CN112198472A - Online remote calibration method and system for partial discharge intelligent sensor - Google Patents
Online remote calibration method and system for partial discharge intelligent sensor Download PDFInfo
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Abstract
The invention relates to an online remote calibration method and system for a partial discharge intelligent sensor, wherein the method comprises the following steps: grouping the intelligent sensors to be verified according to the distance; collecting real-time measurement data of each intelligent sensor in groups; and respectively carrying out clustering operation based on mean shift on the real-time measurement data of each intelligent sensor in each group, and carrying out fault judgment on each intelligent sensor in each group according to a clustering result. Compared with the prior art, the method has the advantages of effectively judging faults and the like.
Description
Technical Field
The invention relates to a method for checking a sensor in an electric power internet of things, in particular to a method and a system for remotely checking a partial discharge intelligent sensor on line.
Background
The construction of modern electric power thing networking provides more convenient, intelligent, safe, economic electric energy supply for people. However, in order to ensure the intellectualization of the power grid, a large number of sensors are operated in the power internet of things to collect environmental variables in real time, so that the performance of a plurality of intelligent sensing devices which are additionally arranged on the site is inevitably uneven. From the source of collection, because the design principle or the quality stability of perception device and reasons such as on-the-spot interference killing feature are poor, the power equipment state data that the device was gathered are not necessarily totally accurate. In the signal transmission process, it is difficult to ensure that data is not distorted in the upward transmission process due to non-uniform signal transmission mode and interface protocol, non-standard data, and the like. After the intelligent sensing devices operate for a long time on the spot, the performance of the intelligent sensing devices is influenced by various reasons, such as vibration, temperature, humidity, large impact signals inside and outside the equipment and the like, which all can damage the characteristics of the sensors, and the on-line verification of the sensing devices is difficult to carry out on the spot. Besides, with the construction of the ubiquitous power internet of things, the number of the sensing devices is rapidly increased, and if sensing equipment which does not reach the standard is put into use, manpower and material resources are consumed to carry out operation and maintenance, so that the sensing equipment is not paid.
At present, relevant technical departments at home and abroad have more traditional calibration work on sensors, and aiming at various types of live detection and on-line monitoring sensors, a method of sending the sensors to relevant authorities for laboratory off-line calibration before installation is adopted, so that the quality of the devices can be ensured to enter a gateway to a certain extent. However, the subsequent temporary and periodic verification problems of a large number of different types of intelligent sensing devices which are already put into operation on site need to be considered seriously. The performance of the intelligent sensing devices can be influenced by a plurality of factors on site in the operation process, such as vibration, temperature, humidity, large impact signals inside and outside the device and the like, which can influence the characteristics of the sensors, and the traditional laboratory offline calibration method is not suitable for on-site online calibration. Therefore, how to scientifically and effectively check performance indexes such as data loss rate, sensitivity and stability of the sensor in operation plays a vital role in ensuring safe and reliable operation of equipment.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an online remote calibration method and system for a partial discharge intelligent sensor, which can effectively judge faults.
The purpose of the invention can be realized by the following technical scheme:
an online remote calibration method for a partial discharge intelligent sensor comprises the following steps:
grouping the intelligent sensors to be verified according to the distance;
collecting real-time measurement data of each intelligent sensor in groups;
and respectively carrying out clustering operation based on mean shift on the real-time measurement data of each intelligent sensor in each group, and carrying out fault judgment on each intelligent sensor in each group according to a clustering result.
Further, the fault determination for each intelligent sensor in each group specifically includes the following steps:
calculating the characteristic value of the sensor data of each intelligent sensor in the group and forming a characteristic vector sample;
performing clustering operation based on mean shift on the feature vector samples;
taking a cluster classification cluster with the largest number of samples as a standard cluster, and calculating the distance between the center coordinates of the rest clusters and the center coordinate of the standard cluster;
and judging whether the distance is greater than the critical distance, if so, judging that all the intelligent sensors in the corresponding cluster have faults.
Further, the mean shift-based clustering operation includes cluster center search, merging similar data clusters, and merging small data clusters.
Further, the kernel function formula adopted by the cluster center search is as follows:
in the formula: g (-) is the negative derivative of the kernel function, xiIs a high-dimensional ball ShK is ShH is the radius of the high-dimensional sphere, mk(x) Is a high-dimensional ball ShThe offset mean vector of the set of points involved.
Further, the real-time measurement data is stored in an edge computing platform, and a computer program for executing the step of performing fault determination on each intelligent sensor in each group is stored in the edge computing platform.
Furthermore, a plurality of edge computing platforms are arranged, and the intelligent sensors to be verified are divided into a plurality of groups according to the distance between each intelligent sensor and each edge computing platform.
