Multi-axis linkage photovoltaic tracking support optimization system and method

Abstract

The invention discloses a multi-axis linkage photovoltaic tracking support optimization system and method, and relates to the technical field of photovoltaic control, and the method comprises the steps: employing a multi-source data fusion analysis algorithm to recognize an optimization set comprising Y target photovoltaic tracking supports from all monitored target photovoltaic tracking supports, a long-distance optimization coordination instruction message is sent to a coordination control center, and for any target photovoltaic tracking support in the optimization set, the coordination control center corresponding to the target photovoltaic tracking support combines a plurality of targets based on a preliminary regulation suggested time period provided by the plurality of area monitoring units, so that the target photovoltaic tracking support in the optimization set is optimized. And performing secondary adaptation on the regulation and control suggestion time period of the target photovoltaic tracking support and the optimization strategy parameters by using a global multi-axis collaborative optimization model to form a final recommended optimization time period. According to the optimization system, through overall planning of multi-area monitoring information, global optimization control of the multi-axis linkage photovoltaic tracking support is realized, and the overall performance and operation reliability of a photovoltaic system are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic control technology, specifically to a multi-axis linkage photovoltaic tracking bracket optimization system and method. Background Technology

[0002] In large-scale photovoltaic power plants, multi-axis linkage photovoltaic tracking brackets improve power generation efficiency by dynamically adjusting tilt and azimuth angles. However, due to the complex environment after the scale is expanded (such as differences in mechanical load), problems such as insufficient sunlight utilization, angle deviation, and drive abnormalities often occur, affecting power generation efficiency and equipment safety. Existing solutions are limited by the sensing range and computing power of a single area, and their optimization strategies are only for local conditions, making it difficult to coordinate the overall situation. In special scenarios such as high wind speed and sudden changes in cloud cover, fragmented local control can easily lead to delays in key optimization needs and spatiotemporal conflicts in the adjustment of multiple photovoltaic tracking brackets, resulting in problems such as waste of sunlight resources and loss of power generation. Especially in large-scale photovoltaic power plants or grid-connected bases, information silos between regions further limit the potential for global collaboration. Therefore, there is an urgent need for a technical solution that integrates multi-region sensing information, intelligently identifies key optimization objects, and generates cross-regional collaborative strategies to solve the performance bottlenecks and potential risks caused by local control. Summary of the Invention

[0003] The purpose of this invention is to provide an optimized system and method for multi-axis linkage photovoltaic tracking brackets to address the shortcomings in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an optimization method for a multi-axis linkage photovoltaic tracking bracket, the optimization method comprising the following steps: A1. The main central control equipment receives local operating status and preliminary control suggestions from G regional monitoring units within the site. The information uploaded by the G regional monitoring units is used to reflect the performance constraints of the current operating status of W target photovoltaic tracking brackets within their monitoring areas. A2. After obtaining the local operating status uploaded by the G regional monitoring units, the main central control device, based on the collected identifiers and real-time operating parameters of the W target photovoltaic tracking brackets and the preliminary control suggestion time periods given by the G regional monitoring units, uses a multi-source data fusion analysis algorithm to identify an optimized set containing Y target photovoltaic tracking brackets from all monitored target photovoltaic tracking brackets. A3. The main central control device sends a long-distance optimization coordination instruction message to the collaborative control center corresponding to each of the target photovoltaic tracking brackets in the optimization set. For any target photovoltaic tracking bracket in the optimization set, the corresponding collaborative control center, based on the preliminary control suggestion time period provided by multiple regional monitoring units, combines multiple targets and uses a global multi-axis collaborative optimization model to perform secondary adaptation of the control suggestion time period and optimization strategy parameters of the target photovoltaic tracking bracket to form the final recommended optimization time period.

[0005] In a preferred embodiment, the criteria for determining the target photovoltaic tracking bracket in the optimization set are: the operating parameters of the target photovoltaic tracking bracket exceed the preset optimization threshold, the current state of the target photovoltaic tracking bracket has an impact on the overall power generation efficiency or equipment safety, or there is a linkage and coupling relationship between the target photovoltaic tracking bracket and other photovoltaic tracking brackets.

[0006] In a preferred embodiment, an optimized set containing Y target photovoltaic tracking brackets is identified from all monitored target photovoltaic tracking brackets using a multi-source data fusion analysis algorithm, including the following steps: The real-time operating parameters of each photovoltaic tracking bracket will be compared with their corresponding preset optimization thresholds one by one to obtain the number of parameters exceeding the limit. Assess the potential impact of the current status of each photovoltaic tracking bracket on the overall power generation efficiency or system safety of the entire site, and calculate the global impact index; Using the multi-axis control group mapping table, other photovoltaic tracking brackets that are coupled with the target photovoltaic tracking bracket are identified, and the number of linkage couplings of the photovoltaic tracking brackets is obtained based on the above analysis. The number of parameters exceeding limits, global impact indicators, and number of linkage couplings are normalized by maximum-minimum values. The number of parameters exceeding limits, global impact indicators, and number of linkage couplings after normalization are summed to obtain the optimization index. Photovoltaic tracking brackets with optimization indexes greater than the optimization threshold are included in the optimization set, which contains Y target photovoltaic tracking brackets.

[0007] In a preferred embodiment, the criteria for determining the target photovoltaic tracking bracket in the optimization set include: the operating parameters of the target photovoltaic tracking bracket exceed the preset optimization threshold, the current state of the target photovoltaic tracking bracket has an impact on the overall power generation efficiency or equipment safety, or there is a linkage and coupling relationship between the target photovoltaic tracking bracket and other photovoltaic tracking brackets.

[0008] In a preferred embodiment, the long-distance optimization coordination instruction message is used to inform the recipient of the Y currently identified target photovoltaic tracking brackets and to push the recommended optimization strategy parameters for each target photovoltaic tracking bracket.

