A method and system for panoramic monitoring of low-voltage distributed photovoltaic power generation, and a computer device.

Abstract

This invention discloses a low-voltage distributed photovoltaic (PV) panoramic monitoring method and system, as well as a computer device, belonging to the field of PV power generation system monitoring and management technology. This low-voltage distributed PV panoramic monitoring method involves each node collecting real-time data on power generation, energy storage, and load. Through autonomous adjustment of energy strategies and collaboration with neighboring nodes, it achieves global optimization, eliminating dependence on centralized systems. A dynamic multi-node self-healing mechanism is introduced to ensure continuous system operation. This method, through distributed monitoring, autonomous decision-making, and intelligent self-healing mechanisms, effectively solves the core pain points of traditional centralized systems, providing an innovative solution for the efficient and reliable operation of large-scale distributed PV systems.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic power generation system monitoring and management technology, and relates to a low-voltage distributed photovoltaic panoramic monitoring method and system, and a computer device. Background Technology

[0002] With the continuous expansion of distributed photovoltaic (PV) power generation systems, efficient monitoring and management of these systems has become a critical issue that urgently needs to be addressed. Traditional monitoring methods primarily employ a centralized architecture, where a central server collects and processes data from multiple PV nodes to achieve unified system scheduling. In early, smaller-scale applications, this centralized monitoring model demonstrated good performance due to its advantages in centralized management. However, as the system scales up and becomes more complex, the limitations of the centralized monitoring model have gradually become apparent.

[0003] First, the communication burden increases significantly under a centralized architecture. As the number of photovoltaic (PV) nodes increases, the efficiency of data transmission and processing decreases sharply, making it difficult for the system to respond promptly to changes in energy demand in local areas. This communication bottleneck is particularly pronounced in large-scale distributed systems, affecting the system's real-time performance and stability. Second, PV nodes in traditional systems lack autonomous decision-making capabilities and rely excessively on the unified scheduling of the central system. This centralized management model limits the ability of nodes to autonomously adjust their power generation, energy storage, and load management strategies based on local environmental conditions, resulting in insufficient flexibility and poor overall responsiveness when dealing with complex and changing environments. Furthermore, existing systems have significant shortcomings in handling node failures. Traditional monitoring models typically rely on manual intervention or central commands for fault handling, lacking effective self-healing mechanisms. In the event of a sudden failure, the system may not be able to recover quickly, leading to a decrease in overall operating efficiency and even affecting power supply stability.

[0004] Therefore, facing the challenges of the expanding scale and increasing complexity of distributed photovoltaic power generation systems, the traditional centralized monitoring model is no longer sufficient to meet practical application needs in terms of communication efficiency, node autonomy, and fault response capabilities. 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 future development trend of large-scale distributed energy systems. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a low-voltage distributed photovoltaic panoramic monitoring method and system, and a computer device, thereby solving the technical problems of the centralized monitoring mode of distributed photovoltaic power generation systems in the prior art, which suffers from heavy communication burden, weak node autonomous decision-making ability, and lack of effective fault self-healing mechanism after the system scale is expanded.

[0006] This invention is achieved through the following technical solution:

[0007] A method for panoramic monitoring of low-voltage distributed photovoltaic systems includes the following steps:

[0008] Acquire energy status data for each photovoltaic node, including power generation status data, energy storage status data, and load status data;

[0009] Each node dynamically adjusts its power generation strategy, energy storage strategy, and load strategy based on its own energy status data and the energy status data of neighboring nodes, and performs autonomous energy management and scheduling.

[0010] When any photovoltaic node fails, it performs self-diagnosis and self-healing. If the failed node cannot heal itself, the neighboring node takes over the load or energy supply of the failed photovoltaic node.

[0011] Preferably, the energy state data is obtained through environmental data, electrical data, and equipment health status data of the photovoltaic node;

[0012] The environmental data includes light intensity, temperature, humidity, and wind speed;

[0013] The node electrical data includes current, voltage, and power.

[0014] Preferably, each node dynamically adjusts its power generation strategy, energy storage strategy, and load strategy based on its own energy state data and the energy state data of neighboring nodes to perform autonomous energy management and scheduling. Specifically:

[0015] The energy status data of each photovoltaic node is packaged and sent to neighboring photovoltaic nodes via a wireless network to synchronize data among multiple photovoltaic nodes.

[0016] Based on the packaged energy status data received, the neighboring photovoltaic nodes calculate the energy flow in real time and dynamically adjust the power generation strategy, energy storage strategy, and load strategy of the photovoltaic nodes to carry out autonomous energy management and scheduling.

[0017] Preferably, each node dynamically adjusts its power generation strategy, energy storage strategy, and load strategy based on its own energy state data and the energy state data of neighboring nodes, enabling autonomous energy management and scheduling. Specifically:

[0018] Each photovoltaic node calculates its current energy surplus or deficit using the energy balance equation based on its own power generation, energy storage level and current load.

[0019] Based on the energy state of its own node and the energy state of neighboring nodes, it determines whether there are nodes that need energy transfer, and dynamically adjusts the energy flow between nodes according to the energy exchange formula to achieve dynamic energy scheduling.

[0020] Meanwhile, during dynamic energy scheduling, for each load device, the node dynamically schedules power supply based on the current available energy and device priority, so that critical load devices are given priority in power supply.