The invention also provides an online remote verification system for the local discharge intelligent sensor, which is used for online verification of a plurality of intelligent sensors to be verified on the power distribution equipment and comprises a cloud platform server and a plurality of edge computing platforms, wherein,
the cloud platform server divides the intelligent sensors to be checked into a plurality of groups according to the distance between each intelligent sensor and each edge computing platform, and establishes corresponding connection between the intelligent sensors in the groups and the edge computing platforms;
and each edge computing platform acquires real-time measurement data of each intelligent sensor in the group, performs mean shift-based clustering operation on the real-time measurement data, and performs fault judgment on each intelligent sensor in the group according to a clustering result.
Further, in the edge computing platform, the fault determination of each intelligent sensor in each group specifically includes:
calculating the characteristic value of the sensor data of each intelligent sensor in the group and forming a characteristic vector sample;
performing clustering operation based on mean shift on the feature vector samples;
taking a cluster classification cluster with the largest number of samples as a standard cluster, and calculating the distance between the center coordinates of the rest clusters and the center coordinate of the standard cluster;
and judging whether the distance is greater than the critical distance, if so, judging that all the intelligent sensors in the corresponding cluster have faults.
Further, the mean shift-based clustering operation includes cluster center search, merging similar data clusters, and merging small data clusters.
Further, the kernel function formula adopted by the cluster center search is as follows:
in the formula: g (-) is the negative derivative of the kernel function, xiIs a high-dimensional ball ShK is ShH is the radius of the high-dimensional sphere, mk(x) Is a high-dimensional ball ShThe offset mean vector of the set of points involved.
In order to realize online remote verification of a partial discharge intelligent sensor, the invention provides a partial discharge sensor edge calculation verification method based on mean shift clustering.
Compared with the prior art, the invention has the following beneficial effects:
the invention groups the intelligent sensors according to the distance, and verifies the sensors in each group in a grouping mode in an online and parallel manner, thereby effectively improving the verification efficiency.
The method is based on a mean shift clustering algorithm, and can effectively distinguish the fault sensor by clustering the data characteristic vector measured by the partial discharge sensor by using the density offset vector.
The method adopted by the invention is compared with other clustering algorithms, and the faulty sensor can be effectively distinguished.
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FIG. 1 is a diagram of a hardware communications architecture of the present invention;
FIG. 2 is a schematic diagram of a verification process according to the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Example 1
The embodiment provides an online remote verification method for a partial discharge intelligent sensor, which comprises the following steps: grouping the intelligent sensors to be verified according to the distance; collecting real-time measurement data of each intelligent sensor in groups; and respectively carrying out clustering operation based on mean shift on the real-time measurement data of each intelligent sensor in each group, and carrying out fault judgment on each intelligent sensor in each group according to a clustering result.
As shown in fig. 2, the fault determination for each intelligent sensor in each group specifically includes the following steps:
1) and calculating characteristic values of the sensor data of each intelligent sensor in the group and forming characteristic vector samples.
Calculating the characteristic value X with certain probability distribution data measured by the partial discharge sensor1、X2……Xn. And the characteristic values are combined into a vector with the dimension n
2) And carrying out clustering operation based on mean shift on the feature vector samples.
And clustering the characteristic vectors of the partial discharge sensors by using a mean shift clustering model to find out the fault sensors scattered outside. The model implements clustering by three steps: searching cluster center, merging similar data clusters and merging decimalAnd (4) clustering. Firstly, a central point x is randomly selected from a feature space with N sample points, and a kernel function is utilized to calculate a high-dimensional sphere S with the point as the center and the radius of hhOffset mean vector m of the contained point setk(x) The formula is as follows:
in the formula: g (-) is the negative derivative of the kernel function, xiIs a high-dimensional ball ShK is ShThe number of points included in (1).
And moving the central point to the position pointed by the mean vector, and continuously iterating until the condition is met, wherein the central point at the moment is the data cluster center. The above steps are then repeated until all sample points are grouped into a certain data cluster. And then combining and combining some data clusters with high similarity into a large data cluster. And finally, merging the small data clusters scattered around the large data cluster into the large data cluster with the highest similarity to complete the whole clustering process.
3) And taking one cluster classification cluster with the largest number of samples as a standard cluster, and calculating the distance l between the center coordinates of the rest clusters and the center coordinate of the standard cluster.
4) And judging whether the distance L is greater than the critical distance L, if so, judging that all the intelligent sensors in the corresponding cluster have faults. The selection of the critical distance L is given by the experience of experts and a large number of test results in combination with the operating environment of the equipment.
In this embodiment, the real-time measurement data is stored in an edge computing platform, and a computer program for executing the step of performing fault determination on each intelligent sensor in each group is stored in the edge computing platform. The edge computing platforms are provided with a plurality of intelligent sensors, and the intelligent sensors to be verified are divided into a plurality of groups according to the distance between each intelligent sensor and each edge computing platform.
The method realizes remote online verification on the partial discharge sensor by using the mean shift clustering algorithm on the edge computing platform, has better robustness and effectiveness, and is suitable for being applied to the low-power-consumption environment of the wireless Internet of things.
