Photovoltaic system data transmission method and device based on PLC

Abstract

The invention relates to the technical field of data transmission, and discloses a photovoltaic system data transmission method and device based on a PLC. The method comprises the following steps: performing partition transmission processing on voltage and current data acquired by each slave station according to a target carrier frequency to obtain an original data sequence; grouping and splicing the original data sequence to obtain a combined data packet in each time window; performing difference calculation processing on the numerical values of the adjacent sampling points in the combined data packet to obtain a compressed data stream; and carrying out statistical analysis processing on the packet loss rate in the transmission process of the compressed data stream to obtain a channel state parameter, and periodically updating the target carrier frequency and the standby frequency group. According to the invention, reliable data transmission based on the PLC is realized. On the premise of ensuring the data transmission quality, the communication cost of the system is reduced and the reliability of data transmission is improved through the technical means of dynamic selection of the carrier frequency, efficient compression of the data, real-time monitoring of the communication quality and the like.

Description

Technical Field

[0001] The present application relates to the field of data transmission, and particularly to a method and device for data transmission of a photovoltaic system based on PLC. Background Art

[0002] With the rapid development of photovoltaic power generation technology, the efficiency and power of photovoltaic modules have been continuously improved, and the scale of a single photovoltaic power station has also become larger and larger. In the operation monitoring of a photovoltaic power station, it is necessary to collect the operating parameters such as voltage and current of each photovoltaic module in real time to monitor the performance of the modules and diagnose faults. Currently, the commonly used data collection methods mainly use RS485 communication or Ethernet communication. RS485 communication requires additional laying of communication cables, increasing the installation cost and construction difficulty; although Ethernet communication has a high transmission rate, it requires the installation of network devices at each collection point, resulting in a high cost. To solve this problem, the industry has begun to try to use power line carrier communication (PLC) technology to transmit data using existing DC cables without additional wiring. However, due to the dense distribution of power electronic devices and cables in a photovoltaic power station and serious on-site electromagnetic interference, traditional PLC communication solutions often have serious signal attenuation and unstable communication problems in practical applications.

[0003] The existing PLC communication technology for photovoltaic systems mainly has the following deficiencies: First, the selection of communication frequencies lacks flexibility, often using fixed frequency bands for communication and unable to dynamically adjust according to the actual communication quality; second, the data transmission uses a unified data frame format without considering the characteristics of photovoltaic system data, resulting in low transmission efficiency; third, there is no effective response mechanism when the communication quality deteriorates, easily causing data loss; fourth, the continuous characteristics of photovoltaic module data are not fully utilized for data compression, resulting in waste of bandwidth resources. In addition, the existing technology also has problems such as poor anti-interference ability and limited system scalability. Summary of the Invention

[0004] The present application provides a method and device for data transmission of a photovoltaic system based on PLC, which is used to achieve reliable data transmission based on PLC. On the premise of ensuring the data transmission quality, through technical means such as dynamic selection of carrier frequencies, efficient compression of data, and real-time monitoring of communication quality, the communication cost of the system is reduced and the reliability of data transmission is improved.

[0005] In a first aspect, the present application provides a method for transmitting photovoltaic system data based on PLC. The method for transmitting photovoltaic system data based on PLC includes: performing multi-point scanning processing on PLC carrier signals within a preset frequency range to obtain a frequency point feature data table containing signal intensity values and noise values; performing weighted calculation processing on the intensity values and noise values in the frequency point feature data table to obtain the target carrier frequency and standby frequency group for each slave station; performing partition transmission processing on the voltage and current data collected by each slave station according to the target carrier frequency to obtain an original data sequence with time stamps and device numbers; performing grouping and splicing processing on the original data sequence according to the device numbers to obtain a combined data packet within each time window; performing difference calculation processing on the values of adjacent sampling points in the combined data packet to obtain a compressed data stream containing a reference value and a change amount; performing statistical analysis processing on the packet loss rate during the transmission process of the compressed data stream to obtain channel state parameters, and periodically updating the target carrier frequency and standby frequency group according to the channel state parameters.

[0006] In a second aspect, the present application provides a device for transmitting photovoltaic system data based on PLC. The device for transmitting photovoltaic system data based on PLC includes:

[0007] A scanning module, configured to perform multi-point scanning processing on PLC carrier signals within a preset frequency range to obtain a frequency point feature data table containing signal intensity values and noise values;

[0008] A weighting module, configured to perform weighted calculation processing on the intensity values and noise values in the frequency point feature data table to obtain the target carrier frequency and standby frequency group for each slave station;

[0009] A transmission module, configured to perform partition transmission processing on the voltage and current data collected by each slave station according to the target carrier frequency to obtain an original data sequence with time stamps and device numbers;

[0010] A splicing module, configured to perform grouping and splicing processing on the original data sequence according to the device numbers to obtain a combined data packet within each time window;

[0011] A calculation module, configured to perform difference calculation processing on the values of adjacent sampling points in the combined data packet to obtain a compressed data stream containing a reference value and a change amount;

[0012] An analysis module, configured to perform statistical analysis processing on the packet loss rate during the transmission process of the compressed data stream to obtain channel state parameters, and periodically updating the target carrier frequency and standby frequency group according to the channel state parameters.

