CN112557793A - Power plug-in health state detection method and device and storage medium - Google Patents
Power plug-in health state detection method and device and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a method, a device and a storage medium for detecting the health state of a power supply plug-in, which are characterized in that the predicted operation parameters of the power supply plug-in to be detected are determined according to the historical operation parameters related to the power supply plug-in to be detected; and determining the health state of the power supply plug-in unit to be detected according to the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the factory data of the power supply plug-in unit to be detected and the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the predicted operation parameter. The worker can conveniently determine whether the power supply plug-in unit to be detected needs to be replaced according to the calculated health state, and reference is provided for the worker to determine whether the power supply plug-in unit needs to be replaced; and the problem that resources are wasted due to the fact that the power supply plug-in which the specified service life is reached but the health state is good is also replaced is solved, and meanwhile, the influence on the normal work of the relay protection device due to the fact that the power supply plug-in which the specified service life is not reached but the health state is poor is always in service can be avoided.
Description
Technical Field
The embodiment of the invention relates to the technical field of relay protection, in particular to a method and a device for detecting the health state of a power plug-in and a storage medium.
Background
In the technical field of power grid relay protection, a relay protection device is important secondary equipment for ensuring safe operation of a power grid, once a power supply plug-in of the relay protection device breaks down, the relay protection device cannot normally operate, a protection function is withdrawn, and accident risk hidden dangers are caused.
In the prior art, usually, power supply plug-ins which run for 6 to 8 years in a relay protection device are replaced according to a specified service life, but the health state of the power supply plug-ins is not detected, so that part of the power supply plug-ins which reach the service life but have good health state are replaced, and resource waste is caused.
Disclosure of Invention
The invention provides a method for detecting the health state of a power supply plug-in, which is used for accurately detecting the health state of the power supply plug-in, so as to provide guidance for whether to replace the power supply plug-in and reduce the waste of resources.
In a first aspect, an embodiment of the present invention provides a method for detecting a health status of a power plug, including:
acquiring current actual measurement operation parameters of a power supply plug-in to be detected;
determining the predicted operation parameters of the power supply plug-in unit to be detected according to the historical operation parameters related to the power supply plug-in unit to be detected;
and determining the health state of the power supply plug-in unit to be detected according to the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the factory data of the power supply plug-in unit to be detected and the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the predicted operation parameter.
Optionally, the historical operating parameters related to the power supply plug-in to be detected include a first historical operating parameter, and the first historical operating parameter is a historical measurement operating parameter of the power supply plug-in the same model as the power supply plug-in to be detected in the quit operation database;
determining the predicted operation parameters of the power supply plug-in unit to be detected according to the historical operation parameters related to the power supply plug-in unit to be detected, wherein the method comprises the following steps:
if the power supply plug-in with the same type as the power supply plug-in to be detected exists in the return equipment database, judging whether the service time of the power supply plug-in to be detected is longer than the longest service time of the power supply plug-in with the same type as the power supply plug-in to be detected in the return database;
if so, determining predicted operation parameters according to the first historical operation parameters and set times of actual measurement operation parameters of the power supply plug-in unit to be detected, wherein the set times of actual measurement operation parameters comprise the m-time actual measurement operation parameters to the n-time actual measurement operation parameters, wherein n is more than or equal to m, and the m-time actual measurement operation parameters are the 1 st time of actual measurement operation parameters exceeding the historical longest service time of the power supply plug-in unit of the same type;
if not, determining the predicted operation parameters according to the first historical operation parameters.
Optionally, the operation parameters of the power supply plug-in include a series equivalent resistance, a series equivalent capacitance and an output voltage, wherein the operation parameter prediction includes a series equivalent resistance prediction, a series equivalent power prediction and an output voltage prediction;
for any predicted operation parameter, determining the predicted operation parameter according to the first historical operation parameter and the set secondary actual measurement operation parameter of the power supply plug-in to be detected, and the method comprises the following steps:
determining average measurement parameters of the power supply plug-ins with the same model as the power supply plug-ins to be detected in the quit equipment database according to a first historical parameter matrix formed by first historical operation parameters of the quit equipment database, wherein the average measurement parameters form an average measurement parameter vector;
expanding the average measurement parameter vector according to the set secondary actual measurement operation parameters;
and determining a predicted operation parameter according to the expanded average measurement parameter vector based on a Holt two-exponential smoothing method.
Optionally, the operation parameters of the power supply plug-in include a series equivalent resistance, a series equivalent capacitance and an output voltage, wherein the operation parameter prediction includes a series equivalent resistance prediction, a series equivalent power prediction and an output voltage prediction;
for any of the predicted operating parameters, determining a predicted operating parameter based on the first historical operating parameter, comprising:
determining average measurement parameters of the power supply plug-ins with the same model as the power supply plug-ins to be detected in the quit equipment database according to a first historical parameter matrix formed by first historical operation parameters of the quit equipment database, wherein the average measurement parameters form an average measurement parameter vector;
determining a first average measurement parameter corresponding to the measurement time which is closest to the service time of the power supply plug-in to be detected in the average measurement parameter vector according to the service time of the power supply plug-in to be detected;
and based on a Holt two-exponential smoothing method, determining the predicted operation parameters according to the first average measurement parameter and the average measurement parameter before the measurement time point corresponding to the first average measurement parameter.
