CN114662947A - Equipment state monitoring method and device, nonvolatile storage medium and irradiation instrument - Google Patents
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Abstract
The invention discloses a method and a device for monitoring equipment state, a nonvolatile storage medium and an irradiator. Wherein, the method comprises the following steps: acquiring environmental data acquired by first target equipment in a target time period; detecting the environmental data by using a preset data detection rule, and taking a monitoring result as a first judgment index; determining a correlation coefficient between the environmental data and a target working parameter, and taking the correlation coefficient as a second judgment index, wherein the target working parameter is a working parameter of second target equipment in a target time period; calculating a first clear sky coefficient sequence according to the environment data, and comparing the first clear sky coefficient sequence with a second clear sky coefficient sequence obtained through prediction to obtain a third judgment index; and determining the working state of the first target equipment according to the first judgment index, the second judgment index and the third judgment index. The invention solves the technical problem that the working state of the irradiation instrument cannot be accurately judged because the operation state of the irradiation instrument is judged based on the monitoring signal in the prior art.
Description
Technical Field
The invention relates to the field of automation, in particular to a method and a device for monitoring equipment state, a nonvolatile storage medium and an irradiator.
Background
At present, with the rapid development of the photovoltaic industry, irradiation data is used as a key parameter of a photovoltaic power generation system, and the importance of the irradiation data in the processes of site selection, transaction, intelligent monitoring, operation and maintenance, accurate evaluation of operation and maintenance effects and the like of a power station is higher and higher, and the irradiation data acquisition by an irradiator is an effective method for acquiring the irradiation data.
However, in the prior art, the method for monitoring the operation state of the irradiation instrument is single, and the operation state of the irradiation instrument can be judged only based on the monitoring signal provided by a manufacturer, but whether the irradiation instrument is in an abnormal state or not cannot be accurately identified by using the monitoring signal.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a device state monitoring method and device, a nonvolatile storage medium and an irradiation instrument, which at least solve the technical problem that the working state of the irradiation instrument cannot be accurately judged because the operation state of the irradiation instrument is judged based on a monitoring signal in the prior art.
According to an aspect of an embodiment of the present invention, there is provided an apparatus condition monitoring method, including: acquiring environmental data collected by first target equipment in a target time period; detecting environmental data according to a preset data detection rule, and taking a monitoring result as a first judgment index; determining a correlation coefficient between the environmental data and a target working parameter, and taking the correlation coefficient as a second judgment index, wherein the target working parameter is a working parameter of second target equipment in a target time period; calculating a first clear sky coefficient sequence according to the environment data, and comparing the first clear sky coefficient sequence with a second clear sky coefficient sequence obtained through prediction to obtain a third judgment index; and determining the working state of the first target equipment according to the first judgment index, the second judgment index and the third judgment index.
In some embodiments of the present application, determining the operating state of the first target device according to the first evaluation indicator, the second evaluation indicator and the third evaluation indicator includes: when any one of the first judgment index, the second judgment index and the third judgment index indicates that the first target equipment is abnormal, determining that the working state of the first target equipment in the target time period is an abnormal working state.
In some embodiments of the present application, when any one of the first evaluation index, the second evaluation index, and the third evaluation index indicates that the first target device is abnormal, determining that the operating state of the first target device within the target time period is an abnormal operating state includes: when the environmental data do not accord with the preset data monitoring rule, determining that a first judgment index indicates that the first target equipment is abnormal; when the correlation coefficient is smaller than the preset correlation coefficient, determining that the second judgment index indicates that the first target equipment is abnormal; and when the similarity of the first clear sky sequence and the second clear sky sequence is lower than the preset similarity, determining that the third judgment index indicates that the first target equipment is abnormal.
In some embodiments of the present application, the preset data detection rule includes at least one of: the method comprises the steps of numerical value missing detection, constant value detection, large number detection and specific time period numerical value detection, wherein the numerical value missing detection comprises the step of detecting whether data missing exists in environment data or not, the constant value detection comprises the step of detecting whether the environment data have the same continuous numerical values of a plurality of data or not, the quantity of the plurality of data is not less than the preset quantity, the large number detection comprises the step of detecting whether the environment data have the data larger than a first preset value or not, and the specific time period numerical value detection comprises the step of detecting whether the collected environment data are a second preset value or not in a specific time period in a target time period.
