CN117269751A - GIS isolating switch switching position confirmation method - Google Patents
GIS isolating switch switching position confirmation method Download PDFInfo
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Classifications
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01H—ELECTRIC SWITCHES; RELAYS; SELECTORS; EMERGENCY PROTECTIVE DEVICES
- H01H31/00—Air-break switches for high tension without arc-extinguishing or arc-preventing means
- H01H31/02—Details
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/327—Testing of circuit interrupters, switches or circuit-breakers
- G01R31/3271—Testing of circuit interrupters, switches or circuit-breakers of high voltage or medium voltage devices
- G01R31/3275—Fault detection or status indication
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Abstract
The invention belongs to the technical field of power equipment monitoring, and relates to a GIS isolating switch switching position confirmation method, which comprises the steps of collecting real-time power of a motor of a GIS isolating switch, and performing data preprocessing to obtain an unknown action power curve; estimating probability density estimation curves corresponding to a standard opening power curve, a standard closing power curve and an unknown action power curve by using a nuclear density estimation method; adopting a cross-validation method to adaptively select the bandwidth of the kernel density estimation; calculating the similarity score of a nuclear density estimation curve corresponding to the unknown action power curve and the nuclear density estimation curves of the standard opening power curve and the standard closing power curve; and judging whether the unknown action power curve is opened or closed based on the similarity score. The invention estimates the probability density estimation curve by the kernel density estimation method and calculates the similarity to judge the opening and closing of the brake, and has the advantages of no dependence on the alignment process, wide applicability and capability of providing probability density information.
Description
Technical Field
The invention belongs to the technical field of power equipment monitoring, and particularly relates to a GIS isolating switch switching position confirmation method.
Background
The gas insulated fully enclosed combined electrical apparatus (GIS) is a key element for realizing the segmentation and isolation of electrical equipment in an electric power system. However, since the mechanical structure of the GIS isolation switch is susceptible to assembly errors and structural damage caused by long-term use, a malfunction is caused. The problem that the opening and closing are not in place is the greatest threat to the safety of the power system and personnel.
The GIS isolating switch separating and closing position detection technology comprises a direct detection method and a state sensing method. The direct detection method is characterized in that the GIS isolating switch is closed in structure, narrow in internal space, compact in transmission mechanism and in high-voltage and high-current environment. The sensor is led in at high potential, the signal wire is difficult to lead out, the signal is interfered by strong electromagnetic environment, the sealing performance of the original structure is destroyed, and the sensor is easy to bring about the reasons of potential insulation hazards and the like, so that the sensor is not suitable for detecting the object.
The state sensing method based on the motor current and the state sensing method based on the vibration signal theoretically have complete information of the mechanical state of the GIS isolating switch, and can monitor the contact state of the contact of the GIS isolating switch. However, the state sensing method based on the motor current cannot reflect the phase change relation between the voltage and the current, and the motor currents of different motor models have a certain degree of dispersibility, so that the current characteristics are not obvious. Even if the current characteristics are obvious, the existing diagnostic algorithm belongs to a physical analysis method, and the defect analysis is carried out on the analysis of the current envelope curve, so that the judgment belongs to threshold judgment, and the two problems are caused.
Based on the state sensing method of the vibration signal, only the vibration frequency of the isolating switch under the action of electromagnetic force is considered, but the natural frequency of the GIS isolating switch body structure is ignored, and the obtained conclusion is difficult to match with the actually measured signal. The data acquisition amount is large, the signal transmission time is long, and the memory space is consumed.
After the separation and closing action instructions of the separation switch are difficult to reliably confirm in the prior art, the key attention point is whether the equipment completes corresponding actions or not, and abnormal separation and closing positions of the isolation switch can be found in time so as to avoid possible running accidents. Which is critical for safe and stable operation of the power system.
Disclosure of Invention
In order to better judge the position of the isolating switch, the invention provides a GIS isolating switch opening and closing position confirmation method, which comprises the steps of estimating a probability density estimation curve corresponding to a standard opening power curve, a standard closing power curve and an unknown action power curve through a nuclear density estimation method, and calculating similarity to judge opening and closing.
