Zero-carbon airport bipv photovoltaic direct-drive low-voltage lighting system

Abstract

This invention relates to the field of electronic information technology and discloses a zero-carbon airport BIPV photovoltaic direct-drive low-voltage lighting system. It addresses the problem in traditional methods where the MPPT algorithm easily gets trapped in local optima under dynamic shading and irradiance variation conditions, failing to achieve efficient global power point tracking. The method includes: a data acquisition module that collects photovoltaic array voltage, current, and power parameters; an environmental monitoring module that detects environmental changes and generates variation signals using irradiance sensors and cameras; an algorithm initialization module that sets the search range and divides the power curve region based on data and signals; a global search module that scans multiple peaks and traverses the global space to identify the maximum power point; a local optimization module that finely tracks and corrects the step size for peak regions; and a control output module that generates commands to drive the inverter to adjust its operating point and provides status feedback. Through module collaboration, efficient operation of the photovoltaic direct-drive low-voltage lighting is achieved, making it suitable for zero-carbon airport environments.

Description

Technical Field

[0001] This invention relates to the field of electronic information technology, specifically to a zero-carbon airport BIPV photovoltaic direct-drive low-voltage lighting system. Background Technology

[0002] With the advancement of global carbon neutrality goals, zero-carbon airports, as the core of sustainable aviation infrastructure, are increasingly emphasizing the application of building-integrated photovoltaics (BIPV) direct-drive low-voltage lighting systems. This involves seamlessly integrating photovoltaic modules into the airport building structure, directly utilizing solar energy to drive low-voltage lighting equipment, achieving energy self-sufficiency and significantly reducing carbon emissions. However, in the complex environment of airports, photovoltaic arrays often face dynamic shading (such as aircraft movement or building shadows) and irradiance variations (such as rapid cloud changes). These factors cause the photovoltaic output power curve to exhibit multi-peak characteristics, posing a severe challenge to the accuracy and response speed of the maximum power point tracking (MPPT) algorithm. In existing technologies, the MPPT algorithm aims to maximize power generation efficiency by monitoring and adjusting the operating point of the photovoltaic system in real time. For example, by improving the DC voltage tracking mechanism, it avoids malfunctions caused by the voltage failing to follow the command in real time, performing well under both dynamic and static conditions and maintaining high efficiency in low-light environments. However, when dealing with multi-peak power curves caused by dynamic shading at airports, it is prone to getting trapped in local optima and cannot effectively achieve global power tracking, leading to a decrease in system efficiency. Similarly, in existing technologies, the MPPT control method based on the improved particle swarm optimization algorithm uses the particle swarm optimization mechanism to search for the global maximum power point, improving the tracking accuracy under partial shading conditions. However, in environments with rapidly changing irradiance, its iterative calculation response is slow, and it is easy to miss the instantaneous optimum due to delay, failing to achieve efficient global power tracking.

[0003] While the aforementioned existing technologies have improved MPPT performance to some extent, they all suffer from the technical problem of easily getting trapped in local optima under dynamic shading and irradiance variation conditions, making it impossible to achieve efficient global power tracking. This not only causes a 10%-20% power loss in photovoltaic systems, but also directly affects the stability and energy self-sufficiency efficiency of BIPV direct-drive low-voltage lighting systems in zero-carbon airports. Therefore, a new optimization method is needed to solve this core technical challenge. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a zero-carbon airport BIPV photovoltaic direct-drive low-voltage lighting system, which solves the problem that the MPPT algorithm in traditional methods is prone to getting trapped in local optima under dynamic shading and irradiance variation conditions, and thus cannot achieve efficient global power tracking.

[0005] To achieve the goal of efficient global power point tracking mentioned in the background section, the present invention provides the following technical solution: Zero-carbon airport BIPV direct-drive low-voltage lighting system includes: Data acquisition module: Collects real-time voltage, current and power parameters of the photovoltaic array, synchronously acquires multi-point data through sensor array, and transmits it to subsequent modules for processing; Environmental monitoring module: detects dynamic shading and radiation variation, monitors environmental changes in real time through radiation sensors and cameras installed at key locations in the airport, and generates variation signals; Algorithm initialization module: Sets the initial search range based on the multi-point electrical parameter data obtained by the data acquisition module and the environmental variation signal output by the environmental monitoring module, divides the power curve region by a preset threshold, and starts global optimization parameter configuration; Global search module: It uses a hybrid optimization algorithm to scan the power curve with multiple peaks, traverses the global space through particle swarm or genetic algorithm, and marks the potential maximum power point region under the constraints of preset candidate threshold based on the power value of each search point, the power difference between adjacent points and the local slope. Local optimization module: Based on the voltage range of the potential maximum power point output by the global search module, the operating point of the photovoltaic array is iteratively adjusted in small steps within this range using the perturbation observation method or the incremental conductance method. The voltage is continuously corrected according to the direction and rate of power change before and after each perturbation until the rate of power change is lower than the preset convergence threshold to determine the local optimal operating point. The perturbation step size is dynamically adjusted in combination with environmental signals such as shading intensity index and irradiance fluctuation rate. Control output module: Generates control commands based on optimization results, drives the inverter to adjust the operating point of the photovoltaic system through pulse width modulation, and feeds back to the data acquisition module in a loop.

[0006] In a preferred embodiment, real-time voltage, current, and power parameters of the photovoltaic array are collected, multi-point data are acquired synchronously through a sensor array, and transmitted to subsequent modules for processing, including: The voltage sensor reduces the voltage through a voltage divider resistor, the Hall closed-loop current sensor measures the DC current, and the power unit calculates the voltage-current product through a multiplication circuit. High-frequency synchronous acquisition using a unified clock is employed to align the time of each channel, statistically analyze the characteristics of the array sub-regions, and filter and threshold check to remove abnormal data. The data is encapsulated into standard frames and sent to subsequent modules via a serial interface. The integrated power management and communication unit performs self-testing, filtering, and verification.

[0007] In a preferred embodiment, detecting dynamic shading and irradiance variation includes: Event determination is based on the changes in the exponent and volatility in the variable signal within a continuous sampling period; The module encapsulates the threshold judgment results of the mutation features into standardized data packets and achieves multi-hop transmission through a low-power mesh topology network; Configure the primary and backup sensor channels to switch to the backup path, and integrate power management circuitry to activate the sampling transmission cycle.

