Photovoltaic power grid low-voltage ride-through coordination method and system

Abstract

The invention discloses a photovoltaic power grid low-voltage ride-through coordination method and system, and relates to the technical field of photovoltaic power grids, and the method comprises the following steps: monitoring the voltage fluctuation in a photovoltaic system in real time, obtaining a voltage steady-state coefficient based on the voltage fluctuation, and transmitting a voltage drop signal if the voltage is lower than the voltage steady-state coefficient; acquiring multiple sections of voltage drop values, and acquiring performance influence data and power grid abnormal influence data of the photovoltaic inverter under each section of voltage drop value; constructing a photovoltaic system voltage drop state evaluation model based on machine learning according to the performance influence data and the power grid abnormal influence data; performing preliminary screening and secondary screening on the output of the photovoltaic system voltage drop state evaluation model to obtain a voltage drop preset value; and the corresponding instruction set is used for judging and controlling the photovoltaic system to carry out low-voltage ride-through operation according to the voltage drop preset value. Through reasonable coordination of low-voltage ride-through, the problem that low-voltage ride-through is abnormal due to unreasonable threshold setting in a photovoltaic power grid is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power grids, and more particularly to a photovoltaic power grid low voltage ride through coordination method and system. Background Art

[0002] In recent years, with technological advances and policy support, photovoltaic power generation has become widely used. Photovoltaic systems use inverters to convert direct current (DC) power to alternating current (AC) power, which is then connected to the grid for transmission. Low-voltage ride-through (LVRT) refers to the ability of photovoltaic systems to continue operating or automatically recover and transmit power to the grid, rather than immediately disconnecting, when a short-term voltage fluctuation or fault occurs on the grid. This requirement for PV system inverters during grid abnormalities ensures that PV power plants can maintain stable operation when the grid voltage temporarily drops below the specified value, avoiding the increased burden on the grid caused by large-scale PV generation disconnection and promoting grid recovery and stability.

[0003] For example, the invention patent announcement with the publication number CN116264400A discloses a method for optimizing the low voltage ride-through of a high-penetration photovoltaic distribution network, including: real-time monitoring of the grid voltage value, judging the grid status: judging whether the grid is in the low voltage ride-through process, and controlling the grid active power and reactive power during the low voltage ride-through process; judging whether the grid is in the low voltage ride-through recovery process, and controlling the grid active power and reactive power during the low voltage ride-through recovery process; judging whether the grid parameters have returned to the level before the low voltage ride-through, and switching the grid back to the original control mode after the low voltage ride-through is restored. When the AC grid voltage drops sharply due to a short circuit fault or an impact, the grid voltage experiences a low voltage ride-through phenomenon. The dual power optimization strategy provided by this application for low voltage ride-through controls the increase of active current while controlling the decrease of reactive current, thereby adjusting the stability of the grid-connected frequency and maximizing the power system's ability to absorb photovoltaic output.

[0004] For example, the invention patent with announcement number CN117134417B discloses a method, device, and storage medium for regulating overcurrent during voltage ride-through of a photovoltaic inverter, including: monitoring the AC side voltage of the photovoltaic inverter of the photovoltaic power station; when the AC side voltage drops and is lower than the low voltage ride-through threshold, low voltage ride-through control is performed; when the AC side voltage rises and is higher than the high voltage ride-through threshold, high voltage ride-through control is performed; when the AC side voltage is between the high voltage ride-through threshold and the low voltage ride-through threshold, conventional control is performed. The technical solution adopted by the present invention actively supports the stability of the grid voltage during continuous low voltage ride-through or high voltage ride-through during the DC commutation failure process; when DC is locked, the voltage support is exited to reduce the instantaneous overvoltage of the DC lockout, thereby supporting the safe and stable operation of the grid and improving the photovoltaic power generation acceptance capacity at the DC sending end.

[0005] The above disclosed technical solutions have at least the following technical problems: When the voltage of a PV grid system drops below a set threshold due to a fault or short circuit, a low voltage ride-through (LVRT) function is activated to maintain grid stability. However, if the threshold is set too high, the LVRT function may be activated too late, causing the equipment to remain in a low voltage state for an extended period, increasing the risk of damage and reducing system stability. If the threshold is set too low, frequent protective actions may occur, impacting normal equipment operation.

