Photovoltaic module parameter monitoring method and device based on multi-channel synchronous potential acquisition

Abstract

The invention relates to the technical field of data processing, and discloses a photovoltaic module parameter monitoring method and device based on multichannel synchronous potential acquisition. The method comprises the following steps: performing parameter configuration on a multi-channel voltage acquisition module in the photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition to obtain acquisition setting parameters; performing synchronous trigger mechanism setting on master and slave devices in the photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition to obtain a multi-channel acquisition trigger signal; performing synchronous sampling and local processing on the voltage signal of the photovoltaic module to obtain a standardized data frame with a timestamp; compressing and transmitting the standardized data frame with the timestamp to obtain a multi-channel synchronous data set; and performing multi-dimensional analysis on the multi-channel synchronous data set to obtain photovoltaic module performance diagnosis data. The photovoltaic module parameter monitoring efficiency and accuracy based on multi-channel synchronous potential acquisition are improved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a photovoltaic module parameter monitoring method and device based on multi-channel synchronous potential acquisition. 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, which makes it difficult to capture instantaneous voltage fluctuations and small performance changes. These methods usually use simple threshold judgments or statistical analysis, which cannot deeply explore the dynamic characteristics and potential faults of photovoltaic modules. At the same time, due to the continuous expansion of the scale of photovoltaic power stations, traditional centralized monitoring systems face tremendous pressure in data transmission and processing, making it difficult to achieve real-time, high-precision monitoring of large-scale photovoltaic modules.

[0003] However, existing monitoring technologies have some 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, while the data analysis method is relatively simple, making it difficult to comprehensively evaluate the health status of photovoltaic modules and predict their performance trends. Summary of the invention

[0004] The present application provides a photovoltaic module parameter monitoring method and device based on multi-channel synchronous potential acquisition, which are used to improve the efficiency and accuracy of photovoltaic module parameter monitoring based on multi-channel synchronous potential acquisition.

[0005] In the first aspect, the present application provides a photovoltaic component parameter monitoring method based on multi-channel synchronous potential acquisition, and the photovoltaic component parameter monitoring method based on multi-channel synchronous potential acquisition includes: performing clock synchronization initialization on the photovoltaic component parameter monitoring system based on multi-channel synchronous potential acquisition to obtain a system time base; based on the system time base, performing parameter configuration on the multi-channel voltage acquisition module in the photovoltaic component parameter monitoring system based on multi-channel synchronous potential acquisition to obtain acquisition setting parameters; according to the acquisition setting parameters, performing synchronous trigger mechanism setting on the master and slave devices in the photovoltaic component parameter monitoring system based on multi-channel synchronous potential acquisition to obtain a multi-channel acquisition trigger signal; through the multi-channel acquisition trigger signal, synchronously sampling and locally processing the photovoltaic component voltage signal to obtain a standardized data frame with a timestamp; compressing and transmitting the standardized data frame with a timestamp to obtain a multi-channel synchronous data set; performing multi-dimensional analysis on the multi-channel synchronous data set to obtain photovoltaic component performance diagnostic data.

[0006] In a second aspect, the present application provides a photovoltaic module parameter monitoring device based on multi-channel synchronous potential acquisition, and the photovoltaic module parameter monitoring device based on multi-channel synchronous potential acquisition includes:

[0007] An initialization module is used to perform clock synchronization initialization on a photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition to obtain a system time reference;

[0008] A configuration module, configured to perform parameter configuration on the multi-channel voltage acquisition module in the photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition based on the system time reference, so as to obtain acquisition setting parameters;

[0009] A setting module, used to set a synchronous trigger mechanism for the master and slave devices in the photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition according to the acquisition setting parameters, so as to obtain a multi-channel acquisition trigger signal;

[0010] A processing module, used to synchronously sample and locally process the voltage signal of the photovoltaic module through the multi-channel acquisition trigger signal to obtain a standardized data frame with a time stamp;

[0011] A transmission module, used for compressing and transmitting the standardized data frame with timestamp to obtain a multi-channel synchronous data set;

[0012] The analysis module is used to perform multi-dimensional analysis on the multi-channel synchronous data set to obtain photovoltaic module performance diagnostic data.

[0013] In the technical solution provided by the present application, by performing clock synchronization initialization on the photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition, a high-precision system time base is obtained, which provides a unified time reference for the entire monitoring process, effectively solves the time consistency problem of multi-channel data acquisition, and improves the accuracy of data analysis. Secondly, the multi-channel voltage acquisition module is parameterized based on the system time base, and the acquisition setting parameters obtained can be dynamically adjusted according to the actual working conditions, which enhances the adaptability and flexibility of the monitoring system. Furthermore, by setting the synchronous trigger mechanism for the master and slave devices, the multi-channel acquisition trigger signal obtained ensures the precise synchronization of the data acquisition of each channel, which provides the possibility of capturing instantaneous voltage fluctuations and minor performance changes. In addition, the photovoltaic module voltage signal is synchronously sampled and locally processed using the multi-channel acquisition trigger signal, and the obtained standardized data frame with a timestamp not only ensures the time correlation of the data, but also provides a standardized data format for subsequent analysis. By compressing and transmitting the standardized data frame, the obtained multi-channel synchronous data set greatly improves the data transmission efficiency and reduces the network burden. Finally, the multi-channel synchronous data set is analyzed in multiple dimensions, and the obtained photovoltaic module performance diagnosis data fully reflects the health status of the component, providing rich information for fault diagnosis and performance prediction. This method achieves real-time and accurate monitoring of PV modules through high-precision time synchronization, flexible parameter configuration, efficient data transmission and comprehensive data analysis, which helps to timely detect potential faults, optimize operation and maintenance strategies, and improve the overall efficiency and reliability of PV 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 A schematic diagram of an embodiment of a photovoltaic module parameter monitoring method based on multi-channel synchronous potential acquisition in an embodiment of the present application;