[0008] In a preferred embodiment, environmental changes are monitored in real time by radiation sensors and cameras installed at key locations in the airport, and variation signals are generated, including: The radiation sensor uses a broadband response silicon-based radiometer to collect radiation intensity, and the camera is equipped with a high-resolution wide-angle lens and infrared auxiliary illumination to capture image sequences. The module self-tests the parameters of the radiation sensor and camera, and cyclically collects radiation and image data. Meteorological data is integrated with wind speed, temperature, and humidity to construct the characteristics of shading intensity and radiation fluctuation. High-intensity events are identified based on thresholds and wirelessly transmitted to the algorithm module.

[0009] In a preferred embodiment, an initial search range is set based on multi-point electrical parameter data acquired by the data acquisition module and environmental variation signals output by the environmental monitoring module. Power curve regions are divided using preset thresholds, and global optimization parameter configuration is initiated, including: It receives collected data and environmental signals, buffers the input through the interface, and performs synchronization verification. Reconstruct the power-voltage curve, calculate the slope change points and peak positions, and identify fluctuation regions by scanning point by point; Trigger the region division logic, generate a sub-interval list from the minimum voltage accumulated data point, record the start voltage, end voltage and average power value, and extend the low power boundary according to the environmental signal; Start parameter configuration, establish the association between signals and search step size boundaries, clean the data through a hierarchical structure and generate a configuration set, and transmit it to the global search module.

[0010] In a preferred embodiment, a hybrid optimization algorithm is used to scan the multi-peak power curve, and a particle swarm optimization or genetic algorithm is used to traverse the global space. Based on the power value of each search point, the power difference between adjacent points, and the local slope, potential maximum power point regions are marked under a preset candidate threshold constraint, including: Receive initialization results, manage the input stream through a queue, and verify data integrity; After self-checking the calculation unit and synchronization status, the system enters the traversal phase and arranges the search entity in the sub-interval. The initial position of the entity is set to the starting or center voltage of the interval. Iteratively update entity locations, summarize historical best interaction states, apply random perturbations to explore uncovered regions, and quantify peak potential to mark candidate points; The peak list is filtered and sorted by power, the corresponding coordinates are recorded, and the data is transmitted to the local optimization module through a standardized interface.

[0011] In a preferred embodiment, based on the voltage range of the potential maximum power point output by the global search module, the operating point of the photovoltaic array is iteratively adjusted in small steps within this range using a perturbation-observation method or an incremental conductance method. The voltage is continuously corrected according to the direction and rate of power change before and after each perturbation, until the power change rate is lower than a preset convergence threshold to determine the local optimal operating point. This includes: Receive the list of peak areas, load the coordinates via the interface, and verify the data integrity. Each region is mapped as an independent unit, and a buffer is allocated to record the starting voltage and boundaries; Starting from the peak voltage, adjust the operating point with small step disturbances and observe whether the power change direction maintains an upward trend or switches in the opposite direction. The system iteratively calculates the power difference and rate of change until the absolute value is below the threshold, locks in the optimal voltage, and transmits the optimal point to the control output module.

[0012] In a preferred embodiment, the disturbance step size is dynamically adjusted by combining environmental signals such as the shading intensity index and irradiance fluctuation rate, including: The disturbance step size is dynamically corrected by integrating environmental signals, and the rate of change is quantified through the deviation monitoring loop; Preset convergence threshold and noise threshold, and determine the offset based on the continuous perturbation power increase; Temporarily adjust the area boundaries and initial locations, summarize the optimal data set, and generate a report; The encapsulated data packet is transmitted to the control output module via the communication link as the adjustment target.

[0013] In a preferred embodiment, control commands are generated based on the optimization results, and the inverter is driven by pulse width modulation to adjust the operating point of the photovoltaic system, which is then cyclically fed back to the data acquisition module, including: Receive local optimum data, write it to the buffer through the communication interface, and check its integrity and consistency. The optimal voltage is extracted as a reference, and the deviation is calculated in combination with the actual voltage to determine the duty cycle adjustment. The pulse width modulation waveform is generated by the microcontroller timer and comparison unit to drive the switching transistor to adjust the array output. Set up a load stabilization loop to monitor current and power changes, automatically adjust the duty cycle according to the deviation to maintain power balance, and configure overvoltage and overcurrent protection to switch power limiting state through hardware comparator; The sampled voltage, current, and power are used to construct a state vector, which is then encapsulated into a feedback frame and transmitted to the data acquisition module via the bus. The main and backup controller channels are configured to switch to the backup path to maintain power supply.

[0014] Compared with existing technologies, the present invention provides a zero-carbon airport BIPV photovoltaic direct-drive low-voltage lighting system, which has the following beneficial effects: 1. This invention uses a data acquisition module to perform high-frequency synchronous sampling of voltage, current, and power at multiple nodes of a photovoltaic array. Combined with an environmental monitoring module, it constructs environmental characteristics such as shading intensity index and irradiance fluctuation rate. This allows the algorithm initialization module to partition and threshold the PV curve under the joint constraints of real-time electrical and environmental quantities, forming an adaptive search boundary and step size configuration that matches the airport scenario. Based on this, a global search module uses a hybrid optimization algorithm to traverse each interval in parallel, introducing diversity maintenance and random perturbation mechanisms. A local optimization module adaptively adjusts the perturbation scale within the candidate peak neighborhood by combining perturbation observation and environmental signals. The control output module then outputs the optimization results. The process transforms the data into inverter-side PWM control commands and establishes a closed-loop control link that integrates acquisition, search, optimization, and execution through state feedback. This enables the photovoltaic array to maintain a global perspective and rapid adjustment capability even in the complex airport environment where dynamic shading and irradiance variations are constantly present. It effectively avoids the MPPT algorithm from getting stuck in local optima, significantly improves the accuracy and response speed of global maximum power point tracking, reduces power loss, and enhances the operational stability and energy self-sufficiency level of the BIPV direct-drive low-voltage lighting system in zero-carbon airports. This solves the problem in traditional methods where the MPPT algorithm is prone to getting stuck in local optima under dynamic shading and irradiance variations, making it unable to achieve efficient global power point tracking.

