CN117706284A - Substation primary equipment discharge early warning detection method and system - Google Patents
Substation primary equipment discharge early warning detection method and system Download PDFInfo
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Abstract
The invention discloses a discharge early warning detection method and system for primary equipment of a transformer substation, wherein the method comprises the following steps: collecting power transmission data and equipment state information of primary equipment of a transformer substation, and preprocessing the power transmission data and the equipment state information; extracting features of the preprocessed data, establishing a discharge detection model, and training the discharge detection model based on a random forest algorithm; inputting real-time operation data of the primary equipment of the transformer substation into a trained discharge detection model, outputting a detection result, and carrying out early warning on discharge faults of the primary equipment of the transformer substation according to the detection result; the method provided by the invention realizes the intelligent detection of the discharge abnormality of the equipment by fusing various data sources and identifying the signals according to the algorithm, and can realize the autonomous discovery and processing of the abnormality through the built-in autonomous decision-making program, thereby greatly improving the safety and the operation efficiency of the equipment.
Description
Technical Field
The invention relates to the technical field of power detection, in particular to a method and a system for early-warning discharge detection of primary equipment of a transformer substation.
Background
The high-voltage power transmission and transformation equipment in the power system works in the atmospheric environment, corona and surface partial discharge phenomena can be generated under certain conditions due to the reduction of the insulation performance, so that the insulation capacity of the equipment is reduced to cause flashover accidents, power supply interruption is caused, inconvenience and loss are brought to industrial and agricultural production and people's life, and personal and equipment safety is even affected in severe cases. Therefore, the method can timely and accurately detect the external insulation discharge of the high-voltage electric equipment, and has important significance for ensuring the reliable operation of the electric power system.
The traditional corona discharge detection method mainly comprises the following steps: observation methods, ultrasonic detection, leakage current on-line monitoring and infrared imaging observation, and the methods have certain defects in practical application: visual inspection is one of the most common methods, but a large number of electrical equipment accidents occur without visible light, and people often can only hear corona discharge sound of 'voice-over', but cannot see discharge; the ultrasonic detection method is difficult to intuitively and accurately position the remote discharge point, and particularly when a plurality of points are discharged simultaneously, the positioning is more difficult; the leakage current online monitoring method needs to be provided with corresponding measuring equipment in advance, and is not suitable for large-area popularization and use; infrared imaging observations can detect discharge accumulation or temperature rise due to internal equipment failure, but this is an indirect measurement method and cannot directly see the discharge.
The ultraviolet imaging detection technology is a new technology for detecting the external insulation state of alternating-current high-voltage circuits and power transmission and transformation equipment in a long distance in recent years, can find equipment defects causing electric field abnormality, accurately position discharge positions, observe discharge conditions, and judge harm of corona discharge to the external insulation of the electrical equipment through analysis. The technology has the advantages of simplicity, high efficiency, visual image, no influence on equipment operation, safety and convenience, and is gradually and widely popularized and applied in the power system. How to evaluate the external insulation performance of electrical equipment by ultraviolet imaging technology and diagnose the health condition of the electrical equipment becomes an urgent problem to be solved. In general, the electric power enterprises take the discharge characteristics as the basic characteristics of the electrical performance of the high-voltage electrical equipment, so that the research on the ultraviolet imaging characteristics of the discharge of the high-voltage electrical equipment is greatly helpful for analyzing the external insulation condition, and has important significance for evaluating whether the external insulation defect and the severity of the defect. The reason for causing the external insulation discharge of the primary equipment is many, and it is necessary to study the corona characteristic changes of the primary equipment in operation by using ultraviolet imaging technology through large-scale and systematic experiments, and study the relation between the changes and the external insulation characteristics of the equipment, so as to accurately evaluate the external state of the equipment. Therefore, the ultraviolet imaging technology is used for researching the external insulation discharge of primary equipment, and has important scientific significance and practical value both from academic and practical application.
Discharging abnormality of power equipment is a common fault in the operation process of a transformer substation, and if the fault cannot be found and processed in time, serious damage of the equipment and safety accidents can be caused. The traditional discharge detection method mainly relies on manual detection and some simple algorithms based on equipment operation data, and has the problems of low detection precision, limited detection range and low detection efficiency.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description summary and in the title of the application, to avoid obscuring the purpose of this section, the description summary and the title of the invention, which should not be used to limit the scope of the invention.
The present invention has been made in view of the above-described problems.
