An economically viable smart photovoltaic system

Abstract

This invention discloses an economically viable intelligent photovoltaic system, comprising: a system design module for designing and optimizing the photovoltaic system; an equipment and data traceability module for real-time monitoring and data collection, forming an information network based on the correlation of the collected data; an inspection and scheduling module for automatically planning and scheduling inspection tasks based on real-time monitoring and collected data; a fault early warning module for analyzing equipment anomalies based on real-time monitoring and collected data and providing early warnings for such anomalies; and a fault self-healing module for rapidly repairing faults detected by the fault early warning module. This achieves an intelligent inspection, early warning, and self-healing mechanism, reducing costs and improving economic efficiency.

Description

Technical Field

[0001] This invention belongs to the field of intelligent photovoltaic technology and relates to an economically viable intelligent photovoltaic system. Background Technology

[0002] With the continuous growth of global energy demand and the increasing awareness of environmental protection, the development and utilization of renewable energy has become a focus of attention for countries around the world. Among them, solar energy, as a clean and renewable energy source, has received particular attention. Photovoltaic systems, as one of the main forms of solar energy utilization, convert solar energy into electrical energy through the photoelectric effect, providing a clean power supply to the power grid, which is of great significance for reducing fossil fuel consumption and greenhouse gas emissions.

[0003] However, traditional photovoltaic (PV) systems have numerous shortcomings in design and operation, particularly in terms of economy, reliability, and intelligence. Firstly, the lack of scientific optimization methods during the system design phase leads to unreasonable system configuration, low power generation efficiency, and high investment costs. Secondly, the lack of effective real-time monitoring and data acquisition methods during system operation makes it impossible to detect and handle equipment failures in a timely manner, affecting the stable operation and power generation efficiency of the system. Furthermore, traditional PV systems lack intelligent inspection, early warning, and self-healing mechanisms, resulting in untimely fault handling and high maintenance costs. Summary of the Invention

[0004] The purpose of this invention is to solve the technical problem that existing photovoltaic systems lack intelligent inspection, early warning and self-healing mechanisms, resulting in untimely fault handling and high maintenance costs, and to provide an economical intelligent photovoltaic system.

[0005] To achieve the above objectives, the present invention employs the following technical solution: This invention provides an economically viable smart photovoltaic system, comprising: The system design module is used for the design and optimization of photovoltaic systems; The equipment and data traceability module is used for real-time monitoring and data collection, and to form an information network by linking the collected data. The inspection scheduling module is used to automatically plan and schedule inspection tasks based on real-time monitoring and collected data. The fault early warning module is used to analyze abnormal equipment conditions based on real-time monitoring and collected data, and to issue early warnings for abnormal conditions. The fault self-healing module is used to quickly repair faults detected by the fault warning module.

[0006] Furthermore, the system design module includes a system optimization module and an adjustment module. The system optimization module is used to optimize the design parameters of the photovoltaic system; the adjustment module is used to adjust the photovoltaic system based on the optimization instructions from the system optimization module.

[0007] Furthermore, the device and data tracing module includes an edge computing module and a causal reasoning engine; the edge computing module is used for real-time monitoring and data collection, and the causal reasoning engine associates the relationships between various data.

[0008] Furthermore, the data includes equipment production batch, material model and batch, factory test data, transportation process data, installation location and batch, installation test and acceptance data, historical alarm and troubleshooting data, voltage, current, temperature and humidity.

[0009] Furthermore, the causal reasoning engine employs a Bayesian network.

[0010] Furthermore, the inspection and scheduling module includes a drone inspection system and an inspection and scheduling platform. The inspection and scheduling platform is used to automatically plan inspection tasks based on real-time monitoring and collected data. The drone inspection system is used to complete the inspection tasks based on the inspection tasks automatically planned by the inspection and scheduling platform.

[0011] Furthermore, the inspection scheduling platform automatically plans inspection tasks using the ant colony algorithm.

[0012] Furthermore, the fault warning module includes a fault analysis module and a fault warning module; the fault analysis module performs fault analysis based on real-time monitoring and collected data; and the fault warning module issues a warning based on the fault analysis results.

[0013] Furthermore, the fault analysis module uses machine learning models and Bayesian networks to analyze faults.