[0009] In a preferred embodiment, step A3 combines multiple objectives, including a full-field illumination model, wind load prediction data, multi-axis linkage constraints, and the objective of maximizing power generation efficiency.

[0010] In a preferred embodiment, a global multi-axis collaborative optimization model is used to perform a secondary adaptation between the suggested control time period and the optimization strategy parameters of the target photovoltaic tracking bracket to form the final recommended optimization time period, including the following steps: The collaborative control center will integrate optimized strategy parameters and preliminary control suggestion periods with locally collected real-time operating status and environmental prediction information to form a local decision dataset. Based on the global multi-axis collaborative optimization model, multiple control periods are traversed or optimized, and the comprehensive performance of each period is analyzed. Based on the analysis results, the collaborative control center dynamically adjusts the preliminary control suggestion time period and strategy parameters, and finally outputs the recommended optimization time period and executable strategy parameters, and generates a control command sequence to be sent to the corresponding photovoltaic tracking bracket controller.

[0011] In a preferred embodiment, the local operating status includes unique identification information of W target photovoltaic tracking brackets within the monitoring area, and real-time operating parameters collected by the photovoltaic tracking brackets, including solar incidence angle, current tilt angle, wind speed, motor status, and tracking error.

[0012] In a preferred embodiment, in step A1, the information uploaded by the G regional monitoring units is used to reflect the performance constraints of the current operating status of the W target photovoltaic tracking brackets within their monitoring areas. The performance constraints include insufficient sunlight utilization, risk of exceeding wind speed limits, tracking angle deviation, and abnormal drive load. G is a positive integer representing the total number of regional monitoring units with monitoring and preliminary decision-making capabilities within the site.

[0013] This application also provides a multi-axis linkage photovoltaic tracking bracket optimization system, including an information receiving module, an optimization set output module, and an optimization recommendation module; Information receiving module: Receives local operating status and preliminary control suggestions from G regional monitoring units within the site. The information uploaded by the G regional monitoring units is used to reflect the performance constraints of the current operating status of W target photovoltaic tracking brackets within their monitoring areas. Optimized set output module: After obtaining the local operating status uploaded by G regional monitoring units, based on the collected identifiers and real-time operating parameters of W target photovoltaic tracking brackets and the preliminary control suggestion time periods given by G regional monitoring units, an optimized set containing Y target photovoltaic tracking brackets is identified from all monitored target photovoltaic tracking brackets using a multi-source data fusion analysis algorithm. The optimization recommendation module sends long-distance optimization coordination instruction messages to the collaborative control center corresponding to each target photovoltaic tracking bracket in the optimization set. For any target photovoltaic tracking bracket in the optimization set, the corresponding collaborative control center, based on the preliminary control suggestion time period provided by multiple regional monitoring units, combines multiple targets and uses a global multi-axis collaborative optimization model to perform secondary adaptation of the control suggestion time period and optimization strategy parameters of the target photovoltaic tracking bracket to form the final recommended optimization time period.

[0014] The technical effects and advantages provided by the present invention in the above technical solution are as follows: 1) This invention sends long-distance optimization and coordination instructions to the collaborative control center corresponding to the key target photovoltaic tracking bracket, and uses a global multi-axis collaborative optimization model at the collaborative control center level to perform secondary adaptation of the control suggestion time period and optimization strategy parameters, forming a final recommended optimization time period that takes into account multi-axis linkage constraints, environmental adaptability and maximization of power generation benefits. This effectively solves technical problems such as the timing conflict of multiple photovoltaic tracking brackets and the contradiction between local optimization and global objectives, and realizes the efficient collaborative operation of the whole-field photovoltaic tracking system in complex environments.

[0015] 2) By receiving local operating status and preliminary control suggestion time periods uploaded by multiple regional monitoring units within the site, the system can comprehensively perceive the operating status of photovoltaic tracking brackets scattered in different regions and their performance constraints, solving the problem of local information silos in the traditional decentralized control mode and providing a complete data foundation for global optimization.

[0016] 3) This invention uses a multi-source data fusion analysis algorithm to intelligently identify the set of key targets that truly need optimization and control from numerous target photovoltaic tracking brackets across the entire field. This screening mechanism based on multi-parameter comprehensive evaluation can accurately locate photovoltaic tracking brackets that have a significant impact on the overall power generation efficiency or equipment safety, avoiding the waste of resources caused by the one-size-fits-all global control in traditional systems. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0018] Figure 1 This is a mind map of the optimization method of the present invention. Detailed Implementation

[0019] 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, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example: This example provides an optimization method for a multi-axis linkage photovoltaic tracking bracket. Please refer to [link / reference]. Figure 1 As shown, the optimization method includes the following steps: A1. Obtain the local operating status uploaded by multiple regional monitoring units: The main central control equipment receives local operating status data from G regional monitoring units within the site (distributed across different areas, each unit responsible for monitoring a group of adjacent photovoltaic tracking brackets). The information uploaded by each regional monitoring unit primarily reflects the performance constraints of the current operating status of W target photovoltaic tracking brackets (i.e., several photovoltaic tracking brackets under the initial control of that regional monitoring unit) within its monitoring area, such as insufficient sunlight utilization, wind speed exceeding limits, tracking angle deviation, and abnormal drive load.

[0021] The local operating status includes at least: unique identification information of W target photovoltaic tracking brackets within the monitoring area, key operating parameters collected in real time for each photovoltaic tracking bracket (such as solar incidence angle, current tilt angle, wind speed, motor status, tracking error, etc.), and preliminary control suggestion time period (i.e. time range for suggested control) generated by the monitoring unit of the area based on the local strategy model.