In a first aspect of the embodiment of the present invention, a method for detecting discharge early warning of primary equipment of a transformer substation is provided, including: collecting power transmission data and equipment state information of primary equipment of a transformer substation, and preprocessing the power transmission data and the equipment state information; performing feature extraction on the preprocessed data, establishing a discharge detection model, and training the discharge detection model based on a random forest algorithm; and inputting the real-time operation data of the primary equipment of the transformer substation into a trained discharge detection model to output a detection result, and carrying out early warning on the discharge fault of the primary equipment of the transformer substation according to the detection result.
As a preferable scheme of the discharge early warning detection method of the primary equipment of the transformer substation, the invention comprises the following steps: the process of the pre-treatment comprises the steps of,
filtering and denoising the acquired power transmission data and equipment state information of the primary equipment of the transformer substation, and cleaning blank values, format errors, logic errors and non-required information in the data;
detecting and rejecting abnormal samples in the data samples by adopting an outlier sample detection strategy based on clustering, namely calculating the kth distance of a point p, calculating the kth reachable distance from a point o to the point p according to the kth distance of the point p, calculating local reachable density according to the kth reachable distance from the point o to the point p, and finally calculating local outlier factors according to the local reachable density;
and carrying out normalization processing on the pre-preprocessed data, and storing the normalized data locally or uploading the normalized data to a database.
As a preferable scheme of the discharge early warning detection method of the primary equipment of the transformer substation, the invention comprises the following steps: feature extraction of the preprocessed data includes,
an effective sampling construction sub-data set is adopted from the data set after normalization processing, a sub-decision tree is constructed by utilizing the sub-data set, and the output result of the random forest is obtained through voting on the judgment result of the sub-decision tree;
each splitting process of the sub-decision tree of the random forest is to randomly select a certain number of features from all the features to be selected, then select the optimal features from the randomly selected features as a result to output, namely, the number M of the input features is used for determining the decision result of one node on the sub-decision tree, the N training samples are sampled for N times in an effective sampling mode to form a training set, the samples which are not extracted are used as prediction evaluation errors, and M features are randomly selected for each node and the optimal splitting mode of the M features is calculated.
As a preferable scheme of the discharge early warning detection method of the primary equipment of the transformer substation, the invention comprises the following steps: training the discharge detection model includes,
carrying out data statistics and analysis on the data extracted by the features, and establishing a discharge detection model according to the statistics and analysis results;
training the discharge detection model by using a random forest algorithm, inputting a light spot area s, a relative light spot area RS, a relative light spot area average value RSP, a one-minute large light spot area frame number N, a relative light spot diameter RD, a pulse current peak value ipeak, a pulse current average value imean, a gain g, a distance d, a relative humidity RH, an air pressure p and a temperature t into the discharge detection model as input parameters for training, and outputting an insulator fault state y, wherein the states are expressed as follows:
,C,RH,p,t)
and deploying the trained discharge detection model into practical application, monitoring the operation condition of the discharge detection model in real time, and feeding back and optimizing according to the monitoring result.
As a preferable scheme of the discharge early warning detection method of the primary equipment of the transformer substation, the invention comprises the following steps: the output of the detection result includes,
inputting real-time operation data of primary equipment of the transformer substation into a trained discharge detection model to output a discharge detection result, and matching the discharge detection result with fault information in a database by using a registration algorithm to obtain a fault type;
the calculation of the registration algorithm includes,
wherein N is c Representing the number of matching point pairs,representing the original matching point pixel coordinates in the original sample,and the coordinate value of the coordinate of the matching point after perspective transformation is represented.
As a preferable scheme of the discharge early warning detection method of the primary equipment of the transformer substation, the invention comprises the following steps: the early warning of the discharge failure of the primary equipment of the transformer substation comprises,
dividing the fault type into two different early warning levels, namely, the fault factors needing to be manually subjected to fault maintenance are primary early warning, and the other fault factors are secondary early warning;
the method comprises the steps of monitoring various faults in primary equipment of a transformer substation in real time, matching fault codes with fault information in a database when the possible faults exist in the primary equipment of the transformer substation, and giving a signal by an early warning system and giving an alarm according to an early warning level if the matching is successful;
and an administrator of the primary equipment of the transformer substation monitors the system state and fault information through a monitoring terminal and timely takes corresponding measures to conduct investigation and treatment.
