Photovoltaic station equipment intelligent fault diagnosis method and device based on deep learning

Abstract

The invention discloses a photovoltaic station equipment intelligent fault diagnosis method and device based on deep learning. The method comprises the following steps: acquiring original information and image information of equipment; generating matched image data from the image information through an image optimization model; matching and generating fault point location information by utilizing the matched image data and the equipment original information; and inputting the fault point location information into an intelligent fault analysis model to obtain a fault judgment result. According to the invention, the technical effect that the fault diagnosis process of the photovoltaic station equipment in the prior art is realized only through a manual or simple judgment rule judgment mode when the photovoltaic station equipment has a fault is solved; the technical problem that fault problems and analysis results cannot be accurately and rapidly judged when the scale is large and fault parameters are complex and diversified is solved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent fault judgment for photovoltaic power stations, and more particularly, to an intelligent fault diagnosis method and device for photovoltaic power station equipment based on deep learning. Background Art

[0002] With the continuous development of intelligent technology, intelligent devices are increasingly used in people's lives, work, and study. Using intelligent technology means has improved the quality of people's lives and increased the efficiency of people's study and work.

[0003] Currently, for the process of judging equipment faults in photovoltaic power stations, manual fault result judgment is usually carried out by using fault instruments or equipment fault parameters, or existing judgment rules are applied for judgment output. However, in the prior art, the fault diagnosis process when faults occur in photovoltaic power station equipment only achieves the technical effect of fault judgment through manual or simple judgment rules, and it is impossible to accurately and quickly judge fault problems and analysis results when the scale is large and the fault parameters are complex and diverse.

[0004] For the above problems, no effective solution has been proposed yet. Summary of the Invention

[0005] Embodiments of the present invention provide an intelligent fault diagnosis method and device for photovoltaic power station equipment based on deep learning, so as to at least solve the technical problem that in the prior art, the fault diagnosis process when faults occur in photovoltaic power station equipment only achieves the technical effect of fault judgment through manual or simple judgment rules, and it is impossible to accurately and quickly judge fault problems and analysis results when the scale is large and the fault parameters are complex and diverse.

[0006] According to one aspect of the embodiments of the present invention, an intelligent fault diagnosis method for photovoltaic power station equipment based on deep learning is provided, including: collecting original equipment information and image information; generating matching image data by passing the image information through an image optimization model; using the matching image data and the original equipment information to match and generate fault location information; inputting the fault location information into an intelligent fault analysis model to obtain a fault judgment result.

[0007] Optionally, the generating matching image data by passing the image information through an image optimization model includes: inputting the image information into the redundant layer of the image optimization model to obtain first image optimization data; inputting the first image optimization data into the noise reduction layer of the image optimization model to obtain the matching image data.

[0008] Optionally, the matching and generating of the fault location information by using the matching image data and the original device information includes: fitting the original device information and the matching image data to obtain the fault location information, where the fault location information includes: fault local image data and fault local parameter data.

[0009] Optionally, before inputting the fault location information into the intelligent fault analysis model to obtain a fault judgment result, the method further includes: constructing the intelligent fault analysis model according to fault historical data.

[0010] According to another aspect of the embodiments of the present invention, there is also provided an intelligent fault diagnosis device for photovoltaic power station equipment based on deep learning, including: an acquisition module, configured to acquire original device information and image information; a generation module, configured to generate matching image data by passing the image information through an image optimization model; a matching module, configured to match and generate fault location information by using the matching image data and the original device information; and an input module, configured to input the fault location information into the intelligent fault analysis model to obtain a fault judgment result.

[0011] Optionally, the generation module includes: a redundancy unit, configured to input the image information into the redundancy layer of the image optimization model to obtain first image optimization data; and a noise reduction unit, configured to input the first image optimization data into the noise reduction layer of the image optimization model to obtain the matching image data.