The invention relates to a GIS isolating switch switching position confirmation method, which comprises the following steps:
step one: collecting real-time power of a motor of the GIS isolating switch, and performing data preprocessing to obtain an unknown action power curve;
step two: estimating probability density estimation curves corresponding to a standard opening power curve, a standard closing power curve and an unknown action power curve by using a nuclear density estimation method;
step three: adopting a cross-validation method to adaptively select the bandwidth of the kernel density estimation;
step four: calculating the similarity score of a nuclear density estimation curve corresponding to the unknown action power curve and the nuclear density estimation curves of the standard opening power curve and the standard closing power curve; and judging whether the unknown action power curve is opened or closed based on the similarity score.
Specifically, the kernel density estimation calculation process is as follows:
；
；
wherein:is a probability density estimate at location x; x is x i Is the ith data point, n is the total number of data points; k (K) h Is a kernel function and h is bandwidth.
Specifically, the cross-validation method includes minimum mean square error cross-validation and minimum cross-validation estimation.
Specifically, the minimum mean square error cross validation: dividing the data set into a training set and a verification set, generally adopting k-fold cross verification, and carrying out kernel density estimation on the training set for each bandwidth value; calculating a mean square error over the validation set using the probability density estimation curve; the bandwidth that minimizes the mean square error is selected as the optimal bandwidth.
Specifically, the minimum cross-validation estimate: dividing the data set into a training set and a verification set; for each bandwidth value, performing kernel density estimation on the training set; calculating an expected mean square error over the validation set using the probability density estimation curve; the bandwidth that minimizes the desired mean square error is selected as the optimal bandwidth.
Specifically, in the first step, the voltage and the current of the motor of the GIS isolating switch in the running process are collected, and the real-time power of the motor is calculated according to the voltage and the current to obtain an unknown action power curve.
Specifically, in the first step, the data preprocessing includes data cleansing, data formatting and data alignment.
Specifically, the data cleansing includes processing missing data, processing outliers, and noise filtering.
Specifically, the data formatting includes: checking the time intervals among the data points of the standard opening power curve, the standard closing power curve and the unknown action power curve to ensure that the data points are consistent in time; the format of the data is checked.
Specifically, the data alignment includes:
time alignment: according to the timestamp information, aligning a standard opening power curve, a standard closing power curve and an unknown action power curve to make the standard opening power curve, the standard closing power curve and the unknown action power curve consistent in time;
interpolation: if the data points are not evenly distributed in time, interpolation methods are used to obtain data for even time steps.
The invention can judge the state in real time when the switch acts, thereby ensuring the stable operation of the power system. The invention adopts the nuclear density estimation method to analyze the similarity of the power curve, and has the following advantages compared with the traditional curve similarity analysis method such as dynamic time warping (DynamicTimeWarping, DTW) and the like, namely:
independent of the alignment process: while the kernel density estimation method does not require a process of curve alignment, DTW and the like generally require a time or space alignment of the curves before performing the similarity analysis, which may involve complex calculations and parameter selection. The kernel density estimation method allows for a free comparison between curves without forced alignment by making a smooth estimation over the entire curve. This allows for more flexibility in dealing with variations between curves and noise.
The applicability is wide: the nuclear density estimation method is a non-parametric method, independent of the specific shape or model of the curve, and thus is applicable to various types of curve data, including continuous, discrete, unimodal, multimodal, etc. In contrast, DTW and like methods generally depend on the specific shape and characteristics of the curve, and may require adjustment of parameters or modification of algorithms for different types of curves.
Providing probability density information: the kernel density estimation method can be used for comparing the similarity of curves and also providing probability density information of each point on the curves. This means that the profile of the curve can be understood in more detail, potential modes or peaks identified and further statistically analyzed. Such probability density information is of great value in many application areas, such as anomaly detection, pattern recognition and statistical analysis.
Non-contact monitoring: the method only needs to compare the existing power curve data, does not need additional physical contact or equipment intervention, and reduces potential risks.