[0022] Where G is a positive integer, representing the total number of regional monitoring units within the station that have independent monitoring and preliminary decision-making capabilities.

[0023] A2. Comprehensive analysis and screening of target photovoltaic tracking brackets that require global collaborative optimization: After obtaining the local operating status uploaded by the G regional monitoring units, the main central control equipment will further integrate and comprehensively analyze this information.

[0024] Specifically, based on the collected identification of W target photovoltaic tracking brackets, real-time operating parameters, and preliminary control suggestions from each regional monitoring unit, the main central control equipment uses a built-in multi-source data fusion analysis algorithm to identify an optimized set (where Y is an integer greater than 0) containing Y target photovoltaic tracking brackets from all monitored target photovoltaic tracking brackets. The target photovoltaic tracking brackets in this optimized set are considered to be key objects that require global coordinated optimization and control within the current site.

[0025] The criteria for determining the target photovoltaic tracking bracket in the optimization set include, but are not limited to: the operating parameters of the target photovoltaic tracking bracket exceed the preset optimization threshold, the current state of the target photovoltaic tracking bracket has a significant impact on the overall power generation efficiency or equipment safety, or there is a strong linkage and coupling relationship between the target photovoltaic tracking bracket and other photovoltaic tracking brackets, and its adjustment may have a chain effect on the surrounding equipment.

[0026] By integrating reported data from multiple areas across the site, and combining the topological relationships, historical operating patterns, and current environmental conditions of each photovoltaic tracking bracket in the overall layout, key photovoltaic tracking brackets are intelligently selected from a global perspective.

[0027] A3. Send global optimization and coordination commands to the overall collaborative control center to achieve cross-regional multi-axis linkage control: After obtaining the optimized set, the main central control device sends a set of long-distance optimization coordination instruction messages to the collaborative control center corresponding to each target photovoltaic tracking bracket in the optimized set.

[0028] The main function of this long-distance optimization and coordination instruction message is to inform the recipient of the Y target photovoltaic tracking brackets that have been identified (i.e., the target objects that need to be controlled and optimized globally) and to push the recommended optimization strategy parameters for each target photovoltaic tracking bracket.

[0029] For any target photovoltaic tracking bracket in the optimization set, its corresponding collaborative control center, based on the preliminary control suggestion time period provided by multiple regional monitoring units, combined with the overall illumination model, wind load prediction data, multi-axis linkage constraints, and the goal of maximizing power generation efficiency, uses the built-in global multi-axis collaborative optimization model to perform a secondary adaptation of the target photovoltaic tracking bracket's control suggestion time period and optimization strategy parameters, and finally forms a recommended optimization time period that takes into account both global efficiency and local feasibility.

[0030] This long-distance optimization and coordination instruction message not only contains the unique identification information of Y target photovoltaic tracking brackets, but also includes optimization strategy parameters tailored for them. It aims to guide the receiver (coordination control center) to implement multi-axis linkage control on a larger scale, thereby realizing the coordinated operation and overall performance improvement of the entire photovoltaic tracking system in complex environments.

[0031] This embodiment provides a multi-axis linkage photovoltaic tracking bracket optimization system, including an information receiving module, an optimization set output module, and an optimization recommendation module; Information receiving module: Receives local operating status and preliminary control suggestion time period from G regional monitoring units within the site. The information uploaded by the G regional monitoring units is used to reflect the performance constraints of the current operating status of W target photovoltaic tracking brackets within their monitoring areas. The local operating status and preliminary control suggestion time period are sent to the optimization set output module and the optimization recommendation module. Optimization set output module: After obtaining the local operating status uploaded by G regional monitoring units, based on the collected identifiers and real-time operating parameters of W target photovoltaic tracking brackets and the preliminary control suggestion time periods given by G regional monitoring units, the multi-source data fusion analysis algorithm is used to identify an optimization set containing Y target photovoltaic tracking brackets from all monitored target photovoltaic tracking brackets, and the optimization set is sent to the optimization recommendation module; The optimization recommendation module sends long-distance optimization coordination instruction messages to the collaborative control center corresponding to each target photovoltaic tracking bracket in the optimization set. For any target photovoltaic tracking bracket in the optimization set, the corresponding collaborative control center, based on the preliminary control suggestion time period provided by multiple regional monitoring units, combines multiple targets and uses a global multi-axis collaborative optimization model to perform secondary adaptation of the control suggestion time period and optimization strategy parameters of the target photovoltaic tracking bracket to form the final recommended optimization time period.

[0032] A1. Obtain the local operating status uploaded by multiple regional monitoring units: The main central control equipment receives local operating status data from G regional monitoring units within the site (distributed across different areas, each unit responsible for monitoring a group of adjacent photovoltaic tracking brackets). The information uploaded by each regional monitoring unit primarily reflects the performance constraints of the current operating status of W target photovoltaic tracking brackets (i.e., several photovoltaic tracking brackets under the initial control of that regional monitoring unit) within its monitoring area, such as insufficient sunlight utilization, wind speed exceeding limits, tracking angle deviation, and abnormal drive load.

[0033] The local operating status includes at least: unique identification information of W target photovoltaic tracking brackets within the monitoring area, key operating parameters collected in real time for each photovoltaic tracking bracket (such as solar incidence angle, current tilt angle, wind speed, motor status, tracking error, etc.), and preliminary control suggestion time period (i.e. time range for suggested control) generated by the monitoring unit of the area based on the local strategy model.

[0034] Where G is a positive integer, representing the total number of regional monitoring units within the station that have independent monitoring and preliminary decision-making capabilities.