[0015] 2. This invention introduces multi-layered protection and redundancy design between the photovoltaic-side optimized link and the load-side execution control. Addressing the risks of flickering, voltage exceeding limits, and even partial power loss in airport BIPV direct-drive low-voltage lighting during load surges, electromagnetic interference, and control unit failures, a unified closed-loop framework covering data acquisition, environmental monitoring, algorithm optimization, and control output is constructed. In the control output module, output voltage, current, and power deviations are monitored in real time. Overvoltage, overcurrent, and limiting thresholds are set according to airport low-voltage power distribution safety regulations and lighting fixture withstand voltage indicators. These thresholds are then combined with hardware comparators and fast shutdown logic to mitigate these issues. It is usually limited within the safety boundary. On the other hand, it adopts a dual-channel architecture of main control and backup processing units. When the main controller is abnormally reset or there is no effective output within a certain number of control cycles, it automatically switches to a simplified PWM strategy to maintain the basic power supply of critical lighting loads. It also transmits fault information and the current operating point back to the front-end optimization module through status feedback. This enables the system to quickly suppress abnormal operating conditions and adaptively correct the operating strategy. As a result, it significantly reduces the probability of lighting interruption and equipment damage under complex electromagnetic environment and frequent operating condition switching conditions, and improves the safety, reliability and operation and maintenance controllability of the zero-carbon airport BIPV photovoltaic direct drive low-voltage lighting system. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the zero-carbon airport BIPV photovoltaic direct-drive low-voltage lighting system of the present invention. Detailed Implementation

[0017] 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. 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.

[0018] Example 1: Figure 1 A zero-carbon airport BIPV photovoltaic direct-drive low-voltage lighting system is presented, including: Data acquisition module: Collects real-time voltage, current and power parameters of the photovoltaic array, synchronously acquires multi-point data through sensor array, and transmits it to subsequent modules for processing; Environmental monitoring module: detects dynamic shading and radiation variation, monitors environmental changes in real time through radiation sensors and cameras installed at key locations in the airport, and generates variation signals; Algorithm initialization module: Sets the initial search range based on the multi-point electrical parameter data obtained by the data acquisition module and the environmental variation signal output by the environmental monitoring module, divides the power curve region by a preset threshold, and starts global optimization parameter configuration; Global search module: It uses a hybrid optimization algorithm to scan the power curve with multiple peaks, traverses the global space through particle swarm or genetic algorithm, and marks the potential maximum power point region under the constraints of preset candidate threshold based on the power value of each search point, the power difference between adjacent points and the local slope. Local optimization module: Based on the voltage range of the potential maximum power point output by the global search module, the operating point of the photovoltaic array is iteratively adjusted in small steps within this range using the perturbation observation method or the incremental conductance method. The voltage is continuously corrected according to the direction and rate of power change before and after each perturbation until the rate of power change is lower than the preset convergence threshold to determine the local optimal operating point. The perturbation step size is dynamically adjusted in combination with environmental signals such as shading intensity index and irradiance fluctuation rate. Control output module: Generates control commands based on optimization results, drives the inverter to adjust the operating point of the photovoltaic system through pulse width modulation, and feeds back to the data acquisition module in a loop.

[0019] The technical connections and implementation logic of the six modules are as follows: The data acquisition module collects the voltage, current, and power parameters of the photovoltaic array in real time through a sensor array and transmits them as basic inputs to the algorithm initialization module. Simultaneously, it provides a state feedback benchmark to the control output module. The environmental monitoring module operates in parallel, detecting dynamic shading and irradiance changes in real time, generating environmental change signals that are sent to both the algorithm initialization module and the local optimization module to introduce external environmental factors. The algorithm initialization module integrates the electrical acquisition data and environmental change signals, sets the search range, configures the algorithm parameters, and triggers the global search module to perform multi-peak traversal of the power curve. The global search module uses a hybrid optimization algorithm to scan for possible power peaks, outputting candidate maximum power points to the local optimization module. The local optimization module performs fine tracking within this neighborhood and adaptively adjusts the step size, achieving progressive optimization from coarse to fine. The control output module generates inverter control commands based on the local optimization results, adjusts the operating point, and sends the updated system state back to the data acquisition module, forming a continuously iterative closed-loop control. This effectively avoids the MPPT algorithm getting trapped in local optima in complex airport shading environments, improving the tracking efficiency of the global maximum power point and environmental adaptability.

[0020] The system collects real-time voltage, current, and power parameters of the photovoltaic array, synchronously acquires data from multiple points via a sensor array, and transmits the data to subsequent modules for processing. The specific implementation is as follows: The data acquisition module is used to collect real-time voltage, current, and power parameters of the photovoltaic array. It synchronously acquires data from multiple points via a sensor array and transmits it to subsequent modules for processing. In the zero-carbon airport BIPV photovoltaic direct-drive low-voltage lighting system, the data acquisition module is activated, and multiple voltage sensors, current sensors, and power calculation units are deployed at the input, output, and intermediate connection points of the photovoltaic array to form a sensor array covering key nodes of the array. The acquisition coverage is no less than 95%, reflecting the distribution characteristics of the entire BIPV structure. The voltage sensors use voltage divider circuits in conjunction with precision resistor networks to convert high-voltage side signals into low-voltage analog quantities suitable for analog-to-digital conversion. The current sensors use closed-loop devices based on the Hall effect to measure DC branch current without cutting the wires, avoiding additional voltage drops that affect efficiency. The power calculation unit is integrated on the acquisition board, using a multiplication circuit to calculate the product of voltage and current in real time and output instantaneous power data. The data acquisition process is performed in a high-frequency synchronous manner, with the sampling frequency set to 100Hz. Time alignment of each channel is achieved through a unified clock source, making the data from multiple points at the same timestamp comparable and reducing timing deviations caused by airport vibration and wind load. Before acquisition, the module performs a self-test process, calibrating the zero point and verifying the linearity of each sensor. For example, a reference voltage is applied to the voltage channel to calculate the offset and write it into the compensation register, keeping the measurement error within 0.05V. After entering the continuous sampling stage, the voltage channel simultaneously records the total output of the array and the voltage of each sub-segment, the current channel monitors the current changes of different branches, and the power unit synthesizes the corresponding power value within the same sampling period, forming a multi-dimensional data set with spatial location markers. To adapt to the complex operating conditions of airports, the photovoltaic array is divided into multiple sub-regions, each equipped with an independent sensor group. The data acquisition module calculates the average, peak, and standard deviation of voltage, current, and power for each sub-region within a sliding time window. This data characterizes the current gradient and power unevenness caused by factors such as aircraft shadows and localized contamination. In the signal processing stage, a hardware low-pass filter is first used to suppress high-frequency interference, with the cutoff frequency set to 50Hz to reduce the influence of electromagnetic sources such as radar and radio equipment. Then, a median filtering algorithm is used to remove isolated pulses, resulting in a smooth electrical quantity sequence that reflects the actual operating conditions. The filtered analog signal is converted into a 16-bit digital quantity by a built-in ADC, with a quantization accuracy better than 0.1%. Based on digitalization, the data acquisition module performs validity checks on voltage, current, and power data, and pre-sets physical range thresholds and rate of change thresholds. The physical range thresholds are set based on the rated voltage and current parameters of the photovoltaic array and in conjunction with safety margins, limiting the voltage to within a certain range. Range, limiting the current to The range; the rate of change threshold is determined by statistically analyzing the fluctuation range of historical operating data, and is used to identify abrupt changes caused by sensor failure or wiring abnormalities; when any sampled data exceeds the physical range or its rate of change exceeds the corresponding threshold, the sampled point is marked as abnormal, and the abnormality type is recorded in the status field of the data frame, so that subsequent modules can choose to remove or process it separately during analysis; Data encapsulation adopts a standard frame structure, packaging voltage, current, power, timestamp, and status flags. The frame format includes a frame header, data body, and CRC tail check to ensure transmission integrity. The physical communication interface can use RS485 bus or CAN bus. RS485 is suitable for long-distance differential transmission covering a wide area of ​​the airport, while CAN bus has bus arbitration and priority management capabilities, allowing priority to send critical monitoring data. The data acquisition module acts as the bus master node, broadcasting the latest data frame to the receiving buffer of subsequent modules at a period of 10ms, and receiving configuration commands from the upper control unit. To reduce energy consumption, the module integrates a power management unit, shutting down some analog front-ends and communication units during non-sampling periods, keeping power consumption in sleep mode below 1mW. To enhance reliability, the sensor array employs a redundant configuration, with primary and backup channels set up at the same critical nodes. When a channel is detected to have prolonged distortion or self-test failure, the acquisition module automatically switches to the backup channel and marks the source of the current channel in the data to ensure data continuity. The entire acquisition process is uniformly scheduled by an embedded microcontroller, which is responsible for timing management, self-testing, filtering, threshold verification, packaging, and communication scheduling. The acquired and verified photovoltaic status data is transmitted via bus to the input interfaces of the environmental monitoring module and the algorithm initialization module, serving as the basis for environmental variation analysis and subsequent maximum power point tracking optimization. This provides real, continuous, and high-resolution electrical data support for the system to achieve stable and efficient operation under dynamic airport conditions.