Drawings
FIG. 1 is a standard split gate power curve;
FIG. 2 is a standard closing power curve;
FIG. 3 is an unknown operating power curve;
FIG. 4 is a graph comparing similarity of nuclear density estimation curves.
Detailed Description
The invention is illustrated in further detail below in connection with examples.
A GIS disconnecting switch switching position confirmation method comprises the following steps:
step one: and collecting real-time power of a motor of the GIS isolating switch, and carrying out data preprocessing to obtain an unknown action power curve.
And collecting the voltage and current of the motor of the GIS isolating switch in the operation process, and calculating the real-time power of the motor according to the voltage and current to obtain an unknown action power curve. The data preprocessing includes data cleansing, data formatting, and data alignment.
Data cleansing is a critical step in ensuring data quality. At this stage, the following tasks need to be performed:
processing missing data: checking and filling missing data points, or taking an interpolation method into consideration to fill in missing values;
processing outliers: identifying and processing possible outliers or outliers to prevent them from affecting the accuracy of the estimation;
noise filtering: it is contemplated that smoothing or filtering techniques may be applied to reduce noise in the data.
Data formatting ensures consistency and availability of data. At this stage, the following tasks need to be performed:
data sampling frequency: checking the time intervals among the data points of the standard opening power curve, the standard closing power curve and the unknown action power curve to ensure that the data points are consistent in time;
data format: the format of the data is checked, for example, whether the data is stored in the proper structure and type.
The data alignment is to ensure that the data of the standard opening power curve, the standard closing power curve and the unknown action power curve are aligned in time for subsequent comparison. The following tasks are performed to achieve data alignment:
time alignment: according to the timestamp information, aligning a standard opening power curve, a standard closing power curve and an unknown action power curve to make the standard opening power curve, the standard closing power curve and the unknown action power curve consistent in time;
interpolation: if the data points are not evenly distributed in time, it is contemplated that interpolation methods can be used to obtain data for even time steps;
the aligned data sets of the standard opening power curve, the standard closing power curve and the unknown action power curve are ensured to contain the same number of data points or time steps so as to carry out subsequent nuclear density estimation and comparison.
Step two: and estimating probability density estimation curves corresponding to the standard opening power curve, the standard closing power curve and the unknown action power curve by using a nuclear density estimation method. The basic principle of kernel density estimation is to estimate the probability density by placing kernel functions around each data point and then summing them. The following is the basic formula for nuclear density estimation:
；
wherein:is a probability density estimate at location x; x is x i Is the ith data point and n is the total number of data points. K (K) h Is a kernel function, typically a density function of a standard normal distribution, in the specific form:
；
where h is the bandwidth used to control the width of the kernel.
The kernel density estimation is a non-parametric method for estimating probability density estimation curves for data. It obtains an estimate of the overall data distribution by placing a kernel around each data point and then summing the kernels. The kernel function is typically a symmetric function centered around zero, which is used to measure the contribution of a data point to the estimate. The density function of a standard normal distribution is typically used as the kernel function, but other kernel functions, such as Epanechnikov kernel, trigonometric kernel, etc., may also be selected.
And (3) nuclear density estimation calculation: for the ith data point x i Calculating the value of the kernel function at position x. This value measures the data point x i Contribution to the estimation at position x. The contributions of all data points are added together and then divided by the sample size n to obtain a probability density estimate at location x。
And visualizing the result of the nuclear density estimation, and drawing an estimated nuclear density estimation curve. This will help to understand intuitively the distribution of data and the probability density estimation of the unknown motion power curve. Kernel density estimation is a useful tool to understand data distribution and make probability density estimation. And obtaining probability density estimation curves of the standard opening power curve, the standard closing power curve and the unknown action power curve by using the kernel density estimation to support subsequent similarity estimation.
Step three: adaptive bandwidth selection.
In the kernel density estimation, the choice of an appropriate bandwidth is critical, as it directly affects the shape and smoothness of the probability density estimation curve. A cross-validation method will be employed to select the bandwidth to ensure accuracy and robustness of the probability density estimation.
Cross-validation is a common bandwidth selection method that selects the best bandwidth by making multiple kernel density estimates over the dataset and evaluating performance. Common cross-validation methods include minimum mean square error cross-validation and minimum cross-validation estimation.
