CN114825636A - Health state monitoring and warning system and method for photovoltaic inverter - Google Patents
Health state monitoring and warning system and method for photovoltaic inverter Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
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- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
- H02J13/00016—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using a wired telecommunication network or a data transmission bus
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Abstract
The invention provides a photovoltaic inverter health state monitoring and warning system and method, and relates to the field of photovoltaic inverter monitoring. The monitoring data of the photovoltaic inverter at a plurality of current moments are acquired and preprocessed through a preprocessing module; the data conversion module converts the preprocessed monitoring data into a two-dimensional image; the first anomaly detection module inputs the two-dimensional image into a preset photovoltaic inverter anomaly detection model; the similarity contrast module acquires and carries out similarity contrast on the real-time image of the photovoltaic inverter and a preset photovoltaic inverter reference image; the second anomaly detection module determines a second anomaly detection result; the service life prediction module inputs the first abnormal detection result and the second abnormal detection result into a preset service life prediction model of the photovoltaic inverter; the health state and alarm module generates a health state report and alarm information, so that the health state of the photovoltaic inverter can be judged in real time, accurately and effectively, and an alarm is given out to early warn.
Description
Technical Field
The invention relates to the field of photovoltaic inverter monitoring, in particular to a photovoltaic inverter health state monitoring and warning system and method.
Background
With the increasing shortage of human energy supply and demand, renewable energy application technology is continuously developed, and the application of new energy is more and more popularized. Solar energy is widely applied by the advantages of abundant resources, wide distribution, reproducibility, no pollution and the like, and particularly, the solar photovoltaic power generation technology develops fastest.
The photovoltaic inverter is a very important component in a photovoltaic power generation system, and the health state of the photovoltaic inverter directly affects the safe and stable operation of a power system. At present, a photovoltaic inverter generally adopts a maintenance mode of regular fixed-point maintenance and maintenance after a fault occurs. Operation and maintenance personnel hardly have a way to know the real-time health state of the photovoltaic inverter and cannot early warn faults in advance.
Disclosure of Invention
The invention aims to provide a photovoltaic inverter health state monitoring and warning system and method, which are used for solving the problems that operation and maintenance personnel in the prior art hardly know the real-time health state of a photovoltaic inverter and cannot early warn faults in advance.
In a first aspect, an embodiment of the present application provides a photovoltaic inverter health status monitoring and warning system, including:
the preprocessing module is used for acquiring and preprocessing the monitoring data of the photovoltaic inverter at a plurality of current moments to generate preprocessed monitoring data;
the data conversion module is used for converting the preprocessed monitoring data into a two-dimensional image by utilizing a gram angular field;
the first anomaly detection module is used for inputting the two-dimensional image into a preset photovoltaic inverter anomaly detection model to obtain a first anomaly detection result;
the similarity comparison module is used for obtaining and comparing the similarity of the real-time image of the photovoltaic inverter with a preset photovoltaic inverter reference image to generate an image similarity result;
the second anomaly detection module is used for determining a second anomaly detection result according to the image similarity result and a preset anomaly judgment rule;
the service life prediction module is used for inputting the first abnormal detection result and the second abnormal detection result into a preset service life prediction model of the photovoltaic inverter to generate a service life prediction result;
and the health state and alarm module is used for generating a health state report according to the life prediction result and generating alarm information according to the life prediction result.
In the implementation process, the monitoring data of the photovoltaic inverter at a plurality of current moments are acquired and preprocessed by the preprocessing module to generate preprocessed monitoring data; then the data conversion module converts the preprocessed monitoring data into a two-dimensional image by utilizing a gram angular field; the first anomaly detection module inputs the two-dimensional image into a preset photovoltaic inverter anomaly detection model to obtain a first anomaly detection result, so that whether the current internal operation of the photovoltaic inverter is abnormal or not can be detected; the similarity comparison module acquires and compares the real-time image of the photovoltaic inverter with a preset photovoltaic inverter reference image to generate an image similarity result; the second anomaly detection module determines a second anomaly detection result according to the image similarity result and a preset anomaly judgment rule, so that whether the current exterior of the photovoltaic inverter is abnormal or not can be detected; the service life prediction module inputs the first abnormal detection result and the second abnormal detection result into a preset service life prediction model of the photovoltaic inverter to generate a service life prediction result; the health state and alarm module generates a health state report according to a life prediction result, generates alarm information according to the life prediction result, and comprehensively evaluates the health state through the internal operation condition and the external condition of the photovoltaic inverter, so that the health state of the photovoltaic inverter can be judged in real time, accurately and effectively, and a warning is given out to warn operation and maintenance personnel in advance, so that the operation and maintenance personnel are helped to make an overhaul plan quickly, and the stable operation of the whole system is ensured.
