CN118760864B - A switch cabinet health status evaluation method and device - Google Patents
A switch cabinet health status evaluation method and device Download PDFInfo
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- 238000011156 evaluation Methods 0.000 title claims description 21
- 230000003862 health status Effects 0.000 title claims description 11
- 238000012423 maintenance Methods 0.000 claims abstract description 142
- 230000036541 health Effects 0.000 claims abstract description 118
- 238000013210 evaluation model Methods 0.000 claims abstract description 48
- 238000000034 method Methods 0.000 claims abstract description 37
- 239000000428 dust Substances 0.000 claims description 34
- 238000004458 analytical method Methods 0.000 claims description 25
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- 238000009825 accumulation Methods 0.000 claims description 20
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- 230000007613 environmental effect Effects 0.000 description 3
- 101100379079 Emericella variicolor andA gene Proteins 0.000 description 2
- 230000032683 aging Effects 0.000 description 2
- 230000008021 deposition Effects 0.000 description 2
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- 238000012935 Averaging Methods 0.000 description 1
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- 230000003287 optical effect Effects 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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Abstract
The application relates to a method and a device for evaluating the health state of a switch cabinet. In the application, the target parameter can be used as an index for measuring whether the switch cabinet needs maintenance or not. Therefore, a health evaluation model is built based on the target parameters, so that the value of the target parameters at the current time point can be evaluated, and the health degree of the switch cabinet at the current time point can be judged. As a criterion for whether the switchgear requires maintenance. The application can help maintenance personnel to know the health state of the switch cabinet, and can respectively judge the health degree according to the target parameters of the switch cabinet under various working environments, thereby making a maintenance plan according to the health degree instead of timing calculation, and further realizing fine management of the switch cabinet.
Description
Technical Field
The invention relates to the field of electrical equipment, in particular to a method and a device for evaluating the health state of a switch cabinet.
Background
Switchgear is an important component in an electrical power system, comprising numerous high-voltage electrical devices. And the use environment of the switch cabinet is complex and is greatly influenced by environmental factors such as temperature, humidity and the like. Therefore, the switch needs to be monitored, and abnormal conditions are found in time, so that measures are rapidly taken to process, the sudden occurrence of equipment faults is avoided, and the reliability and the safety of the power system are improved.
Existing switchgear maintenance is typically performed periodically, for example, every three months or half a year. However, the use conditions of different switch cabinets are different in practice, and some switch cabinets work in a relatively suitable environment, so that the maintenance time interval required in practice is longer, and some switch cabinets work in environments with severe environments and high loads, so that the maintenance time required in practice is shorter, and therefore, the fixed maintenance time is adopted in the prior art, so that the certain switch cabinets cannot be maintained, and the maintenance time of the certain switch cabinets is too frequent, thereby wasting maintenance resources and cost. In view of this, it is necessary to grasp the health of the switch cabinet to provide a reference to maintenance personnel. However, in the prior art, no method for evaluating the health state of a switch cabinet is specially provided for maintenance personnel.
Disclosure of Invention
Therefore, the invention aims to provide a method and a device for evaluating the health state of a switch cabinet, which are used for solving the problem that whether the switch cabinet needs maintenance or not cannot be mastered in the prior art.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the invention relates to a method for evaluating the health state of a switch cabinet, which comprises the following steps:
Acquiring historical sensor data of the switch cabinet in a plurality of historical maintenance periods, wherein the historical sensor data comprise values of sensor parameters corresponding to a plurality of detection time points in the historical maintenance periods, and the sensor parameters comprise temperature, humidity, dust accumulation amount, main line current, main line voltage and fan operation noise level;
performing distribution feature analysis on the sensor data of the plurality of maintenance periods, and taking a sensor parameter meeting a target feature as a target parameter, wherein the target feature comprises: the value of the sensor parameter is trended up or trended down over time;
Constructing a health evaluation model based on values of target parameters of the switch cabinet in a plurality of maintenance periods, wherein the health evaluation model represents a corresponding relation between the values of the target parameters and the health degree of the switch cabinet;
and acquiring the value of the target parameter at the current time point, and determining the health degree of the switch cabinet at the current time point based on the value of the target parameter at the current time point and the health evaluation model.