In some embodiments of the present application, the environmental data includes irradiation data, the target operating parameter includes active power, determining a correlation coefficient between the environmental data and the target operating parameter, and using the correlation coefficient as the second evaluation indicator includes: calculating a covariance between the environmental data and the target operating parameter; calculating a first standard deviation of the environmental data and a second standard deviation of the target operating parameter; the correlation coefficient is determined from the covariance, the first standard deviation and the second standard deviation.
In some embodiments of the present application, calculating a first clear sky coefficient sequence according to the environment data, and comparing the first clear sky coefficient sequence with a second clear sky coefficient sequence obtained by prediction to obtain a third evaluation index includes: determining a second clear sky coefficient in a second clear sky coefficient sequence corresponding to a first clear sky coefficient in the first clear sky coefficient sequence, wherein the first clear sky coefficient and the second clear sky coefficient correspond to the same time point; comparing the deviation proportion of the first clear sky coefficient and the second clear sky coefficient, and determining that the first clear sky coefficient is an abnormal clear sky coefficient under the condition that the deviation proportion is larger than a preset deviation proportion; and determining the proportion of the abnormal clear sky coefficient in the first clear sky coefficient sequence, and taking the proportion as a third judgment index.
In some embodiments of the present application, before calculating a first clear sky coefficient sequence according to environment data and comparing the first clear sky coefficient sequence with a second clear sky coefficient sequence obtained by prediction to obtain a third evaluation index, the apparatus state monitoring method further includes: establishing a clear sky coefficient prediction model; and acquiring a second clear sky coefficient sequence within the target time period output by the clear sky coefficient prediction model.
In some embodiments of the present application, establishing the clear sky coefficient prediction model includes: determining typical environment data as training data in historical environment data collected by first target equipment; determining clear sky coefficients corresponding to the typical environment data as an objective function; and training the machine learning model according to the training data and the target function to obtain a clear sky coefficient prediction model.
According to another aspect of the embodiments of the present invention, there is also provided an apparatus for monitoring a device status, including: the acquisition module is used for acquiring environmental data acquired by the first target equipment within a target time period; the first comparison module is used for detecting the environmental data according to a preset data detection rule and taking a detection result as a first judgment index; the second comparison module is used for determining a correlation coefficient between the environmental data and the target working parameter, and taking the correlation coefficient as a second judgment index, wherein the target working parameter is a working parameter of the second target equipment in a target time period; the third comparison module is used for calculating the first clear sky coefficient sequence according to the environment data and comparing the first clear sky coefficient sequence with the second clear sky coefficient sequence obtained through prediction to obtain a third judgment index; and the processing module is used for determining the working state of the first target equipment according to the first judgment index, the second judgment index and the third judgment index.
According to another aspect of the embodiments of the present invention, there is provided a nonvolatile storage medium including a stored program, wherein a device in which the nonvolatile storage medium is located is controlled to execute a device state monitoring method when the program is executed.
According to another aspect of the embodiment of the invention, an irradiation instrument is provided, which comprises a processor, wherein the processor is used for running a program, and the program executes the device state monitoring method during running.
In the embodiment of the invention, the environmental data acquired by the first target equipment in the target time period is acquired; detecting the environmental data by using a preset data detection rule, and taking a monitoring result as a first judgment index; determining a correlation coefficient between the environmental data and a target working parameter, and taking the correlation coefficient as a second judgment index, wherein the target working parameter is a working parameter of second target equipment in a target time period; calculating a first clear sky coefficient sequence according to the environmental data, and comparing the first clear sky coefficient sequence with a second clear sky coefficient sequence obtained through prediction to obtain a third judgment index; the working state of the first target equipment is determined according to the first judgment index, the second judgment index and the third judgment index, the working state of the first target equipment is comprehensively judged through the first judgment index, the second judgment index and the third judgment index, the purpose of monitoring the working state of the target equipment in a multidimensional way is achieved, the technical effect of accurately identifying whether the target equipment is abnormal is achieved, and the technical problem that the working state of the irradiation instrument cannot be accurately judged due to the fact that the operation state of the irradiation instrument is judged based on monitoring signals in the prior art is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a schematic flow chart of a method for monitoring equipment status according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a third evaluation index determination method according to an embodiment of the invention;
FIG. 3 is a flow chart illustrating a device status determination process according to an embodiment of the invention;
fig. 4 is a schematic structural diagram of an apparatus state monitoring device according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with an embodiment of the present invention, there is provided a method embodiment of a device condition monitoring method, it being noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than presented herein.