[0016] Figure 2 Schematic diagram of an embodiment of a photovoltaic module parameter monitoring device based on multi-channel synchronous potential acquisition in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The embodiment of the present application provides a method and device for monitoring photovoltaic module parameters based on multi-channel synchronous potential acquisition. 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 here can be implemented in an order other than that illustrated or described here. 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 In the embodiment of the present application, an embodiment of a photovoltaic module parameter monitoring method based on multi-channel synchronous potential acquisition includes:

[0019] Step S101, performing clock synchronization initialization on a photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition to obtain a system time reference;

[0020] Step S102: Based on the system time reference, parameter configuration is performed on a multi-channel voltage acquisition module in a photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition to obtain acquisition setting parameters;

[0021] Step S103, according to the acquisition setting parameters, a synchronous trigger mechanism is set for the master and slave devices in the photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition to obtain a multi-channel acquisition trigger signal;

[0022] Step S104, collecting trigger signals through multiple channels, synchronously sampling and locally processing the voltage signals of the photovoltaic modules, and obtaining standardized data frames with timestamps;

[0023] Step S105, compressing and transmitting the standardized data frame with the timestamp to obtain a multi-channel synchronous data set;

[0024] Step S106: Perform multi-dimensional analysis on the multi-channel synchronous data set to obtain photovoltaic module performance diagnostic data.

[0025] It is understandable that the execution subject of the present application can be a photovoltaic module parameter monitoring device based on multi-channel synchronous potential acquisition, or a terminal or a server, which is not limited here. The present application embodiment is described by taking the server as the execution subject as an example.

[0026] Specifically, the monitoring system is initialized for clock synchronization to obtain the system time reference. This process 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 network, the time deviation is calculated and digital filtering is performed, and finally the frequency is adjusted by the phase-locked loop algorithm to ensure the high consistency of the system time. Then, the multi-channel voltage acquisition module is parameterized based on the system time reference. The time resolution of the system time reference is analyzed, the initial sampling frequency value is calculated and the Nyquist theorem is verified to obtain the effective sampling frequency range. The signal-to-noise ratio of the sampling frequency candidate set is evaluated and sorted, and the optimal sampling frequency is selected. The quantization bit number of the analog-to-digital converter is calculated according to the optimal sampling frequency, and the dynamic range is adjusted to obtain the gain control parameter. Finally, the gain control parameter and the optimal sampling frequency are combined to form the acquisition setting parameter.

[0027] According to the acquisition setting parameters, the synchronous trigger mechanism is set for the master and slave devices. The acquisition setting parameters are parsed to obtain the sampling timing requirements and configure the master station trigger strategy. The master station trigger configuration data is encoded into a trigger instruction data packet and transmitted to the slave station through the POWERBUS bus broadcast. After receiving the data, the slave station performs decoding verification, configures the hardware timer, compensates for the clock deviation, sets the timing interrupt, and finally generates a multi-channel acquisition trigger signal. Through the multi-channel acquisition trigger signal, the PV module voltage signal is synchronously sampled and locally processed. In response to the acquisition trigger signal, the analog-to-digital conversion is started to obtain the original digital quantity. The original digital quantity is oversampled and the noise is suppressed by the digital filtering algorithm. The filtered data is extracted and linearly calibrated to obtain the calibrated voltage value. Quantization unit conversion is performed, the current system timestamp is added, and finally it is encapsulated into a standardized data frame with a timestamp.

[0028] The standardized data frames with timestamps are compressed and transmitted. First, data redundancy analysis is performed, and differential encoding is performed based on the redundant features. The differentially encoded data is processed by Huffman coding to obtain a variable-length encoded data stream and divided into blocks. The data block sequence is cyclically redundancy checked and encoded, and transmitted to the master station through the POWERBUS bus. After receiving the data, the master station performs checksum inspection, decodes and restores the original data frame, and finally sorts and aligns it by timestamp to form a multi-channel synchronous data set. Finally, a multi-dimensional analysis is performed on the multi-channel synchronous data set. The voltage fluctuation characteristics are obtained by time series analysis, and the spectrum distribution data is obtained by Fourier transform. Peak detection is performed on the spectrum distribution data to obtain the characteristic frequency set, and fault feature matching is performed through the pattern recognition algorithm. Compare the data of adjacent channels to obtain the difference index between channels, and perform cluster analysis to identify abnormal component groups. Perform trend analysis on the historical data of the abnormal component group to obtain the performance attenuation curve, and predict the remaining life through regression analysis. Comprehensively evaluate the preliminary fault type, abnormal component group and remaining life estimation value to obtain the performance diagnosis data of photovoltaic components.

[0029] For example, a photovoltaic power station installed 1,000 photovoltaic modules, each equipped with a voltage acquisition module. After the clock synchronization is initialized, the system time base accuracy reaches ±50ns. During the acquisition parameter configuration process, the initial sampling frequency is set to 100kHz. After Nyquist theorem verification and signal-to-noise ratio evaluation, the optimal sampling frequency is finally determined to be 50kHz and the quantization accuracy is 16 bits. After the synchronous trigger mechanism is set, the trigger time error of each acquisition module is controlled within ±100ns. In one acquisition cycle, each module collects 100ms of voltage data and generates 5,000 sampling points. After local processing, a standardized data frame with a timestamp is formed, and each frame size is about 10KB. During data compression and transmission, the data volume is compressed to about 40% of the original size through differential coding and Huffman coding. Finally, the multi-dimensional analysis results show that 5 modules have potential hot spot effects, and the performance decay rate of 10 modules is higher than the average level, and the predicted remaining service life is shortened by 20%. These detailed diagnostic data provide power station operation and maintenance personnel with accurate decision-making basis, effectively improving the overall performance and reliability of photovoltaic power stations.