In an embodiment of the present application, further includes:
And comparing the health degree of the switch cabinet at the current time point with a preset threshold value, and sending alarm information to a target object when the health degree of the switch cabinet at the current time point is greater than the preset threshold value.
In one embodiment of the present application, the process for obtaining the dust accumulation amount includes:
acquiring an image of a target area in a switch cabinet, wherein the target area comprises a circuit board, a radiator, a plugging part and an area where a vent is located;
converting the image of the target area into a gray scale image;
and carrying out dust accumulation identification on the gray level image based on a pre-constructed dust identification model to obtain dust accumulation, wherein the dust identification model is obtained by training an artificial neural network by image samples of switch cabinet internal images of different degrees of dust accumulation.
In an embodiment of the present application, performing a distributed feature analysis on the sensor data of the plurality of maintenance periods includes:
Analyzing the distribution characteristics of the sensor data of a single period, wherein the distribution characteristics of the sensor data of the single period comprise trend increment, trend decrement and no increase or decrease trend;
when the trend of the value of the sensor parameter in the history maintenance period exceeding the preset proportion threshold value is decreased, judging that the trend of the value of the sensor parameter is decreased; and when the trend of the value of the sensor parameter in the history maintenance period exceeding the preset proportion threshold value is increased, judging that the trend of the value of the sensor parameter is increased.
In one embodiment of the present application, performing a distributed feature analysis on single cycle sensor data includes:
Mapping values of sensor parameters of a plurality of detection time points in a single history maintenance period into a two-dimensional coordinate system to obtain data coordinates, wherein the abscissa of the two-dimensional coordinate system is time, and the ordinate is a value;
before calculating the data coordinates Average value and average time of data points, and thenAverage value and average time of data points, and based on the previousAverage value and average time of data points to construct a starting reference pointAnd based on the backAverage value of data points and average time end reference point；
Straight line is adopted to make the initial reference pointAnd the ending reference pointConnecting to obtain trend reference line；
Calculating each data point in the data coordinates and the trend reference lineWherein is higher than the trend reference lineIs related to the trend reference lineIs positive and is lower than the trend reference lineIs related to the trend reference lineIs negative;
calculating variance of the plurality of vertical differences, wherein the variance is smaller than a preset threshold value When the value trend of the sensor parameter of the current historical maintenance period is judged to be decreased; when the variance is smaller than a preset threshold value, andAnd when the trend of the value of the sensor parameter of the history maintenance period is judged to be increased, wherein,For the ratio parameter with the value larger than 1, andAs the initial reference pointIs used as a reference to the value of (a),To end the reference pointIs a value of (2).
In one embodiment of the application, constructing a health assessment model based on values of target parameters of a switchgear cabinet over a plurality of maintenance cycles includes:
calculate each target parameter at the first Rate of change of individual maintenance cyclesWherein, the method comprises the steps of, wherein,Wherein, the method comprises the steps of, wherein,Is the firstInitial reference point of each maintenance periodIs used as a reference to the value of (a),Is the firstEnd reference point of each maintenance cycleIs used as a reference to the value of (a),Is the firstThe duration of the individual maintenance periods;
determining a relevance weight for each target parameter based on the rate of change of each target parameter over a plurality of maintenance periods;
Calculating the weight sum of the correlation weights of all the target parameters, and calculating the ratio of the correlation weights of the target parameters to the weight sum;
When the ratio of the correlation weight of any one target parameter to the sum of the weights exceeds a preset proportion value, establishing a single-factor health evaluation model based on the target parameter with the ratio exceeding the preset proportion value;
And when the ratio of the correlation weight of any target parameter to the sum of the weights exceeds a preset proportional value, establishing a multi-factor health evaluation model based on the correlation weights of various target parameters.
