Field of the Invention
This invention relates to real-time monitoring fault detection of large BIPV systems using edge computing with machine learning
Background of the Invention
US10402671B2: Methods and systems are provided for detecting a defect in a solar panel. The method includes initially imaging, via an infrared camera, a group of solar panels. Then, identifying, via a computer system configured for solar panel defect detection, the individual solar panels in the group of solar panels. Finally, identifying, via evaluation of an infrared image obtained by the infrared camera, a defect in at least one of the group of solar panels.
None of the prior art indicates above either alone or in combination with one another discloses what the present invention has disclosed. The present invention is connected to the cloud server (12), and the LoRa RF module (21) in the edge device establishes a LoRa connection with the IoT-powered gateway (11). The IoT-powered gateway (11) connects to the internet with the help of a Wi-Fi module and updates the fault details on the cloud server in the form of internet protocol packets. The information available in the cloud server boosts the authorities to diagnose the fault of PV panels in real time for overcoming further damage to the PV panels. With the assistance of this architecture, the authorities can predict the faults in the PV panels early.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
In this invention, IoT and edge based controller for drones are proposed to conserve the BIPV system energy. Figure 1 illustrates the proposed invention, where the image processing techniques are used to predicted percentage of dust deposition on BIPV modules. Further cleaning was proposed using the robotic arm installed on the building facades. In this invention, an architecture is proposed with the integration of drone technology, edge computing, and AI to realize real-time fault detection. Figure 1 illustrates the working of this real-time fault detection system, where the drones capture the BIPV panel images with the assistance of a thermography camera. The edge device with LoRa in drone (10) is based on edge computing, where it performs the computation process with the help of a co-processor (21) by applying pre-trained machine learning models (22) for identifying the faults from the thermography images.
If the fault is identified, then the edge device updates the type of fault and ID of the panel that is obtained from the thermography images to the authorities connected to the cloud server. To connect to the cloud server (12), the LoRa RF module (21) in edge device establishes a LoRa connection with the IoT powered gateway (11). The IoT powered gateway (11) connects to the internet with the help of a Wi-Fi module and updates the fault details on the cloud server in the form of internet protocol packets. The information available in the cloud server boosts the authorities to diagnose the fault of PV panels in real-time for overcoming further damage to the PV panels. With the assistance of this architecture, the authorities can predict the faults in the PV panels early.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
In this invention, IoT and edge based controller for drones are proposed to conserve the BIPV system energy. Figure 1 illustrates the proposed invention, where the image processing techniques are used to predicted percentage of dust deposition on BIPV modules. Further cleaning was proposed using the robotic arm installed on the building facades. In this invention, an architecture is proposed with the integration of drone technology, edge computing, and AI to realize real-time fault detection. Figure 1 illustrates the working of this real-time fault detection system, where the drones capture the BIPV panel images with the assistance of a thermography camera. The edge device with LoRa in drone (10) is based on edge computing, where it performs the computation process with the help of a co-processor (21) by applying pre-trained machine learning models (22) for identifying the faults from the thermography images. If the fault is identified, then the edge device updates the type of fault and ID of the panel that is obtained from the thermography images to the authorities connected to the cloud server. To connect to the cloud server (12), the LoRa RF module (21) in edge device establishes a LoRa connection with the IoT powered gateway (11). The IoT powered gateway (11) connects to the internet with the help of a Wi-Fi module and updates the fault details on the cloud server in the form of internet protocol packets. The information available in the cloud server boosts the authorities to diagnose the fault of PV panels in real-time for overcoming further damage to the PV panels. With the assistance of this architecture, the authorities can predict the faults in the PV panels early. The different types of faults that arise in the module are the optical degradation in a PV module, electrical mismatches and degradation, and non-classified faults. The optical degradation occurs mainly due to the formation of bubbles, and discoloration of the encapsulation. This can occur internally as well externally; internally poor quality of encapsulation and externally high outdoor temperature and humidity are significant factors. The diagnosis of such faults can be performed through infrared thermography conducted to access the condition of the PV modules. The manual capturing of IR images of a small-scale PV plant of a few kW is possible, however, at a large scale the PV system on building rooftops or integrated on the facades of high-rise buildings requires more reliable and faster technologies.
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
The different types of faults that arise in the module are the optical degradation in a PV module, electrical mismatches and degradation, and non-classified faults. The optical degradation occurs mainly due to the formation of bubbles, and discoloration of the encapsulation. This can occur internally as well externally; internally poor quality of encapsulation and externally high outdoor temperature and humidity are significant factors. The diagnosis of such faults can be performed through infrared thermography conducted to access the condition of the PV modules. The manual capturing of IR images of a small-scale PV plant of a few kW is possible, however, at a large scale the PV system on building rooftops or integrated on the facades of high-rise buildings requires more reliable and faster technologies. Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
These and other advantages of the present subject matter would be described in greater detail with reference to the following figures. It should be noted that the description merely illustrates the principles of the present subject matter. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described herein, embody the principles of the present subject matter and are included within its scope.
In this invention, IoT and edge-based controller for drones are proposed to conserve the BIPV system energy. Figure 1 illustrates the proposed invention, where the image processing techniques are used to predict the percentage of dust deposition on BIPV modules. Further cleaning was proposed using the robotic arm installed on the building facades. In this invention, an architecture is proposed with the integration of drone technology, edge computing, and AI to realize real-time fault detection. Figure 1 illustrates the working of this real-time fault detection system, where the drones capture the BIPV panel images with the assistance of a thermography camera. The edge device with LoRa in drone (10) is based on edge computing, where it performs the computation process with the help of a co-processor (21) by applying pre-trained machine learning models (22) for identifying the faults from the thermography images.
If the fault is identified, then the edge device updates the type of fault and ID of the panel that is obtained from the thermography images to the authorities connected to the cloud server. To connect to the cloud server (12), the LoRa RF module (21) in edge device establishes a LoRa connection with the IoT powered gateway (11). The IoT powered gateway (11) connects to the internet with the help of a Wi-Fi module and updates the fault details on the cloud server in the form of internet protocol packets. The information available in the cloud server boosts the authorities to diagnose the fault of PV panels in real-time for overcoming further damage to the PV panels. With the assistance of this architecture, the authorities can predict the faults in the PV panels early.
ADVANTAGES OF THE INVENTION:

• Real-time monitoring of various faults in the BIPV System
• Develops a database of the different faults in the system for the particular location.
• Helps in the early design and installation phase of the new systems.
• Maintains the output power quality of the BIPV system.

We Claims:

1. REAL-TIME MONITORING FAULT DETECTION OF LARGE BIPV SYSTEM USING EDGE COMPUTING WITH MACHINE LEARNING system comprises edge device with LoRa in drone (10), IoT powered gateway (11), cloud server with web dashboard (12), LoRa RF module (21), and pre-trained machine learning models (22).
2. The system as claimed in claim 1, wherein which is connected to the cloud server (12), the LoRa RF module (21) in the edge device establishes a LoRa connection with the IoT-powered gateway (11).
3. The system as claimed in claim 1, wherein which is an IoT-powered gateway (11) connects to the internet with the help of a Wi-Fi module and updates the fault details on the cloud server in the form of internet protocol packets.
4. The system as claimed in claim 1, wherein information available in the cloud server boosts the authorities to diagnose the fault of PV panels in real-time for overcoming further damage to the PV panels.