Photovoltaic power station remote monitoring method and system based on three-dimensional digital twinning technology

Abstract

The application discloses a photovoltaic power station remote monitoring method and system based on three-dimensional digital twin technology, the method comprising: constructing a three-dimensional twin photovoltaic power station; synchronizing the entity photovoltaic power station data by using the three-dimensional twin photovoltaic power station, and monitoring the real-time operation parameters of the twin equipment, comparing the real-time monitoring operation parameters with the corresponding preset twin equipment parameter threshold, and determining whether to trigger an initial alarm according to the comparison result; combining the twin equipment fault propagation chain rule to determine the fault source corresponding twin equipment and generate a fault source corresponding twin equipment fault alarm or an independent twin equipment fault alarm; for the monitored and obtained twin equipment operation parameters, predicting the independent twin equipment with fault risk or the twin equipment belonging to the fault source, automatically generating an inspection task, and obtaining an inspection path by using an optimal path planning algorithm. The application can improve the visualization effect and fault equipment positioning accuracy of photovoltaic power station remote monitoring.

Description

Technical Field

[0001] This application relates to the technical field of energy monitoring and management, specifically to a method and system for remote monitoring of photovoltaic power plants based on three-dimensional digital twin technology. Background Technology

[0002] With the rapid development of new energy power generation technologies, photovoltaic power plants are expanding in scale, placing higher demands on remote monitoring and maintenance. Traditional power plant monitoring methods rely on two-dimensional maps or simple data reports, making it difficult for operation and maintenance personnel to intuitively understand the three-dimensional spatial layout and equipment status of the power plant. Moreover, this traditional method cannot provide comprehensive information quickly and accurately when dealing with large-scale photovoltaic power plants, greatly limiting operation and maintenance efficiency and fault response speed, which is detrimental to the efficient management and long-term stable operation of the power plant.

[0003] The currently prevalent solution is a power plant monitoring system based on GIS (Geographic Information System) and SCADA (Supervisory Control and Data Acquisition) systems. This system can collect and display power plant operational data, utilizing the geographic information functions of GIS to present information such as the power plant's geographical location to a certain extent, while relying on the SCADA system to collect operational data such as equipment power consumption and temperature. However, in terms of visualization, it cannot provide maintenance personnel with the intuitive understanding of the power plant's spatial layout and the positional relationships between equipment as a 3D display. Regarding interactivity, users cannot flexibly interact with elements within the system, making it difficult to perform in-depth equipment viewing and data analysis; they can only obtain some relatively basic operational data displays.

[0004] Existing power plant monitoring systems lack intuitive displays of three-dimensional spatial information, making it difficult to achieve precise equipment location. When a power plant malfunctions, maintenance personnel cannot quickly pinpoint the exact location of the faulty equipment, requiring significant time for searching and confirmation. This results in low efficiency in rapid fault location and response, increases maintenance costs, and severely impacts the normal operation and economic benefits of photovoltaic power plants. Summary of the Invention

[0005] In order to improve the visualization effect and fault location accuracy of remote monitoring of photovoltaic power plants, reduce fault response time, and improve operation and maintenance efficiency through three-dimensional digital twin technology, this application provides a method and system for remote monitoring of photovoltaic power plants based on three-dimensional digital twin technology.

[0006] Firstly, this application provides a method for remote monitoring of a photovoltaic power station based on three-dimensional digital twin technology, including:

[0007] Construct a 3D twin photovoltaic power station, including a photovoltaic area twin, a substation twin, and a collector line twin. During the construction of the 3D twin photovoltaic power station, the coordinate positions of the twin devices are marked to realize the visualization of the real-world base map and the vectorization of the array of different types of twins. A 3D linkage dashboard is designed to display the core parameters of different types of twin devices and the correlation indicators between cross-type twin devices.

[0008] The system utilizes 3D twin photovoltaic power stations to synchronize data with physical photovoltaic power stations and monitors the operating parameters of twin devices in real time. These real-time operating parameters are compared with corresponding preset threshold values ​​for the twin devices. Based on the comparison results, it determines whether an initial alarm is triggered. For a single twin device that triggers an initial alarm, it checks whether there are associated twin devices that trigger the alarm, and whether these associated twin devices conform to a fault propagation chain. If so, it performs a linked source tracing analysis of the operating parameters of the single twin device that triggered the initial alarm and its associated twin devices to identify the fault source and generate a fault alarm for that twin device. Otherwise, it directly generates an independent twin device fault alarm.

[0009] The system performs correlation clustering analysis on the operating parameters of the twin devices acquired through monitoring. The analyzed operating parameters are then input into a pre-built deep learning-based prediction model for independent twin devices or a fault prediction model for associated twin devices. This predicts which independent twin devices or twin devices are at risk of failure or are sources of failure. The system automatically generates inspection tasks based on the locations of the twin devices predicted to be at risk of failure, and uses an optimal path planning algorithm to obtain inspection paths to assist inspection personnel in their inspections.

[0010] By adopting the above scheme, the location of the twin equipment is marked, realizing the visualization of the twin's real-scene base map and the vectorization of the matrix, improving the visualization level of the power station, and designing a 3D linkage dashboard to facilitate users to fully grasp the status of the power station; synchronize the data of the physical photovoltaic power station and compare the operating parameters to trigger the initial alarm; combine the operating parameter linkage traceability analysis technology to accurately locate the source of the fault, the faulty equipment, and the independent faulty equipment; perform correlation cluster analysis on the operating parameters of the twin equipment and input them into the prediction model to predict the equipment with fault risk, automatically generate inspection tasks and use the optimal path planning algorithm to obtain the inspection path, assist the inspection personnel in the inspection, and improve the inspection efficiency and the timeliness of fault diagnosis.

[0011] Preferred options also include:

[0012] In the process of constructing a 3D twin photovoltaic power station, a matching twin modeling enhancement strategy is obtained based on the core parameter characteristics of different types of twin devices, and the modeling of different types of twins is optimized accordingly.

[0013] The photovoltaic area twin obtains a matching first twin modeling enhancement strategy based on the sensitive characteristics of meteorological data; the first twin modeling enhancement strategy includes: modeling each photovoltaic panel independently and designing an independent photovoltaic panel operating parameter dashboard; integrating meteorological data of the photovoltaic power station and rendering the light intensity, shadow projection, and coverage area of ​​the photovoltaic panel model in real time based on the meteorological data of the photovoltaic power station; and designing a heat map layer to represent the power generation efficiency of the photovoltaic area with color gradients.

[0014] The substation twin obtains a matching second twin modeling enhancement strategy based on the sensitive characteristics of power parameters; the second twin modeling enhancement strategy includes: designing a load heat map to dynamically display the power flow of the substation; designing an equipment health layer to represent the substation equipment health mapping with color gradients; and designing an independent substation operating parameter waveform trend dashboard.

[0015] The collector line twin obtains a matching third twin modeling enhancement strategy based on the sensitive characteristics of meteorological data and power parameters; the third twin modeling enhancement strategy includes: rendering the line in layers according to voltage level, distinguishing cable types with different colors; designing a current density heat map to display the load rate on the cable path; and simulating arc effects at corresponding locations based on the collector line fault data.

[0016] By adopting the above scheme, matching twin modeling enhancement strategies are obtained based on the core parameter characteristics of different types of twin devices to optimize the modeling of different types of twins, improve the visualization and interactive functions of twin models, and further meet the user's viewing and interaction needs.

