Photovoltaic power station component-level data monitoring method and system

Abstract

The invention relates to the technical field of data monitoring, and discloses a photovoltaic power station component-level data monitoring method and system. The method comprises the following steps: performing clock synchronization initialization and network topology construction on a photovoltaic power station component-level data monitoring system to obtain a target time reference and a data acquisition network; performing parameter adaptive configuration on the photovoltaic module voltage acquisition module to obtain module-level acquisition setting parameters; performing multi-channel synchronous sampling on the voltage signal of the photovoltaic module according to the module-level acquisition setting parameter to obtain original voltage data; performing local preprocessing and compressed encoding on the original voltage data to obtain a compressed data packet; transmitting, decoding and restoring the compressed data packet through a data acquisition network to obtain a multi-channel synchronous data set; and performing performance analysis and fault diagnosis on the multi-channel synchronous data set to obtain a component-level monitoring report. According to the invention, the efficiency of photovoltaic power station module-level data monitoring is improved.

Description

Technical Field

[0001] The present application relates to the field of data monitoring technology, and in particular to a method and system for component-level data monitoring in a photovoltaic power station. Background Art

[0002] With the rapid development of photovoltaic power generation technology, performance monitoring and fault diagnosis of photovoltaic modules have become increasingly important. Traditional photovoltaic module monitoring methods mainly rely on single-point sampling or low-frequency data acquisition, usually using centralized inverters or string inverters to monitor the output of the entire photovoltaic array or string. These methods can provide basic power generation and efficiency data, but it is difficult to capture instantaneous voltage fluctuations and minor performance changes at the level of individual components. At the same time, conventional monitoring systems mostly use simple threshold judgments or statistical analysis, which cannot deeply explore the dynamic characteristics and potential faults of photovoltaic modules.

[0003] However, existing monitoring technologies have some significant shortcomings. First, the lack of an accurate clock synchronization mechanism results in poor time consistency of data collected from multiple channels, affecting the accuracy of subsequent analysis. Second, the configuration of acquisition parameters is often fixed and cannot be dynamically adjusted according to actual working conditions, limiting the adaptability of the monitoring system. In addition, the compression efficiency during data transmission is not high, which increases the network burden, especially in large-scale photovoltaic power stations. Finally, the data analysis method is relatively single, making it difficult to comprehensively evaluate the health status of photovoltaic modules and predict their performance trends, and unable to provide sufficiently detailed and timely information support for operation and maintenance decisions. Summary of the invention

[0004] The present application provides a method and system for monitoring component-level data of a photovoltaic power station, which are used to improve the efficiency of component-level data monitoring of a photovoltaic power station.

[0005] In a first aspect, the present application provides a method for component-level data monitoring in a photovoltaic power station, the method comprising: performing clock synchronization initialization and network topology construction on a component-level data monitoring system of the photovoltaic power station to obtain a target time reference and a data acquisition network; based on the target time reference and the data acquisition network, performing parameter adaptive configuration on a photovoltaic component voltage acquisition module to obtain component-level acquisition setting parameters; performing multi-channel synchronous sampling on a photovoltaic component voltage signal according to the component-level acquisition setting parameters to obtain original voltage data; performing local preprocessing and compression encoding on the original voltage data to obtain a compressed data packet; transmitting and decoding and restoring the compressed data packet through the data acquisition network to obtain a multi-channel synchronous data set; performing performance analysis and fault diagnosis on the multi-channel synchronous data set to obtain a component-level monitoring report.

[0006] In a second aspect, the present application provides a photovoltaic power station component-level data monitoring system, the photovoltaic power station component-level data monitoring system comprising:

[0007] A construction module is used to initialize the clock synchronization and construct the network topology of the photovoltaic power station component-level data monitoring system to obtain the target time reference and data acquisition network;

[0008] A configuration module, used to perform parameter adaptive configuration on the photovoltaic module voltage acquisition module based on the target time reference and the data acquisition network to obtain module-level acquisition setting parameters;

[0009] A setting module, used to perform multi-channel synchronous sampling of the photovoltaic module voltage signal according to the module-level acquisition setting parameters to obtain raw voltage data;

[0010] An encoding module, used for locally preprocessing and compressing the original voltage data to obtain a compressed data packet;

[0011] A transmission module, used for transmitting and decoding the compressed data packet through the data acquisition network to obtain a multi-channel synchronous data set;

[0012] The diagnostic module is used to perform performance analysis and fault diagnosis on the multi-channel synchronous data set to obtain a component-level monitoring report.

[0013] In the technical solution provided by the present application, by performing clock synchronization initialization and network topology construction on the photovoltaic power station component-level data monitoring system, a high-precision target time reference and an efficient data acquisition network are obtained, which provides a unified time reference and a reliable data transmission channel for the entire monitoring process, and effectively solves the time consistency and network communication problems of multi-channel data acquisition in large-scale photovoltaic power stations. Secondly, the photovoltaic component voltage acquisition module is parameter-adaptively configured based on the target time reference and the data acquisition network, and the component-level acquisition setting parameters obtained can be dynamically adjusted according to the actual working conditions, thereby improving the accuracy and flexibility of data acquisition. Furthermore, the photovoltaic component voltage signal is sampled synchronously in multiple channels according to the component-level acquisition setting parameters, and the obtained original voltage data ensures the accurate synchronization of data acquisition in each channel, which provides the possibility of capturing instantaneous voltage fluctuations and minor performance changes. The original voltage data is preprocessed and compressed locally, and the compressed data packet obtained greatly reduces the amount of data transmission and improves the network transmission efficiency. The compressed data packet is transmitted and decoded and restored through the data acquisition network, and the obtained multi-channel synchronous data set not only ensures the integrity and time correlation of the data, but also provides a standardized data format for subsequent analysis. Finally, the performance analysis and fault diagnosis of the multi-channel synchronized data set are performed, and the component-level monitoring report obtained fully reflects the operating status, performance change trend and potential failure risk of each photovoltaic component. This method realizes real-time and accurate monitoring of photovoltaic components through high-precision time synchronization, flexible parameter configuration, efficient data compression transmission and comprehensive data analysis, which helps to timely discover potential faults, optimize operation and maintenance strategies, improve the overall efficiency and reliability of photovoltaic power plants, and improve the efficiency of component-level data monitoring of photovoltaic power plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0015] Figure 1 This is a schematic diagram of an embodiment of a photovoltaic power station component-level data monitoring method in an embodiment of the present application;

[0016] Figure 2 Schematic diagram of an embodiment of a photovoltaic power station component-level data monitoring system in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The embodiment of the present application provides a method and system for monitoring component-level data in a photovoltaic power station. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0018] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 , an embodiment of a photovoltaic power station component-level data monitoring method in an embodiment of the present application includes:

[0019] Step S101, performing clock synchronization initialization and network topology construction on a photovoltaic power station component-level data monitoring system to obtain a target time reference and data acquisition network;

[0020] Step S102: Based on the target time reference and the data acquisition network, the photovoltaic module voltage acquisition module is adaptively configured to obtain module-level acquisition setting parameters;

[0021] Step S103, performing multi-channel synchronous sampling on the photovoltaic module voltage signal according to the module-level acquisition setting parameters to obtain the original voltage data;

[0022] Step S104: locally preprocess and compress the original voltage data to obtain a compressed data packet;

[0023] Step S105, transmitting and decoding the compressed data packet through the data acquisition network to obtain a multi-channel synchronous data set;

[0024] Step S106: Perform performance analysis and fault diagnosis on the multi-channel synchronous data set to obtain a component-level monitoring report.