In one embodiment of the present application, determining a relevance weight for each target parameter based on a rate of change of each target parameter over a plurality of maintenance cycles includes:
calculating target parameters Rate of change over multiple maintenance cyclesAverage value of (2)Sum of variances；
Based on the average valueAnd the variance isCalculating relevance index of target parameter，Wherein, the method comprises the steps of, wherein,As a first weight to be used,Is a second weight;
correlation index for all target parameters Normalization processing is carried out to obtain the correlation weight of each target parameter。
In an embodiment of the present application, establishing a single-factor health evaluation model based on a target parameter having a ratio exceeding a preset ratio value includes:
obtaining a reference value of a target parameter with a ratio exceeding a preset ratio value during maintenance And an average of values of the starting reference points of the plurality of maintenance periods；
Value based on the referenceAnd constructing a single-factor health evaluation model by using an average value of values of initial reference points of a plurality of maintenance periods, wherein the mathematical expression of the health evaluation model is as follows:
wherein, For the value of the target parameter whose ratio exceeds the preset ratio value at the current time point,Is the health degree of the switch cabinet.
In an embodiment of the present application, a multi-factor health evaluation model is built based on relevance weights of a plurality of target parameters, including:
Obtaining reference values of a plurality of target parameters in maintenance And an average of values of starting reference points of a plurality of maintenance periods of a plurality of target parameters；
Reference value in maintenance based on multiple target parametersAnd constructing a multi-factor health evaluation model by using an average value of values of initial reference points of a plurality of maintenance periods of a plurality of target parameters, wherein the mathematical expression of the health evaluation model is as follows:
wherein, Is the firstThe values of the individual target parameters at the current point in time,For the health of the switch cabinet,Is the number of target parameters.
The application also provides a switch cabinet health state evaluation device, which comprises:
The system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring historical sensor data of the switch cabinet in a plurality of historical maintenance periods, the historical sensor data comprise values of sensor parameters corresponding to a plurality of detection time points in the maintenance period, and the sensor parameters comprise temperature, humidity, dust accumulation amount, main line current, main line voltage and fan operation noise volume level;
the feature analysis module is used for carrying out distribution feature analysis on the sensor data of the plurality of maintenance periods and taking sensor parameters meeting target features as target parameters, wherein the target features comprise: the value of the sensor parameter is trended up or trended down over time;
the system comprises a model construction module, a health evaluation module and a control module, wherein the model construction module is used for constructing a health evaluation model based on values of target parameters of the switch cabinet in a plurality of maintenance periods, and the health evaluation model represents a corresponding relation between the values of the target parameters and the health degree of the switch cabinet;
And the evaluation module is used for acquiring the value of the target parameter at the current time point and determining the health degree of the switch cabinet at the current time point based on the value of the target parameter at the current time point and the health evaluation model. The invention also provides a storage medium in which a computer program is stored which, when loaded and executed by a processor, implements a switch cabinet health status assessment method as described above.
The present invention also provides an electronic device including: a processor and a memory; wherein the memory is used for storing a computer program; the processor is used for loading and executing the computer program to enable the electronic equipment to execute the switch cabinet health state evaluation method.
The beneficial effects of the application are as follows: according to the switch cabinet health state evaluation method and device, the historical sensor data are subjected to trend analysis to obtain the target parameters related to maintenance. In the application, the target parameter can be used as an index for measuring whether the switch cabinet needs maintenance or not. Therefore, a health evaluation model is built based on the target parameters, so that the value of the target parameters at the current time point can be evaluated, and the health degree of the switch cabinet at the current time point can be judged. As a criterion for whether the switchgear requires maintenance. The application can help maintenance personnel to know the health state of the switch cabinet, and can respectively judge the health degree according to the target parameters of the switch cabinet under various working environments, thereby making a maintenance plan according to the health degree instead of timing calculation, and further realizing fine management of the switch cabinet.