[0017] Preferred options also include:

[0018] By synchronizing data from a physical photovoltaic power station with a three-dimensional twin photovoltaic power station, and monitoring the operating parameters of the twin equipment in real time, the system compares the real-time monitored operating parameters with the corresponding preset twin equipment parameter thresholds. Based on the core parameter characteristics of different types of twin equipment, the system obtains the preset twin equipment parameter thresholds that match them, thereby achieving dynamic adjustment of the preset twin equipment parameter thresholds.

[0019] The first preset parameter threshold for photovoltaic twin devices is obtained based on the sensitive characteristics of meteorological data. This threshold is determined by statistically analyzing various parameter data of photovoltaic twin devices under different historical meteorological data and different historical meteorological data conditions.

[0020] For substation twin equipment, a second preset twin equipment parameter threshold is obtained based on the sensitive characteristics of electrical parameter data. This threshold is determined by statistically analyzing various parameter data of substation twin equipment under different historical real-time loads and different combinations of historical real-time loads.

[0021] For the collector line twin equipment, a third preset twin equipment parameter threshold is obtained based on the sensitivity characteristics of meteorological data and electrical parameter data. This threshold is determined by statistically analyzing various parameter data of the collector line twin equipment under different historical meteorological data and different real-time loads, as well as combinations of different historical meteorological data and different real-time loads.

[0022] By adopting the above scheme, the preset parameter thresholds of twin devices can be dynamically adjusted according to the core parameter characteristics of different types of twin devices, so as to more accurately judge the operating status of twin devices, detect anomalies in a timely manner, and improve the timeliness and accuracy of fault warning.

[0023] Preferred options also include:

[0024] In the process of predicting the risk of failure for independent twin devices or twin devices that are fault sources, and automatically generating inspection tasks for twin devices predicted to have a risk of failure, for each twin device predicted to have a risk of failure, the inspection equipment selection strategy and inspection personnel selection strategy for the twin device are obtained and matched according to the risk type and level of the predicted failure risk. The inspection equipment is selected according to the matched inspection equipment selection strategy, and the inspection personnel are selected according to the matched inspection personnel selection strategy, thereby generating the corresponding inspection task for the twin device predicted to have a risk of failure. Among them, different types of twin devices are preset with corresponding inspection equipment types and inspection personnel levels under different combinations of failure types and levels.

[0025] In the process of obtaining inspection paths using the optimal path planning algorithm, the generated inspection tasks corresponding to all twin devices predicted to have fault risks are statistically analyzed. The independent twin devices with fault risks or twin devices with related relationships are distinguished. For the inspection task point sets corresponding to several independent twin devices in the photovoltaic area, several independent twin devices in the substation, several independent twin devices in the collector line, and twin devices and their related twin devices, the optimal path planning algorithm that is pre-matched is obtained for each of them, and the optimal path aggregation of multiple inspection task point sets is obtained.

[0026] By adopting the above scheme, inspection tasks are generated by matching inspection equipment and personnel selection strategies according to the type and level of fault risk, ensuring that appropriate inspection resources are selected to deal with different fault situations, and improving the targeting and efficiency of inspections. By distinguishing different types of twin equipment and related equipment, the optimal path planning algorithm is matched for different sets of inspection task points to obtain the optimal path aggregation, which can optimize inspection routes, reduce inspection time and costs, and improve overall inspection efficiency.

[0027] Preferred options also include:

[0028] Based on the operating monitoring parameters of the independent twin device that generates the fault alarm, or the twin device belonging to the fault source and the corresponding associated twin device, the corresponding input is matched with the twin device fault diagnosis model or the associated twin device fault diagnosis model, and the fault diagnosis result is obtained, including: fault type and level; wherein, different types of twin devices have pre-set matching twin device fault diagnosis models based on deep learning, and for different combinations of associated twin devices, pre-set matching associated twin device fault diagnosis models based on deep learning;

[0029] Obtain the location of the fault source twin device that generates the fault alarm and its associated twin devices or independent twin devices; determine whether there are any twin devices of the fault source twin device that generates the fault alarm and its associated twin devices or independent twin devices located in a preset key location; based on the result of whether the fault source twin device that generates the fault alarm and its associated twin devices or independent twin devices are located in a preset key location, as well as the fault type and level, match the corresponding fault response strategy level.

[0030] Among them, whether the twin device that generates the fault alarm is located in a preset key location and whether there are preset matching fault response strategy levels under different combinations of fault types and levels; different levels of fault response strategies include: directly obtaining equipment fault handling suggestions that match the fault type and level, selecting experts to assist in fault decision-making and then obtaining equipment fault handling suggestions that match the fault type and level, or directly obtaining equipment fault handling suggestions that match the fault type and level after experts make fault decisions.

[0031] By adopting the above scheme, the corresponding fault diagnosis model is matched according to the operating monitoring parameters of the twin equipment that generates the fault alarm, and the fault type and level are accurately obtained. Then, by combining whether the faulty equipment is located in a preset key location, an appropriate fault response strategy level is matched, which provides direct suggestions, expert-assisted decision-making suggestions, or expert direct decision-making suggestions for equipment fault handling, thereby improving the pertinence and efficiency of fault handling.

[0032] Preferred options also include:

[0033] Record the fault source twin device that generates the fault alarm and the corresponding fault diagnosis results of its associated twin device or independent twin device. For each recorded twin device, generate a work order containing location coordinates, fault type and level, and historical fault data, and associate it with the maintenance manual of the twin device to create an operation and maintenance work order. For each operation and maintenance work order, record the entire lifecycle information of the creation, allocation, execution and closure of the inspection work order.

[0034] By adopting the above solution, the fault diagnosis results are recorded and work orders containing relevant information are generated and linked to the maintenance manual, which helps maintenance personnel to quickly understand the fault situation and repair it according to the manual; the entire life cycle information of the work order is recorded, which improves the efficiency of work order processing.

[0035] Preferred options also include:

[0036] During the construction of the 3D twin photovoltaic power station, a twin device search dashboard with a tree-like device list was also designed to support users in querying each twin device through fuzzy search and displaying the real-time operating parameters of the queried twin devices.

[0037] By adopting the above solution, users can query each twin device through fuzzy search, which makes it easy for users to quickly locate the device. It can also display the real-time operating parameters of the queried twin devices, helping users to fully understand the power plant's operating status and improve interactivity.

[0038] Secondly, this application provides a remote monitoring system for photovoltaic power plants based on three-dimensional digital twin technology, comprising:

[0039] The 3D twin photovoltaic power station construction module is used to construct 3D twin photovoltaic power stations, including photovoltaic area twins, substation twins, and collector line twins. During the construction of the 3D twin photovoltaic power station, the coordinate positions of the twin devices are marked to realize the visualization of the real-world base map and the vectorization of the matrix of different types of twins. A 3D linkage dashboard is designed to display the core parameters of different types of twin devices and the correlation indicators between cross-type twin devices.