[0025] It is understandable that the execution subject of the present application may be a photovoltaic power station component-level data monitoring system, or a terminal or a server, which is not specifically limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0026] In the embodiment of the present application, the monitoring system is firstly initialized for clock synchronization and network topology construction. This step starts the GPS module, parses the GPS raw data to obtain accurate time information, performs time format conversion and time zone adjustment, generates a system reference timestamp and distributes it to the master station. The master station time reference is transmitted to the slave station through the IEEE 1588 precision time protocol, calculates the time deviation and performs digital filtering, and finally adjusts the frequency through the phase-locked loop algorithm to ensure the high consistency of the system time. At the same time, the POWERBUS bus is initialized, the slave control unit is scanned, the component mapping table is established, the network topology analysis and communication path optimization are performed, and an efficient data acquisition network is formed. Based on the target time reference and the data acquisition network, the photovoltaic component voltage acquisition module is adaptively configured with parameters. This process analyzes historical operating data, evaluates current environmental conditions, estimates the voltage variation range, and dynamically adjusts the ADC reference voltage and sampling frequency. At the same time, the data compression parameters are calculated according to the network bandwidth, and the component-level acquisition setting parameters are comprehensively formed and sent to each slave control unit.

[0027] Subsequently, multi-channel synchronous sampling is performed according to the component-level acquisition setting parameters. The master station plans the sampling timing based on the target time base and broadcasts the synchronous trigger instruction. After receiving the instruction, the slave station configures the hardware timer and activates the ADC for sampling at the precise moment. The sampled data undergoes oversampling, digital filtering, extraction and linear calibration to finally form the raw voltage data with timestamp. Local preprocessing and compression encoding of the raw voltage data is the next key step. First, data redundancy analysis is performed and differential encoding is used to reduce the amount of data. Then variable-length encoding is performed through the Huffman coding algorithm to further compress the data. The encoded data stream is processed in blocks, and cyclic redundancy check codes and frame headers are added to form the final compressed data packet.

[0028] The compressed data packets are transmitted to the master station through the data acquisition network. The transmission process uses time division multiplexing technology to avoid data conflicts. After receiving the data, the master station performs a cyclic redundancy check to ensure data integrity. The valid data packets are restored to the original voltage data frame after Huffman decoding and differential decoding. Then they are sorted and multi-channel aligned according to the timestamp to form a multi-channel synchronous data set. Finally, the multi-channel synchronous data set is subjected to performance analysis and fault diagnosis. This process includes time series analysis, fast Fourier transform, peak detection and pattern recognition to identify abnormal components and potential faults. At the same time, the performance degradation and remaining life of the components are predicted through trend analysis and regression analysis. The comprehensive evaluation results form a detailed component-level monitoring report.

[0029] For example, suppose a power station with 1,000 photovoltaic modules is monitored using this method. After clock synchronization is initialized, the system time accuracy reaches ±50 nanoseconds. During the parameter adaptive configuration process, the ADC reference voltage is set to 45V (estimated voltage range 30-40V) based on historical data and current light intensity, and the sampling frequency is dynamically adjusted between 1-100Hz. In one sampling cycle, each component collects 100ms of voltage data and generates 1,000 sampling points. The raw data is differentially encoded and Huffman encoded, and the data volume is compressed to 30% of the original size. After transmission to the master station, the data restoration accuracy reaches 99.99%. The performance analysis results show that 5 components have potential hot spot effects, and the performance decay rate of 10 components is 20% higher than the average level, and the predicted remaining service life is shortened by 2 years.

[0030] The efficiency of component-level data monitoring in photovoltaic power plants is improved. By performing clock synchronization initialization and network topology construction on the component-level data monitoring system of photovoltaic power plants, a high-precision target time reference and an efficient data acquisition network are obtained, which provides a unified time reference and a reliable data transmission channel for the entire monitoring process, and effectively solves the time consistency and network communication problems of multi-channel data acquisition in large-scale photovoltaic power plants. Secondly, the parameters of the photovoltaic component voltage acquisition module are adaptively configured based on the target time reference and data acquisition network, and the component-level acquisition setting parameters obtained can be dynamically adjusted according to the actual working conditions, which improves the accuracy and flexibility of data acquisition. Furthermore, the photovoltaic component voltage signal is sampled synchronously in multiple channels according to the component-level acquisition setting parameters, and the obtained raw voltage data ensures the precise synchronization of data acquisition in each channel, which makes it possible to capture instantaneous voltage fluctuations and minor performance changes. The raw voltage data is locally preprocessed and compressed, and the compressed data packet obtained greatly reduces the amount of data transmission and improves the network transmission efficiency. The compressed data packet is transmitted and decoded and restored through the data acquisition network, and the obtained multi-channel synchronous data set not only ensures the integrity and time correlation of the data, but also provides a standardized data format for subsequent analysis. Finally, the performance analysis and fault diagnosis of the multi-channel synchronized data set are performed, and the component-level monitoring report obtained fully reflects the operating status, performance change trend and potential failure risk of each photovoltaic component. This method realizes real-time and accurate monitoring of photovoltaic components through high-precision time synchronization, flexible parameter configuration, efficient data compression transmission and comprehensive data analysis, which helps to timely discover potential faults, optimize operation and maintenance strategies, improve the overall efficiency and reliability of photovoltaic power plants, and improve the efficiency of component-level data monitoring of photovoltaic power plants.