[0012] Optionally, the matching module includes: a fitting unit, configured to fit the original device information and the matching image data to obtain the fault location information, where the fault location information includes: fault local image data and fault local parameter data.

[0013] Optionally, the device further includes: a construction module, configured to construct the intelligent fault analysis model according to fault historical data.

[0014] According to another aspect of the embodiments of the present invention, there is also provided a non-volatile storage medium, where the non-volatile storage medium includes a stored program, and when the program runs, it controls a device where the non-volatile storage medium is located to execute an intelligent fault diagnosis method for photovoltaic power station equipment based on deep learning.

[0015] According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a processor and a memory; computer-readable instructions are stored in the memory, and the processor is configured to run the computer-readable instructions, where when the computer-readable instructions run, they execute an intelligent fault diagnosis method for photovoltaic power station equipment based on deep learning.

[0016] In the embodiments of the present invention, the original information and image information of the acquisition device are adopted; the image information is passed through an image optimization model to generate matching image data; the matching image data and the original device information are used to match and generate fault location information; the fault location information is input into an intelligent fault analysis model to obtain a fault judgment result, which solves the technical problem that in the prior art, the fault diagnosis process when a photovoltaic power station device fails is only judged by manual or simple judgment rules, and it is impossible to accurately and quickly judge the fault problem and analysis result when the scale is large and the fault parameters are complex and diverse. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 is a flowchart of an intelligent fault diagnosis method for photovoltaic power station devices based on deep learning according to an embodiment of the present invention;

[0019] Figure 2 is a structural block diagram of an intelligent fault diagnosis device for photovoltaic power station devices based on deep learning according to an embodiment of the present invention;

[0020] Figure 3 is a block diagram of a terminal device for executing the method according to the present invention according to an embodiment of the present invention;

[0021] Figure 4 is a storage unit for holding or carrying program code for implementing the method according to the present invention according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0023] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0024] According to an embodiment of the present invention, a method embodiment of an intelligent fault diagnosis method for photovoltaic power station equipment based on deep learning is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0025] Embodiment 1

[0026] Figure 1 is a flowchart of an intelligent fault diagnosis method for photovoltaic power station equipment based on deep learning according to an embodiment of the present invention, as Figure 1 shown, the method includes the following steps:

[0027] Step S102, collect the original information and image information of the equipment.

[0028] Step S104, pass the image information through an image optimization model to generate matching image data.

[0029] Step S106, use the matching image data and the original information of the equipment to match and generate fault location information.

[0030] Step S108, input the fault location information into an intelligent fault analysis model to obtain a fault judgment result.

[0031] Optionally, passing the image information through an image optimization model to generate matching image data includes: inputting the image information into the redundant layer of the image optimization model to obtain first image optimization data; inputting the first image optimization data into the noise reduction layer of the image optimization model to obtain the matching image data.

[0032] Optionally, the matching and generating of the fault location information by using the matching image data and the original device information includes: fitting the original device information and the matching image data to obtain the fault location information, where the fault location information includes: fault local image data and fault local parameter data.

[0033] Optionally, before inputting the fault location information into the intelligent fault analysis model to obtain a fault judgment result, the method further includes: constructing the intelligent fault analysis model according to fault historical data.

[0034] Through the above embodiments, the technical problem in the prior art that the fault diagnosis process when a photovoltaic power station device fails is only judged by manual or simple judgment rules to achieve fault judgment, and it is impossible to accurately and quickly judge the fault problem and analysis result when the scale is large and the fault parameters are complex and diverse is solved.

[0035] Embodiment 2

[0036] Figure 2 is a structural block diagram of an intelligent fault diagnosis device for photovoltaic power station equipment according to an embodiment of the present invention, as Figure 2 shown, the device includes:

[0037] An acquisition module 20, configured to acquire original device information and image information.

[0038] A generation module 22, configured to generate matching image data by passing the image information through an image optimization model.