[0040] The 3D twin photovoltaic power station application module is used to synchronize data from the physical photovoltaic power station with the 3D twin photovoltaic power station, monitor the real-time operating parameters of the twin devices, and compare the real-time monitored operating parameters with the corresponding preset twin device parameter thresholds. Based on the comparison results, it determines whether an initial alarm is triggered. For a single twin device that triggers an initial alarm, it checks whether there are related twin devices that triggered the initial alarm and whether they conform to the fault propagation chain law. If so, it performs a linkage tracing analysis of the operating parameters of the single twin device that triggered the initial alarm and its related twin devices to determine... The system generates a fault alarm for the corresponding twin device based on the fault source; otherwise, it directly generates a fault alarm for the independent twin device. It performs correlation clustering analysis on the monitored twin device operating parameters, inputs the analyzed operating parameters into a pre-built deep learning-based prediction model for independent twin devices or a fault prediction model for associated twin devices, predicts the independent twin devices with fault risk or the twin devices belonging to the fault source, and automatically generates inspection tasks for the predicted locations of the twin devices with fault risk. It also uses an optimal path planning algorithm to obtain the inspection path to assist inspection personnel in conducting inspections.

[0041] By adopting the above scheme, a three-dimensional twin photovoltaic power station is constructed and the coordinate positions of the twin devices are marked, realizing the visualization of the real-world base map and the vectorization of the matrix for different types of twins. The core parameters and the correlation indicators between cross-type twin devices are displayed, improving the visualization level of the power station. By using the synchronous data of the three-dimensional twin photovoltaic power station to monitor operating parameters and compare them with thresholds, the initial alarm is triggered in a timely manner. By linking the operating parameters of the twin devices for source tracing analysis, the source of the fault can be identified and the fault alarm can be generated. For equipment with fault risk, inspection tasks are automatically generated and inspection paths are obtained to assist inspection personnel in inspection and improve the efficiency of fault prevention and handling.

[0042] Thirdly, this application provides a computer-readable storage medium including a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to perform the method described above.

[0043] Fourthly, this application provides a computer device, the computer device including a memory, a processor and a program stored in the memory and executable thereon, the program being executed by the processor to implement the steps of the method described above.

[0044] In summary, this application has the following beneficial effects:

[0045] 1. By constructing a 3D twin photovoltaic power station, the coordinate positions of the twin equipment are marked, enabling visualization of real-world base maps and vectorization of different types of twins. This improves the visualization effect and equipment positioning accuracy of remote monitoring of photovoltaic power stations, solving the problem that traditional monitoring methods cannot intuitively display the 3D spatial layout and equipment status of the power station. By synchronizing data from the 3D twin photovoltaic power station with the physical photovoltaic power station, real-time monitoring of operating parameters, generation of fault alarms, fault prediction, and intelligent inspection are performed. This allows for rapid location of fault points and automatic generation of inspection tasks and paths, reducing fault response time, improving operation and maintenance efficiency, and solving the problem of low efficiency in rapid fault location and response in existing monitoring systems.

[0046] 2. Utilize 3D twin photovoltaic power plants to record fault diagnosis results and generate work orders containing relevant information, linking them to maintenance manuals. Statistically analyze the defect distribution patterns of various types of twin equipment to generate heat maps, enabling power plant defect and work order management applications. Based on the operating monitoring parameters of the twin equipment generating fault alarms, match corresponding fault diagnosis models to accurately obtain fault diagnosis results and match corresponding fault handling suggestions, realizing power plant data analysis and decision support applications.

[0047] 3. Provides an equipment search dashboard that supports fuzzy search, displays real-time operating information of the equipment, improves the user experience, and makes it easier for users to fully understand the operating status of the power station. Attached Figure Description

[0048] Figure 1 This is a flowchart of the remote monitoring method for photovoltaic power plants based on three-dimensional digital twin technology described in a specific embodiment;

[0049] Figure 2 This is a schematic diagram of the structure of the photovoltaic power station remote monitoring system based on three-dimensional digital twin technology described in a specific embodiment;

[0050] Figure 3 This is a technical architecture diagram of the photovoltaic power station remote monitoring system based on three-dimensional digital twin technology described in a specific embodiment. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0052] like Figure 1 As shown in the figure, this application discloses a method for remote monitoring of a photovoltaic power station based on three-dimensional digital twin technology, including the following steps:

[0053] S1. Construct a three-dimensional twin photovoltaic power station.

[0054] Considering the structure of a photovoltaic power station, a three-dimensional twin photovoltaic power station is constructed, including a photovoltaic area twin, a substation twin, and a collector line twin.

[0055] To ensure the accuracy of subsequent fault location, during the construction of a photovoltaic area twin based on digital twin technology, precise coordinate positions of equipment such as photovoltaic modules, inverters, and transformer substations within the area are marked to ensure consistency between the virtual model and the actual layout. The specific actual coordinate positions are measured and recorded using high-precision measuring instruments such as GPS locators and total stations. For example, since photovoltaic modules are typically arranged in a matrix, the position of each module is accurately recorded during coordinate marking. Through precise coordinate marking, the real-world base map of the photovoltaic area can be visualized and the matrix vectorized, allowing users to intuitively understand the layout of the photovoltaic area.

[0056] Furthermore, during the construction of the photovoltaic twin, software development tools and graphical interface design software were used to design a component parameter dashboard to display the location, operating parameters, and other core data of each component device, as well as the core data correlation indicators between various twin devices. Considering the linkage between the photovoltaic area, substation, and collection lines in an actual photovoltaic power station, for example, a sudden drop in photovoltaic area irradiance leads to a decrease in collection line current, which in turn leads to a decrease in substation power output, a three-dimensional linkage dashboard was also designed to display the photovoltaic area's power generation, power output, component temperature, and other core parameters, as well as the correlation indicators between cross-type twin devices such as substations and collection lines, including the amount of electricity transmitted to the substation.

[0057] Correspondingly, in the process of constructing a substation twin based on digital twin technology, coordinate annotation is also required for main transformers, GIS equipment, SVG equipment, etc. The main transformer is responsible for raising or lowering voltage and is equipped with heat sinks and cooling oil. The GIS equipment uses gas insulation and monitors parameters such as circuit breaker status and gas concentration. The SVG equipment can adjust reactive power to ensure stable grid operation and monitors switchgear status and meter readings. Coordinate annotation enables visualization and matrix vectorization of the substation's real-world base map, and a substation twin equipment parameter dashboard is designed to display the location, operating parameters, and other core data of each twin device, as well as the core data correlation indicators between the twin devices. Similarly, in the process of constructing the substation twin, a 3D linkage dashboard is designed to display core parameters of the main transformer, such as oil temperature, winding temperature, and voltage and current on the high-voltage and low-voltage sides, as well as correlation indicators with the photovoltaic area and collector lines, such as the amount of electricity received from the photovoltaic area and the amount of electricity transmitted to the collector lines.

[0058] Accordingly, in the process of constructing a digital twin of a power collection line, three-dimensional models of the power collection line towers and lines are built, and the positions of the towers and the routes of the lines are marked with coordinates. After marking, the real-world base map of the power collection line can be visualized and vectorized into a matrix. A power collection line twin equipment parameter dashboard is designed to display the location, operating parameters, and other core data of each twin device, as well as the core data correlation indicators between the twin devices. Similarly, in the process of constructing the power collection line twin, a three-dimensional linkage dashboard is designed to display the core parameters of the power collection line, such as current, voltage, and power, as well as the correlation indicators with photovoltaic areas and substations, such as the amount of electricity collected from the photovoltaic area and the amount of electricity transmitted to the substation.