[0030] In the embodiment of the present application, by performing clock synchronization initialization on the photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition, a high-precision system time base is obtained, which provides a unified time reference for the entire monitoring process, effectively solves the time consistency problem of multi-channel data acquisition, and improves the accuracy of data analysis. Secondly, the multi-channel voltage acquisition module is parameterized based on the system time base, and the acquisition setting parameters obtained can be dynamically adjusted according to the actual working conditions, which enhances the adaptability and flexibility of the monitoring system. Furthermore, by setting the synchronous trigger mechanism for the master and slave devices, the multi-channel acquisition trigger signal obtained ensures the precise synchronization of the data acquisition of each channel, which provides the possibility of capturing instantaneous voltage fluctuations and minor performance changes. In addition, the photovoltaic module voltage signal is synchronously sampled and locally processed using the multi-channel acquisition trigger signal, and the obtained standardized data frame with a timestamp not only ensures the time correlation of the data, but also provides a standardized data format for subsequent analysis. By compressing and transmitting the standardized data frame, the obtained multi-channel synchronous data set greatly improves the data transmission efficiency and reduces the network burden. Finally, the multi-channel synchronous data set is analyzed in multiple dimensions, and the obtained photovoltaic module performance diagnosis data fully reflects the health status of the component, providing rich information for fault diagnosis and performance prediction. This method achieves real-time and accurate monitoring of PV modules through high-precision time synchronization, flexible parameter configuration, efficient data transmission and comprehensive data analysis, which helps to timely detect potential faults, optimize operation and maintenance strategies, and improve the overall efficiency and reliability of PV power plants.

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

[0032] (1) Starting the GPS module in the photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition to obtain GPS raw data, and parsing and processing 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) Transmitting the master station time reference over the network to obtain slave station synchronization data, and calculating the time deviation of the slave station synchronization data to obtain a time correction parameter;

[0036] (5) The time correction parameter is digitally filtered to obtain a smooth correction value, and the frequency of the smooth correction value is adjusted through a phase-locked loop algorithm to obtain a system time reference.

[0037] Specifically, the GPS module is first started 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. Next, 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. Then, the localized time data is timestamped to create a system reference timestamp. The timestamp usually contains year, month, day, hour, minute, second, and millisecond or microsecond accuracy information. The generated system reference timestamp is then distributed to the master station device of the system. After receiving the timestamp, the master station device uses it as the master station time reference for subsequent time synchronization operations.

[0038] The master time reference is transmitted to each slave device in the system through the network. Special time synchronization protocols such as Precision Time Protocol (PTP) or Network Time Protocol (NTP) are used during network transmission to minimize the impact of network delay on time synchronization. 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 local clock of the slave device and the master time reference. Finally, the calculated time correction parameter is digitally filtered. The purpose of digital filtering is to reduce the impact of transient noise and interference and obtain a smoother and more stable correction value. Common filtering methods include moving average filtering and Kalman filtering. 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 time reference). This process not only adjusts the time deviation, but also corrects the frequency deviation, and finally obtains a highly accurate system time reference.

[0039] For example, suppose a monitoring system with 100 photovoltaic modules is undergoing clock synchronization initialization. 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 8th 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, the master station transmits the time reference to all slave stations through the network. 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. Finally, the PLL algorithm adjusts the local clock frequency based on this correction value, gradually reducing the deviation to within 50 microseconds.

[0040] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0041] (1) Analyze the time resolution of the system time base to obtain the minimum time unit, and calculate the sampling frequency based on the minimum time unit to obtain the initial sampling frequency value;

[0042] (2) The initial sampling frequency value is verified by Nyquist theorem to obtain the effective sampling frequency range, and the effective sampling frequency range is segmented to obtain a sampling frequency candidate set;

[0043] (3) performing signal-to-noise ratio evaluation on each frequency in the sampling frequency candidate set to obtain a signal-to-noise ratio data set, and sorting the signal-to-noise ratio data set to obtain the optimal sampling frequency;

[0044] (4) calculating the number of quantization bits of the analog-to-digital converter based on the optimal sampling frequency to obtain a quantization precision parameter, and performing dynamic range adjustment on the quantization precision parameter to obtain a gain control parameter;

[0045] (5) The gain control parameter and the optimal sampling frequency are combined to obtain the acquisition setting parameters.

[0046] Specifically, the first step is to perform a time resolution analysis on the system time base. The time resolution analysis aims to determine the smallest time unit that the system can recognize, usually in nanoseconds or picoseconds. This minimum time unit directly affects the upper limit of the sampling frequency. Based on the minimum time unit, the initial sampling frequency value is calculated using a reciprocal relationship. For example, if the minimum time unit is 10 nanoseconds, the initial sampling frequency value is 100MHz. Subsequently, the initial sampling frequency value is verified by the Nyquist theorem. The Nyquist theorem states that the sampling frequency should be at least twice the highest frequency of the signal to avoid aliasing effects. By analyzing the spectral characteristics of the PV module voltage signal, the highest frequency component of the signal is determined, and then the effective sampling frequency range is obtained. For example, if the highest frequency of the PV module voltage signal is 50kHz, the effective sampling frequency range should not be less than 100kHz. This effective frequency range is divided into segments, usually using a logarithmic distribution, to obtain a sampling frequency candidate set. This candidate set contains multiple possible sampling frequency values, providing a basis for subsequent optimization selection.