Drawings
The invention is further described below with reference to the accompanying drawings and examples:
FIG. 1 is a flow chart of a method of evaluating the health status of a switchgear in accordance with one embodiment of the present application;
Fig. 2 is a block diagram of a switch cabinet health evaluation apparatus according to an embodiment of the present application.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present invention by way of illustration, and only the layers related to the present invention are shown in the drawings and are not drawn according to the number, shape and size of the layers in actual implementation, and the form, number and proportion of the layers in actual implementation may be arbitrarily changed, and the layer layout may be more complex.
In the following description, numerous details are discussed to provide a more thorough explanation of embodiments of the present invention, however, it will be apparent to one skilled in the art that embodiments of the present invention may be practiced without these specific details.
In the application, sensors such as a temperature sensor, a humidity sensor, a dust concentration sensor, a camera, a current transformer, a voltage transformer, a microphone and the like are arranged in the switch cabinet in advance to collect various parameters of the switch cabinet during working.
In each maintenance period in the early stage, various parameters in the switch cabinet are collected at fixed time, so that the parameters are used as basic data for constructing a health evaluation model. In the present application, the inherent correlation between parameters and switch cabinet maintenance is found based on the above-mentioned historical data. So that the reference guidance work is performed in the subsequent maintenance work. The specific process is as follows:
Fig. 1 is a flowchart of a method for evaluating the health status of a switchgear according to an embodiment of the present application, as shown in fig. 1: the method for evaluating the health state of the switch cabinet of the embodiment may include steps S110 to S140:
S110, acquiring historical sensor data of the switch cabinet in a plurality of historical maintenance periods, wherein the historical sensor data comprise values of sensor parameters corresponding to a plurality of detection time points in the historical maintenance periods, and the sensor parameters comprise temperature, humidity, dust accumulation amount, main line current, main line voltage and fan operation noise volume level;
Wherein the application is prior to performing the health assessment. A large amount of historical data needs to be collected to construct an evaluation model. In general, as the length of switchgear operation increases, a number of parameters may change, which are generally related to changes in equipment aging, wear, environmental factors, and electrical performance. The method specifically comprises the following steps:
(1) Temperature and humidity, switch cabinet long-term work, heat dissipation mouth deposition leads to hot air to be difficult to in time discharge, and heat gathers and leads to inside temperature to rise. Meanwhile, the performance of the sealing rubber strip is reduced, water vapor is easy to enter the switch cabinet, and the humidity is increased.
(2) Dust accumulation amount, after the switch cabinet works for a long time, dust can be accumulated on the surfaces of the insulators and the buses, the radiator and the fan blades, the wiring terminals and the connectors, the inner corners and gaps, the cabinet doors and the side plates, the ventilation openings and the heat dissipation holes, the installation base, the support and the like. The heating value of the internal components is increased, and the performance is weakened.
(3) The main line current and the main line voltage are affected by temperature or component aging, and the main line current or the main line voltage in the switch cabinet also changes.
(4) The level of the noise of the fan operation is increased due to the temperature rise in the switch cabinet, and the corresponding level of the noise of the fan operation is also increased under the control of the temperature control system in order to enhance heat dissipation.
In the application, the temperature, humidity, main line current, main line voltage and fan operation noise level can be directly collected by the sensor. The dust accumulation amount cannot be directly collected through the sensor, so that the dust accumulation amount in the switch cabinet is identified through the deeply-learned identification model. The method specifically comprises the following steps:
s111, acquiring an image of a target area in the switch cabinet, wherein the target area comprises a circuit board, a radiator, a plugging part and an area where a vent is located;
wherein, the regional deposition of circuit board, radiator, grafting position and vent place can influence the inside components and parts performance of cubical switchboard. The camera of the present application therefore monitors mainly these positions.
S112, converting the image of the target area into a gray level image; in addition, high pass filtering is required.
