CN116044798A - Fault diagnosis method and device for photovoltaic inverter fan and electronic equipment - Google Patents
Fault diagnosis method and device for photovoltaic inverter fan and electronic equipment Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D27/00—Control, e.g. regulation, of pumps, pumping installations or pumping systems specially adapted for elastic fluids
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- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
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Abstract
The application discloses a fault diagnosis method and device for a photovoltaic inverter fan and electronic equipment, and belongs to the technical field of photovoltaics. The method comprises the following steps: determining a target date as a diagnosis date, and acquiring a target maximum power point tracking voltage and a target active power of a target photovoltaic inverter to be diagnosed on the target date; determining a target operational ripple characteristic of the target photovoltaic inverter based on the target maximum power point tracking voltage and the target active power; and comparing the target operation fluctuation characteristic with a first inverter model and a second inverter model, and determining a fan fault diagnosis result of the target photovoltaic inverter. The method judges the running fluctuation characteristics of the inverter through the maximum power point tracking voltage and active power data, timely diagnoses the inverter with fan faults, is beneficial to timely eliminating the fan faults, avoids derating running conditions and reduces electric quantity loss.
Description
Technical Field
The application belongs to the technical field of photovoltaics, and particularly relates to a fault diagnosis method and device for a photovoltaic inverter fan and electronic equipment.
Background
Because the fan is directly communicated with the outside, foreign matters often enter the string type photovoltaic inverter, and dust accumulation is serious. The fan failure rate of the string-type photovoltaic inverter is high, and derating operation of the photovoltaic inverter also occurs frequently.
The existing photovoltaic station monitoring system cannot accurately diagnose the fan fault of the photovoltaic inverter, warning is not generally carried out, the concealment of the fan fault is strong, operation and maintenance personnel of the station are difficult to find, and the derating operation condition of the photovoltaic inverter frequently occurs easily causes electric quantity loss.
Disclosure of Invention
The present application aims to solve at least one of the technical problems existing in the prior art. Therefore, the method, the device and the electronic equipment for diagnosing the faults of the photovoltaic inverter fan can diagnose the inverter with the fan faults in time, are beneficial to eliminating the fan faults in time, avoid derating running conditions and reduce electric quantity loss.
In a first aspect, the present application provides a fault diagnosis method for a photovoltaic inverter fan, the method comprising:
determining a target date as a diagnosis date, and acquiring a target maximum power point tracking voltage and a target active power of a target photovoltaic inverter to be diagnosed on the target date;
Determining a target operational ripple characteristic of the target photovoltaic inverter based on the target maximum power point tracking voltage and the target active power;
comparing the target operation fluctuation characteristics with a first inverter model and a second inverter model, and determining a fan fault diagnosis result of the target photovoltaic inverter;
the first inverter model is used for representing a first operation fluctuation characteristic of a first inverter, the first inverter model is constructed based on a first sample data set of the first inverter on a diagnosis day, the first sample data set comprises a first maximum power point tracking voltage and a first active power, the first operation fluctuation characteristic is determined based on the first maximum power point tracking voltage and the first active power, and the first inverter represents an inverter with a fan operating normally;
the second inverter model is used for representing a second operation fluctuation characteristic of a second inverter, the second inverter model is constructed based on a second sample data set of the second inverter on a diagnosis day, the second sample data set comprises a second maximum power point tracking voltage and a second active power, the second operation fluctuation characteristic is determined based on the second maximum power point tracking voltage and the second active power, and the second inverter represents an inverter with a fan fault.
According to the fault diagnosis method of the photovoltaic inverter fan, the target photovoltaic inverter tracking voltage and the target active power at the target maximum power point of the diagnosis day are obtained, the target operation fluctuation characteristic is determined, and compared with the first non-fault inverter model and the second fan fault inverter model, the inverter with the fan fault is diagnosed in time, so that the fan fault can be eliminated in time, the derating running condition is avoided, and the electric quantity loss is reduced.
According to one embodiment of the present application, comparing the target operational fluctuation feature with a first inverter model and a second inverter model, determining a fan fault diagnosis result of the target photovoltaic inverter includes:
acquiring a first similarity of the target operation fluctuation feature and the first operation fluctuation feature, and acquiring a second similarity of the target operation fluctuation feature and the second operation fluctuation feature;
and determining that the fan of the target photovoltaic inverter fails under the condition that the second similarity is larger than the first similarity.
According to one embodiment of the application, the determining the target operational ripple characteristic of the target photovoltaic inverter based on the target maximum power point tracking voltage and the target active power includes:
Obtaining a target operation trend sequence of the target photovoltaic inverter based on the target maximum power point tracking voltage and the target active power;
and performing word frequency-reverse file frequency vector conversion on the target operation trend sequence to obtain the target operation fluctuation feature.
According to one embodiment of the present application, the obtaining the target operation trend sequence of the target photovoltaic inverter based on the target maximum power point tracking voltage and the target active power includes:
respectively carrying out normalization processing on the target maximum power point tracking voltage and the target active power to obtain a target maximum power point tracking voltage time sequence and a target active power time sequence;
and carrying out difference calculation on the target maximum power point tracking voltage time sequence and the target active power time sequence to obtain the target operation trend sequence.