Optionally, the historical operating parameters related to the power plug-in to be detected include a second historical operating parameter, where the second historical operating parameter is a historical measurement operating parameter of the power plug-in to be detected in the operation database;
determining the predicted operation parameters of the power supply plug-in unit to be detected according to the historical operation parameters related to the power supply plug-in unit to be detected, wherein the method comprises the following steps:
and if the power supply plug-in with the same model as the power supply plug-in to be detected does not exist in the quit transportation equipment database, determining the predicted operation parameters according to the second historical operation parameters.
Optionally, the operation parameters of the power supply plug-in include a series equivalent resistance, a series equivalent capacitance and an output voltage, wherein the operation parameter prediction includes a series equivalent resistance prediction, a series equivalent power prediction and an output voltage prediction;
for any of the predicted operating parameters, determining the predicted operating parameter based on the second historical operating parameter, comprising:
and determining the predicted operation parameters according to a second historical operation parameter vector formed by the second historical operation parameters based on a Holt two-exponential smoothing method.
Optionally, the health status of the power plug-in includes a health status value and a health level;
according to the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the factory data of the power supply plug-in unit to be detected and the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the prediction operation parameter, the method comprises the following steps:
detecting the matching degree of the actually measured operation parameters of the power supply plug-in unit to be detected and the factory data of the power supply plug-in unit to be detected according to the currently actually measured series equivalent capacitor and the corresponding factory series equivalent capacitor of the power supply plug-in unit to be detected, the currently actually measured series equivalent resistor and the corresponding factory series equivalent resistor, and the currently actually measured output voltage and the corresponding factory output voltage of the power supply plug-in unit to be detected;
detecting the matching degree of the actually measured operation parameters of the power supply plug-in unit to be detected and the predicted operation parameters of the power supply plug-in unit to be detected according to the currently actually measured series equivalent capacitor and the corresponding predicted series equivalent capacitor of the power supply plug-in unit to be detected, the currently actually measured series equivalent resistor and the corresponding predicted series equivalent resistor and the currently actually measured output voltage and the corresponding predicted output voltage of the power supply plug-in unit to be detected;
detecting a health state value according to the matching degree of the actually measured operation parameters of the power supply plug-in unit to be detected and the factory data of the power supply plug-in unit to be detected and the matching degree of the actually measured operation parameters of the power supply plug-in unit to be detected and the predicted operation parameters of the power supply plug-in unit to be detected;
and determining the health level of the power plug-in according to the health state value and the pre-classified health level.
Optionally, after determining the health state of the power plug-in to be detected according to the matching degree between the current actual measurement operating parameter of the power plug-in to be detected and the factory data of the power plug-in to be detected, and the matching degree between the current actual measurement operating parameter of the power plug-in to be detected and the predicted operating parameter, the method further includes:
judging whether the current measurement has the retreating equipment, if so, supplementing the current measurement parameters to a retreating equipment database;
if not, the current measurement parameters are all supplemented to the on-the-fly database.
In a second aspect, an embodiment of the present invention further provides a device for detecting a health status of a power plug, including:
the acquisition module is used for acquiring the current actual measurement operation parameters of the power supply plug-in unit to be detected;
the predicted operation parameter determining module is used for determining the predicted operation parameters of the power supply plug-in unit to be detected according to the historical operation parameters related to the power supply plug-in unit to be detected;
and the health state determining module is used for determining the health state of the power supply plug-in unit to be detected according to the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the factory data of the power supply plug-in unit to be detected and the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the predicted operation parameter.
In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the power plug-in state detection method of the first aspect.
The embodiment of the invention provides a method, a device and a storage medium for detecting the health state of a power supply plug-in, wherein the method comprises the steps of determining the predicted operation parameters of the power supply plug-in to be detected according to the historical operation parameters related to the power supply plug-in to be detected; and determining the health state of the power supply plug-in unit to be detected according to the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the factory data of the power supply plug-in unit to be detected and the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the predicted operation parameter. The worker can conveniently determine whether the power supply plug-in unit to be detected needs to be replaced according to the calculated health state, and reference is provided for the worker to determine whether the power supply plug-in unit needs to be replaced; and the problem that resources are wasted due to the fact that the power supply plug-in which the specified service life is reached but the health state is good is also replaced is solved, and meanwhile, the influence on the normal work of the relay protection device due to the fact that the power supply plug-in which the specified service life is not reached but the health state is poor is always in service can be avoided.
Drawings
Fig. 1 is a flowchart of a method for detecting a health status of a power plug-in according to an embodiment of the present invention.
