CN113791324A - Pollution insulator flashover risk early warning system and method - Google Patents
Pollution insulator flashover risk early warning system and method Download PDFInfo
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Abstract
The invention relates to a pollution insulator flashover risk early warning system.A cutoff ring is used for collecting leakage current; the signal processing module is connected with the cutoff ring and used for conditioning the leakage current; the microclimate data acquisition circuit is used for acquiring environmental temperature and humidity data; the ARM processor is respectively connected with the signal processing module and the microclimate data acquisition circuit and is used for sampling data through an A/D sampling port of the ARM processor; the main control single chip microcomputer is connected with the ARM processor and used for awakening the ARM processor at the sampling moment so as to perform pollution insulator flashover risk early warning. The early warning method corresponding to the system can process the data sample by using the fuzzy neural network detection model to obtain the pollution degree evaluation result of the insulator, so that the grading early warning is carried out on the flashover risk of the pollution insulator. The intelligent power supply system can adapt to complex field environments, greatly reduces pollution flashover probability, causes economic loss and work difficulty, and practically improves reliability of the power supply system.
Description
Technical Field
The invention relates to the field of insulator detection, in particular to a pollution insulator flashover risk early warning system and method.
Background
The insulator is one of the most common electrical equipment in a power system, and dirt can be deposited on the surface of the insulator running outdoors. In a dry state, dirt does not greatly affect the insulating capability of the insulator, the dirt insulator basically does not discharge, the weather conditions that the insulator is wet and dirt loss is not caused are the biggest challenges faced by the dirt insulator, such as fog, downy rain, light rain, snow melting, condensation and other weather conditions, at the moment, the soluble electrolyte in the dirt absorbs water to form a layer of conductive water film, the insulating property of the insulator is reduced, and the probability of flashover is increased. Because environmental factors in one area are basically consistent, the insulator pollution accumulation conditions are similar, once one insulator is in flashover, thousands of insulators in the same area are positioned at the edge of flashover, large-area power failure is easily caused, and immeasurable loss is caused. Particularly in the beginning of the 21 st century, the atmospheric environment is continuously deteriorated, the rainfall acidification is increasingly serious, large-area pollution flashover accidents are frequently caused, and the safe operation of a power grid is seriously threatened.
The discharge of the pollution insulator is the result of the combined action of the pollution type, the accumulation amount, the material of the insulator, the umbrella skirt shape, the wind power, the humidity and the operating voltage. A large amount of information including sound, heat, light, electromagnetic waves and the like is released from the process from discharging to flashover of the polluted insulator, the information is processed and analyzed, relevant characteristic quantities representing the insulation state of the insulator can be obtained, the accuracy of evaluating the running state of the insulator can be improved by selecting the proper characteristic quantities, and early warning is further realized when danger comes.
With continuous exploration of pollution flashover theory and continuous progress of technology, various pollution insulator flashover risk early warning methods based on background algorithm appear. The development is mature as follows: the method comprises the steps of a pollution insulator flashover early warning method based on a fuzzy principle, a pollution insulator flashover early warning method based on a neural network, a pollution insulator flashover early warning method based on a least square support vector machine and the like. The pollution insulator flashover risk early warning models have the advantages and the disadvantages, the basic input quantity is based on leakage current, the pollution flashover mechanism and the leakage current characteristic are not deeply researched and effectively extracted, and the characteristic parameters are single. The weight of pollution degree discharge along the surface between the characteristic parameters is not researched, so that the prediction effect is not ideal. Therefore, a complete method for warning the flashover risk of the contaminated insulator is urgently needed to be provided.
Disclosure of Invention
The invention aims to provide a pollution insulator flashover risk early warning system and method, which can timely and completely carry out pollution insulator flashover risk early warning.
In order to achieve the purpose, the invention provides the following scheme:
a filthy insulator flashover risk early warning system, the risk early warning system includes: the system comprises a cutoff ring, a signal processing module, a microclimate data acquisition circuit, a master control single chip microcomputer and an ARM processor;
the cutoff ring is used for collecting leakage current;
the signal processing module is connected with the cutoff ring and used for conditioning the leakage current;
the microclimate data acquisition circuit is used for acquiring environmental temperature and humidity data;
the ARM processor is respectively connected with the signal processing module and the microclimate data acquisition circuit and is used for sampling data through an A/D sampling port of the ARM processor;
the main control single chip microcomputer is connected with the ARM processor and used for awakening the ARM processor at the sampling moment so as to perform pollution insulator flashover risk early warning.