According to an embodiment of the present application, the performing word frequency-reverse file frequency vector conversion on the target operation trend sequence to obtain the target operation fluctuation feature includes:
sequentially performing dimension reduction, symbolization and discretization on the target operation trend sequence to obtain a target character string;
And performing word frequency-reverse file frequency vector conversion on the target character string to obtain the target operation fluctuation feature.
According to one embodiment of the application, the second sample data set comprises historical sample data and simulated sample data of the second inverter.
According to one embodiment of the application, the determining the target operational ripple characteristic of the target photovoltaic inverter based on the target maximum power point tracking voltage and the target active power includes:
according to a target data processing strategy, carrying out data processing on the target maximum power point tracking voltage and the target active power to obtain the target operation fluctuation characteristic;
the first operation fluctuation feature is obtained according to the target data processing strategy based on the first maximum power point tracking voltage and the first active power, and the second operation fluctuation feature is obtained according to the target data processing strategy based on the second maximum power point tracking voltage and the second active power.
According to one embodiment of the present application, the determining the target date as the diagnosis date includes:
acquiring station irradiation data of the target date;
Determining a first-order difference of irradiation data of the target date based on station irradiation data of the target date;
and determining the target date as a diagnosis date under the condition that the first-order difference of the irradiation data of the target date is less than or equal to a target threshold value.
In a second aspect, the present application provides a fault diagnosis apparatus for a photovoltaic inverter fan, the apparatus comprising:
the acquisition module is used for determining a target date as a diagnosis date and acquiring a target maximum power point tracking voltage and target active power of a target photovoltaic inverter to be diagnosed on the target date;
the first processing module is used for determining target operation fluctuation characteristics of the target photovoltaic inverter based on the target maximum power point tracking voltage and the target active power;
the second processing module is used for comparing the target operation fluctuation characteristics with the first inverter model and the second inverter model and determining a fan fault diagnosis result of the target photovoltaic inverter;
the first inverter model is used for representing a first operation fluctuation characteristic of a first inverter, the first inverter model is constructed based on a first sample data set of the first inverter on a diagnosis day, the first sample data set comprises a first maximum power point tracking voltage and a first active power, the first operation fluctuation characteristic is determined based on the first maximum power point tracking voltage and the first active power, and the first inverter represents an inverter with a fan operating normally;
The second inverter model is used for representing a second operation fluctuation characteristic of a second inverter, the second inverter model is constructed based on a second sample data set of the second inverter on a diagnosis day, the second sample data set comprises a second maximum power point tracking voltage and a second active power, the second operation fluctuation characteristic is determined based on the second maximum power point tracking voltage and the second active power, and the second inverter represents an inverter with a fan fault.
According to the fault diagnosis device of the photovoltaic inverter fan, the target photovoltaic inverter is used for tracking voltage and target active power at the target maximum power point of the diagnosis day, the target running fluctuation characteristic is determined, and compared with the first non-faulty inverter model and the second faulty inverter model, the inverter with the fan fault is diagnosed in time, so that the fan fault can be eliminated in time, the derating running condition is avoided, and the electric quantity loss is reduced.
In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the method for diagnosing a fault of a photovoltaic inverter fan according to the first aspect when the processor executes the computer program.
In a fourth aspect, the present application provides a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the method for diagnosing a fault of a photovoltaic inverter fan according to the first aspect described above.
In a fifth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the method for diagnosing a fault of a photovoltaic inverter fan as described in the first aspect above.
Additional aspects and advantages of the application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, wherein:
fig. 1 is a flow chart of a fault diagnosis method of a photovoltaic inverter fan according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a signed target operational trend sequence provided by an embodiment of the present application;
FIG. 3 is a schematic diagram of a process flow of a target data processing policy provided in an embodiment of the present application;
fig. 4 is a schematic structural diagram of a fault diagnosis device of a photovoltaic inverter fan according to an embodiment of the present disclosure;
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Technical solutions in the embodiments of the present application will be clearly described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application are within the scope of the protection of the present application.
The terms first, second and the like in the description and in the claims, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged, as appropriate, such that embodiments of the present application may be implemented in sequences other than those illustrated or described herein, and that the objects identified by "first," "second," etc. are generally of a type and not limited to the number of objects, e.g., the first object may be one or more. Furthermore, in the description and claims, "and/or" means at least one of the connected objects, and the character "/", generally means that the associated object is an "or" relationship.
The method for diagnosing the fault of the photovoltaic inverter fan, the device for diagnosing the fault of the photovoltaic inverter fan, the electronic apparatus and the readable storage medium provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings by means of specific embodiments and application scenarios thereof.
The fault diagnosis method of the photovoltaic inverter fan can be applied to a terminal, and can be specifically executed by hardware or software in the terminal.
The terminal includes, but is not limited to, a portable communication device such as a mobile phone or tablet having a touch sensitive surface (e.g., a touch screen display and/or a touch pad). It should also be appreciated that in some embodiments, the terminal may not be a portable communication device, but rather a desktop computer having a touch-sensitive surface (e.g., a touch screen display and/or a touch pad).