[0039] A matching module 24, configured to match and generate fault location information by using the matching image data and the original device information.

[0040] An input module 26, configured to input the fault location information into an intelligent fault analysis model to obtain a fault judgment result.

[0041] Optionally, the generation module includes: a redundancy unit, configured to input the image information into a redundancy layer of the image optimization model to obtain first image optimization data; a noise reduction unit, configured to input the first image optimization data into a noise reduction layer of the image optimization model to obtain the matching image data.

[0042] Optionally, the matching module includes: a fitting unit, configured to fit the original device information and the matching image data to obtain the fault location information, where the fault location information includes: fault local image data and fault local parameter data.

[0043] Optionally, the device further includes: a construction module, configured to construct the intelligent fault analysis model according to the fault history data.

[0044] Through the above embodiments, the technical problem in the prior art that the fault diagnosis process when a fault occurs in a photovoltaic power station device is only judged by manual or simple judgment rules to achieve the technical effect of fault judgment is solved, and the technical problem that it is impossible to accurately and quickly judge the fault problem and analysis result when the scale is large and the fault parameters are complex and diverse is solved.

[0045] According to another aspect of the embodiments of the present invention, there is also provided a non-volatile storage medium, where the non-volatile storage medium includes a stored program, and when the program runs, it controls a device where the non-volatile storage medium is located to execute an intelligent fault diagnosis method for photovoltaic power station devices based on deep learning.

[0046] Specifically, the above method includes: collecting device original information and image information; passing the image information through an image optimization model to generate matching image data; using the matching image data and the device original information to match and generate fault location information; inputting the fault location information into the intelligent fault analysis model to obtain a fault judgment result. Optionally, the passing the image information through an image optimization model to generate matching image data includes: inputting the image information into a redundancy layer of the image optimization model to obtain first image optimization data; inputting the first image optimization data into a noise reduction layer of the image optimization model to obtain the matching image data. Optionally, the using the matching image data and the device original information to match and generate fault location information includes: fitting the device original information and the matching image data to obtain the fault location information, where the fault location information includes: fault local image data, fault local parameter data. Optionally, before inputting the fault location information into the intelligent fault analysis model to obtain a fault judgment result, the method further includes: constructing the intelligent fault analysis model according to the fault history data.

[0047] According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a processor and a memory; the memory stores computer-readable instructions, and the processor is configured to run the computer-readable instructions, where when the computer-readable instructions run, they execute an intelligent fault diagnosis method for photovoltaic power station devices based on deep learning.

[0048] Specifically, the above method includes: collecting original device information and image information; generating matching image data by passing the image information through an image optimization model; using the matching image data and the original device information to match and generate fault location information; and inputting the fault location information into an intelligent fault analysis model to obtain a fault judgment result. Optionally, the step of generating matching image data by passing the image information through an image optimization model includes: inputting the image information into the redundant layer of the image optimization model to obtain first image optimization data; and inputting the first image optimization data into the noise reduction layer of the image optimization model to obtain the matching image data. Optionally, the step of using the matching image data and the original device information to match and generate fault location information includes: fitting the original device information and the matching image data to obtain the fault location information, where the fault location information includes: fault local image data and fault local parameter data. Optionally, before inputting the fault location information into the intelligent fault analysis model to obtain a fault judgment result, the method further includes: constructing the intelligent fault analysis model according to fault historical data.

[0049] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0050] In the above embodiments of the present invention, the descriptions of the various embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0051] In the several embodiments provided in the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the units or modules can be in electrical or other forms.

[0052] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0053] In addition, Figure 3 is a schematic hardware structure diagram of a terminal device provided in an embodiment of the present application. As Figure 3As shown in the figure, the terminal device may include an input device 30, a processor 31, an output device 32, a memory 33, and at least one communication bus 34. The communication bus 34 is used to implement communication connections between components. The memory 33 may include high-speed RAM memory, and may also include non-volatile storage NVM, such as at least one disk memory. Various programs can be stored in the memory 33 to complete various processing functions and implement the method steps of this embodiment.