[0021] Dynamic shading and radiation variation are detected by using radiation sensors and cameras installed at key locations in the airport to monitor environmental changes in real time and generate variation signals. The specific implementation is as follows: The environmental monitoring module is used to detect dynamic shading and irradiance variations. It monitors environmental changes in real time and generates variation signals through irradiance sensors and cameras installed at key locations in the airport. In the BIPV photovoltaic direct-drive low-voltage lighting system of the zero-carbon airport, the environmental monitoring module starts up and operates based on the photovoltaic status data output by the aforementioned data acquisition module. Silicon-based irradiance sensors and high-definition cameras are deployed around the photovoltaic array at the edge of the roof, near the apron, and on the top of the terminal building to capture the impact of building shadows, aircraft movement, and cloud changes on the photovoltaic array. The monitoring range covers an area of ​​approximately 500m around the array. The irradiance sensor is a silicon-based pyranometer with a wide-spectrum response, and the high-definition camera has a 1080p resolution and is equipped with a wide-angle lens and an infrared auxiliary lighting unit to support continuous monitoring day and night. The monitoring process begins with the initialization of the environmental monitoring module. First, a self-diagnostic procedure is executed to test the irradiance sensor against a standard light source, ensuring that the measurement deviation is controlled within 1W / m². Then, the camera's focal length and exposure are adaptively adjusted to adapt to the variable lighting conditions at the airport. After completing the self-test, a real-time detection loop is entered. The irradiance sensor samples at a 1-minute interval, and the average irradiance level and fluctuation amplitude are obtained through integration and statistical calculations. The difference between the peak and trough values ​​of irradiance is recorded when the clouds move rapidly, serving as one of the characteristics of irradiance variation. The camera acquires image sequences at 30fps, and the shadow areas and their movement trajectories are identified by using the brightness distribution and edge gradient changes of adjacent frames, thereby estimating the shading area and shading speed. To improve the accuracy of environmental characterization, the environmental monitoring module accesses airport meteorological station data via an interface to obtain wind speed, temperature, and humidity information. This data is then aligned with local sensor data by timestamp. Wind speed is used to correct short-term fluctuations in irradiance readings, and temperature and humidity are used to assist in determining cloud cover and haze conditions. Based on this data, the module constructs environmental variation characteristics. In this embodiment, these include a shading intensity index and irradiance volatility. The shading intensity index, ranging from 0 to 100, represents the proportion of shading coverage in the photovoltaic area. Irradiance volatility is quantified by the standard deviation of the irradiance sequence within a given time window. Alarm thresholds and sensitivity thresholds are preset for both types of characteristics. The thresholds are determined based on the rated irradiance requirements of the photovoltaic modules and historical operational statistics. The number of continuous sampling periods is preset to 3-5 based on the sampling period and system response time, and can be adjusted through a parameter configuration table. When the shading intensity index continuously exceeds the alarm threshold and the irradiance volatility is higher than the corresponding sensitivity threshold within the preset continuous sampling period, the operating state is determined as a high-intensity dynamic shading event. The environmental monitoring module encodes and packages the above-mentioned variation characteristics and threshold judgment results into standardized data packets. The data packets contain fields such as signal type, feature value, threshold status and timestamp, and can be encapsulated in JSON format. The data packets are transmitted to the algorithm initialization module through the ZigBee wireless network, which utilizes its low power consumption and mesh topology to achieve multi-hop coverage in airport scenarios, with end-to-end latency controlled within 50ms, and link layer encryption is enabled to improve anti-interference capability. To ensure monitoring reliability, primary and backup sensors are configured at key irradiation measurement points. When the primary channel is detected to be continuously distorted or the self-test fails, the system automatically switches to the backup channel. When necessary, the irradiation intensity can be estimated based on the brightness of the camera image as auxiliary information. The module integrates a power management circuit, which activates high-power components only during the sampling and transmission cycle and enters a low-power mode at other times. In this embodiment, the average power consumption is controlled within 5W. Finally, the shading intensity index, irradiance fluctuation rate, and threshold judgment results output by the environmental monitoring module are used as environmental variation signals. After being aligned with the aforementioned photovoltaic electrical data in time, they are input into the algorithm initialization module to dynamically adjust the maximum power point search range and parameter configuration. This enables the system to maintain high MPPT convergence performance and operational stability even under the complex and rapidly changing irradiance conditions at the airport.