Minimum mean square error cross validation: the goal of this approach is to select a bandwidth that minimizes the mean square error between the kernel density estimate and the true probability density curve. The method comprises the following specific steps: dividing the data set into a training set and a validation set, typically employing k-fold cross validation, where k is the number of folds for cross validation; for each possible bandwidth value, performing kernel density estimation on the training set; calculating a mean square error over the validation set using the probability density estimation curve; the bandwidth that minimizes the mean square error is selected as the optimal bandwidth.
Minimum cross-validation estimation: the goal of this approach is to select a bandwidth that minimizes the expected mean square error of the cross-validation estimate. The method comprises the following specific steps: dividing the data set into a training set and a verification set; for each possible bandwidth value, performing kernel density estimation on the training set; calculating an expected mean square error over the validation set using the probability density estimation curve; the bandwidth that minimizes the desired mean square error is selected as the optimal bandwidth.
The invention comprehensively adopts the two bandwidth verification methods, has the best effect, and the effect is different according to actual data.
The selection of bandwidth typically involves the use of cross-validation methods, with minimum mean square error cross-validation and minimum cross-validation estimation being common selection methods. The rationale for these methods is to select the best bandwidth by estimating the kernel density function over a training set and then evaluating the estimated quality over a validation set.
The choice of bandwidth allows for a trade-off between smoothness and adaptability. Smaller bandwidths may result in probability density estimation curves that are closer to the original data, but may be too sharp. A larger bandwidth results in a smoother probability density estimation curve, but may be too flat, losing detail of the original data.
By cross-validation, a bandwidth can be found that allows the probability density estimation curve to have a smaller mean square error or expected mean square error over the validation set. This bandwidth can be used to estimate the probability density estimation curve of the unknown motion power curve for subsequent similarity assessment.
In summary, the goal of bandwidth selection is to find a value that best balances the kernel density estimate between smoothness and fitness to ensure that the kernel density estimate accurately reflects the distribution characteristics of the data. The cross-validation method is a common, data-based way to achieve this goal. The bandwidth adaptive method can automatically select a proper bandwidth according to the characteristics of data without manually adjusting or presetting a bandwidth value. This adaptation makes the method more versatile and can adapt to changes in different data sets. Bandwidth adaptation can better handle non-uniformities and noise in the data than traditional fixed bandwidth methods.
Step four: calculating the similarity score of a nuclear density estimation curve corresponding to the unknown action power curve and the nuclear density estimation curves of the standard opening power curve and the standard closing power curve; based on the similarity score, the likelihood of an unknown action power curve being open or closed is interpreted, providing visual or interpretable information of the results to support the final decision.
An example of the application of the present invention is shown in fig. 1-4, where fig. 1 is a standard split power curve, with the horizontal axis representing data points and the vertical axis representing values in watts (W). Fig. 2 is a standard closing power curve, and fig. 3 is an unknown operating power curve. Fig. 4 is a graph showing a comparison of similarity of core density estimation curves, curve 1 is a core density estimation curve corresponding to a standard opening power curve, curve 2 is a core density estimation curve corresponding to a standard closing power curve, curve 3 is a core density estimation curve corresponding to an unknown action power curve, the horizontal axis represents a data value, and the vertical axis represents probability density without dimension. The similarity score of the curve 3 compared with the curve 1 is 0.0000000954, and the similarity score of the curve 3 compared with the curve 2 is 0.0000040416, so that the closing power curve of the GIS disconnecting switch is judged in fig. 3.
The invention adopts the nuclear density estimation method to analyze the similarity of the curves, and has the following three main advantages compared with the traditional curve similarity analysis method using dynamic time warping (DynamicTimeWarping, DTW) and the like:
independent of the alignment process: while the kernel density estimation method does not require a process of curve alignment, DTW and the like generally require a time or space alignment of the curves before performing the similarity analysis, which may involve complex calculations and parameter selection. The kernel density estimation method allows for a free comparison between curves without forced alignment by making a smooth estimation over the entire curve. This allows for more flexibility in dealing with variations between curves and noise. This makes the kernel density estimation method easier to implement, especially for situations where noise or variations are present in the actual data.