[0006] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0007] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a photovoltaic power grid low voltage ride-through coordination method and system, which solves the problem of low voltage ride-through abnormalities caused by unreasonable threshold settings in the photovoltaic power grid through reasonable coordination of low voltage ride-through.

[0008] To achieve the above object, the present invention provides the following technical solutions: A photovoltaic power grid low voltage ride-through coordination method and system includes the following steps: real-time monitoring of voltage fluctuations in the photovoltaic system, obtaining a voltage steady-state coefficient based on the voltage fluctuations, and sending a voltage drop signal to the photovoltaic system if the voltage is lower than the voltage steady-state coefficient; obtaining multiple voltage drop values ​​based on the voltage drop signal, and obtaining performance impact data of the photovoltaic inverter and grid abnormality impact data under each voltage drop value; constructing a photovoltaic system voltage drop state assessment model based on machine learning according to the performance impact data and grid abnormality impact data; performing preliminary and secondary screening on the output of the photovoltaic system voltage drop state assessment model to obtain a voltage drop preset value; and determining and controlling the corresponding instruction set for the photovoltaic system to perform a low voltage ride-through operation based on the voltage drop preset value.

[0009] In a preferred embodiment, the performance influencing data of the photovoltaic inverter includes a photovoltaic inverter sensitivity coefficient, and the specific method for obtaining the photovoltaic inverter sensitivity coefficient is as follows: obtaining the photovoltaic inverter input signal and the light radiation and temperature data around the photovoltaic inverter under the current voltage drop; performing data analysis and feature extraction on the light radiation and temperature data around the photovoltaic inverter to obtain the light radiation influencing factor and the temperature influencing factor that affect the propagation of the voltage signal; obtaining the output signal waveform after the photovoltaic inverter is activated based on the photovoltaic inverter input signal; analyzing the output signal waveform to obtain the response time interval between the photovoltaic inverter receiving the input signal and then outputting the signal; and calculating the photovoltaic inverter sensitivity coefficient based on the response time interval combined with the light radiation influencing factor and the temperature influencing factor based on a preset photovoltaic inverter sensitivity formula.

[0010] In a preferred embodiment, the light radiation and temperature data around the photovoltaic inverter are subjected to data analysis and feature extraction to obtain the light radiation influencing factor and temperature influencing factor that affect the propagation of the voltage signal. The specific steps are as follows: collecting light radiation and temperature data for feature extraction to obtain weight coefficients of light radiation and temperature data, and removing random fluctuations in the data based on a smoothing algorithm; drawing a scatter plot of the impact of light radiation and temperature data on the signal of the photovoltaic inverter for the processed light radiation and temperature data; obtaining the changing trend of the impact of light radiation and temperature data on the signal propagation of the photovoltaic inverter based on the scatter plot of signal impact; and quantifying the changing trend based on regression analysis to obtain the light radiation influencing factor and temperature influencing factor respectively.

[0011] In a preferred embodiment, the photovoltaic inverter stability coefficient is specifically obtained by the following method: obtaining a waveform diagram of the photovoltaic inverter output power; analyzing the change trend of the output power before and after the voltage drop based on the output power waveform diagram according to the preset standard output power to obtain the efficiency of the power conversion unit; obtaining the working status of the historical protection device in the photovoltaic inverter under the voltage drop; performing regression analysis based on the historical working status data of the protection device to obtain the working status influencing factors of the protection device, the working status influencing factors including the fault repair data of the protection device and the aging rate of the protection device; calculating the fault repair data and the aging rate of the protection device based on the preset protection device sensitivity formula to obtain the protection device sensitivity coefficient; calculating the efficiency of the power conversion unit and the protection device sensitivity coefficient based on the preset photovoltaic inverter stability formula to obtain the photovoltaic inverter stability coefficient.