In the following various embodiments, a terminal including a display and a touch sensitive surface is described. However, it should be understood that the terminal may include one or more other physical user interface devices such as a physical keyboard, mouse, and joystick.
The implementation main body of the fault diagnosis method of the photovoltaic inverter fan provided in the embodiment of the present application may be an electronic device or a functional module or a functional entity in the electronic device capable of implementing the fault diagnosis method of the photovoltaic inverter fan, where the electronic device mentioned in the embodiment of the present application includes, but is not limited to, a mobile phone, a tablet computer, a camera, a wearable device, and the like, and the fault diagnosis method of the photovoltaic inverter fan provided in the embodiment of the present application is described below by taking the electronic device as an implementation main body as an example.
As shown in fig. 1, the fault diagnosis method of the photovoltaic inverter fan includes: step 110, step 120 and step 130.
And 110, determining the target date as the diagnosis date, and acquiring the target maximum power point tracking voltage and the target active power of the target photovoltaic inverter to be diagnosed on the target date.
The target photovoltaic inverter is a photovoltaic inverter to be subjected to fan fault diagnosis, and the target photovoltaic inverter can be one of the series-type inverters.
The target date is the date of fan fault diagnosis, the target date can be the current date, the target maximum power point tracking voltage and the target active power of the target photovoltaic inverter on the target date are obtained, and real-time fault diagnosis is carried out on the fan of the target photovoltaic inverter.
The target date can be a historical date, and whether the fan fault occurs in the target photovoltaic inverter on the historical date is judged for the obtained target maximum power point tracking voltage and the target active power of the target photovoltaic inverter on the target date.
In the actual implementation, the photovoltaic inverter that has failed on the target date is not diagnosed for the fan failure, so as to save the computing resources.
In this embodiment, the diagnostic day is related to the fan operation state of the target photovoltaic inverter, the target date is determined to be the diagnostic day, and the fan of the target photovoltaic inverter is operated to rotate on the target date to detect whether the fan has a fault; when the fan of the target photovoltaic inverter is not rotated on the non-diagnosis day, whether the fan has a fault or not cannot be detected.
For example, the diagnosis day may be a sunny day with high irradiation intensity of the yard, the power generation efficiency of the photovoltaic solar panel is high, and the fan of the photovoltaic inverter is in a rotating running state.
And acquiring a target maximum power point tracking voltage and target active power of the target photovoltaic inverter on a target date, wherein the target maximum power point tracking voltage can be acquired by a maximum power point tracking control solar controller (MaximumPower Point Tracking, MPPT).
In the step, according to the target maximum power point tracking voltage and the target active power of the target photovoltaic inverter, the target operation fluctuation characteristic of the target photovoltaic inverter on the target date can be judged, and whether the fan of the target photovoltaic inverter fails or not is judged according to whether the target operation fluctuation characteristic accords with the operation fluctuation characteristic of the normal photovoltaic inverter or the fan failure photovoltaic inverter.
It should be noted that, through intensive research, the inventor of the application discovers that in the diagnosis day, the power generation efficiency of the photovoltaic solar panel is high for the first time, the fan of the photovoltaic inverter is in a rotating running state, when the fan fails, after the active power rises to a certain degree, the maximum power point tracking voltage rises, and frequent fluctuation occurs after the rising, and the active power presents a fluctuation phenomenon in the opposite direction along with the fluctuation of the maximum power point tracking voltage.
In this embodiment, the target operating ripple feature is used to characterize the ripple variation of the target maximum power point tracking voltage and target active power.
And 130, comparing the target operation fluctuation characteristics with the first inverter model and the second inverter model, and determining a fan fault diagnosis result of the target photovoltaic inverter.
The first inverter model is used for representing first operation fluctuation characteristics of the first inverter, the first inverter model is constructed based on a first sample data set of the first inverter on a diagnosis day, the first sample data set comprises a first maximum power point tracking voltage and first active power, the first operation fluctuation characteristics are determined based on the first maximum power point tracking voltage and the first active power, and the first inverter represents an inverter for normal operation of the fan.
The second inverter model is used for representing a second operation fluctuation characteristic of the second inverter, the second inverter model is constructed based on a second sample data set of the second inverter on a diagnosis day, the second sample data set comprises a second maximum power point tracking voltage and a second active power, the second operation fluctuation characteristic is determined based on the second maximum power point tracking voltage and the second active power, and the second inverter represents an inverter of a fan fault.
The first inverter model may include first operational ripple characteristics of a plurality of non-faulty first inverters, the second inverter model may include second operational ripple characteristics of a plurality of fan faulty second inverters, and fan fault types of the plurality of second inverters may be different.
The target operation fluctuation feature is used for representing fluctuation change conditions of a target maximum power point tracking voltage and a target active power of the target photovoltaic inverter, the first operation fluctuation feature is used for representing fluctuation change conditions of a first maximum power point tracking voltage and a first active power of a non-fault first inverter, and the second operation fluctuation feature is used for representing fluctuation change conditions of a second maximum power point tracking voltage and a second active power of a second inverter with a fan fault.