The applicability is wide: the nuclear density estimation method is a non-parametric method, independent of the specific shape or model of the curve, and thus is applicable to various types of curve data, including continuous, discrete, unimodal, multimodal, etc. In contrast, DTW and like methods generally depend on the specific shape and characteristics of the curve, and may require adjustment of parameters or modification of algorithms for different types of curves. DTW and like methods are typically based on specific curve models or assumptions, which may limit their applicability. If the shape of the curve does not meet these assumptions, performance may be degraded.
Providing probability density information: the kernel density estimation method can be used for comparing the similarity of curves and also providing probability density information of each point on the curves. This means that the profile of the curve can be understood in more detail, potential modes or peaks identified and further statistically analyzed. Such probability density information is of great value in many application areas, such as anomaly detection, pattern recognition and statistical analysis.
While embodiments of the present invention have been shown and described in detail with reference to the foregoing detailed description, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations may be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. The GIS disconnecting switch switching position confirmation method is characterized by comprising the following steps:
step one: collecting real-time power of a motor of the GIS isolating switch, and performing data preprocessing to obtain an unknown action power curve;
step two: estimating probability density estimation curves corresponding to a standard opening power curve, a standard closing power curve and an unknown action power curve by using a nuclear density estimation method;
step three: adopting a cross-validation method to adaptively select the bandwidth of the kernel density estimation;
step four: calculating the similarity score of a nuclear density estimation curve corresponding to the unknown action power curve and the nuclear density estimation curves of the standard opening power curve and the standard closing power curve; and judging whether the unknown action power curve is opened or closed based on the similarity score.
2. The method for confirming the on-off position of a GIS isolating switch according to claim 1, wherein the core density estimation calculation process is as follows:
；
；
wherein:is a probability density estimate at location x; x is x i Is the ith data point, n is the total number of data points; k (K) h Is a kernel function and h is bandwidth.
3. The method of claim 1, wherein the cross-validation method comprises minimum mean square error cross-validation and minimum cross-validation estimation.
4. A GIS isolation switch on-off position confirmation method according to claim 3, wherein the minimum mean square error cross-validation: dividing the data set into a training set and a verification set, generally adopting k-fold cross verification, and carrying out kernel density estimation on the training set for each bandwidth value; calculating a mean square error over the validation set using the probability density estimation curve; the bandwidth that minimizes the mean square error is selected as the optimal bandwidth.
5. A GIS disconnector switching position confirmation method according to claim 3, characterized in that the minimum cross-validation estimate: dividing the data set into a training set and a verification set; for each bandwidth value, performing kernel density estimation on the training set; calculating an expected mean square error over the validation set using the probability density estimation curve; the bandwidth that minimizes the desired mean square error is selected as the optimal bandwidth.
6. The method for confirming the switching position of the GIS isolating switch according to claim 1, wherein in the first step, the voltage and the current of the motor of the GIS isolating switch in the running process are collected, and the real-time power of the motor is calculated according to the voltage and the current to obtain an unknown action power curve.
7. The method for confirming the opening and closing positions of the GIS isolating switch according to claim 1, wherein in the first step, the data preprocessing comprises data cleaning, data formatting and data alignment.
8. The method of claim 7, wherein the data cleaning includes processing missing data, processing outliers and noise filtering.
9. The method for confirming the opening and closing positions of a GIS isolation switch according to claim 7, wherein the data formatting includes: checking the time intervals among the data points of the standard opening power curve, the standard closing power curve and the unknown action power curve to ensure that the data points are consistent in time; the format of the data is checked.
10. The method for confirming the opening and closing positions of a GIS isolation switch according to claim 7, wherein the data alignment comprises:
time alignment: according to the timestamp information, aligning a standard opening power curve, a standard closing power curve and an unknown action power curve to make the standard opening power curve, the standard closing power curve and the unknown action power curve consistent in time;
interpolation: if the data points are not evenly distributed in time, interpolation methods are used to obtain data for even time steps.
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