Low-voltage distributed photovoltaic panoramic monitoring method and system, and computer device

Abstract

The invention discloses a low-voltage distributed photovoltaic panorama monitoring method and system and a computer device, and belongs to the technical field of photovoltaic power generation system monitoring and management. According to the low-voltage distributed photovoltaic panorama monitoring method, each node collects power generation, energy storage and load data in real time, and the energy strategy is automatically adjusted; and the global optimization is realized by cooperating with the adjacent nodes, and the dependence on a centralized system is eliminated. And a dynamic multi-node self-healing mechanism is introduced to ensure continuous operation of the system. According to the method, through distributed monitoring, autonomous decision making and intelligent self-healing mechanisms, the core pain point of a traditional centralized system is effectively solved, and an innovative solution is provided for efficient and reliable operation of a large-scale distributed photovoltaic system.

Description

Technical Field

[0001] The present invention belongs to the technical field of monitoring and management of photovoltaic power generation systems, and relates to a low-voltage distributed photovoltaic panoramic monitoring method and system, and a computer device. Background Art

[0002] With the continuous expansion of the application scale of distributed photovoltaic power generation systems, how to efficiently monitor and manage these systems has become a key problem to be solved urgently. Traditional monitoring methods mainly adopt a centralized architecture, that is, data of multiple photovoltaic nodes are centrally collected and processed through a central server, and then unified scheduling of the system is realized. In the early small-scale application scenarios, this centralized monitoring mode showed good operation effects due to its advantages of centralized management. However, with the expansion of the system scale and the increase of complexity, the limitations of the centralized monitoring mode gradually appear.

[0003] First of all, the communication burden under the centralized architecture increases significantly. When the number of photovoltaic nodes increases, the efficiency of data transmission and processing drops sharply, resulting in the system being difficult to respond to changes in energy demands in local areas in a timely manner. Especially in large-scale distributed systems, the communication bottleneck problem is particularly prominent, affecting the real-time performance and stability of the system. Secondly, the photovoltaic nodes in traditional systems lack the ability of autonomous decision-making and overly rely on the unified scheduling of the central system. This centralized management mode limits the ability of nodes to independently adjust power generation, energy storage, and load management strategies according to local environmental conditions, resulting in insufficient flexibility of the system in dealing with complex and changeable environments and poor overall response ability. In addition, existing systems have obvious shortcomings in dealing with node failures. Traditional monitoring modes usually rely on manual intervention or central instructions for fault handling and lack an effective self-healing mechanism. Once a sudden failure occurs, the system may not be able to recover quickly, resulting in a decrease in overall operation efficiency and even affecting power supply stability.

[0004] Therefore, in the face of the challenges of the expansion of the scale and the increase of complexity of distributed photovoltaic power generation systems, the traditional centralized monitoring mode is difficult to meet the actual application requirements in terms of communication efficiency, node autonomy, and fault response ability. To improve the overall performance and reliability of the system, it is urgent to explore more efficient and flexible distributed monitoring and management strategies to adapt to the development trend of future large-scale distributed energy systems. Summary of the Invention

[0005] Aiming at the problems existing in the prior art, the present invention provides a low-voltage distributed photovoltaic panoramic monitoring method and system, and a computer device, so as to solve the technical problems that the centralized monitoring mode of distributed photovoltaic power generation systems in the prior art has a large communication burden, weak node autonomous decision-making ability, and lack of an effective fault self-healing mechanism after the system scale expands.

[0006] The present invention is realized through the following technical solutions: A low-voltage distributed photovoltaic panoramic monitoring method includes the following steps: Obtain the energy status data of each photovoltaic node, where the energy status data includes power generation status data, energy storage status data, and load status data; Each node dynamically adjusts the power generation strategy, energy storage strategy, and load strategy of any photovoltaic node based on its own energy status data and the energy status data of neighboring nodes, and performs autonomous energy management and scheduling; When any photovoltaic node fails, self-diagnosis and self-healing are performed. If the faulty node cannot self-heal, the neighboring node takes over the load or energy supply of the faulty photovoltaic node.

[0007] Preferably, the energy status data is obtained through the environmental data, node electrical data, and equipment health status data of the photovoltaic node; The environmental data includes light intensity, temperature, humidity, and wind speed; The node electrical data includes current, voltage, and power.

[0008] Preferably, each node dynamically adjusts the power generation strategy, energy storage strategy, and load strategy of any photovoltaic node based on its own energy status data and the energy status data of neighboring nodes, and the specific implementation of autonomous energy management and scheduling is as follows: Pack the energy status data of each photovoltaic node, and send the packed energy status data to neighboring photovoltaic nodes through a wireless network to synchronize data among multiple photovoltaic nodes; The neighboring photovoltaic nodes calculate the energy flow direction in real time according to the received packed energy status data, and dynamically adjust the power generation strategy, energy storage strategy, and load strategy of the photovoltaic nodes to perform autonomous energy management and scheduling.

[0009] Preferably, each node dynamically adjusts the power generation strategy, energy storage strategy, and load strategy of any photovoltaic node based on its own energy status data and the energy status data of neighboring nodes, and the specific implementation of autonomous energy management and scheduling is as follows: Each photovoltaic node calculates the current energy surplus or deficit using an energy balance equation according to its own power generation, energy storage level, and current load; Based on the energy status of its own node and the energy status of neighboring nodes, it is judged whether there are nodes that need energy transmission, and the energy flow between nodes is dynamically adjusted according to the energy exchange formula to achieve dynamic energy scheduling; At the same time, during dynamic energy scheduling, for each load device, the node dynamically schedules according to the current available energy and device priority, so that key load devices are preferentially powered.