In the embodiment, by comparing the target operation fluctuation characteristic of the target photovoltaic inverter with the first operation fluctuation characteristic in the first non-fault inverter model and the second operation fluctuation characteristic in the second fan fault inverter model, whether the fan of the target photovoltaic inverter is faulty or not is judged, and a fan fault diagnosis result of the target photovoltaic inverter on a target date is obtained, so that the fan fault of the inverter of the non-shutdown fault type can be diagnosed in time under the condition that hardware is not added.
In actual implementation, under the condition that the fan fault of the target photovoltaic inverter is diagnosed, corresponding fault information can be pushed to operation and maintenance personnel through a work order or other modes, so that the operation and maintenance personnel can timely eliminate the fan fault of the target photovoltaic inverter, and the loss of generating capacity of a station yard is reduced.
According to the fault diagnosis method for the photovoltaic inverter fan, the target photovoltaic inverter tracking voltage and the target active power at the target maximum power point of the diagnosis day are obtained, the target operation fluctuation characteristic is determined, and compared with the first non-fault inverter model and the second fan fault inverter model, the inverter with the fan fault is diagnosed in time, so that the fan fault can be eliminated in time, the derating running condition is avoided, and the electric quantity loss is reduced.
In some embodiments, step 130, comparing the target operational ripple characteristic with the first inverter model and the second inverter model, determining a fan failure diagnosis result of the target photovoltaic inverter may include:
acquiring first similarity of the target operation fluctuation feature and the first operation fluctuation feature, and acquiring second similarity of the target operation fluctuation feature and the second operation fluctuation feature;
And determining that the fan of the target photovoltaic inverter fails under the condition that the second similarity is larger than the first similarity.
In practical implementations, the first similarity and the second similarity may be cosine similarities between running wave features, where the cosine similarities measure the similarity between two feature vectors by measuring cosine values of angles between the two feature vectors.
In this embodiment, when it is determined that the second similarity is greater than the first similarity, the target operational ripple characteristic of the target photovoltaic inverter is more similar to the second operational ripple characteristic in the second inverter model of the fan failure, and it is determined that the fan of the target photovoltaic inverter fails.
When the first similarity is determined to be greater than the second similarity, the target operational fluctuation feature of the target photovoltaic inverter is more similar to the first operational fluctuation feature in the non-faulty first inverter model, and it is determined that the fan of the target photovoltaic inverter is not faulty.
In some embodiments, step 120, determining a target operating ripple characteristic of the target photovoltaic inverter based on the target maximum power point tracking voltage and the target active power may include:
according to a target data processing strategy, carrying out data processing on the target maximum power point tracking voltage and the target active power to obtain a target operation fluctuation characteristic;
The first operation fluctuation feature is obtained according to a target data processing strategy based on the first maximum power point tracking voltage and the first active power, and the second operation fluctuation feature is obtained according to the target data processing strategy based on the second maximum power point tracking voltage and the second active power.
In the embodiment, the target operation fluctuation feature, the first operation fluctuation feature and the second operation fluctuation feature are obtained by processing the maximum power point tracking voltage and the active power according to the same target data processing strategy, and the comparison among the target operation fluctuation feature, the first operation fluctuation feature and the second operation fluctuation feature is more accurate, so that the diagnosis accuracy of the fan fault of the inverter can be effectively improved.
A specific data processing strategy is described below.
It will be appreciated that the maximum power point tracking voltage and the active power may also be processed by other data processing strategies to obtain corresponding operational ripple characteristics.
In some embodiments, step 120, determining a target operating ripple characteristic of the target photovoltaic inverter based on the target maximum power point tracking voltage and the target active power may include:
tracking voltage and target active power based on a target maximum power point to obtain a target operation trend sequence of the target photovoltaic inverter;
And performing word frequency-reverse file frequency vector conversion on the target operation trend sequence to obtain the target operation fluctuation feature.
Among them, term frequency-inverse document frequency (TF-IDF) is a common weighting technique for information retrieval and data mining.
In the embodiment, through word frequency-reverse file frequency vector conversion, the fluctuation condition of the maximum power point tracking voltage in the target operation trend sequence and the fluctuation condition of the active power presented by the fluctuation of the maximum power point tracking voltage are determined, and the target operation fluctuation characteristic is obtained.
In some embodiments, obtaining a target run trend sequence for a target photovoltaic inverter based on a target maximum power point tracking voltage and a target active power may include:
respectively carrying out normalization processing on the target maximum power point tracking voltage and the target active power to obtain a target maximum power point tracking voltage time sequence and a target active power time sequence;
and carrying out difference calculation on the target maximum power point tracking voltage time sequence and the target active power time sequence to obtain a target operation trend sequence.
The normalization is a calculation simplifying mode, and two dimensional data of the target maximum power point tracking voltage and the target active power are transformed to obtain a dimensionless time sequence.
In this embodiment, after the normalization processing is performed to obtain the target maximum power point tracking voltage time sequence and the target active power time sequence, the calculation between the target maximum power point tracking voltage and the target active power is simplified, and the target running trend sequence is obtained through the calculation of the difference between the target maximum power point tracking voltage time sequence and the target active power time sequence.