In a second aspect of the embodiment of the present invention, a system for early-warning and detecting discharge of primary equipment of a substation is provided, including:
the data acquisition processing unit is used for acquiring power transmission data and equipment state information of primary equipment of the transformer substation and preprocessing the power transmission data and the equipment state information;
the model building training unit is used for extracting characteristics of the preprocessed data, building a discharge detection model and training the discharge detection model based on a random forest algorithm;
and the detection early warning unit is used for inputting the real-time operation data of the primary equipment of the transformer substation into the trained discharge detection model to output a detection result, and carrying out early warning on the discharge fault of the primary equipment of the transformer substation according to the detection result.
As a preferable scheme of the primary equipment discharge early warning detection system of the transformer substation, the invention comprises the following steps: also included is a method of manufacturing a semiconductor device,
the data storage unit is used for storing the normalized data locally or uploading the normalized data to a database, monitoring the operation condition of the discharge detection model in real time, and feeding back and optimizing according to the monitoring result;
and the control terminal is used for an administrator of the primary equipment of the transformer substation to monitor the system state and fault information through the monitoring terminal and timely take corresponding measures to conduct investigation and treatment.
In a third aspect of embodiments of the present invention, there is provided a computer device comprising a memory storing a computer program and a processor configured to invoke instructions stored in the memory to perform the steps of the method of any of the embodiments of the present invention.
In a fourth aspect of embodiments of the present invention, there is provided a computer readable storage medium having stored thereon computer program instructions comprising:
the computer program instructions, when executed by a processor, implement the steps of the method according to any of the embodiments of the present invention.
Compared with the prior art, the invention has the beneficial effects that:
according to the substation primary equipment discharge early warning detection method and system, through fusion of various data sources and recognition of signals according to the algorithm, the intelligent detection of equipment discharge abnormality is achieved through the electrical signals, the acoustic signals and the infrared signals, autonomous discovery and abnormal condition processing can be achieved through the built-in autonomous decision making program, and safety and operation efficiency of equipment are greatly improved. The invention carries out continuous observation values in a period of time on the input parameters and then carries out treatment, carries out statistics treatment on the observation values in a period of time of the relative spot area mean value RSP and the pulse current peak value ipeak, then compares the variation trend of the input parameter statistics values in different stages, and inputs the difference values of the input parameter statistics values into a system for model training, thereby achieving the early warning purpose.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
fig. 1 is an overall flowchart of a method and a system for detecting discharge early warning of primary equipment of a transformer substation.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present invention have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the invention. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1, in one embodiment of the present invention, a method for detecting discharge early warning of primary equipment of a transformer substation is provided, which is used for evaluating external insulation performance of electrical equipment and diagnosing health status of the electrical equipment through ultraviolet imaging technology. The method specifically comprises the following steps:
s1: and collecting power transmission data and equipment state information of primary equipment of the transformer substation, and preprocessing the power transmission data and the equipment state information. It should be noted that:
the preprocessing process comprises the steps of carrying out filtering and noise reduction processing on the acquired power transmission data and equipment state information of primary equipment of the transformer substation, and cleaning blank values, format errors, logic errors and non-demand information in the data;
further, detection and elimination are carried out on samples with anomalies in the data samples by adopting an outlier sample detection strategy based on clustering, namely, the kth distance of the point p is calculated, and the kth distance is expressed as follows:
d k (p)＝d(p,o)
the method meets the following conditions: at least k points o 'epsilon C { x not equal to p } which do not contain p in the set, and d (p, o') is less than or equal to d (p, o); at most, k-1 points o 'epsilon C { x +.p } excluding p in the set satisfy d (p, o') < d (p, o);
the kth reachable distance from point o to point p is calculated from the kth distance from point p, expressed as:
reach-dist k (p,o)＝max{k-dist(o)d(p,o)}
the local reachable density is calculated from the kth reachable distance from point o to point p, expressed as:
calculating local outliers from the local reachable densities, expressed as:
wherein, the point o and the point p are data points corresponding to each driving stroke, d (p, o) is the distance between the data points p and o, N k (p) is the kth distance neighborhood of point p, LOF k (p) the closer to 1, the closer to its neighborhood point density the point p is, the more likely the point p is to belong to the same cluster as the neighborhood; LOF (Low-Density filter) k The smaller (p) is less than 1, the density of the description point p is higher than the density of the neighborhood point, and p is a dense point; LOF (Low-Density filter) k The greater (p) is greater than 1, indicating that the density of the point p is less than the density of its neighborhood points, the more likely the point p is an outlier;
further, the pre-processed data is normalized, and the normalized data is stored locally or uploaded to a database.