[0010] Preferably, the energy balance equation is:

[0011] Among them, is the power generation power of node at time moment, is the power released from the energy storage unit by node at time moment, is the load power demand of node at time moment, is the power generation priority weight, energy storage priority weight; The energy exchange formula is:

[0012] Among them, is the energy exchanged from node to node at time moment, is the energy exchange coefficient between node and node ; is the excess power of node at time moment, is the power gap of node at time moment.

[0013] Preferably, when any photovoltaic node fails, self-diagnosis and self-healing are performed. Specifically: Each node continuously monitors its own state through a preset autoregressive model, obtains the error value at the current moment, and compares the error value at the current moment with the preset error threshold. If the error value at the current moment is greater than the preset error threshold, mark the node as a potential failure node; Then perform self-healing by reducing the output power or switching to an alternative path. If the self-healing is successful, restore the node to the normal working state.

[0014] Preferably, if the failed node cannot self-heal, the adjacent node takes over the load or energy supply of the failed photovoltaic node. Specifically: Isolate the failed node, and when the failed node recovers, rejoin the system and restore the normal scheduling function of the node.

[0015] A low-voltage distributed photovoltaic panoramic monitoring system includes: A data acquisition module, which is used to obtain the energy state data of each photovoltaic node, and the energy state data includes power generation state data, energy storage state data and load state data; A data analysis and decision-making module, which is used to dynamically adjust the power generation strategy, energy storage strategy and load strategy of any photovoltaic node based on its own energy state data and the energy state data of neighboring nodes for each node, and perform autonomous energy management and scheduling; A fault diagnosis and self-healing module, which is used to perform self-diagnosis and self-healing when any photovoltaic node fails. If the faulty node cannot self-heal, the neighboring node takes over the load or energy supply of the faulty photovoltaic node.

[0016] A computer device includes a memory, a processor and a computer program stored on the memory, and the processor executes the computer program to implement the steps of the above method.

[0017] Compared with the prior art, the present invention has the following beneficial technical effects: In the low-voltage distributed photovoltaic panoramic monitoring method of the present invention, first, each photovoltaic node in this application conducts local communication through a self-organizing network, only needs to exchange data with neighboring nodes, greatly reducing the data transmission volume of the central node. The node makes real-time decisions based on local data (such as adjusting the power generation strategy), reducing the dependence on the central server and lowering the communication frequency and bandwidth requirements; second, each node autonomously generates an optimization strategy according to its own and neighboring nodes' energy states (power generation, energy storage, load) (such as preferentially using energy storage power supply or adjusting the inverter output), improving the system's real-time response ability, adapting to dynamic environmental changes, enhancing energy utilization efficiency, and improving the node's autonomous decision-making ability; in addition, in this application, the node monitors the health status in real time (such as component failure, communication interruption), tries to restart or switch to an alternative link. If self-healing fails, the neighboring node takes over the functions of the faulty node through dynamic load distribution or energy sharing, ensuring the continuous operation of the system. The multi-node cooperation avoids the spread of single-point failures, improves the system's robustness, significantly reduces the downtime, lowers the maintenance cost, and guarantees the high availability of the system. This method effectively solves the core pain points of traditional centralized systems through distributed monitoring, autonomous decision-making and intelligent self-healing mechanisms, providing an innovative solution for the efficient and reliable operation of large-scale distributed photovoltaic systems. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is a schematic flow chart of a low - voltage distributed photovoltaic panoramic monitoring method in Embodiment 1 of the present invention; Figure 2 It is a schematic flow chart of a low - voltage distributed photovoltaic panoramic monitoring method in Embodiment 2 of the present invention; Figure 3 It is a schematic hierarchical structure diagram of a low - voltage distributed photovoltaic panoramic monitoring method in Embodiment 2 of the present invention; Figure 4 It is a schematic structural diagram of a low - voltage distributed photovoltaic panoramic monitoring system in Embodiment 3 of the present invention. Detailed implementation manners

[0020] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. The components of the embodiments of the present invention usually described and illustrated herein can be arranged and designed in various different configurations.

[0021] Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of the present invention.

[0022] The present invention will be further described in detail below with reference to the accompanying drawings: Embodiment 1 As Figure 1 shown, the present invention discloses a low - voltage distributed photovoltaic panoramic monitoring method, including the following steps: S1: Obtain the energy state data of each photovoltaic node, where the energy state data includes power generation state data, energy storage state data, and load state data; The energy state data is obtained through the environmental data, node electrical data, and equipment health status data of the photovoltaic node; The environmental data includes light intensity, temperature, humidity, and wind speed; The node electrical data includes current, voltage, and power.

[0023] S2: Each node dynamically adjusts the power generation strategy, energy storage strategy, and load strategy of any photovoltaic node based on its own energy state data and the energy state data of neighboring nodes for autonomous energy management and scheduling; Specifically: Pack the energy status data of each photovoltaic node, and send the packed energy status data to neighboring photovoltaic nodes through a wireless network to synchronize the data among multiple photovoltaic nodes; During dynamic adjustment, each photovoltaic node calculates its current energy surplus or deficit using an energy balance equation based on its own power generation, energy storage level, and current load, and determines whether there are nodes that require energy transfer based on the energy status of its own node and neighboring nodes. Then, it dynamically adjusts the energy flow between nodes according to the energy exchange formula to achieve dynamic energy scheduling.

[0024] Among them, the energy balance equation is:

[0025] Among them, is the energy balance equation of node i at time t, is node at time the power generation power, is node at time the power released from the energy storage unit, is node at time the load power demand, is the power generation priority weight, the energy storage priority weight; The energy exchange formula is:

[0026] Among them, is at time the energy exchanged from node to node , is node and node the energy exchange coefficient between, is node at time the excess power, is node at time the power gap.

[0027] When any photovoltaic node fails, self-diagnosis and self-healing are performed. If the faulty node cannot self-heal, neighboring nodes take over the load or energy supply of the faulty photovoltaic node.