[0035] Regional monitoring units are distributed in different physical areas within the photovoltaic power station. They are typically deployed and planned according to the spatial layout, electrical zoning, control domain division, or topology of the photovoltaic tracking bracket array. Each regional monitoring unit has a relatively independent monitoring coverage and preliminary decision-making capability, and is responsible for the preliminary generation of status monitoring and control strategies for a group of adjacent or related photovoltaic tracking brackets within its area.

[0036] Specifically, the photovoltaic power station is divided into G regional monitoring units, where G is a positive integer representing the total number of regional units within the station that have independent monitoring and local control strategy generation capabilities. Each regional monitoring unit (ZMUk, k∈[1,G]) is associated with a set of spatially adjacent photovoltaic tracking brackets. In this scheme, the set of photovoltaic tracking brackets is defined as the target monitoring object set of the regional monitoring unit, with a quantity of W (W is also a positive integer, which may vary depending on the region). That is, each ZMUk is responsible for monitoring and initially controlling the W target photovoltaic tracking brackets within the region.

[0037] The target photovoltaic tracking bracket refers to the photovoltaic tracking bracket unit that is currently within the direct sensing and control range of the monitoring unit in this area. Its operating status and control response are the basic data objects for local optimization and subsequent global coordination.

[0038] During each control cycle (or according to an event-triggered mechanism), each regional monitoring unit (ZMUk) will upload a local operational status report to its superior central control device. This report is a comprehensive status description generated by ZMUk based on the analysis results of real-time sensing data and local strategy models of its W target photovoltaic tracking brackets. It is mainly used to provide feedback to the main control layer on the operational health, performance, potential risks, and preliminary control suggestions of the photovoltaic tracking brackets in the current region.

[0039] Each local operational status report should contain at least the following three types of key information: 1) Unique identification information of the target photovoltaic tracking bracket This data section is used to clearly identify the W target photovoltaic tracking brackets monitored by the current area monitoring unit ZMUk, ensuring that the main central control equipment can accurately locate each specific physical photovoltaic tracking bracket when performing data fusion, status correlation, and optimization decisions. Unique identification information typically includes: photovoltaic tracking bracket number (e.g., TPTSS_ID), area number (Zone_ID), physical location code (e.g., coordinates or array index), etc. Its data structure can be a string, integer code, or composite key-value pair, maintaining global uniqueness and consistency within the system.

[0040] 2) Real-time key operating parameters of each photovoltaic tracking bracket This section contains core monitoring data, which is collected in real time and pre-processed before being reported by the sensor system, drive control system, and edge computing module deployed on each target photovoltaic tracking bracket. Key operating parameters include the following: The solar incidence angle represents the angle between the current sunlight and the normal to the photovoltaic panel, and is one of the key inputs determining whether the tracking angle is optimal. The current tilt angle is the actual tilt angle of the photovoltaic panel, reflecting the actual physical attitude of the photovoltaic tracking bracket. Real-time collected wind speed data is used to assess whether the current wind conditions exceed the safe operating threshold of the photovoltaic tracking bracket. Motor operating status includes motor current, voltage, temperature, and operating mode (such as automatic tracking, forced stop, limit protection, etc.). Tracking error represents the deviation between the current actual angle of the photovoltaic tracking bracket and the theoretical optimal angle, and is a core indicator for measuring tracking accuracy. The above parameters are usually organized in structured data form, such as serialized transmission using JSON, Protocol Buffers, or a custom binary protocol. Each parameter item is bound to the corresponding photovoltaic tracking bracket ID to ensure data traceability and parsing accuracy.

[0041] 3) Preliminary adjustment recommendations for the period This information is generated by the regional monitoring unit based on its built-in local policy model. This rule-based model's function is to preliminarily determine, based on the currently collected operating parameters, whether there are any abnormal situations requiring intervention or optimization opportunities, and output a suggested time range (period) for control operations. This period is typically represented in the form of a timestamp interval, such as [T s ,T e ] represents from time point T s To T e The period between these two points is a suitable window for implementing regulatory actions.

[0042] The decision-making logic takes into account the following factors: Will the current wind speed decrease to below the safe threshold in the future? Is it about to enter a period of high sunlight intensity and significant tracking benefits? Is there an opportunity for the equipment to stabilize and be suitable for fine-tuning the angle? What is the optimal control timing derived by the local strategy model based on historical patterns and the current situation? It should be noted that this preliminary control suggestion is a regional and local optimization suggestion. It does not consider the linkage effect of other photovoltaic tracking brackets across the entire site, the global optimality of the overall illumination model, or the constraints of multi-axis coordination. Therefore, it is only a reference input for the main central control equipment to perform subsequent global integration and in-depth optimization.

[0043] At the data receiving end, the main central control equipment will receive LOSR data packets sequentially or in parallel from G regional monitoring units. Each data packet corresponds to a ZMUk and its managed W target photovoltaic tracking brackets. The main control equipment will perform unified reception buffering, format verification, data decoding, and structured storage on these data.

[0044] A2. Comprehensive analysis and screening of target photovoltaic tracking brackets that require global collaborative optimization: After obtaining the local operating status uploaded by the G regional monitoring units, the main central control equipment will further integrate and comprehensively analyze this information.

[0045] Specifically, based on the collected identification of W target photovoltaic tracking brackets, real-time operating parameters, and preliminary control suggestions from each regional monitoring unit, the main central control equipment uses a built-in multi-source data fusion analysis algorithm to identify an optimized set (where Y is an integer greater than 0) containing Y target photovoltaic tracking brackets from all monitored target photovoltaic tracking brackets. The target photovoltaic tracking brackets in this optimized set are considered to be key objects that require global coordinated optimization and control within the current site.