S113, carrying out dust accumulation recognition on the gray level image based on a pre-constructed dust recognition model to obtain dust accumulation, wherein the dust recognition model is obtained by training an artificial neural network through image samples of switch cabinet internal images with different degrees of dust accumulation.
Specifically, the training process of the dust recognition model includes:
Collecting a large number of sample images, wherein the sample images comprise images of dust accumulated in the switch cabinet;
The sample image is converted to a gray scale image and high pass filtered to preserve the contour of the dust area.
The images processed as described above are labeled, for example, 10%,20%. The more the dust is deposited, the larger the number of labels is.
And dividing the processed image into a training set, a testing set and a verification set, and training the artificial neural network. A dust recognition model is obtained.
S120, carrying out distribution feature analysis on the sensor data of the plurality of maintenance periods, and taking sensor parameters meeting target features as target parameters, wherein the target features comprise: the value of the sensor parameter is trended up or trended down over time;
According to the application, by carrying out distribution feature analysis on the sensor data of a plurality of maintenance periods, the sensor parameters can change unidirectionally along with the increase of the service time, so that the sensor parameters are used as a standard for measuring the health degree of the switch cabinet.
For example, as the amount of dust is collected, the temperature gradually increases after a period of time (the heat dissipation fan cannot dissipate heat in time), and thus the trend is increasing. The fan is gradually powered up to correspond to the gradually increased internal temperature, which causes the trend of the magnitude of the fan operation noise to increase (but may also be affected by the air conditioning system). Thus, although in theory the sensor parameters may be used as a reference factor for measuring the health of the switchgear. However, the values of some sensor parameters cannot show theoretical unidirectional trend and cannot be used as reliable judgment factors under the influence of external conditions.
Therefore, in order to find out which parameter factors can be used as reliable judgment factors, the application adopts the following processes to carry out distribution characteristic analysis, and the method specifically comprises the following steps:
S121, carrying out distribution characteristic analysis on the sensor data of a single period, wherein the distribution characteristics of the sensor data of the single period comprise trend increment, trend decrement and no increase or decrease trend;
S122, when the trend of the value of the sensor parameter in the history maintenance period exceeds a preset proportion threshold value is decreased, judging that the trend of the value of the sensor parameter is decreased; and when the trend of the value of the sensor parameter in the history maintenance period exceeding the preset proportion threshold value is increased, judging that the trend of the value of the sensor parameter is increased.
Trend analysis is first performed for a single cycle for each sensor parameter, then statistics are performed if a parameter is trend increasing or trend decreasing for more than 95% of the cycles. Then it can be determined that the sensor parameter has a tendency to increase or decrease in value during a maintenance period with a high probability under the current environmental conditions. Thus avoiding the existence of a situation in which some sensor parameters are susceptible to external conditions and cannot be used as reference factors.
The trend analysis method for a single cycle is as follows:
S1211, mapping values of sensor parameters of a plurality of detection time points in a single history maintenance period into a two-dimensional coordinate system to obtain data coordinates, wherein the abscissa of the two-dimensional coordinate system is time, and the ordinate is a value; so as to facilitate data statistics and processing;
S1212, calculating the previous position in the data coordinates Average value and average time of data points, and thenAverage value and average time of data points, and based on the previousAverage value and average time of data points to construct a starting reference pointAnd based on the backAverage value of data points and average time end reference point；
The application calculates the average value of the first 3 data points as the initial reference point of the current maintenance periodAnd takes the average time as the initial reference pointIs defined by the abscissa of the (c). The application calculates the average value of the 3 data points as the ending reference point of the current maintenance periodAnd takes its average time as the ending reference pointIs defined by the abscissa of the (c). Averaging can effectively reduce reference point errors caused by sensor data errors.
S1213, using a straight line to reference the initial reference pointAnd the ending reference pointConnecting to obtain trend reference line；
S1214, calculating each data point in the data coordinates and the trend reference lineWherein is higher than the trend reference lineIs related to the trend reference lineIs positive and is lower than the trend reference lineIs related to the trend reference lineIs negative;
The vertical difference is from each data point to the trend reference line For reflecting the distance of a plurality of data points along a trend reference lineThe degree of distribution.