[0028] Specifically, each node continuously monitors its own state through a preset autoregressive model, obtains the error value at the current moment, and compares the error value at the current moment with a preset error threshold. If the error value at the current moment is greater than the preset error threshold, the node is marked as a potential fault node; Then, it performs self-healing by reducing the output power or switching to an alternative path. If the self-healing is successful, the node is restored to the normal operating state.

[0029] The autoregressive model for fault detection is as follows:

[0030] where is the system state at time moment, is the order of the autoregressive model, is the autoregressive coefficient, is time moment, and the error term is the error value mentioned above.

[0031] The present invention discloses a low-voltage distributed photovoltaic panoramic monitoring method. By constructing a self-organizing monitoring network for distributed photovoltaic nodes, it realizes autonomous energy management and cooperative scheduling of nodes, and improves the system fault tolerance through a multi-node cooperative self-healing mechanism. It combines swarm intelligence to optimize energy sharing and load distribution, and uses intelligent data modeling to generate a panoramic monitoring map to support real-time optimization of the system.

[0032] Embodiment 2 As Figure 2 and Figure 3 shown, the present embodiment provides a low-voltage distributed photovoltaic panoramic monitoring method, which includes multiple photovoltaic nodes. The method includes the following steps: S1: Enable the sensors of each photovoltaic node to collect the environmental data, node electrical data, and device health status data of each photovoltaic node; The environmental data includes light intensity, temperature, humidity, and wind speed, which are used to predict the power generation and adjust the efficiency of energy storage. The node electrical data includes current, voltage, and power, which are used to monitor the power generation, load, and status of energy storage devices.

[0033] Specifically, in the present invention, a photovoltaic node includes a photovoltaic module, an energy storage device, an inverter, and a load device. Each photovoltaic node deploys a multi-modal sensor system to realize real-time monitoring of the node and its environmental state. Specifically, the sensor system of the node is divided into the following categories: Environmental sensors: used to collect external environmental data, including light intensity, temperature, humidity, wind speed, etc. These data can reflect the influence of the external environment on photovoltaic power generation, so as to be used to adjust power generation and energy storage strategies.

[0034] Electrical sensors: Used to monitor the electrical state inside the node, including current, voltage, and power. This data can reflect the power generation status of photovoltaic modules, the charging / discharging conditions of energy storage devices, and the operating conditions of inverters.

[0035] Monitoring of device health status data: By monitoring the operating status of devices through sensors such as temperature and vibration, it is possible to detect the aging of photovoltaic modules, the lifespan of energy storage batteries, and the fault omens of inverters, ensuring that the devices can operate efficiently in a healthy state.

[0036] In this step, all sensors are initialized and start continuously collecting data. This data is preprocessed locally at the node to ensure the accuracy and validity of the data. The data collected by the sensors is not only used to monitor the current state but also for energy scheduling and load management in subsequent steps. The preprocessing mainly refers to data cleaning, integration of data from different sensor nodes, data conversion, elimination of abnormal data, etc.

[0037] S2: Each photovoltaic node exchanges energy status data with neighboring nodes through a wireless communication network; Step S2 specifically includes the following steps: S2.1: Initialization of the wireless communication module: Start the wireless communication module of each node and establish a communication connection with neighboring nodes using low-power wireless communication technology; S2.2: Synchronization of state information between nodes: Each photovoltaic node packs its current energy status information and sends it to neighboring nodes through the wireless network, forming a data synchronization mechanism between nodes; S2.3: Creation of the neighborhood energy status matrix: Each node creates a neighborhood energy status matrix M(t) based on communication with neighboring nodes. The elements in the matrix represent the power generation, energy storage, and load status of each node; S2.4: Calculation of energy flow direction: Based on the received neighborhood status matrix data, the node calculates the possible energy flow direction in real time and determines whether the nodes with excess power need to transfer the excess electricity to the nodes with insufficient power.

[0038] The expression of the neighborhood energy status matrix M(t) in step S2.3 is as follows:

[0039] Where, represents the state relationship between node and node , including power generation, load, and energy storage status.

[0040] Specifically, in this embodiment, each photovoltaic node establishes a communication connection with neighboring nodes by activating a wireless communication module. The wireless communication technologies used include low-power communication technologies such as Long Range (LoRa), NarrowBand Internet of Things (NB-IoT), Wireless Fidelity Mesh (WiFiMesh), or 5th Generation Mobile Networks (5G). The selection of these technologies depends on the distance between nodes, data transmission requirements, and energy constraint conditions. When the distance between different nodes is relatively far, wide-area low-power communication technologies such as LoRa or NB-IoT can ensure a wider coverage range and lower power consumption for the nodes. In areas where nodes are relatively dense, WiFiMesh or 5G can provide higher transmission rates and network stability.

[0041] The initialization process includes: Self-check of the communication module: Each node first self-checks whether its wireless communication module is working properly and checks the hardware status such as the antenna and signal strength to ensure the reliability of the communication link.

[0042] Search for neighboring nodes: The node activates the neighboring node search function and detects the presence of surrounding nodes and their communication parameters, such as signal strength and communication delay, by broadcasting and receiving signals.

[0043] Establish a communication link: Once neighboring nodes are found, the system establishes a stable communication link based on the signal strength and link quality to form a wireless communication network among the nodes.

[0044] Through these steps, the node ensures connection with other surrounding photovoltaic nodes, forms a communication grid within the area, and provides a communication foundation for subsequent data exchange and energy state synchronization.

[0045] In this embodiment, the synchronization of status information between nodes is achieved through the transmission of data packets. Each photovoltaic node packs its energy state data, including information such as power generation, energy storage status, and load demand, and then broadcasts it to surrounding neighboring nodes through the wireless communication network. These data packets not only contain the energy state at the current moment but also include the status data received from other neighboring nodes in the previous time period to ensure the continuity and integrity of data transmission.