[0046] The criteria for determining the target photovoltaic tracking bracket in the optimization set include, but are not limited to: the operating parameters of the target photovoltaic tracking bracket exceed the preset optimization threshold, the current state of the target photovoltaic tracking bracket has a significant impact on the overall power generation efficiency or equipment safety, or there is a strong linkage and coupling relationship between the target photovoltaic tracking bracket and other photovoltaic tracking brackets, and its adjustment may have a chain effect on the surrounding equipment.

[0047] By integrating reported data from multiple areas across the site, and combining the topological relationships, historical operating patterns, and current environmental conditions of each photovoltaic tracking bracket in the overall layout, key photovoltaic tracking brackets are intelligently selected from a global perspective.

[0048] Based on the preprocessed full-field data, the main central control equipment invokes its built-in multi-source data fusion analysis algorithm to perform a multi-dimensional comprehensive evaluation of each target photovoltaic tracking bracket TPTSSi (i=1,2,...,W×G). Specifically, this includes the following sub-logic: The real-time operating parameters of each photovoltaic tracking bracket (such as wind speed WS, tracking error TE, motor temperature MT, etc.) will be compared with their corresponding preset optimization thresholds one by one. These thresholds are control boundaries pre-set based on equipment specifications, operating experience, safety standards, and historical statistical patterns. For example: If the current wind speed WS of a photovoltaic tracking bracket exceeds its structural allowable wind speed threshold, it is considered to be at risk of wind speed exceeding the limit; if the tracking error TE is continuously higher than the set accuracy threshold, the tracking performance is considered to be not optimal; if the motor current or temperature exceeds the safe operating range, it is considered to be an abnormal drive load.

[0049] For each photovoltaic tracking bracket (TPTSSi), all its key operating parameters are iterated and compared with the corresponding parameter thresholds in the local operating status report. If one or more parameters exceed the threshold, the photovoltaic tracking bracket is marked as a "parameter abnormality suspicious object" and enters the subsequent comprehensive analysis queue.

[0050] Assess the potential impact of the current state of each photovoltaic tracking bracket on the overall power generation efficiency or system safety of the entire site: If a photovoltaic tracking bracket is located in the area with the best solar resources in the entire field (such as a high-irradiance array area), but its current tilt angle deviates from the optimal angle by more than the deviation threshold, its adjustment may bring significant power generation gains, so it should be given priority. If a photovoltaic tracking bracket is a neighboring photovoltaic tracking bracket to a junction node in the field, its abnormal state may trigger cascading failures or local power fluctuations. If a photovoltaic tracking bracket is in a historical pattern of frequent failures, it is identified as a systemic potential hazard.

[0051] Based on pre-configured importance weighting factors for photovoltaic tracking brackets (such as power generation contribution (obtained by dividing the power generation of the photovoltaic panel of the tracking bracket by the total power generation of the station; the greater the power generation contribution, the higher the power generation gain brought by the photovoltaic tracking bracket, indicating that it needs to be given priority) and fault frequency (obtained by dividing the number of faults of the photovoltaic tracking bracket within the monitoring window by the length of the monitoring window; the greater the fault frequency, the higher the need for priority)), the system calculates a global impact index for each photovoltaic tracking bracket (the global impact index is obtained by summing the power generation contribution and fault frequency).

[0052] For groups of photovoltaic tracking brackets that are physically adjacent, drive-linked, synchronously tracking, or structurally coupled (e.g., photovoltaic tracking brackets in the same shaft group, the same array row, or the same wind direction block), further analysis is conducted on the potential chain effects of the adjustment behavior of the target photovoltaic tracking bracket on surrounding equipment, as detailed below: Adjusting the tilt angle of a photovoltaic (PV) tracking bracket can cause shading of neighboring PV tracking brackets; as a member of a multi-axis synchronous control group, its individual adjustment may disrupt the group's coordination and consistency; and since PV tracking brackets share motor drives with other devices, their regulation may affect the stability of the same group of devices. Using a pre-constructed multi-axis control group mapping table, other PV tracking brackets with strong coupling relationships to the target PV tracking bracket are identified. Based on the above analysis, the number of linkage couplings among the PV tracking brackets (i.e., the total number of PV tracking brackets affected by the target PV tracking bracket) is obtained.

[0053] Based on the above comprehensive judgment results (i.e., the number of parameters exceeding limits, global impact indicators, and the number of linkage couplings), the main central control equipment will execute a multi-condition fusion decision logic (perform maximum-minimum value normalization on the number of parameters exceeding limits, global impact indicators, and the number of linkage couplings, sum the normalized number of parameters exceeding limits, global impact indicators, and the number of linkage couplings to obtain the optimization index, and include the photovoltaic tracking brackets with optimization indices greater than the optimization threshold into the optimization set), and finally select the optimization set from all W×G target photovoltaic tracking brackets. This set contains Y target photovoltaic tracking brackets (Y≥1).

[0054] The final optimized set is in the form of structured data, which includes the unique identifier TPTSS_ID of each selected photovoltaic tracking bracket; the relevant real-time operating parameters and historical background information of the photovoltaic tracking bracket; the multi-dimensional criteria for its selection (such as exceeding limits, high impact, coupling sensitivity, etc.); the source of the corresponding regional monitoring unit and the preliminary control suggestion period.

[0055] A3. Send global optimization and coordination commands to the overall collaborative control center to achieve cross-regional multi-axis linkage control: After obtaining the optimized set, the main central control device sends a set of long-distance optimization coordination instruction messages to the collaborative control center corresponding to each target photovoltaic tracking bracket in the optimized set.

[0056] The main function of this long-distance optimization and coordination instruction message is to inform the recipient of the Y target photovoltaic tracking brackets that have been identified (i.e., the target objects that need to be controlled and optimized globally) and to push the recommended optimization strategy parameters for each target photovoltaic tracking bracket.

