the action of different surface filth and environment relative humidity, the external insulation and heating characteristics of the lightning arrester are greatly changed, and the infrared detection effect of the fault lightning arrester is directly influenced. Meanwhile, the heating characteristics of different lightning arresters under different fault conditions (such as damp, aging, short circuit, dirt, abnormal operation and the like) are obviously different. Particularly, the thermal infrared imager only detects the surface temperature but cannot acquire the internal temperature of the arrester, most early faults of the arrester originate from internal moisture and valve plate degradation, and it is very important to know the internal temperature distribution of the zinc oxide arrester in advance, so that the internal fault inversion detection method based on the surface temperature rise of the arrester is provided.
Disclosure of Invention
The invention aims to provide an internal fault inversion detection method based on surface temperature rise of a lightning arrester, which can achieve the effects of realizing internal temperature calculation of a zinc oxide lightning arrester, predicting the damage condition of a zinc oxide lightning arrester device body in advance, avoiding the influence of short circuit on system power supply and personal safety, and effectively making up the defects of infrared detection by deducing an internal temperature distribution rule by utilizing the direction of the surface temperature distribution rule through a simulation model and a big data algorithm.
The invention discloses an internal fault inversion detection method based on surface temperature rise of an arrester, which adopts the technical scheme that the internal fault inversion detection method based on the surface temperature rise of the arrester comprises the following steps:
step 1, establishing an MOA temperature field calculation model, setting environment and fault parameters, obtaining a temperature-fluid field of a lightning arrester, simultaneously obtaining the temperature rise of the MOA under different environmental conditions and different fault conditions, selecting MOA surface temperature characteristic position points and internal hot point temperatures, and constructing a forward modeling database;
step 4, determining the fault position and judging the fault degree according to the distribution characteristics and the internal temperature distribution data;
and 5, comparing the distribution characteristics with the internal temperature inversion values and experimental measured values of the internal temperature distribution data, and calculating the sum of squares of errors, the average absolute error, the average absolute percentage error, the mean square error and the mean square percentage error.
As a preferred scheme, the temperature rise of the MOA under different environmental conditions and different fault conditions is obtained by forward calculation.
As a preferable scheme, the internal hot spot temperature inversion model based on the MOA surface temperature adopts an artificial intelligence algorithm and selects one of a neural network or a vector machine SVM.
Preferably, the sum of squared errors is expressed as
The mean absolute error is expressed as
The average absolute percentage error is formulated as
The mean square error formula is
The mean square percentage error is formulated as
And Ai is the test value of the ith prediction sample, Pi is the prediction value of the ith prediction sample, and N is the number of the prediction samples.
As a preferred scheme, the internal fault inversion detection method based on the surface temperature rise of the lightning arrester comprises a lightning arrester temperature forward calculation module, a lightning arrester internal temperature field reverse deduction module, an internal fault position judgment module and an error analysis module, wherein the lightning arrester temperature forward calculation module, the lightning arrester internal temperature field reverse deduction module, the internal fault position judgment module and the error analysis module are sequentially connected.
Preferably, the platform and the system of the internal fault inversion detection method based on the surface temperature rise of the lightning arrester are stored in an APP of a computer framework and are driven to run by a burned program, and the platform and the system comprise a bus framework, a processor, a memory and a bus interface, wherein the bus framework can comprise any number of interconnected buses and bridges, the bus framework links various circuits including one or more processors represented by the processor and a memory represented by the memory, the bus framework can also link various other circuits such as a peripheral device, a voltage stabilizer, a power management circuit and the like, the bus interface provides an interface between the bus framework and a receiver and a transmitter, and the receiver and the transmitter can be the same element, namely a transceiver, and provide a unit for communicating with various other systems on a transmission medium.
The internal fault inversion detection method based on the surface temperature rise of the lightning arrester has the beneficial effects that:
through the overall structure of equipment, can realize that the inside temperature of zinc oxide arrester calculates, foresee zinc oxide arrester equipment body damaged condition in advance, avoid short circuit influence system power supply and personal safety to through simulation model, big data algorithm, utilize surface temperature distribution rule direction to deduce inside temperature distribution rule, can effectively compensate infrared detection's not enough.