[0046] The synchronization process includes the following sub-steps: Data packet construction: Each node packs its current power generation , energy storage status (t), and load situation Pack it and mark it with a timestamp to ensure that the receiving end can judge its validity based on the freshness of the data.

[0047] Data broadcasting: Nodes broadcast their status information through a wireless communication network and ensure the freshness of the data through a timestamp mechanism to prevent outdated data from interfering with decisions.

[0048] Neighborhood data caching: After each node receives data from other neighboring nodes, it performs local caching and determines whether to update the local neighborhood data cache based on the timestamp.

[0049] To improve the reliability of data synchronization, the system adopts an acknowledgment (ACK) mechanism to ensure that each node can receive packets from all neighboring nodes. If a packet is lost during communication, the node will automatically request retransmission to ensure the integrity of the status information synchronization.

[0050] In this embodiment, based on the received energy status data of neighboring nodes, each node creates an energy status matrix M(t). This matrix is used to describe the energy status of the entire neighborhood at the current moment, including the power generation, energy storage, and load conditions of each node. The form of the matrix is as follows:

[0051] Among them, the elements in the matrix represent the energy status relationship between node and node at time . Specifically, includes the following content: Power generation : The power generation power of node at time .

[0052] Energy storage status : The energy storage level of node at time , that is, the energy stored in the battery or energy storage unit.

[0053] Load demand : The load power demand of node at time , representing the total power demand of all load devices of the current node.

[0054] The matrix M(t) will be continuously updated in each cycle, and the node dynamically updates the element values in the matrix according to the received neighborhood data. This matrix is not only used for the energy scheduling decision of the local node but also provides a data basis for the energy flow in the entire neighborhood.

[0055] During the creation of the energy state matrix, the node determines whether energy balance or transmission is required within its neighborhood by comparing the differences among the power generation, energy storage level, and load demand.

[0056] In this embodiment, based on the data of the energy state matrix M(t), each node calculates the energy flow between itself and its neighboring nodes to determine whether energy exchange is needed. This process is achieved through the following formula:

[0057] where : the energy (power) transmitted from node to node at time . A positive value indicates that node transmits energy to node , and a negative value indicates that node receives energy from node

[0058] : the energy exchange coefficient between node and node , which depends on the physical distance, communication delay, and energy transmission loss between the nodes. This coefficient can be dynamically adjusted continuously through the learning and feedback of historical data to improve the efficiency of energy transmission.

[0059] : the current energy surplus of node , representing the part where its power generation and energy storage exceed the load demand.

[0060] : the current energy deficit of node , representing the part where its load demand exceeds the power generation and energy storage.

[0061] By calculating , the node can determine whether it needs to transmit excess energy to neighboring nodes or obtain energy from neighboring nodes to make up for its own deficit. This formula can ensure the energy balance and optimization within the local range of the system.

[0062] The calculation of the energy flow does not solely rely on the current state data. The node can also combine weather forecasts and load predictions to adjust the energy transmission strategy in advance. For example, when it is predicted that the future load in a certain area will increase significantly, neighboring nodes can provide energy support in advance to ensure the stability of the system during the load peak period.

[0063] S3: Autonomously execute energy management and scheduling based on the local state of the node and the energy data of neighboring nodes; Step S3 specifically includes the following steps: S3.1: Local energy balance calculation: Each node calculates its current energy surplus or deficit using the energy balance equation based on its power generation, energy storage level, and current load. S3.2: Neighborhood energy exchange determination: Based on the node's energy state and the neighborhood state matrix M(t), determine whether there are nodes that need energy transfer, and dynamically adjust the energy flow between nodes i and j according to the energy exchange formula. S3.3: Dynamically schedule energy: For each load device, the node dynamically schedules according to the current available energy and device priority to supply power to critical load devices first. S3.4: Adaptive adjustment to environmental changes: The node dynamically adjusts its power generation and energy storage strategies by continuously collecting environmental data.

[0064] The calculation formula of the energy balance equation in step S3.1 is as follows:

[0065] Among them, is the power generation power of node at time , that is, the electricity generated by the current photovoltaic panel, is the power released from the energy storage unit of node at time , that is, the electricity provided by the battery or energy storage device, is the load power demand of node at time , representing the total power demand of all current load devices of this node, is the power generation priority weight, the energy storage priority weight; is the energy balance equation of node i at time t.

[0066] The energy exchange formula in step S3.2 is as follows:

[0067] Among them, is the energy (power) exchanged from node to node at time , is the energy exchange coefficient between node and node , is the excess power of node at time , is the power gap of node at time , indicating the node The part where the load demand exceeds its own power generation and energy storage; In step S3.3, the load distribution formula is as follows:

[0068] Where, is the power allocated to the load device at time , is the priority weight of the load device , is the total number of load devices on the node, is the total power available for allocation at the node at time , that is, the remaining energy after the current node's power generation and energy storage minus the base load.

[0069] Specifically, in this embodiment, each photovoltaic node first calculates the current energy surplus or deficit through the local energy balance equation. The power generation power , energy storage power and load demand of the node are the key inputs for calculation. The energy balance calculation formula is as follows:

[0070] Where: : The total power that the photovoltaic node can dispatch at time .

[0071] : The current power generation power of node , which is determined by the power generation efficiency of the photovoltaic module and environmental factors (such as light intensity).

[0072] : The power currently released from the energy storage unit of node , that is, the energy provided by the battery or other energy storage devices.

[0073] : The current load power demand of node , that is, the total power demand of all load devices.

[0074] The power generation priority weight, which represents the weight of the node when preferentially using the current power generation power, and is usually related to the light intensity.

[0075] The energy storage priority weight, which represents the weight of the node when preferentially using the energy storage, and is usually determined according to the charge state of the energy storage unit.