Fig. 2 is a flowchart of a method for detecting a health status of a power plug according to a second embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a power plug health status detection apparatus according to a third embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a method for detecting a health status of a power plug-in according to an embodiment of the present invention, where the embodiment is applicable to a situation of detecting a health status of a power plug-in, and the method can be executed by a power plug-in health status detecting apparatus, and specifically includes the following steps:
specifically, in order to ensure safe and stable operation of the power grid, regular or irregular measurement of the operation parameters of the power plug-in the relay protection device is required. The operating parameters of the power supply plug-in unit may include, but are not limited to, a series equivalent resistance, a series equivalent capacitance, and an output voltage. The current actual measurement running parameter of the power plug-in to be detected can adopt a method of measuring and averaging for multiple times within a set time, for example, the set time is 30 minutes, then each running parameter is measured for multiple times within 30 minutes, and the current actual measurement running parameter is obtained by averaging multiple measured values of the same running parameter, so that the accuracy of the current actual measurement running parameter is ensured, and the influence of single measurement inaccuracy on the health state detection result is avoided.
the historical operating parameters related to the power supply plug-in to be detected can include the historical measuring operating parameters of the power supply plug-in which the power supply plug-in to be detected has been returned and which is of the same type as the power supply plug-in to be detected, and can also include the historical measuring operating parameters of the power supply plug-in to be detected before the current measurement. In this step, the future operating condition of the power supply plug-in to be detected can be predicted according to the historical operating parameters related to the power supply plug-in to be detected, so as to obtain the predicted operating parameters of the power supply plug-in to be detected. The predicted operating parameters include at least the predicted operating parameters corresponding to the current measurement time point.
And step 130, determining the health state of the power supply plug-in unit to be detected according to the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the factory data of the power supply plug-in unit to be detected and the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the predicted operation parameter.
After the predicted operation parameters of the power supply plug-in to be detected are determined, the health state of the power supply plug-in to be detected can be determined. Specifically, the matching degree (hereinafter referred to as a first matching degree) between the current actual measurement operation parameter and the factory data of the power supply plug-in to be detected can be calculated through a preset first calculation formula, wherein the factory data of the power supply plug-in to be detected can be stored in a transportation equipment database in advance; the matching degree of the current actual measurement operation parameter and the predicted operation parameter (hereinafter referred to as a second matching degree) can also be calculated through a preset second calculation formula, and then the health state of the power supply plug-in to be detected is determined according to the first matching degree and the second matching degree. The first matching degree, the second matching degree and the health state can have preset corresponding relations, and then after the first matching degree and the second matching degree are calculated, the health state of the power supply plug-in to be detected is determined according to the preset corresponding relations, so that a worker can conveniently determine whether the power supply plug-in to be detected needs to be replaced according to the calculated health state, and reference is provided for the worker whether the power supply plug-in is replaced; and the problem that resources are wasted due to the fact that the power supply plug-in which the specified service life is reached but the health state is good is also replaced is solved, and meanwhile, the influence on the normal work of the relay protection device due to the fact that the power supply plug-in which the specified service life is not reached but the health state is poor is always in service can be avoided.
In the method for detecting the health state of the power plug-in, the predicted operation parameters of the power plug-in to be detected are determined according to the historical operation parameters related to the power plug-in to be detected; and determining the health state of the power supply plug-in unit to be detected according to the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the factory data of the power supply plug-in unit to be detected and the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the predicted operation parameter. The worker can conveniently determine whether the power supply plug-in unit to be detected needs to be replaced according to the calculated health state, and reference is provided for the worker to determine whether the power supply plug-in unit needs to be replaced; and the problem that resources are wasted due to the fact that the power supply plug-in which the specified service life is reached but the health state is good is also replaced is solved, and meanwhile, the influence on the normal work of the relay protection device due to the fact that the power supply plug-in which the specified service life is not reached but the health state is poor is always in service can be avoided.
Example two
Fig. 2 is a flowchart of a method for detecting a health status of a power plug-in according to the second embodiment of the present invention, where optionally, the historical operating parameters related to the power plug-in to be detected include a first historical operating parameter, and the first historical operating parameter is a historical measured operating parameter of a power plug-in the same model as the power plug-in to be detected in the quit operation database;
referring to fig. 2, the power plug-in health status detection method includes:
when the method for monitoring the health state of the power plug-in is executed, historical operating parameter data in a transport equipment database and/or a return equipment database can be called, wherein the transport equipment database can be used for storing historical operating parameters of the relay protection device during transport of the power plug-in and factory data of the transport equipment before the time point of the current measured operating parameter, and the current measured operating parameters can be added to the transport equipment database after the current measured operating parameters are measured; the retreating equipment database can be used for storing the measured operation parameters of the retreated power supply plug-ins in the process of the life cycle of the relay protection device. In this step, the service time of the power supply plug-in to be detected and the longest service time of the power supply plug-in with the same type as the power supply plug-in to be detected in the decommissioning equipment database can be compared, and further the historical operating parameters related to the power supply plug-in to be detected are determined.
If so, the following step 231 is executed,
231, determining predicted operation parameters according to the first historical operation parameters and set times of actual measurement operation parameters of the power supply plug-in unit to be detected, wherein the set times of actual measurement operation parameters comprise the operation parameters from the m time of actual measurement to the n time of actual measurement, wherein n is more than or equal to m, and the operation parameters from the m time of actual measurement are the operation parameters of the 1 st time of actual measurement exceeding the historical longest service time of the power supply plug-in unit of the same type;
the operation parameters of the power supply plug-in unit comprise a series equivalent resistor, a series equivalent capacitor and output voltage, wherein the operation parameter prediction comprises series equivalent resistor prediction, series equivalent power supply prediction and output voltage prediction.