[0021] Preferably, the energy balance equation is:

[0022]

[0023] in, For nodes In time Power generation at any given moment For nodes In time The power constantly released from the energy storage unit For nodes In time The load power requirement at any given time. As the priority weight for power generation, Energy storage priority weighting;

[0024] The energy exchange formula is as follows:

[0025]

[0026] in, In time Time, node To the node The energy exchanged For nodes and nodes The energy exchange coefficient between them For nodes In time Excess power at any given moment For nodes In time The power gap at any given moment.

[0027] Preferably, when any photovoltaic node fails, it performs self-diagnosis and self-healing, specifically as follows:

[0028] 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 fault node.

[0029] Then, self-healing is performed by reducing the output power or switching to a backup path. If self-healing is successful, the node is restored to normal working condition.

[0030] Preferably, if the faulty node cannot heal itself, the load or energy supply of the faulty photovoltaic node is taken over by a neighboring node. Specifically, the faulty node is isolated, and when the faulty node recovers, it is rejoined to restore the normal scheduling function of the node.

[0031] A low-voltage distributed photovoltaic panoramic monitoring system includes:

[0032] The data acquisition module is used to acquire the energy status data of each photovoltaic node, including power generation status data, energy storage status data and load status data.

[0033] The data analysis and decision-making module is used by each node 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, so as to carry out autonomous energy management and scheduling.

[0034] The fault diagnosis and self-healing module is used to perform self-diagnosis and self-healing when any photovoltaic node fails. If the faulty node cannot heal itself, the neighboring node will take over the load or energy supply of the faulty photovoltaic node.

[0035] A computer device includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described above.

[0036] Compared with the prior art, the present invention has the following beneficial technical effects:

[0037] This invention discloses a panoramic monitoring method for low-voltage distributed photovoltaic systems. Firstly, each photovoltaic node communicates locally via a self-organizing network, exchanging data only with neighboring nodes, significantly reducing the data transmission volume of the central node. Nodes make real-time decisions based on local data (such as adjusting power generation strategies), reducing reliance on the central server and lowering communication frequency and bandwidth requirements. Secondly, each node autonomously generates optimization strategies (such as prioritizing energy storage power supply or adjusting inverter output) based on its own and neighboring nodes' energy status (power generation, energy storage, load), improving the system's real-time response capability, adapting to dynamic environmental changes, enhancing energy utilization efficiency, and improving the node's autonomous decision-making ability. Furthermore, in this invention, nodes monitor their health status in real time (such as component failures or communication interruptions), attempting to restart or switch to backup links. If self-healing fails, neighboring nodes take over the functions of the faulty node through dynamic load allocation or energy sharing, ensuring continuous system operation. Multi-node collaboration avoids the propagation of single-point failures, improving system robustness, significantly reducing downtime, lowering maintenance costs, and ensuring high system availability. This method effectively addresses 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. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a flowchart illustrating a panoramic monitoring method for low-voltage distributed photovoltaic systems according to Embodiment 1 of the present invention.

[0040] Figure 2 This is a flowchart illustrating a panoramic monitoring method for low-voltage distributed photovoltaic systems according to Embodiment 2 of the present invention.

[0041] Figure 3 This is a schematic diagram of the hierarchical structure of a low-voltage distributed photovoltaic panoramic monitoring method in Embodiment 2 of the present invention;

[0042] Figure 4 This is a schematic diagram of a low-voltage distributed photovoltaic panoramic monitoring system according to Embodiment 3 of the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0044] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0045] The present invention will now be described in further detail with reference to the accompanying drawings:

[0046] Example 1

[0047] like Figure 1 As shown, this invention discloses a panoramic monitoring method for low-voltage distributed photovoltaic systems, comprising the following steps:

[0048] S1: Obtain the energy status data of each photovoltaic node, including power generation status data, energy storage status data, and load status data;

[0049] The energy status data is obtained through environmental data, electrical data, and equipment health status data of the photovoltaic nodes.

[0050] The environmental data includes light intensity, temperature, humidity, and wind speed;

[0051] The node electrical data includes current, voltage, and power.

[0052] 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, and performs autonomous energy management and scheduling.

[0053] Specifically, the energy status data of each photovoltaic node is packaged and sent to neighboring photovoltaic nodes via a wireless network to synchronize data among multiple photovoltaic nodes.

[0054] During dynamic adjustment, each photovoltaic node calculates its current energy surplus or deficit using the energy balance equation based on its own power generation, energy storage level, and current load. It also determines whether there are nodes that need energy transmission based on its own energy state and the energy state of neighboring nodes, and dynamically adjusts the energy flow between nodes according to the energy exchange formula to achieve dynamic energy scheduling.

[0055] The energy balance equation is as follows:

[0056]

[0057] in, Here is the energy balance equation for node i at time t. For nodes In time Power generation at any given moment For nodes In time The power constantly released from the energy storage unit For nodes In time The load power requirement at any given time. As the priority weight for power generation, Energy storage priority weighting;

[0058] The energy exchange formula is as follows:

[0059]

[0060] in, In time Time, node To the node The energy exchanged For nodes and nodes The energy exchange coefficient between them For nodes In time Excess power at any given moment For nodes In time The power gap at any given moment.

[0061] When any photovoltaic node fails, it performs self-diagnosis and self-healing. If the failed node cannot heal itself, the neighboring node takes over the load or energy supply of the failed photovoltaic node.

[0062] Specifically, 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 fault node.

[0063] Then, self-healing is performed by reducing the output power or switching to a backup path. If self-healing is successful, the node is restored to normal working condition.

