Centralized photovoltaic power station full life cycle intelligent management and optimization system

Abstract

The invention provides a centralized photovoltaic power station full-life-cycle intelligent management and optimization system. The system comprises a quantum sensing network module used for collecting high-precision data and performing secure transmission; the intelligent prediction and optimization engine module is used for fault prediction and operation optimization; the self-adaptive photovoltaic power station management module is used for dynamic optimization and power grid access; the ecological balance and environmental protection module is used for maintaining an ecological system; the intelligent operation and safety management module is used for remote monitoring and autonomous maintenance; the energy conversion and storage system module is used for diversified energy utilization; and the virtual reality and augmented reality interaction module is used for remote training and maintenance guidance. According to the invention, high-precision data acquisition, intelligent fault prediction, dynamic layout optimization and efficient operation and maintenance of the centralized photovoltaic power station are realized by integrating advanced technologies such as a quantum technology, artificial intelligence and a block chain, and the management efficiency, the operation reliability and the ecological friendliness of the photovoltaic power station are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of large-scale centralized photovoltaic power stations on the ground, and in particular to a full life cycle intelligent management and optimization system for centralized photovoltaic power stations. Background Art

[0002] As a clean and renewable form of energy, solar energy plays an increasingly important role in the global energy transformation and the realization of carbon neutrality goals. Large-scale centralized photovoltaic power stations are built in areas with abundant solar resources, vast and relatively cheap land, such as deserts and Gobi, which can make full use of solar resources and achieve efficient solar energy conversion. However, the power station consists of a large photovoltaic array composed of thousands of photovoltaic modules, facing a more complex operating environment and higher technical requirements. The full life cycle management of large-scale centralized photovoltaic power stations involves multiple stages such as site selection, design, construction, operation, maintenance and decommissioning. Each stage requires comprehensive consideration of technical, economic and environmental factors.

[0003] At present, the management of photovoltaic power stations mainly relies on traditional monitoring systems and manual operations. These systems usually use conventional sensors for data collection, use simple mathematical models for analysis and prediction, and conduct operation management through manual decision-making. In terms of inverter and photovoltaic array maintenance, most of them adopt regular maintenance or post-fault repair strategies, which are inefficient and costly. In addition, the existing management system often lacks comprehensive consideration of the natural ecological environment, which is not conducive to long-term sustainable development.

[0004] With the continuous expansion of the scale of large photovoltaic bases and the increasingly wide distribution range, traditional management methods have been unable to meet the growing efficiency and reliability needs. In particular, in terms of data collection accuracy, fault prediction capability, layout optimization design, ecological impact assessment, operation and maintenance efficiency, and energy utilization, the existing technology still has many shortcomings. These problems not only affect the economic benefits of photovoltaic power generation, but also partially restrict the further development of the photovoltaic industry. Therefore, an innovative system that can comprehensively improve the management level of photovoltaic power stations is urgently needed to meet the current technical challenges. Summary of the invention

[0005] The purpose of the present invention is to propose a centralized photovoltaic power station full life cycle intelligent management and optimization system to solve the problems of the prior art.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is: A centralized photovoltaic power station full life cycle intelligent management and optimization system, including: Quantum perception network module for collecting high-precision data and secure transmission; Intelligent prediction and optimization engine module, used for fault prediction and operation optimization; Adaptive photovoltaic power plant management module for dynamic optimization and grid access; Ecological balance and environmental protection module, used to maintain the ecosystem; Intelligent operation and security management module for remote monitoring and autonomous maintenance; Energy conversion and storage system modules for diversified energy utilization; Virtual reality and augmented reality interactive modules for remote training and maintenance guidance.

[0007] Further preferably, the quantum perception network module includes: Diamond quantum sensors based on nitrogen vacancy centers have nano-Tesla-level sensitivity; Quantum entanglement distributed measurement system, used to achieve remote synchronous measurement; The quantum key distribution system based on the E91 protocol has a key generation rate greater than 1Mbps.

[0008] Further preferably, the intelligent prediction and optimization engine module includes: A hybrid architecture fault prediction model based on long short-term memory network and convolutional neural network; Reinforcement learning optimization module using the Proximal Policy Optimization algorithm.

[0009] Further preferably, the adaptive photovoltaic power station management module includes: Generative Adversarial Network (GAN) and Markov Chain Hybrid (MCM) model for dynamic optimization of photovoltaic power plants; Blockchain electricity trading platform based on Hyperledger Fabric.