[0014] Furthermore, the fault self-healing module includes a fault self-healing identification module and a fault self-repair module; the fault self-healing identification module is used to identify fault types that can heal themselves; the fault self-repair module automatically repairs the fault based on the results of the fault self-healing identification module.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention discloses an economically viable intelligent photovoltaic system. The system design module performs precise calculations on the design and optimization of the photovoltaic system to ensure reasonable system configuration and high power generation efficiency. This not only reduces investment costs but also improves the system's economic viability and market competitiveness. The equipment and data traceability module can monitor and collect the photovoltaic system's operating data in real time and form an information network based on the relationships between these data. This helps in the timely detection and handling of equipment faults, ensuring data accuracy and reliability. Simultaneously, this module provides strong data support for subsequent system maintenance and optimization. The inspection scheduling module automatically plans and schedules inspection tasks based on real-time monitoring and collected data. This significantly reduces the workload of manual inspections and improves inspection efficiency and accuracy. Furthermore, this module can dynamically adjust the inspection plan according to the system's operating status to ensure stable system operation. The fault early warning module, based on real-time monitoring and collected data, conducts in-depth analysis of abnormal equipment conditions in the photovoltaic system and accurately warns of potential faults. This helps maintenance personnel take timely measures to prevent fault escalation and reduce system downtime and maintenance costs. The fault self-healing module automatically initiates a rapid repair program based on the fault information detected by the fault early warning module. This significantly shortens fault repair time and improves system reliability and stability. In addition, this module can also record the fault handling process, providing a reference for subsequent system optimization and maintenance.

[0016] Furthermore, the adjustment module of this invention, based on instructions from the system optimization module, performs real-time adjustments to the photovoltaic system, such as adjusting the tilt angle of the components or tracking the system direction, to adapt to changes in lighting conditions. This function ensures the efficient operation of the photovoltaic system under different weather conditions, further improving power generation efficiency. The causal reasoning engine uses a Bayesian network to connect the relationships between various data points, constructing an information network. Through this engine, potential correlations between devices and fault propagation paths can be identified, providing valuable reference information for maintenance personnel and helping to detect potential faults in advance and take corrective measures. The inspection scheduling platform uses an ant colony algorithm to automatically plan inspection tasks, comprehensively considering factors such as equipment operating status, historical fault records, and weather conditions, providing the optimal inspection route and schedule for the UAV inspection system. This function significantly improves inspection efficiency and reduces maintenance costs. The fault analysis module uses machine learning models and Bayesian networks to analyze real-time monitoring and collected data, identifying potential fault modes and predicting the probability and timing of fault occurrence. This function helps maintenance personnel take proactive measures to avoid the occurrence or escalation of faults.

[0017] Furthermore, this invention analyzes the information provided by the fault early warning module to identify self-healing fault types and automatically repairs them. For faults that cannot heal themselves, the system provides detailed fault reports and repair suggestions to guide maintenance personnel in quickly resolving the issues. This function significantly improves the reliability and stability of photovoltaic power plants. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A block diagram of an intelligent photovoltaic system for economic considerations in this invention.

[0020] The module consists of: 1-System design module; 2-Equipment and data traceability module; 3-Inspection and scheduling module; 4-Fault early warning module; 5-Fault self-healing module. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and marked in the accompanying drawings can generally be arranged and designed in various different configurations.

[0022] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0023] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0024] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper," "lower," "horizontal," or "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, terms such as "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0025] Furthermore, the use of the term "horizontal" does not imply that the component must be absolutely horizontal, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0026] In the description of the embodiments of the present invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention according to the specific circumstances.

[0027] The present invention will now be described in further detail with reference to the accompanying drawings: See Figure 1 This invention provides an economically viable intelligent photovoltaic system that integrates a system design module, an equipment and data traceability module, an inspection and scheduling module, a fault early warning module, and a fault self-healing module, aiming to improve the economic efficiency, reliability, and intelligence level of the photovoltaic system.

[0028] The system design module utilizes advanced simulation software and algorithms to optimize the layout, capacity, and component selection of the photovoltaic system based on local sunlight conditions, topography, grid connection requirements, and other factors. Through multiple iterative calculations, it ensures that the system achieves optimal economic efficiency and power generation while meeting power generation demands.

[0029] The aforementioned equipment and data traceability module are used to monitor and collect real-time operational data of the photovoltaic system, including key parameters such as illuminance, temperature, current, and voltage. This data is transmitted to a cloud server via IoT technology and undergoes data cleaning, integration, and correlation analysis to form an information network. This information network clearly displays the relationships between various devices, facilitating maintenance personnel to quickly locate problematic equipment.

[0030] The inspection scheduling module automatically plans and schedules inspection tasks based on real-time monitoring data provided by the equipment and data traceability modules. This module employs advanced algorithms, comprehensively considering factors such as equipment operating status, historical fault records, and weather conditions, to provide maintenance personnel with the optimal inspection routes and schedules. This significantly improves inspection efficiency and reduces maintenance costs.

[0031] The fault early warning module utilizes big data analytics and machine learning technologies to deeply mine and analyze data collected by the equipment and data tracing modules. When abnormal equipment parameters are detected or deviate from normal ranges, the module can quickly issue an early warning signal and provide possible causes and solutions for the fault. This helps maintenance personnel take timely measures to prevent the fault from escalating.