[0064] The autoregressive model for fault detection is as follows:

[0065]

[0066] in, For time The system state at any given moment. Let be the order of the autoregressive model. These are the autoregressive coefficients. For time The error term at time is the error value mentioned above.

[0067] This invention discloses a panoramic monitoring method for low-voltage distributed photovoltaic systems. By constructing a self-organizing monitoring network for distributed photovoltaic nodes, it enables autonomous energy management and collaborative scheduling of nodes. It also improves system fault tolerance through a multi-node collaborative self-healing mechanism, optimizes energy sharing and load distribution through swarm intelligence, and generates a panoramic monitoring map using intelligent data modeling to support real-time system optimization.

[0068] Example 2

[0069] like Figure 2 as well as Figure 3 As shown, this embodiment provides a method for panoramic monitoring of low-voltage distributed photovoltaic systems, including multiple photovoltaic nodes. The method includes the following steps:

[0070] S1: Activate the sensors of each photovoltaic node to collect environmental data, node electrical data and equipment health status data of each photovoltaic node;

[0071] Environmental data, including light intensity, temperature, humidity, and wind speed, is used to predict power generation and energy storage efficiency adjustments. Node electrical data, including current, voltage, and power, is used to monitor power generation, load, and the status of energy storage devices.

[0072] Specifically, in this invention, a photovoltaic node includes photovoltaic modules, energy storage devices, inverters, and load devices. Each photovoltaic node is equipped with a multimodal sensor system to achieve real-time monitoring of the node and its environment. Specifically, the node's sensor system is divided into the following categories:

[0073] Environmental sensors: These are used to collect external environmental data, including light intensity, temperature, humidity, and wind speed. This data reflects the impact of the external environment on photovoltaic power generation, which can then be used to adjust power generation and energy storage strategies.

[0074] Electrical sensors: Used to monitor the electrical status within the node, including current, voltage, and power. This data reflects the power generation status of the photovoltaic modules, the charging / discharging status of the energy storage devices, and the operation of the inverter.

[0075] Equipment health status monitoring: By monitoring the operating status of equipment through sensors such as temperature and vibration, it is possible to detect the aging of photovoltaic modules, the lifespan of energy storage batteries, and early signs of inverter failure, ensuring that the equipment can operate efficiently in a healthy state.

[0076] In this step, all sensors are initialized and begin continuously acquiring data. This data is preprocessed locally at each node to ensure accuracy and validity. The sensor data is used not only to monitor the current status but also for energy scheduling and load management in subsequent steps. Preprocessing mainly involves data cleaning, integration of data from different sensor nodes, data transformation, and removal of outlier data.

[0077] S2: Each photovoltaic node exchanges energy status data with neighboring nodes through a wireless communication network;

[0078] Step S2 specifically includes the following steps:

[0079] S2.1: Wireless communication module initialization: Start the wireless communication module of each node and establish a communication connection with neighboring nodes using low-power wireless communication technology;

[0080] S2.2: Inter-node state information synchronization: Each photovoltaic node packages its current energy state information and sends it to neighboring nodes through a wireless network, forming an inter-node data synchronization mechanism;

[0081] S2.3: Energy State Matrix Creation: Each node creates a neighborhood energy state 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.

[0082] S2.4: Energy Flow Calculation: Based on the received neighborhood state matrix data, the node calculates the possible energy flow in real time and determines whether the node with excess energy needs to transfer the excess power to the node with insufficient energy.

[0083] The expression for the neighborhood energy state matrix M(t) in step S2.3 is as follows:

[0084] in, Representative node With nodes The state relationships include power generation, load, and energy storage status.

[0085] Specifically, in this embodiment, each photovoltaic node establishes a communication connection with neighboring nodes by activating its 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 choice of these technologies depends on the distance between nodes, data transmission requirements, and energy constraints. When the distance between different nodes is large, using wide-area low-power communication technologies such as LoRa or NB-IoT can ensure a wider coverage area and lower power consumption. In areas where nodes are relatively densely packed, WiFiMesh or 5G can provide higher transmission rates and network stability.

[0086] The initialization process includes:

[0087] Communication module self-test: Each node first self-tests whether its wireless communication module is working properly, and checks the hardware status such as antenna and signal strength to ensure the reliability of the communication link.

[0088] Neighbor Node Search: A node initiates a neighbor node search function by broadcasting and receiving signals to detect the presence of surrounding nodes and their communication parameters, such as signal strength and communication latency.

[0089] Establishing a communication link: Once a neighboring node is found, the system establishes a stable communication link based on signal strength and link quality, forming a wireless communication network between nodes.

[0090] Through these steps, nodes ensure their connection with other surrounding photovoltaic nodes, forming a communication grid within the region, providing a communication foundation for subsequent data exchange and energy status synchronization.

[0091] In this embodiment, the synchronization of state information between nodes is achieved through the transmission of data packets. Each photovoltaic node packages its energy state data, including information such as power generation, energy storage status, and load demand, and then broadcasts it to neighboring nodes via a wireless communication network. These data packets not only contain the current energy state but also include state data received from other neighboring nodes in the previous time period, ensuring the continuity and integrity of data transmission.

[0092] The synchronization process includes the following sub-steps:

[0093] Packet construction: Each node displays its current power generation. Energy storage status (t) and load conditions Package the data and mark it with a timestamp to ensure that the receiving end can determine its validity based on the data's freshness.

[0094] Data broadcasting: Nodes broadcast their status information through wireless communication networks and use timestamp mechanisms to ensure the freshness of the data and prevent outdated data from interfering with decision-making.

