CN110926651A - Power distribution cabinet detection method and device - Google Patents
Power distribution cabinet detection method and device Download PDFInfo
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Abstract
The invention provides a power distribution cabinet detection method and a power distribution cabinet detection device, wherein the power distribution cabinet detection method comprises the steps of obtaining a power distribution cabinet temperature data value; dividing the temperature data value of the power distribution cabinet into a common temperature data value and a temperature data value to be monitored; inputting the common temperature data value into a trained Bagging algorithm model to obtain a predicted value of the temperature to be monitored, which is output by the Bagging algorithm model; calculating a difference value between the temperature data value to be monitored and the predicted value of the temperature to be monitored; and detecting the power distribution cabinet according to the relation between the difference value and the trained confidence interval. According to the method, the working temperature of the important module of the power distribution cabinet is predicted through the Bagging algorithm model to obtain a corresponding predicted value, then the actual temperature value obtained through measurement is compared with the corresponding predicted value, whether the obtained difference value falls into a confidence interval or not is judged, and therefore the working temperature of the important module of the power distribution cabinet can be effectively monitored, and the monitoring accuracy is improved.
Description
Technical Field
The invention relates to the technical field of power distribution cabinet detection, in particular to a power distribution cabinet detection method and device.
Background
A power distribution cabinet or a power distribution box is final-stage equipment of a power distribution system and is mainly used for occasions with concentrated loads and more loops.
At present, various detection methods for a power distribution cabinet are provided, the detection methods are mainly classified into a circuit detection type and a temperature detection type, the circuit detection type is complex in structure, and the detection of a main working module of the power distribution cabinet is mainly realized by an external circuit module; the temperature detection type is characterized in that a probe is arranged on the main working module, and an early warning temperature is set, when the temperature detected by the probe is higher than the early warning temperature, an alarm is sent out, the cost of the mode is low, but the false alarm rate is high due to the limitation of an algorithm.
Disclosure of Invention
In view of the above deficiencies of the prior art, the present invention provides a method and an apparatus for detecting a power distribution cabinet, which are directed to solving one of the technical problems in the background art.
The technical problem to be solved by the invention is realized by adopting the following technical scheme:
as a first aspect of the invention, a power distribution cabinet detection method is provided, which comprises the steps of
Acquiring a temperature data value of the power distribution cabinet;
dividing the temperature data value of the power distribution cabinet into a common temperature data value and a temperature data value to be monitored;
inputting the common temperature data value into a trained Bagging algorithm model to obtain a predicted value of the temperature to be monitored, which is output by the Bagging algorithm model;
calculating a difference value between the temperature data value to be monitored and the predicted value of the temperature to be monitored;
and detecting the power distribution cabinet according to the relation between the difference value and the trained confidence interval.
As an optional implementation mode of the invention, the training process of the confidence interval is
Acquiring historical data of the temperature of the power distribution cabinet;
inputting common temperature data values in the historical data into a trained Bagging algorithm model as a training set to obtain a set of temperature predicted values to be monitored, which are output by the Bagging algorithm model;
calculating a difference value between a temperature data value to be monitored corresponding to the common temperature data value and a predicted temperature value to be monitored to obtain a difference value set;
calculating an average value and a standard deviation corresponding to the average value from the difference value set;
and establishing a confidence interval according to the average value and the standard deviation.
In an alternative embodiment of the present invention, the confidence interval is, where the mean value of the difference is represented and the standard deviation of the difference is represented.
As an optional implementation manner of the present invention, the training process of the Bagging algorithm model includes acquiring historical data of the temperature of the power distribution cabinet;
taking historical data of the temperature of the power distribution cabinet as a training set, taking a common temperature data value in the training set as an input value, and taking the temperature data value to be monitored as an output value;
sampling with the put back t times from the training set;
aiming at each sampling result, training by using a decision tree algorithm to obtain a weak hypothesis model;
aiming at a new sample s with a known common temperature data value, each hypothesis model can obtain a prediction output value, and the prediction output value with the highest ticket obtaining is the predicted value of the temperature to be monitored of the new sample s.
As a second aspect of the invention, a power distribution cabinet detection device is provided, which comprises
The first acquisition module is used for acquiring a temperature data value of the power distribution cabinet;
the distribution module is used for dividing the temperature data value of the power distribution cabinet into a common temperature data value and a temperature data value to be monitored;
the first calculation module is used for inputting the common temperature data value into a trained Bagging algorithm model to obtain a predicted value of the temperature to be monitored, which is output by the Bagging algorithm model;
the second calculation module is used for calculating the difference value between the temperature data value to be monitored and the predicted value of the temperature to be monitored;
and the detection module is used for detecting the power distribution cabinet according to the relation between the difference value and the trained confidence interval.