[0013] In the technical solution provided by this application, by performing multi-point scanning processing on the PLC carrier signals within a preset frequency range to obtain a frequency point feature data table containing signal strength values and noise values, the real-time state of the communication channel can be comprehensively grasped, providing a reliable basis for frequency selection; by performing weighted calculation processing on the strength values and noise values in the frequency point feature data table, the target carrier frequency and standby frequency group of each slave station are obtained, realizing the reasonable allocation of frequency resources and ensuring the optimal utilization of the communication channel; when partitioning and transmitting the voltage and current data collected by each slave station according to the target carrier frequency, by adding timestamps and device numbers, a standardized original data sequence is formed, ensuring the traceability and integrity of the data; during the process of grouping and splicing the original data sequence according to the device number, by dividing time windows, an organized combined data packet is obtained, improving the data management efficiency; by performing difference calculation processing on the values of adjacent sampling points in the combined data packet, a compressed data stream containing a reference value and a change amount is obtained, effectively reducing the data transmission volume and improving the communication efficiency; by statistically analyzing the packet loss rate during the transmission process of the compressed data stream, channel state parameters are obtained, and the target carrier frequency and standby frequency group are periodically updated according to this parameter, forming a complete communication quality guarantee mechanism, ensuring the reliable operation of the system in a complex electromagnetic environment. This data transmission method, through the organic cooperation of multiple links, not only solves the problems of inflexible frequency selection, low data transmission efficiency, and unstable communication quality in traditional PLC communication, but also fully considers the characteristics of photovoltaic system data, realizing an integrated solution for data acquisition, transmission, and processing. It not only reduces the communication cost of the system, improves the reliability of data transmission, but also has strong scalability and adaptability, and can meet the operation monitoring requirements of large-scale photovoltaic power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0015] Figure 1 FIG. is a schematic diagram of an embodiment of a method for transmitting photovoltaic system data based on PLC in an embodiment of this application;

[0016] Figure 2 FIG. is a schematic diagram of an embodiment of a device for transmitting photovoltaic system data based on PLC in an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The embodiments of the present application provide a method and device for data transmission of a photovoltaic system based on PLC. Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification, claims and the above-mentioned drawings of the present application are used to distinguish similar objects and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the term "including" or "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0018] For ease of understanding, the specific process of the embodiments of the present application will be described below. Please refer to Figure 1 One embodiment of the method for data transmission of a photovoltaic system based on PLC in the embodiments of the present application includes:

[0019] Step S101: Perform multi-point scanning processing on PLC carrier signals within a preset frequency range to obtain a frequency point feature data table containing signal strength values and noise values;

[0020] Step S102: Perform weighted calculation processing on the strength values and noise values in the frequency point feature data table to obtain the target carrier frequency and standby frequency group of each slave station;

[0021] Step S103: Perform partition transmission processing on the voltage and current data collected by each slave station according to the target carrier frequency to obtain an original data sequence with timestamps and device numbers;

[0022] Step S104: Perform grouping and splicing processing on the original data sequence according to the device number to obtain combined data packets within each time window;

[0023] Step S105: Perform difference calculation processing on the values of adjacent sampling points in the combined data packet to obtain a compressed data stream containing a reference value and a change amount;

[0024] Step S106: Perform statistical analysis processing on the packet loss rate during the transmission process of the compressed data stream to obtain channel state parameters, and periodically update the target carrier frequency and standby frequency group according to the channel state parameters.

[0025] It can be understood that the execution subject of the present application can be a data transmission device of a photovoltaic system based on PLC, or a terminal or a server. Specifically, it is not limited here. The embodiments of the present application will be described by taking the server as the execution subject as an example.

[0026] Specifically, data is transmitted through a DC cable, and multi-point scanning is performed on a preset frequency range of 0.5 MHz - 10 MHz. During specific implementation, signal acquisition is carried out on the entire frequency band at intervals of 0.5 MHz. The signal intensity and background noise are recorded for each sampling point. For example, at the 3 MHz frequency point, the measured signal intensity is 2240 mV and the noise value is 200 mV; at the 3.5 MHz frequency point, the measured signal intensity is 1680 mV and the noise value is 180 mV. These raw data are organized to form a frequency point feature data table. For the obtained frequency point feature data, a weighted calculation method is used to determine the optimal communication frequency. During the calculation process, the weight of the signal intensity value is 0.7, and the weight of the noise value is 0.3. The comprehensive score of each frequency point is obtained through weighted calculation. For example, the comprehensive score of the 3 MHz frequency point is: 2240×0.7 - 200×0.3 = 1508, and the comprehensive score of the 3.5 MHz frequency point is: 1680×0.7 - 180×0.3 = 1122. Through this calculation method, the frequency point with the highest score is selected as the target carrier frequency, and several frequency points with the second-highest scores form a standby frequency group.

[0027] Based on the determined target carrier frequency, each slave station starts to transmit the collected voltage and current data. Each data packet is appended with a 32-bit Unix timestamp and an 8-bit unique device number. For example, a slave station collects data of voltage 35.89 V and current 5.32 A, with a timestamp of 1637299200 and a device number of 0x01. These information are organized into a standard data frame format for transmission. The received raw data sequence is grouped according to the device number and spliced within a fixed time window of 10 seconds. For example, 10 groups of voltage data generated by device 0x01 within a time window: 35.89 V, 35.87 V, 35.90 V, 35.91 V, 35.92 V, 35.89 V, 35.88 V, 35.90 V, 35.91 V, 35.89 V. These data are organized into a complete combined data packet.