[0059] Therefore, a panoramic view of the photovoltaic power station is displayed on the constructed 3D twin photovoltaic power station model. To facilitate real-time viewing of the panoramic layout and equipment distribution of the photovoltaic power station and to intuitively grasp the operating status of the power station, a twin equipment search dashboard with a tree-like equipment list was also designed during the construction process. This allows users to query each twin device through fuzzy search and displays the real-time operating parameters of the queried twin devices. To further enable users to select the panoramic layout of twin devices in all types of areas, the mid-range layout of a cluster of twin devices of a single type, and the close-up layout of a single cluster of twin devices according to their needs, LOD dynamic layering technology is used to render the 3D twin photovoltaic power station in layers, and users can view the layered layout of the photovoltaic power station according to their needs.

[0060] Furthermore, considering the varying visualization and interaction requirements for different twins, and to further adapt to user needs and innovate visualization and interaction designs, the method also includes:

[0061] In the process of constructing a 3D twin photovoltaic power station, matching twin modeling enhancement strategies are obtained based on the core parameter characteristics of different types of twin devices, and the modeling of different types of twins is optimized accordingly; the twin modeling enhancement strategies include twin modeling and visualization optimization strategies and twin interaction design optimization strategies;

[0062] Considering that the core parameters of the photovoltaic twin equipment include power generation, power output, and module temperature, these core parameters are significantly affected by the environment. For example, the higher the light intensity, the higher the power generation and power output of the photovoltaic modules. Therefore, the core parameter characteristics are all sensitive to meteorological data and can be identified as meteorological data-sensitive features. The photovoltaic twin obtains a first twin modeling enhancement strategy matching these meteorological data-sensitive features. This first twin modeling enhancement strategy includes: independently modeling each photovoltaic module, constructing a 1:1 model based on parameters such as photovoltaic panel tilt angle, arrangement density, and support structure. The system features a 3D model with surface materials simulating reflectivity and thermal conductivity. It also includes an independent dashboard for photovoltaic (PV) panel operation parameters, allowing users to view real-time power, temperature, and aging rate information. Integrated with PV power plant meteorological data, the system renders the PV panel model's light intensity, shadow projection, and coverage area in real-time. For example, it uses color gradients (dark blue to bright yellow) to represent PV panel light intensity; a semi-transparent white layer to simulate snow accumulation; and dynamically projects shadows based on the sun's angle. A heatmap layer is designed with color gradients to characterize PV area power generation efficiency, such as red for high efficiency and blue for low efficiency. The system also allows for timeline rollback on the operation parameter panel, supporting the reproduction of historical weather data and comparison of power generation curves and equipment wear under different weather conditions.

[0063] Considering that the core parameters of the substation's twin equipment include: the main transformer's oil temperature, winding temperature, and voltage and current on the high-voltage and low-voltage sides, these core parameters are all significantly affected by electrical parameters, such as...

[0064] During peak electricity consumption periods, i.e. when the substation load is high and the current is high, the corresponding core parameter characteristics are all deeply bound to power parameters, and the core parameters can be identified as circuit parameter sensitive features. The substation twin obtains a matching second twin modeling enhancement strategy based on the power parameter sensitive features. The second twin modeling enhancement strategy includes: designing a load heat map to dynamically display the substation's power flow direction, using fluid animation to simulate the power flow path (e.g., from photovoltaic zone to voltage boosting to grid connection); designing an equipment health layer to represent the substation equipment health mapping with color gradients (e.g., green-red); and designing an independent substation operating parameter waveform trend dashboard, such as: clicking on a circuit breaker to pop up a voltage / current waveform diagram to identify harmonic distortion.

[0065] Considering that the core parameters of the collector line's twin equipment include current, voltage, and power, and since electromechanical lines are not in a closed environment like substations, and towers and lines are generally in high-altitude, open-air environments, these core parameters are significantly affected by power parameters and weather data. Therefore, the collector line twin obtains a matching third twin modeling enhancement strategy based on the sensitivity characteristics of meteorological data and power parameters. This third twin modeling enhancement strategy includes: rendering the line in layers according to voltage level (current collector - current distribution - step-up), distinguishing cable types with different colors (e.g., red for high-voltage cables); designing a current density heatmap to display the load rate along the cable path (e.g., green for normal, red for overload); simulating arc effects at corresponding locations based on collector line fault data; and using a semi-transparent material to display the underground cable route, with key nodes (combiner boxes, transfer wells) marked with numbers.

[0066] By synchronizing data from a 3D twin photovoltaic power station with that of a physical photovoltaic power station, real-time monitoring of the operating parameters of the twin equipment is achieved.

[0067] Specifically, through data transmission interfaces and communication protocols such as MQTT and OPC UA, the operational data collected by sensors in the physical power station is transmitted to the 3D twin photovoltaic power station. Real-time operational parameters are monitored for each twin device using various sensors, such as current sensors, voltage sensors, and temperature sensors, to monitor multiple types of real-time operational parameters, including the output power and temperature of the photovoltaic modules, and the oil and winding temperatures of the main transformer.

[0068] S3. Compare the operating parameters obtained by real-time monitoring of the twin device with the corresponding preset twin device parameter thresholds. Based on the comparison results, determine whether an initial alarm is triggered, and then analyze the correlation between the twin devices that trigger the initial alarm to determine the final twin device fault alarm.

[0069] To avoid false alarms caused by data silos, the operating parameters of the twin device and its associated twin devices are queried and combined for fault source analysis to ultimately obtain accurate fault alarms; specifically including:

[0070] For each twin device, the real-time monitored operating parameters are compared with corresponding preset twin device parameter thresholds. Based on the comparison result, it is determined whether to trigger an initial alarm. That is, if the real-time monitored operating parameters exceed the corresponding preset twin device parameter thresholds, an initial alarm is triggered for that twin device. Considering that each twin device has multiple real-time operating parameters, corresponding preset twin device parameter thresholds are obtained for comparison. For example, the output power of photovoltaic module A corresponds to a preset photovoltaic module output power threshold, and the temperature of photovoltaic module B corresponds to a preset photovoltaic module temperature threshold. Each type of preset parameter threshold is determined based on the device's design requirements and historical operating data.

[0071] For a single twin device that triggers the initial alarm, indicating a very high risk of failure, in order to rule out the possibility that this failure is caused by other faulty twin devices, it is necessary to determine whether there are any associated twin devices that trigger the initial alarm and whether they conform to the fault propagation chain pattern. The fault propagation chain pattern between the associated devices of each twin device and between the associated devices of twin devices is determined by fault correlation analysis based on the historical operating data of the faulty twin devices.

[0072] If the determination result is that there is an associated twin device that triggered the initial alarm and that it conforms to the fault propagation chain law with the associated twin device that triggered the initial alarm, then a linkage source tracing analysis of the operating parameters of the single twin device that triggered the initial alarm and its associated twin devices is performed to determine the twin device corresponding to the fault source and generate a fault alarm for the twin device corresponding to the fault source; otherwise, an independent twin device fault alarm is directly generated. Among them, the linkage source tracing analysis of the operating parameters of associated twin devices uses a fault source tracing model built based on deep learning algorithms to analyze and obtain the twin devices that belong to the fault source among the associated twin devices. This model is trained and generated based on the historical operating parameters of associated twin devices and the twin devices that belong to the fault source.