As an alternative embodiment of the invention, the device further comprises
The second acquisition module is used for acquiring historical data of the temperature of the power distribution cabinet;
the first training module is used for inputting common temperature data values in the historical data into a trained Bagging algorithm model as a training set to obtain a set of temperature predicted values to be monitored, wherein the set is output by the Bagging algorithm model;
the third calculation module is used for calculating the difference value between the temperature data value to be monitored corresponding to the common temperature data value and the predicted temperature value to be monitored to obtain a difference value set;
a fourth calculating module, configured to calculate an average value and a standard deviation corresponding to the average value from the difference set;
and the establishing module is used for establishing a confidence interval according to the average value and the standard deviation.
As an alternative embodiment of the invention, the device further comprises
The third acquisition module is used for acquiring historical data of the temperature of the power distribution cabinet;
the second training module is used for taking historical data of the temperature of the power distribution cabinet as a training set, taking a common temperature data value in the training set as an input value and taking the temperature data value to be monitored as an output value;
the sampling module is used for sampling with the time t from the training set;
the obtaining module is used for training by using a decision tree algorithm aiming at each sampling result to obtain a weak hypothesis model;
and the fifth calculation module is used for obtaining a prediction output value for each hypothesis model aiming at a new sample s with a known common temperature data value, wherein the prediction output value with the highest ticket obtaining is the predicted value of the temperature to be monitored of the new sample s.
The invention has the beneficial effects that:
according to the method, the working temperature of the important module of the power distribution cabinet is predicted through the Bagging algorithm model to obtain a corresponding predicted value, then the actual temperature value obtained through measurement is compared with the corresponding predicted value, whether the obtained difference value falls into a confidence interval or not is judged, and therefore the working temperature of the important module of the power distribution cabinet can be effectively monitored, and the monitoring accuracy is improved.
Drawings
The invention is further illustrated with reference to the following figures and examples.
Fig. 1 is a logic schematic diagram of a power distribution cabinet detection method according to the present embodiment;
fig. 2 is a logic diagram of a confidence interval of the power distribution cabinet detection method according to the embodiment;
fig. 3 is a logic diagram of a Bagging algorithm model of the power distribution cabinet detection method according to the embodiment;
fig. 4 is a schematic diagram of the power distribution cabinet detection apparatus according to the embodiment.
Detailed Description
The following embodiments are provided to describe the embodiments of the present invention, and to further describe the detailed description of the embodiments of the present invention, such as the shapes, configurations, mutual positions and connection relationships of the components, the functions and operation principles of the components, the manufacturing processes and operation methods, etc., so as to help those skilled in the art to more fully, accurately and deeply understand the inventive concept and technical solutions of the present invention.
As a first aspect of the present invention, as shown in fig. 1, there is provided a detection method of a power distribution cabinet, including
S10, obtaining a temperature data value of the power distribution cabinet;
s20, dividing the temperature data value of the power distribution cabinet into a common temperature data value and a temperature data value to be monitored;
s30, inputting the common temperature data value into the trained Bagging algorithm model to obtain a predicted value of the temperature to be monitored, which is output by the Bagging algorithm model;
s40, calculating the difference value between the temperature data value to be monitored and the predicted value of the temperature to be monitored;
and S50, detecting the power distribution cabinet according to the relation between the difference and the trained confidence interval.
In this embodiment, various temperature data values of the power distribution cabinet, such as the ambient temperature and the operating temperature of each module, are detected, the temperature data values are divided into common temperature data values and temperature data values to be monitored, the common temperature data values are input into a trained Bagging algorithm model to obtain predicted values of the temperature to be monitored output by the Bagging algorithm model, a difference between the temperature data values to be monitored and the predicted values of the temperature to be monitored is calculated, the difference is compared with a trained confidence interval, if the difference falls into the trained confidence interval, it is indicated that the module corresponding to the temperature data values to be monitored of the power distribution cabinet is in a normal operating state, if the difference exceeds the trained confidence interval, it is indicated that the module corresponding to the temperature data values to be monitored of the power distribution cabinet is abnormal, and an alarm is given to the outside, so that the maintenance personnel can maintain and repair the device.