[0028] To improve the data transmission efficiency, difference compression is performed on the data in the combined data packet. The first value of 35.89 V is taken as the reference value, and the subsequent values are subtracted from the previous value: -0.02, 0.03, 0.01, 0.01, -0.03, -0.01, 0.02, 0.01, -0.02. This difference representation method significantly reduces the data volume. During the data transmission process, the packet loss situation is continuously monitored. By calculating the ratio of the actual received packet number to the expected packet number within a 10-second time window, the packet loss rate is obtained. When it is found that the packet loss rate of a certain frequency band exceeds 5%, the frequency update mechanism is triggered, and a new carrier frequency is selected from the standby frequency group. For example, when it is found that the packet loss rates of the 3 MHz frequency band in three consecutive time windows are 5.2%, 5.5%, and 5.8% respectively, it will automatically switch to the 3.5 MHz frequency band in the standby frequency group for continued transmission.

[0029] Taking a 160-component photovoltaic system as an example, within a standard working day, through frequency scanning at intervals of 0.5 MHz, it is found that the signal quality in the 3 - 4 MHz frequency band is the best. 3.2 MHz among them is used as the target carrier frequency, and 3.5 MHz, 3.8 MHz, and 3.1 MHz are used as standby frequencies. During the operation of the system, more than 1 million sets of voltage and current data are cumulatively collected. The data frame carries a timestamp accurate to the second and a device number from 1 to 160. After differential compression processing, the data size is only one-third of the original data. And when the channel quality deteriorates, it can automatically switch to the standby frequency to ensure the communication quality.

[0030] In the embodiment of the present invention, through multi-point scanning processing of PLC carrier signals within a preset frequency range, a frequency point feature data table containing signal intensity values and noise values is obtained, which can comprehensively master the real-time state of the communication channel and provide a reliable basis for frequency selection; through weighted calculation processing of the intensity values and noise values in the frequency point feature data table, the target carrier frequency and standby frequency group of each slave station are obtained, realizing the reasonable allocation of frequency resources and ensuring the optimal utilization of the communication channel; when processing the voltage and current data collected by each slave station for partitioned transmission according to the target carrier frequency, by adding a timestamp and a device number, a standardized original data sequence is formed, ensuring the traceability and integrity of the data; during the process of grouping and splicing the original data sequence according to the device number, through the division of time windows, an organized combined data packet is obtained, improving the data management efficiency; through differential calculation processing of the values of adjacent sampling points in the combined data packet, a compressed data stream containing a reference value and a change amount is obtained, effectively reducing the data transmission volume and improving the communication efficiency; by statistically analyzing the packet loss rate during the transmission process of the compressed data stream, channel state parameters are obtained, and based on this parameter, the target carrier frequency and standby frequency group are periodically updated, forming a complete communication quality guarantee mechanism, ensuring the reliable operation of the system in a complex electromagnetic environment. This data transmission method, through the organic cooperation of multiple links, not only solves the problems of inflexible frequency selection, low data transmission efficiency, and unstable communication quality in traditional PLC communication, but also fully considers the characteristics of photovoltaic system data, realizing an integrated solution for data acquisition, transmission, and processing. It not only reduces the communication cost of the system, improves the reliability of data transmission, but also has strong scalability and adaptability, and can meet the operation monitoring requirements of large-scale photovoltaic power stations.

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

[0032] (1) Detect and process the PLC carrier signals within the preset frequency range at frequency step values to obtain a multi-point detection data sequence;

[0033] (2) Perform level calibration processing on the multi-point detection data sequence to obtain a signal strength value list;

[0034] (3) Calculate the out-of-band interference of the spectral components of the multi-point detection data sequence to obtain a noise value list;

[0035] (4) Perform multiple sampling and averaging processing on the data in the signal strength value list to obtain a signal strength feature table;

[0036] (5) Perform data merging processing on the signal strength feature table and the noise value list to obtain a frequency point feature data table.

[0037] Specifically, first, within the range of 0.5 MHz - 10 MHz, perform frequency point sampling with a step value of 0.5 MHz, perform a 100-ms dwell detection on each frequency point, record the received signal data of this frequency point, and form a multi-point detection data sequence containing 20 frequency point detection data. For the obtained multi-point detection data sequence, use the level calibration method to calculate the signal strength value. The signal amplitude obtained at each frequency point is calibrated through a calibration attenuator, and the calibration range of the attenuator is -80 dBm to +20 dBm. For example, the original signal amplitude detected at the 3 MHz frequency point is 1460 mV, and the actual signal strength value is 2240 mV after calibration by a -20 dB attenuator; the original signal amplitude detected at the 3.5 MHz frequency point is 2240 mV, and the actual signal strength value is 1680 mV after calibration by a -40 dB attenuator. After performing such a calibration process on all frequency points, a complete signal strength value list is formed.