[0073] Furthermore, to further optimize the intelligent alarm function and achieve tiered early warning and accurate positioning, the method also includes:

[0074] To achieve dynamic adjustment of preset twin device parameter thresholds, the following method is proposed: Synchronize data from a physical photovoltaic power station using a 3D twin photovoltaic power station, monitor the operating parameters of the twin device in real time, and compare the real-time monitored operating parameters with the corresponding preset twin device parameter thresholds. This method obtains the preset twin device parameter thresholds that match the core parameter characteristics of different types of twin devices.

[0075] To obtain the first preset parameter thresholds for photovoltaic (PV) twin devices based on the sensitivity characteristics of meteorological data, these thresholds are determined by statistically analyzing various parameters of the PV twin devices under different historical meteorological data and conditions. For example, the temperature difference threshold between adjacent PV modules is set to no more than 5 degrees Celsius under normal weather conditions and no more than 8 degrees Celsius under extreme weather conditions. In addition to statistical methods, a parameter threshold model for PV twin devices can be trained using historical meteorological data and various parameter data under different historical meteorological data conditions. Real-time meteorological data and specific parameters of the PV twin devices are input into the parameter threshold model to obtain the corresponding parameter thresholds.

[0076] To address the issue of substation twin equipment, a second preset parameter threshold for matching electrical parameter data is obtained. This threshold is determined by statistically analyzing various parameter data of the substation twin equipment under different historical real-time loads and combinations thereof. For example, the transformer oil temperature threshold is set to 90 degrees Celsius during peak electricity demand and 85 degrees Celsius during off-peak electricity demand, but the maximum limit for the top layer oil temperature should generally not exceed 95 degrees Celsius. Furthermore, a parameter threshold model for substation twin equipment can be trained using various parameter data of photovoltaic twin equipment under different historical real-time loads and conditions. The real-time load and specific parameters of the substation twin equipment are then input into the parameter threshold model to obtain the corresponding parameter thresholds.

[0077] To obtain a third preset parameter threshold for the collector twin device based on the sensitivity characteristics of meteorological data and electrical parameter data, the threshold is determined by statistically analyzing various parameter data of the collector twin device under different historical meteorological data, different real-time loads, and different combinations of historical meteorological data and real-time loads. Furthermore, a parameter threshold model for the collector twin device can be obtained by training with various parameter data of the collector twin device under different historical real-time meteorological data, different combinations of real-time loads, and different combinations of historical real-time meteorological data and real-time loads. The real-time load, real-time meteorological data, and specific parameters of the collector twin device are input into the parameter threshold model to obtain the specific parameter thresholds of the collector twin device.

[0078] S4. Based on the operating parameters of the twin devices obtained from monitoring, predict the independent twin devices with fault risks or the twin devices that are fault sources, automatically generate inspection tasks, and use the optimal path planning algorithm to obtain inspection paths to assist inspection personnel in carrying out inspections.

[0079] Specifically, besides considering comprehensive monitoring of photovoltaic areas, substations, and power collection lines based on 3D twin photovoltaic power station models, including functions such as equipment status monitoring, historical data querying, and early warning information acquisition, 3D twin photovoltaic power station models can also be used to implement intelligent inspection applications for photovoltaic power stations. This can predict equipment failure trends, optimize inspection paths, reduce unplanned downtime, and improve the overall performance of the power station. Specifically, this includes:

[0080] The system performs correlation clustering analysis on the operating parameters of the monitored twin devices. The analyzed operating parameters are then input into either an independent twin device prediction model or a related twin device fault prediction model based on deep learning. Specifically, operating parameters belonging to independent twin devices are assigned to the independent twin device prediction model, while the analyzed twin devices and their corresponding related twin devices are input into the related twin device fault prediction model. This predicts which independent twin devices or twin devices are at risk of failure, and simultaneously obtains the type and level of the predicted fault risk. Inspection tasks are automatically generated based on the locations of the twin devices predicted to have fault risk; each location of a twin device at fault risk is identified as an inspection task point. An optimal path planning algorithm is used to obtain the inspection path to assist inspection personnel in their work.

[0081] Furthermore, considering that the required inspection personnel and equipment differ under different fault risk conditions, in order to further improve inspection efficiency, personnel and equipment can be pre-allocated for each inspection task point and directly reflected in the inspection task; the method also includes:

[0082] In the process of predicting the risk of failure for independent twin devices or twin devices that are fault sources, and automatically generating inspection tasks for twin devices predicted to have a risk of failure, for each twin device predicted to have a risk of failure, the selection strategy for inspection equipment and the selection strategy for inspection personnel are obtained and matched according to the risk type and level of the predicted failure risk. Inspection equipment is selected according to the matched inspection equipment selection strategy, and inspection personnel are selected according to the matched inspection personnel selection strategy, thus generating the corresponding inspection task for the twin device predicted to have a risk of failure. Among them, different types of twin devices are preset with appropriate inspection equipment types and inspection personnel levels under different combinations of fault types and levels. The specific matching rules can be set according to expert suggestions or determined according to the matching rules that achieve the preset inspection efficiency based on historical inspection efficiency. For example, if the twin device in the photovoltaic area is at high risk of overheating, it is matched with a drone + infrared light inspection equipment and inspection personnel of level L4 (L4>L2). If it is at medium risk of overheating, it is matched with a handheld EL detector and inspection personnel of level L2.

[0083] In the process of obtaining inspection paths using optimal path planning algorithms, all inspection tasks generated corresponding to twin devices predicted to have fault risks are statistically analyzed. This distinguishes between independent twin devices with fault risks and twin devices with related relationships. For each set of inspection task points corresponding to several independent twin devices in a photovoltaic area, several independent twin devices in a substation, several independent twin devices in a collector line, and twin devices and their related twin devices, a pre-matched optimal path planning algorithm is obtained, resulting in the optimal path aggregation for multiple inspection task point sets. Specifically, [the specific details are not provided in the original text]. The optimal path planning algorithm for pre-matching inspection task points of the same type of set can be set according to expert suggestions or determined based on the optimal path planning algorithm that matches the preset inspection efficiency based on historical inspection efficiency. For example: for multi-target inspection task points in the set of inspection task points corresponding to several independent twin devices in photovoltaic areas, an improved ant colony algorithm is used; for point-to-point sets in the set of inspection task points corresponding to several independent twin devices in substations, the A* algorithm is used; for the set of inspection task points corresponding to several independent twin devices in collector lines, the three-dimensional Dijkstra algorithm is used; and for the set of inspection task points corresponding to twin devices and their associated twin devices, a genetic algorithm is used.

[0084] In one specific embodiment, in addition to using a 3D twin photovoltaic power station model to display the photovoltaic power station scene, monitor its operation, and implement intelligent alarms and intelligent inspections, the method can also use the 3D twin photovoltaic power station model to perform defect management after issuing an intelligent alarm. The method further includes:

[0085] Considering that the twin devices generating fault alarms include independent devices and devices that conform to the fault propagation chain pattern, corresponding fault diagnosis models are pre-built to more accurately determine the fault type and level. Specifically, different types of twin devices have pre-set matching twin device fault diagnosis models built based on deep learning, and different combinations of related twin devices have pre-set matching related twin device fault diagnosis models built based on deep learning.

[0086] Based on the operating monitoring parameters of the independent twin device that generates the fault alarm, or the twin device belonging to the fault source and the corresponding associated twin device, the corresponding input is matched with the twin device fault diagnosis model or the associated twin device fault diagnosis model, and the fault diagnosis results are obtained, including: fault type and level.