[0012] In a preferred embodiment, the power grid abnormality impact data is specifically obtained by the following method: obtaining the status data of the power grid equipment, and obtaining the load data of the power grid equipment based on feature analysis and extraction; performing differential analysis on the load data of the power grid equipment based on preset stable load data to obtain the load difference value of the power grid equipment; monitoring the synchronization of different areas of the power grid system to obtain the synchronization rate of the power grid system; and calculating the power grid abnormality impact data based on the preset power grid abnormality impact data formula according to the load difference value of the power grid equipment and the synchronization rate of the power grid system.

[0013] In a preferred embodiment, the output of the photovoltaic system voltage drop state assessment model is subjected to preliminary screening and secondary screening to obtain a voltage drop preset value, and the specific steps are as follows: obtaining a voltage drop state assessment coefficient based on the output of the photovoltaic system voltage drop state assessment model; monitoring the state of the protection device in the photovoltaic system, and obtaining a voltage drop value at which the photovoltaic system frequently performs protection actions due to a small voltage drop; using the voltage drop state assessment coefficient corresponding to the voltage drop value at which protection actions are frequently performed as the limit minimum value; obtaining a voltage drop value at which the photovoltaic system voltage drops too much, causing the inverter to disconnect; and using the voltage drop state assessment coefficient corresponding to the voltage drop value at which the inverter is disconnected. The voltage drop state assessment coefficient corresponding to the voltage drop value is taken as the maximum limit; all voltage drop state assessment coefficients are obtained and preliminarily screened based on the minimum limit and the maximum limit; voltage drop state assessment coefficients that are less than the minimum limit and greater than the maximum limit are excluded to obtain a preliminarily screened voltage drop state assessment data set; the voltage drop state assessment data set is averaged and the average is set as a voltage drop state assessment stable value; the voltage drop state assessment data set is secondary screened by comparison based on the voltage drop state assessment stable value, and the optimal voltage drop state assessment coefficient is obtained based on the gradient downward method; The voltage drop value corresponding to the optimal voltage drop state evaluation coefficient is set as a voltage drop preset value.

[0014] In a preferred embodiment, the corresponding instruction set for judging and controlling the photovoltaic system to perform low voltage ride-through operation based on the voltage drop preset value has the following specific steps: if the voltage drop value is less than the voltage drop preset value, the photovoltaic system does not perform low voltage ride-through operation, and the system monitors it in real time; if the voltage drop value is greater than or equal to the voltage drop preset value, the system automatically schedules the photovoltaic system to perform low voltage ride-through operation.

[0015] In a preferred embodiment, the light radiation influence factor and the temperature influence factor are calculated using the following formula: The specific calculation formula of the photovoltaic inverter sensitivity coefficient is as follows: In the formula, S is the sensitivity coefficient of the photovoltaic inverter, is the response time interval, is the light radiation influencing factor, is the temperature influence factor, is the standard data of photovoltaic inverter sensitivity, is an adjustable constant, is the light radiation, is the light radiation weight coefficient, is the temperature data, is the temperature data weight coefficient.

[0016] In a preferred embodiment, the voltage drop state assessment model has a specific calculation formula as follows: In the formula, is the voltage drop status assessment coefficient, is the grid abnormality impact data, S is the photovoltaic inverter sensitivity coefficient, is the photovoltaic inverter stability coefficient, is the weight coefficient of the power grid abnormality impact data, is the sensitivity coefficient of the photovoltaic inverter, is the weight coefficient of the PV inverter stability factor.

[0017] In a preferred embodiment, a system for a photovoltaic power grid low voltage ride-through coordination method is characterized by comprising a signal transmission module, a data acquisition module, a model construction module, a data screening module and an instruction generation module: the signal transmission module is used to monitor voltage fluctuations in the photovoltaic system in real time, obtain a voltage steady-state coefficient based on the voltage fluctuation, and send a voltage drop signal to the photovoltaic system if the voltage is lower than the voltage steady-state coefficient; the data acquisition module is used to obtain multiple voltage drop values ​​based on the voltage drop signal, and obtain performance impact data of the photovoltaic inverter and grid abnormality impact data under each voltage drop value; the model construction module is used to construct a photovoltaic system voltage drop status assessment model based on machine learning according to the performance impact data and grid abnormality impact data; the data screening module is used to perform preliminary screening and secondary screening on the output of the photovoltaic system voltage drop status assessment model to obtain a voltage drop preset value; the instruction generation module is used to judge and control the photovoltaic system to perform a corresponding instruction set for low voltage ride-through operation based on the voltage drop preset value.