Fig. 1 is a method for monitoring a state of a device according to an embodiment of the present invention, as shown in fig. 1, the method including the steps of:
step S102, acquiring environmental data collected by first target equipment in a target time period;
in some embodiments of the present application, the target time period may be set by a user, including setting a duration of the target time period, a starting time point and an ending time point of the target time period, and the like. In addition, the target time period may be a future upcoming time period, or may be a historical time period. When the target time period is set as the future time period, the scheme provided by the application can monitor the operation state of the first target device in the operation process of the first target device. When the target time period is set as the historical time period, the scheme provided by the application can be used for rechecking the historical operating state of the first target device.
It should be noted that the first target device described in this application may be an irradiation instrument, and the device state monitoring method provided in this application may be executed in the irradiation instrument, or may be executed in any electronic device that has a certain computing capability and has an authority to read environmental data collected by the irradiation instrument, including but not limited to a local server or a cloud server.
Step S104, detecting the environmental data by a preset data detection rule, and taking a detection result as a first judgment index;
in some embodiments of the present application, the preset data detection rule includes at least one of: the method comprises the steps of numerical value missing detection, constant value detection, large number detection and numerical value detection in a specific time period. The numerical value missing detection comprises detecting whether data missing exists in the environment data, the constant value detection comprises detecting whether the environment data has a plurality of continuous data with the same numerical value, the number of the plurality of data is not less than a preset number, the majority detection comprises detecting whether the environment data has data which is greater than a first preset value, and the specific time period numerical value detection comprises detecting whether the acquired environment data is a second preset value within a specific time period in the target time period.
Specifically, in some embodiments of the present application, the environmental data collected by the irradiator includes irradiation data and ambient environmental data, where the ambient environmental data includes temperature, humidity, barometric pressure, wind speed, wind direction, pm2.5 longitude and latitude, altitude, and the like. The data can be stored in a database after being collected, so that the collected data can be detected according to preset data monitoring rules through database sql statements, and the detection comprises collection point number statistics, single-point value threshold judgment and night numerical value judgment to realize missing, constant values (for example, continuous 6-point values are the same), common-large numbers (for example, the value is larger than 1600), and irregular diagnosis (the value of a night table is larger than zero) and the like in collection monitoring.
Step S106, determining a correlation coefficient between the environmental data and a target working parameter, and taking the correlation coefficient as a second judgment index, wherein the target working parameter is a working parameter of second target equipment in a target time period;
in some embodiments of the present application, the environmental data includes irradiance data, the target operating parameter includes active power, determining a correlation coefficient between the environmental data and the target operating parameter, and using the correlation coefficient as the second criterion includes: calculating a covariance between the environmental data and the target operating parameter; calculating a first standard deviation of the environmental data and a second standard deviation of the target operating parameter; determining the correlation coefficient according to the covariance, the first standard deviation and the second standard deviation.
Specifically, the second target device may be an inverter sample board machine in the photovoltaic power station, where selecting the sample board machine may reduce an influence of factors such as limited power existing in an actual production process. The working parameters of the second target device comprise the active power of the sample board machine.
When the second evaluation index is actually calculated, the environmental data and the active power of the sample board machine collected by the irradiator in a preset time period (for example, eight am to four pm) can be selected, 0.5h is taken as a data sampling frequency, and a pearson correlation coefficient is used for performing curve correlation analysis on the environmental data and the active power:
in the above formula, X represents active power, Y represents environment data, and sigmaXDenotes the standard deviation, σ, of the active powerYStandard deviation of the environmental data, cov (X, Y) covariance of the active power and the environmental data, E () expected value of the specific data, u +XIndicating the desired value of active power, uYRepresenting the expected value of the environmental data.