[0031] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0032] (1) Start the GPS module in the photovoltaic power station component-level data monitoring system to obtain GPS raw data, and parse and process the GPS raw data to obtain accurate time information;

[0033] (2) converting the precise time information into a time format to obtain unified time format data, and adjusting the unified time format data by time zone to obtain localized time data;

[0034] (3) Generate a timestamp for the localized time data to obtain a system reference timestamp, and distribute the system reference timestamp to obtain a master station time reference;

[0035] (4) The master station time reference is transmitted over the network through the IEEE 1588 precision time protocol to obtain the slave station synchronization data, and the time deviation of the slave station synchronization data is calculated to obtain the time correction parameter;

[0036] (5) performing digital filtering on the time correction parameter to obtain a smooth correction value, and performing frequency adjustment on the smooth correction value through a phase-locked loop algorithm to obtain a target time reference;

[0037] (6) Initializing the bus in the photovoltaic power station component-level data monitoring system to obtain an initialized bus, and scanning the slave control units through the initialized bus to obtain a slave control unit list;

[0038] (7) Associating the slave control unit list with the component ID to obtain a component mapping table, and performing network topology analysis on the component mapping table to obtain a network structure diagram;

[0039] (8) Optimizing the communication path of the network structure diagram to obtain an optimized communication path, and configuring the communication parameters of the slave control unit based on the optimized communication path to obtain a data acquisition network.

[0040] Specifically, the component-level data monitoring method of a photovoltaic power station first starts the GPS module to obtain GPS raw data. The GPS module receives signals from satellites and decodes them to obtain raw data containing information such as time and location. These raw data are parsed and processed to extract accurate time information. The parsing process includes steps such as verifying data integrity, decoding time information, and performing error correction, and finally obtaining time data accurate to nanoseconds. The precise time information is converted into a unified time format, such as Coordinated Universal Time (UTC). This step ensures that all devices in the system use the same time standard. Subsequently, the unified time format data is adjusted to a time zone according to the geographical location of the system to obtain localized time data. The time zone adjustment takes into account the local time zone offset and possible daylight saving time rules to ensure that the time data is consistent with the actual local time.

[0041] Then, the localized time data is timestamped to create a system reference timestamp. The timestamp usually contains information on year, month, day, hour, minute, second, and millisecond or microsecond accuracy. The generated system reference timestamp is then distributed to the master device of the system. After receiving the timestamp, the master device uses it as the master time reference for subsequent time synchronization operations. The master time reference is transmitted over the network via the IEEE 1588 Precision Time Protocol (PTP). The PTP protocol achieves high-precision time synchronization by exchanging timestamp information and accurately measuring network delays and clock deviations. After receiving the master time reference, the slave device compares it with the local clock and calculates the time deviation. This time deviation is the time correction parameter, which reflects the difference between the slave device's local clock and the master time reference.

[0042] The calculated time correction parameters are digitally filtered to reduce the impact of instantaneous noise and interference, and obtain a smoother and more stable correction value. Common filtering methods include moving average filtering, Kalman filtering, etc. The smooth correction value obtained after filtering can better reflect the actual clock deviation. Subsequently, the smooth correction value is frequency adjusted by the phase-locked loop (PLL) algorithm. The PLL algorithm dynamically adjusts the frequency of the local clock to synchronize it with the reference clock (i.e., the master station time reference). This process not only adjusts the time deviation, but also corrects the frequency deviation, and finally obtains a highly accurate target time reference. Initialize the POWERBUS bus in the component-level data monitoring system of the photovoltaic power station. POWERBUS is a low-voltage DC carrier power supply bus that can transmit power and data signals at the same time, simplifying the system wiring. The initialization process includes setting communication parameters, configuring bus protocols, etc. After the initialization is completed, the slave control units are scanned through this initialization bus, and all slave control units connected to the bus are identified and listed to form a slave control unit list.

[0043] The obtained slave control unit list is associated with the component ID, and the corresponding relationship between each slave control unit and the PV components it monitors is established to form a component mapping table. This mapping table is the basis for subsequent data collection and analysis. Then, the network topology analysis of the component mapping table is performed to determine the physical connection relationship and logical communication path between each slave control unit, and generate a network structure diagram. The communication path is optimized based on the generated network structure diagram. The optimization process takes into account factors such as data transmission efficiency, network load balancing, and communication reliability to obtain the optimized communication path. Based on this optimized communication path, the communication parameters of each slave control unit are configured, including communication address, transmission rate, data format, etc., to finally form a complete data acquisition network.

[0044] For example, suppose a power station with 1,000 photovoltaic modules uses this method to initialize the monitoring system. After the GPS module is started, the raw data is received and the precise time information is obtained after parsing as "2023-12-15 08:30:15.123456789". The time format conversion unifies it into the UTC format, such as "1639554615.123456789". Assuming that the system is located in the East Eight District, the localized time "2023-12-15 16:30:15.123456789" is obtained after the time zone adjustment. After the system reference timestamp is generated and distributed to the master station, it is transmitted to all slave stations through the IEEE 1588 protocol. After a slave station receives the master station time reference, it compares it with the local clock and calculates the time deviation as +2.5 milliseconds. This deviation value is subjected to moving average filtering to obtain a smooth correction value of +2.3 milliseconds. The PLL algorithm adjusts the local clock frequency according to this correction value, gradually reducing the deviation to within 50 microseconds. After the POWERBUS bus is initialized, 998 valid slave control units are scanned (2 units are offline). After the component ID is associated, a mapping table is generated. The network topology analysis shows that the system has a tree structure with 5 layers. After the communication path is optimized, the data transmission delay of the farthest slave control unit is reduced from the original 500 milliseconds to 200 milliseconds.

[0045] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0046] (1) Statistically analyzing the historical operating data of the photovoltaic modules to obtain the historical voltage range, and evaluating the current environmental conditions based on the historical voltage range to obtain the estimated voltage change range;

[0047] (2) performing dynamic range calculation on the estimated voltage variation range to obtain an ADC reference voltage value, and configuring an analog-to-digital converter based on the ADC reference voltage value to obtain ADC configuration parameters;

[0048] (3) Analyze the change rate of the light intensity data to obtain the light change characteristics, and dynamically adjust the sampling frequency based on the light change characteristics to obtain the sampling frequency parameters;

[0049] (4) Measuring the bandwidth of the data collection network to obtain available bandwidth data, and calculating data compression parameters based on the available bandwidth data to obtain compression coding parameters;

[0050] (5) Integrate the ADC configuration parameters, sampling frequency parameters, and compression encoding parameters to obtain component-level acquisition setting parameters, and send the parameters to the slave control unit through the data acquisition network to obtain a configuration completion signal.