[0047] Next, the signal-to-noise ratio is evaluated for each frequency in the sampling frequency candidate set. The signal-to-noise ratio (SNR) is an important indicator of signal quality and is calculated as the logarithm of the ratio of signal power to noise power. For each candidate sampling frequency, the corresponding signal-to-noise ratio is calculated through simulated sampling or actual testing to form a signal-to-noise ratio data set. This data set is then sorted, usually in descending order, so that the sampling frequency with the highest signal-to-noise ratio can be selected as the optimal sampling frequency. Based on the optimal sampling frequency, the number of quantization bits of the analog-to-digital converter is calculated to obtain the quantization accuracy parameter. The number of quantization bits is related to the dynamic range of the signal and the required signal-to-noise ratio. The quantization accuracy parameter is dynamically adjusted to obtain the gain control parameter. The dynamic range adjustment takes into account the amplitude variation range of the actual signal to ensure that the input range of the ADC matches the signal amplitude, thereby maximizing the effective number of bits. The gain control parameter is usually expressed as a multiplier or attenuation factor, which is used to adjust the signal amplitude to adapt to the input range of the ADC. Finally, the gain control parameter is combined with the optimal sampling frequency to form a complete acquisition setting parameter. These parameters include sampling frequency, ADC resolution, gain setting, etc., which provide specific configuration guidance for the subsequent data acquisition process.

[0048] For example, assuming that the time base resolution of a photovoltaic module monitoring system is 1 nanosecond, the initial sampling frequency obtained by preliminary calculation is 1GHz. Through spectrum analysis, it is found that the effective frequency components of the photovoltaic module voltage signal are mainly concentrated below 100kHz. Applying the Nyquist theorem, the effective sampling frequency range is determined to be 200kHz to 1GHz. This range is logarithmically segmented, and the sampling frequency candidate set is obtained as {200kHz, 500kHz, 1MHz, 2MHz, 5MHz, 10MHz}. The signal-to-noise ratio of each candidate frequency is evaluated, and the SNR data set obtained is {80dB, 85dB, 88dB, 90dB, 89dB, 87dB}. After sorting, 2MHz is selected as the optimal sampling frequency. Based on the SNR requirement of 90dB, the required ADC bit number is calculated to be 15 bits. Considering that the typical range of the photovoltaic module voltage signal is 0-60V, and the full-scale range of the 15-bit ADC is 5V, the gain control parameter is calculated to be 1 / 12. Finally, the acquisition setting parameters were determined as follows: sampling frequency 2MHz, ADC resolution 15 bits, and input gain 1 / 12.

[0049] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0050] (1) Analyze the acquisition setting parameters to obtain the sampling timing requirements, and configure the trigger strategy of the master station device based on the sampling timing requirements to obtain the master station trigger configuration data;

[0051] (2) Encoding the trigger configuration data of the master station to obtain a trigger instruction data packet, and broadcasting the trigger instruction data packet through the POWERBUS bus to obtain the slave station receiving data;

[0052] (3) decoding and verifying the data received from the slave station to obtain the slave station trigger parameters, and configuring the hardware timer of the slave station device based on the slave station trigger parameters to obtain the timing trigger settings;

[0053] (4) performing clock deviation compensation on the timing trigger setting to obtain the compensated trigger moment, and performing timing interrupt setting on the compensated trigger moment to obtain an interrupt trigger signal;

[0054] (5) The interrupt trigger signal is distributed synchronously across multiple channels to obtain a multi-channel acquisition trigger signal.

[0055] Specifically, the acquisition setting parameters are parsed to obtain the sampling timing requirements. This process includes extracting key information such as sampling frequency, sampling period, and number of sampling points. Based on these sampling timing requirements, the trigger strategy is configured for the master station device. The trigger strategy configuration includes setting parameters such as trigger period, trigger delay, trigger pulse width, and finally forming the master station trigger configuration data. The master station trigger configuration data is encoded and processed to generate a trigger instruction data packet. The encoding process usually adopts a specific protocol format, such as packaging information such as trigger time, trigger interval, sampling duration, etc. according to a predefined field order and length. The encoded trigger instruction data packet is broadcasted through the POWERBUS bus. The POWERBUS bus is a low-voltage DC carrier power supply bus with the characteristics of sharing a bus for signal and power supply, which can effectively simplify system wiring. Broadcast transmission ensures that all slave devices can receive the trigger instruction at the same time.

[0056] After receiving the broadcast data, the slave device first decodes and verifies it. The decoding process is the reverse process of encoding, which restores the received data packet to understandable trigger parameters. The verification steps include checking data integrity, checksum verification, etc. to ensure that the received data is not damaged. After decoding and verification, the slave trigger parameters are obtained. Based on these parameters, the hardware timer of the slave device is configured and the timing trigger is set. The hardware timer configuration includes setting the initial count value, interrupt trigger conditions, etc. to achieve accurate timing trigger function. Considering the clock deviation that may exist between different slave devices, it is necessary to compensate for the clock deviation of the timing trigger setting. The compensation process usually calculates the clock deviation of each slave based on the previous clock synchronization results, and applies this deviation value to the calculation of the trigger time. The compensated trigger time is more accurate and can ensure the synchronization of multiple slave devices in global time. The timing interrupt is set for the compensated trigger time to generate an interrupt trigger signal. The timing interrupt is a precise hardware trigger mechanism that can trigger the processor to perform specific operations at a preset time.