Example 2
The embodiment provides an online remote verification system for a partial discharge intelligent sensor, which is used for performing online verification on a plurality of intelligent sensors to be verified on power distribution equipment, and as shown in fig. 1, the online remote verification system comprises a cloud platform server and a plurality of edge computing platforms, wherein the cloud platform server divides the intelligent sensors to be verified into a plurality of groups according to the distance between each intelligent sensor and each edge computing platform, and establishes corresponding connection between the intelligent sensors in the groups and the edge computing platforms; and each edge computing platform acquires real-time measurement data of each intelligent sensor in the group, performs mean shift-based clustering operation on the real-time measurement data, and performs fault judgment on each intelligent sensor in the group according to a clustering result. For the intelligent partial discharge sensors installed on the power equipment, the verification system receives data information of a group of partial discharge sensors through the Internet of things sink node by using the edge computing platform, clusters partial discharge data measured by a plurality of sensors by using a mean shift clustering algorithm, and judges the partial discharge sensor with a fault, so that the function of online remote verification of the intelligent partial discharge sensors is achieved. The rest is the same as example 1.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (10)
1. An online remote calibration method for a partial discharge intelligent sensor is characterized by comprising the following steps:
grouping the intelligent sensors to be verified according to the distance;
collecting real-time measurement data of each intelligent sensor in groups;
and respectively carrying out clustering operation based on mean shift on the real-time measurement data of each intelligent sensor in each group, and carrying out fault judgment on each intelligent sensor in each group according to a clustering result.
2. The online remote verification method for the partial discharge intelligent sensor according to claim 1, wherein the fault determination for each intelligent sensor in each group specifically comprises the following steps:
calculating the characteristic value of the sensor data of each intelligent sensor in the group and forming a characteristic vector sample;
performing clustering operation based on mean shift on the feature vector samples;
taking a cluster classification cluster with the largest number of samples as a standard cluster, and calculating the distance between the center coordinates of the rest clusters and the center coordinate of the standard cluster;
and judging whether the distance is greater than the critical distance, if so, judging that all the intelligent sensors in the corresponding cluster have faults.
3. The on-line remote verification method for a partial discharge intelligent sensor according to claim 1, wherein the mean shift-based clustering operation comprises cluster center search, merging similar data clusters, and merging small data clusters.
4. The on-line remote verification method for the smart sensor with partial discharge of claim 3, wherein the kernel function formula adopted by the cluster center search is as follows:
in the formula: g (-) is the negative derivative of the kernel function, xiIs a high-dimensional ball ShK is ShH is the radius of the high-dimensional sphere, mk(x) Is a high-dimensional ball ShIs comprised ofA biased mean vector of the set of points.
5. The method of claim 2, wherein the real-time measurement data is stored in an edge computing platform, and the edge computing platform has a computer program stored therein for performing the step of determining the failure of each smart sensor in each group.
6. The on-line remote calibration method for the partial discharge intelligent sensor according to claim 5, wherein a plurality of edge computing platforms are provided, and the intelligent sensors to be calibrated are divided into a plurality of groups according to the distance between each intelligent sensor and each edge computing platform.
7. An online remote verification system for a partial discharge intelligent sensor is characterized by being used for online verification of a plurality of intelligent sensors to be verified on power distribution equipment and comprising a cloud platform server and a plurality of edge computing platforms, wherein,
the cloud platform server divides the intelligent sensors to be checked into a plurality of groups according to the distance between each intelligent sensor and each edge computing platform, and establishes corresponding connection between the intelligent sensors in the groups and the edge computing platforms;
and each edge computing platform acquires real-time measurement data of each intelligent sensor in the group, performs mean shift-based clustering operation on the real-time measurement data, and performs fault judgment on each intelligent sensor in the group according to a clustering result.
8. The system of claim 7, wherein the fault determination of each intelligent sensor in each group in the edge computing platform specifically comprises:
calculating the characteristic value of the sensor data of each intelligent sensor in the group and forming a characteristic vector sample;
performing clustering operation based on mean shift on the feature vector samples;
taking a cluster classification cluster with the largest number of samples as a standard cluster, and calculating the distance between the center coordinates of the rest clusters and the center coordinate of the standard cluster;
and judging whether the distance is greater than the critical distance, if so, judging that all the intelligent sensors in the corresponding cluster have faults.
9. The on-line remote verification system for a partial discharge intelligent sensor according to claim 7, wherein the mean shift-based clustering operation comprises cluster center search, merging similar data clusters, and merging small data clusters.
10. The online remote verification system for the partial discharge intelligent sensor according to claim 9, wherein the kernel function formula adopted by the cluster center search is as follows:
in the formula: g (-) is the negative derivative of the kernel function, xiIs a high-dimensional ball ShK is ShH is the radius of the high-dimensional sphere, mk(x) Is a high-dimensional ball ShThe offset mean vector of the set of points involved.
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