Based on the first aspect, in some embodiments of the invention, the preprocessing module comprises:
the data acquisition unit is used for acquiring monitoring data of the photovoltaic inverter at a plurality of current moments;
the data filling unit is used for performing data filling on the monitoring data of the photovoltaic inverter at a plurality of current moments to generate initial preprocessing data;
and the data normalization unit is used for performing normalization processing on the initial preprocessing data to obtain preprocessing monitoring data.
Based on the first aspect, in some embodiments of the invention, the data normalization unit comprises:
and the normalization subunit is used for performing normalization processing on the initial preprocessing data by adopting Z-Score normalization to obtain preprocessing monitoring data.
Based on the first aspect, in some embodiments of the invention, the similarity contrast module comprises:
the sparse coding unit is used for respectively carrying out sparse coding on the real-time image of the photovoltaic inverter and a preset reference photovoltaic inverter image to generate a real-time image code and a reference image code;
and the similarity calculation unit is used for calculating the similarity between the real-time image of the photovoltaic inverter and a preset reference photovoltaic inverter image by using the Euclidean distance according to the real-time image code and the reference image code to obtain an image similarity result.
Based on the first aspect, in some embodiments of the present invention, the method further includes:
the historical data acquisition module is used for acquiring historical monitoring data of the photovoltaic inverters;
the historical data conversion module is used for converting historical monitoring data of the photovoltaic inverters into historical image data by utilizing the gram angle field;
and the anomaly detection model training module is used for generating a photovoltaic inverter anomaly detection model by adopting generation countermeasure network training according to historical image data.
Based on the first aspect, in some embodiments of the invention, the health status and alarm module includes:
the health report generating unit is used for generating a health state report according to the life prediction result;
and the alarm generation judging unit is used for judging whether the life prediction result is smaller than a preset threshold value, if so, generating alarm information, and if not, ending the process.
In a second aspect, an embodiment of the present application provides a method for monitoring and alarming a health state of a photovoltaic inverter, including the following steps:
acquiring and preprocessing monitoring data of the photovoltaic inverter at a plurality of current moments to generate preprocessed monitoring data;
converting the preprocessed monitoring data into a two-dimensional image by utilizing a gram angular field;
inputting the two-dimensional image into a preset photovoltaic inverter abnormity detection model to obtain a first abnormity detection result;
the method comprises the steps of obtaining and comparing the similarity of a real-time image of a photovoltaic inverter with a preset photovoltaic inverter reference image to generate an image similarity result;
determining a second abnormal detection result according to the image similarity result and a preset abnormal judgment rule;
inputting the first abnormal detection result and the second abnormal detection result into a preset photovoltaic inverter service life prediction model to generate a service life prediction result;
and generating a health state report according to the life prediction result, and generating alarm information according to the life prediction result.
In the implementation process, the monitoring data of the photovoltaic inverter at a plurality of current moments are obtained and preprocessed to generate preprocessed monitoring data; then, converting the preprocessed monitoring data into a two-dimensional image by utilizing a gram angular field; then, inputting the two-dimensional image into a preset photovoltaic inverter abnormity detection model to obtain a first abnormity detection result, so that whether the current internal operation of the photovoltaic inverter is abnormal or not can be detected; then, obtaining and comparing the similarity of the real-time image of the photovoltaic inverter with a preset photovoltaic inverter reference image to generate an image similarity result; determining a second abnormal detection result according to the image similarity result and a preset abnormal judgment rule, so that whether the current exterior of the photovoltaic inverter is abnormal or not can be detected; inputting the first abnormal detection result and the second abnormal detection result into a preset photovoltaic inverter service life prediction model to generate a service life prediction result; and finally, generating a health state report according to the life prediction result, generating alarm information according to the life prediction result, and comprehensively evaluating the health state through the internal operation condition and the external condition of the photovoltaic inverter, so that the health state of the photovoltaic inverter can be accurately and effectively judged in real time, and a warning is given out to warn operation and maintenance personnel in advance, so that the operation and maintenance personnel can be reminded to make a maintenance plan quickly, and the stable operation of the whole system is ensured.