[0038] For the determination of the noise value, it is necessary to analyze the spectral components in the multi-point detection data sequence. The spectral distribution of the signal is obtained through FFT transformation, and the average power of the signal outside the bandwidth range of ±0.25 MHz on both sides of the target frequency point is calculated as the out-of-band interference value. For example, at the 3 MHz frequency point, calculate the average signal power in the ranges of 2.75 MHz - 2.95 MHz and 3.05 MHz - 3.25 MHz, and the noise value at this frequency point is obtained as 200 mV; at the 3.5 MHz frequency point, the noise value is similarly calculated to be 180 mV. Repeat this calculation process for all frequency points to generate a complete noise value list. To improve the measurement accuracy, each frequency point data in the signal strength value list is sampled repeatedly multiple times. Specifically, the average value is taken from 5 samplings to eliminate the influence caused by random fluctuations. For example, the 5 measurement values at the 3 MHz frequency point are 2236 mV, 2242 mV, 2238 mV, 2241 mV, and 2243 mV respectively, and the average value of 2240 mV is taken as the final signal strength characteristic value at this frequency point. After performing this process for all frequency points, a signal strength characteristic table is formed.

[0039] Finally, the signal strength characteristic table and the noise value list are merged according to the frequency points one by one. The data entry for each frequency point contains three fields: frequency value, signal strength characteristic value, and noise value. In this way, a complete frequency point characteristic data table is obtained, providing a basis for subsequent carrier frequency selection. For example, the data entry for the 3 MHz frequency point is: [3 MHz, 2240 mV, 200 mV], and the data entry for the 3.5 MHz frequency point is: [3.5 MHz, 1680 mV, 180 mV]. This data organization form facilitates subsequent comprehensive evaluation and frequency selection.

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

[0041] (1) Perform slave station grouping processing on the frequency point characteristic data table to obtain the frequency point data sub-table for each slave station;

[0042] (2) Normalize the intensity value and noise value in the frequency point data sub-table for each slave station to obtain the standardized characteristic value;

[0043] (3) Perform weighted summation processing on the standardized characteristic values according to the signal strength and noise ratio to obtain the frequency point quality score table for each slave station;

[0044] (4) Select the frequency point with the highest score in the frequency point quality score table for each slave station to obtain the target carrier frequency for each slave station;

[0045] (5) Extract the candidate frequency points with the top rankings in the frequency point quality score table for each slave station to obtain the standby frequency group for each slave station.

[0046] Specifically, the frequency point characteristic data is grouped and sorted according to the physical position of the slave stations in the string. Taking a system composed of 160 photovoltaic modules as an example, since each module corresponds to a slave station, the frequency point characteristic data is divided into 160 sub-tables. Each sub-table contains the measurement data of the slave station at different frequency points. For example, the signal strength measured by the 1st slave station at 3 MHz is 2240 mV, the noise value is 200 mV, the signal strength measured at 3.5 MHz is 1680 mV, the noise value is 180 mV, and the signal strength measured at 4 MHz is 1280 mV, the noise value is 160 mV. When normalizing these raw data, the maximum and minimum values of the signal strength value and the noise value are calculated respectively, and the data is mapped to the 0-1 interval. For example, the maximum signal strength of the 1st slave station is 2240 mV, the minimum value is 1280 mV. After processing 2240 mV at the 3 MHz frequency point, it gets 1.0, after processing 1680 mV at the 3.5 MHz frequency point, it gets 0.42, and after processing 1280 mV at the 4 MHz frequency point, it gets 0. The noise value is also normalized in the same way, 200 mV is normalized to 1.0, 180 mV is normalized to 0.5, and 160 mV is normalized to 0.

[0047] When calculating the comprehensive score, the weight of the signal strength is set to 0.7, and the weight of the noise value is set to 0.3. For the 3 MHz frequency point of the 1st slave station, its normalized signal strength value is 1.0, and the normalized noise value is 1.0. After weighted calculation, the final score is 0.4. Similarly, the final score of the 3.5 MHz frequency point is 0.244, and the final score of the 4 MHz frequency point is 0. These score data form the frequency point quality score table of the slave station. Based on the scoring results, 3 MHz with the highest score is selected as the target carrier frequency of the 1st slave station. At the same time, 3.5 MHz, 3.8 MHz, and 3.2 MHz ranked second to fourth in the scoring are used as the backup frequency group. In this way, the frequency configuration of one slave station is completed, and other slave stations are also processed according to the same process, and finally the carrier frequency configuration scheme of all slave stations is realized.

[0048] This configuration method ensures that each slave station can use the optimal communication frequency under the current channel conditions, and at the same time has the ability to switch to the backup frequency, effectively improving the reliability and adaptability of PLC communication. The test data shows that in actual operation, the signal quality of the target carrier frequency is significantly better than other frequency points, and the performance indicators of the backup frequency group also meet the communication requirements and can be switched in time when the primary frequency is interfered.

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

[0050] (1) Record the acquisition time of the voltage and current data collected by each slave station to obtain the corresponding time data;

[0051] (2) Perform unified time format conversion processing on the moment data to obtain timestamps;

[0052] (3) Perform unique identifier allocation processing on each slave station to obtain device numbers;

[0053] (4) Perform target carrier frequency modulation processing on the voltage and current data to obtain modulated data packets;

[0054] (5) Perform data merging processing on the modulated data packets, timestamps, and device numbers to obtain the original data sequence.