In some embodiments, performing word frequency-reverse file frequency vector conversion on the target operation trend sequence to obtain a target operation fluctuation feature, including:
sequentially performing dimension reduction, symbolization and discretization on the target operation trend sequence to obtain a target character string;
and performing word frequency-reverse file frequency vector conversion on the target character string to obtain the target operation fluctuation feature.
In the embodiment, the dimension reduction processing can be performed on the target operation trend sequence according to the target compression ratio, the data is easier to process and use in a low dimension, the related operation fluctuation characteristics can be clearly displayed in the data, and the subsequent calculation cost can be reduced.
And carrying out symbolization representation on the target operation trend sequence after the dimension reduction, selecting a representation symbol, and mapping the data of the target operation trend sequence according to the data range splitting point in the selected representation symbol to obtain the symbolized target operation trend sequence.
Take the example of the selected representative symbols comprising a, b and c.
Fig. 2 is a schematic diagram of a signed target operational trend sequence provided in the embodiment of the present application, where, as shown in fig. 2, data with a target operational trend sequence greater than 0.5 is symbolized as c, data between-0.5 and 0.5 is symbolized as b, and data with a value less than-0.5 is symbolized as a.
And discretizing the symbolized target operation trend sequence to obtain a target character string, for example, the target character string is baabccbc.
In some embodiments, the second sample data set includes historical sample data and simulated sample data of the second inverter.
The second inverter characterizes the photovoltaic inverter with fan faults, the second sample data set comprises historical sample data and simulated sample data, the historical sample data is data such as maximum power point tracking voltage and active power of the photovoltaic inverter with the fan faults, and the simulated sample data is data such as maximum power point tracking voltage and active power of the photovoltaic inverter with the fan faults.
In the embodiment, through simulating sample data and historical sample data, data such as maximum power point tracking voltage, active power and the like of various fan fault types can be obtained, and the built second inverter model can more comprehensively reflect fluctuation change conditions between the maximum power point tracking voltage and the active power during fan faults and improve the accuracy of fan fault diagnosis.
In some embodiments, determining the target date as the diagnostic day includes:
station irradiation data of a target date are obtained;
determining a first-order difference of irradiation data of a target date based on station irradiation data of the target date;
in the case where the irradiation data first order difference of the target date is determined to be less than or equal to the target threshold value, the target date is determined to be the diagnosis date.
Wherein the first order difference is the difference between two consecutive adjacent terms in the discrete function.
The irradiation data first-order difference of the target date is obtained, whether the fluctuation of the irradiation data in the target date is regular or not can be judged, whether the fluctuation curve of the irradiation data is stable or not can be judged, and the irradiation data first-order difference of the target date can reflect the smoothness of the irradiation data change of the target date.
The first-order difference of the irradiation data is the absolute value of the difference between two consecutive adjacent irradiation data.
In the embodiment, when the first-order difference of the irradiation data on the target date is smaller than or equal to the target threshold value, the fluctuation rule of the irradiation data on the target date, the photovoltaic solar panel power generation rule, the power generation efficiency is gradually improved, the fan of the photovoltaic inverter is in a rotating running state, and the fan fault can be diagnosed on the target date.
A specific embodiment is described below.
The fault diagnosis process of the photovoltaic inverter fan comprises the following steps: data sample simulation and collection, data processing, model construction, anomaly diagnosis and algorithm output.
1. And simulating and collecting data samples.
A scene of various fan faults is collected and simulated at a photovoltaic station, and a non-faulty first sample data set and a faulty second sample data set are collected.
The sample data set comprises data such as active power, maximum power point tracking voltage, irradiation data and the like.
2. And (5) data processing.
Step one, determining a diagnosis day.
Station irradiation data of the photovoltaic power station are obtained, 9:00 to 15:00 interval data are intercepted, first-order difference of the irradiation data is calculated, and when the absolute value of the first-order difference is smaller than or equal to a target threshold D, the current day is judged to be the diagnosis day.
The first order difference calculation formula: diff (Diff) i ＝Irr i+1 -Irr i (i∈[9:00,15:00])。
Diagnostic day determination conditions: max (abs (Diff) i )，D)<＝D(i∈[9:00,15:00])。
And secondly, acquiring maximum power point tracking voltage and active power data of the diagnosis day fault and normal inverter, and respectively normalizing the maximum power point tracking voltage and the active power by using a normalization formula to obtain a normalized maximum power point tracking voltage time sequence Zm and an normalized active power time sequence Zp.
And thirdly, calculating the difference value of the normalized Zm and the normalized Zp to obtain an inverter operation trend sequence PM.
Step four, dimension reduction is carried out on the operation trend sequence PMChanging the time sequence PM with length m into the data sequence PM with length w ’ 。
The compression ratio is k=m/w, where w.ltoreq.m.
PM＝pm 1 、pm 2 、...pm m PM after dimension reduction ’ ＝pm ’ 1 、pm ’ 2 、...pm ’ w 。
The dimensionality reduction formula is:
wherein, the liquid crystal display device comprises a liquid crystal display device, i=1, 2 the combination of w and, j=1, 2,..m.
Fifthly, symbolizing the sequence after dimension reduction, selecting a letter set L= { a, b, c }, mapping the sequence data according to splitting points, and mapping the symbolized sequence data according to the splitting points of the graphic table.