[0095] Neighborhood data caching: After receiving data from other neighboring nodes, each node caches the data locally and determines whether to update its local neighborhood data cache based on the timestamp.

[0096] To improve the reliability of data synchronization, the system employs an acknowledgment (ACK) mechanism to ensure that each node receives data packets from all neighboring nodes. If a packet is lost during communication, the node will automatically request a retransmission to ensure the integrity of state information synchronization.

[0097] In this embodiment, based on the received energy state data of neighboring nodes, an energy state matrix M(t) is created for each node. This matrix describes the energy state of the entire neighborhood at the current moment, including the power generation, energy storage, and load status of each node. The matrix has the following form:

[0098]

[0099] Among them, the elements in the matrix Indicates time Time, node and nodes The energy state relationship between them. Specifically, Includes the following:

[0100] Electricity generation :node In time Power generation at any given moment.

[0101] Energy storage status :node In time The energy storage level at any given time, that is, the energy stored in a battery or energy storage unit.

[0102] Load requirements :node In time The load power demand at any given moment represents the total power demand of all load devices at that node.

[0103] The matrix M(t) is continuously updated in each period, with each node dynamically updating the element values ​​based on the received neighborhood data. This matrix is ​​used not only for energy scheduling decisions at the local node but also provides the data foundation for energy flow throughout the entire neighborhood.

[0104] During the creation of the energy state matrix, nodes determine whether energy balancing or transmission is needed within their neighborhood by comparing the differences between power generation, energy storage levels, and load demand.

[0105] In this embodiment, based on the energy state matrix M(t), each node calculates the energy flow between itself and its neighboring nodes to determine whether energy exchange is necessary. This process is implemented using the following formula:

[0106]

[0107] in, :node In time Time to node Transmitted energy (power), positive values ​​indicate nodes To the node Energy is transferred; negative values ​​indicate energy transfer from the node. Receive energy.

[0108] :node and nodes The energy exchange coefficient between nodes depends on the physical distance between them, communication latency, and energy transmission losses. This coefficient can be dynamically adjusted continuously through learning from and receiving feedback from historical data to improve the efficiency of energy transmission.

[0109] :node The current energy surplus represents the portion of power generation and energy storage that exceeds load demand.

[0110] :node The energy gap at any given moment represents the portion of the load demand that exceeds the power generation and energy storage capacity.

[0111] Through calculation Nodes can determine whether they need to transfer excess energy to neighboring nodes or obtain energy from neighboring nodes to make up for their own energy deficit. This formula ensures that the system achieves energy balance and optimization within a local range.

[0112] The calculation of energy flow does not solely rely on current state data. Nodes can also combine weather forecasts and load predictions to adjust energy transmission strategies in advance. For example, when it is predicted that the load in a certain area will increase significantly in the future, neighboring nodes can provide energy support in advance to ensure the stability of the system during peak load periods.

[0113] S3: Based on the local state of the node and the energy data of neighboring nodes, it autonomously performs energy management and scheduling;

[0114] Step S3 specifically includes the following steps:

[0115] 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;

[0116] S3.2: Neighborhood Energy Exchange Determination: Based on the energy state of the node and the neighborhood state matrix M(t), determine whether there is a node that needs energy transfer, and dynamically adjust the energy flow between nodes i and j according to the energy exchange formula;

[0117] S3.3: Dynamic energy scheduling: For each load device, the node dynamically schedules power according to the currently available energy and device priority, so that critical load devices are given priority in power supply;

[0118] S3.4: Adaptive adjustment to environmental changes: Nodes dynamically adjust power generation and energy storage strategies by continuously collecting environmental data.

[0119] The energy balance equation calculation formula in step S3.1 is as follows:

[0120] in, For nodes In time The power generation capacity at any given moment, that is, the electricity generated by the photovoltaic panel at that moment. For nodes In time The power constantly released from the energy storage unit, i.e., the electricity provided by the battery or energy storage device. For nodes In time The load power demand at any given time represents the total power demand of all load devices at that node. As the priority weight for power generation, Energy storage priority weighting; Let be the energy balance equation for node i at time t.

[0121] The energy exchange formula in step S3.2 is as follows:

[0122]

[0123] in, In time Time, node To the node The exchange of energy (power). For nodes and nodes The energy exchange coefficient between them For nodes In time Excess power at any given moment For nodes In time The power gap at time t represents the node The load demand exceeds its own power generation and energy storage capacity;

[0124] The load distribution formula in step S3.3 is as follows:

[0125]

[0126] in, In time Time allocation to load devices power, For load devices Priority weights, This represents the total number of load devices on the node. For nodes in time The total power available for allocation at any given time is the energy remaining after the current node generates and stores power, minus the base load.

[0127] Specifically, in this embodiment, each photovoltaic node first calculates its current energy surplus or deficit using a local energy balance equation. The node's power generation... Energy storage capacity and load requirements This is the key input for the calculation. The energy balance calculation formula is as follows:

[0128]

[0129] in:

[0130] Photovoltaic nodes In time The total power that can be scheduled at any time.

[0131] :node The current power generation capacity is determined by the power generation efficiency of photovoltaic modules and environmental factors (such as sunlight intensity).

[0132] :node The power currently released from the energy storage unit, i.e. the energy provided by the battery or other energy storage device.

[0133] :node Current load power requirements, i.e., the total power requirements of all load devices.

[0134] The power generation priority weight represents the weight of a node when it prioritizes using its current power generation capacity, and is usually related to sunlight intensity.