[0051] Specifically, the historical operating data of the photovoltaic module is statistically analyzed to obtain the historical voltage range. This process includes calculating the maximum, minimum, average and standard deviation of the voltage in the past period of time (such as the last 30 days). Then, based on this historical voltage range, combined with the current environmental conditions (such as temperature and light intensity), an evaluation is performed to estimate the voltage change range in the future. The estimation process takes into account the influence coefficient of temperature on the output voltage of the component and the relationship curve between light intensity and open circuit voltage. The estimated voltage change range is dynamically calculated to obtain the ADC reference voltage value. The calculation process considers reserving a certain margin to cope with possible voltage fluctuations. For example, if the estimated voltage range is 30-40V, the ADC reference voltage may be set to 45V. Based on this ADC reference voltage value, the analog-to-digital converter is configured, including setting the number of quantization bits, sampling frequency range, etc., to obtain the ADC configuration parameters. These parameters directly affect the accuracy and resolution of data acquisition.

[0052] At the same time, the rate of change of the light intensity data is analyzed to obtain the characteristics of light change. This analysis process includes calculating the first-order derivative and second-order derivative of the light intensity, identifying the period of rapid change and the stable period. Based on these characteristics of light change, the sampling frequency is dynamically adjusted. When the light changes rapidly (such as the shadow effect caused by the movement of clouds), the sampling frequency is increased to capture transient changes; during the period of stable light, the sampling frequency is reduced to save system resources. The sampling frequency parameters obtained by this dynamic adjustment strategy can optimize the use of system resources while ensuring data quality. In order to ensure the reliability of data transmission, the bandwidth of the data acquisition network is measured to obtain the available bandwidth data. The measurement process may be carried out using a network throughput test or a specific protocol based on the POWERBUS bus. Based on this available bandwidth data, the data compression parameters are calculated. The selection of compression parameters requires a trade-off between data compression rate and computational complexity to ensure that enough data information is transmitted within the limited network bandwidth. The compression coding parameters obtained in this process include the number of differential coding bits, Huffman coding table, etc.

[0053] Finally, the ADC configuration parameters, sampling frequency parameters, and compression encoding parameters are integrated to form complete component-level acquisition setting parameters. These parameters are sent to each slave control unit through the data acquisition network. After receiving the parameters, each slave control unit performs local configuration and returns a configuration completion signal to ensure the consistency of parameter settings for the entire system.

[0054] For example, assume that a component of a 1MW photovoltaic power station has historical operating data showing a voltage range of 30-42V, an average of 36V, and a standard deviation of 2V. The current ambient temperature is 25°C and the light intensity is 800W / m2. Based on these data, the voltage change range is estimated to be 32-40V in the next 4 hours. Considering the 5V margin, the ADC reference voltage is set to 45V. A 16-bit ADC is selected, with a resolution of 45V / 2^16≈0.69mV. Analysis of light intensity data shows that the light change rate is the highest during the morning period of 8:00-10:00, reaching 100W / (m2·h). Therefore, the sampling frequency is set to 100Hz during this period and 10Hz for other periods. The network bandwidth test results show that the available bandwidth is 100kbps. Based on this, the differential encoding bit is set to 8 bits, and an appropriate Huffman coding table is selected, which is expected to compress the data volume to 40% of the original data. These parameters are integrated and sent to the slave control unit, which completes the configuration within 5 seconds and returns a confirmation signal.

[0055] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0056] (1) Planning the sampling sequence based on the target time reference to obtain the sampling trigger moment, and broadcasting the sampling trigger moment through the data acquisition network to obtain a synchronous trigger instruction;

[0057] (2) receiving and parsing the synchronization trigger instruction to obtain local trigger parameters, and configuring the hardware timer based on the local trigger parameters to obtain the timing trigger setting;

[0058] (3) activating the ADC according to the timing trigger setting to obtain a sampling start signal, and performing analog-to-digital conversion on the PV module voltage based on the sampling start signal to obtain a digital voltage value;

[0059] (4) performing oversampling processing on the digital voltage value to obtain an oversampled data set, and performing noise suppression on the oversampled data set through a digital filtering algorithm to obtain filtered data;

[0060] (5) extracting the filtered data to obtain downsampled data, and linearly calibrating the downsampled data to obtain a calibrated voltage value;

[0061] (6) The calibrated voltage value is converted into a quantized unit to obtain standard unit voltage data, and the current system timestamp is added to the standard unit voltage data to obtain the original voltage data.

[0062] Specifically, the sampling sequence is planned based on the established target time reference to obtain the sampling trigger moment. This process takes into account the preset sampling frequency and system clock accuracy, and calculates a series of precise sampling time points. Subsequently, these sampling trigger moments are broadcast to all slave control units through the data acquisition network to form a synchronous trigger instruction. After receiving the synchronous trigger instruction, each slave control unit performs parsing and processing to extract local trigger parameters. These parameters include information such as trigger moment, sampling duration, and sampling frequency. Based on these local trigger parameters, the hardware timer is accurately configured and the timing trigger is set. The configuration of the hardware timer includes setting the initial count value, interrupt trigger conditions, etc., to accurately trigger the sampling action at the specified time.

[0063] When the timer reaches the set value, the ADC is automatically triggered to start working and generate a sampling start signal. This signal activates the sampling circuit of the ADC and starts analog-to-digital conversion of the voltage of the photovoltaic module. The ADC converts the continuous analog voltage signal into a discrete digital quantity to obtain the initial digital voltage value. The accuracy of this process directly affects the accuracy of subsequent data analysis. In order to improve the signal quality, the initial digital voltage value is oversampled. Oversampling is a technique that samples a signal at a rate higher than the Nyquist frequency, which can improve the signal-to-noise ratio and the effective number of bits. For example, if the final required sampling rate is 1kHz, oversampling may be performed at a frequency of 8kHz to obtain an oversampled data set. This data set contains 8 times more data points than are actually needed.

[0064] The oversampled data set is noise-reduced by a digital filtering algorithm. Common digital filtering algorithms include finite impulse response (FIR) filters and infinite impulse response (IIR) filters. The filtering process can remove high-frequency noise and improve the signal-to-noise ratio of the signal. The filtered data retains the useful components of the signal while significantly reducing the impact of interference. The filtered data needs to be decimated, i.e., downsampled. The decimation process usually selects one of every N samples, where N is the ratio of the oversampling rate to the target sampling rate. This process reduces the amount of data while maintaining the frequency band characteristics of the signal. The downsampled data is linearly calibrated to compensate for the nonlinear error and gain error of the ADC. Linear calibration usually uses a multi-point calibration method to establish a calibration curve using known reference voltage points, and then correct the collected data.

[0065] Finally, the calibrated voltage value is quantized and converted to standard voltage units (usually volts). This process involves the calculation of the ADC reference voltage and resolution. The converted standard unit voltage data is added with the current system timestamp to form the final raw voltage data. The accuracy of the timestamp usually matches the synchronization accuracy of the system and can be accurate to microseconds or nanoseconds.