Based on the second aspect, in some embodiments of the present invention, the monitoring data of the photovoltaic inverter at a plurality of current times are obtained and preprocessed, and the step of generating the preprocessed monitoring data includes the following steps:
acquiring monitoring data of the photovoltaic inverter at a plurality of current moments;
carrying out data filling on the monitoring data of the photovoltaic inverter at a plurality of current moments to generate initial preprocessing data;
and carrying out normalization processing on the initial preprocessing data to obtain preprocessing monitoring data.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory for storing one or more programs; a processor. The program or programs, when executed by a processor, implement the method of any of the second aspects as described above.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the method according to any one of the above second aspects.
The embodiment of the invention at least has the following advantages or beneficial effects:
the embodiment of the invention provides a photovoltaic inverter health state monitoring and warning system and method, wherein monitoring data of a photovoltaic inverter at a plurality of current moments are acquired and preprocessed through a preprocessing module to generate preprocessed monitoring data; then the data conversion module converts the preprocessed monitoring data into a two-dimensional image by utilizing a gram angular field; the first anomaly detection module inputs the two-dimensional image into a preset photovoltaic inverter anomaly detection model to obtain a first anomaly detection result, so that whether the current internal operation of the photovoltaic inverter is abnormal or not can be detected; the similarity comparison module acquires and compares the real-time image of the photovoltaic inverter with a preset photovoltaic inverter reference image to generate an image similarity result; the second anomaly detection module determines a second anomaly detection result according to the image similarity result and a preset anomaly judgment rule, so that whether the current exterior of the photovoltaic inverter is abnormal or not can be detected; the service life prediction module inputs the first abnormal detection result and the second abnormal detection result into a preset service life prediction model of the photovoltaic inverter to generate a service life prediction result; the health state and alarm module generates a health state report according to a life prediction result, generates alarm information according to the life prediction result, and comprehensively evaluates the health state through the internal operation condition and the external condition of the photovoltaic inverter, so that the health state of the photovoltaic inverter can be judged in real time, accurately and effectively, and a warning is given out to warn operation and maintenance personnel in advance, so that the operation and maintenance personnel are helped to make an overhaul plan quickly, and the stable operation of the whole system is ensured.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a block diagram of a health status monitoring and warning system of a photovoltaic inverter according to an embodiment of the present invention;
FIG. 2 is a block diagram of a preprocessing module according to an embodiment of the present invention;
fig. 3 is a flowchart of a method for monitoring and alarming a health status of a photovoltaic inverter according to an embodiment of the present invention;
FIG. 4 is a detailed step diagram of step S110 provided by the embodiment of the present invention;
fig. 5 is a block diagram of an electronic device according to an embodiment of the present invention.
Icon: 110-a pre-processing module; 120-a data conversion module; 130-a first anomaly detection module; 140-similarity contrast module; 150-a second anomaly detection module; 160-a life prediction module; 170-health status and alarm module; 101-a memory; 102-a processor; 103-communication interface.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. 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 application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, 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 an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Examples
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the individual features of the embodiments can be combined with one another without conflict.
Referring to fig. 1, fig. 1 is a block diagram of a health status monitoring and warning system for a photovoltaic inverter according to an embodiment of the present invention. This photovoltaic inverter health status monitoring and alarm system includes:
the preprocessing module 110 is configured to acquire and preprocess monitoring data of the photovoltaic inverter at multiple current moments to generate preprocessed monitoring data; the above-mentioned multiple current time instants may refer to multiple time instants in the current day, such as: the working time of the photovoltaic inverter is from seven am to six pm every day, and the monitoring data of the photovoltaic inverter is acquired once every 10 minutes, so that the monitoring data of a plurality of moments in the day can be obtained. The monitoring data of the photovoltaic inverter comprise three-phase voltage, three-phase current, inverter conversion efficiency, inverter power factor, input power and output power of the inverter and the like.