[0022] The initial search range is set based on the multi-point electrical parameter data acquired by the data acquisition module and the environmental variation signal output by the environmental monitoring module. The power curve region is divided by a preset threshold, and the global optimization parameter configuration is initiated. The specific implementation is as follows: The algorithm initialization module is used to set the initial search range based on the aforementioned collected data and environmental signals, and to complete the initial configuration of power curve division and global optimization parameters. In the zero-carbon airport BIPV photovoltaic direct-drive low-voltage lighting system, following the voltage, current and power parameter data packets transmitted in the previous text, as well as the shading intensity index and irradiance fluctuation rate signals generated in the previous text, the algorithm initialization module writes the above inputs into the buffer through a dedicated interface and performs synchronization and integrity verification. In this embodiment, a verification mechanism can be used to detect transmission errors and control the error rate within the order of 0.01% to ensure that subsequent calculations are based on reliable data. The configuration process begins with data analysis of the power-voltage curve. The module reconstructs the PV curve based on the sampling results, calculates the slope change points and local peak positions, and scans the curve data point by point through the processing unit to identify areas with significant power fluctuations. When the power change amplitude in a voltage neighborhood exceeds a preset fluctuation threshold, the region re-division logic is triggered. The fluctuation threshold can be set based on the statistical distribution of historical airport operation data, typically around 20%, corresponding to a typical cloud interference level. After triggering, the module accumulates data points along the voltage axis from the minimum operating voltage until it detects a turning point where the power changes from rising to falling, generating a sub-interval and forming an interval list in sequence. Each interval records the starting voltage, ending voltage, and average power value to characterize possible multi-peak structures. When the environmental monitoring module outputs a high shading intensity index, the algorithm initialization module can appropriately expand the boundary of the low-power interval and increase the scanning density within that interval to enhance the coverage of subtle peaks. After completing the curve division, the algorithm initialization module starts the global optimization parameter configuration. First, the module sets the basic search step size according to the length of each sub-interval and the effective voltage range. In this embodiment, the step size can be set to 1 / 100 of the voltage range and can be adjusted through the parameter table. Second, the module corrects the search boundary with reference to the irradiance fluctuation rate. When the irradiance fluctuation rate is higher than the preset sensitivity threshold, the voltage boundary of the corresponding interval is expanded by a certain proportion. The expansion range can be about 10% of the original boundary to accommodate instantaneous fluctuations. The sensitivity threshold can be set in combination with the historical irradiance fluctuation distribution and the allowable fluctuation range of the component. Then, the module establishes parameter mapping relationships, associating the shading intensity index, irradiance fluctuation rate with configuration parameters such as search range width and step size upper limit. For example, when the shading intensity index exceeds the preset shading threshold (which can be selected as 50 and determined based on historical shading data and rated operating conditions), a wider initial search range is prioritized. At the same time, the module combines temperature information to correct the power estimation, so as to reduce the impact of thermal effects on peak identification. The overall configuration logic adopts a hierarchical structure, with the bottom layer responsible for data cleaning and interval statistics, and the upper layer generating search boundaries and parameter configuration sets, thereby improving the robustness of the configuration results. To enhance adaptability to complex airport scenarios, the algorithm initialization module integrates an adaptive threshold adjustment mechanism to continuously monitor the statistical characteristics of the input signal. When the irradiance fluctuation rate exceeds the sensitivity threshold in multiple consecutive sampling periods, the module automatically increases the segmentation accuracy. The number of consecutive periods can be set to 3-5 based on the sampling period and system response time. When the segmentation accuracy is improved, the number of sampling points within the interval can be increased from the default value (e.g., 10 points) to a higher value (e.g., 20 points) to refine the characterization of the PV curve details. This allows the fluctuation threshold, sensitivity threshold, and related granular parameters to adaptively adjust with the level of environmental disturbance. Their initial selection is based on historical airport data statistics and equipment rated operating conditions, thereby ensuring that the configuration process has a repeatable and verifiable source of parameters. After completing the above processing, the algorithm initialization module generates initialization result output, including the list of divided sub-intervals, the corrected search boundaries, and the corresponding parameter configuration set. This is transmitted to the global search module via a high-speed data bus. The output can be encapsulated in a structured data format such as JSON or XML, explicitly providing the starting voltage, ending voltage, recommended step size, and environment-related configuration flags for each interval. This allows the global search module to directly use these boundaries as traversal starting points and constraints, and to use the parameter configuration set as the basis for the scanning strategy.