[0076] Through this formula, a node can determine whether there is currently an energy surplus (i.e., ) or an energy deficit (i.e., ), and prepare for subsequent energy exchanges.

[0077] After the local energy balance calculation, each node needs to decide whether to perform an energy exchange. In this embodiment, the node analyzes the energy states of other nodes in its neighborhood to determine whether there are neighboring nodes that can provide energy support or accept excess energy. The node uses the neighborhood energy state matrix M(t) created in step S2 to calculate the energy exchange amount between two nodes through the following energy exchange formula:

[0078] where, : At time , the energy (power) transmitted from node to node . If the value is positive, it means that node transmits energy to node ; if it is negative, it means that node obtains energy from node .

[0079] : The energy exchange coefficient between node and node . This coefficient is affected by factors such as physical distance, transmission medium, and energy loss. The node dynamically adjusts this coefficient based on historical data to maximize the efficiency of energy exchange.

[0080] : The current energy surplus of node , which represents the part where the power generation and energy storage power exceed the load demand.

[0081] : The current energy deficit of node , which represents the part where its load demand exceeds the power generation and energy storage power.

[0082] Through this formula, a node can determine whether there is a need for energy transmission and dynamically perform energy exchanges based on the energy states of other nodes in its neighborhood. For example, when a node has a large amount of energy surplus, it can transmit the excess energy to a node with insufficient energy through the exchange coefficient, and vice versa.

[0083] Energy exchange is not a fixed one-to-one relationship. Complex multi-node energy transmission paths can be formed among multiple nodes to ensure more optimized energy flow in the entire system and reduce energy waste.

[0084] After a node determines its local energy state, in this embodiment, the node performs dynamic energy scheduling according to the priorities of its load devices. When the node has available energy (i.e., ), the system needs to allocate power to each load according to the priorities of the devices. The formula for load allocation is as follows:

[0085] Where, : Time The power allocated to the load device at time, representing the actual power obtained by the device.

[0086] The priority weight of the load device , reflecting the importance of the device in energy allocation. Critical loads (such as communication devices and monitoring devices) have higher weights, while secondary loads have lower weights.

[0087] The total number of load devices on the node.

[0088] : The total power available for allocation at time by the node, that is, the remaining energy of the current power generation and energy storage power minus the base load.

[0089] Through this formula, the node dynamically allocates energy according to the importance of each load device. Devices with higher priorities will be powered first when energy is limited, while non-critical devices may have their power restricted or be temporarily powered off.

[0090] For example, in the case of insufficient energy, critical devices (such as data transmission modules) will be given priority to ensure power supply, while non-critical devices (such as air conditioners and lighting devices) may have their power supply temporarily reduced or turned off to ensure the normal operation of the basic functions of the system.

[0091] In this embodiment, the photovoltaic node continuously collects environmental data (such as light intensity, temperature, humidity, etc.) and dynamically adjusts the power generation and energy storage strategies according to changes in the external environment. The power generation and energy storage efficiency of the photovoltaic node are directly affected by environmental factors. Therefore, the node must make adaptive adjustments according to environmental changes to maximize the operating efficiency of the system and prevent equipment overload or loss.

[0092] Power generation strategy adjustment: When the light intensity increases, the node will automatically increase the power generation priority weight , ensuring that the photovoltaic modules can maximize the use of solar energy for power generation; when the light weakens, the node will reduce the power generation power and instead give priority to using the energy storage device for power supply. This can effectively avoid the ineffective power generation of photovoltaic modules under low-efficiency lighting conditions and reduce energy waste.

[0093] Energy storage management optimization: The charging and discharging strategies of energy storage devices are affected by factors such as temperature and device health status. The node adjusts the charging and discharging strategies according to the current power, temperature and other information of the energy storage device. For example, when the temperature is too high, the node will reduce the charging power of the energy storage device to avoid damage to the battery life caused by overheating; when the battery is nearly full, the system will give priority to powering the load to reduce the charging pressure on the energy storage device.

[0094] This environment-adaptive adjustment mechanism can ensure that the photovoltaic nodes maintain stable and efficient operation under changing external environments, while preventing damage to photovoltaic modules and energy storage devices due to overuse or extreme environmental conditions.

[0095] S4: Aggregate the data of multiple photovoltaic nodes through the regional gateway to form a regional panoramic monitoring map; Step S4 specifically includes the following steps: S4.1: Regional gateway initialization: Start the regional gateway, connect to all photovoltaic nodes in the region through wireless communication technology, and receive the data summary of each node; S4.2: Regional data aggregation: The regional gateway aggregates the energy status, power generation, energy storage level, load demand and device health status of all nodes to form a regional-level energy flow view; S4.3: Panoramic monitoring data modeling: According to the data of each node, the gateway generates a regional panoramic monitoring map, including the energy flow between nodes, load demand, node health conditions and changes in environmental data, for users or the central collaboration platform to view; S4.4: Generation of regional energy scheduling suggestions: Based on the regional panoramic map, the gateway calculates the possible energy flow directions within the current region, provides regional energy scheduling suggestions, and optimizes the energy distribution strategy to balance power generation and load demand.

[0096] Specifically, in this embodiment, the regional gateway generates a regional panoramic monitoring map based on the aggregated energy data. This panoramic monitoring map not only shows the power generation, energy storage and load status of each node, but also can reflect the energy flow situation between nodes. Through data modeling, the gateway can intuitively display the energy distribution within the entire region. The specific data modeling process is as follows: Generation of energy flow map: According to the energy exchange data between each node , the regional gateway generates an energy flow map within the region, showing the flow paths of energy from power generation nodes to load nodes and energy storage nodes. The width and color of each path can represent the magnitude and direction of energy flow.