[0010] Further preferably, the ecological balance and environmental protection module includes: Bio-electromagnetic field simulation system based on superconducting quantum interference device; Marine ecosystem balance prediction model based on graph neural network.

[0011] Further preferably, the intelligent operation and maintenance and safety management module includes: Magnetic adsorption climbing maintenance robot; Autonomous inspection drones with AI video and infrared thermal imaging capabilities; Holographic remote control system based on 5G network.

[0012] Further preferably, the energy conversion and storage system module includes: Mobile hydrogen production platform using proton exchange membrane electrolyzer; Hydrogen transport pipeline lined with stainless steel and high-strength composite materials.

[0013] Further preferably, the virtual reality and augmented reality interaction module includes: High-resolution and low-latency virtual reality remote training system; Augmented reality maintenance guidance system using optical waveguide display technology.

[0014] A method for implementing a centralized photovoltaic power station full life cycle intelligent management and optimization system, comprising: S1: Deploy a quantum sensing network to collect operating data of photovoltaic power plants; S2: Collect external environmental data of the photovoltaic power station through inspection drones; S3: Uses the intelligent prediction and optimization engine to combine the operating data in S1 and the environmental data in S2 to perform fault prediction, optimize operating parameters, power prediction, and environmental prediction; S4: According to the optimized operating parameters in S3, the operating parameters of the photovoltaic power station are adjusted through the adaptive photovoltaic power station management system; S5: Based on the fault prediction results obtained in S3, the maintenance robot is called to perform equipment maintenance through the intelligent operation and maintenance and safety management platform; S6: According to the power prediction results obtained in S3, the solar energy that cannot be connected to the grid is converted into hydrogen energy storage through the energy conversion and storage system; S7: According to the environmental prediction results obtained in S3, the photovoltaic power station operation strategy is adjusted through the adaptive photovoltaic power station management system; S8: Through the environmental data obtained in S2 and the maintenance process data obtained in S5, combined with the virtual reality and augmented reality interactive system, immersive training and remote operation experience are provided.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a centralized photovoltaic power station full life cycle intelligent management and optimization system, which realizes a comprehensive upgrade of centralized photovoltaic power station management by integrating multiple cutting-edge technologies such as quantum perception, artificial intelligence, blockchain and new materials. It significantly improves the accuracy and reliability of data collection, enhances the fault prediction and operation optimization capabilities, realizes dynamic optimization of power stations, and greatly improves the operation and maintenance efficiency and safety.

[0016] At the same time, the present invention also effectively reduces the impact on the ecological environment, improves energy efficiency, and provides strong support for sustainable development. In addition, the application of virtual reality and augmented reality technology optimizes the training and maintenance process and improves the skill level of personnel. In general, the present invention systematically solves the key problems in the full life cycle management of photovoltaic power stations, significantly improves the overall performance and economic benefits, reduces operating costs, and extends the service life of equipment. It provides innovative solutions for the rapid and healthy development of the photovoltaic industry, and helps promote the widespread application of clean energy and the optimization of energy structure.

[0017] The main advantages of the present invention are: 1. High-precision data collection: The quantum sensing network achieves nano-Tesla-level magnetic field measurement accuracy, greatly improving the accuracy of data collection; 2. Intelligent prediction and optimization: The intelligent engine based on deep learning and reinforcement learning significantly improves the fault prediction capability and operation parameter optimization effect; 3. Dynamic management: The adaptive photovoltaic power station management system can adjust the operation strategy according to the real-time situation to maximize the power generation efficiency; 4. Eco-friendly: The ecological balance and environmental protection module minimizes the impact of photovoltaic power plants on the ecosystem; 5. Efficient operation and maintenance: The intelligent operation and maintenance and security management platform greatly improves the operation and maintenance efficiency, reduces labor costs and security risks; 8. Improved training effect: Virtual reality and augmented reality interactive systems greatly improve training effect and maintenance quality; Through comprehensive intelligent management, the present invention can significantly improve the overall performance of photovoltaic power stations, including improving power generation efficiency, reducing operation and maintenance costs, extending equipment life, and reducing environmental impact. At the same time, the modular design and advanced system architecture of the present invention also provide a good foundation for future technology upgrades and functional expansion. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 : It is a module schematic diagram of a centralized photovoltaic power station full life cycle intelligent management and optimization system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0020] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0021] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0022] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0023] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0024] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0025] Example 1 As attached Figure 1 As shown, this embodiment provides a centralized photovoltaic power station full life cycle intelligent management and optimization system, including: 1) Quantum perception network module The quantum perception network module of this system is the foundation of the entire system, responsible for high-precision data collection and secure transmission. It mainly consists of the following three parts: 1.1) Diamond quantum sensors based on nitrogen vacancy centers Advanced sensors are installed in key parts of the photovoltaic array, such as the inverter MPPT branch and the photovoltaic module loop structure. It uses the quantum properties of nitrogen vacancy centers to achieve nano-Tesla-level magnetic field measurement accuracy.