[0057] Finally, the interrupt trigger signal is distributed to multiple channels synchronously. The synchronous distribution process ensures that all acquisition channels start data acquisition at the same time. This is usually achieved through hardware circuits, such as using a multiplexer or a dedicated synchronous trigger circuit to distribute a single interrupt trigger signal to multiple acquisition channels at the same time, thereby obtaining a multi-channel acquisition trigger signal.

[0058] For example, suppose a photovoltaic module monitoring system contains 100 acquisition channels, the sampling frequency is set to 10kHz, and each acquisition lasts for 1 second. After parsing the acquisition setting parameters, the sampling timing requirements are obtained: the sampling period is 100μs, and 10,000 sampling points are collected each time. Based on these requirements, the master station trigger strategy is configured: trigger once every 60 seconds, and the trigger pulse width is 1μs. These configuration data are encoded into a 32-byte trigger instruction data packet and broadcasted at a rate of 9600bps through the POWERBUS bus. After receiving the data packet, the slave device decodes the trigger parameters and configures the initial value of the hardware timer to 600000000 (assuming the timer clock is 10MHz). Considering that the clock deviation of a certain slave station is +5μs / s, the trigger time after 60 seconds is compensated, and the actual timer value set is 599999700. When the timer counts to the set value, an interrupt trigger signal is generated and distributed to all acquisition channels simultaneously through 100 parallel output circuits to achieve accurate multi-channel synchronous acquisition.

[0059] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0060] (1) responding to a multi-channel acquisition trigger signal to obtain a sampling start instruction, and performing analog-to-digital conversion on the photovoltaic module voltage signal based on the sampling start instruction to obtain an original digital quantity;

[0061] (2) oversampling the original digital quantity to obtain an oversampled data set, and performing noise suppression on the oversampled data set through a digital filtering algorithm to obtain filtered data;

[0062] (3) extracting the filtered data to obtain downsampled data, and linearly calibrating the downsampled data to obtain a calibrated voltage value;

[0063] (4) performing quantitative unit conversion on the calibrated voltage value to obtain standard unit voltage data, and adding the current system timestamp to the standard unit voltage data to obtain time-correlated voltage data;

[0064] (5) Encapsulate the time-correlated voltage data into data frames to obtain standardized data frames with timestamps.

[0065] Specifically, when the acquisition module receives the trigger signal, it immediately generates a sampling start instruction and activates the analog-to-digital converter (ADC). The ADC then samples and quantizes the voltage signal of the photovoltaic module, converting the continuous analog voltage signal into a discrete digital quantity. In this process, the resolution and sampling rate of the ADC directly affect the accuracy and time resolution of the original digital quantity. After obtaining the original digital quantity, the acquisition module performs oversampling processing. Oversampling is a technology that improves signal resolution by sampling the signal at a rate higher than the Nyquist frequency to obtain redundant data points. For example, if the final required sampling rate is 10kHz, oversampling may be performed at a rate of 100kHz, and the resulting oversampled data set contains 10 times more data points than actually required. These additional data points provide more information for subsequent digital processing.

[0066] Next, the oversampled data set is digitally filtered to suppress noise. Common digital filtering algorithms include finite impulse response (FIR) filters and infinite impulse response (IIR) filters. Taking the FIR filter as an example, its basic principle is to perform a weighted average on a series of input samples. The filtering process can be expressed as a convolution operation, in which the input signal is convolved with the impulse response of the filter. The filtered data retains the useful components of the signal while significantly reducing the impact of high-frequency noise. The filtered data needs to be decimated, that is, 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. For example, if the oversampling rate is 10 times the target sampling rate, 1 of every 10 samples is selected. 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 a known reference voltage point, and then corrects the collected data.

[0067] The calibrated voltage value needs to be converted to a quantized unit, converting the original code value of the ADC into a standard voltage unit (usually volts). This process involves the calculation of the ADC reference voltage and resolution. For example, for a 12-bit ADC, the full scale is 5V, and the voltage represented by each code value is 5V / 4096. Multiply the code value by this coefficient to get the actual voltage value. The converted standard unit voltage data is added with the current system timestamp to form time-correlated voltage data. The accuracy of the timestamp usually matches the synchronization accuracy of the system and can be accurate to microseconds or nanoseconds. Finally, the time-correlated voltage data is encapsulated in a data frame. The data frame usually contains fields such as frame header, data length, channel identifier, timestamp, voltage data, checksum, etc. The encapsulated data frame becomes a standardized data format, which is convenient for subsequent transmission and processing.

[0068] For example, suppose that a certain acquisition channel in a photovoltaic module monitoring system receives a trigger signal and starts sampling the photovoltaic module voltage. The ADC resolution is 16 bits, the reference voltage is 3.3V, and the target sampling rate is 1kHz. Oversampling is performed at a rate of 10kHz, and 10 sample points are collected every millisecond. The original digital quantity may be a series of ADC code values ​​such as {32768, 32770, 32765, 32772, 32769, 32767, 32771, 32768, 32766, 32770}. Apply a 50-order FIR low-pass filter to these data for noise suppression, and the filtered data may become {32768, 32769, 32768, 32769, 32768, 32768, 32769, 32768, 32768, 32769}. Then perform a 10:1 decimation to obtain the downsampled data 32768. Assuming the linear calibration function is y=1.0002x+0.5, the ADC code value after calibration is 32775. Convert it to a voltage value: (32775 / 65536)×3.3V=1.6501V. After adding the timestamp "2023-12-15 10:30:00.000123", it is finally encapsulated into a data frame: {frame header, length, channel ID, 2023-12-15 10:30:00.000123, 1.6501, checksum}.