Optionally, the signal processing module includes an impedance matching network, a filter circuit, an amplifier circuit, a peak holding circuit, and a multiplexing switch, which are connected in sequence; the impedance matching network is connected with the cut-off ring, and the multiplexing switch is connected with the A/D sampling port.
Optionally, the risk early warning system includes: a CF memory card;
the CF storage card is connected with the ARM processor and used for storing the data collected by the A/D sampling port.
Optionally, the risk early warning system includes: a solar power supply system;
the solar power supply system provides working voltage for the risk early warning system.
Optionally, the solar power supply system includes: solar panel and lithium cell.
Optionally, the master control single chip microcomputer is connected with the ARM processor and the solar power supply system through an I2C bus respectively.
A pollution insulator flashover risk early warning method comprises the following steps:
acquiring a dirty insulator data sample; the dirty insulator data sample comprises: leakage current and ambient temperature and humidity data;
processing the dirty insulator data sample by using a fuzzy neural network detection model to obtain a dirty insulator flashover risk early warning result;
the fuzzy neural network detection model establishing process comprises the following steps:
determining fuzzy evaluation parameters; the fuzzy evaluation parameters comprise: input parameters and output parameters, variable weight method weight, membership function and fuzzy rule base;
and establishing a fuzzy neural network detection model according to the fuzzy evaluation parameters.
Optionally, the input parameters include: leakage current's virtual value, amplitude and pulse frequency, and ambient temperature and humidity data, output parameter is insulator filthy degree grade, includes: normal filth NL, normal filth CM, more severe filth MS and severe filth SR.
Optionally, the fuzzy neural network detection model includes: the device comprises an input layer, a quantization layer, a hidden layer and an output layer;
the quantization layer is used for fuzzifying the input parameters to obtain fuzzified input parameters;
the hidden layer is used for mapping the fuzzified input parameters to output to obtain a fuzzy output value;
and the output layer is used for outputting the insulator pollution degree grade according to the fuzzy output value.
Optionally, in the hidden layer, the membership function is used to process the input parameters for fuzzification.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the method, a fuzzy neural network detection model is established by utilizing the leakage current acquired by the cutoff ring, the environmental temperature and humidity data acquired by the microclimate data acquisition circuit and the insulator pollution degree grade, so that a pollution insulator data sample is processed, and a corresponding pollution insulator flashover risk early warning result is obtained. The pollution insulator flashover risk early warning method can improve timeliness and accuracy of insulator running state early warning, a corresponding early warning system has the advantages of energy conservation and environmental protection, convenience and quickness, great practicability and adaptability to complex field environments, pollution flashover probability and economic loss caused by pollution flashover are greatly reduced, operation and maintenance work of electric power workers is greatly facilitated, and reliability of a power supply system is practically improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a general block diagram of a foul insulator flashover risk early warning system provided by the present invention;
FIG. 2 is a schematic circuit diagram of a signal processing module according to the present invention;
fig. 3 is a flowchart of the method for warning the flashover risk of the contaminated insulator according to 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.
The invention aims to provide a pollution insulator flashover risk early warning system and method, which can adapt to a complex field environment, greatly reduce pollution flashover probability and economic loss, greatly facilitate operation and maintenance work of electric power workers, and practically improve the reliability of a power supply system.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
The invention provides a pollution insulator flashover risk early warning system, as shown in figure 1, the risk early warning system comprises: the device comprises a cutoff ring, a signal processing module, a microclimate data acquisition circuit, a master control single chip microcomputer and an ARM processor.
The cutoff ring is used for collecting leakage current.
The signal processing module is connected with the cutoff ring and used for conditioning leakage current.
The microclimate data acquisition circuit is used for acquiring environmental temperature and humidity data.
The ARM processor is respectively connected with the signal processing module and the microclimate data acquisition circuit and is used for sampling data through an A/D sampling port of the ARM processor.
The main control single chip microcomputer is connected with the ARM processor and used for awakening the ARM processor at the sampling moment to perform pollution insulator flashover risk early warning.
The cutoff ring is in close contact with the insulator through the conductive silver adhesive and the auxiliary fixing device, the surface current of the insulator is cut off, and the leakage current can be calculated through resistance sampling and voltage measurement. High sensitivity and no waveform distortion.