In some embodiments of the application, a strong positive correlation exists between the active power of a sample board machine in a photovoltaic power station and irradiation data at the same time, and the larger the irradiation amount is, the larger the active power is. If the correlation coefficient calculated by the above formula is abnormal, if the correlation coefficient is less than zero, the second evaluation index may be considered to indicate that the irradiation instrument has a fault.
Step S108, calculating a first clear sky coefficient sequence according to the environmental data, and comparing the first clear sky coefficient sequence with a second clear sky coefficient sequence obtained through prediction to obtain a third judgment index;
in some embodiments of the present application, a first clear sky coefficient sequence is calculated according to the environment data, and is compared with a second clear sky coefficient sequence obtained through prediction, and obtaining a third evaluation index includes determining a second clear sky coefficient in the second clear sky coefficient sequence corresponding to a first clear sky coefficient in the first clear sky coefficient sequence, where the first clear sky coefficient and the second clear sky coefficient correspond to a same time point; comparing the deviation ratio of the first clear sky coefficient and the second clear sky coefficient, and determining that the first clear sky coefficient is an abnormal clear sky coefficient under the condition that the deviation ratio is greater than a preset deviation ratio; and determining the proportion of the abnormal clear sky coefficients in the first clear sky coefficient sequence, and taking the proportion as the third judgment index.
Specifically, the calculation formula of the clear sky coefficient is as follows:
wherein, FZCJ represents the irradiation data collected by the irradiation meter, LLFZ represents the theoretical irradiation outside the atmosphere, and the calculation principle of the theoretical irradiation outside the atmosphere is as follows:
LLFZ＝1367*ER*(sin(lat)*sin(ED)+cos(lat)*cos(ED)*cos(tl)
in the formula, ER represents a day-ground distance coefficient, lat represents latitude, tl represents a time angle, and ED represents a solar altitude angle, and the factors can be calculated and obtained based on a celestial body geometric principle and combined with time and longitude and latitude data.
In some embodiments of the present application, before calculating a first clear sky coefficient sequence according to the environmental data, and comparing the first clear sky coefficient sequence with a second clear sky coefficient sequence obtained by prediction to obtain a third evaluation index, the method for monitoring the device state further includes: establishing a clear sky coefficient prediction model; and acquiring the second clear sky coefficient sequence within the target time period output by the clear sky coefficient prediction model.
In some embodiments of the present application, the step of establishing a clear sky coefficient prediction model includes determining typical environmental data as training data from historical environmental data collected by the first target device; determining a clear sky coefficient corresponding to the typical environment data as an objective function; and training a machine learning model according to the training data and the objective function to obtain the clear sky coefficient prediction model.
In some embodiments of the present application, determining the clear sky coefficient as the objective function means that the predicted clear sky coefficient output by the model should be as close as possible to the clear sky coefficient as the objective function.
Specifically, as shown in fig. 2, when the third evaluation index is determined, the XGBoost sequence analysis method may be used to input characteristic data of temperature, humidity, air pressure, wind speed, wind direction, pm2.5, longitude and latitude, altitude, and equipment installation azimuth on a typical day as model characteristic input X, and perform sequence trend model construction with clear sky coefficients as objective functions Y to obtain a clear sky coefficient sequence prediction model. The finally obtained model can output a clear sky coefficient sequence in a target time period. When the clear sky coefficient calculated through the real data deviates from the clear sky coefficient prediction sequence curve value output by the model and reaches a certain range, if the clear sky coefficient prediction sequence curve value reaches 30%, the data is judged to be abnormal, if the number of abnormal points in the sampling period exceeds a certain proportion of the sum of the acquisition points, if the number of abnormal points exceeds half of the sum of the acquisition points, the data of the irradiation instrument is judged to have drift, and the third judgment index can be considered to indicate that the irradiation instrument works abnormally.
Step S110, determining the working state of the first target device according to the first evaluation indicator, the second evaluation indicator and the third evaluation indicator.