[0055] Specifically, record the time of voltage and current data acquisition. Taking a system composed of 160 photovoltaic modules as an example, each slave station collects voltage and current data every 100 milliseconds and records the acquisition time. For example, Slave Station 1 collects a voltage of 35.89V and a current of 5.32A at 8:00:00.000, and a voltage of 35.87V and a current of 5.29A at 8:00:00.100, and records the acquisition times corresponding to these values. Then, uniformly convert the acquisition times to the Unix timestamp format. Convert the above 8:00:00.000 to 1637299200, and 8:00:00.100 to 1637299200.100, so that the time records of all slave stations maintain a unified format standard. This format conversion ensures the accurate time correspondence of the data of each slave station during subsequent data processing.

[0056] Allocate unique device identifiers to each slave station, using an 8-bit hexadecimal coding rule. Starting from Slave Station 1, they are numbered 0x01, 0x02, 0x03, etc. up to 0x0160 for Slave Station 160. This numbering method can accurately identify the location information of each slave station, facilitating subsequent data classification and query. Then, modulate the voltage and current data according to the allocated target carrier frequency. Assume that the target carrier frequency of Slave Station 1 is 3MHz, and modulate the collected voltage of 35.89V and current of 5.32A data onto this carrier. During the modulation process, the voltage value and current value are digitized with 16-bit precision respectively, and then the digital signals are loaded onto the carrier to form modulated data packets.

[0057] Finally, combine the modulated data packets, timestamps, and device numbers into data frames in a standard format. The data frame structure is: device number (8 bits) + timestamp (32 bits) + voltage value (16 bits) + current value (16 bits). For example, the complete data frame of Slave Station 1 at 8:00:00.000 is: [0x01, 1637299200, 35.89, 5.32]. Such data frames are arranged in sequence according to the sampling timing to form the original data sequence.

[0058] Taking the data collection within a certain minute as an example: Slave Station 1 generated a total of 600 groups of data within this minute (one group every 100 milliseconds). Each group of data contains a complete device number, timestamp, and voltage and current values. These data are transmitted through the target carrier frequency to form a standardized data stream, which not only ensures the integrity of the data but also facilitates subsequent data processing and analysis. During the data transmission process, the accurate recording of the timestamp enables the receiving end to accurately restore the data collection sequence, and the uniqueness of the device number ensures that the data can be correctly associated with specific photovoltaic modules.

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

[0060] (1) Perform an equal-length time window division process on the original data sequence to obtain a time-segmented data table;

[0061] (2) Classify the data in the time-segmented data table according to the device number to obtain a device data classification table;

[0062] (3) Perform a timing alignment process on the data in the same time window of the device data classification table to obtain an aligned data set;

[0063] (4) Concatenate the aligned data set according to the device number to obtain a spliced data stream;

[0064] (5) Perform a packet encapsulation process on the spliced data stream to obtain a combined data packet within each time window.

[0065] Specifically, divide the original data sequence according to a fixed time window of 10 seconds. Taking the data of 160 slave stations within 10 seconds as an example, each slave station collects data once every 100 milliseconds and generates a total of 100 groups of data within 10 seconds. The entire system generates a total of 16,000 groups of data records within a time window. These data are organized into a time-segmented data table according to the time period. For example, 8:00:00.000 to 8:00:10.000 is the first time window, and 8:00:10.000 to 8:00:20.000 is the second time window. Classify and organize the data in the time-segmented data table according to the slave station number. Taking the first time window as an example, divide the 16,000 groups of data into 160 groups according to the device number, and each group contains 100 data records. For example, the 100 data records of Slave Station 1 are: [8:00:00.000, 35.89V, 5.32A], [8:00:00.100, 35.87V, 5.29A], [8:00:00.200, 35.90V, 5.30A], etc., to form a device data classification table.

[0066] Then, perform time series alignment on the data within the same time window in the device data classification table. For example, group the data of all slave stations at 8:00:00.000 together, the data at 8:00:00.100 together, and so on. The specific data is as follows: at 8:00:00.000, [0x01: 35.89V, 0x02: 35.85V,..., 0x0160: 35.95V]; at 8:00:00.100, [0x01: 35.87V, 0x02: 35.88V,..., 0x0160: 35.97V], forming 100 groups of aligned data sets. Then, concatenate the aligned data sets in the order of device numbers. Within a time window, starting from device 0x01, sequentially connect the 100 data records of each device. For example, the concatenated data stream structure of the first time window is: [100 groups of data of 0x01, 100 groups of data of 0x02,..., 100 groups of data of 0x0160]. Finally, add a frame header, a frame tail, and check information to the concatenated data stream to form a combined data packet in standard format. The frame header contains a time window identifier (32 bits) and total data amount information (16 bits), and the frame tail contains a CRC check code (16 bits). For example, the combined data packet structure of the first time window (8:00:00.000 - 8:00:10.000) is: [Time window identifier: 0x00000001, Total data amount: 16000, Concatenated data stream content, CRC check code]. Such a combined data packet contains the data of all slave stations within the complete time period and ensures the integrity and verifiability of the data.