In order to prevent the weak signal output by the interception ring from being interfered, the system adopts a double-layer shielded cable and a self-adaptive noise cancellation method; meanwhile, the collected signals are isolated in a multilayer mode by means of optical coupling isolation sheets and the like, and damage of large-current signals to the circuit is prevented.
The pollution insulator flashover risk early warning system has the advantages of being energy-saving, environment-friendly, light and convenient, has great practicability, can adapt to complex field environments, greatly reduces pollution flashover probability and economic loss caused by pollution flashover, greatly facilitates operation and maintenance of electric power workers, and practically improves reliability of a power supply system.
Fig. 2 shows a specific circuit schematic diagram of a signal processing module, which includes an impedance matching network, a filter circuit, an amplifier circuit, a peak hold circuit and a multiplexing switch connected in sequence; the impedance matching network is connected with the cut-off ring, and the multiplex switch is connected with the A/D sampling port.
The current signal collected by the cut-off ring is subjected to circuit links such as impedance matching, filtering, amplifying and the like, and finally is sampled by an A/D sampling port of the ARM processor.
The risk early warning system includes: a CF memory card; the CF storage card is connected with the ARM processor and used for storing data collected by the A/D sampling port for being transmitted back to the background early warning system for processing and analysis at proper time, and therefore flashover early warning of the polluted insulator is achieved.
The risk early warning system includes: solar energy power supply system, solar energy power supply system includes: solar panel and lithium cell provide 5V operating voltage for risk early warning system.
The main control single chip microcomputer is respectively connected with the ARM processor and the solar power supply system through an I2C bus, and the ARM processor is awakened when the timing acquisition time is up.
The application also provides a risk early warning method corresponding to the pollution insulator flashover risk early warning system, and the risk early warning method comprises the following steps:
acquiring a dirty insulator data sample; the dirty insulator data sample includes: leakage current and ambient temperature and humidity data.
And processing a dirty insulator data sample by using a fuzzy neural network detection model to obtain a dirty insulator flashover risk early warning result.
The fuzzy neural network detection model establishing process comprises the following steps:
determining fuzzy evaluation parameters; the fuzzy evaluation parameters comprise: input parameters and output parameters, variable weight method weight, membership function and fuzzy rule base.
And establishing a fuzzy neural network detection model according to the fuzzy evaluation parameters.
The input parameters include: the effective value, the amplitude and the pulse frequency of the leakage current, and the environmental temperature and humidity data, and the input parameters are all factors which comprehensively influence the pollution flashover of the insulator; the output parameter is the insulator filthy degree grade, including: normal filth NL, normal filth CM, more severe filth MS and severe filth SR, the collective form of these four states is called the comment set.
The weight variation method basically follows the rules of expert experience and an analytic hierarchy process, the weight is properly adjusted, the weight difference of five characteristic parameters is reduced after the adjustment of the transverse sorting factor and the balance coefficient is considered, and certain reliability is achieved.
The method comprises the steps of utilizing a fuzzy algorithm to evaluate the pollution degree of an insulator, firstly carrying out fuzzification processing on input parameters, namely processing data collected by a sensor through a membership function to realize fuzzification of the input parameters. Quantitative degree reflecting each parameter belonging to four pollution degrees
For a fuzzy rule base: when extracting rules from field data, firstly calculating the membership degrees of five characteristic quantities of a first group of data in different partitions according to a membership function, then determining the partition where the maximum membership degree corresponding to each characteristic quantity is located, generating a first fuzzy rule, then calculating a second group of data in the same method, if the generated second fuzzy rule is the same as the first one, adding 1 to the occurrence frequency of the first rule, and if not, taking the first rule as the second rule. And calculating all sample data in sequence according to the rule extraction method, and obtaining all fuzzy rules required by the system and the occurrence times of each rule under the condition that the sample data is enough. The occurrence frequency of a rule marks the importance degree of the rule, if the occurrence probability of the rule is too low, the rule can be eliminated according to actual needs or related experiences, and finally an optimal fuzzy rule base can be obtained. Once the construction of the fuzzy rule base is completed, the structure of the whole pollution degree early warning model is determined accordingly. The mapping relation of the pollution degree parameters from input to output can be realized by utilizing the early warning model.
The first layer of the model is an input layer, and each node of the layer represents an obtained parameter value reflecting the pollution characteristics of the insulator, namely a leakage current parameter and environmental temperature and humidity data characteristic parameter value screened by the text. The input layer does not perform any mathematical operation on the input parameters, but directly passes on to the next layer.