[0066] For example, assume a 100MW PV power plant component-level monitoring system, which contains 100,000 PV components. The system is set to perform synchronous sampling on all components once per second. Based on the target time base, the system calculates the sampling trigger time of the next second as "2023-12-16 10:30:00.000000", and broadcasts the synchronous trigger instruction through the POWERBUS bus. After receiving the instruction, each slave control unit configures the local hardware timer and sets the trigger time to "10:30:00.000000". When the local clock reaches the set time, the ADC is activated and starts sampling the PV component voltage. A 16-bit ADC is used, the reference voltage is 50V, and the theoretical resolution is 50V / 65536≈0.76mV. The system oversamples at a frequency of 8kHz and collects 8 data points within 1ms. These 8 data points are processed by the FIR low-pass filter to filter out the noise components above 1kHz. Then 8:1 extraction is performed, and the first data point is selected as the final sampling value. Assume that the eight oversampled data points (ADC raw values) of a component are {32768, 32770, 32765, 32772, 32769, 32767, 32771, 32768}, and after filtering and decimation, 32768 is obtained. Apply the linear calibration function y = 1.0002x + 0.5, and the calibrated ADC value is 32775. Convert it to a voltage value: (32775 / 65536) × 50V = 25.0057V. Finally, add the precise timestamp "2023-12-16 10:30:00.000000" to form the complete raw voltage data.

[0067] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0068] (1) performing data redundancy analysis on the original voltage data to obtain redundant features, and performing differential encoding on the data based on the redundant features to obtain differentially encoded data;

[0069] (2) performing variable-length coding on the differentially coded data by using a Huffman coding algorithm to obtain a variable-length coded data stream, and performing block processing on the variable-length coded data stream to obtain a data block sequence;

[0070] (3) Perform cyclic redundancy check encoding on the data block sequence to obtain a data packet with a checksum, and add a frame header to the data packet with the checksum to obtain a compressed data packet.

[0071] Specifically, data redundancy analysis is performed on the original voltage data to identify repeated patterns and redundant information in the data. Redundant features include continuous identical values, slowly changing trends, or periodic fluctuations. This analysis process usually involves calculating the difference between adjacent data points, identifying repeated sequences, and evaluating the statistical characteristics of the data. Based on these redundant features, differential coding technology is used to process the data. Differential coding uses the difference between adjacent data points as a new data sequence instead of storing absolute values. This method is particularly suitable for photovoltaic module voltage data because voltage values ​​usually show slowly changing characteristics. Differential coding effectively reduces the absolute value range of the data, thereby improving the efficiency of subsequent compression. The differentially encoded data is variable-length encoded using the Huffman coding algorithm. Huffman coding is a lossless compression algorithm based on the frequency of data occurrence, which assigns shorter codes to frequently occurring data and longer codes to rare data. In photovoltaic module parameter monitoring, small changes in voltage values ​​are usually more frequent, so they are assigned shorter codes. The Huffman coding process includes building a frequency table, generating a Huffman tree, and creating a coding table. The encoding process converts the original data into a variable-length bit string to form a variable-length coded data stream. This data stream is then processed by block processing, which divides the continuous data stream into fixed-size or variable-size data blocks. Block processing facilitates data transmission and error recovery, and each data block can be processed and retransmitted independently.

[0072] Finally, the block sequence is cyclically redundancy checked (CRC) encoded, and a checksum is generated and attached to the data packet. CRC is an error detection coding technology that calculates the check bit by polynomial division. The CRC encoding process includes selecting a generating polynomial, initializing the CRC register, performing CRC calculations, and attaching a checksum. This check mechanism can effectively detect data errors during transmission and improve the reliability of data transmission. The data packet with the checksum is then added with frame header information, including metadata such as the data packet length, timestamp, and data type, to finally form a complete compressed data packet.

[0073] For example, suppose the raw voltage data (unit: volt) collected by a photovoltaic module for 10 consecutive seconds is: [35.2, 35.3, 35.2, 35.4, 35.3, 35.2, 35.3, 35.5, 35.4, 35.3]. Data redundancy analysis finds that these values ​​have a small range of variation and are suitable for differential encoding. After differential encoding, the difference sequence is obtained: [0.1, -0.1, 0.2, -0.1, -0.1, 0.1, 0.2, -0.1, -0.1]. The absolute value range of this difference sequence is significantly smaller than the original data. Next, the frequency statistics of this difference sequence are performed, and the results are: 0.1 appears 3 times, -0.1 appears 4 times, and 0.2 appears 2 times. Based on this frequency statistics, a Huffman tree is constructed and the codes are assigned: -0.1 is encoded as 0, 0.1 is encoded as 10, and 0.2 is encoded as 11. Applying this encoding scheme, the difference sequence is converted into a bit stream: 10 0 11 0 0 10 11 0 0. This bit stream is divided into two data blocks, each with 4 bytes. A 32-bit CRC checksum is calculated for each data block, assuming that the obtained checksum is 0xA5B6C7D8. Finally, the frame header information is added, including the packet length (8 bytes), timestamp (2023-12-16 10:30:00), data type (voltage), etc. In this way, the original 80 bytes of data (10 32-bit floating point numbers) are compressed into a compressed data packet of about 20 bytes containing the actual data, CRC checksum and frame header, with a compression rate of 75%.

[0074] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0075] (1) Time division multiplexing is performed on the compressed data packets to obtain the transmission timing, and data is transmitted through the data acquisition network based on the transmission timing to obtain the data received by the master station;

[0076] (2) performing a cyclic redundancy check on the data received by the master station to obtain a data integrity verification result, and screening valid data based on the data integrity verification result to obtain a valid data packet;

[0077] (3) performing Huffman decoding on the valid data packet to obtain decoded data, and performing differential decoding on the decoded data to obtain an original voltage data frame;

[0078] (4) extracting the timestamp in the original voltage data frame to obtain time information, and reordering the data based on the time information to obtain sorted data;

[0079] (5) Perform multi-channel alignment on the sorted data to obtain a multi-channel synchronized data set.