[0018] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By comprehensively analyzing the PV system's voltage sag status assessment coefficients, combined with the status of protective devices and inverter disconnection conditions, a multi-level screening and optimization process was established to accurately determine the voltage sag preset value. This method effectively eliminates abnormal data, reduces the negative impact of frequent protective device activation and inverter disconnection, and improves system stability and reliability. Furthermore, a secondary screening and optimization process based on the gradient-down method helps precisely adjust the voltage sag preset value, ensuring stable operation of the PV system under various voltage sag conditions.

[0019] 2. By comprehensively analyzing the PV inverter's input signal, ambient light radiation, and temperature data, combined with the response time interval of the output signal waveform, the sensitivity coefficient of the PV inverter is accurately calculated. Feature extraction reveals the influence of light radiation and temperature on voltage signal transmission. This quantifies the impact of external environmental factors on inverter performance and optimizes the system response by combining the PV inverter's sensitivity formula. This method helps improve the adaptability and stability of the PV inverter under different environmental conditions, optimizes its output signal, and thus enhances the overall efficiency and reliability of the PV system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic structural diagram of a photovoltaic power grid low voltage ride-through coordination method provided in an embodiment of the present application.

[0021] Figure 2 A schematic diagram of the structure of a photovoltaic power grid low voltage ride through coordination system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0023] Example 1, Figure 1 A schematic diagram of a photovoltaic grid low voltage ride-through coordination method provided in an embodiment of the present application includes the following steps: S1, real-time monitoring of voltage fluctuations in the photovoltaic system, obtaining a voltage steady-state coefficient based on the voltage fluctuations, and sending a voltage drop signal to the photovoltaic system if the voltage is lower than the voltage steady-state coefficient.

[0024] In this example, voltage drops are typically caused by load changes, equipment failures, or transient fluctuations in the power grid. By acquiring real-time voltage fluctuation data, the system can quickly identify voltage drops and promptly issue an alarm or initiate an emergency response mechanism. The voltage stability coefficient refers to the standard voltage that maintains normal and stable operation in a photovoltaic system. The specific method for obtaining the voltage stability coefficient is as follows: The system monitors and acquires the voltage of the photovoltaic system at multiple time intervals in real time. These voltage intervals are integrated into a data set, and the standard deviation of the data set is calculated. This standard deviation is set as the voltage stability coefficient. By calculating the voltage stability coefficient, the photovoltaic system can assess in real time whether the voltage is within the normal operating range. If the voltage monitored in the photovoltaic system falls below the voltage stability coefficient for a predetermined period of time, the monitoring device sends a voltage drop signal to the photovoltaic system.

[0025] S2, based on the voltage drop signal, obtains multiple voltage drop values, and obtains performance impact data of the photovoltaic inverter and grid abnormality impact data under each voltage drop value.

[0026] In this example, the performance impact data of the photovoltaic inverter refers to the impact on the performance of the photovoltaic inverter when the voltage drops. The performance impact data of the photovoltaic inverter includes the photovoltaic inverter sensitivity coefficient and the photovoltaic inverter stability coefficient.

[0027] The PV inverter sensitivity coefficient refers to the impact on the PV inverter's sensitivity during a voltage drop, which can cause a delay in the PV inverter's output to maintain the PV system voltage stability after receiving the PV system voltage signal. Obtaining the PV inverter sensitivity coefficient has the following advantages for analyzing the performance of PV inverters during voltage drops: Improving the predictability of inverter responses to voltage variations: PV inverter performance can be significantly impacted by grid voltage fluctuations, particularly during voltage dips (such as voltage sags or voltage defects). Determining the inverter's voltage sensitivity coefficient allows for accurate prediction of its response during voltage dips. For example, an inverter may enter grid-trip protection mode when voltage drops below a certain threshold, and the sensitivity coefficient helps understand the extent of this change.