Referring to fig. 2, fig. 2 is a block diagram of a preprocessing module according to an embodiment of the present invention. In order to make the data easier to analyze and process, missing value supplement, normalization and other operations need to be performed on the monitored data, and the above preprocessing process can be performed by the following units:
the data acquisition unit is used for acquiring monitoring data of the photovoltaic inverter at a plurality of current moments; the above-mentioned acquisition may be obtained by the inverter monitoring system, or may be obtained by various sensors, such as the case temperature of the inverter.
The data filling unit is used for performing data filling on the monitoring data of the photovoltaic inverter at a plurality of current moments to generate initial preprocessing data; the data filling comprises missing value supplement, and the quality and the utilization rate of the monitoring data are improved. The data filling may be performed by using a linear interpolation method, or may be performed by using a random forest algorithm to fill missing monitoring data. The above linear interpolation method and the random forest algorithm are both prior art, and are not described herein again.
And the data normalization unit is used for performing normalization processing on the initial preprocessing data to obtain preprocessing monitoring data. Since the monitored data includes many parameters, and the unit or the representation mode of each parameter is different, in order to improve the operation efficiency, normalization processing is required to normalize the data. The normalization processing may be performed by adopting standard deviation normalization, or the normalization processing may be performed on the initial preprocessing data by adopting Z-Score normalization through a normalization subunit, so as to obtain the preprocessing monitoring data. The average value of the data after Z-Score processing is equal to 0, the standard deviation of the data is equal to 1, and the Z-Score processing can reflect that the initial preprocessed data has several standard deviations from the average value and also ensure the comparability of the data. The Z-Score normalization is well known in the art and will not be described herein.
A data conversion module 120 for converting the preprocessed monitoring data into a two-dimensional image using a gram angular field; the Gram Angular Field (GAF) represents the time series by a polar coordinate system instead of a cartesian coordinate system, and encodes the univariate time series data into an image by capturing autocorrelation. The preprocessed monitoring data can be converted into image sequence data representation by adopting a gram angular field, so that the abnormity detection can be carried out through images at a later stage. The above-mentioned conversion of time-series data into image data using the gram angle field is prior art and will not be described herein again.
The first anomaly detection module 130 is configured to input the two-dimensional image into a preset photovoltaic inverter anomaly detection model to obtain a first anomaly detection result; the preset photovoltaic inverter abnormity detection model is obtained by training a countermeasure network generated based on GAN, most of photovoltaic inverter monitoring data are normal data, the abnormal data are few, and the data sample distribution is extremely unbalanced, so the aim of abnormity detection is achieved by learning the normal data distribution by the countermeasure network. The photovoltaic inverter abnormality detection model can be obtained by training the following units:
the historical data acquisition module is used for acquiring historical monitoring data of the photovoltaic inverters; the historical monitoring data is used as sample data, wherein a time node t can be set, the historical monitoring data of the photovoltaic inverter in w days before the time t is used as a sample by the model, if the photovoltaic inverter fails in w time points, namely the operation data is abnormal or the photovoltaic inverter cannot normally operate, the historical monitoring data is defined as an abnormal sample, otherwise, the historical monitoring data is defined as a normal sample.
The historical data conversion module is used for converting historical monitoring data of the photovoltaic inverters into historical image data by utilizing the gram angle field; historical monitoring data can be converted into image sequence data representation by using the gram angle field, so that the training by using the generation countermeasure network can be conveniently carried out at the later stage.
And the anomaly detection model training module is used for generating a photovoltaic inverter anomaly detection model by adopting generation countermeasure network training according to historical image data. The generation countermeasure network is composed of a generator and a discriminator, the generator is mainly composed of an encoder-decoder-encoder network, and two mapping relations of the original image mapping reconstruction image and the encoding of the original image mapping reconstruction image are learned, so that the input image is mapped into a low-dimensional vector and reconstructed into a generated output image; the discriminator is formed by a network of encoders that map the images generated by the generator to potential vector representations. Through alternate training of the generator and the discriminator. In the stage of reasoning anomaly, because the learned data samples are normal samples, the difference between the potential vector obtained after the model network is trained under the normal samples and the potential vector obtained after the model network is subjected to the encoder-decoder-encoder network for the first time is not too large, but because the difference obtained by the abnormal samples through the two-time encoding is larger, when the difference between the potential vectors obtained through the two-time encoding is larger than a certain threshold value, the data can be judged to be anomalous, and the historical image data is trained to obtain the photovoltaic inverter anomaly detection model.