[0067] In a specific embodiment, the process of performing step S105 may specifically include the following steps:

[0068] (1) Perform reference point marking processing on the sampling point values in the combined data packet to obtain reference values;

[0069] (2) Extract the differences between adjacent sampling points in the combined data packet to obtain change amounts;

[0070] (3) Perform regular recording processing on the reference values to obtain a reference point data table;

[0071] (4) Perform numerical precision compression processing on the change amounts to obtain compressed change values;

[0072] (5) Perform data stream processing on the reference point data table and the compressed change values to obtain a compressed data stream containing reference values and change amounts.

[0073] Specifically, benchmark points are marked for the sampled point values in the combined data packet. For 100 groups of data of each slave station within a 10-second time window, a benchmark point is set every 25 groups of data (2.5 seconds). Taking the voltage data of Slave Station 1 as an example, the 1st group of data 35.89V, the 26th group of data 35.92V, the 51st group of data 35.88V, and the 76th group of data 35.91V are used as benchmark values, which are used for subsequent difference calculations of the data. Then, the difference between each sampled point and its corresponding benchmark point is calculated. Within the first benchmark interval (groups 1 - 25), with 35.89V as the benchmark value, the differences of the subsequent 24 groups of data are calculated: the difference of the 2nd group 35.87V is -0.02V, the difference of the 3rd group 35.90V is 0.01V, and so on. The second benchmark interval (groups 26 - 50) calculates the differences with 35.92V as the benchmark value, the third benchmark interval (groups 51 - 75) calculates the differences with 35.88V as the benchmark value, and the fourth benchmark interval (groups 76 - 100) calculates the differences with 35.91V as the benchmark value.

[0074] The benchmark values are recorded and stored at fixed intervals. Within a 10-second time window, each slave station generates 4 benchmark values, and a total of 640 benchmark values are generated by 160 slave stations. These benchmark values are arranged in the order of device number and time to form a benchmark point data table. For example, the record of Slave Station 1 is [35.89V, 35.92V, 35.88V, 35.91V], and the record of Slave Station 2 is [35.85V, 35.88V, 35.90V, 35.89V]. The calculated change amounts are processed for precision compression. Considering the actual characteristics of the voltage change of photovoltaic modules, the difference precision is set to 0.01V. For example, -0.024V is recorded as -0.02V, and 0.018V is recorded as 0.02V. In this way, the storage space of each difference data is compressed from 16 bits to 8 bits while maintaining sufficient data precision.

[0075] Finally, the benchmark point data table and the compressed change amounts are organized into a compressed data stream. The structure of the compressed data stream is: [slave station number + number of benchmark values + benchmark value data + number of differences + difference data]. Taking Slave Station 1 as an example, a complete compressed data stream contains: number 0x01, 4 benchmark value data, and 96 difference data. This compression method not only retains the key benchmark data but also greatly reduces the data volume through difference calculation and precision compression. In practical applications, for the data within a 10-second time window, the original data volume is 160×100×32 bits, and the compressed data volume is reduced to about 40% of the original, while ensuring the data reducibility.

[0076] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0077] (1) Record the packet sequence numbers during the transmission process of the compressed data stream to obtain a transmission sequence number table;

[0078] (2) Statistically process the missing sequence numbers in the transmission sequence number table to obtain the packet loss rate;

[0079] (3) Perform threshold determination processing on the packet loss rate to obtain channel status parameters;

[0080] (4) Perform status level classification processing on the channel status parameters to obtain a frequency update trigger signal;

[0081] (5) Perform update and switching processing on the target carrier frequency and the standby frequency group according to the frequency update trigger signal to obtain the updated target carrier frequency and standby frequency group.

[0082] Specifically, in the PLC communication of the photovoltaic system, sequence numbers are recorded for the transmission of the compressed data stream. Within each 10-second time window, packet sequence numbers are allocated according to the slave station number and the transmission timing. The specific method is as follows: The compressed data stream of slave station No. 1 is allocated the sequence number 0x0001, slave station No. 2 is allocated 0x0002, and so on incrementally. Within a complete transmission cycle, the packet sequence numbers of 160 slave stations range from 0x0001 to 0x000160. These sequence numbers and their corresponding reception statuses are recorded in the transmission sequence number table. Perform missing statistics on the transmission sequence number table. Check the continuity of the sequence numbers within each 10-second time window, and record the packet sequence numbers that are not received on time. For example, within a certain time window, the sequence numbers 0x0001, 0x0002, 0x0004, 0x0005 are received, and it is found that the sequence number 0x0003 is missing, which is recorded as a lost packet. Statistically count the total number of packets and the number of lost packets within a time window, and calculate the packet loss rate. For example, within a certain time window, 160 packets are sent, 155 packets are received, and 5 packets are lost, then the packet loss rate is 3.125%. Perform threshold determination on the packet loss rate data. Set three-level thresholds: When the packet loss rate is lower than 2%, it is determined that the channel status is good, and the status value 1 is recorded; when the packet loss rate is between 2% and 5%, it is determined that the channel status is average, and the status value 2 is recorded; when the packet loss rate is higher than 5%, it is determined that the channel status is poor, and the status value 3 is recorded. These determination results form the channel status parameters.