The second layer of the model is a quantization layer, which is simply a layer that fuzzifies the data passed from the input layer. Firstly, defining fuzzy subsets for input parameters, and then converting the input parameters into specific membership numerical values on the fuzzy subsets by using a membership function.
The third layer of the model is a hidden layer, and the hidden layer is used for mapping the fuzzified input parameters to output to obtain a fuzzy output value.
The fourth layer of the model is an output layer, the output layer is a single node, the evaluation result of the pollution degree of the insulator is given, and the transfer function of the evaluation result adopts a linear function.
By utilizing the method and the device, input data are divided into NL, CM, MS and SR, the number of samples which are distributed uniformly is selected for the pollution degree of each type, and the samples are not repeated and covered mutually, so that the pollution degree evaluation result of the insulator is obtained, and the grading early warning is carried out on the pollution insulator flashover risk.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The method disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the description of the method part.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. The utility model provides a filthy insulator flashover risk early warning system which characterized in that, the risk early warning system includes: the system comprises a cutoff ring, a signal processing module, a microclimate data acquisition circuit, a master control single chip microcomputer and an ARM processor;
the cutoff ring is used for collecting leakage current;
the signal processing module is connected with the cutoff ring and used for conditioning the leakage current;
the microclimate data acquisition circuit is used for acquiring environmental temperature and humidity data;
the ARM processor is respectively connected with the signal processing module and the microclimate data acquisition circuit and is used for sampling data through an A/D sampling port of the ARM processor;
the main control single chip microcomputer is connected with the ARM processor and used for awakening the ARM processor at the sampling moment so as to perform pollution insulator flashover risk early warning.
2. The pollution insulator flashover risk early warning system according to claim 1, wherein the signal processing module comprises an impedance matching network, a filter circuit, an amplifying circuit, a peak holding circuit and a multiplexing switch which are connected in sequence; the impedance matching network is connected with the cut-off ring, and the multiplexing switch is connected with the A/D sampling port.
3. The contamination insulator flashover risk pre-warning system of claim 1, wherein the risk pre-warning system comprises: a CF memory card;
the CF storage card is connected with the ARM processor and used for storing the data collected by the A/D sampling port.
4. The contamination insulator flashover risk pre-warning system of claim 1, wherein the risk pre-warning system comprises: a solar power supply system;
the solar power supply system provides working voltage for the risk early warning system.
5. The pollution insulator flashover risk early warning system according to claim 4, wherein the solar power supply system comprises: solar panel and lithium cell.
6. The pre-warning system for flashover risk of filthy insulators according to claim 4, wherein the main control single chip microcomputer is connected with the main control single chip microcomputer through I2And the C bus is respectively connected with the ARM processor and the solar power supply system.
7. A pollution insulator flashover risk early warning method is characterized by comprising the following steps:
acquiring a dirty insulator data sample; the dirty insulator data sample comprises: leakage current and ambient temperature and humidity data;
processing the dirty insulator data sample by using a fuzzy neural network detection model to obtain a dirty insulator flashover risk early warning result;
the fuzzy neural network detection model establishing process comprises the following steps:
determining fuzzy evaluation parameters; the fuzzy evaluation parameters comprise: input parameters and output parameters, variable weight method weight, membership function and fuzzy rule base;
and establishing a fuzzy neural network detection model according to the fuzzy evaluation parameters.
8. The pre-warning method for the flashover risk of the filthy insulator according to claim 7, wherein the input parameters comprise: leakage current's virtual value, amplitude and pulse frequency, and ambient temperature and humidity data, output parameter is insulator filthy degree grade, includes: normal filth NL, normal filth CM, more severe filth MS and severe filth SR.
9. The pre-warning method for the flashover risk of the filthy insulator according to claim 8, wherein the fuzzy neural network detection model comprises: the device comprises an input layer, a quantization layer, a hidden layer and an output layer;
the quantization layer is used for fuzzifying the input parameters to obtain fuzzified input parameters;
the hidden layer is used for mapping the fuzzified input parameters to output to obtain a fuzzy output value;
and the output layer is used for outputting the insulator pollution degree grade according to the fuzzy output value.
10. The pre-warning method for flashover risk of a filthy insulator according to claim 9, wherein in the hidden layer, the membership function is used for processing the input parameters to perform fuzzification processing.
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| CN117368797A (en) * | 2023-11-17 | 2024-01-09 | 国网青海省电力公司海南供电公司 | Composite insulator flashover early warning method based on leakage current and EFS |
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Application publication date: 20211214 |