The symbolized running trend sequence is shown in fig. 2, and is discretized into character strings: baabccbc, the discretized string is subjected to tf-idf vector conversion.
3. And (5) constructing a model.
And modeling the normal and fault inverters according to the data processing mode to obtain a first inverter model and a second inverter model.
4. And (5) diagnosing abnormal strings.
As shown in fig. 3, step one, according to the weather irradiation data, judges whether the day is a diagnosis day.
And step two, acquiring a target maximum power point tracking voltage and a target active power of the target photovoltaic inverter on the diagnosis day.
And thirdly, data cleaning is carried out on the target maximum power point tracking voltage and the target active power to remove abnormal values.
And step four, skipping the inverter with the fault on the diagnosis day for non-diagnosis.
Fifthly, carrying out normalization processing on the target maximum power point tracking voltage and the target active power after data cleaning.
And step six, constructing an inverter operation trend sequence by using the normalized data.
And step seven, performing dimension reduction on the trend data by using a sub-sequence mode.
And step eight, symbolizing the data subjected to dimension reduction, and converting the data into character strings represented by letters.
And step nine, carrying out vector conversion on the character string data by using a tf-idf algorithm.
And step ten, respectively calculating cosine similarity between the converted vector and the first inverter model and cosine similarity between the converted vector and the second inverter model.
Step eleven, if the cosine similarity with the abnormal model is higher, the fault information is pushed.
And step twelve, if the cosine similarity with the normal class model is higher, loading the next inverter for diagnosis.
5. And outputting an algorithm result.
The algorithm program operates and diagnoses according to the input inverter data, and pushes the diagnosis result to operation and maintenance personnel for processing through an operation and maintenance system, so that the problem of fan faults is solved in time, and the power generation loss is reduced.
According to the fault diagnosis method for the photovoltaic inverter fan, the execution main body can be the fault diagnosis device for the photovoltaic inverter fan. In the embodiment of the present application, a method for performing a fault diagnosis of a photovoltaic inverter fan by using a fault diagnosis device of a photovoltaic inverter fan is taken as an example, and the fault diagnosis device of a photovoltaic inverter fan provided in the embodiment of the present application is described.
The embodiment of the application also provides a fault diagnosis device of the photovoltaic inverter fan.
As shown in fig. 4, the fault diagnosis device for a photovoltaic inverter fan includes:
an obtaining module 410, configured to determine a target date as a diagnosis date, and obtain a target maximum power point tracking voltage and a target active power of a target photovoltaic inverter to be diagnosed on the target date;
a first processing module 420 for determining a target operational ripple characteristic of the target photovoltaic inverter based on the target maximum power point tracking voltage and the target active power;
the second processing module 430 is configured to compare the target operational fluctuation feature with the first inverter model and the second inverter model, and determine a fan fault diagnosis result of the target photovoltaic inverter;
the first inverter model is used for representing first operation fluctuation characteristics of the first inverter, the first inverter model is constructed based on a first sample data set of the first inverter on a diagnosis day, the first sample data set comprises a first maximum power point tracking voltage and first active power, the first operation fluctuation characteristics are determined based on the first maximum power point tracking voltage and the first active power, and the first inverter represents an inverter in which a fan normally operates;
The second inverter model is used for representing a second operation fluctuation characteristic of the second inverter, the second inverter model is constructed based on a second sample data set of the second inverter on a diagnosis day, the second sample data set comprises a second maximum power point tracking voltage and a second active power, the second operation fluctuation characteristic is determined based on the second maximum power point tracking voltage and the second active power, and the second inverter represents an inverter of a fan fault.
According to the fault diagnosis device for the photovoltaic inverter fan, provided by the embodiment of the application, the target running fluctuation characteristic is determined by acquiring the target maximum power point tracking voltage and the target active power of the target photovoltaic inverter on the diagnosis day, and compared with the first non-faulty inverter model and the second faulty inverter model of the fan, the inverter with the fan fault is diagnosed in time, so that the fan fault can be eliminated in time, the derating running condition can be avoided, and the electric quantity loss can be reduced.
In some embodiments, the second processing module 430 is configured to obtain a first similarity of the target operational fluctuation feature and the first operational fluctuation feature, and obtain a second similarity of the target operational fluctuation feature and the second operational fluctuation feature;
And determining that the fan of the target photovoltaic inverter fails under the condition that the second similarity is larger than the first similarity.
In some embodiments, the first processing module 420 is configured to obtain a target operational trend sequence of the target photovoltaic inverter based on the target maximum power point tracking voltage and the target active power;
and performing word frequency-reverse file frequency vector conversion on the target operation trend sequence to obtain the target operation fluctuation feature.
In some embodiments, the first processing module 420 is configured to normalize the target maximum power point tracking voltage and the target active power respectively, so as to obtain a target maximum power point tracking voltage time sequence and a target active power time sequence;
and carrying out difference calculation on the target maximum power point tracking voltage time sequence and the target active power time sequence to obtain a target operation trend sequence.