S1215, calculating variance of the plurality of vertical differences, wherein the variance is smaller than a preset threshold valueWhen the value trend of the sensor parameter of the current historical maintenance period is judged to be decreased; when the variance is smaller than a preset threshold value, andAnd when the trend of the value of the sensor parameter of the history maintenance period is judged to be increased, wherein,For the ratio parameter with the value larger than 1, andAs the initial reference pointIs used as a reference to the value of (a),To end the reference pointIs a value of (2).
The method judges a plurality of data points along a trend reference line based on varianceThe extent of the distribution, if the variance is small, indicates that the multiple data points are along a trend reference lineDistributed. If the variance is large, it means that the distribution of a plurality of data points is disordered, and if the distribution is irregular, the ascending or descending trend of the distribution cannot be judged.
In addition, in order to ensure that a plurality of data points have a significant tendency to rise or fall, the present application sets two screening conditions, namelyOr alternatively. And the variance is less than a preset threshold.
S130, constructing a health evaluation model based on values of target parameters of the switch cabinet in a plurality of maintenance periods, wherein the health evaluation model represents a corresponding relation between the values of the target parameters and the health degree of the switch cabinet;
after screening out the target parameters, the health of the switch cabinet is evaluated quickly. According to the application, the health evaluation is performed by constructing a health evaluation model.
The method specifically comprises the following steps:
S1301, calculating each target parameter at the first Rate of change of individual maintenance cyclesWherein, the method comprises the steps of, wherein,Wherein, the method comprises the steps of, wherein,Is the firstInitial reference point of each maintenance periodIs used as a reference to the value of (a),Is the firstEnd reference point of each maintenance cycleIs used as a reference to the value of (a),Is the firstThe duration of the individual maintenance periods;
In the maintenance period, the value change amplitude and regularity of different target parameters are different. Therefore, in order to unify dimensions, the application calculates each target parameter in the first Rate of change of individual maintenance cyclesRate of change hereRefers to the rate of change per unit time. For example 2%/montath (month). And then based on its rate of changeAnd determining the relevance weights of the different target parameters according to regularity.
S1302, determining a relevance weight of each target parameter based on the change rate of each target parameter in a plurality of maintenance periods;
the determining process of the correlation weight comprises the following steps:
S13021 calculating target parameters Rate of change over multiple maintenance cyclesAverage value of (2)Sum of variances；
S13022, based on the average valueAnd the variance isCalculating relevance index of target parameter，Wherein, the method comprises the steps of, wherein,As a first weight to be used,Is a second weight;
Wherein the rate of change The larger the target parameter is, the description isCan be more easily found with the increase of the service time. The higher the weight. Meanwhile, the smaller the variance is, the change rate is explainedThe more stable it is, the more regular it can be. Thus, the application is realized by introducing the change rateRate of changeIs used to calculate the correlation.
S13023 correlation index for all target parametersNormalization processing is carried out to obtain the correlation weight of each target parameter。
And finally, carrying out normalization processing. So that all the relevance weightsThe sum is 1, so that the subsequent processing and calculation are convenient.
S1303, calculating the weight sum of the correlation weights of all the target parameters, and calculating the ratio of the correlation weights of the target parameters to the weight sum;
S1304, when the ratio of the correlation weight and the weight sum of any one target parameter exceeds a preset proportion value, establishing a single-factor health evaluation model based on the target parameter with the ratio exceeding the preset proportion value;
In some cases, if the correlation weight of a certain target parameter is particularly high, the duty cycle may be above 50%. The model can be simplified and a single factor model can be built. The model is concise while the evaluation accuracy can be ensured. And is not affected by other factors.