[0135] Energy storage priority weight represents the weight of a node when it has priority in using energy storage, and is usually determined based on the charging status of the energy storage unit.

[0136] Using this formula, nodes can determine whether there is an energy surplus (i.e., ) or energy gap (i.e. And to prepare for subsequent energy exchange.

[0137] After calculating the local energy balance, each node needs to decide whether to exchange energy. In this embodiment, the node analyzes the energy state of other nodes in its neighborhood to determine if there are any 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 using the following energy exchange formula:

[0138]

[0139] in, In time Time, node To the node Transmitted energy (power). A positive value indicates a node... To the node Transmitting energy; if negative, it indicates a node From node Obtain energy.

[0140] :node and nodes The energy exchange coefficient between nodes. This coefficient is affected by factors such as physical distance, transmission medium, and energy loss. Nodes dynamically adjust this coefficient based on historical data to maximize the efficiency of energy exchange.

[0141] :node The current energy surplus represents the portion of power generation and energy storage that exceeds load demand.

[0142] :node The current energy gap represents the portion of its load demand that exceeds the power generation and storage capacity.

[0143] This formula allows nodes to determine if there is a need for energy transfer and to dynamically exchange energy based on the energy status of other nodes in their neighborhood. For example, when a node has a large energy surplus, it can transfer the excess energy to nodes with insufficient energy through the exchange coefficient, and vice versa.

[0144] Energy exchange is not a fixed one-to-one relationship. Multiple nodes can form complex multi-node energy transmission paths to ensure that the energy flow of the entire system is more optimized and to reduce energy waste.

[0145] Once a node determines its local energy state, in this embodiment, the node performs dynamic energy scheduling based on the priority of its load devices. When a node has available energy (i.e.... When this happens, the system needs to allocate power to each load according to the device's priority. The formula for load allocation is as follows:

[0146]

[0147] in, :time Time allocation to load devices The power rating indicates the actual amount of electricity received by the device.

[0148] load device The priority weight reflects the importance of the device in energy allocation. Critical loads (such as communication equipment and monitoring equipment) have higher weights, while secondary loads have lower weights.

[0149] The total number of load devices on the node.

[0150] Node in time The total power available for allocation at any given time is the current power generation and storage capacity minus the remaining energy from the base load.

[0151] This formula allows nodes to dynamically allocate energy based on the importance of each load device. Higher-priority devices will receive power first when energy is limited, while non-critical devices may have their power limited or be temporarily shut down.

[0152] For example, in the event of energy shortages, critical equipment (such as data transmission modules) will receive priority power, while non-critical equipment (such as air conditioning and lighting equipment) may have their power supply temporarily reduced or shut down to ensure that the system's basic functions continue to operate normally.

[0153] In this embodiment, the photovoltaic node continuously collects environmental data (such as light intensity, temperature, humidity, etc.) and dynamically adjusts its 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 adaptively adjust according to environmental changes to maximize system operating efficiency and prevent equipment overload or damage.

[0154] Power generation strategy adjustment: When sunlight intensity increases, nodes will automatically increase their power generation priority weight. This ensures that photovoltaic modules can maximize the use of solar energy for power generation; when sunlight weakens, the nodes reduce their power output and prioritize the use of energy storage devices. This effectively avoids photovoltaic modules generating electricity ineffectively under low-light conditions, reducing energy waste.

[0155] Energy storage management optimization: The charging and discharging strategies of energy storage devices are affected by factors such as temperature and device health status. Nodes adjust their charging and discharging strategies based on information such as the current power level and temperature of the energy storage devices. For example, when the temperature is too high, the node will reduce the charging power of the energy storage devices to avoid overheating and damage to battery life; when the battery is close to being fully charged, the system will prioritize powering the load to reduce the charging pressure on the energy storage devices.

[0156] This environmental adaptive adjustment mechanism ensures that 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.

[0157] S4: Aggregate data from multiple photovoltaic nodes through the regional gateway to form a panoramic monitoring map of the region;

[0158] Step S4 specifically includes the following steps:

[0159] S4.1: Area Gateway Initialization: Start the area gateway, connect all photovoltaic nodes in the area through wireless communication technology, and receive the data summary from each node;

[0160] S4.2: Regional Data Aggregation: The regional gateway aggregates data on the energy status, power generation, energy storage level, load demand, and equipment health status of all nodes to form a regional-level energy flow view;

[0161] S4.3: Panoramic monitoring data modeling: Based on the data from each node, the gateway generates a panoramic monitoring map of the area, including energy flow between nodes, load demand, node health status and changes in environmental data, for users or the central collaboration platform to view.

[0162] S4.4: Regional Energy Dispatch Recommendation Generation: Based on the regional panoramic map, the gateway calculates the possible energy flow directions within the current region, provides regional energy dispatch recommendations, and optimizes energy allocation strategies to balance power generation and load demand.

[0163] Specifically, in this embodiment, the regional gateway generates a panoramic monitoring map of the region based on aggregated energy data. This panoramic monitoring map not only displays the power generation, energy storage, and load status of each node, but also reflects the energy flow between nodes. Through data modeling, the gateway can intuitively display the energy distribution throughout the entire region. The specific data modeling process is as follows:

[0164] Energy flow diagram generation: based on energy exchange data between nodes. The regional gateway generates an energy flow map within the region, showing the flow path of energy from 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.

[0165] Node Health Status Graph: Based on the health status data of each node (such as temperature and fault information), the regional gateway generates a node health status monitoring graph, indicating the operating status of each node and potentially faulty nodes. This status graph provides maintenance personnel with key reference information to help identify and prevent potential equipment problems.