The similarity comparison module 140 is configured to obtain and perform similarity comparison between the real-time image of the photovoltaic inverter and a preset reference image of the photovoltaic inverter, and generate an image similarity result; the photovoltaic inverter reference image may be an image of the photovoltaic inverter when the photovoltaic inverter leaves a factory, or may be a real-time image of the photovoltaic inverter at the previous time. Whether the outside of the current photovoltaic inverter is abnormal or not can be judged by comparing the real-time images.
The similarity comparison can be obtained by Euclidean distance calculation, and comprises the following units:
the sparse coding unit is used for respectively carrying out sparse coding on the real-time image of the photovoltaic inverter and a preset reference photovoltaic inverter image to generate a real-time image code and a reference image code; the sparse coding described above is an unsupervised learning method that finds a set of "overcomplete" basis vectors to represent sample data more efficiently. The goal of the sparse coding algorithm is to find a set of basis vectors so that we can represent the input vector as a linear combination of these basis vectors. The above sparse coding belongs to the prior art, and is not described herein again.
And the similarity calculation unit is used for calculating the similarity between the real-time image of the photovoltaic inverter and a preset reference photovoltaic inverter image by using the Euclidean distance according to the real-time image code and the reference image code to obtain an image similarity result. Euclidean distance is the most common distance measurement, the absolute distance between each point in a multi-dimensional space is measured, the distance of two images existing in the space can be measured by calculating the Euclidean distance between real-time image coding and reference image coding, the farther the distance is, the larger the difference between the two images is, the lower the similarity is, otherwise, the higher the similarity is.
The second anomaly detection module 150 is configured to determine a second anomaly detection result according to the image similarity result and according to a preset anomaly judgment rule; the preset abnormality judgment rule refers to comparing the similarity result with a preset similarity threshold, wherein if the image similarity result exceeds the preset similarity threshold, the second abnormality detection result is abnormal, otherwise, the second abnormality detection result is normal. Whether the photovoltaic inverter is damaged or not can be judged through the second abnormal detection result.
The service life prediction module 160 is configured to input the first abnormal detection result and the second abnormal detection result into a preset service life prediction model of the photovoltaic inverter, and generate a service life prediction result; the preset photovoltaic inverter service life prediction model is obtained by training through a machine learning algorithm according to historical abnormal data, and the service life of the photovoltaic inverter can be comprehensively predicted through the first abnormal detection result and the second abnormal detection result.
And the health state and alarm module 170 is configured to generate a health state report according to the life prediction result, and generate alarm information according to the life prediction result. Wherein, include the following unit:
the health report generating unit is used for generating a health state report according to the life prediction result; the health state can be evaluated according to the life prediction result to obtain a corresponding health score, so that a health state report is formed, and the health state of the current photovoltaic inverter can be clearly and intuitively known through the health state report.
And the alarm generation judging unit is used for judging whether the life prediction result is smaller than a preset threshold value, if so, generating alarm information, and if not, ending the process. The preset threshold value can be set according to experience, and a user is reminded to overhaul the photovoltaic inverter by generating alarm information.
In the implementation process, the monitoring data of the photovoltaic inverter at a plurality of current moments are acquired and preprocessed by the preprocessing module 110 to generate preprocessed monitoring data; then the data conversion module 120 converts the preprocessed monitoring data into a two-dimensional image using a gram angular field; the first anomaly detection module 130 inputs the two-dimensional image into a preset photovoltaic inverter anomaly detection model to obtain a first anomaly detection result, so that whether the current internal operation of the photovoltaic inverter is abnormal or not can be detected; the similarity comparison module 140 obtains and compares the real-time image of the photovoltaic inverter with a preset reference image of the photovoltaic inverter to generate an image similarity result; the second anomaly detection module 150 determines a second anomaly detection result according to the image similarity result and a preset anomaly judgment rule, so that whether the current exterior of the photovoltaic inverter is abnormal or not can be detected; the service life prediction module 160 inputs the first abnormal detection result and the second abnormal detection result into a preset service life prediction model of the photovoltaic inverter to generate a service life prediction result; health status and warning module 170 generates the health status report according to the life prediction result, and generates warning information according to the life prediction result, and through the comprehensive evaluation of the health status from the internal operation condition and the external condition of the photovoltaic inverter, the health status of the photovoltaic inverter can be judged in real time, accurately and effectively, and a warning is given out, so that early warning is given in advance, operation and maintenance personnel are reminded, and the operation and maintenance personnel are helped to make maintenance plans quickly, and the stable operation of the whole system is guaranteed.