[0097] Node health status diagram: Based on the health status data of each node (such as temperature, fault information), the regional gateway generates a node health status monitoring diagram, indicating the operating status of each node and potential faulty nodes. This status diagram can provide key reference information for operation and maintenance personnel to help discover and prevent potential equipment problems.

[0098] Environmental impact diagram: Based on the data of environmental sensors on the nodes (such as light intensity, temperature, etc.), a regional environmental impact monitoring diagram is generated, showing the power generation efficiency and load changes of different nodes under different environmental conditions.

[0099] The integration and modeling of these panoramic data enable users or systems to have an intuitive and comprehensive understanding of the operating status of the entire region, providing a visual reference for subsequent regional optimization and scheduling.

[0100] S5: Dynamically adjust power generation, energy storage, and load strategies based on node status, regional energy distribution, and load priorities; Step S5 specifically includes the following steps: S5.1: Node status priority analysis: According to the energy status of each node and the energy distribution within the region, analyze the current power generation efficiency, energy storage level, load demand, and their priorities of each node; S5.2: Dynamic adjustment of load priorities: Use the load priority algorithm to adjust the power distribution of each load device according to the total available energy of the system , S5.3: Optimization of energy storage strategy: According to the current status of the energy storage device, when the energy is sufficient, preferentially store the excess energy, and when the energy is insufficient, preferentially release the energy storage to support high-priority loads; S5.4: Adjustment of power generation strategy: According to environmental changes, adjust the power generation of photovoltaic modules in real time to avoid overload or inefficient operation.

[0101] Specifically, in this embodiment, first, node status priority analysis is performed. The purpose is to calculate the priority of each node based on the current energy status of each node and prepare for subsequent energy distribution and scheduling. The system mainly calculates the priority through the following data: The energy status of the node: including the current power generation of the node , energy storage level ,

[0102] load demand

[0103] Regional energy distribution: Considering the overall energy distribution within the region, the system will prioritize nodes with energy gaps and increase their priorities to ensure that these nodes can obtain energy scheduling support within the region first.

[0104] In this embodiment, for the load devices on each node, according to their load priorities Dynamic adjustment is performed. There may be multiple different types of load devices on a node, such as critical devices (data communication modules, monitoring devices, etc.) and non-critical devices (lighting, air conditioning, etc.). In the case of insufficient energy supply, the system needs to reasonably allocate energy according to the load priorities. The specific formula for dynamic adjustment of load priorities is as follows:

[0105] is the time at which power is allocated to the load device and represents the energy actually allocated to this device. is the priority weight of the load device and is usually preset by the user or the system. The weight value of critical devices is higher, and the weight value of secondary devices is lower. is the total number of load devices on the node. The time is the total available power of the node at a certain moment, that is, the remaining power after subtracting the base load from the power generation and the energy storage.

[0106] Through this formula, when the energy is insufficient, the node can preferentially allocate energy to high-priority devices, while low-priority devices may be temporarily powered off or have their power reduced in extreme cases to ensure the key functions of the system are guaranteed.

[0107] In this embodiment, the management strategy of the energy storage device is achieved by optimizing the charging and discharging processes. The energy storage device plays an energy buffering role in the photovoltaic system. It can store excess energy when the power generation is excessive and release the stored energy when the power generation is insufficient. The optimization of the system's energy storage strategy is carried out according to the following principles; Adjustment of energy storage priority: When the energy is sufficient, the system gives priority to storing excess energy. When the energy storage device is close to full charge, the system will appropriately reduce the charging power to avoid overcharging the energy storage device. Conversely, when the energy supply is insufficient, the energy storage device gives priority to powering critical load devices to ensure the normal operation of the node.

[0108] Temperature and energy storage management: The system will adjust the charging and discharging strategies of the energy storage device according to the current ambient temperature. For example, when the temperature of the energy storage device is high, the system will reduce the charging power to prevent the battery from being damaged by overheating.

[0109] The formula for optimizing the energy storage strategy is as follows: = f(temperature, charge_ ) Among them, the energy storage power is a function dynamically adjusted by node i according to the ambient temperature and the battery charge level.

[0110] This optimization of the dynamic energy storage strategy can ensure that the system reasonably utilizes the energy storage device under different energy supply and demand conditions, extends the service life of the energy storage device, and improves the energy utilization rate at the same time.

[0111] In this embodiment, the adjustment of the power generation strategy mainly depends on the real-time feedback of environmental data, such as light intensity, temperature, humidity, etc. The power generation power of the photovoltaic module highly depends on environmental factors. The system dynamically adjusts the power generation strategy by collecting environmental data to ensure that the photovoltaic system operates under the best conditions.

[0112] Light intensity feedback: When the light intensity increases, the system will increase the power generation power and give priority to using solar energy resources for power generation; when the light intensity decreases, the system will reduce the power generation power of the photovoltaic module and give priority to using the energy storage device or obtaining energy from other nodes.

[0113] Temperature management: When the temperature is too high, the system will reduce the power generation power to avoid damage to the photovoltaic module due to overheating; at a suitable temperature, the system will operate the photovoltaic module at the highest efficiency point to maximize the power generation.

[0114] The adjustment of the power generation strategy is achieved through the following formula: = g( , ) Among them, the power generation power is a function of the light intensity and temperature, representing the power generation strategy of node i under different environmental conditions.

[0115] Through the dynamic adjustment of the power generation strategy, the system can ensure that under different environmental conditions, the power generation efficiency of the photovoltaic module is maximized, and at the same time, the equipment is protected from overheating or inefficient operation.

[0116] S6: When a node fails, perform self-diagnosis and self-healing, and cooperate with neighboring nodes to take over the load or energy supply of the failed node.