[0026] Its sensitivity can be expressed by the following formula:

[0027] in, is the magnetic field measurement accuracy (unit: T), is Planck's constant, is the Lande factor (approximately 2, dimensionless), is the Bohr magneton, is the number of nitrogen vacancy centers, is the coherence time (in seconds).

[0028] This high-precision measurement can capture tiny changes in the operation of the photovoltaic system, providing a reliable data basis for fault prediction and performance optimization.

[0029] 1.2) Quantum entanglement distributed measurement system 50-100 quantum nodes are established within the wind farm to form a quantum sensing network. The system uses the quantum entanglement effect to achieve remote synchronous measurement, overcoming the synchronization error problem in traditional distributed measurement systems.

[0030] The system is based on Bell states:

[0031] in, represents the entangled state, and They represent the ground state and excited state of the two quantum bits respectively, and the subscripts A and B represent two remote locations.

[0032] In this way, the entire photovoltaic power station can be accurately and collaboratively measured to obtain comprehensive information on the overall operating status of the photovoltaic power station.

[0033] 1.3) Quantum Key Distribution System The quantum key distribution system using the E91 protocol has achieved a key generation rate of more than 1 Mbps. This method ensures the absolute security of data transmission, effectively prevents data leakage and hacker attacks, and provides a solid guarantee for the information security of the entire system.

[0034] 2) Intelligent prediction and optimization engine module The intelligent prediction and optimization engine module is the brain of the system, responsible for receiving and analyzing data, and performing fault prediction, optimizing operating parameters, power prediction, and environmental prediction.

[0035] Contains the following core components: 2.1) Hybrid architecture fault prediction model This model combines the advantages of long short-term memory network (LSTM) and convolutional neural network (CNN). LSTM is good at processing time series data, while CNN is good at extracting spatial features. Through this hybrid architecture model, the historical operation data and current operation status data of the equipment can be considered at the same time, which greatly improves the accuracy and timeliness of fault prediction; 2.2) Reinforcement Learning Optimization Module The reinforcement learning module of the Proximal Policy Optimization (PPO) algorithm is used to continuously optimize the operating parameters of the photovoltaic power station. This module finds the best operating strategy through continuous learning and adjustment, which not only increases power generation but also extends the service life of the equipment.

[0036] 3) Adaptive photovoltaic power station management module The adaptive photovoltaic power plant management module enables the photovoltaic power plant to self-adjust according to the real-time operating parameters, mainly including: 3.1) Generative Adversarial Network (GAN) + Markov Chain Hybrid (MCM) Model This model can accurately simulate the dynamics of photovoltaic power stations under complex meteorological conditions. By analyzing the impact of factors such as the atmosphere, cloud cover, aerosols and water vapor on solar radiation in real time, the system can dynamically adjust the operating parameters of the photovoltaic system to maximize power generation efficiency.

[0037] 3.2) Blockchain Power Trading Platform The blockchain power trading platform built on Hyperledger Fabric realizes safe, transparent and efficient power trading, simplifies the trading process and improves the efficiency of power station electricity trading.

[0038] 4) Ecological balance and environmental protection module This module is dedicated to reducing the impact of centralized photovoltaic power plants on natural ecosystems. Mainly include: 4.1) Bioelectromagnetic field simulation system The biological electromagnetic field simulation system based on superconducting quantum interference device (SQUID) can accurately simulate and monitor the electromagnetic environment of organisms. Through this system, the impact of photovoltaic power stations on the biological environment can be evaluated and corresponding protective measures can be taken.

[0039] 4.2) Natural ecosystem balance prediction model The natural ecosystem balance prediction model built based on graph neural network (GNN) can comprehensively evaluate the impact of centralized photovoltaic power stations on local ecosystems. This model takes into account the complex relationship between multiple environments and provides a scientific basis for ecological protection decision-making.

[0040] 5) Intelligent operation and maintenance and security management module The intelligent operation and maintenance and safety management module is designed to improve the operation and maintenance efficiency and safety of photovoltaic power plants.