Based on the same inventive concept, the invention further provides a photovoltaic inverter health state monitoring and warning method, please refer to fig. 3, and fig. 3 is a flowchart of a photovoltaic inverter health state monitoring and warning method provided by an embodiment of the invention. The photovoltaic inverter health state monitoring and alarming method comprises the following steps:
step S110: acquiring and preprocessing monitoring data of the photovoltaic inverter at a plurality of current moments to generate preprocessed monitoring data;
step S120: converting the preprocessed monitoring data into a two-dimensional image by utilizing a gram angular field;
step S130: inputting the two-dimensional image into a preset photovoltaic inverter abnormity detection model to obtain a first abnormity detection result;
step S140: obtaining and comparing the similarity of a real-time image of the photovoltaic inverter with a preset photovoltaic inverter reference image to generate an image similarity result;
step S150: determining a second abnormal detection result according to the image similarity result and a preset abnormal judgment rule;
step S160: inputting the first abnormal detection result and the second abnormal detection result into a preset photovoltaic inverter service life prediction model to generate a service life prediction result;
step S170: and generating a health state report according to the life prediction result, and generating alarm information according to the life prediction result.
In the implementation process, the monitoring data of the photovoltaic inverter at a plurality of current moments are obtained and preprocessed to generate preprocessed monitoring data; then, converting the preprocessed monitoring data into a two-dimensional image by utilizing a gram angular field; then, inputting the two-dimensional image into a preset photovoltaic inverter abnormity detection model to obtain a first abnormity detection result, so that whether the current internal operation of the photovoltaic inverter is abnormal or not can be detected; then, obtaining and comparing the similarity of the real-time image of the photovoltaic inverter with a preset photovoltaic inverter reference image to generate an image similarity result; determining a second abnormal detection result according to the image similarity result and a preset abnormal judgment rule, so that whether the current exterior of the photovoltaic inverter is abnormal or not can be detected; inputting the first abnormal detection result and the second abnormal detection result into a preset photovoltaic inverter service life prediction model to generate a service life prediction result; and finally, generating a health state report according to the life prediction result, generating alarm information according to the life prediction result, and comprehensively evaluating the health state through the internal operation condition and the external condition of the photovoltaic inverter, so that the health state of the photovoltaic inverter can be accurately and effectively judged in real time, and a warning is given out to warn operation and maintenance personnel in advance, so that the operation and maintenance personnel can be reminded to make a maintenance plan quickly, and the stable operation of the whole system is ensured.
Referring to fig. 4, fig. 4 is a detailed step diagram of step S110 according to an embodiment of the present invention. The method comprises the following steps of obtaining and preprocessing monitoring data of the photovoltaic inverter at a plurality of current moments, and generating the preprocessed monitoring data:
firstly, acquiring monitoring data of the photovoltaic inverter at a plurality of current moments;
then, performing data filling on the monitoring data of the photovoltaic inverter at a plurality of current moments to generate initial preprocessing data;
then, normalization processing is carried out on the initial preprocessing data to obtain preprocessing monitoring data.
Referring to fig. 5, fig. 5 is a schematic structural block diagram of an electronic device according to an embodiment of the present disclosure. The electronic device comprises a memory 101, a processor 102 and a communication interface 103, wherein the memory 101, the processor 102 and the communication interface 103 are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 101 may be configured to store software programs and modules, such as program instructions/modules corresponding to the photovoltaic inverter health monitoring and warning system provided in the embodiments of the present application, and the processor 102 executes various functional applications and data processing by executing the software programs and modules stored in the memory 101. The communication interface 103 may be used for communicating signaling or data with other node devices.