[0057] For any target photovoltaic tracking bracket in the optimization set, its corresponding collaborative control center, based on the preliminary control suggestion time period provided by multiple regional monitoring units, combined with the overall illumination model, wind load prediction data, multi-axis linkage constraints, and the goal of maximizing power generation efficiency, uses the built-in global multi-axis collaborative optimization model to perform a secondary adaptation of the target photovoltaic tracking bracket's control suggestion time period and optimization strategy parameters, and finally forms a recommended optimization time period that takes into account both global efficiency and local feasibility.

[0058] This long-distance optimization and coordination instruction message not only contains the unique identification information of Y target photovoltaic tracking brackets, but also includes optimization strategy parameters tailored for them. It aims to guide the receiver (coordination control center) to implement multi-axis linkage control on a larger scale, thereby realizing the coordinated operation and overall performance improvement of the entire photovoltaic tracking system in complex environments.

[0059] Based on the output optimization set, the main central control equipment constructs and sends customized long-distance optimization coordination command messages to the Cooperative Control Center (CCHi) corresponding to each target photovoltaic tracking bracket in the set. This command message is a key data carrier for cross-regional control interaction within the system, and its function is to: clearly inform the recipient which target photovoltaic tracking brackets have been identified by the global optimization decision-making layer as key objects requiring priority control; and push a set of optimization strategy parameters tailored to each target photovoltaic tracking bracket, including: Recommended target tilt angle or attitude adjustment range; suggested tracking mode switching strategies (such as dynamic tracking, wind speed avoidance mode, safety lock mode, etc.); optimization target weights related to power generation gain, light utilization rate, shadow avoidance, etc.; linkage constraints or coordination priorities with adjacent photovoltaic tracking brackets; and contextual information related to the photovoltaic tracking bracket, such as current operating parameter snapshots, area number, multi-axis group affiliation, historical control records, etc., to assist the coordination control center in localization adaptation and decision-making.

[0060] The command message data structure is organized in a structured form, including a unique identifier for the target photovoltaic tracking bracket, a global optimization strategy parameter set, information on associated regions and multi-axis groups, a preliminary control suggestion period (derived from the regional monitoring unit), and global optimization objectives (such as maximizing power generation efficiency, wind speed safety constraints, and improving tracking accuracy). This command is transmitted to the corresponding collaborative control center via a highly reliable communication network within the site (such as industrial Ethernet).

[0061] Each collaborative control center that receives a long-distance optimization coordination command is responsible for implementing further localized strategy adaptation and optimization decisions for the target photovoltaic tracking brackets under its jurisdiction. It performs secondary adaptation of the control suggestion period and optimization strategy parameters to form a set of recommended optimization period and execution strategy combination that takes into account both the global optimization goal and the feasibility of local control.

[0062] To achieve this goal, the collaborative control center operates a global multi-axis collaborative optimization model. Its inputs include not only the optimization strategy parameters and preliminary control suggestion periods issued by the main control layer, but also the full-field illumination model, wind load prediction data, multi-axis linkage constraints, and the objective function for maximizing power generation benefits. The full-field illumination model provides predictions of solar radiation intensity and incident angle trends for all regions over a future period, used to assess theoretical power generation benefits under different control periods. Wind load prediction data includes predicted values ​​of wind speed, wind direction, and their changes for each region over a future period, used to assess wind speed exceedance risks and safety constraints. Multi-axis linkage constraints define the motion synchronization requirements, collision avoidance rules, drive priorities, and other collaborative control logic among the same multi-axis group, the same array row, and adjacent photovoltaic tracking brackets. The power generation benefit maximization objective function serves as the core objective of optimization decision-making, with power generation gain, tracking accuracy improvement, and overall system efficiency as quantitative indicators.

[0063] The processing logic is as follows: The collaborative control center first integrates the optimization strategy parameters and preliminary control suggestion periods issued by the master controller with the locally collected real-time operating status and environmental prediction information to form a complete local decision dataset. Based on the global multi-axis collaborative optimization model, it traverses or optimizes multiple possible control periods (such as multiple time points or windows within the preliminary control suggestion period) and analyzes the comprehensive performance of each period in the following dimensions: Potential for improving power generation efficiency (e.g., theoretical power generation gain, improved solar utilization); degree of compliance with safety constraints (e.g., whether wind speed is within a safe range, whether the driving load is controllable); feasibility of multi-axis coordination (e.g., whether there is any conflict with the operation of adjacent photovoltaic tracking brackets, whether synchronization requirements are met); stability of control execution (e.g., motor response capability, control delay, historical execution success rate).

[0064] Based on the above multidimensional analysis results, the collaborative control center dynamically adjusts the preliminary control suggestion time period and strategy parameters, such as fine-tuning the target tilt angle, delaying or advancing the control window, adjusting the linkage sequence or priority, and finally outputting a recommended optimization time period and a set of executable strategy parameters. It also generates a specific control command sequence and sends it to the corresponding photovoltaic tracking bracket controller to drive it to execute multi-axis linkage control actions as planned.

[0065] In a large-scale photovoltaic power plant, the power plant is divided into multiple areas, each monitored by a regional monitoring unit (ZMU), while the ultimate control of each photovoltaic tracking bracket (hereinafter referred to as "bracket") is the responsibility of the corresponding collaborative control center (CCH).

[0066] The focus is currently on one specific target photovoltaic tracking bracket—bracket A (TPTSS_A), which is located in Zone 3, controlled by the Coordination Control Center CCH_3, and selected into the optimization set (Y = multiple brackets, including bracket A) by the main central control device (CMCU) in step A2.

[0067] Upon receiving the Long-Distance Coordination Optimization Command (LOCDM) from the main central control device, CCH_3 first obtains the following key information: 1) Optimization strategy parameters (TOSPs) issued by the master controller Improve the light utilization rate and power generation gain of support A under the current weather conditions; adjust the current tilt angle to the theoretical optimal angle (e.g., 32.5°) and switch to dynamic tracking enhancement mode; this support and the adjacent supports B and C belong to the same multi-axis linkage group, and attention should be paid to synchronization.