For any predicted operation parameter, in the step 231, determining the predicted operation parameter according to the first historical operation parameter and the set secondary measured operation parameter of the power plug-in to be detected includes:
step 2311, determining average measurement parameters of power supply plug-ins of the same type as the power supply plug-ins to be detected in the quit equipment database according to a first historical parameter matrix formed by first historical operation parameters of the quit equipment database, wherein the average measurement parameters form an average measurement parameter vector;
exemplary, first historical operating parameter matrix of series equivalent resistance
The following were used:
wherein A ispRepresenting a series equivalent resistance vector of the previous measurement corresponding to the pth power supply plug-in with the same type as the power supply plug-in to be detected (including the series equivalent resistance vector of the pth power supply plug-in), wherein p is 1, 2 … … k … … n, n represents the number of the power supply plug-ins with the same type as the power supply plug-in to be detected in the retired device database, and k is less than or equal to n; t represents the total number of measurements made on the retired power plug-ins in the retired equipment database.
And the measured value of the b-th series equivalent resistance of the a-th plug-in unit is shown, wherein a is more than or equal to 1 and less than or equal to n, and b is more than or equal to 1 and less than or equal to t. And arranging the first historical operation parameter matrix according to the measurement time from left to right, wherein the leftmost column corresponds to the earliest measurement of each plug-in, the rest columns correspond to the measurement values of the plug-ins at the time point of the earliest measurement of the plug-ins, if a certain plug-in has no measurement value at the time node, the average value of the two previous and next measurements is taken as supplement, and if no measurement value is arranged at the right side of the plug-in, the corresponding node is filled with 0.
Combining the first historical operating parameter matrix, if the power supply plug-in corresponding to A1 has no measured value on the second and third days after commissioning (corresponding to other power supply plug-ins such as A2, the measured value is measured on the third day), and if the measured parameter is present on the fourth day, taking the average value of the adjacent measured values before and after the measured value to fill the data on the second and third days; the measurement value of the a2 card is not measured the next day after the card is put into operation, and if the card has a measurement value on the third day, the measurement value processing method on the second day is the same as above, but if the card has no measurement value after the fourth day (which means that the card is returned after only three days of operation, and the measurement value is not updated), the values in the fourth row and the following are all 0. The above padding values are all virtual measurement data.
Then according to the first historical operation parameter matrix of the series equivalent resistance Er
The average measurement parameter vector can be calculated
Wherein k is1,k2……ktThe number of data items corresponding to the 1 st column and the 2 nd column … …, respectively, which are not 0.
Step 2312, expanding the average measurement parameter vector according to the set secondary actual measurement operation parameters;
specifically, when the service time of the power supply plug-in to be detected exceeds the longest service time of the power supply plug-in of the same model in the retirement database, the set secondary actual measurement operation parameters of the power supply plug-in to be detected can be successively added to the average measurement parameter vector according to the measurement time to obtain an updated average measurement parameter vector, illustratively, the set secondary actual measurement operation parameters comprise the m-th actual measurement operation parameter to the n-th actual measurement operation parameter, wherein n is more than or equal to m, and the m-th actual measurement operation parameter is the 1-st actual measurement operation parameter exceeding the historical longest service time of the power supply plug-in of the same model; sequentially adding the series equivalent resistance values from the m-th actual measurement operation parameter to the n-th actual measurement operation parameter
And (6) finally.
And 2313, determining a predicted operation parameter according to the expanded average measurement parameter vector based on a Holt two-exponential smoothing method.
Taking the calculation of the predicted operation parameter of the series equivalent resistance Er, namely the predicted series equivalent resistance as an example for explanation, based on the Holt two-exponential smoothing method, the specific process of determining the predicted operation parameter according to the average measurement parameter vector is as follows:
based on the following formula:
in the formula
For the time node corresponding to the current measurement, the initial value
b1＝0，atThe formed array is the corresponding curve after the smoothing treatment. Alpha and beta are smoothing parameters, and alpha and beta values can be initialized before step 210 of the power plug-in health state detection method of the embodiment, and in the process of iteration by using the above formula (3) and formula (4), the values of alpha and beta can be determined by performing test adjustment on the actual values of alpha and beta and determining a target smoothing curve (a final desired smoothing curve).
After the smoothing parameters alpha and beta are obtained, the measured values in the formula (3) are obtained
Replacement by predicting series equivalent resistance
The iteration is carried out by the following method:
bt＝β(at-at-1)+(1-β)bt-1 (7)
in the formula
Predicting the starting time, namely predicting parameters after the time from the time; initial value
To predict the series equivalent resistance at the start time, bt0The predicted starting time trend value obtained in equation (4) (i.e., b calculated for the last iteration of equation (4))tValue).
And predicting the series equivalent resistance after the future T time on the basis of the predicted starting moment. The specific prediction modes of the predicted series equivalent capacitance corresponding to the series equivalent capacitance EL and the predicted output voltage corresponding to the output voltage Uo are the same, and are not described herein again.