[0054] Optionally, the above-mentioned processor 31 can be implemented, for example, as a central processing unit (CPU), an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field-programmable gate array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components. The processor 31 is coupled to the above-mentioned input device 30 and output device 32 through a wired or wireless connection.

[0055] Optionally, the above-mentioned input device 30 can include various input devices. For example, it can include at least one of a user interface for users, a device interface for devices, a programmable interface for software, a camera, and a sensor. Optionally, the device interface for devices can be a wired interface for data transmission between devices, or a hardware insertion interface for data transmission between devices (such as a USB interface, a serial port, etc.); Optionally, the user interface for users can be, for example, control buttons for users, a voice input device for receiving voice input, and a touch sensing device for users to receive user touch input (such as a touch screen with touch sensing function, a touchpad, etc.); Optionally, the above-mentioned programmable interface for software can be, for example, an entry for users to edit or modify programs, such as an input pin interface or an input interface of a chip, etc.; Optionally, the above-mentioned transceiver can be a radio frequency transceiver chip with communication functions, a baseband processing chip, and transceiver antennas, etc. Audio input devices such as microphones can receive voice data. The output device 32 can include output devices such as a display and a speaker.

[0056] In this embodiment, the processor of the terminal device includes functions for executing each module of the data processing device in each device. The specific functions and technical effects can be referred to the above-mentioned embodiments, and will not be elaborated here.

[0057] Figure 4 This is a schematic diagram of the hardware structure of the terminal device provided in another embodiment of this application. Figure 4 is a pair of Figure 3 In a specific embodiment during the implementation process. As Figure 4 shown, the terminal device of this embodiment includes a processor 41 and a memory 42.

[0058] The processor 41 executes the computer program code stored in the memory 42 to implement the method in the above embodiments.

[0059] The memory 42 is configured to store various types of data to support the operation of the terminal device. Examples of such data include instructions for any application or method operating on the terminal device, such as messages, pictures, videos, etc. The memory 42 may include a random access memory (RAM), and may also include non-volatile memory, such as at least one disk memory.

[0060] Optionally, the processor 41 is disposed in the processing component 40. The terminal device may further include: a communication component 43, a power component 44, a multimedia component 45, an audio component 46, an input / output interface 47, and / or a sensor component 48. The specific components included in the terminal device are set according to actual requirements, and this embodiment does not limit this.

[0061] The processing component 40 generally controls the overall operation of the terminal device. The processing component 40 may include one or more processors 41 to execute instructions to complete all or part of the steps of the above method. In addition, the processing component 40 may include one or more modules to facilitate the interaction between the processing component 40 and other components. For example, the processing component 40 may include a multimedia module to facilitate the interaction between the multimedia component 45 and the processing component 40.

[0062] The power component 44 provides power for various components of the terminal device. The power component 44 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the terminal device.

[0063] The multimedia component 45 includes a display screen that provides an output interface between the terminal device and the user. In some embodiments, the display screen may include a liquid crystal display (LCD) and a touch panel (TP). If the display screen includes a touch panel, the display screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors can not only sense the boundaries of touch or swipe actions, but also detect the duration and pressure associated with the touch or swipe operation.

[0064] The audio component 46 is configured to output and / or input audio signals. For example, the audio component 46 includes a microphone (MIC), which is configured to receive external audio signals when the terminal device is in an operating mode, such as a voice recognition mode. The received audio signals can be further stored in the memory 42 or transmitted via the communication component 43. In some embodiments, the audio component 46 further includes a speaker for outputting audio signals.

[0065] The input / output interface 47 provides an interface between the processing component 40 and a peripheral interface module, and the peripheral interface module can be a click wheel, buttons, etc. These buttons can include, but are not limited to: volume buttons, start buttons, and lock buttons.