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FIG. 1 is a general schematic of the present invention;
FIG. 2 is a schematic view of the overall frame structure of the present invention;
fig. 3 is a schematic block diagram of the overall structural steps of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-3, an embodiment of the present invention is shown: an internal fault inversion detection method based on surface temperature rise of a lightning arrester comprises the following steps:
step 1, establishing an MOA temperature field calculation model, setting environment and fault parameters, obtaining a temperature-fluid field of a lightning arrester, simultaneously obtaining the temperature rise of the MOA under different environmental conditions and different fault conditions, selecting MOA surface temperature characteristic position points and internal hot point temperatures, and constructing a forward modeling database;
specifically, the method comprises the following steps: the MOA temperature field calculation model is used for the lightning arrester temperature field-electromagnetic field coupling simulation calculation, the obstructed fault and the surface temperature distribution characteristic of the lightning arrester under the environmental condition are obtained, and the conditions of different environmental conditions and different faults comprise the conditions of moisture, aging, short circuit, dirt, abnormal operation and the like.
specifically, the method comprises the following steps: the internal hot spot temperature inversion model of the MOA surface temperature is used for constructing an internal hot spot temperature reverse deduction model based on the surface temperature of a zinc oxide arrester (MOA).
specifically, the method comprises the following steps: and the difference of the MOA surface temperature distribution can be obtained according to the MOA surface temperature distribution rule, and if the difference of the MOA surface temperature distribution appears, the position is a fault position.
Step 4, determining the fault position and judging the fault degree according to the distribution characteristics and the internal temperature distribution data;
and 5, comparing the distribution characteristics with the internal temperature inversion value and the experimental measured value of the internal temperature distribution data, and calculating the square sum of errors, the average absolute error, the average absolute percentage error, the mean square error and the mean square percentage error, wherein the square sum of errors, the average absolute error, the average absolute percentage error, the mean square error and the mean square percentage error are used for analyzing the error between the temperature data acquired by the inversion module and the actually measured temperature data.
And forward calculation is adopted to obtain the temperature rise of the MOA under different environmental conditions and different fault conditions.
The internal hot spot temperature inversion model based on the MOA surface temperature adopts an artificial intelligence algorithm and selects one of a neural network or a vector machine SVM.
The sum of the squares of errors is formulated as
The mean absolute error is expressed as
The average absolute percentage error is formulated as
The mean square error formula is
The mean square percentage error is formulated as
And Ai is the test value of the ith prediction sample, Pi is the prediction value of the ith prediction sample, and N is the number of the prediction samples.
The internal fault inversion detection method based on the surface temperature rise of the lightning arrester comprises a lightning arrester temperature forward calculation module, a lightning arrester internal temperature field reverse deduction module, an internal fault position judgment module and an error analysis module, wherein the lightning arrester temperature forward calculation module, the lightning arrester internal temperature field reverse deduction module, the internal fault position judgment module and the error analysis module are sequentially connected.
Specifically, the method comprises the following steps: the lightning arrester temperature field forward calculation module is used for lightning arrester temperature field-electromagnetic field coupling simulation calculation to obtain the surface temperature distribution characteristics of the lightning arrester under the obstructed faults and the environmental conditions; the lightning arrester internal temperature field reverse deduction module is used for constructing an internal hot spot temperature reverse deduction model based on the surface temperature of a zinc oxide lightning arrester (MOA), and the construction of the inversion model is based on an artificial intelligence algorithm, such as a neural network, a Support Vector Machine (SVM) and the like; the internal fault position judging module is used for analyzing the MOA surface temperature distribution rule, obtaining distribution characteristics, predicting the internal hot spot temperature and judging the fault position; and the error analysis module is used for analyzing the error between the temperature data acquired by the inversion module and the actually measured temperature data.