(1) The construction method of the single factor model comprises the following steps:
(1-1) obtaining a reference value of a target parameter with a ratio exceeding a preset ratio value during maintenance And an average of values of the starting reference points of the plurality of maintenance periods；
(1-2) Taking a value based on the referenceAnd constructing a single-factor health evaluation model by using an average value of values of initial reference points of a plurality of maintenance periods, wherein the mathematical expression of the health evaluation model is as follows:
wherein, For the value of the target parameter whose ratio exceeds the preset ratio value at the current time point,Is the health degree of the switch cabinet.
Wherein due to the average valueIs determined based on the typical value of the start of each maintenance period, and the start of each maintenance period is the end of the last maintenance period (i.e. the last maintenance is completed), so that the performance indexes of the switch cabinet just after maintenance are almost consistent, and the average value is the average valueAll have references. Reference valueDetermined by expert opinion.
Representing: from the maintenance period, the value of the target parameter changesAt this point, a high probability should be required to maintain the switch at a high cost.The actual change amount of the target parameter is represented by the value of the target parameter compared with the initial value. The ratio of the two is taken, so that the use degree (percentage) can be known, and the difference between the use degree and 1 is obtained, thereby obtaining the health degree.
S1305, when the ratio of the correlation weight of any target parameter to the sum of the weights exceeds a preset proportional value, a multi-factor health evaluation model is built based on the correlation weights of various target parameters.
(2) The construction method of the multi-factor model comprises the following steps:
(2-1) obtaining reference values of a plurality of target parameters during maintenance And an average of values of starting reference points of a plurality of maintenance periods of a plurality of target parameters；
(2-2) Reference valuation at maintenance based on multiple target parametersAnd constructing a multi-factor health evaluation model by using an average value of values of initial reference points of a plurality of maintenance periods of a plurality of target parameters, wherein the mathematical expression of the health evaluation model is as follows:
wherein, Is the firstThe values of the individual target parameters at the current point in time,For the health of the switch cabinet,Is the number of target parameters.
The principle of the multi-factor model is consistent with that of the single factor model, except that the weights calculated in the previous description are introduced. And weighting and summing the corresponding usage degrees of the multiple target parameters to obtain the total usage degree percentage. And finally, obtaining the health degree by differentiating the health degree with 1.
And S140, acquiring the value of the target parameter at the current time point, and determining the health degree of the switch cabinet at the current time point based on the value of the target parameter at the current time point and the health evaluation model.
Specifically, the health degree of the switch cabinet at the current time point can be determined by substituting the value of the target parameter at the current time point into a corresponding formula.
S150, comparing the health degree of the switch cabinet at the current time point with a preset threshold value, and sending alarm information to a target object when the health degree of the switch cabinet at the current time point is greater than the preset threshold value.
Finally, if the health degree is less than 10%, the alarm information can be sent to maintenance personnel to prompt that the current switch cabinet needs to be maintained.
According to the switch cabinet health state evaluation method, the historical sensor data is subjected to trend analysis to obtain the target parameters related to maintenance. In the application, the target parameter can be used as an index for measuring whether the switch cabinet needs maintenance or not. Therefore, a health evaluation model is built based on the target parameters, so that the value of the target parameters at the current time point can be evaluated, and the health degree of the switch cabinet at the current time point can be judged. As a criterion for whether the switchgear requires maintenance. The application can help maintenance personnel to know the health state of the switch cabinet, and can respectively judge the health degree according to the target parameters of the switch cabinet under various working environments, thereby making a maintenance plan according to the health degree instead of timing calculation, and further realizing fine management of the switch cabinet.