[0080] Specifically, the compressed data packets are time-division multiplexed to obtain the transmission timing. Time-division multiplexing is a technology that staggers the transmission of multiple data streams in time, which can effectively utilize the limited network bandwidth. The design of the transmission timing takes into account the data volume and network topology of each slave control unit to ensure the smooth transmission of the data stream. Based on this transmission timing, data is transmitted through the data acquisition network (such as the POWERBUS bus) to send the compressed data packets of each slave control unit to the master station. After the master station receives the data, it first performs a cyclic redundancy check (CRC). CRC is an error detection method that verifies the integrity of the data by comparing the received checksum with the recalculated checksum. This process includes initializing the CRC register, performing polynomial division on the received data, and comparing the calculated result with the received checksum. If the two match, the data is considered complete; otherwise, it is determined that an error occurred in the data transmission process. Based on this data integrity verification result, the valid data is screened, and the data packets that failed the verification are eliminated, and only the valid data packets that are complete and correct are retained.

[0081] Huffman decoding is performed on valid data packets. Huffman decoding is a reverse process that uses the same encoding table as encoding to convert the variable-length bit string back to the original symbol sequence. The decoding process includes reading the bit stream, traversing the Huffman tree until reaching the leaf node, outputting the corresponding symbol, and then repeating this process until all data is processed. The data obtained after decoding is still in the form of differential encoding and needs to be further differentially decoded. The differential decoding process is to restore the difference sequence to the original absolute value sequence, usually starting from a known initial value, and gradually accumulating the difference to obtain the original data. In this way, the original voltage data frame is restored. The timestamp in the original voltage data frame is extracted to obtain the time information. The timestamp is usually included in the header or tail of the data frame and records the exact moment of data acquisition. The extracted time information is used for subsequent data sorting and alignment. Based on this time information, the data is reordered. The purpose of reordering is to ensure that the data from different slave control units are correctly arranged in the order of acquisition time. This step is crucial for subsequent data analysis, especially when analyzing performance differences between components.

[0082] Finally, the sorted data is subjected to multi-channel alignment to obtain a multi-channel synchronized data set. Multi-channel alignment is the process of accurately matching the data from different photovoltaic modules in time. This process takes into account the possible slight time difference between different channels, and makes the data points of all channels completely aligned in time by interpolation or discarding some data points. The multi-channel synchronized data set obtained in this way provides a reliable basis for subsequent module performance comparison and fault diagnosis.

[0083] For example, assume that a monitoring system for a 100MW photovoltaic power plant contains 100,000 photovoltaic modules and collects data once per second. In a certain second, the system receives 99,998 valid compressed data packets, each of which is about 20 bytes in size. Time division multiplexing processing transmits these data packets to the master station at a rate of 1000 packets per millisecond according to the predetermined timing. After receiving the data, the master station performs a CRC check and finds that 5 of the packets fail the check. These packets are marked as invalid and discarded. Huffman decoding is performed on the remaining 99,993 valid data packets. Assume that the encoding table used is: -0.1 is encoded as 0, 0.1 is encoded as 10, and 0.2 is encoded as 11. The decoding process restores the bit stream "10 0 11 0 0 10 11 0 0" to the difference sequence [0.1,-0.1,0.2,-0.1,-0.1,0.1,0.2,-0.1,-0.1]. Differential decoding starts from the initial value of 35.2V, accumulates the difference sequence, and obtains the original voltage data [35.2, 35.3, 35.2, 35.4, 35.3, 35.2, 35.3, 35.5, 35.4, 35.3]. After extracting the timestamp, it is found that due to network delay, the data arrival order is not completely consistent with the acquisition order. The timing reordering arranges the data according to the timestamp from "2023-12-1610:30:00.000" to "2023-12-1610:30:00.999". Multi-channel alignment processing ensures that all 99,993 channels have corresponding data values ​​at each millisecond time point, and individual missing data points are supplemented by linear interpolation. The resulting multi-channel synchronized data set is a 99,993 x 1000 matrix, with each row representing a PV module and each column representing a millisecond sampling point, providing a high-quality data foundation for subsequent large-scale PV power plant performance analysis.

[0084] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0085] (1) Perform time series analysis on multi-channel synchronous data sets to obtain voltage fluctuation characteristics, and then perform frequency domain conversion on the voltage fluctuation characteristics using the fast Fourier transform algorithm to obtain spectrum distribution data;

[0086] (2) Perform peak detection on the spectrum distribution data to obtain a characteristic frequency set, and use a pattern recognition algorithm to match the fault characteristics of the characteristic frequency set to obtain a preliminary fault type;

[0087] (3) Comparing and analyzing the adjacent channel data in the multi-channel synchronous data set to obtain the inter-channel difference index, and performing cluster analysis based on the inter-channel difference index to obtain the abnormal component group;

[0088] (4) Perform trend analysis on the historical data of the abnormal component group to obtain a performance degradation curve, and perform life prediction on the performance degradation curve through regression analysis to obtain an estimated remaining life;

[0089] (5) Comprehensively evaluate the preliminary fault types, abnormal component groups, and remaining life estimates to obtain the component-level monitoring report.

[0090] Specifically, a time series analysis is performed on the multi-channel synchronous data set to extract the voltage fluctuation characteristics. This process includes calculating statistical indicators (such as mean, standard deviation, skewness and kurtosis), identifying trends and periodic patterns, and detecting abnormal fluctuations. Subsequently, these time domain features are converted to the frequency domain through the fast Fourier transform (FFT) algorithm. FFT is an algorithm that efficiently calculates discrete Fourier transforms and can reveal the various frequency components contained in the signal. The spectrum distribution data obtained in this step reflects the frequency characteristics of voltage fluctuations and helps to identify periodic interference and abnormal oscillations. Peak detection is performed on the spectrum distribution data to identify significant frequency components and form a characteristic frequency set. Peak detection usually uses a local maximum search algorithm and sets an appropriate threshold to filter noise. After obtaining the characteristic frequency set, fault feature matching is performed through a pattern recognition algorithm. This step uses a pre-established fault feature library to compare the detected characteristic frequencies with known fault modes. Common pattern recognition methods include support vector machines (SVM), k-nearest neighbor algorithms (KNN) or decision trees. The matching results give preliminary fault types, such as hot spot effects, microcracks or interconnection failures.

[0091] At the same time, the adjacent channel data in the multi-channel synchronous data set are compared and analyzed to calculate the difference indicators between channels. These indicators can include voltage difference, power difference, correlation coefficient, etc. Through this comparison, components with abnormal performance can be quickly identified. Cluster analysis is performed based on these difference indicators to group components with similar performance, thereby identifying abnormal component groups. Commonly used algorithms for cluster analysis include K-means, hierarchical clustering, or DBSCAN. This step helps to quickly locate components with abnormal performance, especially in large-scale photovoltaic arrays. For the identified abnormal component groups, their historical data is further analyzed to obtain the performance attenuation curve. This process involves trend analysis of long-term data, including calculating moving averages, identifying seasonal patterns and long-term trends, etc. The performance attenuation curve is fitted by regression analysis to predict the future performance of the component. Commonly used regression models include linear regression, exponential regression, or polynomial regression. The most appropriate model is selected according to the actual attenuation trend. Based on the regression model, the time required for the component to reach a specific performance threshold (such as 80% of the rated power) is calculated to obtain the estimated value of the remaining life.