[0069] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0070] (1) performing data redundancy analysis on the standardized data frame with timestamp to obtain redundant features, and performing differential encoding on the data based on the redundant features to obtain differentially encoded data;

[0071] (2) performing Huffman coding on the differentially coded data 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;

[0072] (3) Perform cyclic redundancy check encoding on the data block sequence to obtain a data packet with a checksum, and transmit the data packet with the checksum through the POWERBUS bus to obtain data received by the master station;

[0073] (4) Verify and check the data received by the master station to obtain a valid data packet, and decode and restore the valid data packet to obtain the original data frame;

[0074] (5) Sort and align the original data frames by timestamps to obtain a multi-channel synchronized data set.

[0075] Specifically, data redundancy analysis is performed on standardized data frames with timestamps to identify repetitive patterns and redundant information in the data. Redundant features include continuous identical values, slowly changing trends, or periodic fluctuations. 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, effectively reducing the absolute value range of the data, thereby improving the efficiency of subsequent compression. The differentially encoded data is further processed by Huffman coding to obtain a variable-length coded data stream. 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 variable-length coded data stream is then processed in blocks, which divides the continuous data stream into fixed-size or variable-size data blocks. Block processing is conducive to data transmission and error recovery, and each data block can be processed and retransmitted independently. Cyclic redundancy check (CRC) encoding is performed on the block-by-block data block sequence, and a checksum is generated and attached to the data packet. CRC is an error detection coding technology that calculates the check bit through polynomial division and can effectively detect data errors during transmission. Data packets with checksums are transmitted through the POWERBUS bus. POWERBUS is a low-voltage DC carrier power supply bus that can transmit power and data signals at the same time, simplifying system wiring. During transmission, data packets may have errors due to electromagnetic interference or signal attenuation.

[0076] After receiving the data, the master station first performs a checksum check. By recalculating the CRC value of the received data and comparing it with the received checksum, it can be determined whether an error occurred during the data transmission process. Only the data packets that pass the check are considered valid data packets, and the erroneous data packets are discarded or requested to be retransmitted. The valid data packets are decoded and restored. This process includes two steps: Huffman decoding and differential decoding. Huffman decoding reconverts the variable-length code into a fixed-length value, and differential decoding restores the difference data to the original absolute value sequence. Finally, the restored original data frames are sorted and aligned according to the timestamp. Since multi-channel data may be out of order due to transmission delays or processing time differences, it is necessary to reorder according to the timestamp information in each data frame. The sorted data is time-aligned to ensure that the data points of different channels correspond accurately in time, forming a multi-channel synchronous data set. This synchronous data set provides the basis for subsequent data analysis and fault diagnosis.

[0077] For example, suppose a photovoltaic array contains 100 components, each of which collects 10 voltage data points per second. Each data point contains a 16-bit voltage value and a 32-bit timestamp, and the original data frame size is 48 bits. After processing data for 1 minute, the total data volume is 100×10×60×48=2,880,000 bits. Data redundancy analysis found that the voltage change between adjacent data points usually does not exceed 0.1V, corresponding to about 328 code values ​​in a 16-bit ADC. Differential encoding converts the 16-bit voltage value into a 9-bit difference value (range -256 to 255). Huffman encoding further encodes frequently occurring small differences (such as -1, 0, 1) into 1-2 bits and large differences into 8-9 bits. The average length of each difference code is reduced to 5 bits. The timestamp uses relative time encoding, which only records the offset from the reference time, and is represented by 12 bits on average. After encoding, the average length of each data point is reduced to 17 bits. The data is divided into blocks of 100 points, and a 16-bit CRC checksum is added to each block. The final amount of data transmitted is about 100×10×60×17 / 100×116=1,183,200 bits, with a compression rate of 41%. After receiving the data, the master station first performs a CRC check. If it finds that 5 data blocks fail the check, it requires retransmission. The data that passes the check is decoded and restored to obtain the original voltage value and timestamp. Finally, the data of 100 channels are sorted and aligned by timestamp to form a multi-channel synchronous data set with 60,000 time points, each of which contains 100 channel voltage values.

[0078] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0079] (1) Perform time series analysis on multi-channel synchronous data sets to obtain voltage fluctuation characteristics, and perform Fourier transform based on the voltage fluctuation characteristics to obtain spectrum distribution data;

[0080] (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;

[0081] (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;

[0082] (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;

[0083] (5) Comprehensively evaluate the preliminary fault types, abnormal component groups, and remaining life estimates to obtain PV module performance diagnostic data.

[0084] Specifically, a time series analysis is performed on a multi-channel synchronous data set to obtain voltage fluctuation characteristics. This process involves calculating statistical characteristics of voltage data, such as mean, standard deviation, skewness and kurtosis, as well as time domain characteristics, such as rise time, fall time and steady-state error. Based on these voltage fluctuation characteristics, a Fourier transform is performed to convert the time domain signal into a frequency domain representation to obtain spectrum distribution data. The Fourier transform reveals the various frequency components and their amplitudes contained in the signal, which helps to identify periodic changes and abnormal fluctuations. Next, the spectrum distribution data is peak detected 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.

[0085] At the same time, the adjacent channel data in the multi-channel synchronous data set are compared and analyzed to calculate the difference index between channels. The difference index can include voltage difference, power difference, correlation coefficient, etc. Based on these difference indicators, cluster analysis is performed 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 are further analyzed to obtain the performance attenuation curve. The performance attenuation curve reflects the trend of component output capacity over time. 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.