The application also provides a switch cabinet health state evaluation device, which comprises:
The system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring historical sensor data of the switch cabinet in a plurality of historical maintenance periods, the historical sensor data comprise values of sensor parameters corresponding to a plurality of detection time points in the maintenance period, and the sensor parameters comprise temperature, humidity, dust accumulation amount, main line current, main line voltage and fan operation noise volume level;
the feature analysis module is used for carrying out distribution feature analysis on the sensor data of the plurality of maintenance periods and taking sensor parameters meeting target features as target parameters, wherein the target features comprise: the value of the sensor parameter is trended up or trended down over time;
the system comprises a model construction module, a health evaluation module and a control module, wherein the model construction module is used for constructing a health evaluation model based on values of target parameters of the switch cabinet in a plurality of maintenance periods, and the health evaluation model represents a corresponding relation between the values of the target parameters and the health degree of the switch cabinet;
And the evaluation module is used for acquiring the value of the target parameter at the current time point and determining the health degree of the switch cabinet at the current time point based on the value of the target parameter at the current time point and the health evaluation model. The invention also provides a storage medium in which a computer program is stored which, when loaded and executed by a processor, implements a switch cabinet health status assessment method as described above.
According to the switch cabinet health state evaluation device, the historical sensor data is subjected to trend analysis to obtain the target parameters related to maintenance. In the application, the target parameter can be used as an index for measuring whether the switch cabinet needs maintenance or not. Therefore, a health evaluation model is built based on the target parameters, so that the value of the target parameters at the current time point can be evaluated, and the health degree of the switch cabinet at the current time point can be judged. As a criterion for whether the switchgear requires maintenance. The application can help maintenance personnel to know the health state of the switch cabinet, and can respectively judge the health degree according to the target parameters of the switch cabinet under various working environments, thereby making a maintenance plan according to the health degree instead of timing calculation, and further realizing fine management of the switch cabinet.
The present embodiment also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements any of the methods of the present embodiments, wherein the method is the execution logic of the present system.
The embodiment also provides an electronic terminal, including: a processor and a memory;
the memory is used for storing a computer program, and the processor is used for executing the computer program stored in the memory, so that the terminal executes any one of the methods in the embodiment.
The computer readable storage medium in this embodiment, as will be appreciated by those of ordinary skill in the art: all or part of the steps for implementing the method embodiments described above may be performed by computer program related hardware. The aforementioned computer program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver, and a communication interface, where the memory and the communication interface are connected to the processor and the transceiver and complete communication with each other, the memory is used to store a computer program, the communication interface is used to perform communication, and the processor and the transceiver are used to run the computer program, so that the electronic terminal performs each step of the above method.
In this embodiment, the memory may include a random access memory (Random Access Memory, abbreviated as RAM), and may further include a non-volatile memory (non-volatile memory), such as at least one disk memory.
The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, abbreviated as CPU), a network processor (Network Processor, abbreviated as NP), etc.; but may also be a digital signal processor (DIGITAL SIGNAL Processing, DSP), application Specific Integrated Circuit (ASIC), field-Programmable gate array (FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components.
In the above embodiments, while the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications and variations of these embodiments will be apparent to those skilled in the art in light of the foregoing description. The embodiments of the invention are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims.
The above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. Accordingly, it is intended that all equivalent modifications and variations of the invention be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.
Claims (6)
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| CN110826934A (en) * | 2019-11-21 | 2020-02-21 | 广州供电局有限公司 | Method, device and system for evaluating health degree of medium-voltage switch cabinet |
| CN116095922A (en) * | 2023-03-13 | 2023-05-09 | 广州易而达科技股份有限公司 | Lighting lamp control method and device, lighting lamp and storage medium |
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| CN116611028B (en) * | 2023-06-19 | 2025-02-14 | 国网甘肃省电力公司天水供电公司 | Switchgear cable joint performance degradation assessment method and system based on IoT perception |
| CN117555237A (en) * | 2023-12-15 | 2024-02-13 | 湖南第一师范学院 | Real-time optimal control method for electric appliances based on Internet of things |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110826934A (en) * | 2019-11-21 | 2020-02-21 | 广州供电局有限公司 | Method, device and system for evaluating health degree of medium-voltage switch cabinet |
| CN116095922A (en) * | 2023-03-13 | 2023-05-09 | 广州易而达科技股份有限公司 | Lighting lamp control method and device, lighting lamp and storage medium |
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