In some embodiments of the present application, determining the operating state of the first target device according to the first criterion, the second criterion, and the third criterion includes: when any one of the first judgment index, the second judgment index and the third judgment index indicates that the first target equipment is abnormal, determining that the working state of the first target equipment in the target time period is an abnormal working state.
In some embodiments of the present application, when any one of the first evaluation index, the second evaluation index, and the third evaluation index indicates that the first target device is abnormal, determining that the operating state of the first target device within the target time period is an abnormal operating state includes: when the detection result is that the environmental data do not accord with the preset data monitoring rule, determining that the first judgment index indicates that the first target equipment is abnormal; when the correlation coefficient is smaller than the preset correlation coefficient, determining that the second judgment index indicates that the first target equipment is abnormal; and when the similarity of the first clear sky sequence and the second clear sky sequence is lower than the preset similarity, determining that the third judgment index indicates that the first target equipment is abnormal.
Specifically, as shown in fig. 3, by the device status monitoring method provided by the present application, it is achieved that the working status of the target device is determined jointly from both the collection monitoring of data and the numerical analysis of data. As shown in fig. 3, when the working state of the target device is analyzed from the collection monitoring perspective, it may be analyzed whether the data collected by the target device has a deficiency, a constant value, a large number or an irregular value (for example, the irradiation data at night is not zero). And when determining whether the target equipment is abnormal from the angle of numerical analysis, the method also comprises two detection methods of periodic abnormal detection and numerical drift detection. When periodic anomaly detection is carried out, a typical day (when the weather is clear) is selected, a data sampling time interval is set, then curve correlation analysis of the active power of the sample board machine and irradiation data collected by the irradiation instrument is carried out, and when the data exceeding a preset proportion have correlation coefficients, anomaly of target equipment is determined. When the count value is detected to drift, a typical day in the target time period also needs to be selected, the clear sky coefficient at the moment is calculated, then the clear sky coefficient is compared with the predicted clear sky coefficient, and when the deviation between the clear sky coefficient and the predicted clear sky coefficient is large, the target equipment is determined to be in an abnormal state.
In summary, the equipment state monitoring method provided by the application can quantitatively evaluate and monitor the operation state of the irradiation instrument from multiple dimensions based on the output data of the irradiation instrument, and compared with the prior art, the monitoring result is more accurate; on the other hand, the method and the device realize quantitative acquisition of historical operating states of the irradiation instrument in different environments by combining environmental factor data and station-side power generation active power data and based on a machine learning method, can dynamically and continuously monitor potential changes possibly occurring in the states of the irradiation instrument, and can also accurately judge the working state of the irradiation instrument in different environmental conditions.
According to an embodiment of the present invention, an apparatus condition monitoring device is provided. Fig. 4 is a device state monitoring apparatus according to an embodiment of the present invention, as shown in fig. 4, the apparatus including: the acquisition module 40 is configured to acquire environmental data acquired by a first target device within a target time period; the first comparison module 42 is configured to detect the environmental data according to a preset data detection rule, and use a detection result as a first evaluation index; a second comparing module 44, configured to determine a correlation coefficient between the environmental data and a target operating parameter, and use the correlation coefficient as a second evaluation indicator, where the target operating parameter is an operating parameter of a second target device in the target time period; a third comparing module 46, configured to calculate a first clear sky coefficient sequence according to the environment data, and compare the first clear sky coefficient sequence with a second clear sky coefficient sequence obtained through prediction to obtain a third evaluation index; and the processing module 48 is configured to determine the working state of the first target device according to the first evaluation indicator, the second evaluation indicator and the third evaluation indicator.
It should be noted that the device state monitoring apparatus shown in fig. 4 can be used to execute the device state monitoring method shown in fig. 1, and therefore, the explanation about the device state monitoring method in fig. 1 also applies to the embodiment of the present application.