[0023] A hybrid optimization algorithm is used to scan the multi-peak power curve. The global space is traversed using particle swarm optimization or genetic algorithms. Based on the power value of each search point, the power difference between adjacent points, and the local slope, potential regions with maximum power are marked under a preset candidate threshold constraint. Specifically, the implementation is as follows: The global search module is used to scan the multi-peak region of the power curve based on a hybrid optimization algorithm. It traverses the global search space and identifies potential maximum power points using a particle swarm optimization algorithm or a genetic algorithm. In the zero-carbon airport BIPV photovoltaic direct-drive low-voltage lighting system, after the algorithm initialization is completed, the global search module is activated, enabling it to perform a global search on the power curve based on the sub-interval list, search boundary, and parameter configuration set given by the initialization results. The module first receives and caches the input data through a dedicated interface. In this embodiment, a FIFO queue can be used to manage the input stream, and a verification field can be used to check the data integrity and timing consistency to adapt to the continuous operation requirements under the scenario of rapid changes in airport irradiance and shading. The search process begins with module self-check and initialization. First, it checks the computing unit, storage resources, and clock synchronization status. In this embodiment, the delay of a single self-check and switching is controlled within approximately 10ms. After completing the self-check, it enters the global traversal stage. The module treats the divided PV curve as a one-dimensional voltage space to be searched and arranges multiple search entities on each sub-interval. The search entities can be particles in a particle swarm or individuals in a genetic algorithm. Each sub-interval is allocated several search entities according to its length and importance. Their initial positions can be set as the starting voltage, center voltage, or voltage points selected by a random strategy. The initial step size is given by the parameter configuration set mentioned above. During the search iteration, the module updates the entity positions based on the power feedback of the current position in each iteration cycle. Each cycle can perform approximately 10 to 20 position updates. The specific number of updates can be adjusted through the parameter table to achieve a balance between search accuracy and computational load. To improve search comprehensiveness and suppress premature convergence, the global search module incorporates a diversity maintenance and mutation mechanism. Within a preset iteration interval, the module aggregates the historical optimal positions of each search entity and performs information exchange. It can be configured to trigger state fusion every 5 iterations to guide entities out of local highs. Simultaneously, random perturbations are applied to some entities, superimposing small-amplitude offsets near their current voltage positions to explore voltage ranges that are initially uncovered or significantly affected by environmental abrupt changes. After each position update, the module calculates the corresponding power value and quantifies peak potential based on the power difference between adjacent positions and changes in local slope. When the power difference exceeds a preset candidate threshold, the position is marked as a candidate peak point, and its voltage coordinates, power value, and sub-interval identifier are recorded. The candidate threshold can be statistically set based on the fluctuation characteristics of historical PV curves and measurement noise levels to distinguish between normal fluctuations and changes with peak significance. In this embodiment, it can be approximately 5% and can be adjusted through a parameter configuration table. To enhance adaptability to the complex airport environment, the global search module receives the occlusion intensity index and irradiance fluctuation rate output earlier, using them as environmental feedback factors. When the occlusion intensity index exceeds the aforementioned preset occlusion threshold, or the irradiance fluctuation rate remains excessively high for a certain period, the module can dynamically increase the number of search entities or shorten the iteration cycle to improve the search density during critical periods, resulting in a significantly higher effective coverage than schemes with fixed step size and fixed number of entities. Simultaneously, the module configures redundant search paths. When an abnormal interruption of the main search thread is detected or available computing resources are limited, it automatically switches to simplified mode, performing rapid scanning only within the high-probability interval given by the algorithm initialization module to maintain continuous tracking of the maximum power point region. The location update strategy can be based on power gradient guidance, while the mutation probability is linearly adjusted according to the magnitude of environmental fluctuation, enabling the search strategy to adaptively change with the level of external disturbances. Once the global search reaches the preset number of iterations or meets the convergence condition, the module summarizes and filters the marked candidate peaks, removing points with low power contribution or obvious duplication, and generates a peak region list. Each record in the list includes the starting voltage, peak voltage, corresponding estimated power, and simplified confidence index of the peak region, and can be sorted by power or confidence. The global search module transmits this peak list to the local optimization module through a standardized interface. The data can be encapsulated in binary encoded data frames with verification fields or in structured formats such as JSON and XML to ensure transmission reliability and parsing convenience. Based on this, the local optimization module uses the peak voltage as the initial region for fine tracking, and on this basis, completes the detailed configuration of the local search range and step size, realizing a smooth connection from global coarse positioning to local fine optimization. This ensures that maximum power point tracking has a sufficient candidate peak base and global perspective under airport dynamic shading and irradiance variation conditions.

[0024] Based on the voltage range of the potential maximum power point output by the global search module, the operating point of the photovoltaic array is iteratively adjusted in small steps within this range using either the perturbation observation method or the incremental conductance method. The voltage is continuously corrected according to the direction and rate of power change before and after each perturbation until the rate of power change is lower than a preset convergence threshold to determine the local optimal operating point. The perturbation step size is dynamically adjusted in conjunction with environmental signals such as the shading intensity index and irradiance fluctuation rate. The specific implementation is as follows: The local optimization module is used to perform fine tracking of the peak regions identified by the global search. It uses a perturbation observation method to gradually approach the local optimum and dynamically adjusts the step size in combination with environmental signals. In the zero-carbon airport BIPV photovoltaic direct-drive low-voltage lighting system, the local optimization module receives the starting voltage, peak voltage and estimated power value of each peak region through a dedicated data interface based on the peak region list output above. After parsing the input data, it writes it into the internal buffer and checks the integrity and format of the data through a verification field to ensure that the subsequent optimization process is based on reliable candidate region data. The tracking process begins with module activation and region mapping, mapping each peak region as an independent optimization unit, allocating a dedicated buffer, and recording the region's initial voltage, allowable boundaries, and associated environmental identifiers to isolate the optimization process between different regions. This is followed by an iterative approximation phase, where the module uses the voltage point near the peak as the initial operating point and employs a small-step perturbation observation strategy to adjust the operating point voltage. The initial perturbation step size can be set to approximately 0.05V. After each perturbation, the current power is calculated based on the acquired voltage and current and compared with the previous sampled power. When the power increases, the current disturbance direction is maintained; when the power decreases, the direction is reversed to make the stepping direction converge along the power increase trend. The disturbance step size is adaptively adjusted based on environmental signals such as the shading intensity index and irradiance fluctuation rate output in the previous text. When the irradiance fluctuation rate is high or the shading intensity is large, the step size can be reduced to about 0.01V to improve the approximation accuracy and suppress oscillations. When the environment is relatively stable, the step size can be increased to about 0.1V to accelerate convergence. The upper and lower limits of the step size are configured through the parameter table, and their selection is based on the rated voltage level of the component and the statistical results of the adjustment effect in historical operation. To improve the accuracy of the approximation process, the local optimization module sets up a power change monitoring loop. After each disturbance, it calculates the power difference and the rate of change. The rate of change is obtained by the difference between the current power and the previous sampled power, and is combined with the local voltage gradient to determine whether the current operating point is close to the peak value. When the absolute value of the power change rate is lower than the preset convergence threshold, the current point is considered to have reached the local optimum. The convergence threshold can be statistically set based on the noise level and allowable power fluctuation range of the measured PV curve at the airport. In this embodiment, it can be taken as about 0.5%, and can be adjusted through a configuration table. At the same time, the module uses the temperature signal to compensate for the power measurement results, reducing the thermal drift caused by component temperature rise. If the power increase is not significant in multiple consecutive perturbations (the number of consecutive perturbations can be set to 3), and the rate of change in each perturbation is lower than the noise threshold, then it is determined that there may be peak offset or input uncertainty in the current region. The noise threshold can be set according to the statistical distribution of power measurement noise in the same measured dataset. At this time, the module can temporarily relax the voltage boundary of the region or appropriately adjust the initial point position to cover the possible offset peak. When the input peak region data is found to be seriously abnormal or the optimization process fails multiple times, the module can fall back to the fast scanning strategy of the preset sub-range and only perform simplified approximation in the high probability interval given by the algorithm initialization module to maintain the continuous operation of the local optimization process. After local optimization is completed, the module summarizes the final local optimum of each peak region and generates an optimization result dataset. In this embodiment, each record includes at least the region identifier, optimal voltage, corresponding stable power, number of convergence iterations, and convergence judgment flag. The optimization results can be encapsulated into structured data packets, such as binary data frames with check fields or formats such as JSON and XML, and transmitted to the control output module through a high-speed communication link. Based on this, the control output module uses the optimal voltage or equivalent operating point parameter as the target setpoint for the inverter or regulating unit, and updates the control strategy and output commands accordingly. This achieves a smooth connection from global search, local approximation to actual control execution, thereby enabling maximum power point tracking to have fine-grained adjustment capabilities and high tracking accuracy under conditions of airport dynamic shading and irradiance variation.