[0117] Step S6 specifically includes the following steps: S6.1: Fault detection and diagnosis: Each node continuously monitors its own state through an autoregressive model. If the error ϵ(t) exceeds a predetermined threshold, the node is marked as a potential fault node; S6.2: Self-healing attempt: When a node detects a fault in itself, it attempts to self-heal by reducing its output power or switching to an alternative path. If successful, it resumes its normal operating state; S6.3: Neighborhood collaborative takeover: If a node cannot self-heal, neighboring nodes take over its load or energy supply; S6.4: Fault isolation and recovery: With the assistance of the regional gateway, the faulty node is isolated. When the faulty node recovers, it rejoins the system and resumes its normal scheduling function.

[0118] The autoregressive model formula for fault detection in step S6.1 is as follows:

[0119] where, is the system state at time , is the order of the autoregressive model, are the autoregressive coefficients, is the error term at time , is time, is the node.

[0120] Specifically, in this embodiment, each photovoltaic node continuously monitors its own operating state and performs real-time fault detection through an autoregressive model. The autoregressive model predicts the normal operating state of the node based on historical data and identifies faults by monitoring the difference between the actual state and the predicted state. The fault detection formula is as follows:

[0121] where, is the current state (such as voltage, current, temperature, etc.) of node i at time , representing the actual working state of the system. are the coefficients of the autoregressive model, representing the influence of historical states on the current state. These coefficients are trained using historical data. is the order of the autoregressive model, indicating how many historical time moments of data are used for prediction. is time , i.e., the deviation between the predicted value and the actual value.

[0122] When the error term ϵ(t) exceeds a predetermined fault threshold, the system determines that the current node may have a fault and marks it as a potential faulty node. At this time, the system will initiate a further fault self-healing mechanism.

[0123] In this embodiment, when a node detects that it may have a fault, the system will attempt to perform node self-healing. The self-healing attempt mainly includes the following steps: Output power reduction: The node first reduces its own output power to avoid further damage caused by overload or other abnormal operations. Reducing the power can alleviate the faults caused by overheating or excessive current in a certain part of the node.

[0124] Standby path switching: The system will attempt to switch to a standby path (such as a standby line or a standby device), thereby bypassing the faulty component and restoring part of the functions of the node. For example, if a certain photovoltaic component fails, the system can switch the power transmission to other unaffected components to reduce energy loss.

[0125] The attempt of self-healing is completed automatically. If the node returns to the normal operating state through these methods, the system will continue to monitor the operation of the node and mark it as a successful self-healing.

[0126] In this embodiment, when the node cannot be restored through the self-healing mechanism, the system will start the neighborhood collaborative takeover mechanism. The collaborative takeover is achieved through real-time communication with neighboring nodes. The neighboring nodes will temporarily take over the load or energy supply of the faulty node to ensure the stability of the entire system. The specific operations include: Load takeover: The neighboring node analyzes the load requirements of the faulty node , and calculates whether it can take over the load. If the neighboring node has sufficient energy reserves or power generation capacity, the system will, through dynamic energy scheduling, allocate the load of the faulty node to the neighboring node to ensure the continuity of the load power supply.

[0127] Energy supply takeover: When the faulty node is responsible for energy transmission, the neighboring node replaces the faulty node to conduct energy transmission through the energy exchange mechanism. The specific formula for energy supply takeover is as follows:

[0128] Among them, the neighboring node calculates its own energy surplus and the energy gap of the neighboring node , and dynamically adjusts the energy exchange amount to ensure the continuity of energy transmission within the area.

[0129] Through the neighborhood collaborative takeover, the load and energy supply of the faulty node can be taken over by the neighboring nodes in a short time to ensure the stability of the overall operation of the system.

[0130] In this embodiment, if the node cannot be restored through self-healing and requires longer maintenance, the system will isolate the faulty node to avoid its impact on other normally operating nodes. The process of fault isolation is as follows: Faulty node isolation: The system temporarily isolates the faulty node from the network through the regional gateway, stops communicating and transferring energy with this node, and avoids its impact on the normal operation of other nodes in the region.

[0131] System automatic adjustment: After the faulty node is isolated, the regional gateway will automatically adjust the energy scheduling strategy in the neighborhood and reallocate the energy supply and demand in the region. For example, the system will recalculate the energy state matrix M(t) and make up for the energy gap caused by node isolation through neighborhood cooperation.

[0132] When the faulty node returns to normal through repair or system self-check, the system will automatically rejoin it to the network and restore its role in energy scheduling.

[0133] The present invention constructs a self-organizing monitoring network through distributed photovoltaic nodes. Each node has autonomous energy management and load scheduling capabilities and works in cooperation with other nodes through wireless communication technology to form global optimization, getting rid of the dependence on traditional centralized monitoring systems, enhancing the flexibility and reliability of the system. In case of emergencies or local faults, neighboring nodes can perform real-time collaborative scheduling and adaptive adjustment, greatly reducing the problem of overall system efficiency reduction caused by single-point node failures. In addition, the present invention introduces a dynamic multi-node collaborative self-healing mechanism. Nodes can not only restore some functions through local self-healing mechanisms but also, after self-healing fails, have neighboring nodes cooperate to take over the load and energy supply of the faulty node. It not only relies on the intelligent decision-making of a single node but also integrates the collaborative response among multiple nodes, ensuring the continuous operation of the system in the case of local node failures and significantly enhancing the fault tolerance and robustness of the system. The present invention realizes distributed intelligent cooperation among photovoltaic nodes by simulating the mode of swarm intelligence. Nodes influence each other through local rules and autonomously form swarm intelligence behaviors, showing high self-adaptability in energy sharing, load distribution, and overall system optimization. The panoramic monitoring system of the present invention not only collects the energy data of multiple photovoltaic nodes but also generates real-time energy flow diagrams, node health diagrams, and load demand diagrams through intelligent data modeling technology, enabling users or automated control systems to intuitively understand the operating state of the entire system, quickly discover bottlenecks in energy scheduling, and support system decision-making optimization.