According to the method, the working temperature of the important module of the power distribution cabinet is predicted through the Bagging algorithm model to obtain a corresponding predicted value, then the actual temperature value obtained through measurement is compared with the corresponding predicted value, whether the obtained difference value falls into a confidence interval or not is judged, and therefore the working temperature of the important module of the power distribution cabinet can be effectively monitored, and the monitoring accuracy is improved.
As an alternative implementation, as shown in fig. 2, the training process of the confidence interval is S51, obtaining historical data of the temperature of the power distribution cabinet;
s52, inputting common temperature data values in the historical data into a trained Bagging algorithm model as a training set to obtain a set of temperature predicted values to be monitored, which are output by the Bagging algorithm model;
s53, calculating the difference value between the temperature data value to be monitored corresponding to the common temperature data value and the predicted temperature value to be monitored, and obtaining a difference value set;
s54, calculating a mean value and a standard deviation corresponding to the mean value from the difference value set;
and S55, establishing a confidence interval according to the average value and the standard deviation.
Optionally, the confidence interval is
Wherein the content of the first and second substances,
represents the mean value of the difference;
As an alternative implementation, as shown in fig. 3, the training process of the Bagging algorithm model includes
S31, acquiring historical data of the temperature of the power distribution cabinet;
s32, taking historical data of the temperature of the power distribution cabinet as a training set, taking a common temperature data value in the training set as an input value, and taking the temperature data value to be monitored as an output value;
s33, sampling with the sample put back for t times from the training set;
s34, aiming at each sampling result, training by using a decision tree algorithm to obtain a weak hypothesis model;
and S35, aiming at a new sample S with a known common temperature data value, each hypothesis model can obtain a prediction output value, and the prediction output value with the highest ticket obtaining is the predicted value of the temperature to be monitored of the new sample S.
As a second aspect of the invention, as shown in FIG. 4, there is provided a detecting device for a power distribution cabinet, comprising
The first acquisition module 10 is used for acquiring a temperature data value of the power distribution cabinet;
the distribution module 20 is configured to divide the temperature data value of the power distribution cabinet into a common temperature data value and a temperature data value to be monitored;
the first calculation module 30 is configured to input the common temperature data value into a trained Bagging algorithm model, and obtain a predicted value of the temperature to be monitored, which is output by the Bagging algorithm model;
the second calculating module 40 is configured to calculate a difference between the temperature data value to be monitored and the predicted temperature value to be monitored;
and the detection module 50 is used for detecting the power distribution cabinet according to the relation between the difference value and the trained confidence interval.
In this embodiment, various temperature data values of the power distribution cabinet, such as the ambient temperature and the operating temperature of each module, are detected, the temperature data values are divided into common temperature data values and temperature data values to be monitored, the common temperature data values are input into a trained Bagging algorithm model to obtain predicted values of the temperature to be monitored output by the Bagging algorithm model, a difference between the temperature data values to be monitored and the predicted values of the temperature to be monitored is calculated, the difference is compared with a trained confidence interval, if the difference falls into the trained confidence interval, it is indicated that the module corresponding to the temperature data values to be monitored of the power distribution cabinet is in a normal operating state, if the difference exceeds the trained confidence interval, it is indicated that the module corresponding to the temperature data values to be monitored of the power distribution cabinet is abnormal, and an alarm is given to the outside, so that the maintenance personnel can maintain and repair the device.
As an optional implementation mode, the device further comprises
The second acquisition module is used for acquiring historical data of the temperature of the power distribution cabinet;
the first training module is used for inputting common temperature data values in the historical data into a trained Bagging algorithm model as a training set to obtain a set of temperature predicted values to be monitored, wherein the set is output by the Bagging algorithm model;
the third calculation module is used for calculating the difference value between the temperature data value to be monitored corresponding to the common temperature data value and the predicted temperature value to be monitored to obtain a difference value set;
a fourth calculating module, configured to calculate an average value and a standard deviation corresponding to the average value from the difference set;
and the establishing module is used for establishing a confidence interval according to the average value and the standard deviation.
As an optional implementation mode, the device further comprises
The third acquisition module is used for acquiring historical data of the temperature of the power distribution cabinet;
the second training module is used for taking historical data of the temperature of the power distribution cabinet as a training set, taking a common temperature data value in the training set as an input value and taking the temperature data value to be monitored as an output value;
the sampling module is used for sampling with the time t from the training set;
the obtaining module is used for training by using a decision tree algorithm aiming at each sampling result to obtain a weak hypothesis model;
and the fifth calculation module is used for obtaining a prediction output value for each hypothesis model aiming at a new sample s with a known common temperature data value, wherein the prediction output value with the highest ticket obtaining is the predicted value of the temperature to be monitored of the new sample s.