[0041] Mainly include: 5.1) Magnetic climbing maintenance robot This robot can climb freely on the surface of photovoltaic panels to perform inspection and simple maintenance tasks, greatly reducing the risks of manual maintenance and improving maintenance efficiency and frequency.

[0042] 5.2) Autonomous Cruise UAV The drone equipment is equipped with AI video and infrared thermal imaging devices, and can fly autonomously according to the predetermined inspection route. It is capable of carrying out long-term and large-scale photovoltaic power station inspection tasks in relatively complex meteorological environments.

[0043] 5.3) Holographic remote control system The holographic remote control system based on the 5G network enables remote guidance and operation by experts, reduces the need for on-site attendance by experts, and improves the efficiency of problem solving.

[0044] 6) Energy conversion and storage system module The energy conversion and storage system improves the energy efficiency of centralized photovoltaic power plants, mainly including: 6.1) Mobile hydrogen production platform A mobile hydrogen production platform using a proton exchange membrane (PEM) electrolyzer with a hydrogen production efficiency of It can be expressed as:

[0045] in, is the number of moles of hydrogen produced (unit: mol), is the higher heating value of hydrogen (unit: J / mol), For input electric power (unit: W), part of the electricity that cannot be connected to the grid will be converted into hydrogen energy and stored, thus improving energy utilization and providing support for the future hydrogen energy economy.

[0046] 6.2) Hydrogen transportation pipeline The hydrogen transmission pipeline lined with stainless steel and high-strength composite materials ensures the safe and efficient transmission of hydrogen to the hydrogen consumption point.

[0047] 7) Virtual reality and augmented reality interaction module Virtual reality and augmented reality interactive systems improve training efficiency and maintenance quality, mainly including: 7.1) Virtual Reality Remote Training System The high-resolution, low-latency virtual reality remote training system provides operators with an immersive training experience.

[0048] 7.2) Augmented Reality Maintenance Guidance System The augmented reality maintenance guidance system using optical waveguide display technology provides on-site maintenance personnel with intuitive and real-time operation guidance.

[0049] The architecture and integration of this system are as follows: Adopt distributed microservice architecture to ensure the scalability of the system; Implement DevOps and GitOps methodologies for continuous integration and deployment; Use distributed time series database to store massive sensor data; Use federated learning technology to train AI models and protect data privacy; Implement a security model based on zero-trust architecture and homomorphic encryption technology to ensure system security.

[0050] It should be noted that in actual applications, the specific implementation of this system may need to be appropriately adjusted and optimized according to the geographical location, climate conditions, environment and other factors of the specific centralized photovoltaic power station. In addition, with the continuous development of related technologies, the various modules of this system can also be continuously upgraded to maintain its technological advancement and practicality.

[0051] Example 2 This embodiment provides a method for implementing a centralized photovoltaic power station full life cycle intelligent management and optimization system, including: S1: Deploy a quantum sensing network to collect operating data of photovoltaic power plants; S1-1: Install diamond quantum sensors based on nitrogen vacancy centers in key locations such as inverters, transformers and photovoltaic modules; S1-1: Establish a quantum entanglement distributed measurement system and set up 50-100 quantum nodes within the power station.

[0052] S1-3: Implement the E91 quantum key distribution protocol to ensure data transmission security.

[0053] S2: Collect external environmental data of the photovoltaic power station through inspection drones; S3: Uses the intelligent prediction and optimization engine to combine the operating data in S1 and the environmental data in S2 to perform fault prediction, optimize operating parameters, power prediction, and environmental prediction; S4: According to the optimized operating parameters in S3, the operating parameters of the photovoltaic power station are adjusted through the adaptive photovoltaic power station management system; S5: Based on the fault prediction results obtained in S3, the maintenance robot is called to perform equipment maintenance through the intelligent operation and maintenance and safety management platform; S5-1: Deploy a magnetic climbing maintenance robot to perform regular inspection tasks; S5-2: Regularly activate autonomous cruise drones to inspect photovoltaic power plants; S5-3: Enable the holographic remote control system to achieve remote guidance from experts; S6: According to the power prediction results obtained in S3, the solar energy that cannot be connected to the grid is converted into hydrogen energy storage through the energy conversion and storage system; S6-1: Operate a mobile hydrogen production platform to convert solar energy that cannot be connected to the grid into hydrogen energy; S6-2: Lay hydrogen pipelines to achieve efficient transmission of hydrogen energy; S7: According to the environmental prediction results obtained in S3, the photovoltaic power station operation strategy is adjusted through the adaptive photovoltaic power station management system; S8: Through the environmental data obtained in S2 and the maintenance process data obtained in S5, combined with the virtual reality and augmented reality interactive system, immersive training and remote operation experience are provided.