[0068] 2) Preliminary control recommendations (PCTW) provided by the regional monitoring unit Recommended control window: 13:00–14:30 (during this period, wind speed is expected to decrease and irradiance will increase).

[0069] 3) Local real-time running status (collected by CCH_3 itself) The current tilt angle is 30.2° (deviating from the theoretical value by approximately 2.3°); the current wind speed is 4.8 m / s (close to but not exceeding the limit); the motor is in normal condition with low response delay; the tracking error of 0.8° is slightly high; the historical control success rate is 92%.

[0070] 4) Environmental forecast information (provided by the station's meteorological module) Wind speed forecast for the next hour: 5.2 m / s at 13:00 (slightly exceeding the safe threshold of 5.0 m / s), gradually decreasing to 4.5 m / s after 13:30; Predicted solar incidence angle: The optimal angle range for the day will be reached around 13:30; Irradiation intensity forecast: It will increase significantly from 13:15, with the peak period being 13:30–14:00.

[0071] Multidimensional analysis and time-period optimization based on the Global Multi-Axis Collaborative Optimization Model (GMCOM): The Global Multi-Axis Collaborative Optimization Model (GMCOM) within CCH_3 will, based on the above inputs, traverse or optimize multiple candidate control time points or windows (such as 13:00, 13:15, 13:30, 13:45, 14:00, 14:15, etc.) within the initial control suggestion period (13:00–14:30), analyzing the performance of each period in the following four core dimensions: ① Potential for improving power generation efficiency Does the light intensity and incident angle during this period favor increasing power generation? Can adjusting the tilt angle significantly reduce tracking error and improve solar energy conversion efficiency? Example results show that the irradiance is highest between 13:30 and 14:00, and the incident angle is close to optimal. Adjusting the tilt angle during this period is expected to increase power generation by approximately 1.8%, and reduce the tracking error to below 0.3°.

[0072] ② Degree of satisfaction of safety constraints Whether the wind speed during the control period is within the safe range; whether the drive motor will be overloaded; whether the structural stress is controllable. Example results: at 13:00, the wind speed is 5.2m / s, exceeding the safe threshold of 5.0m / s, posing a risk; after 13:30, the wind speed drops to 4.5m / s, which is within the safe range.

[0073] ③ Feasibility of multi-axis collaboration Does the adjustment action conflict with the actions of adjacent supports (such as supports B and C)? Does it meet the requirements of synchronization and sequence within the same multi-axis group? For example, if support A adjusts its tilt angle at 13:30, while supports B and C are scheduled to make fine adjustments at 13:35, there will be a brief linkage conflict, requiring staggered or synchronized execution.

[0074] ④ Control execution stability The motor response speed, historical control success rate, and control signal delay all support accurate execution within this time period. Example results show that historical data from CCH_3 indicates good control command response, no motor abnormalities, and an execution success rate exceeding 95% during the 13:30–14:00 time period.

[0075] Based on the above multidimensional assessment, the GMCOM model dynamically optimizes and adjusts the original recommended control period (13:00–14:30) and strategy parameters, specifically including: The original suggested window of 13:00–14:30 was narrowed and optimized to 13:30–13:45 (at which wind speed is safe, irradiance is optimal, linkage conflicts are minimal, and control is stable); the target tilt angle was fine-tuned to 32.3° (instead of 32.5°) to reduce drive impact; the "Dynamic Tracking Enhancement Mode" was maintained, but the action sequence was prioritized in coordination with adjacent supports B and C; it was suggested that support A perform tilt angle adjustments before supports B and C to avoid shadow occlusion. Finally, the CCH_3 output was: A recommended optimization period is 13:30–13:45; a set of executable strategy parameters (target tilt angle, control mode, linkage priority, etc.); and specific control command sequences are generated (e.g., Position_Set(32.3°), Mode_Switch(Enhanced_Tracking), Sync_Group(ABC), Execute_at(13:30:00)). CCH_3 sends the above control command sequence to the drive controller and tracking actuator of support A through the local control bus, ensuring that it completes the tilt angle adjustment action on time, safely, and collaboratively around 13:30, while maintaining good linkage timing with adjacent supports, realizing the precise implementation of multi-axis linkage control.

[0076] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0077] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to specific implementations. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An optimization method for a multi-axis linkage photovoltaic tracking bracket, characterized in that: The optimization method includes the following steps: A1. The main central control equipment receives local operating status and preliminary control suggestions from G regional monitoring units within the site. The information uploaded by the G regional monitoring units is used to reflect the performance constraints of the current operating status of W target photovoltaic tracking brackets within their monitoring areas. A2. After obtaining the local operating status uploaded by the G regional monitoring units, the main central control device, based on the collected identifiers and real-time operating parameters of the W target photovoltaic tracking brackets and the preliminary control suggestion time periods given by the G regional monitoring units, uses a multi-source data fusion analysis algorithm to identify an optimized set containing Y target photovoltaic tracking brackets from all monitored target photovoltaic tracking brackets. A3. The main central control device sends a long-distance optimization coordination instruction message to the collaborative control center corresponding to each of the target photovoltaic tracking brackets in the optimization set. For any target photovoltaic tracking bracket in the optimization set, the corresponding collaborative control center, based on the preliminary control suggestion time period provided by multiple regional monitoring units, combines multiple targets and uses a global multi-axis collaborative optimization model to perform secondary adaptation of the control suggestion time period and optimization strategy parameters of the target photovoltaic tracking bracket to form the final recommended optimization time period.