[0086] Finally, the preliminary fault types, abnormal component groups and remaining life estimates are comprehensively evaluated to obtain comprehensive PV module performance diagnostic data. The comprehensive 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 diagnostic results include not only the current performance status, but also the potential failure risks and expected life, providing comprehensive support for operation and maintenance decisions.

[0087] For example, suppose an array of 1,000 PV modules is subjected to multi-channel synchronous potential acquisition. Time series analysis of the voltage data of one of the modules shows that its standard deviation is 0.5V, which is significantly higher than the 0.2V of a normal module. After Fourier transformation, significant peaks appear at 50Hz and 100Hz, with amplitudes of 0.3V and 0.2V, respectively. Peak detection incorporates these two frequencies into the characteristic frequency set. The pattern recognition algorithm matches this feature with the fault library and preliminarily determines it as inverter ripple interference. Comparative analysis shows that the voltage difference between this module and adjacent modules is 2.5V on average, exceeding the normal threshold of 1V. Cluster analysis classifies it into the "high voltage difference" group, which contains 15 modules in total. Analysis of the historical data of this group shows that its output power has dropped by 5% in the past year. Regression analysis using an exponential decay model predicts a remaining life of 8 years. Comprehensive evaluation takes into account inverter interference, abnormal voltage difference, and accelerated decay, and the final diagnosis result is "moderate performance degradation, it is recommended to optimize inverter parameters and monitor closely".

[0088] The photovoltaic module parameter monitoring method based on multi-channel synchronous potential acquisition in the embodiment of the present application is described above. The photovoltaic module parameter monitoring device based on multi-channel synchronous potential acquisition in the embodiment of the present application is described below. Figure 2 In the embodiment of the present application, an embodiment of a photovoltaic module parameter monitoring device based on multi-channel synchronous potential acquisition includes:

[0089] Initialization module 201, used to perform clock synchronization initialization on the photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition to obtain a system time reference;

[0090] Configuration module 202, configured to perform parameter configuration on the multi-channel voltage acquisition module in the photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition based on the system time reference, and obtain acquisition setting parameters;

[0091] A setting module 203 is used to set a synchronous trigger mechanism for the master and slave devices in the photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition according to the acquisition setting parameters to obtain a multi-channel acquisition trigger signal;

[0092] The processing module 204 is used to synchronously sample and locally process the photovoltaic module voltage signal through the multi-channel acquisition trigger signal to obtain a standardized data frame with a time stamp;

[0093] The transmission module 205 is used to compress and transmit the standardized data frame with the timestamp to obtain a multi-channel synchronous data set;

[0094] The analysis module 206 is used to perform multi-dimensional analysis on the multi-channel synchronous data set to obtain photovoltaic module performance diagnostic data.

[0095] Through the cooperation of the above components, the clock synchronization initialization of the photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition is performed to obtain a high-precision system time base, which provides a unified time reference for the entire monitoring process, effectively solves the time consistency problem of multi-channel data acquisition, and improves the accuracy of data analysis. Secondly, the multi-channel voltage acquisition module is configured based on the system time base, and the acquisition setting parameters obtained can be dynamically adjusted according to the actual working conditions, which enhances the adaptability and flexibility of the monitoring system. Furthermore, by setting the synchronous trigger mechanism for the master and slave devices, the multi-channel acquisition trigger signal obtained ensures the precise synchronization of data acquisition in each channel, which provides the possibility of capturing instantaneous voltage fluctuations and minor performance changes. In addition, the multi-channel acquisition trigger signal is used to synchronously sample and locally process the photovoltaic module voltage signal, and the obtained standardized data frame with timestamp not only ensures the time correlation of the data, but also provides a standardized data format for subsequent analysis. By compressing and transmitting the standardized data frame, the obtained multi-channel synchronous data set greatly improves the data transmission efficiency and reduces the network burden. Finally, the multi-dimensional analysis of the multi-channel synchronous data set is performed, and the obtained photovoltaic module performance diagnosis data fully reflects the health status of the module, providing rich information for fault diagnosis and performance prediction. This method achieves real-time and accurate monitoring of PV modules through high-precision time synchronization, flexible parameter configuration, efficient data transmission and comprehensive data analysis, which helps to timely detect potential faults, optimize operation and maintenance strategies, and improve the overall efficiency and reliability of PV power plants.

[0096] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. 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 module parameter monitoring method based on multi-channel synchronous potential acquisition, characterized in that: The photovoltaic module parameter monitoring method based on multi-channel synchronous potential acquisition includes: Perform clock synchronization initialization on the photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition to obtain the system time reference; Based on the system time reference, parameter configuration is performed on the multi-channel voltage acquisition module in the photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition to obtain acquisition setting parameters; According to the acquisition setting parameters, a synchronous trigger mechanism is set for the master and slave devices in the photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition to obtain a multi-channel acquisition trigger signal; The photovoltaic module voltage signal is synchronously sampled and locally processed through the multi-channel acquisition trigger signal to obtain a standardized data frame with a time stamp; Compressing and transmitting the standardized data frame with the timestamp to obtain a multi-channel synchronous data set; The multi-channel synchronous data set is subjected to a multi-dimensional analysis to obtain photovoltaic module performance diagnostic data.

2. The photovoltaic module parameter monitoring method based on multi-channel synchronous potential acquisition according to claim 1 is characterized in that: The clock synchronization initialization of the photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition to obtain the system time reference includes: Starting the GPS module in the photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition 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; Transmitting the master station time reference via a network to obtain slave station synchronization data, and performing time deviation calculation on the slave station synchronization data to obtain a time correction parameter; The time correction parameter is digitally filtered to obtain a smooth correction value, and the smooth correction value is frequency adjusted through a phase-locked loop algorithm to obtain the system time reference.