The platform and the system of the internal fault inversion detection method based on the surface temperature rise of the lightning arrester are stored in an APP of a computer framework and are driven to run through a burning program, and comprise a bus framework, a processor, a memory and a bus interface, wherein the bus framework can comprise any number of interconnected buses and bridges, the bus framework can link various circuits including one or more processors represented by the processor and the memory represented by the memory, the bus framework can also link various other circuits such as peripheral equipment, a voltage stabilizer, a power management circuit and the like, the bus interface provides an interface between the bus framework and a receiver and a transmitter, and the receiver and the transmitter can be the same element, namely a transceiver, and provide a unit for communicating with various other systems on a transmission medium.
Finally, it should be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (6)
1. An internal fault inversion detection method based on surface temperature rise of a lightning arrester is characterized by comprising the following steps:
the method comprises the following steps:
step 1, establishing an MOA temperature field calculation model, setting environment and fault parameters, obtaining a temperature-fluid field of a lightning arrester, simultaneously obtaining the temperature rise of the MOA under different environmental conditions and different fault conditions, selecting MOA surface temperature characteristic position points and internal hot point temperatures, and constructing a forward modeling database;
step 2, constructing an internal hot spot temperature inversion model based on MOA surface temperature;
step 3, analyzing an MOA surface temperature distribution rule according to an internal hot spot temperature inversion model based on the MOA surface temperature to obtain distribution characteristics and internal temperature distribution data;
step 4, determining the fault position and judging the fault degree according to the distribution characteristics and the internal temperature distribution data;
and 5, comparing the distribution characteristics with the internal temperature inversion values and experimental measured values of the internal temperature distribution data, and calculating the sum of squares of errors, the average absolute error, the average absolute percentage error, the mean square error and the mean square percentage error.
2. The internal fault inversion detection method based on the surface temperature rise of the lightning arrester as claimed in claim 1, characterized in that: and forward calculation is adopted to obtain the temperature rise of the MOA under different environmental conditions and different fault conditions.
3. The internal fault inversion detection method based on the surface temperature rise of the lightning arrester as claimed in claim 1, characterized in that: the internal hot spot temperature inversion model based on the MOA surface temperature adopts an artificial intelligence algorithm and selects one of a neural network or a vector machine SVM.
4. The internal fault inversion detection method based on the surface temperature rise of the lightning arrester as claimed in claim 1, characterized in that: the sum of the squares of errors is formulated as
The mean absolute error is expressed as
The average absolute percentage error is formulated as
The mean square error formula is
The mean square percentage error is formulated as
And Ai is the test value of the ith prediction sample, Pi is the prediction value of the ith prediction sample, and N is the number of the prediction samples.
5. The internal fault inversion detection method based on the surface temperature rise of the lightning arrester as claimed in claim 1, characterized in that: the lightning arrester temperature forward calculation module, the lightning arrester internal temperature field reverse deduction module, the internal fault position judgment module and the error analysis module are sequentially connected.
6. The method for detecting the inversion of the internal fault based on the surface temperature rise of the lightning arrester as claimed in claims 1 to 5, wherein the method comprises the following steps: the platform and the system of the internal fault inversion detection method based on the surface temperature rise of the lightning arrester are stored in an APP of a computer framework and are driven to run through a burning program, and comprise a bus framework, a processor, a memory and a bus interface, wherein the bus framework can comprise any number of interconnected buses and bridges, the bus framework can link various circuits including one or more processors represented by the processor and the memory represented by the memory, the bus framework can also link various other circuits such as peripheral equipment, a voltage stabilizer, a power management circuit and the like, the bus interface provides an interface between the bus framework and a receiver and a transmitter, and the receiver and the transmitter can be the same element, namely a transceiver, and provide a unit for communicating with various other systems on a transmission medium.
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- 2021-12-14 CN CN202111528537.1A patent/CN114279573A/en active Pending
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|---|---|---|---|---|
| KR20070074321A (en) * | 2006-01-09 | 2007-07-12 | 길경석 | Arrester diagnosis technique and device by measurement of temperature |
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