[0032] The fault self-healing module is activated immediately upon detection of a fault by the fault warning module. Based on the fault type, location, and severity, this module automatically selects an appropriate repair strategy. For example, for minor line contact issues, the module can achieve rapid self-healing by remotely adjusting equipment parameters or sending commands to on-site equipment. For complex faults, the module provides detailed fault reports and repair suggestions to guide maintenance personnel in quickly resolving the problem.

[0033] Another embodiment of the present invention provides an economically viable intelligent photovoltaic system, including a system design module, an equipment and data traceability module, an inspection and scheduling module, a fault early warning module, and a fault self-healing module. These modules work together to improve the economic efficiency, reliability, and intelligence level of the photovoltaic system.

[0034] The system design module includes a system optimization module and a tuning module: The system optimization module utilizes advanced simulation software and particle swarm optimization to optimize the design parameters of the photovoltaic system (such as component layout, inverter selection, cable specifications, etc.). Simultaneously, considering multiple objectives such as economy, power generation efficiency, and reliability, it finds the optimal solution through a multi-objective optimization algorithm. Based on the optimal solution, it outputs the optimized photovoltaic system design scheme, including component layout diagram, inverter configuration table, cable specification table, etc.

[0035] This invention utilizes Internet of Things (IoT) technology to monitor the real-time operating status of photovoltaic power plants, including key parameters such as irradiance, temperature, humidity, current, and voltage. The optimal parameters generated by the system optimization module are sent to the adjustment module to execute corresponding adjustment operations, which may include adjusting the tilt angle of the modules, tracking the system direction, and optimizing the inverter's operating parameters.

[0036] The device and data tracing module include an edge computing module and a causal reasoning engine: The edge computing module is deployed at the photovoltaic power station site to monitor and collect data in real time, including equipment production batches, material models and batches, factory test data, transportation process data, installation location and batches, installation test and acceptance data, real-time voltage, current, temperature and humidity, etc.

[0037] The causal reasoning engine employs a Bayesian network to connect various data points and construct an information network. By analyzing this information network, potential correlation paths between devices can be identified, providing strong support for subsequent inspections and fault early warnings.

[0038] The inspection and scheduling module includes an inspection and scheduling platform and a drone inspection system: The inspection scheduling platform employs an ant colony algorithm to automatically plan inspection tasks based on real-time monitoring and collected data. This algorithm comprehensively considers factors such as equipment operating status, historical fault records, and weather conditions to provide the optimal inspection route and schedule for the drone inspection system.

[0039] The drone inspection system completes the inspection tasks of the photovoltaic power station according to the inspection tasks automatically planned by the inspection dispatch platform. The drones are equipped with high-definition cameras and infrared thermal imagers, which can monitor the operating status and abnormal conditions of the equipment in real time.

[0040] The fault early warning module includes a fault analysis module and a fault early warning module: The fault analysis module employs machine learning models and Bayesian networks to analyze faults in real-time monitored and collected data. By analyzing abnormal changes in equipment parameters, it identifies potential fault modes and predicts the likelihood and timing of fault occurrence.

[0041] Based on fault analysis results, the fault early warning module sends warning signals to maintenance personnel and provides possible causes and solutions for the faults. Warning signals can be sent via SMS, email, or app push notifications to ensure that maintenance personnel can take timely action.

[0042] The fault self-healing module includes a fault self-healing identification module and a fault self-repair module: The fault self-healing identification module analyzes the information provided by the fault early warning module to identify fault types that can heal themselves. For example, for minor line contact problems, the system can automatically adjust equipment parameters or send commands to field equipment to achieve rapid fault self-healing.

[0043] The fault self-healing module automatically repairs faults based on the results of the fault self-healing identification module. For faults that cannot heal themselves, the system can provide detailed fault reports and repair suggestions to guide maintenance personnel in quickly resolving the issues.

[0044] The simulation software and particle swarm optimization algorithm used in the system design optimization process of this invention optimize the design parameters of the photovoltaic system (such as component layout, inverter selection, cable specifications, etc.). Specifically, reputable and widely used simulation software, such as MATLAB and ANSYS, are selected, as these software programs typically have high accuracy and reliability. Before optimizing the system design, the simulation model is verified to ensure its accuracy. This can be done by comparing the model with actual data or with the results of other simulation software. The particle swarm optimization algorithm is selected for optimization. After the photovoltaic system is put into operation, its operating status is continuously monitored, and the optimization effect is evaluated regularly to ensure the effectiveness and sustainability of the optimization scheme.