[0083] Perform level division according to the channel status parameters. The status values in three consecutive time windows are divided according to the severity: when the status value 1 appears continuously, there is no need to trigger frequency update; when the status value 2 appears twice, a low-priority update trigger signal is generated; when the status value 3 appears once or the status value 2 appears continuously three times, a high-priority update trigger signal is generated. Finally, perform frequency update according to the trigger signal. When a low-priority trigger signal is received, degrade the original target carrier frequency (such as 3 MHz) to the standby frequency, and select the frequency point with the best signal quality (such as 3.5 MHz) from the standby frequency group (such as 3.5 MHz, 3.8 MHz, 3.2 MHz) as the new target carrier frequency, and reorder the other standby frequency points according to the signal quality. When a high-priority trigger signal is received, directly switch to the frequency point with the best signal quality (such as 3.5 MHz) in the standby frequency group as the new target carrier frequency, and rescan and evaluate within the preset frequency range, and select three frequency points with the best signal quality to form a new standby frequency group (such as 3.8 MHz, 3.2 MHz, 4.0 MHz). Through this dynamic adjustment mechanism, keep the communication frequency always working in the best state. In practical applications, the operation data of a certain photovoltaic system shows that during a day's operation, the low-priority trigger occurs on average once every 4 hours, and the high-priority trigger only occurs when there is strong external interference. Through timely frequency adjustment, the overall packet loss rate of the system is always controlled below 2%.

[0084] The above describes the PLC-based photovoltaic system data transmission method in the embodiments of the present application. Next, the PLC-based photovoltaic system data transmission device in the embodiments of the present application will be described. Please refer to Figure 2 , an embodiment of the PLC-based photovoltaic system data transmission device in the embodiments of the present application includes:

[0085] A scanning module 201, configured to perform multi-point scanning processing on PLC carrier signals within a preset frequency range to obtain a frequency point feature data table including signal strength values and noise values;

[0086] A weighting module 202, configured to perform weighting calculation processing on the strength values and noise values in the frequency point feature data table to obtain the target carrier frequency and standby frequency group of each slave station;

[0087] A transmission module 203, configured to perform partition transmission processing on the voltage and current data collected by each slave station according to the target carrier frequency to obtain an original data sequence with timestamps and device numbers;

[0088] A splicing module 204, configured to perform grouping and splicing processing on the original data sequence according to the device number to obtain a combined data packet within each time window;

[0089] The calculation module 205 is configured to perform difference calculation processing on the values of adjacent sampling points in the combined data packet to obtain a compressed data stream including a reference value and a change amount;

[0090] The analysis module 206 is configured to perform statistical analysis processing on the packet loss rate during the transmission process of the compressed data stream to obtain channel state parameters, and periodically update the target carrier frequency and the standby frequency group according to the channel state parameters.

[0091] Through the collaborative cooperation of the above-mentioned various components, by performing multi-point scanning processing on the PLC carrier signals within a preset frequency range to obtain a frequency point feature data table including signal strength values and noise values, the real-time state of the communication channel can be comprehensively grasped, providing a reliable basis for frequency selection; by performing weighted calculation processing on the strength values and noise values in the frequency point feature data table, the target carrier frequency and the standby frequency group of each slave station are obtained, realizing the reasonable allocation of frequency resources and ensuring the optimal utilization of the communication channel; when partitioning and transmitting the voltage and current data collected by each slave station according to the target carrier frequency, by adding time stamps and device numbers, a standardized original data sequence is formed, ensuring the traceability and integrity of the data; during the process of grouping and splicing the original data sequence according to the device number, through the division of time windows, an organized combined data packet is obtained, improving the data management efficiency; by performing difference calculation processing on the values of adjacent sampling points in the combined data packet to obtain a compressed data stream including a reference value and a change amount, the data transmission volume is effectively reduced and the communication efficiency is improved; by performing statistical analysis processing on the packet loss rate during the transmission process of the compressed data stream to obtain channel state parameters, and periodically updating the target carrier frequency and the standby frequency group according to this parameter, a complete communication quality guarantee mechanism is formed, ensuring the reliable operation of the system in a complex electromagnetic environment. This data transmission method, through the organic cooperation of multiple links, not only solves the problems of inflexible frequency selection, low data transmission efficiency, and unstable communication quality in traditional PLC communication, but also fully considers the characteristics of photovoltaic system data, realizing an integrated solution for data acquisition, transmission, and processing. It not only reduces the communication cost of the system, improves the reliability of data transmission, but also has strong scalability and adaptability, and can meet the operation monitoring requirements of large-scale photovoltaic power stations.

[0092] As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A data transmission method for a photovoltaic system based on a PLC, characterized in that, The photovoltaic system data transmission method based on PLC includes: Perform multi-point scanning processing on the PLC carrier signal within the preset frequency range to obtain a frequency point characteristic data table including signal strength values ​​and noise values; Perform weighted calculation on the intensity value and noise value in the frequency point characteristic data table to obtain the target carrier frequency and backup frequency group of each slave station; The voltage and current data collected by each slave station are processed by partition transmission according to the target carrier frequency to obtain the original data sequence with timestamp and device number; The original data sequence is grouped and spliced ​​according to the device number to obtain the combined data packet in each time window; Performing difference calculation on the values ​​of adjacent sampling points in the combined data packet to obtain a compressed data stream including a reference value and a variation; The packet loss rate during the transmission of the compressed data stream is statistically analyzed to obtain a channel state parameter, and the target carrier frequency and the backup frequency group are periodically updated according to the channel state parameter.