If not, the following step 232 is executed,
specifically, the operation parameters of the power supply plug-in unit comprise a series equivalent resistor, a series equivalent capacitor and an output voltage, wherein the operation parameter prediction comprises series equivalent resistor prediction, series equivalent power supply prediction and output voltage prediction;
for any of the predicted operating parameters, step 232, determining the predicted operating parameter based on the first historical operating parameter, includes:
step 2321, determining average measurement parameters of power supply plug-ins of the same type as the power supply plug-ins to be detected in the return equipment database according to a first historical parameter matrix formed by first historical operating parameters of the return equipment database, wherein the average measurement parameters form an average measurement parameter vector;
still taking the calculation of the predicted operation parameter of the series equivalent resistance Er, i.e. the predicted series equivalent resistance as an example, the first historical operation parameter matrix of the series equivalent resistance Er
Formula (1) can be referred to; corresponding average measured parameter vector of series equivalent resistance Er
Reference may be made to equation (2).
Step 2322, determining a first average measurement parameter corresponding to the measurement time closest to the service time of the power supply plug-in to be detected in the average measurement parameter vector according to the service time of the power supply plug-in to be detected;
specifically, the first average measurement parameter corresponding to the measurement time closest to the service time of the power supply to be detected can be determined according to the service time of the power supply to be detected and the interval time of each adjacent element in the average measurement parameter vector.
Step 2323, based on the Holt two-exponential smoothing method, the predicted operation parameter is determined according to the first average measurement parameter and the average measurement parameter before the measurement time point corresponding to the first average measurement parameter.
Specifically, the values of the smoothing parameters α and β may still be determined by using the formulas (3) and (4), but in this embodiment, when the formulas (3) and (4) are used for iterative computation, t ∈ [2, 3.]T' is a measurement time point corresponding to the first average measurement parameter, an initial value
b1＝0。
After the smoothing parameters are obtained, the determination of the predicted series equivalent resistance can still be performed by using equations (5) to (7). The specific prediction modes of the predicted series equivalent capacitance corresponding to the series equivalent capacitance EL and the predicted output voltage corresponding to the output voltage Uo are the same, and are not described herein again.
Optionally, the historical operating parameters related to the power plug-in to be detected include a second historical operating parameter, where the second historical operating parameter is a historical measurement operating parameter of the power plug-in to be detected in the operation database;
the method for detecting the health status of the power plug-in further includes:
and 240, if the power supply plug-in with the same model as the power supply plug-in to be detected does not exist in the quit transportation equipment database, determining the predicted operation parameters according to the second historical operation parameters.
When the power supply plug-in unit with the same model as the power supply plug-in unit to be detected does not exist in the return equipment database, the operation parameters of the power supply plug-in unit to be detected at the current measurement time point can be predicted according to the previous measurement data (second historical operation parameters) of the power supply plug-in unit to be detected before the current measurement time point.
Specifically, the operation parameters of the power supply plug-in unit comprise a series equivalent resistance, a series equivalent capacitance and an output voltage, wherein the operation parameter prediction comprises series equivalent resistance prediction, series equivalent power supply prediction and output voltage prediction. Optionally, for any of the predicted operating parameters, the determining the predicted operating parameter according to the second historical operating parameter in step 240 includes:
and 241, determining a predicted operation parameter according to a second historical operation parameter vector formed by the second historical operation parameter based on a Holt two-exponential smoothing method.
Wherein the second historical operating parameter vector a' may be represented as:
wherein the content of the first and second substances,
and c is more than or equal to 1 and less than or equal to y-1, y represents the time point of the current measured operation parameter, and y-1 represents the time point of the previous measurement of the current measurement.
Then, according to the second historical operating parameter, iterative calculation is carried out based on the formulas (3) to (4) to obtain values of alpha and beta; the predicted series equivalent resistance is then obtained according to equations (5) - (7). The specific prediction modes of the predicted series equivalent capacitance corresponding to the series equivalent capacitance EL and the predicted output voltage corresponding to the output voltage Uo are the same, and are not described herein again.
Optionally, the health status of the power plug-in includes a health status value and a health level; after the above steps 230 and 240, the method further includes:
specifically, the first matching degree P1 can be calculated by the following formula:
in the formula Er、EL、UoRespectively the current measured values of the series equivalent resistance, the series equivalent capacitance and the output voltage of the power supply plug-in to be detected, ErN、ELN、UNThe power supply plug-in unit is respectively a series equivalent resistance factory value, a series equivalent capacitance factory value and a rated output voltage of the power supply plug-in unit to be detected.
Step 252, detecting the matching degree (hereinafter referred to as a second matching degree) of the actual measurement operation parameters of the power plug-in to be detected and the prediction operation parameters of the power plug-in to be detected according to the current actual measurement series equivalent capacitor and the corresponding prediction series equivalent capacitor of the power plug-in to be detected, the current actual measurement series equivalent resistor and the corresponding prediction series equivalent resistor, and the current actual measurement output voltage and the corresponding prediction output voltage;
specifically, the second matching degree P2 can be calculated by the following formula:
in the formula
And respectively predicting the series equivalent resistance, the series equivalent capacitance and the output voltage of the power supply plug-in to be detected, wherein each predicted operation parameter respectively corresponds to the predicted operation parameter at the current measurement moment.
Step 253, detecting a health state value according to the matching degree of the actually measured operation parameters of the power supply plug-in to be detected and the factory data of the power supply plug-in to be detected and the matching degree of the actually measured operation parameters of the power supply plug-in to be detected and the predicted operation parameters of the power supply plug-in to be detected;
specifically, the health state value G may be calculated by the following formula:
and step 254, determining the health level of the power plug-in according to the health state value and the pre-classified health level.