2. The method for optimizing a multi-axis linkage photovoltaic tracking bracket according to claim 1, characterized in that: The criteria for determining the target photovoltaic tracking bracket in the optimization set are as follows: the operating parameters of the target photovoltaic tracking bracket exceed the preset optimization threshold, the current state of the target photovoltaic tracking bracket has an impact on the overall power generation efficiency or equipment safety, or there is a linkage and coupling relationship between the target photovoltaic tracking bracket and other photovoltaic tracking brackets.

3. The method for optimizing a multi-axis linkage photovoltaic tracking bracket according to claim 2, characterized in that: An optimized set containing Y target photovoltaic tracking brackets is identified from all monitored target photovoltaic tracking brackets using a multi-source data fusion analysis algorithm, including the following steps: The real-time operating parameters of each photovoltaic tracking bracket will be compared with their corresponding preset optimization thresholds one by one to obtain the number of parameters exceeding the limit. Assess the potential impact of the current status of each photovoltaic tracking bracket on the overall power generation efficiency or system safety of the entire site, and calculate the global impact index; Using the multi-axis control group mapping table, other photovoltaic tracking brackets that are coupled with the target photovoltaic tracking bracket are identified, and the number of linkage couplings of the photovoltaic tracking brackets is obtained based on the above analysis. The number of parameters exceeding limits, global impact indicators, and number of linkage couplings are normalized by maximum-minimum values. The number of parameters exceeding limits, global impact indicators, and number of linkage couplings after normalization are summed to obtain the optimization index. Photovoltaic tracking brackets with optimization indexes greater than the optimization threshold are included in the optimization set, which contains Y target photovoltaic tracking brackets.

4. The method for optimizing a multi-axis linkage photovoltaic tracking bracket according to claim 2, characterized in that: The criteria for determining the target photovoltaic tracking bracket in the optimization set include: the operating parameters of the target photovoltaic tracking bracket exceed the preset optimization threshold, the current state of the target photovoltaic tracking bracket has an impact on the overall power generation efficiency or equipment safety, or there is a linkage and coupling relationship between the target photovoltaic tracking bracket and other photovoltaic tracking brackets.

5. The method for optimizing a multi-axis linkage photovoltaic tracking bracket according to claim 2, characterized in that: The long-distance optimization coordination instruction message is used to inform the recipient of the Y target photovoltaic tracking brackets that have been identified, and to push the recommended optimization strategy parameters for each target photovoltaic tracking bracket.

6. The method for optimizing a multi-axis linkage photovoltaic tracking bracket according to claim 1, characterized in that: In step A3, multiple objectives are combined, including the full-field illumination model, wind load prediction data, multi-axis linkage constraints, and the objective of maximizing power generation efficiency.

7. The method for optimizing a multi-axis linkage photovoltaic tracking bracket according to claim 6, characterized in that: The global multi-axis collaborative optimization model is used to perform a secondary adaptation between the suggested control time period and the optimization strategy parameters of the target photovoltaic tracking bracket, forming the final recommended optimization time period, including the following steps: The collaborative control center will integrate optimized strategy parameters and preliminary control suggestion periods with locally collected real-time operating status and environmental prediction information to form a local decision dataset. Based on the global multi-axis collaborative optimization model, multiple control periods are traversed or optimized, and the comprehensive performance of each period is analyzed. Based on the analysis results, the collaborative control center dynamically adjusts the preliminary control suggestion time period and strategy parameters, and finally outputs the recommended optimization time period and executable strategy parameters, and generates a control command sequence to be sent to the corresponding photovoltaic tracking bracket controller.

8. The method for optimizing a multi-axis linkage photovoltaic tracking bracket according to claim 1, characterized in that: The local operating status includes the unique identification information of W target photovoltaic tracking brackets within the monitoring area, and the real-time operating parameters collected by the photovoltaic tracking brackets, including solar incidence angle, current tilt angle, wind speed, motor status, and tracking error.

9. The method for optimizing a multi-axis linkage photovoltaic tracking bracket according to claim 8, characterized in that: In step A1, the information uploaded by the G regional monitoring units is used to reflect the performance constraints of the current operating status of the W target photovoltaic tracking brackets within their monitoring areas. The performance constraints include insufficient sunlight utilization, risk of exceeding wind speed limits, tracking angle deviation, and abnormal drive load. G is a positive integer, representing the total number of regional monitoring units with monitoring and preliminary decision-making capabilities within the site.

10. A multi-axis linkage photovoltaic tracking bracket optimization system, used to implement the optimization method according to any one of claims 1-9, characterized in that: It includes an information receiving module, an optimized set output module, and an optimized recommendation module; Information receiving module: Receives local operating status and preliminary control suggestions from G regional monitoring units within the site. The information uploaded by the G regional monitoring units is used to reflect the performance constraints of the current operating status of W target photovoltaic tracking brackets within their monitoring areas. Optimized set output module: After obtaining the local operating status uploaded by G regional monitoring units, based on the collected identifiers and real-time operating parameters of W target photovoltaic tracking brackets and the preliminary control suggestion time periods given by G regional monitoring units, an optimized set containing Y target photovoltaic tracking brackets is identified from all monitored target photovoltaic tracking brackets using a multi-source data fusion analysis algorithm. The optimization recommendation module sends long-distance optimization coordination instruction messages to the collaborative control center corresponding to each target photovoltaic tracking bracket in the optimization set. For any target photovoltaic tracking bracket in the optimization set, the corresponding collaborative control center, based on the preliminary control suggestion time period provided by multiple regional monitoring units, combines multiple targets and uses a global multi-axis collaborative optimization model to perform secondary adaptation of the control suggestion time period and optimization strategy parameters of the target photovoltaic tracking bracket to form the final recommended optimization time period.

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