[0025] Based on the optimization results, control commands are generated, and the inverter is driven by pulse width modulation to adjust the operating point of the photovoltaic system. The commands are then cyclically fed back to the data acquisition module. The specific implementation is as follows: The control output module is used to generate control commands based on the optimization results, drive the inverter to adjust the operating point of the photovoltaic system through pulse width modulation, and feed back the operating status to the aforementioned data acquisition module. In the BIPV photovoltaic direct-drive low-voltage lighting system of the zero-carbon airport, following the local optimal point data output above, the control output module receives the optimal voltage, stable power and area identifier corresponding to each peak area through a high-speed communication interface, writes the above data into the command buffer, and uses check and error correction coding to detect the integrity and consistency of the data in order to adapt to the transmission interference in the complex electromagnetic environment of the airport. The instruction generation process begins with target voltage analysis; the optimal voltage of the currently selected area is extracted as a reference value, and the deviation is calculated based on the actual voltage sampled in real time. The adjustment amount of the PWM duty cycle is determined according to the magnitude of the deviation. The duty cycle calculation is completed by the timer and comparison unit inside the microcontroller. A proportional or deviation integral algorithm can be used, and a hardware multiplier is used to achieve fast calculation, so that the single calculation cycle is controlled in the millisecond range, not exceeding 1ms. Subsequently, the module generates a PWM waveform based on the calculation results. The carrier frequency can be set according to the electromagnetic compatibility and dynamic response requirements of the inverter and low-voltage lighting load, and can be about 20kHz. It is used to drive power switching devices such as MOSFETs or IGBTs to adjust the photovoltaic array output to a working state close to the local optimum. The module monitors the output current with feedback from the current sensor and implements amplitude limiting control on the output voltage. The allowable fluctuation range can be determined according to the airport low-voltage lighting power quality standards and the voltage withstand capability of the lamps. In this embodiment, it is controlled within the target voltage ±0.5V range to ensure the safety and stability of the load operation. To adapt to the dynamic changes of direct-drive low-voltage lighting loads, the control output module is equipped with a load stabilization loop to continuously monitor changes in the current and power of the lighting branch. When the load increases, causing the current to rise and the output voltage to decrease, the module automatically increases the PWM duty cycle according to the voltage deviation to increase the output power. When the load decreases, the duty cycle is reduced accordingly to maintain power balance and avoid over-adjustment that could cause oscillation. Meanwhile, the module is equipped with overvoltage and overcurrent protection mechanisms. The protection threshold can be set by referring to the airport low-voltage power distribution safety specifications and the withstand voltage and rated current indicators of related equipment, and a safety margin is reserved. When the actual voltage or current exceeds the preset safety limit, the hardware comparator quickly pulls down the gate drive signal or triggers the protection branch, so that the inverter enters the power limiting or shutdown state, thereby preventing equipment damage and the spread of abnormal operating conditions. To achieve closed-loop control, the control output module returns the adjusted system state to the front-end optimization link. The module periodically samples voltage, current, and real-time power using an ADC, and constructs an operating state vector by combining the current deviation and protection status flags. This vector is then encapsulated into a structured feedback frame, which includes a synchronization header, status flag bits, and quantized voltage, current, power, and deviation information. The feedback frame is cyclically transmitted to the status input interface of the data acquisition module mentioned above via the system's internal bus. The feedback update frequency can be set based on the system's dynamic response time; in this embodiment, it can be set to approximately 10 times per second. This allows the data acquisition module to synchronously reflect the current control state when constructing photovoltaic operating data, thereby forming a continuous closed-loop iteration between acquisition, optimization, and control. To improve reliability in airport scenarios, the control output module is also configured with redundant control paths, adopting a dual-channel architecture of a main microcontroller and a backup processing unit. When the main control unit is detected to have abnormally reset or no effective output within several control cycles, the system automatically switches to the backup processing unit. The backup unit uses a simplified PWM control strategy to maintain basic power supply within a fixed or slowly adjusted duty cycle range, and improves continuous operation under fault conditions through dual power supply. Through the above-mentioned control command generation, power regulation and status feedback process, the control output module, on the one hand, timely converts the local optimization results into the actual operating point adjustment of the inverter and load side, and on the other hand, provides real-time operating status input to the data acquisition and optimization module, forming a closed-loop control link that connects acquisition, search, optimization and execution within the system. This enables the zero-carbon airport BIPV photovoltaic direct-drive low-voltage lighting system to maintain a stable and efficient operating state under dynamic shading and irradiance variation conditions.

[0026] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0027] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0028] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0029] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0030] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0031] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0032] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0033] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0034] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0035] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A zero-carbon airport BIPV photovoltaic direct-drive low-voltage lighting system, characterized in that, include: Data acquisition module: Collects real-time voltage, current and power parameters of the photovoltaic array, synchronously acquires multi-point data through sensor array, and transmits it to subsequent modules for processing; Environmental monitoring module: detects dynamic shading and radiation variation, monitors environmental changes in real time through radiation sensors and cameras installed at key locations in the airport, and generates variation signals; Algorithm initialization module: Sets the initial search range based on the multi-point electrical parameter data obtained by the data acquisition module and the environmental variation signal output by the environmental monitoring module, divides the power curve region by a preset threshold, and starts global optimization parameter configuration; Global search module: It uses a hybrid optimization algorithm to scan the power curve with multiple peaks, traverses the global space through particle swarm or genetic algorithm, and marks the potential maximum power point region under the constraints of preset candidate threshold based on the power value of each search point, the power difference between adjacent points and the local slope. Local optimization module: Based on the voltage range of the potential maximum power point output by the global search module, the operating point of the photovoltaic array is iteratively adjusted in small steps within this range using the perturbation observation method or the incremental conductance method. The voltage is continuously corrected according to the direction and rate of power change before and after each perturbation until the rate of power change is lower than the preset convergence threshold to determine the local optimal operating point. The perturbation step size is dynamically adjusted in combination with environmental signals such as shading intensity index and irradiance fluctuation rate. Control output module: Generates control commands based on optimization results, drives the inverter to adjust the operating point of the photovoltaic system through pulse width modulation, and feeds back to the data acquisition module in a loop.