S2: and extracting features of the preprocessed data, establishing a discharge detection model, and training the discharge detection model based on a random forest algorithm. It should be noted that:
feature extraction of the preprocessed data includes,
an effective sampling construction sub-data set is adopted from the data set after normalization processing, a sub-decision tree is constructed by utilizing the sub-data set, and the output result of the random forest is obtained through voting of the judgment result of the sub-decision tree;
each splitting process of the sub-decision tree of the random forest is to randomly select a certain number of features from all the features to be selected, then select the optimal features from the randomly selected features as a result to output, namely, the number M of the input features is used for determining the decision result of one node on the sub-decision tree, samples are taken N times from N training samples in an effective sampling mode to form a training set, samples which are not taken are used as prediction evaluation errors, and M features are randomly selected for each node and the optimal splitting mode of the M features is calculated.
Further, training the discharge detection model includes,
carrying out data statistics and analysis on the data extracted by the features, and establishing a discharge detection model according to the statistics and analysis results; training a discharge detection model by using a random forest algorithm, taking a light spot area s, a relative light spot area RS, a relative light spot area average value RSP, a one-minute large light spot area frame number N, a relative light spot diameter RD, a pulse current peak value ipeak, a pulse current average value imean, a gain g, a distance d, relative humidity RH, air pressure p and temperature t as input parameters to input the training in the discharge detection model, and outputting an insulator fault state y, wherein the training is represented as follows:
y＝f(s，RS，RSP，NRD，ipeak，imean，g，d，RH，p，t)
and deploying the trained discharge detection model into practical application, monitoring the running condition of the discharge detection model in real time, and feeding back and optimizing according to the monitoring result.
S3: and inputting real-time operation data of the primary equipment of the transformer substation into the trained discharge detection model to output a detection result, and carrying out early warning on discharge faults of the primary equipment of the transformer substation according to the detection result. It should be noted that:
the output of the detection result includes,
inputting real-time operation data of primary equipment of a transformer substation into a trained discharge detection model to output a discharge detection result, and matching the discharge detection result with fault information in a database by using a registration algorithm to obtain a fault type;
in particular, the calculation of the registration algorithm includes,
wherein N is c Representing the number of matching point pairs,representing the original matching point pixel coordinates in the original sample,and the coordinate value of the coordinate of the matching point after perspective transformation is represented.
Further, the early warning of the discharge failure of the primary equipment of the transformer substation comprises,
dividing the fault type into two different early warning grades, namely, the fault factor requiring manual fault maintenance is first-level early warning, and the other fault factors are second-level early warning;
the method comprises the steps of monitoring various faults in primary equipment of a transformer substation in real time, matching fault codes with fault information in a database when the possible faults exist in the primary equipment of the transformer substation, and giving a signal by an early warning system and giving an alarm according to an early warning level if the matching is successful;
an administrator of primary equipment of the transformer substation monitors system state and fault information through a monitoring terminal and timely takes corresponding measures to conduct investigation and processing.
From the above, the beneficial effects of the invention are as follows: according to the substation primary equipment discharge early warning detection method and system, through fusion of various data sources and recognition of signals according to the algorithm, the intelligent detection of equipment discharge abnormality is achieved through the electrical signals, the acoustic signals and the infrared signals, autonomous discovery and abnormal condition processing can be achieved through the built-in autonomous decision making program, and safety and operation efficiency of equipment are greatly improved. The invention carries out continuous observation values in a period of time on the input parameters and then carries out treatment, carries out statistics treatment on the observation values in a period of time of the relative spot area mean value RSP and the pulse current peak value ipeak, then compares the variation trend of the input parameter statistics values in different stages, and inputs the difference values of the input parameter statistics values into a system for model training, thereby achieving the early warning purpose.
In a second aspect of the present disclosure, a system for early warning and detecting discharge of primary equipment of a transformer substation is provided, including:
the data acquisition processing unit is used for acquiring power transmission data and equipment state information of primary equipment of the transformer substation and preprocessing the power transmission data and the equipment state information;
the model building training unit is used for extracting characteristics of the preprocessed data, building a discharge detection model and training the discharge detection model based on a random forest algorithm;
and the detection early warning unit is used for inputting real-time operation data of the primary equipment of the transformer substation into the trained discharge detection model to output a detection result, and carrying out early warning on discharge faults of the primary equipment of the transformer substation according to the detection result.