[0087] In addition to conducting inspections based on predicted fault points, the operation and maintenance task points can also be determined based on the location of the fault source twin device that generates the fault alarm and its associated twin device or independent twin device. The optimal path planning algorithm is used to obtain the operation and maintenance path to assist operation and maintenance personnel in performing fault operation and maintenance.

[0088] In addition, for defect management, the distribution patterns of defects in various types of twin devices can be statistically analyzed to generate defect heat maps; the correlation patterns of defects across different types of twin devices can be analyzed to establish causal chains and generate correlation relationships between twin devices across different types.

[0089] In one specific embodiment, in addition to using a 3D twin photovoltaic power station model to display the photovoltaic power station scene, monitor its operation, and implement intelligent alarms and intelligent inspections, the method can also use the 3D twin photovoltaic power station model to perform decision analysis and report generation after intelligent alarms. The method further includes:

[0090] Considering the characteristics of different regions or cities and different photovoltaic power plants, some regions or cities and some photovoltaic power plants are important electricity consumption areas. For these key areas, more accurate fault diagnosis and response are needed.

[0091] Obtain the location of the fault source twin device that generates the fault alarm and its associated twin devices or independent twin devices; determine whether there are any twin devices of the fault source twin device that generates the fault alarm and its associated twin devices or independent twin devices located in a preset key location; based on the result of whether the fault source twin device that generates the fault alarm and its associated twin devices or independent twin devices are located in a preset key location, as well as the fault type and level, match the corresponding fault response strategy level.

[0092] Among them, whether the twin device that generates the fault alarm is located in a preset key location and whether there are preset matching fault response strategy levels under different combinations of fault types and levels; different levels of fault response strategies include: directly obtaining equipment fault handling suggestions that match the fault type and level, selecting experts to assist in fault decision-making and then obtaining equipment fault handling suggestions that match the fault type and level, or directly obtaining equipment fault handling suggestions that match the fault type and level after experts make fault decisions.

[0093] Specifically, for fault response strategy level 1 (the twin device generating the fault alarm is not located in a preset key location, and the fault type and fault level are not limited), direct fault handling suggestions matching the fault type and level are obtained; for fault response strategy level 2 (the twin device generating the fault alarm is located in a preset key location, the fault type is a common fault type, and the fault level is not limited), the corresponding operating monitoring parameters of the fault source twin device generating the fault alarm and its associated twin devices or independent twin devices are sent to experts for assisted fault decision-making, and after joint fault decision, equipment fault handling suggestions matching the fault type and level are obtained; for fault response strategy level 3 (the twin device generating the fault alarm is located in a preset key location, the fault type is an unconventional fault type, and the fault level is not limited), the corresponding operating monitoring parameters of the fault source twin device generating the fault alarm and its associated twin devices or independent twin devices are sent to experts for fault decision-making, and after the experts make a fault decision, equipment fault handling suggestions matching the fault type and level are obtained.

[0094] Following the above fault decision analysis, a fault report is generated based on the fault results of the fault-generating twin device and its associated or independent twin devices, along with corresponding fault handling suggestions. Similarly, operational reports for each twin can also be generated according to user needs.

[0095] In one specific embodiment, besides using a 3D twin photovoltaic power station model to display the photovoltaic power station scene, perform operation monitoring, and realize intelligent alarms and intelligent inspections, the method can also generate and process work orders when performing fault maintenance or predictive fault inspections after intelligent alarms are triggered using the 3D twin photovoltaic power station model, thereby improving the efficiency of maintenance or inspections. The method further includes:

[0096] Record the fault source twin device that generates the fault alarm and the corresponding fault diagnosis results of its associated twin device or independent twin device. For each recorded twin device, generate a work order containing location coordinates, fault type and level, and historical fault data, and associate it with the maintenance manual of the twin device to create an operation and maintenance work order. For each operation and maintenance work order, record the entire lifecycle information of the creation, allocation, execution and closure of the inspection work order.

[0097] It can also record the fault prediction results of twin devices and their associated twin devices or independent twin devices that are predicted to have fault risks. For each recorded twin device, it generates a work order containing location coordinates, predicted fault type and level, and historical fault data, and associates it with the maintenance manual of the twin device to create an inspection work order. For each inspection work order, it records the entire lifecycle information of the work order creation, allocation, execution and closure.

[0098] like Figure 2 As shown in the figure, this application discloses a remote monitoring system for a photovoltaic power station based on three-dimensional digital twin technology, specifically including:

[0099] The 3D twin photovoltaic power station construction module 101 is used to construct a 3D twin photovoltaic power station, including a photovoltaic area twin, a substation twin, and a collector line twin. During the construction of the 3D twin photovoltaic power station, the coordinate positions of the twin devices are marked to realize the visualization of the real-world base map and the vectorization of the matrix of different types of twins. A 3D linkage dashboard is designed to display the core parameters of different types of twin devices and the correlation indicators between cross-type twin devices.

[0100] The 3D twin photovoltaic power station application module 102 is used to synchronize data from the physical photovoltaic power station with the 3D twin photovoltaic power station, monitor the real-time operating parameters of the twin devices, and compare the real-time monitored operating parameters with the corresponding preset twin device parameter thresholds. Based on the comparison results, it determines whether an initial alarm is triggered. For a single twin device that is determined to have triggered an initial alarm, it is determined whether there are related twin devices that triggered the initial alarm and whether they conform to the fault propagation chain law. If so, a linkage tracing analysis of the operating parameters of the single twin device that triggered the initial alarm and its related twin devices is performed. Identify the twin device corresponding to the fault source and generate a fault alarm for the corresponding twin device; otherwise, directly generate a fault alarm for the independent twin device. Perform correlation clustering analysis on the operating parameters of the twin devices obtained from monitoring, and input the analyzed operating parameters into the independent twin device prediction model or the associated twin device fault prediction model based on deep learning to predict the independent twin devices with fault risk or the twin devices that belong to the fault source. Automatically generate inspection tasks for the locations of the twin devices predicted to have fault risk, and use the optimal path planning algorithm to obtain the inspection path to assist the inspection personnel in carrying out the inspection.

[0101] The 3D twin photovoltaic power station application module 102 is also used to input the corresponding matching twin device fault diagnosis model or related twin device fault diagnosis model and obtain the fault diagnosis results based on the operation monitoring parameters of the independent twin device that generates the fault alarm or the twin device belonging to the fault source and the corresponding associated twin device, including: fault type and level; wherein, different types of twin devices have preset matching twin device fault diagnosis models based on deep learning, and for different combinations of associated twin devices, preset matching related twin device fault diagnosis models based on deep learning; obtain the location of the fault source twin device that generates the fault alarm and its associated twin device or independent twin device, determine whether the fault source twin device that generates the fault alarm and its associated twin device or independent twin device are located in a preset key location; and match the fault response strategy level according to the result of whether the fault source twin device that generates the fault alarm and its associated twin device or independent twin device are located in a preset key location and the fault type and level.

[0102] The 3D twin photovoltaic power station application module 102 is also used to record the fault source twin device that generates the fault alarm and its associated twin device or independent twin device corresponding to the fault diagnosis results. For each recorded twin device, a work order containing location coordinates, fault type and level, and historical fault data is generated, and the maintenance manual of the twin device is associated to create an operation and maintenance work order. For each operation and maintenance work order, the creation, allocation, execution and closure of the inspection work order are recorded.