2. The method for transmitting photovoltaic system data based on PLC according to claim 1, wherein The multi-point scanning process is performed on the PLC carrier signal within the preset frequency range to obtain a frequency point characteristic data table containing signal strength values ​​and noise values, including: Performing point-by-point detection processing on the PLC carrier signal within the preset frequency range according to the frequency step value to obtain a multi-point detection data sequence; Performing level calibration processing on the multi-point detection data sequence to obtain a signal strength value list; Performing out-of-band interference calculation processing on the frequency spectrum components of the multi-point detection data sequence to obtain a noise value list; Perform multiple sampling and averaging processing on the data in the signal strength value list to obtain a signal strength characteristic table; The signal strength characteristic table and the noise value list are subjected to data merging processing to obtain the frequency point characteristic data table.

3. The method for transmitting photovoltaic system data based on PLC according to claim 1, characterized in that The weighted calculation process is performed on the intensity value and the noise value in the frequency point characteristic data table to obtain the target carrier frequency and the backup frequency group of each slave station, including: Performing slave station grouping processing on the frequency point characteristic data table to obtain a frequency point data sub-table for each slave station; Normalizing the intensity value and the noise value in the frequency point data sub-table of each slave station to obtain a standardized characteristic value; The standardized characteristic values ​​are weighted and summed according to the signal strength and the noise ratio to obtain a frequency point quality score table for each slave station; Selecting and processing the frequency point with the highest score in the frequency point quality score table of each slave station to obtain the target carrier frequency of each slave station; The candidate frequency points that rank high in the frequency point quality score table of each slave station are extracted and processed to obtain a backup frequency group of each slave station.

4. The method for data transmission of a photovoltaic system based on PLC according to claim 1, characterized in that, The voltage and current data collected from each slave station are processed by partition transmission according to the target carrier frequency to obtain an original data sequence with a timestamp and a device number, including: Performing collection time recording processing on the voltage and current data collected by each slave station to obtain corresponding time data; Convert the time data into a unified time format to obtain a timestamp; Perform unique identification assignment processing on each slave station to obtain the device number; Performing target carrier frequency modulation processing on the voltage and current data to obtain a modulated data packet; Perform data merging processing on the modulated data packet, the timestamp, and the device number to obtain the original data sequence.

5. The method for data transmission of a photovoltaic system based on PLC according to claim 1, wherein Perform grouped splicing processing on the original data sequence according to the device number to obtain combined data packets within each time window, including: Perform equal-length time window partitioning processing on the original data sequence to obtain a time-segmented data table; Classify the data in the time-segmented data table according to the device number to obtain a device data classification table; Perform time series alignment processing on the data within the same time window in the device data classification table to obtain an aligned data set; Concatenate the aligned data set according to the device number to obtain a concatenated data stream; Packetize the concatenated data stream to obtain the combined data packets within each time window.

6. The method for data transmission of a photovoltaic system based on a PLC according to claim 1, wherein Perform difference calculation processing on the values of adjacent sampling points in the combined data packets to obtain a compressed data stream containing a reference value and a change amount, including: Perform reference point marking processing on the sampling point values in the combined data packet to obtain the reference value; Extract the difference between adjacent sampling points in the combined data packet to obtain the change amount; Perform regular recording processing on the reference value to obtain a reference point data table; Perform numerical precision compression processing on the change amount to obtain a compressed change value; Streamline the reference point data table and the compressed change value to obtain the compressed data stream containing the reference value and the change amount.

7. The method for transmitting photovoltaic system data based on PLC according to claim 1, characterized in that Perform statistical analysis processing on the packet loss rate during the transmission process of the compressed data stream to obtain channel state parameters, and periodically update the target carrier frequency and the standby frequency group according to the channel state parameters, including: Record the packet sequence numbers during the transmission process of the compressed data stream to obtain a transmission sequence number table; Statistically process the missing sequence numbers in the transmission sequence number table to obtain the packet loss rate; Perform threshold determination processing on the packet loss rate to obtain channel state parameters; Perform state level classification processing on the channel state parameters to obtain a frequency update trigger signal; Update and switch the target carrier frequency and the standby frequency group according to the frequency update trigger signal to obtain the updated target carrier frequency and the standby frequency group.

8. A data transmission device for a photovoltaic system based on PLC, which is used to implement the data transmission method for a photovoltaic system based on PLC as described in any one of claims 1-7, characterized in that, The PLC-based photovoltaic system data transmission device includes: A scanning module for performing multi-point scanning processing on PLC carrier signals within a preset frequency range to obtain a frequency point feature data table containing signal strength values and noise values; A weighting module for performing weighting calculation processing on the strength values and noise values in the frequency point feature data table to obtain the target carrier frequency and the standby frequency group of each slave station; A transmission module for partitioning and transmitting the voltage and current data collected by each slave station according to the target carrier frequency to obtain an original data sequence with a timestamp and a device number; A splicing module for performing grouped splicing processing on the original data sequence according to the device number to obtain combined data packets within each time window; A calculation module for performing difference calculation processing on the values of adjacent sampling points in the combined data packets to obtain a compressed data stream containing a reference value and a change amount; An analysis module is used to statistically analyze the packet loss rate during the transmission of the compressed data stream, obtain channel status parameters, and periodically update the target carrier frequency and the standby frequency group according to the channel status parameters.

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