Further, a substation primary equipment early warning detecting system that discharges still includes:
the data storage unit is used for storing the normalized data locally or uploading the normalized data to the database, monitoring the operation condition of the discharge detection model in real time, and feeding back and optimizing according to the monitoring result;
and the control terminal is used for monitoring the system state and fault information through the monitoring terminal by an administrator of the primary equipment of the transformer substation and timely taking corresponding measures for investigation and treatment.
In a third aspect of the present disclosure, a computer device is provided that includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by the processor to implement a method for file synchronization of a terminal device and a carrier module. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
In a fourth aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions comprising:
the computer program instructions, when executed by a processor, implement a method of any of the preceding.
The present invention may be a method, apparatus, system, and/or computer program product, which may include a computer-readable storage medium having computer-readable program instructions embodied thereon for performing various aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
Example 2
The embodiment is different from the first embodiment in that a verification test of a method and a system for detecting discharge early warning of primary equipment of a transformer substation is provided, and the technical effects adopted in the method provided by the invention are verified and explained.
In the embodiment, the signal collector is used for collecting the power transmission data and the equipment state information of primary equipment of the transformer substation in real time, preprocessing the power transmission data and the equipment state information, and collecting the data as shown in a table 1;
table 1: data acquisition table.
| Time (seconds) | Current (A) | Voltage (V) | Fault type | Fault location |
| 0.00 | 0 | 220 | Normal state | Without any means for |
| 0.01 | 100 | 220 | Short circuit | Substation transformer |
| 0.02 | 150 | 215 | Short circuit | Substation transformer |
| 0.03 | 200 | 210 | Short circuit | Substation transformer |
Extracting features of the preprocessed data, establishing a discharge detection model, and training the discharge detection model based on a random forest algorithm; and inputting real-time operation data of the primary equipment of the transformer substation into the trained discharge detection model to output a detection result, and carrying out early warning on discharge faults of the primary equipment of the transformer substation according to the detection result.
In the embodiment, the method and the traditional early warning method respectively measure and compare the prediction time of the same fault type, the automatic test equipment is started, MATLB software programming is used for realizing the simulation test of the embodiment, the program is compiled and operated on Microsoft Visual Studio 2017, simulation data are obtained according to experimental results, and the comparison results are shown in Table 2.
Table 2: the comparison result of the method and the traditional early warning method is provided.
| Experimental sample | Conventional method | The method of the invention |
| Detection efficiency | 75％ | 99％ |
| Time | ＞10min | ＜5s |
| Cost of labor | High height | Low and low |
| Accuracy rate of | 80％ | 98％ |
Compared with the traditional early warning method, the method has the advantages that the average prediction time is greatly reduced, and the prediction accuracy and efficiency are greatly improved; therefore, the method provided by the invention realizes the intelligent detection of the discharge abnormality of the equipment by fusing various data sources and identifying signals according to algorithms, including electric signals, acoustic signals and infrared signals, and can realize the autonomous discovery and the abnormal condition treatment through a built-in autonomous decision-making program, thereby greatly improving the safety and the operation efficiency of the equipment.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. The utility model provides a substation primary equipment early warning detection method that discharges which characterized in that includes:
collecting power transmission data and equipment state information of primary equipment of a transformer substation, and preprocessing the power transmission data and the equipment state information;
performing feature extraction on the preprocessed data, establishing a discharge detection model, and training the discharge detection model based on a random forest algorithm;
and inputting the real-time operation data of the primary equipment of the transformer substation into a trained discharge detection model to output a detection result, and carrying out early warning on the discharge fault of the primary equipment of the transformer substation according to the detection result.
2. The substation primary equipment discharge early warning detection method according to claim 1, wherein: the process of the pre-treatment comprises the steps of,
filtering and denoising the acquired power transmission data and equipment state information of the primary equipment of the transformer substation, and cleaning blank values, format errors, logic errors and non-required information in the data;
detecting and rejecting abnormal samples in the data samples by adopting an outlier sample detection strategy based on clustering, namely calculating the kth distance of a point p, calculating the kth reachable distance from a point o to the point p according to the kth distance of the point p, calculating local reachable density according to the kth reachable distance from the point o to the point p, and finally calculating local outlier factors according to the local reachable density;
and carrying out normalization processing on the pre-preprocessed data, and storing the normalized data locally or uploading the normalized data to a database.