[0045] This invention achieves intelligent inspection scheduling, that is, it uses ant colony optimization to automatically plan inspection tasks based on real-time monitoring and collected data, specifically as follows: First, key data, such as equipment operating status and environmental parameters, are collected in real time through sensors and edge computing modules deployed at the photovoltaic power plant site. Then, the collected data undergoes preprocessing, including data cleaning and transformation, to ensure data quality and usability.

[0046] Based on the specific conditions and inspection requirements of the photovoltaic power station, an ant colony algorithm model was constructed. The ant colony algorithm is an optimization algorithm that simulates the foraging behavior of ants. It places multiple virtual "ants" in the search space, allowing them to find the optimal solution according to certain rules. Parameters of the ant colony algorithm, such as the number of ants, the number of iterations, and the pheromone update strategy, were set according to the actual situation. These parameters affect the convergence speed and the accuracy of the final result. Preprocessed data was input into the constructed ant colony algorithm model, and the algorithm was run to generate the optimal inspection path. During the algorithm's operation, the algorithm parameters could be dynamically adjusted based on real-time data changes to improve the algorithm's adaptability and efficiency. Based on the optimal inspection path generated by the ant colony algorithm, specific inspection tasks were generated and sent to the UAV inspection system. The UAV inspection system automatically executed the inspection tasks based on the received tasks, monitoring the equipment's operating status and any abnormalities in real time.

[0047] This invention utilizes advanced simulation software and particle swarm optimization to comprehensively optimize the design parameters of the photovoltaic system through system optimization and adjustment modules, thereby improving the system's power generation efficiency and economy. An edge computing module and a causal reasoning engine monitor and collect data in real time, constructing an information network to provide strong support for inspection and fault early warning. The inspection scheduling platform employs an ant colony algorithm to automatically plan inspection tasks based on real-time monitoring and collected data, improving inspection efficiency and accuracy. The fault analysis module uses machine learning models and Bayesian networks to analyze faults in real-time monitoring and collected data, improving the accuracy and timeliness of fault early warning. The fault self-healing module, through fault self-healing identification and fault self-repair modules, achieves rapid fault self-healing, reducing downtime and maintenance costs. In summary, the intelligent photovoltaic system of this invention achieves efficient and reliable operation through the collaborative work of the above modules, while reducing costs and improving economic benefits.

[0048] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An intelligent photovoltaic system considering economy, characterized by, The system comprises: a system design module for the design and optimization of a photovoltaic system; a device and data traceability module for real-time monitoring and data collection, and forming an information network from the correlation of the collected data; a patrol scheduling module for automatically planning and scheduling patrol tasks based on real-time monitoring and data collection; a fault warning module for analyzing device abnormal conditions based on real-time monitoring and data collection, and warning of abnormal conditions; a fault self-healing module for rapid repair of faults based on fault detection by the fault warning module.

2. The cost conscious intelligent photovoltaic system of claim 1, wherein, The system design module comprises a system optimization module for optimizing design parameters of the photovoltaic system, and an adjustment module for adjusting the photovoltaic system based on optimization instructions from the system optimization module.

3. The cost conscious intelligent photovoltaic system of claim 1, wherein, The device and data traceability module comprises an edge computing module for real-time monitoring and data collection, and a causal relationship reasoning engine for correlating the relationships between various data.

4. The cost conscious intelligent photovoltaic system of claim 3, wherein, The data includes device production lot, material model and lot, factory test data, transportation process data, installation location and lot, installation test and acceptance data, historical warning and defect elimination data, voltage, current, temperature, and humidity.

5. The cost conscious intelligent photovoltaic system of claim 3, wherein, The causal relationship reasoning engine uses a Bayesian network.

6. The cost conscious, intelligent photovoltaic system of claim 1, wherein, The patrol scheduling module comprises a UAV patrol system and a patrol scheduling platform, the patrol scheduling platform is used to automatically plan patrol tasks based on real-time monitoring and data collection, and the UAV patrol system is used to complete patrol tasks based on the automatically planned patrol tasks of the patrol scheduling platform.

7. The cost conscious intelligent photovoltaic system of claim 6, wherein, The patrol scheduling platform uses an ant colony algorithm to automatically plan patrol tasks.

8. The cost conscious, intelligent photovoltaic system of claim 1, wherein, The fault warning module comprises a fault analysis module and a fault warning module; The fault analysis module analyzes faults based on real-time monitoring and data collection, and the fault warning module warns based on fault analysis results.

9. The cost conscious intelligent photovoltaic system of claim 8, wherein, The fault analysis module uses a machine learning model and a Bayesian network to analyze faults.

10. The cost-effective smart photovoltaic system of claim 1, wherein, The fault self-healing module comprises a fault self-healing identification module and a fault self-repair module; the fault self-healing identification module is used to identify fault types that can be self-healed; The fault self-repair module automatically repairs faults based on the results of the fault self-healing identification module.

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