The Memory 101 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The processor 102 may be an integrated circuit chip having signal processing capabilities. The Processor 102 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
It will be appreciated that the configuration shown in fig. 5 is merely illustrative and that the electronic device may include more or fewer components than shown in fig. 5 or have a different configuration than shown in fig. 5. The components shown in fig. 5 may be implemented in hardware, software, or a combination thereof.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules 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 application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including 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 application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (10)
1. A photovoltaic inverter health status monitoring and warning system characterized by comprising:
the preprocessing module is used for acquiring and preprocessing the monitoring data of the photovoltaic inverter at a plurality of current moments to generate preprocessed monitoring data;
the data conversion module is used for converting the preprocessed monitoring data into a two-dimensional image by utilizing a gram angular field;
the first anomaly detection module is used for inputting the two-dimensional image into a preset photovoltaic inverter anomaly detection model to obtain a first anomaly detection result;
the similarity comparison module is used for obtaining and comparing the similarity of the real-time image of the photovoltaic inverter with a preset photovoltaic inverter reference image to generate an image similarity result;
the second anomaly detection module is used for determining a second anomaly detection result according to the image similarity result and a preset anomaly judgment rule;
the service life prediction module is used for inputting the first abnormal detection result and the second abnormal detection result into a preset service life prediction model of the photovoltaic inverter to generate a service life prediction result;
and the health state and alarm module is used for generating a health state report according to the life prediction result and generating alarm information according to the life prediction result.
2. The pv inverter health monitoring and alert system of claim 1, wherein the pre-processing module comprises:
the data acquisition unit is used for acquiring monitoring data of the photovoltaic inverter at a plurality of current moments;
the data filling unit is used for performing data filling on the monitoring data of the photovoltaic inverter at a plurality of current moments to generate initial preprocessing data;
and the data normalization unit is used for performing normalization processing on the initial preprocessing data to obtain preprocessing monitoring data.
3. The pv inverter health monitoring and warning system of claim 2, wherein the data normalization unit comprises:
and the normalization subunit is used for performing normalization processing on the initial preprocessing data by adopting Z-Score normalization to obtain preprocessing monitoring data.
4. The pv inverter health monitoring and alert system of claim 1, wherein the similarity comparison module comprises:
the sparse coding unit is used for respectively carrying out sparse coding on the real-time image of the photovoltaic inverter and a preset reference photovoltaic inverter image to generate a real-time image code and a reference image code;
and the similarity calculation unit is used for calculating the similarity between the real-time image of the photovoltaic inverter and a preset reference photovoltaic inverter image by using the Euclidean distance according to the real-time image code and the reference image code to obtain an image similarity result.
5. The pv inverter health monitoring and alert system of claim 1, further comprising:
the historical data acquisition module is used for acquiring historical monitoring data of the photovoltaic inverters;
the historical data conversion module is used for converting historical monitoring data of the photovoltaic inverters into historical image data by utilizing the gram angle field;
and the anomaly detection model training module is used for generating a photovoltaic inverter anomaly detection model by adopting generation countermeasure network training according to historical image data.
6. The pv inverter health monitoring and alert system of claim 1, wherein the health and alert module comprises:
the health report generating unit is used for generating a health state report according to the life prediction result;
and the alarm generation judging unit is used for judging whether the life prediction result is smaller than a preset threshold value, if so, generating alarm information, and if not, ending the process.
7. A health state monitoring and alarming method for a photovoltaic inverter is characterized by comprising the following steps:
acquiring and preprocessing monitoring data of the photovoltaic inverter at a plurality of current moments to generate preprocessed monitoring data;
converting the preprocessed monitoring data into a two-dimensional image by utilizing a gram angular field;
inputting the two-dimensional image into a preset photovoltaic inverter abnormity detection model to obtain a first abnormity detection result;
obtaining and comparing the similarity of a real-time image of the photovoltaic inverter with a preset photovoltaic inverter reference image to generate an image similarity result;
determining a second abnormal detection result according to the image similarity result and a preset abnormal judgment rule;
inputting the first abnormal detection result and the second abnormal detection result into a preset photovoltaic inverter service life prediction model to generate a service life prediction result;
and generating a health state report according to the life prediction result, and generating alarm information according to the life prediction result.
8. The pv inverter health monitoring and warning method of claim 7, wherein the step of obtaining and pre-processing the monitoring data of the pv inverter at a plurality of current times to generate pre-processed monitoring data comprises the steps of:
acquiring monitoring data of the photovoltaic inverter at a plurality of current moments;
carrying out data filling on the monitoring data of the photovoltaic inverter at a plurality of current moments to generate initial preprocessing data;
and carrying out normalization processing on the initial preprocessing data to obtain preprocessing monitoring data.