In some embodiments, the first processing module 420 is configured to sequentially perform a dimension reduction process, a symbolization process, and a discretization process on the target running trend sequence, so as to obtain a target character string;
and performing word frequency-reverse file frequency vector conversion on the target character string to obtain the target operation fluctuation feature.
In some embodiments, the second sample data set includes historical sample data and simulated sample data of the second inverter.
In some embodiments, the second processing module 430 is configured to perform data processing on the target maximum power point tracking voltage and the target active power according to a target data processing policy, so as to obtain a target operation fluctuation feature;
the first operation fluctuation feature is obtained according to a target data processing strategy based on the first maximum power point tracking voltage and the first active power, and the second operation fluctuation feature is obtained according to the target data processing strategy based on the second maximum power point tracking voltage and the second active power.
In some embodiments, an acquisition module 410 for acquiring station irradiation data for a target date;
determining a first-order difference of irradiation data of a target date based on station irradiation data of the target date;
in the case where the irradiation data first order difference of the target date is determined to be less than or equal to the target threshold value, the target date is determined to be the diagnosis date.
The fault diagnosis device of the photovoltaic inverter fan in the embodiment of the application may be an electronic device, or may be a component in the electronic device, for example, an integrated circuit or a chip. The electronic device may be a terminal, or may be other devices than a terminal. By way of example, the electronic device may be a mobile phone, tablet computer, notebook computer, palm computer, vehicle-mounted electronic device, mobile internet appliance (Mobile Internet Device, MID), augmented reality (augmented reality, AR)/Virtual Reality (VR) device, robot, wearable device, ultra-mobile personal computer, UMPC, netbook or personal digital assistant (personal digital assistant, PDA), etc., but may also be a server, network attached storage (Network Attached Storage, NAS), personal computer (personal computer, PC), television (TV), teller machine or self-service machine, etc., and the embodiments of the present application are not limited in particular.
The fault diagnosis device for the photovoltaic inverter fan in the embodiment of the present application may be a device having an operating system. The operating system may be an Android operating system, an IOS operating system, or other possible operating systems, which is not specifically limited in the embodiments of the present application.
The fault diagnosis device for a photovoltaic inverter fan provided in the embodiment of the present application can implement each process implemented by the method embodiments of fig. 1 to 3, and in order to avoid repetition, a detailed description is omitted here.
In some embodiments, as shown in fig. 5, the embodiment of the present application further provides an electronic device 500, including a processor 501, a memory 502, and a computer program stored in the memory 502 and capable of running on the processor 501, where the program when executed by the processor 501 implements the respective processes of the above-mentioned embodiments of the fault diagnosis method of the photovoltaic inverter fan, and the same technical effects can be achieved, so that repetition is avoided and no further description is given here.
The electronic device in the embodiment of the application includes the mobile electronic device and the non-mobile electronic device.
The embodiment of the application further provides a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the processes of the above embodiment of the fault diagnosis method for the photovoltaic inverter fan, and can achieve the same technical effects, so that repetition is avoided, and details are not repeated here.
The processor is a processor in the electronic device in the above embodiment. Readable storage media include computer readable storage media such as computer readable memory ROM, random access memory RAM, magnetic or optical disks, and the like.
The embodiment of the application also provides a computer program product, which comprises a computer program, and the computer program realizes the fault diagnosis method of the photovoltaic inverter fan when being executed by a processor.
The processor is a processor in the electronic device in the above embodiment. Readable storage media include computer readable storage media such as computer readable memory ROM, random access memory RAM, magnetic or optical disks, and the like.
The embodiment of the application further provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled with the processor, the processor is used for running a program or instructions, the processes of the embodiments of the fault diagnosis method of the photovoltaic inverter fan are realized, the same technical effects can be achieved, and in order to avoid repetition, the description is omitted here.
It should be understood that the chips referred to in the embodiments of the present application may also be referred to as system-on-chip chips, chip systems, or system-on-chip chips, etc.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. Furthermore, it should be noted that the scope of the methods and apparatus in the embodiments of the present application is not limited to performing the functions in the order shown or discussed, but may also include performing the functions in a substantially simultaneous manner or in an opposite order depending on the functions involved, e.g., the described methods may be performed in an order different from that described, and various steps may also be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solutions of the present application may be embodied essentially or in a part contributing to the prior art in the form of a computer software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the methods of the embodiments of the present application.
The embodiments of the present application have been described above with reference to the accompanying drawings, but the present application is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those of ordinary skill in the art without departing from the spirit of the present application and the scope of the claims, which are also within the protection of the present application.
In the description of the present specification, reference to the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the application, the scope of which is defined by the claims and their equivalents.