2. The zero-carbon airport BIPV photovoltaic direct-drive low-voltage lighting system according to claim 1, characterized in that, The system collects real-time voltage, current, and power parameters of the photovoltaic array, synchronously acquires data from multiple points via a sensor array, and transmits the data to subsequent modules for processing, including: The voltage sensor reduces the voltage through a voltage divider resistor, the Hall closed-loop current sensor measures the DC current, and the power unit calculates the voltage-current product through a multiplication circuit. High-frequency synchronous acquisition using a unified clock is employed to align the time of each channel, statistically analyze the characteristics of the array sub-regions, and filter and threshold check to remove abnormal data. The data is encapsulated into standard frames, sent to subsequent modules via a serial interface, and self-testing, filtering, and verification are performed by the integrated power management and communication unit.

3. The zero-carbon airport BIPV photovoltaic direct-drive low-voltage lighting system according to claim 1, characterized in that, Detecting dynamic shading and irradiance variations, including: Event determination is based on the changes in the exponent and volatility in the variable signal within a continuous sampling period; The module encapsulates the threshold judgment results of the mutation features into standardized data packets and achieves multi-hop transmission through a low-power mesh topology network; Configure the primary and backup sensor channels to switch to the backup path, and integrate power management circuitry to activate the sampling transmission cycle.

4. The zero-carbon airport BIPV photovoltaic direct-drive low-voltage lighting system according to claim 1, characterized in that, Environmental changes are monitored in real time by radiation sensors and cameras installed at key locations in the airport, and variation signals are generated, including: The radiation sensor uses a broadband response silicon-based radiometer to collect radiation intensity, and the camera is equipped with a high-resolution wide-angle lens and infrared auxiliary illumination to capture image sequences. The module self-tests the parameters of the radiation sensor and camera, and cyclically collects radiation and image data. Meteorological data is integrated with wind speed, temperature, and humidity to construct the characteristics of shading intensity and radiation fluctuation. High-intensity events are identified based on thresholds and wirelessly transmitted to the algorithm module.

5. The zero-carbon airport BIPV photovoltaic direct-drive low-voltage lighting system according to claim 1, characterized in that, Based on the multi-point electrical parameter data acquired by the data acquisition module and the environmental variation signals output by the environmental monitoring module, the initial search range is set. Power curve regions are divided using preset thresholds, and global optimization parameter configuration is initiated, including: It receives collected data and environmental signals, buffers the input through the interface, and performs synchronization verification. Reconstruct the power-voltage curve, calculate the slope change points and peak positions, and identify fluctuation regions by scanning point by point; The trigger area division logic generates a sub-interval list from the minimum voltage accumulated data point, records the start voltage, end voltage and average power value, and extends the low power boundary according to the environmental signal; Start parameter configuration, establish the association between signals and search step size boundaries, clean the data through a hierarchical structure and generate a configuration set, and transmit it to the global search module.

6. The zero-carbon airport BIPV photovoltaic direct-drive low-voltage lighting system according to claim 1, characterized in that, A hybrid optimization algorithm is used to scan the multi-peak power curve. The global space is traversed using particle swarm optimization or genetic algorithms. Based on the power value of each search point, the power difference between adjacent points, and the local slope, potential maximum power point regions are marked under a preset candidate threshold constraint, including: Receive initialization results, manage the input stream through a queue, and verify data integrity; After self-checking the calculation unit and synchronization status, the system enters the traversal phase and arranges the search entity in the sub-interval. The initial position of the entity is set to the starting or center voltage of the interval. Iteratively update entity locations, summarize historical best interaction states, apply random perturbations to explore uncovered regions, and quantify peak potential to mark candidate points; The peak list is filtered and sorted by power, the corresponding coordinates are recorded, and the data is transmitted to the local optimization module through a standardized interface.

7. The zero-carbon airport BIPV photovoltaic direct-drive low-voltage lighting system according to claim 1, characterized in that, Based on the voltage range of the potential maximum power point output by the global search module, the operating point of the photovoltaic array is iteratively adjusted in small steps within this range using either the perturbation-observation method or the incremental conductance method. The voltage is continuously corrected according to the direction and rate of power change before and after each perturbation, until the rate of power change is lower than a preset convergence threshold to determine the local optimal operating point, including: Receive the list of peak areas, load the coordinates via the interface, and verify the data integrity. Each region is mapped as an independent unit, and a buffer is allocated to record the starting voltage and boundaries; Starting from the peak voltage, adjust the operating point with small step disturbances and observe whether the power change direction maintains an upward trend or switches in the opposite direction. The system iteratively calculates the power difference and rate of change until the absolute value is below the threshold, locks in the optimal voltage, and transmits the optimal point to the control output module.

8. The zero-carbon airport BIPV photovoltaic direct-drive low-voltage lighting system according to claim 1, characterized in that, The disturbance step size is dynamically adjusted by combining environmental signals such as the shading intensity index and irradiance volatility, including: The disturbance step size is dynamically corrected by integrating environmental signals, and the rate of change is quantified through the deviation monitoring loop; Preset convergence threshold and noise threshold, and determine the offset based on the continuous perturbation power increase; Temporarily adjust the area boundaries and initial locations, summarize the optimal data set, and generate a report; The encapsulated data packet is transmitted to the control output module via the communication link as the adjustment target.

9. The zero-carbon airport BIPV photovoltaic direct-drive low-voltage lighting system according to claim 1, characterized in that, Based on the optimization results, control commands are generated, which drive the inverter to adjust the operating point of the photovoltaic system via pulse width modulation, and are cyclically fed back to the data acquisition module, including: Receive local optimum data, write it to the buffer through the communication interface, and check its integrity and consistency. The optimal voltage is extracted as a reference, and the deviation is calculated in combination with the actual voltage to determine the duty cycle adjustment. The pulse width modulation waveform is generated by the microcontroller timer and comparison unit to drive the switching transistor to adjust the array output. Set up a load stabilization loop to monitor current and power changes, automatically adjust the duty cycle according to the deviation to maintain power balance, and configure overvoltage and overcurrent protection to switch power limiting state through hardware comparator; The sampled voltage, current, and power are used to construct a state vector, which is then encapsulated into a feedback frame and transmitted to the data acquisition module via the bus. The main and backup controller channels are configured to switch to the backup path to maintain power supply.

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