[0103] In one specific embodiment, the system further includes a three-dimensional twin photovoltaic power station construction optimization module 103.

[0104] This is used to obtain matching twin modeling enhancement strategies based on the core parameter characteristics of different types of twin devices during the construction of a 3D twin photovoltaic power station, and to optimize the modeling of different types of twins accordingly; the twin modeling enhancement strategies include twin modeling and visualization optimization strategies and twin interaction design optimization strategies.

[0105] In one specific embodiment, the system further includes a 3D twin photovoltaic power station application optimization module 104, used to synchronize data from the physical photovoltaic power station using the 3D twin photovoltaic power station, and to monitor the real-time operating parameters of the twin devices. During the comparison of the real-time monitored operating parameters with corresponding preset twin device parameter thresholds, preset twin device parameter thresholds are obtained based on the core parameter characteristics of different types of twin devices, achieving dynamic adjustment of the preset twin device parameter thresholds. It is also used to predict the location of independent twin devices or twin devices belonging to fault sources with potential fault risks, and to automatically generate inspection tasks for twin devices predicted to have potential fault risks. For each twin device predicted to have potential fault risks, the system obtains and matches the twin device's inspection equipment selection strategy and inspection progress based on the predicted fault risk type and level. The inspection personnel selection strategy selects inspection equipment and personnel according to the matching inspection equipment selection strategy, generating corresponding inspection tasks for the twin equipment predicted to have fault risks. During the process of obtaining inspection paths using the optimal path planning algorithm, all generated inspection tasks for twin equipment predicted to have fault risks are statistically analyzed. This distinguishes between independent twin equipment with fault risks and twin equipment with related relationships. For each set of inspection task points corresponding to several independent twin equipment in the photovoltaic area, several independent twin equipment in the substation, several independent twin equipment in the collector line, and twin equipment and its related twin equipment, the pre-matched optimal path planning algorithm is obtained, resulting in the optimal path aggregation for multiple inspection task point sets.

[0106] Using the above system, remote monitoring of the entire photovoltaic power station based on 3D digital twin technology can be achieved; for example... Figure 3 As shown, after the physical data of the photovoltaic power station is collected and processed and synchronized to the twin system, the photovoltaic power station is visualized based on the 3D modeling and visualization layer, various applications are performed based on the application layer, and various interactions are completed based on the user interaction layer. The user interaction is set up with security and permission management, such as controlling the user's access and operation permissions to the system according to the user's role and permissions to ensure the security of the system. Among them, the interaction data uses SSL / TLS encryption technology to ensure the secure transmission of data and AES encryption algorithm to ensure the secure storage of data to prevent data leakage and tampering.

[0107] This application also discloses a computer-readable storage medium.

[0108] Specifically, the computer-readable storage medium stores a computer program that can be loaded by a processor and executed, such as the aforementioned remote monitoring method for photovoltaic power plants based on three-dimensional digital twin technology. The computer-readable storage medium includes, for example, various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0109] This application also discloses a computer device.

[0110] Specifically, the computer device includes a memory and a processor. The memory stores a computer program that can be loaded and executed by the processor to perform the aforementioned remote monitoring method for photovoltaic power plants based on three-dimensional digital twin technology.

[0111] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A method for remote monitoring of photovoltaic power plants based on three-dimensional digital twin technology, characterized in that, include: Construct a 3D twin photovoltaic power station, including a photovoltaic area twin, a substation twin, and a collector line twin; during the construction of the 3D twin photovoltaic power station, the coordinate positions of the twin equipment are marked to realize the visualization of the real-world base map and the vectorization of the matrix of different types of twins; A 3D interactive dashboard was designed to display the core parameters of different types of twin devices and the correlation indicators between cross-type twin devices; The data of the physical photovoltaic power station is synchronized using a three-dimensional twin photovoltaic power station, and the operating parameters of the twin equipment are monitored in real time. The real-time monitored operating parameters are compared with the corresponding preset twin equipment parameter thresholds, and the initial alarm is triggered based on the comparison results. For a single twin device that triggers the initial alarm, determine whether there is an associated twin device that triggers the initial alarm and whether it conforms to the fault propagation chain rule with the associated twin device that triggers the initial alarm; if so, perform a linkage source tracing analysis of the operating parameters of the single twin device that triggers the initial alarm and its associated twin devices to determine the twin device corresponding to the fault source and generate a fault alarm for the twin device corresponding to the fault source. Otherwise, directly generate a fault alarm for the independent twin device; The system performs correlation clustering analysis on the operating parameters of the twin devices acquired through monitoring. The analyzed operating parameters are then input into a pre-built deep learning-based prediction model for independent twin devices or a fault prediction model for associated twin devices. This predicts which independent twin devices are at risk of failure or which twin devices are considered fault sources. Inspection tasks are automatically generated based on the locations of the predicted fault-risk twin devices, and optimal path planning algorithms are used to obtain inspection paths to assist inspection personnel. The system also includes: In the process of constructing a 3D twin photovoltaic power station, a matching twin modeling enhancement strategy is obtained based on the core parameter characteristics of different types of twin devices, and the modeling of different types of twins is optimized accordingly. The photovoltaic area twin obtains a matching first twin modeling enhancement strategy based on the sensitive characteristics of meteorological data; the first twin modeling enhancement strategy includes: modeling each photovoltaic panel independently and designing an independent photovoltaic panel operating parameter dashboard; integrating meteorological data of the photovoltaic power station and rendering the light intensity, shadow projection, and coverage area of ​​the photovoltaic panel model in real time based on the meteorological data of the photovoltaic power station; and designing a heat map layer to represent the power generation efficiency of the photovoltaic area with color gradients. The substation twin obtains a matching second twin modeling enhancement strategy based on the sensitive characteristics of power parameters; the second twin modeling enhancement strategy includes: designing a load heat map to dynamically display the power flow of the substation; designing an equipment health layer to represent the substation equipment health mapping with color gradients; and designing an independent substation operating parameter waveform trend dashboard. The collector line twin obtains a matching third twin modeling enhancement strategy based on the sensitive characteristics of meteorological data and power parameters; the third twin modeling enhancement strategy includes: rendering the line in layers according to voltage level, distinguishing cable types with different colors; designing a current density heat map to display the load rate on the cable path; and simulating arc effects at corresponding locations based on the collector line fault data.

2. The method for remote monitoring of photovoltaic power plants based on three-dimensional digital twin technology according to claim 1, characterized in that, Also includes: By synchronizing data from a physical photovoltaic power station with a three-dimensional twin photovoltaic power station, and monitoring the operating parameters of the twin equipment in real time, the system compares the real-time monitored operating parameters with the corresponding preset twin equipment parameter thresholds. Based on the core parameter characteristics of different types of twin equipment, the system obtains the preset twin equipment parameter thresholds that match them, thereby achieving dynamic adjustment of the preset twin equipment parameter thresholds. The first preset parameter threshold for photovoltaic twin devices is obtained based on the sensitive characteristics of meteorological data. This threshold is determined by statistically analyzing various parameter data of photovoltaic twin devices under different historical meteorological data and different historical meteorological data conditions. For substation twin equipment, a second preset twin equipment parameter threshold is obtained based on the sensitive characteristics of electrical parameter data. This threshold is determined by statistically analyzing various parameter data of substation twin equipment under different historical real-time loads and different combinations of historical real-time loads. For the collector line twin equipment, a third preset twin equipment parameter threshold is obtained based on the sensitivity characteristics of meteorological data and electrical parameter data. This threshold is determined by statistically analyzing various parameter data of the collector line twin equipment under different historical meteorological data and different real-time loads, as well as combinations of different historical meteorological data and different real-time loads.