[0054] S8-1: Conduct VR-based remote training courses to improve personnel skills; S8-2: Use the AR maintenance guidance system to assist on-site maintenance operations.

[0055] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A centralized photovoltaic power station full life cycle intelligent management and optimization system, characterized in that: include: Quantum perception network module for collecting high-precision data and secure transmission; Intelligent prediction and optimization engine module, used for fault prediction and operation optimization; Adaptive photovoltaic power plant management module for dynamic optimization and grid access; Ecological balance and environmental protection module, used to maintain the ecosystem; Intelligent operation and security management module for remote monitoring and autonomous maintenance; Energy conversion and storage system modules for diversified energy utilization; Virtual reality and augmented reality interactive modules for remote training and maintenance guidance.

2. According to claim 1, a centralized photovoltaic power station full life cycle intelligent management and optimization system is characterized in that: The quantum perception network module includes: Diamond quantum sensors based on nitrogen vacancy centers have nano-Tesla-level sensitivity; Quantum entanglement distributed measurement system, used to achieve remote synchronous measurement; The quantum key distribution system based on the E91 protocol has a key generation rate greater than 1Mbps.

3. According to claim 1, a centralized photovoltaic power station full life cycle intelligent management and optimization system is characterized in that: The intelligent prediction and optimization engine module includes: A hybrid architecture fault prediction model based on long short-term memory network and convolutional neural network; Reinforcement learning optimization module using the Proximal Policy Optimization algorithm.

4. According to claim 1, a centralized photovoltaic power station full life cycle intelligent management and optimization system is characterized in that: The adaptive photovoltaic power station management module includes: Generative Adversarial Network (GAN) and Markov Chain Hybrid (MCM) model for dynamic optimization of photovoltaic power plants; Blockchain electricity trading platform based on Hyperledger Fabric.

5. According to claim 1, a centralized photovoltaic power station full life cycle intelligent management and optimization system is characterized in that: The ecological balance and environmental protection module includes: Bio-electromagnetic field simulation system based on superconducting quantum interference device; Marine ecosystem balance prediction model based on graph neural network.

6. According to claim 1, a centralized photovoltaic power station full life cycle intelligent management and optimization system is characterized in that: The intelligent operation and maintenance and safety management module includes: Magnetic adsorption climbing maintenance robot; Autonomous inspection drones with AI video and infrared thermal imaging capabilities; Holographic remote control system based on 5G network.

7. According to claim 1, a centralized photovoltaic power station full life cycle intelligent management and optimization system is characterized in that: The energy conversion and storage system module includes: Mobile hydrogen production platform using proton exchange membrane electrolyzer; Hydrogen transport pipeline lined with stainless steel and high-strength composite materials.

8. The intelligent management and optimization system for the entire life cycle of a centralized photovoltaic power station according to claim 1 is characterized in that: The virtual reality and augmented reality interaction module includes: High-resolution and low-latency virtual reality remote training system; Augmented reality maintenance guidance system using optical waveguide display technology.

9. A method for implementing a centralized photovoltaic power station full life cycle intelligent management and optimization system, characterized in that: The following steps are involved: S1: Deploy a quantum sensing network to collect operating data of photovoltaic power plants; S2: Collect external environmental data of the photovoltaic power station through inspection drones; S3: Uses the intelligent prediction and optimization engine to combine the operating data in S1 and the environmental data in S2 to perform fault prediction, optimize operating parameters, power prediction, and environmental prediction; S4: According to the optimized operating parameters in S3, the operating parameters of the photovoltaic power station are adjusted through the adaptive photovoltaic power station management system; S5: Based on the fault prediction results obtained in S3, the maintenance robot is called to perform equipment maintenance through the intelligent operation and maintenance and safety management platform; S6: According to the power prediction results obtained in S3, the solar energy that cannot be connected to the grid is converted into hydrogen energy storage through the energy conversion and storage system; S7: According to the environmental prediction results obtained in S3, the photovoltaic power station operation strategy is adjusted through the adaptive photovoltaic power station management system; S8: Through the environmental data obtained in S2 and the maintenance process data obtained in S5, combined with the virtual reality and augmented reality interactive system, immersive training and remote operation experience are provided.

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