The invention has been described in an illustrative manner, and it is to be understood that the invention is not limited to the precise form disclosed, and that various insubstantial modifications of the inventive concepts and solutions, or their direct application to other applications without such modifications, are intended to be covered by the scope of the invention. The protection scope of the present invention shall be subject to the protection scope defined by the claims.
Claims (7)
1. A detection method of a power distribution cabinet is characterized in that: comprises that
Acquiring a temperature data value of the power distribution cabinet;
dividing the temperature data value of the power distribution cabinet into a common temperature data value and a temperature data value to be monitored;
inputting the common temperature data value into a trained Bagging algorithm model to obtain a predicted value of the temperature to be monitored, which is output by the Bagging algorithm model;
calculating a difference value between the temperature data value to be monitored and the predicted value of the temperature to be monitored;
and detecting the power distribution cabinet according to the relation between the difference value and the trained confidence interval.
2. The power distribution cabinet detection method according to claim 1, characterized in that: the training process of the confidence interval is
Acquiring historical data of the temperature of the power distribution cabinet;
inputting common temperature data values in the historical data into a trained Bagging algorithm model as a training set to obtain a set of temperature predicted values to be monitored, which are output by the Bagging algorithm model;
calculating a difference value between a temperature data value to be monitored corresponding to the common temperature data value and a predicted temperature value to be monitored to obtain a difference value set;
calculating an average value and a standard deviation corresponding to the average value from the difference value set;
and establishing a confidence interval according to the average value and the standard deviation.
3. The power distribution cabinet detection method according to claim 2, characterized in that: the confidence interval is
Wherein
Represents the mean value of the difference, SδThe standard deviation of the difference is indicated.
4. The power distribution cabinet detection method according to claim 1 or 2, characterized in that: the training process of the Bagging algorithm model comprises the following steps
Acquiring historical data of the temperature of the power distribution cabinet;
taking historical data of the temperature of the power distribution cabinet as a training set, taking a common temperature data value in the training set as an input value, and taking the temperature data value to be monitored as an output value;
sampling with the put back t times from the training set;
aiming at each sampling result, training by using a decision tree algorithm to obtain a weak hypothesis model;
aiming at a new sample s with a known common temperature data value, each hypothesis model can obtain a prediction output value, and the prediction output value with the highest ticket obtaining is the predicted value of the temperature to be monitored of the new sample s.
5. The utility model provides a switch board detection device which characterized in that: comprises that
The first acquisition module is used for acquiring a temperature data value of the power distribution cabinet;
the distribution module is used for dividing the temperature data value of the power distribution cabinet into a common temperature data value and a temperature data value to be monitored;
the first calculation module is used for inputting the common temperature data value into a trained Bagging algorithm model to obtain a predicted value of the temperature to be monitored, which is output by the Bagging algorithm model;
the second calculation module is used for calculating the difference value between the temperature data value to be monitored and the predicted value of the temperature to be monitored;
and the detection module is used for detecting the power distribution cabinet according to the relation between the difference value and the trained confidence interval.
6. The power distribution cabinet detection device according to claim 5, wherein: the device also comprises
The second acquisition module is used for acquiring historical data of the temperature of the power distribution cabinet;
the first training module is used for inputting common temperature data values in the historical data into a trained Bagging algorithm model as a training set to obtain a set of temperature predicted values to be monitored, wherein the set is output by the Bagging algorithm model;
the third calculation module is used for calculating the difference value between the temperature data value to be monitored corresponding to the common temperature data value and the predicted temperature value to be monitored to obtain a difference value set;
a fourth calculating module, configured to calculate an average value and a standard deviation corresponding to the average value from the difference set;
and the establishing module is used for establishing a confidence interval according to the average value and the standard deviation.
7. The power distribution cabinet detection device according to claim 5, wherein: the device also comprises
The third acquisition module is used for acquiring historical data of the temperature of the power distribution cabinet;
the second training module is used for taking historical data of the temperature of the power distribution cabinet as a training set, taking a common temperature data value in the training set as an input value and taking the temperature data value to be monitored as an output value;
the sampling module is used for sampling with the time t from the training set;
the obtaining module is used for training by using a decision tree algorithm aiming at each sampling result to obtain a weak hypothesis model;
and the fifth calculation module is used for obtaining a prediction output value for each hypothesis model aiming at a new sample s with a known common temperature data value, wherein the prediction output value with the highest ticket obtaining is the predicted value of the temperature to be monitored of the new sample s.
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Application publication date: 20200327 |