Specifically, the health level can be divided in advance, and then the health level to which the calculated health state value belongs determines the health level of the power plug-in, and finally the detection of the health state of the power plug-in is realized. For example, the health status value and the health class of the power plug-in may be as shown in table 1:
TABLE 1
| Health grade | Health care | Good effect | In general | Is poor |
| G | 90≤G≤100 | 80≤G<90 | 70≤G<80 | G<70 |
Optionally, after the step 250, the method further includes:
judging whether the current measurement has the retreating equipment or not, and if so, supplementing the current measurement parameters to the retreating equipment database; if not, the current measurement parameters are all supplemented to the on-the-fly database.
According to the technical scheme of the embodiment, the health state of the plug-in is comprehensively obtained by determining the health state according to the first historical operating parameter, or the first historical operating parameter and the set secondary actual measurement operating parameter of the power plug-in to be detected or the second historical operating parameter according to the fact that whether the power plug-in with the same model as the power plug-in to be detected exists in the quit transport database or not and whether the service time of the power plug-in to be detected exceeds the longest service time of the power plug-in with the same model in the quit transport database, obtaining the predicted operating parameter based on a Holt two-index smoothing method, and determining the health state according to the predicted operating parameter obtained by calculation, the factory data of the power plug-in to be detected and the actual measurement operating parameter.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a power plug-in health status detection apparatus according to a third embodiment of the present invention, which can execute the power plug-in health status detection method according to any of the embodiments described above, and the specific structure of the power plug-in health status detection apparatus is as follows:
an obtaining module 310, configured to obtain a current actual measurement operating parameter of a power plug-in to be detected;
the predicted operation parameter determining module 320 is used for determining the predicted operation parameters of the power supply plug-in unit to be detected according to the historical operation parameters related to the power supply plug-in unit to be detected;
the health status determining module 330 is configured to determine the health status of the power plug-in to be detected according to the matching degree between the current actual measurement operating parameter of the power plug-in to be detected and the factory data of the power plug-in to be detected, and the matching degree between the current actual measurement operating parameter of the power plug-in to be detected and the predicted operating parameter.
According to the health state detection device for the power supply plug-ins, the predicted operation parameters of the power supply plug-ins to be detected are determined by the predicted operation parameter determination module according to the historical operation parameters related to the power supply plug-ins to be detected; the health state determining module determines the health state of the power supply plug-in unit to be detected according to the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the factory data of the power supply plug-in unit to be detected and the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the prediction operation parameter. The worker can conveniently determine whether the power supply plug-in unit to be detected needs to be replaced according to the calculated health state, and reference is provided for the worker to determine whether the power supply plug-in unit needs to be replaced; and the problem that resources are wasted due to the fact that the power supply plug-in which the specified service life is reached but the health state is good is also replaced is solved, and meanwhile, the influence on the normal work of the relay protection device due to the fact that the power supply plug-in which the specified service life is not reached but the health state is poor is always in service can be avoided.
Example four
A fourth embodiment of the present invention provides a storage medium containing computer-executable instructions, where the computer-executable instructions are executed by a computer processor to perform a method for detecting a health status of a power supply plug-in, and the method includes:
acquiring current actual measurement operation parameters of a power supply plug-in to be detected;
determining the predicted operation parameters of the power supply plug-in unit to be detected according to the historical operation parameters related to the power supply plug-in unit to be detected;
and determining the health state of the power supply plug-in unit to be detected according to the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the factory data of the power supply plug-in unit to be detected and the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the predicted operation parameter.
Optionally, the computer executable instruction, when executed by the computer processor, may be further used to implement a technical solution of a power supply plug-in health status detection method provided by any embodiment of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes instructions for enabling a computer electronic device (which may be a personal computer, a server, or a network electronic device) to execute the methods according to the embodiments of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method for detecting the health state of a power plug-in is characterized by comprising the following steps:
acquiring current actual measurement operation parameters of a power supply plug-in to be detected;
determining the predicted operation parameters of the power supply plug-in unit to be detected according to the historical operation parameters related to the power supply plug-in unit to be detected;
and determining the health state of the power supply plug-in unit to be detected according to the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the factory data of the power supply plug-in unit to be detected and the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the predicted operation parameter.
2. The method for detecting the health status of the power supply plug-in unit according to claim 1, wherein the historical operating parameters related to the power supply plug-in unit to be detected include a first historical operating parameter, and the first historical operating parameter is a historical measured operating parameter of the power supply plug-in unit of the same type as the power supply plug-in unit to be detected in a quit operation database;
the determining the predicted operation parameters of the power supply plug-in unit to be detected according to the historical operation parameters related to the power supply plug-in unit to be detected comprises the following steps:
if the retired equipment database has the power supply plug-in with the same type as the power supply plug-in to be detected, judging whether the service time of the power supply plug-in to be detected is longer than the longest service time of the power supply plug-in with the same type as the power supply plug-in to be detected in the retired database;
if so, determining the predicted operation parameters according to the first historical operation parameters and set times of actual measurement operation parameters of the power supply plug-in unit to be detected, wherein the set times of actual measurement operation parameters comprise the m-time actual measurement operation parameters to the n-time actual measurement operation parameters, wherein n is more than or equal to m, and the m-time actual measurement operation parameters are the 1 st actual measurement operation parameters exceeding the historical longest service time of the power supply plug-in unit of the same type;
if not, determining the predicted operation parameters according to the first historical operation parameters.