[0092] Finally, the preliminary fault types, abnormal component groups, and remaining life estimates are comprehensively evaluated to generate a component-level monitoring report. This evaluation process takes into account the weights and interrelationships of various indicators, and may use fuzzy logic or expert system methods to make decisions. The comprehensive evaluation not only considers the current performance status, but also includes potential failure risks and expected life, providing comprehensive support for operation and maintenance decisions.

[0093] For example, suppose a monitoring system for a 100MW PV power station is analyzing data for one day, involving 100,000 PV modules, with each module collecting data once per second. A time series analysis of the 24-hour voltage data of one of the modules was performed, and the average voltage was calculated to be 36.5V with a standard deviation of 0.8V. FFT analysis found obvious peaks at 50Hz and 100Hz, with amplitudes of 0.2V and 0.1V respectively. These characteristic frequencies were matched with the fault library through the pattern recognition algorithm, and were initially judged to be inverter ripple interference. At the same time, through comparative analysis, it was found that the average voltage difference between this module and the adjacent modules was 1.5V, exceeding the normal threshold of 0.5V. Cluster analysis classified it into the "low voltage abnormality" group, which contained 50 modules in total. Analysis of the one-year historical data of this group found that its output power annual attenuation rate was 2.5%, which was higher than the normal value of 1.5%. Using the exponential decay model for regression analysis, the remaining service life was predicted to be 12 years, which was lower than the design life of 20 years. The comprehensive assessment took into account inverter interference, low voltage anomalies and accelerated decay, and the final diagnosis result was "moderate performance degradation, it is recommended to optimize inverter parameters and monitor closely". This detailed component-level monitoring report provides clear action guidance for operation and maintenance personnel, helping to take timely measures to extend component life and optimize system performance.

[0094] The above describes the photovoltaic power station component-level data monitoring method in the embodiment of the present application. The following describes the photovoltaic power station component-level data monitoring system in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of the photovoltaic power station component-level data monitoring system includes:

[0095] Construction module 201 is used to initialize clock synchronization and construct network topology for the photovoltaic power station component-level data monitoring system to obtain a target time reference and data acquisition network;

[0096] A configuration module 202 is used to perform parameter adaptive configuration on the photovoltaic module voltage acquisition module based on the target time reference and the data acquisition network to obtain module-level acquisition setting parameters;

[0097] A setting module 203 is used to perform multi-channel synchronous sampling on the photovoltaic module voltage signal according to the module-level acquisition setting parameters to obtain raw voltage data;

[0098] The encoding module 204 is used to perform local preprocessing and compression encoding on the original voltage data to obtain a compressed data packet;

[0099] The transmission module 205 is used to transmit and decode the compressed data packet through the data acquisition network to obtain a multi-channel synchronous data set;

[0100] The diagnosis module 206 is used to perform performance analysis and fault diagnosis on the multi-channel synchronous data set to obtain a component-level monitoring report.

[0101] Through the cooperation of the above components, the clock synchronization initialization and network topology construction of the photovoltaic power station component-level data monitoring system are carried out to obtain a high-precision target time reference and an efficient data acquisition network, which provides a unified time reference and a reliable data transmission channel for the entire monitoring process, and effectively solves the time consistency and network communication problems of multi-channel data acquisition in large-scale photovoltaic power stations. Secondly, the parameters of the photovoltaic component voltage acquisition module are adaptively configured based on the target time reference and the data acquisition network, and the component-level acquisition setting parameters obtained can be dynamically adjusted according to the actual working conditions, which improves the accuracy and flexibility of data acquisition. Furthermore, the photovoltaic component voltage signal is sampled synchronously in multiple channels according to the component-level acquisition setting parameters, and the obtained original voltage data ensures the accurate synchronization of data acquisition in each channel, which makes it possible to capture instantaneous voltage fluctuations and minor performance changes. The original voltage data is preprocessed and compressed locally, and the compressed data packet obtained greatly reduces the amount of data transmission and improves the network transmission efficiency. The compressed data packet is transmitted and decoded and restored through the data acquisition network, and the obtained multi-channel synchronous data set not only ensures the integrity and time correlation of the data, but also provides a standardized data format for subsequent analysis. Finally, the performance analysis and fault diagnosis of the multi-channel synchronized data set are performed, and the component-level monitoring report obtained fully reflects the operating status, performance change trend and potential failure risk of each photovoltaic component. This method realizes real-time and accurate monitoring of photovoltaic components through high-precision time synchronization, flexible parameter configuration, efficient data compression transmission and comprehensive data analysis, which helps to timely discover potential faults, optimize operation and maintenance strategies, improve the overall efficiency and reliability of photovoltaic power plants, and improve the efficiency of component-level data monitoring of photovoltaic power plants.

[0102] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A photovoltaic power station component-level data monitoring method, characterized in that: The photovoltaic power station component-level data monitoring method comprises: Perform clock synchronization initialization and network topology construction on the component-level data monitoring system of the photovoltaic power station to obtain the target time reference and data acquisition network; Based on the target time reference and the data acquisition network, the photovoltaic module voltage acquisition module is adaptively configured to obtain module-level acquisition setting parameters; According to the component-level acquisition setting parameters, multi-channel synchronous sampling is performed on the photovoltaic component voltage signal to obtain raw voltage data; Performing local preprocessing and compression encoding on the original voltage data to obtain a compressed data packet; The compressed data packets are transmitted and decoded and restored through the data acquisition network to obtain a multi-channel synchronous data set; Performance analysis and fault diagnosis are performed on the multi-channel synchronous data set to obtain a component-level monitoring report.

2. The photovoltaic power station component-level data monitoring method according to claim 1, characterized in that: The clock synchronization initialization and network topology construction of the photovoltaic power station component-level data monitoring system to obtain the target time reference and data acquisition network include: Starting the GPS module in the photovoltaic power station component-level data monitoring system to obtain GPS raw data, and parsing and processing the GPS raw data to obtain accurate time information; Performing time format conversion on the precise time information to obtain unified time format data, and performing time zone adjustment on the unified time format data to obtain localized time data; Generating a timestamp for the localized time data to obtain a system reference timestamp, and distributing the system reference timestamp to obtain a master station time reference; The master station time reference is transmitted over the network through the IEEE 1588 precision time protocol to obtain slave station synchronization data, and the time deviation of the slave station synchronization data is calculated to obtain a time correction parameter; Performing digital filtering on the time correction parameter to obtain a smooth correction value, and performing frequency adjustment on the smooth correction value through a phase-locked loop algorithm to obtain the target time reference; Initializing a bus in the photovoltaic power station component-level data monitoring system to obtain an initialized bus, and scanning slave control units through the initialized bus to obtain a slave control unit list; Associating the slave control unit list with component IDs to obtain a component mapping table, and performing network topology analysis on the component mapping table to obtain a network structure diagram; The communication path of the network structure diagram is optimized to obtain an optimized communication path, and communication parameters of the slave control unit are configured based on the optimized communication path to obtain the data acquisition network.