[0134] Embodiment 3 As Figure 4 shown, the present invention also discloses a low-voltage distributed photovoltaic panoramic monitoring system, including: A data acquisition module, which is used to obtain the energy state data of each photovoltaic node, and the energy state data includes power generation state data, energy storage state data, and load state data; The data analysis and decision-making module is used for each node to dynamically adjust the power generation strategy, energy storage strategy, and load strategy of any photovoltaic node based on its own energy state data and the energy state data of neighboring nodes, so as to perform autonomous energy management and scheduling. The fault diagnosis and self-healing module is used for self-diagnosis and self-healing when any photovoltaic node fails. If the faulty node cannot self-heal, the neighboring nodes will take over the load or energy supply of the faulty photovoltaic node.

[0135] In addition, a schematic diagram of a terminal device provided by an embodiment of the present invention is shown. The terminal device in this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0136] The computer program can be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention.

[0137] The terminal device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory.

[0138] The processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0139] The memory can be used to store the computer program and / or module. The processor realizes various functions of the terminal device by running or executing the computer program and / or module stored in the memory, and by calling the data stored in the memory.

[0140] If the modules / units integrated in the terminal device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0141] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A low-voltage distributed photovoltaic panoramic monitoring method, characterized in that, It includes the following steps: Obtain the energy status data of each photovoltaic node, where the energy status data includes power generation status data, energy storage status data, and load status data; Each node dynamically adjusts the power generation strategy, energy storage strategy, and load strategy of any photovoltaic node based on its own energy status data and the energy status data of neighboring nodes for autonomous energy management and scheduling; When any photovoltaic node fails, self-diagnosis and self-healing are performed. If the faulty node cannot self-heal, the neighboring nodes take over the load or energy supply of the faulty photovoltaic node.

2. The low-voltage distributed photovoltaic panoramic monitoring method according to claim 1, wherein The energy status data is obtained through the environmental data, node electrical data, and equipment health status data of the photovoltaic node.

3. The low-voltage distributed photovoltaic panoramic monitoring method according to claim 2, characterized in that, The environmental data includes light intensity, temperature, humidity, and wind speed; the node electrical data includes current, voltage, and power.

4. A low-voltage distributed photovoltaic panoramic monitoring method according to claim 1, characterized in that Each node dynamically adjusts the power generation strategy, energy storage strategy, and load strategy of any photovoltaic node based on its own energy status data and the energy status data of neighboring nodes for autonomous energy management and scheduling specifically as follows: Pack the energy status data of each photovoltaic node and send the packed energy status data to neighboring photovoltaic nodes through a wireless network to synchronize data among multiple photovoltaic nodes; The neighboring photovoltaic nodes calculate the energy flow direction in real time based on the received packed energy status data and dynamically adjust the power generation strategy, energy storage strategy, and load strategy of the photovoltaic nodes for autonomous energy management and scheduling.

5. A low-voltage distributed photovoltaic panoramic monitoring method according to claim 1, characterized in that, Each node dynamically adjusts the power generation strategy, energy storage strategy, and load strategy of any photovoltaic node based on its own energy status data and the energy status data of neighboring nodes for autonomous energy management and scheduling specifically as follows: Each photovoltaic node calculates the current energy surplus or deficit using an energy balance equation based on its own power generation, energy storage level, and current load; Based on the energy status of its own node and the energy status of neighboring nodes, it determines whether there are nodes that need energy transfer and dynamically adjusts the energy flow between nodes according to an energy exchange formula to achieve dynamic energy scheduling; Meanwhile, during dynamic energy scheduling, for each load device, the node dynamically schedules according to the current available energy and device priority to supply power preferentially to critical load devices.

6. A low-voltage distributed photovoltaic panoramic monitoring method according to claim 5, characterized in that The energy balance equation is: Among them, is the energy balance equation of node i at time t, is the power generation power of node at time is the power released from the energy storage unit by node at time is the load power demand of node at time is the power generation priority weight, energy storage priority weight; The energy exchange formula is: Among them, At time , the energy exchanged between node and node is , and the energy exchange coefficient between node and node is . The excess power of node at time is , and the power gap of node at time is 7. A low-voltage distributed photovoltaic panoramic monitoring method according to claim 1, characterized in that, When any photovoltaic node fails, self-diagnosis and self-healing are performed specifically as follows: Each node continuously monitors its own status through a preset autoregressive model, obtains the error value at the current moment, and compares the error value at the current moment with a preset error threshold. If the error value at the current moment is greater than the preset error threshold, the node is marked as a potential faulty node; Then, self-healing is performed by reducing the output power or switching to a standby path. If self-healing is successful, the node is restored to the normal working state.

8. A low-voltage distributed photovoltaic panoramic monitoring method according to claim 1, characterized in that If the faulty node cannot self-heal, the neighboring nodes take over the load or energy supply of the faulty photovoltaic node specifically as follows: isolate the faulty node, and when the faulty node recovers, it rejoins the system to restore the normal scheduling function of the node.

9. A low-voltage distributed photovoltaic panoramic monitoring system, characterized in that, It includes: A data acquisition module, which is used to obtain the energy status data of each photovoltaic node, and the energy status data includes power generation status data, energy storage status data and load status data; A data analysis and decision-making module, which is used to dynamically adjust the power generation strategy, energy storage strategy and load strategy of any photovoltaic node based on its own energy status data and the energy status data of neighboring nodes for each node, and perform autonomous energy management and scheduling; A fault diagnosis and self-healing module, which is used to perform self-diagnosis and self-healing when any photovoltaic node fails. If the faulty node cannot self-heal, the neighboring node takes over the load or energy supply of the faulty photovoltaic node.

10. A computer device, comprising a memory, a processor, and a computer program stored on the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 8.

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