3. The method for remote monitoring of photovoltaic power plants based on three-dimensional digital twin technology according to claim 1, characterized in that, Also includes: In the process of predicting independent twin devices or twin devices that are fault sources and automatically generating inspection tasks for twin devices predicted to have fault risks, for each twin device predicted to have fault risks, the inspection equipment selection strategy and inspection personnel selection strategy for the twin device are obtained and matched according to the risk type and level of the predicted fault risks. Inspection equipment is selected according to the matched inspection equipment selection strategy, and inspection personnel are selected according to the matched inspection personnel selection strategy, thereby generating the corresponding inspection task for the twin device predicted to have fault risks. Among them, different types of twin devices are preset with appropriate inspection equipment types and inspection personnel levels under different combinations of fault types and levels. In the process of obtaining inspection paths using the optimal path planning algorithm, the generated inspection tasks corresponding to all twin devices predicted to have fault risks are statistically analyzed. The independent twin devices with fault risks or twin devices with related relationships are distinguished. For the inspection task point sets corresponding to several independent twin devices in the photovoltaic area, several independent twin devices in the substation, several independent twin devices in the collector line, and twin devices and their related twin devices, the optimal path planning algorithm that is pre-matched is obtained for each of them, and the optimal path aggregation of multiple inspection task point sets is obtained.

4. The method for remote monitoring of photovoltaic power plants based on three-dimensional digital twin technology according to claim 1, characterized in that, Also includes: Based on the operating monitoring parameters of the independent twin device that generates the fault alarm, or the twin device belonging to the fault source and the corresponding associated twin device, the corresponding input is matched with the twin device fault diagnosis model or the associated twin device fault diagnosis model, and the fault diagnosis result is obtained, including: fault type and level; wherein, different types of twin devices have pre-set matching twin device fault diagnosis models based on deep learning, and for different combinations of associated twin devices, pre-set matching associated twin device fault diagnosis models based on deep learning; Obtain the location of the fault source twin device that generates the fault alarm and its associated twin devices or independent twin devices; determine whether there are any twin devices of the fault source twin device that generates the fault alarm and its associated twin devices or independent twin devices located in a preset key location; based on the result of whether the fault source twin device that generates the fault alarm and its associated twin devices or independent twin devices are located in a preset key location, as well as the fault type and level, match the corresponding fault response strategy level. Among them, whether the twin device that generates the fault alarm is located in a preset key location and whether there are preset matching fault response strategy levels under different combinations of fault types and levels; different levels of fault response strategies include: directly obtaining equipment fault handling suggestions that match the fault type and level, selecting experts to assist in fault decision-making and then obtaining equipment fault handling suggestions that match the fault type and level, or directly obtaining equipment fault handling suggestions that match the fault type and level after experts make fault decisions.

5. The method for remote monitoring of photovoltaic power plants based on three-dimensional digital twin technology according to claim 4, characterized in that, Also includes: Record the fault source twin device that generates the fault alarm and the corresponding fault diagnosis results of its associated twin device or independent twin device. For each recorded twin device, generate a work order containing location coordinates, fault type and level, and historical fault data, and associate it with the maintenance manual of the twin device to create an operation and maintenance work order. For each operation and maintenance work order, record the entire lifecycle information of the creation, allocation, execution and closure of the inspection work order.

6. The method for remote monitoring of photovoltaic power plants based on three-dimensional digital twin technology according to claim 1, characterized in that, Also includes: During the construction of the 3D twin photovoltaic power station, a twin device search dashboard with a tree-like device list was also designed to support users in querying each twin device through fuzzy search and displaying the real-time operating parameters of the queried twin devices.

7. A remote monitoring system for photovoltaic power plants based on three-dimensional digital twin technology, characterized in that, include: The 3D twin photovoltaic power station construction module is used to construct 3D twin photovoltaic power stations, including photovoltaic area twins, substation twins, and collector line twins. During the construction of the 3D twin photovoltaic power station, the coordinate positions of the twin devices are marked to realize the visualization of the real-world base map and the vectorization of the matrix of different types of twins. A 3D interactive dashboard was designed to display the core parameters of different types of twin devices and the correlation indicators between cross-type twin devices; The 3D twin photovoltaic power station application module is used to synchronize the data of the physical photovoltaic power station with the 3D twin photovoltaic power station, monitor the real-time operating parameters of the twin equipment, compare the real-time monitored operating parameters with the corresponding preset twin equipment parameter thresholds, and determine whether to trigger an initial alarm based on the comparison results. For a single twin device that triggers the initial alarm, it is determined whether there is an associated twin device that also triggers the initial alarm, and whether this associated twin device conforms to the fault propagation chain rule. If so, a linkage source analysis of the operating parameters of the single twin device that triggers the initial alarm and its associated twin devices is performed to determine the twin device corresponding to the fault source and generate a fault alarm for the corresponding twin device. Otherwise, an independent twin device fault alarm is directly generated. The association clustering analysis is performed on the operating parameters of the twin devices obtained by monitoring. The analyzed operating parameters are then input into a pre-built independent twin device prediction model or an associated twin device fault prediction model based on deep learning to predict independent twin devices with fault risk or twin devices that belong to the fault source. Inspection tasks are automatically generated for the locations of the twin devices predicted to have fault risk, and the optimal path planning algorithm is used to obtain the inspection path to assist inspection personnel in carrying out inspections. The 3D twin photovoltaic power station construction optimization module is used to obtain matching twin modeling enhancement strategies based on the core parameter characteristics of different types of twin devices during the construction of a 3D twin photovoltaic power station, and optimize the modeling of different types of twins accordingly. The photovoltaic twin obtains a first twin modeling enhancement strategy that matches the sensitive characteristics of meteorological data; The first twin modeling enhancement strategy includes: independently modeling each photovoltaic panel module and designing an independent photovoltaic panel module operation parameter dashboard; integrating photovoltaic power station meteorological data and rendering the photovoltaic panel module's light intensity, shadow projection, and coverage area in real time based on the photovoltaic power station meteorological data; designing a heat map layer to represent the photovoltaic area's power generation efficiency with color gradients; the substation twin obtains a matching second twin modeling enhancement strategy based on the power parameter sensitivity characteristics; the second twin modeling enhancement strategy includes: designing a load heat map to dynamically display the substation's power flow; designing an equipment health layer to represent the substation equipment health mapping with color gradients; designing an independent substation operation parameter waveform trend dashboard; the collector line twin obtains a matching third twin modeling enhancement strategy based on the meteorological data sensitivity characteristics and power parameter sensitivity characteristics; the third twin modeling enhancement strategy includes: rendering lines in layers according to voltage levels and distinguishing cable types with different colors; designing a current density heat map to display the load rate on the cable path; and simulating arc effects at corresponding locations based on collector line fault data.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform the method as described in any one of claims 1 to 6.

9. A computer device, characterized in that, The computer device includes a memory, a processor, and a program stored in and executable on the memory, the program being executed by the processor to implement the steps of the method as described in any one of claims 1 to 6.

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