3. The photovoltaic power station component-level data monitoring method according to claim 1, characterized in that: The method of adaptively configuring the photovoltaic module voltage acquisition module based on the target time reference and the data acquisition network to obtain the module-level acquisition setting parameters includes: Performing statistical analysis on historical operating data of photovoltaic modules to obtain a historical voltage range, and evaluating current environmental conditions based on the historical voltage range to obtain an estimated voltage change range; Performing dynamic range calculation on the estimated voltage variation range to obtain an ADC reference voltage value, and configuring an analog-to-digital converter based on the ADC reference voltage value to obtain ADC configuration parameters; Analyzing the rate of change of the light intensity data to obtain light change characteristics, and dynamically adjusting the sampling frequency based on the light change characteristics to obtain sampling frequency parameters; Measuring the bandwidth of the data acquisition network to obtain available bandwidth data, and calculating data compression parameters based on the available bandwidth data to obtain compression encoding parameters; The ADC configuration parameters, the sampling frequency parameters and the compression coding parameters are integrated to obtain the component-level acquisition setting parameters, and the parameters are sent to the slave control unit through the data acquisition network to obtain a configuration completion signal.

4. The photovoltaic power station component-level data monitoring method according to claim 1, characterized in that: The method of performing multi-channel synchronous sampling on the photovoltaic module voltage signal according to the module-level acquisition setting parameters to obtain the original voltage data includes: Planning the sampling timing based on the target time reference to obtain a sampling trigger moment, and broadcasting the sampling trigger moment through the data acquisition network to obtain a synchronization trigger instruction; Receiving and parsing the synchronization trigger instruction to obtain local trigger parameters, and configuring the hardware timer based on the local trigger parameters to obtain timing trigger settings; Activating the ADC according to the timing trigger setting to obtain a sampling start signal, and performing analog-to-digital conversion on the photovoltaic module voltage based on the sampling start signal to obtain a digital voltage value; Oversampling the digital voltage value to obtain an oversampled data set, and performing noise suppression on the oversampled data set using a digital filtering algorithm to obtain filtered data; Performing decimation processing on the filtered data to obtain downsampled data, and performing linear calibration on the downsampled data to obtain a calibrated voltage value; The calibrated voltage value is converted into a quantized unit to obtain standard unit voltage data, and a current system timestamp is added to the standard unit voltage data to obtain the original voltage data.

5. The photovoltaic power station component-level data monitoring method according to claim 1, characterized in that: The locally preprocessing and compressing the raw voltage data to obtain a compressed data packet includes: Performing data redundancy analysis on the original voltage data to obtain redundant features, and performing differential encoding on the data based on the redundant features to obtain differentially encoded data; Performing variable-length coding processing on the differentially coded data by using a Huffman coding algorithm to obtain a variable-length coded data stream, and performing block processing on the variable-length coded data stream to obtain a data block sequence; Cyclic redundancy check encoding is performed on the data block sequence to obtain a data packet with a checksum, and a frame header is added to the data packet with the checksum to obtain the compressed data packet.

6. The photovoltaic power station component-level data monitoring method according to claim 1, characterized in that: The method of transmitting and decoding the compressed data packet through the data acquisition network to obtain a multi-channel synchronous data set includes: Performing time division multiplexing processing on the compressed data packets to obtain a transmission timing, and transmitting data through the data acquisition network based on the transmission timing to obtain data received by the master station; Performing a cyclic redundancy check on the data received by the master station to obtain a data integrity verification result, and screening valid data based on the data integrity verification result to obtain a valid data packet; Performing Huffman decoding on the valid data packet to obtain decoded data, and performing differential decoding on the decoded data to obtain an original voltage data frame; Extracting the timestamp in the original voltage data frame to obtain time information, and reordering the data based on the time information to obtain sorted data; The sorted data is subjected to multi-channel alignment processing to obtain the multi-channel synchronized data set.

7. The photovoltaic power station component-level data monitoring method according to claim 6, characterized in that: The performing of performance analysis and fault diagnosis on the multi-channel synchronous data set to obtain a component-level monitoring report includes: Performing time series analysis on the multi-channel synchronous data set to obtain voltage fluctuation characteristics, and performing frequency domain conversion on the voltage fluctuation characteristics by means of a fast Fourier transform algorithm to obtain spectrum distribution data; Performing peak detection on the frequency spectrum distribution data to obtain a characteristic frequency set, and performing fault feature matching on the characteristic frequency set through a pattern recognition algorithm to obtain a preliminary fault type; Comparatively analyzing adjacent channel data in the multi-channel synchronous data set to obtain inter-channel difference indicators, and performing cluster analysis based on the inter-channel difference indicators to obtain abnormal component groups; Performing trend analysis on historical data of the abnormal component group to obtain a performance decay curve, and performing life prediction on the performance decay curve through regression analysis to obtain a remaining life estimation value; The preliminary fault type, the abnormal component group and the estimated remaining life are comprehensively evaluated to obtain the component-level monitoring report.

8. A photovoltaic power station component-level data monitoring system, characterized in that: Used to execute the photovoltaic power station component-level data monitoring method according to any one of claims 1 to 7, the photovoltaic power station component-level data monitoring system comprises: A construction module is used to initialize the clock synchronization and construct the network topology of the photovoltaic power station component-level data monitoring system to obtain the target time reference and data acquisition network; A configuration module, used to perform parameter adaptive configuration on the photovoltaic module voltage acquisition module based on the target time reference and the data acquisition network to obtain module-level acquisition setting parameters; A setting module, used to perform multi-channel synchronous sampling of the photovoltaic module voltage signal according to the module-level acquisition setting parameters to obtain raw voltage data; An encoding module, used for locally preprocessing and compressing the original voltage data to obtain a compressed data packet; A transmission module, used for transmitting and decoding the compressed data packet through the data acquisition network to obtain a multi-channel synchronous data set; The diagnostic module is used to perform performance analysis and fault diagnosis on the multi-channel synchronous data set to obtain a component-level monitoring report.

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