3. The substation primary equipment discharge early warning detection method according to claim 2, wherein: feature extraction of the preprocessed data includes,
an effective sampling construction sub-data set is adopted from the data set after normalization processing, a sub-decision tree is constructed by utilizing the sub-data set, and the output result of the random forest is obtained through voting on the judgment result of the sub-decision tree;
each splitting process of the sub-decision tree of the random forest is to randomly select a certain number of features from all the features to be selected, then select the optimal features from the randomly selected features as a result to output, namely, the number M of the input features is used for determining the decision result of one node on the sub-decision tree, the N training samples are sampled for N times in an effective sampling mode to form a training set, the samples which are not extracted are used as prediction evaluation errors, and M features are randomly selected for each node and the optimal splitting mode of the M features is calculated.
4. The substation primary equipment discharge early warning detection method according to claim 3, wherein: training the discharge detection model includes,
carrying out data statistics and analysis on the data extracted by the features, and establishing a discharge detection model according to the statistics and analysis results;
training the discharge detection model by using a random forest algorithm, inputting a light spot area s, a relative light spot area RS, a relative light spot area average value RSP, a one-minute large light spot area frame number N, a relative light spot diameter RD, a pulse current peak value ipeak, a pulse current average value imean, a gain g, a distance d, a relative humidity RH, an air pressure p and a temperature t into the discharge detection model as input parameters for training, and outputting an insulator fault state y, wherein the states are expressed as follows:
y＝f(s，RS，RSP，N，RD，ipeak，imean,g，d，RH，p，t)
and deploying the trained discharge detection model into practical application, monitoring the operation condition of the discharge detection model in real time, and feeding back and optimizing according to the monitoring result.
5. The primary equipment discharge early warning detection method of the transformer substation according to claim 4, wherein the method comprises the following steps of: the output of the detection result includes,
inputting real-time operation data of primary equipment of the transformer substation into a trained discharge detection model to output a discharge detection result, and matching the discharge detection result with fault information in a database by using a registration algorithm to obtain a fault type;
the calculation of the registration algorithm includes,
where Nc represents the number of matching point pairs,representing the original matching point pixel coordinates in the original sample,and the coordinate value of the coordinate of the matching point after perspective transformation is represented.
6. The substation primary equipment discharge early warning detection method according to claim 5, wherein the method comprises the following steps of: the early warning of the discharge failure of the primary equipment of the transformer substation comprises,
dividing the fault type into two different early warning levels, namely, the fault factors needing to be manually subjected to fault maintenance are primary early warning, and the other fault factors are secondary early warning;
the method comprises the steps of monitoring various faults in primary equipment of a transformer substation in real time, matching fault codes with fault information in a database when the possible faults exist in the primary equipment of the transformer substation, and giving a signal by an early warning system and giving an alarm according to an early warning level if the matching is successful;
and an administrator of the primary equipment of the transformer substation monitors the system state and fault information through a monitoring terminal and timely takes corresponding measures to conduct investigation and treatment.
7. A system for implementing the substation primary equipment discharge early warning detection method according to any one of claims 1 to 6, characterized by comprising:
the data acquisition processing unit is used for acquiring power transmission data and equipment state information of primary equipment of the transformer substation and preprocessing the power transmission data and the equipment state information;
the model building training unit is used for extracting characteristics of the preprocessed data, building a discharge detection model and training the discharge detection model based on a random forest algorithm;
and the detection early warning unit is used for inputting the real-time operation data of the primary equipment of the transformer substation into the trained discharge detection model to output a detection result, and carrying out early warning on the discharge fault of the primary equipment of the transformer substation according to the detection result.
8. The substation primary equipment discharge early warning detection system of claim 7, wherein: also included is a method of manufacturing a semiconductor device,
the data storage unit is used for storing the normalized data locally or uploading the normalized data to a database, monitoring the operation condition of the discharge detection model in real time, and feeding back and optimizing according to the monitoring result;
and the control terminal is used for an administrator of the primary equipment of the transformer substation to monitor the system state and fault information through the monitoring terminal and timely take corresponding measures to conduct investigation and treatment.
9. A computer device comprising a memory storing a computer program and a processor, characterized in that the processor is configured to invoke instructions stored in the memory to perform the steps of the method of any of claims 1-6.
10. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the steps of the method of any of claims 1 to 6.
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