According to an embodiment of the present invention, a nonvolatile storage medium is provided, which includes a stored program, wherein, when the program is running, a device in which the nonvolatile storage medium is located is controlled to execute the following device status monitoring method: acquiring environmental data acquired by first target equipment in a target time period; detecting environmental data according to a preset data detection rule, and taking a monitoring result as a first judgment index; determining a correlation coefficient between the environmental data and a target working parameter, and taking the correlation coefficient as a second judgment index, wherein the target working parameter is a working parameter of second target equipment in a target time period; calculating a first clear sky coefficient sequence according to the environment data, and comparing the first clear sky coefficient sequence with a second clear sky coefficient sequence obtained through prediction to obtain a third judgment index; and determining the working state of the first target equipment according to the first judgment index, the second judgment index and the third judgment index.
According to an embodiment of the present invention, there is provided an irradiation instrument. The irradiation instrument comprises a processor, wherein the processor is used for running a program, and the program executes the following equipment state monitoring method during running: acquiring environmental data collected by first target equipment in a target time period; detecting environmental data according to a preset data detection rule, and taking a monitoring result as a first judgment index; determining a correlation coefficient between the environmental data and a target working parameter, and taking the correlation coefficient as a second judgment index, wherein the target working parameter is a working parameter of second target equipment in a target time period; calculating a first clear sky coefficient sequence according to the environment data, and comparing the first clear sky coefficient sequence with a second clear sky coefficient sequence obtained through prediction to obtain a third judgment index; and determining the working state of the first target equipment according to the first judgment index, the second judgment index and the third judgment index.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or may not be executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, and various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (11)
1. A method for monitoring a condition of a device, comprising:
acquiring environmental data collected by first target equipment in a target time period;
detecting the environmental data by using a preset data detection rule, and taking a detection result as a first judgment index;
determining a correlation coefficient between the environmental data and a target working parameter, and taking the correlation coefficient as a second judgment index, wherein the target working parameter is a working parameter of second target equipment in the target time period;
calculating a first clear sky coefficient sequence according to the environment data, and comparing the first clear sky coefficient sequence with a second clear sky coefficient sequence obtained through prediction to obtain a third judgment index;
and determining the working state of the first target equipment according to the first judgment index, the second judgment index and the third judgment index.
2. The device status monitoring method according to claim 1, wherein determining the operating status of the first target device according to the first evaluation indicator, the second evaluation indicator and the third evaluation indicator comprises:
when any one of the first evaluation index, the second evaluation index and the third evaluation index indicates that the first target device is abnormal, determining that the working state of the first target device in the target time period is an abnormal working state.
3. The apparatus state monitoring method according to claim 2, wherein when any one of the first evaluation index, the second evaluation index, and the third evaluation index indicates that the first target apparatus is abnormal, determining that the operating state of the first target apparatus in the target period of time is an abnormal operating state comprises:
when the detection result is that the environmental data do not accord with the preset data monitoring rule, determining that the first judgment index indicates that the first target device is abnormal;
when the correlation coefficient is smaller than a preset correlation coefficient, determining that the second judgment index indicates that the first target device is abnormal;
and when the similarity between the first clear sky sequence and the second clear sky sequence is lower than a preset similarity, determining that the third judgment index indicates that the first target equipment is abnormal.
4. The device status monitoring method according to claim 1, wherein the preset data detection rule comprises at least one of: the method comprises the steps of numerical value missing detection, constant value detection, large number detection and specific time period numerical value detection, wherein the numerical value missing detection comprises the step of detecting whether data missing exists in the environment data or not, the constant value detection comprises the step of detecting whether the environment data have the same numerical value of a plurality of continuous data or not, the quantity of the plurality of data is not less than the preset quantity, the large number detection comprises the step of detecting whether the environment data have the data which is larger than a first preset value or not, and the specific time period numerical value detection comprises the step of detecting whether the collected environment data are a second preset value or not in a specific time period in the target time period.
5. The equipment condition monitoring method of claim 1, wherein the environmental data comprises irradiance data, the target operating parameter comprises active power, determining a correlation coefficient between the environmental data and the target operating parameter, and using the correlation coefficient as a second criterion comprises:
calculating a covariance between the environmental data and the target operating parameter;
calculating a first standard deviation of the environmental data and a second standard deviation of the target operating parameter;
determining the correlation coefficient according to the covariance, the first standard deviation and the second standard deviation.