[0166] Environmental Impact Map: Based on data from environmental sensors at the nodes (such as light intensity and temperature), an environmental impact monitoring map of the area is generated, showing the power generation efficiency and load changes of different nodes under different environmental conditions.

[0167] The integration and modeling of this panoramic data enables users or systems to have an intuitive and comprehensive understanding of the operational status of the entire region, providing a visual reference for subsequent regional optimization and scheduling.

[0168] S5: Dynamically adjust power generation, energy storage and load strategies based on node status, regional energy distribution and load priority;

[0169] Step S5 specifically includes the following steps:

[0170] S5.1: Node Status Priority Analysis: Based on 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 priority of each node;

[0171] S5.2: Dynamic Load Priority Adjustment: Utilizes a load priority algorithm based on the total available energy of the system. Adjust the power distribution of each load device;

[0172] S5.3: Energy storage strategy optimization: Based on the current status of the energy storage device, when energy is sufficient, excess energy is stored first, and when energy is insufficient, the stored energy is released first to support high-priority loads.

[0173] S5.4: Power generation strategy adjustment: Adjust the power generation of photovoltaic modules in real time according to environmental changes to avoid overload or inefficient operation.

[0174] Specifically, in this embodiment, node state priority analysis is performed first. The purpose is to calculate the priority of each node based on its current energy state, and to prepare for subsequent energy allocation and scheduling. The system mainly uses the following data for priority calculation:

[0175] Node energy state: including the node's current power generation. Energy storage level Load requirements And the energy fluctuations of this node in historical data.

[0176] Energy supply and demand balance: Calculate the energy supply and demand balance of nodes based on power generation and load demand. If a node's energy supply exceeds its load demand, it has a lower priority; if a node's energy supply is insufficient and it is in a critical state, it has a higher priority.

[0177] Regional energy distribution: Considering the overall energy distribution within the region, the system will prioritize nodes with energy gaps and increase their priority to ensure that these nodes can receive priority energy scheduling support within the region.

[0178] In this embodiment, for each load device on a node, the load priority is determined according to its load priority. Dynamic adjustments are made. Nodes may contain various types of load devices, such as critical equipment (data communication modules, monitoring equipment, etc.) and non-critical equipment (lighting, air conditioning, etc.). When energy supply is insufficient, the system needs to allocate energy rationally according to load priority. The specific dynamic load priority adjustment formula is as follows:

[0179]

[0180] For time Time allocation to load devices The power rating indicates the actual energy allocated to the device. For load devices The priority weights are usually preset by the user or the system. Critical devices have higher weight values, while secondary devices have lower weight values. This represents the total number of load devices on the node. time The total available power at any given time point is the remaining power after subtracting the base load from the generated power and stored energy.

[0181] This formula allows nodes to prioritize energy allocation to high-priority devices when energy is insufficient, while low-priority devices may temporarily lose power or reduce power in extreme cases, ensuring that critical system functions are guaranteed.

[0182] In this embodiment, the management strategy for the energy storage device is achieved by optimizing the charging and discharging processes. The energy storage device acts as an energy buffer in the photovoltaic system, storing excess energy when power generation is excessive and releasing it when power generation is insufficient. The system's energy storage strategy optimization is based on the following principles;

[0183] Energy storage priority adjustment: When energy is abundant, the system prioritizes storing excess energy. When energy storage devices are near full charge, the system will appropriately reduce charging power to prevent overcharging. Conversely, when energy supply is insufficient, energy storage devices will prioritize powering critical load devices to ensure the normal operation of nodes.

[0184] Temperature and Energy Storage Management: The system adjusts the charging and discharging strategies of the energy storage device based on the current ambient temperature. For example, when the energy storage device temperature is high, the system reduces the charging power to prevent the battery from overheating and being damaged.

[0185] The energy storage strategy optimization formula is as follows:

[0186] =f(temperature,charge_ )

[0187] Among them, energy storage power It is a function that dynamically adjusts node i based on ambient temperature and battery charging level.

[0188] This dynamic energy storage strategy optimization can ensure that the system makes reasonable use of energy storage devices under different energy supply and demand conditions, extends the service life of energy storage devices, and improves energy utilization efficiency.

[0189] In this embodiment, the adjustment of the power generation strategy mainly relies on real-time feedback of environmental data, such as light intensity, temperature, and humidity. The power generation of photovoltaic modules is highly dependent on environmental factors. The system dynamically adjusts the power generation strategy by collecting environmental data to ensure that the photovoltaic system operates under optimal conditions.

[0190] Light intensity feedback: When the light intensity increases, the system will increase the power generation and prioritize the use of solar energy resources for power generation; when the light intensity decreases, the system will reduce the power generation of the photovoltaic modules and prioritize the use of energy storage devices or obtaining energy from other nodes.

[0191] Temperature management: When the temperature is too high, the system will reduce the power generation to prevent the photovoltaic modules from overheating and being damaged; while at a suitable temperature, the system will operate the photovoltaic modules at their highest efficiency point to maximize power generation.

[0192] The adjustment of the power generation strategy is achieved through the following formula:

[0193] =g( , )

[0194] Among them, power generation It is a function of light intensity and temperature, representing the power generation strategy of node i under different environmental conditions.

[0195] By dynamically adjusting the power generation strategy, the system can ensure that the power generation efficiency of photovoltaic modules is maximized under different environmental conditions, while protecting the equipment from overheating or inefficient operation.