3. The method according to claim 2, wherein the operating parameters of the power supply plug-in include a series equivalent resistance, a series equivalent capacitance, and an output voltage, wherein the predicted operating parameters include a predicted series equivalent resistance, a predicted series equivalent power, and a predicted output voltage;
for any predicted operation parameter, determining the predicted operation parameter according to the first historical operation parameter and the set secondary actual measurement operation parameter of the power supply plug-in to be detected comprises the following steps:
determining average measurement parameters of the power supply plug-ins with the same model as the power supply plug-ins to be detected in the quit equipment database according to a first historical parameter matrix formed by first historical operation parameters of the quit equipment database, wherein the average measurement parameters form an average measurement parameter vector;
expanding the average measurement parameter vector according to the set secondary actual measurement operation parameters;
and determining the predicted operation parameters according to the expanded average measurement parameter vector based on a Holt two-exponential smoothing method.
4. The method according to claim 2, wherein the operating parameters of the power supply plug-in include a series equivalent resistance, a series equivalent capacitance, and an output voltage, wherein the predicted operating parameters include a predicted series equivalent resistance, a predicted series equivalent power, and a predicted output voltage;
for any predicted operating parameter, said determining said predicted operating parameter from said first historical operating parameter comprises:
determining average measurement parameters of the power supply plug-ins with the same model as the power supply plug-ins to be detected in the quit equipment database according to a first historical parameter matrix formed by first historical operation parameters of the quit equipment database, wherein the average measurement parameters form an average measurement parameter vector;
determining a first average measurement parameter corresponding to the measurement time which is closest to the service time of the power supply plug-in to be detected in the average measurement parameter vector according to the service time of the power supply plug-in to be detected;
and determining the predicted operation parameters according to the first average measurement parameter and the average measurement parameter before the measurement time point corresponding to the first average measurement parameter based on a Holt two-exponential smoothing method.
5. The method for detecting the health status of the power plug-in unit according to claim 1, wherein the historical operating parameters related to the power plug-in unit to be detected comprise second historical operating parameters, and the second historical operating parameters are historically measured operating parameters of the power plug-in unit to be detected in an operation database;
the determining the predicted operation parameters of the power supply plug-in unit to be detected according to the historical operation parameters related to the power supply plug-in unit to be detected comprises the following steps:
and if the power supply plug-in with the same model as the power supply plug-in to be detected does not exist in the quit transportation equipment database, determining the predicted operation parameters according to the second historical operation parameters.
6. The method according to claim 5, wherein the operating parameters of the power supply plug-in include a series equivalent resistance, a series equivalent capacitance, and an output voltage, wherein the predicted operating parameters include a predicted series equivalent resistance, a predicted series equivalent power, and a predicted output voltage;
for any predicted operating parameter, said determining said predicted operating parameter from said second historical operating parameter comprises:
and determining the predicted operation parameters according to a second historical operation parameter vector formed by the second historical operation parameters based on a Holt two-exponential smoothing method.
7. The power plug-in health status detection method according to any one of claims 3, 4 or 6, wherein the health status of the power plug-in comprises a health status value and a health level;
according to the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the factory data of the power supply plug-in unit to be detected and the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the prediction operation parameter, the method comprises the following steps:
detecting the matching degree of the actually measured operation parameters of the power supply plug-in unit to be detected and the factory data of the power supply plug-in unit to be detected according to the currently actually measured series equivalent capacitor and the corresponding factory series equivalent capacitor of the power supply plug-in unit to be detected, the currently actually measured series equivalent resistor and the corresponding factory series equivalent resistor, and the currently actually measured output voltage and the corresponding factory output voltage of the power supply plug-in unit to be detected;
detecting the matching degree of the actually measured operation parameters of the power plug-in to be detected and the predicted operation parameters of the power plug-in to be detected according to the currently actually measured series equivalent capacitor and the corresponding predicted series equivalent capacitor of the power plug-in to be detected, the currently actually measured series equivalent resistor and the corresponding predicted series equivalent resistor of the power plug-in to be detected and the currently actually measured output voltage and the corresponding predicted output voltage;
detecting the health state value according to the matching degree of the actually measured operation parameter of the power supply plug-in to be detected and the factory data of the power supply plug-in to be detected and the matching degree of the actually measured operation parameter of the power supply plug-in to be detected and the predicted operation parameter of the power supply plug-in to be detected;
and determining the health level of the power plug-in according to the health state value and the pre-divided health level.
8. The method for detecting the health status of the power supply plug-in unit according to claim 2 or 5, wherein after determining the health status of the power supply plug-in unit to be detected according to the matching degree between the current measured operating parameter of the power supply plug-in unit to be detected and the factory data of the power supply plug-in unit to be detected and the matching degree between the current measured operating parameter of the power supply plug-in unit to be detected and the predicted operating parameter, the method further comprises:
judging whether the current measurement has the retreating equipment or not, and if so, supplementing the current measurement parameters to the retreating equipment database;
if not, the current measurement parameters are all supplemented to the on-the-fly database.