[0066] The sensor component 48 includes one or more sensors for providing a status assessment of various aspects of the terminal device. For example, the sensor component 48 can detect the on / off state of the terminal device, the relative positioning of components, the presence or absence of user contact with the terminal device. The sensor component 48 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact, including detecting the distance between the user and the terminal device. In some embodiments, the sensor component 48 can further include a camera, etc.

[0067] The communication component 43 is configured to facilitate communication between the terminal device and other devices in a wired or wireless manner. The terminal device can access a wireless network based on a communication standard, such as WiFi, 2G, or 3G, or a combination thereof. In one embodiment, the terminal device can include a SIM card slot for inserting a SIM card, enabling the terminal device to log in to the GPRS network and establish communication with a server via the Internet.

[0068] As can be seen from the above, in Figure 4 the embodiments, the communication component 43, the audio component 46, as well as the input / output interface 47 and the sensor component 48 can all be used as Figure 3 implementation manners of the input device in the embodiments.

[0069] In several embodiments provided in the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are merely illustrative. For example, the division of the units can be a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the couplings or direct couplings or communication connections shown or discussed with each other can be through some interfaces, and the indirect couplings or communication connections of units or modules can be in an electrical or other form.

[0070] The unit described as a separation component may or may not be physically separated. The component displayed as a unit may or may not be a physical unit, that is, it may be located in one place or may be distributed over multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0071] In addition, each functional unit in various embodiments of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0072] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: USB flash drive, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other various media that can store program codes.

[0073] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A photovoltaic field station equipment intelligent fault diagnosis method based on deep learning, characterized in that: include: Collect equipment original information and image information; Passing the image information through an image optimization model to generate matching image data; Using the matching image data and the original device information, matching and generating fault point information; The fault point information is input into the intelligent fault analysis model to obtain the fault judgment result.

2. The method according to claim 1, characterized in that The generating matching image data by passing the image information through the image optimization model comprises: Inputting the image information into the redundant layer of the image optimization model to obtain first image optimization data; The first image optimization data is input into the denoising layer of the image optimization model to obtain the matching image data.

3. The method according to claim 1, characterized in that: The matching and generating fault point information by using the matching image data and the original device information includes: The original device information and the matching image data are fitted to obtain the fault point information, wherein the fault point information includes: fault local image data and fault local parameter data.

4. The method according to claim 1, characterized in that: Before inputting the fault point information into the intelligent fault analysis model to obtain the fault judgment result, the method further includes: The intelligent fault analysis model is constructed according to the fault history data.

5. A photovoltaic field station equipment intelligent fault diagnosis device based on deep learning, characterized in that: include: The acquisition module is used to acquire the original information and image information of the device; A generation module, used for generating matching image data by passing the image information through an image optimization model; A matching module, used to match and generate fault point information using the matching image data and the original information of the device; The input module is used to input the fault point information into the intelligent fault analysis model to obtain the fault judgment result.

6. The device according to claim 5, characterized in that The generation module comprises: A redundancy unit, used for inputting the image information into a redundancy layer of the image optimization model to obtain first image optimization data; A denoising unit is used to input the first image optimization data into the denoising layer of the image optimization model to obtain the matching image data.

7. The device according to claim 5, characterized in that The matching module comprises: A fitting unit is used to fit the original device information and the matching image data to obtain the fault point information, wherein the fault point information includes: fault local image data and fault local parameter data.

8. The device according to claim 5, characterized in that The device also includes: A construction module is used to construct the intelligent fault analysis model according to the fault history data.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein the program controls the device where the non-volatile storage medium is located to execute the method according to any one of claims 1 to 4 when the program is executed.

10. An electronic device, characterized in that: It comprises a processor and a memory; the memory stores computer-readable instructions, and the processor is used to execute the computer-readable instructions, wherein the computer-readable instructions execute the method described in any one of claims 1 to 4 when executed.

Images