[0196] S6: In the event of a node failure, it performs self-diagnosis and self-healing, and takes over the load or energy supply of the failed node in cooperation with neighboring nodes.

[0197] Step S6 specifically includes the following steps:

[0198] S6.1: Fault Detection and Diagnosis: Each node continuously monitors its own status through an autoregressive model. If the error... If (t) exceeds the predetermined threshold, the node is marked as a potential faulty node;

[0199] 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 a backup path. If successful, it returns to normal operation.

[0200] S6.3: Neighborhood Cooperative Takeover: If a node cannot heal itself, neighboring nodes take over its load or energy supply;

[0201] S6.4: Fault Isolation and Recovery: With the assistance of the regional gateway, the faulty node is isolated. When the faulty node recovers, it is rejoined to the system and its normal scheduling function is restored.

[0202] The autoregressive model formula for fault detection in step S6.1 is shown below:

[0203]

[0204] in, For time The system state at any given moment. Let be the order of the autoregressive model. These are the autoregressive coefficients. For time Error term at time, For time, For nodes.

[0205] Specifically, in this embodiment, each photovoltaic node continuously monitors its own operating status and performs real-time fault detection using an autoregressive model. The autoregressive model predicts the normal operating status of the node based on historical data and identifies faults by monitoring the difference between the actual and predicted states. The fault detection formula is as follows:

[0206]

[0207] in, For node i in time The current state at any given moment (such as voltage, current, temperature, etc.) represents the actual operating state of the system. These are the coefficients of the autoregressive model, representing the influence of historical states on the current state. These coefficients are trained using historical data. The order of the autoregressive model indicates how much historical data is used for prediction. For time The error term at time is the deviation between the predicted value and the actual value.

[0208] When the error term (t) When the predetermined fault threshold is exceeded, the system will determine that the current node may be faulty and mark it as a potential faulty node. At this time, the system will activate a further fault self-healing mechanism.

[0209] In this embodiment, when a node detects a potential failure, the system will attempt self-healing. The self-healing attempt mainly includes the following steps:

[0210] Reduced output power: The node first reduces its own output power to avoid further damage caused by overload or other operational abnormalities. Reducing power can mitigate faults caused by overheating or excessive current in a part of the node.

[0211] Alternate path switching: The system will attempt to switch to an alternative path (such as a backup line or backup equipment) to bypass the failed component and restore some of the node's functionality. For example, if a photovoltaic module fails, the system can switch power transmission to other unaffected components, reducing energy loss.

[0212] The self-healing attempt is done automatically. If the node recovers to normal working condition through these methods, the system will continue to monitor the node's operation and mark it as successfully self-healed.

[0213] In this embodiment, when a node cannot recover through the self-healing mechanism, the system will initiate a neighborhood cooperative takeover mechanism. Cooperative takeover is achieved through real-time communication with neighboring nodes, which will temporarily take over the load or energy supply of the failed node to ensure the stability of the entire system. Specific operations include:

[0214] Load takeover: Neighboring nodes analyze the load requirements of the failed node. The system calculates whether it can take over the load. If neighboring nodes have sufficient energy reserves or power generation capacity, the system will use dynamic energy scheduling to distribute the load of the failed node to neighboring nodes to ensure the continuity of power supply to the load.

[0215] Energy supply takeover: When a faulty node is responsible for energy transmission, a neighboring node takes over the energy transmission through an energy exchange mechanism. The specific formula for energy supply takeover is as follows:

[0216]

[0217] Among them, neighboring nodes By calculating its own energy surplus and neighboring nodes energy gap Dynamically adjust the amount of energy exchange This ensures the continuity of energy transmission within the region.

[0218] Through neighborhood collaborative takeover, the load and energy supply of a faulty node can be taken over by neighboring nodes in a short period of time, ensuring the overall stability of the system.

[0219] In this embodiment, if a node cannot recover through self-healing and requires a longer maintenance period, the system will isolate the faulty node to prevent it from affecting other normally operating nodes. The fault isolation process is as follows:

[0220] Fault node isolation: The system temporarily disconnects the faulty node from the network through the regional gateway, stopping communication and energy transmission with the node to prevent it from affecting the normal operation of other nodes in the region.

[0221] Automatic system adjustment: After a faulty node is isolated, the regional gateway will automatically adjust the energy scheduling strategy within its neighborhood, redistributing energy supply and demand within the region. For example, the system will recalculate the energy state matrix M(t) and, through neighborhood cooperation, compensate for the energy gap caused by node isolation.

[0222] When a faulty node recovers to normal through maintenance or system self-test, the system will automatically rejoin it into the network and restore its role in energy dispatch.

[0223] This invention constructs a self-organizing monitoring network using distributed photovoltaic nodes. Each node possesses autonomous energy management and load scheduling capabilities and collaborates with other nodes via wireless communication technology to achieve global optimization. This eliminates reliance on traditional centralized monitoring systems, enhancing system flexibility and reliability. In the event of sudden events or localized failures, neighboring nodes can coordinate and adjust in real time, significantly reducing the overall system efficiency degradation caused by single-point node failures. Furthermore, this invention introduces a dynamic multi-node collaborative self-healing mechanism. Nodes can not only recover some functionality through local self-healing but also, in the event of self-healing failure, neighboring nodes can collaboratively take over the load and energy supply of the failed node. This approach integrates not only intelligent decision-making by individual nodes but also collaborative responses between multiple nodes, ensuring continuous system operation even in the event of localized node failures and significantly improving system fault tolerance and robustness. This invention achieves distributed intelligent collaboration among photovoltaic nodes by simulating swarm intelligence. Nodes influence each other through local rules, autonomously forming swarm intelligence behavior and exhibiting high adaptability in energy sharing, load allocation, and overall system optimization. The panoramic monitoring system of this invention not only collects energy data from multiple photovoltaic nodes, but also generates real-time energy flow diagrams, node health diagrams, and load demand diagrams through intelligent data modeling technology. This enables users or automated control systems to intuitively understand the operating status of the entire system, quickly identify bottlenecks in energy scheduling, and support system decision optimization.