9. A power plug-in health status detection apparatus, comprising:
the acquisition module is used for acquiring the current actual measurement operation parameters of the power supply plug-in unit to be detected;
the predicted operation parameter determining module is used for determining the predicted operation parameters of the power supply plug-in unit to be detected according to the historical operation parameters related to the power supply plug-in unit to be detected;
and the health state determining module is used for determining the health state of the power supply plug-in unit to be detected according to the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the factory data of the power supply plug-in unit to be detected and the matching degree of the current actual measurement operation parameter of the power supply plug-in unit to be detected and the predicted operation parameter.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method for detecting a status of a power plug-in according to any one of claims 1 to 8.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113933633A (en) * | 2021-10-14 | 2022-01-14 | 广东电网有限责任公司东莞供电局 | Detection apparatus for power plug-in components |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012007947A (en) * | 2010-06-23 | 2012-01-12 | Panasonic Electric Works Co Ltd | Receptacle measurement device |
| US20120025842A1 (en) * | 2010-07-30 | 2012-02-02 | Gm Global Technology Operations, Inc. | Method for monitoring a power coupler for a plug-in electric vehicle |
| CN106777603A (en) * | 2016-12-02 | 2017-05-31 | 国网江苏省电力公司镇江供电公司 | Relay protection device of intelligent substation life-span prediction method |
| CN106872851A (en) * | 2017-04-13 | 2017-06-20 | 国家电网公司 | The health status detection means and detection method of new relay protecting power plug-in unit |
| CN109558992A (en) * | 2018-12-17 | 2019-04-02 | 广州甘来信息科技有限公司 | Based on sale peak value prediction technique, device, equipment and the storage medium from the machine of dealer |
| CN110119339A (en) * | 2019-05-07 | 2019-08-13 | 上海电气集团股份有限公司 | Appraisal procedure, system, equipment and the storage medium of the health status of industrial equipment |
| CN110221139A (en) * | 2019-05-05 | 2019-09-10 | 新奥数能科技有限公司 | A kind of failure prediction method of dry-type transformer, apparatus and system |
| CN110247396A (en) * | 2019-07-17 | 2019-09-17 | 国网山东省电力公司青岛供电公司 | State Estimation for Distribution Network and system based on adaptive robust Unscented kalman filtering |
| CN111007327A (en) * | 2018-10-08 | 2020-04-14 | 株洲中车时代电气股份有限公司 | Current transformer and capacitor state monitoring method and device thereof |
| CN111382172A (en) * | 2018-12-29 | 2020-07-07 | 中国电力科学研究院有限公司 | Power distribution terminal backup power supply multivariate health degree self-checking method and system |
| CN112014737A (en) * | 2020-08-27 | 2020-12-01 | 湖北亿纬动力有限公司 | Method, device, equipment and storage medium for detecting health state of battery core |
-
2020
- 2020-12-04 CN CN202011410444.4A patent/CN112557793B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012007947A (en) * | 2010-06-23 | 2012-01-12 | Panasonic Electric Works Co Ltd | Receptacle measurement device |
| US20120025842A1 (en) * | 2010-07-30 | 2012-02-02 | Gm Global Technology Operations, Inc. | Method for monitoring a power coupler for a plug-in electric vehicle |
| CN106777603A (en) * | 2016-12-02 | 2017-05-31 | 国网江苏省电力公司镇江供电公司 | Relay protection device of intelligent substation life-span prediction method |
| CN106872851A (en) * | 2017-04-13 | 2017-06-20 | 国家电网公司 | The health status detection means and detection method of new relay protecting power plug-in unit |
| CN111007327A (en) * | 2018-10-08 | 2020-04-14 | 株洲中车时代电气股份有限公司 | Current transformer and capacitor state monitoring method and device thereof |
| CN109558992A (en) * | 2018-12-17 | 2019-04-02 | 广州甘来信息科技有限公司 | Based on sale peak value prediction technique, device, equipment and the storage medium from the machine of dealer |
| CN111382172A (en) * | 2018-12-29 | 2020-07-07 | 中国电力科学研究院有限公司 | Power distribution terminal backup power supply multivariate health degree self-checking method and system |
| CN110221139A (en) * | 2019-05-05 | 2019-09-10 | 新奥数能科技有限公司 | A kind of failure prediction method of dry-type transformer, apparatus and system |
| CN110119339A (en) * | 2019-05-07 | 2019-08-13 | 上海电气集团股份有限公司 | Appraisal procedure, system, equipment and the storage medium of the health status of industrial equipment |
| CN110247396A (en) * | 2019-07-17 | 2019-09-17 | 国网山东省电力公司青岛供电公司 | State Estimation for Distribution Network and system based on adaptive robust Unscented kalman filtering |
| CN112014737A (en) * | 2020-08-27 | 2020-12-01 | 湖北亿纬动力有限公司 | Method, device, equipment and storage medium for detecting health state of battery core |
Non-Patent Citations (1)
| Title |
|---|
| 叶佳明等: "基于失效统计分析的电源插件健康状态检测装置设计", 《湖南电力》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113933633A (en) * | 2021-10-14 | 2022-01-14 | 广东电网有限责任公司东莞供电局 | Detection apparatus for power plug-in components |
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