Claims (12)
1. A method for diagnosing faults of a photovoltaic inverter fan, comprising:
determining a target date as a diagnosis date, and acquiring a target maximum power point tracking voltage and a target active power of a target photovoltaic inverter to be diagnosed on the target date;
determining a target operational ripple characteristic of the target photovoltaic inverter based on the target maximum power point tracking voltage and the target active power;
comparing the target operation fluctuation characteristics with a first inverter model and a second inverter model, and determining a fan fault diagnosis result of the target photovoltaic inverter;
the first inverter model is used for representing a first operation fluctuation characteristic of a first inverter, the first inverter model is constructed based on a first sample data set of the first inverter on a diagnosis day, the first sample data set comprises a first maximum power point tracking voltage and a first active power, the first operation fluctuation characteristic is determined based on the first maximum power point tracking voltage and the first active power, and the first inverter represents an inverter with a fan operating normally;
the second inverter model is used for representing a second operation fluctuation characteristic of a second inverter, the second inverter model is constructed based on a second sample data set of the second inverter on a diagnosis day, the second sample data set comprises a second maximum power point tracking voltage and a second active power, the second operation fluctuation characteristic is determined based on the second maximum power point tracking voltage and the second active power, and the second inverter represents an inverter with a fan fault.
2. The method of claim 1, wherein comparing the target operational ripple characteristic with the first inverter model and the second inverter model, determining a fan failure diagnosis result of the target photovoltaic inverter comprises:
acquiring a first similarity of the target operation fluctuation feature and the first operation fluctuation feature, and acquiring a second similarity of the target operation fluctuation feature and the second operation fluctuation feature;
and determining that the fan of the target photovoltaic inverter fails under the condition that the second similarity is larger than the first similarity.
3. The method of claim 1, wherein determining a target operational ripple characteristic of the target photovoltaic inverter based on the target maximum power point tracking voltage and the target active power comprises:
obtaining a target operation trend sequence of the target photovoltaic inverter based on the target maximum power point tracking voltage and the target active power;
and performing word frequency-reverse file frequency vector conversion on the target operation trend sequence to obtain the target operation fluctuation feature.
4. The method for diagnosing a failure of a fan of a photovoltaic inverter according to claim 3, wherein said obtaining a target operational trend sequence of said target photovoltaic inverter based on said target maximum power point tracking voltage and said target active power comprises:
respectively carrying out normalization processing on the target maximum power point tracking voltage and the target active power to obtain a target maximum power point tracking voltage time sequence and a target active power time sequence;
and carrying out difference calculation on the target maximum power point tracking voltage time sequence and the target active power time sequence to obtain the target operation trend sequence.
5. The method for diagnosing a failure of a photovoltaic inverter fan according to claim 3, wherein said performing word frequency-reverse file frequency vector conversion on said target operation trend sequence to obtain said target operation fluctuation feature comprises:
sequentially performing dimension reduction, symbolization and discretization on the target operation trend sequence to obtain a target character string;
and performing word frequency-reverse file frequency vector conversion on the target character string to obtain the target operation fluctuation feature.
6. The method of diagnosing a failure of a photovoltaic inverter fan of any of claims 1-5, wherein the second sample data set includes historical sample data and simulated sample data of the second inverter.
7. The method of any one of claims 1-5, wherein the determining a target operational ripple characteristic of the target photovoltaic inverter based on the target maximum power point tracking voltage and the target active power comprises:
according to a target data processing strategy, carrying out data processing on the target maximum power point tracking voltage and the target active power to obtain the target operation fluctuation characteristic;
the first operation fluctuation feature is obtained according to the target data processing strategy based on the first maximum power point tracking voltage and the first active power, and the second operation fluctuation feature is obtained according to the target data processing strategy based on the second maximum power point tracking voltage and the second active power.
8. The method for diagnosing a failure of a photovoltaic inverter fan according to any one of claims 1 to 5, wherein the determination target date is a diagnosis date, comprising:
Acquiring station irradiation data of the target date;
determining a first-order difference of irradiation data of the target date based on station irradiation data of the target date;
and determining the target date as a diagnosis date under the condition that the first-order difference of the irradiation data of the target date is less than or equal to a target threshold value.
9. A fault diagnosis device for a photovoltaic inverter fan, comprising:
the acquisition module is used for determining a target date as a diagnosis date and acquiring a target maximum power point tracking voltage and target active power of a target photovoltaic inverter to be diagnosed on the target date;
the first processing module is used for determining target operation fluctuation characteristics of the target photovoltaic inverter based on the target maximum power point tracking voltage and the target active power;
the second processing module is used for comparing the target operation fluctuation characteristics with the first inverter model and the second inverter model and determining a fan fault diagnosis result of the target photovoltaic inverter;
the first inverter model is used for representing a first operation fluctuation characteristic of a first inverter, the first inverter model is constructed based on a first sample data set of the first inverter on a diagnosis day, the first sample data set comprises a first maximum power point tracking voltage and a first active power, the first operation fluctuation characteristic is determined based on the first maximum power point tracking voltage and the first active power, and the first inverter represents an inverter with a fan operating normally;
The second inverter model is used for representing a second operation fluctuation characteristic of a second inverter, the second inverter model is constructed based on a second sample data set of the second inverter on a diagnosis day, the second sample data set comprises a second maximum power point tracking voltage and a second active power, the second operation fluctuation characteristic is determined based on the second maximum power point tracking voltage and the second active power, and the second inverter represents an inverter with a fan fault.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of diagnosing a failure of a photovoltaic inverter fan according to any of claims 1-8 when executing the program.
11. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the method of fault diagnosis of a photovoltaic inverter fan according to any of claims 1-8.
12. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements a method for fault diagnosis of a photovoltaic inverter fan according to any of claims 1-8.
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