[0224] Example 3

[0225] like Figure 4 As shown, the present invention also discloses a low-voltage distributed photovoltaic panoramic monitoring system, comprising:

[0226] The data acquisition module is used to acquire the energy status data of each photovoltaic node, including power generation status data, energy storage status data and load status data.

[0227] The data analysis and decision-making module is used by each node 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, so as to carry out autonomous energy management and scheduling.

[0228] The fault diagnosis and self-healing module is used to perform self-diagnosis and self-healing when any photovoltaic node fails. If the faulty node cannot heal itself, the neighboring node will take over the load or energy supply of the faulty photovoltaic node.

[0229] Additionally, a schematic diagram of a terminal device according to an embodiment of the present invention is provided. This terminal device 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, it implements the steps in the various method embodiments described above. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the various device embodiments described above.

[0230] The computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention.

[0231] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0232] The processor may be a central processing unit (CPU), or 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.

[0233] The memory can be used to store the computer program and / or module. The processor implements various functions of the terminal device by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0234] If the modules / units integrated into the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0235] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A low-voltage distributed photovoltaic panoramic monitoring method, characterized in that, It comprises the following steps: Obtaining energy state data of each photovoltaic node, the energy state data comprising power generation state data, energy storage state data and load state data; 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 adjacent nodes, and performs autonomous energy management and scheduling; When any photovoltaic node fails, self-diagnosis and self-recovery are performed, and if the failed node cannot be self-recovered, the adjacent nodes take over the load or energy supply of the failed photovoltaic node; The energy state data is obtained through the environmental data, node electrical data and equipment health state data of the photovoltaic node; The environmental data comprises light intensity, temperature, humidity and wind speed; the node electrical data comprises current, voltage and power. 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 adjacent nodes, and performs autonomous energy management and scheduling, specifically: The energy state data of each photovoltaic node is packaged, and the packaged energy state data is sent to adjacent photovoltaic nodes through a wireless network, so that the data of multiple photovoltaic nodes are synchronized; Adjacent photovoltaic nodes calculate the energy flow direction in real time according to the received packaged energy state data, dynamically adjust the power generation strategy, energy storage strategy and load strategy of the photovoltaic nodes, and perform autonomous energy management and scheduling; 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 adjacent nodes, and performs autonomous energy management and scheduling, specifically: Each photovoltaic node calculates the current energy surplus or gap using an energy balance equation according to its own power generation, energy storage level and current load; Based on the energy state of the node itself and the energy state of the adjacent nodes, it is determined whether there is a node that needs energy transmission, and the energy flow between the nodes is dynamically adjusted according to the energy exchange formula to realize 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 to prioritize power supply for critical load devices; The energy balance equation is: wherein, is the energy balance equation for node i at time t, is the power generation of node i at time t, is the power generation of node i at time t, is the power generation of node i at time t, is the power generation of node i at time t, is the power generation of node i at time t, is the power released from the energy storage unit at time t, is the power released from the energy storage unit at time t, is the load power demand of node i at time t, is the load power demand of node i at time t, is the generation priority weight, is the energy storage priority weight. When any photovoltaic node fails, self-diagnosis and self-recovery are performed, specifically: Each node continuously monitors its own state through a preset autoregressive model, obtains the error value at the current time, and compares the error value at the current time with the preset error threshold value, if the error value at the current time is greater than the preset error threshold value, the node is marked as a potential failure node; Then self-recovery is performed by reducing the output power or switching to a backup path, and if the self-recovery is successful, the node returns to the normal working state.

2. The low-voltage distributed photovoltaic panoramic monitoring method according to claim 1, characterized in that, If the failed node cannot be self-recovered, the adjacent nodes take over the load or energy supply of the failed photovoltaic node, specifically: the failed node is isolated, and when the failed node recovers, it rejoins the system and restores the normal scheduling function of the node.

3. A low voltage distributed photovoltaic panoramic monitoring system, characterized in that, A low-voltage distributed photovoltaic panoramic monitoring method for realizing the method of any one of claims 1-2, comprising: a data acquisition module, configured to acquire energy state data of each photovoltaic node, the energy state data including power generation state data, energy storage state data and load state data; a data analysis and decision module, configured to dynamically adjust power generation strategy, energy storage strategy and load strategy of any photovoltaic node based on energy state data of the photovoltaic node and energy state data of adjacent photovoltaic nodes, and to perform autonomous energy management and scheduling; a fault diagnosis and self-recovery module, configured to perform self-diagnosis and self-recovery when any photovoltaic node fails, and to take over load or energy supply of the failed photovoltaic node by adjacent photovoltaic nodes if the failed photovoltaic node cannot be self-recovered.

4. A computer apparatus comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program, when executed by the processor, causes the processor to perform the method of any one of claims 1 to 3. The processor executes the computer program to implement the steps of the method according to any one of claims 1-2.

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