3. The photovoltaic module parameter monitoring method based on multi-channel synchronous potential acquisition according to claim 1 is characterized in that: The multi-channel voltage acquisition module in the photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition is configured based on the system time reference to obtain acquisition setting parameters, including: Performing time resolution analysis on the system time reference to obtain a minimum time unit, and calculating a sampling frequency based on the minimum time unit to obtain an initial sampling frequency value; Performing Nyquist theorem verification on the initial sampling frequency value to obtain a valid sampling frequency range, and dividing the valid sampling frequency range into sections to obtain a sampling frequency candidate set; Performing signal-to-noise ratio evaluation on each frequency in the sampling frequency candidate set to obtain a signal-to-noise ratio data set, and sorting the signal-to-noise ratio data set to obtain an optimal sampling frequency; Calculating the number of quantization bits of the analog-to-digital converter based on the optimal sampling frequency to obtain a quantization precision parameter, and performing dynamic range adjustment on the quantization precision parameter to obtain a gain control parameter; The gain control parameter and the optimal sampling frequency are combined to obtain the acquisition setting parameter.

4. The photovoltaic module parameter monitoring method based on multi-channel synchronous potential acquisition according to claim 1 is characterized in that: The step of setting a synchronous trigger mechanism for the master and slave devices in the photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition according to the acquisition setting parameters to obtain a multi-channel acquisition trigger signal includes: Parsing the acquisition setting parameters to obtain sampling timing requirements, and configuring the trigger strategy for the master station device based on the sampling timing requirements to obtain master station trigger configuration data; Encoding the master station trigger configuration data to obtain a trigger instruction data packet, and broadcasting the trigger instruction data packet through the POWERBUS bus to obtain slave station received data; Decoding and verifying the data received by the slave station to obtain a slave station trigger parameter, and configuring a hardware timer of the slave station device based on the slave station trigger parameter to obtain a timing trigger setting; Performing clock deviation compensation on the timing trigger setting to obtain a compensated trigger moment, and performing a timing interrupt setting on the compensated trigger moment to obtain an interrupt trigger signal; The interrupt trigger signal is distributed synchronously in multiple channels to obtain the multi-channel acquisition trigger signal.

5. The photovoltaic module parameter monitoring method based on multi-channel synchronous potential acquisition according to claim 1 is characterized in that: The multi-channel acquisition trigger signal is used to synchronously sample and locally process the photovoltaic module voltage signal to obtain a standardized data frame with a timestamp, including: Responding to the multi-channel acquisition trigger signal to obtain a sampling start instruction, and performing analog-to-digital conversion on the photovoltaic component voltage signal based on the sampling start instruction to obtain an original digital quantity; Oversampling the original digital quantity to obtain an oversampled data set, and performing noise suppression on the oversampled data set through 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; Performing quantitative unit conversion on the calibrated voltage value to obtain standard unit voltage data, and adding a current system timestamp to the standard unit voltage data to obtain time-correlated voltage data; The time-correlated voltage data is packaged into a data frame to obtain the standardized data frame with a timestamp.

6. The photovoltaic module parameter monitoring method based on multi-channel synchronous potential acquisition according to claim 1 is characterized in that: The step of compressing and transmitting the standardized data frame with the timestamp to obtain a multi-channel synchronous data set includes: Performing data redundancy analysis on the standardized data frame with timestamp to obtain redundant features, and performing differential encoding on the data based on the redundant features to obtain differentially encoded data; Performing Huffman coding on the differentially coded data 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; Performing cyclic redundancy check encoding on the data block sequence to obtain a data packet with a checksum, and transmitting the data packet with the checksum via a POWERBUS bus to obtain master station received data; Verifying and checking the data received by the master station to obtain a valid data packet, and decoding and restoring the valid data packet to obtain an original data frame; The original data frames are sorted and aligned according to timestamps to obtain the multi-channel synchronized data set.

7. The photovoltaic module parameter monitoring method based on multi-channel synchronous potential acquisition according to claim 1 is characterized in that: The multi-dimensional analysis of the multi-channel synchronous data set to obtain photovoltaic module performance diagnostic data includes: Performing time series analysis on the multi-channel synchronous data set to obtain voltage fluctuation characteristics, and performing Fourier transform based on the voltage fluctuation characteristics to obtain frequency 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 photovoltaic component performance diagnostic data.

8. A photovoltaic module parameter monitoring device based on multi-channel synchronous potential acquisition, used to implement the photovoltaic module parameter monitoring method based on multi-channel synchronous potential acquisition as described in any one of claims 1 to 7, characterized in that: The photovoltaic module parameter monitoring device based on multi-channel synchronous potential acquisition includes: An initialization module is used to perform clock synchronization initialization on a photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition to obtain a system time reference; A configuration module, configured to perform parameter configuration on the multi-channel voltage acquisition module in the photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition based on the system time reference, so as to obtain acquisition setting parameters; A setting module, used to set a synchronous trigger mechanism for the master and slave devices in the photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition according to the acquisition setting parameters, so as to obtain a multi-channel acquisition trigger signal; A processing module, used to synchronously sample and locally process the voltage signal of the photovoltaic module through the multi-channel acquisition trigger signal to obtain a standardized data frame with a time stamp; A transmission module, used for compressing and transmitting the standardized data frame with timestamp to obtain a multi-channel synchronous data set; The analysis module is used to perform multi-dimensional analysis on the multi-channel synchronous data set to obtain photovoltaic module performance diagnostic data.

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