9. An electronic device, comprising:
a memory for storing one or more programs;
a processor;
the one or more programs, when executed by the processor, implement the method of any of claims 7-8.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 7-8.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115829168A (en) * | 2023-02-16 | 2023-03-21 | 苏州洪昇新能源科技有限公司 | Remote coordination operation and maintenance management system for new energy equipment based on data analysis |
| CN115864995A (en) * | 2023-02-16 | 2023-03-28 | 东方电气集团科学技术研究院有限公司 | Inverter conversion efficiency diagnosis method and device based on big data mining |
| CN116191680A (en) * | 2023-04-25 | 2023-05-30 | 丰德信电力发展股份有限公司 | Monitoring management system applied to photovoltaic power generation |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104734362A (en) * | 2015-04-07 | 2015-06-24 | 上海理工大学 | Photovoltaic inverter health status monitoring and alarm system |
| CN109844667A (en) * | 2017-05-31 | 2019-06-04 | 伸和控制工业股份有限公司 | State monitoring apparatus, state monitoring method and program |
| CN111902712A (en) * | 2018-03-29 | 2020-11-06 | 三菱电机株式会社 | Abnormality detection device and abnormality detection method |
| CN111914920A (en) * | 2020-07-24 | 2020-11-10 | 山东工商学院 | Sparse coding-based similarity image retrieval method and system |
| CN113008583A (en) * | 2020-11-10 | 2021-06-22 | 大连理工大学 | Method and device for monitoring state and automatically alarming abnormality of rotating machine |
| CN113092900A (en) * | 2021-03-22 | 2021-07-09 | 阳光新能源开发有限公司 | State detection method and device of photovoltaic inverter and computer readable storage medium |
| CN113673564A (en) * | 2021-07-16 | 2021-11-19 | 深圳供电局有限公司 | Electricity stealing sample generation method and device, computer equipment and storage medium |
| CN114238692A (en) * | 2022-02-23 | 2022-03-25 | 北京嘉沐安科技有限公司 | Network live broadcast-oriented video big data accurate retrieval method and system |
-
2022
- 2022-05-26 CN CN202210582855.4A patent/CN114825636A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104734362A (en) * | 2015-04-07 | 2015-06-24 | 上海理工大学 | Photovoltaic inverter health status monitoring and alarm system |
| CN109844667A (en) * | 2017-05-31 | 2019-06-04 | 伸和控制工业股份有限公司 | State monitoring apparatus, state monitoring method and program |
| CN111902712A (en) * | 2018-03-29 | 2020-11-06 | 三菱电机株式会社 | Abnormality detection device and abnormality detection method |
| CN111914920A (en) * | 2020-07-24 | 2020-11-10 | 山东工商学院 | Sparse coding-based similarity image retrieval method and system |
| CN113008583A (en) * | 2020-11-10 | 2021-06-22 | 大连理工大学 | Method and device for monitoring state and automatically alarming abnormality of rotating machine |
| CN113092900A (en) * | 2021-03-22 | 2021-07-09 | 阳光新能源开发有限公司 | State detection method and device of photovoltaic inverter and computer readable storage medium |
| CN113673564A (en) * | 2021-07-16 | 2021-11-19 | 深圳供电局有限公司 | Electricity stealing sample generation method and device, computer equipment and storage medium |
| CN114238692A (en) * | 2022-02-23 | 2022-03-25 | 北京嘉沐安科技有限公司 | Network live broadcast-oriented video big data accurate retrieval method and system |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115829168A (en) * | 2023-02-16 | 2023-03-21 | 苏州洪昇新能源科技有限公司 | Remote coordination operation and maintenance management system for new energy equipment based on data analysis |
| CN115864995A (en) * | 2023-02-16 | 2023-03-28 | 东方电气集团科学技术研究院有限公司 | Inverter conversion efficiency diagnosis method and device based on big data mining |
| CN115829168B (en) * | 2023-02-16 | 2023-09-08 | 长峡数字能源科技(湖北)有限公司 | New energy equipment remote coordination operation and maintenance management system based on data analysis |
| CN116191680A (en) * | 2023-04-25 | 2023-05-30 | 丰德信电力发展股份有限公司 | Monitoring management system applied to photovoltaic power generation |
| CN116191680B (en) * | 2023-04-25 | 2023-07-18 | 丰德信电力发展股份有限公司 | Monitoring management system applied to photovoltaic power generation |
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