6. The device status monitoring method according to claim 1, wherein calculating a first clear sky coefficient sequence according to the environmental data, and comparing the first clear sky coefficient sequence with a second clear sky coefficient sequence obtained by prediction to obtain a third evaluation index comprises:
determining a second clear sky coefficient in the second clear sky coefficient sequence corresponding to a first clear sky coefficient in the first clear sky coefficient sequence, wherein the first clear sky coefficient and the second clear sky coefficient correspond to the same time point;
comparing the deviation ratio of the first clear sky coefficient and the second clear sky coefficient, and determining that the first clear sky coefficient is an abnormal clear sky coefficient under the condition that the deviation ratio is greater than a preset deviation ratio;
and determining the proportion of the abnormal clear sky coefficient in the first clear sky coefficient sequence, and taking the proportion as the third judgment index.
7. The apparatus state monitoring method according to claim 1, wherein before calculating a first clear sky coefficient sequence according to the environmental data and comparing the first clear sky coefficient sequence with a second clear sky coefficient sequence obtained by prediction to obtain a third evaluation index, the apparatus state monitoring method further comprises:
establishing a clear sky coefficient prediction model;
and acquiring the second clear sky coefficient sequence within the target time period output by the clear sky coefficient prediction model.
8. The equipment state monitoring method according to claim 7, wherein establishing a clear sky coefficient prediction model comprises:
determining typical environmental data as training data in historical environmental data collected by the first target device;
determining a clear sky coefficient corresponding to the typical environment data as an objective function;
and training a machine learning model according to the training data and the objective function to obtain the clear sky coefficient prediction model.
9. An apparatus condition monitoring device, comprising:
the acquisition module is used for acquiring environmental data acquired by the first target equipment within a target time period;
the first comparison module is used for detecting the environmental data according to a preset data detection rule and taking a detection result as a first judgment index;
the second comparison module is used for determining a correlation coefficient between the environmental data and a target working parameter, and taking the correlation coefficient as a second judgment index, wherein the target working parameter is a working parameter of second target equipment in the target time period;
the third comparison module is used for calculating the first clear sky coefficient sequence according to the environment data and comparing the first clear sky coefficient sequence with the second clear sky coefficient sequence obtained through prediction to obtain a third judgment index;
and the processing module is used for determining the working state of the first target equipment according to the first judgment index, the second judgment index and the third judgment index.
10. A non-volatile storage medium, comprising a stored program, wherein the program, when executed, controls a device in which the storage medium is located to perform the device status monitoring method according to any one of claims 1 to 8.
11. An irradiation instrument comprising a processor, wherein the processor is configured to run a program, wherein the program is configured to execute the device status monitoring method according to any one of claims 1 to 8 when the program is run.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150100266A1 (en) * | 2012-05-14 | 2015-04-09 | Commissariat A L'energie Atomique Et Aux Energies Alternatives | Estimation of drift in a solar radiation sensor |
| US20160019323A1 (en) * | 2013-03-14 | 2016-01-21 | Omron Corporation | Solar power generation system, abnormality determination processing device, abnormality determination processing method, and program |
| CN109842371A (en) * | 2019-03-19 | 2019-06-04 | 黎和平 | A kind of method and apparatus positioning photovoltaic power generation exception |
| CN113361737A (en) * | 2020-03-05 | 2021-09-07 | 中国电力科学研究院有限公司 | Abnormity early warning method and system for photovoltaic module |
-
2022
- 2022-03-29 CN CN202210320792.5A patent/CN114662947A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150100266A1 (en) * | 2012-05-14 | 2015-04-09 | Commissariat A L'energie Atomique Et Aux Energies Alternatives | Estimation of drift in a solar radiation sensor |
| US20160019323A1 (en) * | 2013-03-14 | 2016-01-21 | Omron Corporation | Solar power generation system, abnormality determination processing device, abnormality determination processing method, and program |
| CN109842371A (en) * | 2019-03-19 | 2019-06-04 | 黎和平 | A kind of method and apparatus positioning photovoltaic power generation exception |
| CN113361737A (en) * | 2020-03-05 | 2021-09-07 | 中国电力科学研究院有限公司 | Abnormity early warning method and system for photovoltaic module |
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