[0028] Optimizing the inverter's anti-interference capability and stability: By obtaining the sensitivity coefficient, the inverter's stability during voltage drops can be effectively assessed, determining whether it is prone to frequent shutdowns or restarts, thereby improving system stability and reliability. Understanding the inverter's voltage sensitivity can help designers select appropriate inverter configurations and improve the system's anti-interference capabilities, especially in areas with unstable power grid quality.

[0029] Supporting inverter performance optimization and intelligent control: Obtaining the inverter's voltage sensitivity coefficient helps optimize the inverter's intelligent control strategy. For example, the inverter can adaptively adjust its operating mode based on voltage fluctuations, thereby reducing the impact of voltage drops on system performance. The sensitivity coefficient is particularly important in dynamic voltage control, frequency regulation, and fault recovery, helping to achieve intelligent inverter operation and efficient performance.

[0030] The specific method for obtaining the photovoltaic inverter sensitivity coefficient is as follows: Obtain the input signal of the photovoltaic inverter and the light radiation and temperature data around the photovoltaic inverter during the current voltage drop; Performing data analysis and feature extraction on the light radiation and temperature data around the photovoltaic inverter to obtain a light radiation influencing factor and a temperature influencing factor that affect voltage signal propagation; Obtaining an output signal waveform of the photovoltaic inverter based on the photovoltaic inverter input signal; The output signal waveform is analyzed to obtain the response time interval between when the photovoltaic inverter receives the input signal and then outputs the signal; The photovoltaic inverter sensitivity coefficient is calculated based on the preset photovoltaic inverter sensitivity formula according to the response time interval combined with the light radiation influence factor and the temperature influence factor.

[0031] The specific steps of performing data analysis and feature extraction on the light radiation and temperature data around the photovoltaic inverter to obtain the light radiation influencing factor and the temperature influencing factor that affect the propagation of the voltage signal are as follows: Collect light radiation and temperature data for feature extraction, obtain weight coefficients of light radiation and temperature data, and remove random fluctuations in the data based on a smoothing algorithm; Draw a scatter diagram of the effect of the processed light radiation and temperature data on the signal of the photovoltaic inverter; Obtain the changing trend of the impact of light radiation and temperature data on the signal propagation of photovoltaic inverters based on the scatter plot of signal impact; The change trend is quantified based on regression analysis to obtain the light radiation influencing factor and the temperature influencing factor respectively.

[0032] Generally, the stronger the light radiation, the higher the output voltage. The effect of temperature on the photovoltaic system is usually negative. The higher the temperature, the lower the efficiency of the photovoltaic module, resulting in a voltage drop.

[0033] The specific calculation formulas for the light radiation influence factor and the temperature influence factor are as follows:

[0034]

[0035] The specific calculation formula of the photovoltaic inverter sensitivity coefficient is as follows:

[0036] In the formula, S is the sensitivity coefficient of the photovoltaic inverter, is the response time interval, is the light radiation influencing factor, is the temperature influence factor, is the standard data of photovoltaic inverter sensitivity, is an adjustable constant, is the light radiation, is the light radiation weight coefficient, is the temperature data, is the temperature data weight coefficient.

[0037] It's important to note that the adjustable constant reflects the PV inverter's sensitivity to voltage variations. The purpose of analyzing and extracting features from the solar radiation and temperature data surrounding the PV inverter is to identify and quantify the impact of these environmental factors on the PV system's output voltage and performance. Analyzing the response time interval between the PV inverter receiving an input signal and then issuing an output signal can better reflect the inverter's sensitivity. A longer response time indicates a greater vulnerability to voltage drops and a lower sensitivity.

[0038] The stability factor of a photovoltaic inverter refers to the impact on the inverter's output efficiency and adaptability when the voltage drops, causing instability in the photovoltaic inverter and reducing the low-voltage ride-through control performance of the photovoltaic system.

[0039] The specific method for obtaining the photovoltaic inverter stability coefficient is as follows: Get the waveform of the photovoltaic inverter output power; Based on the output power waveform, the efficiency of the power conversion unit is obtained by analyzing the change trend of the output power before and after the voltage drop according to the preset standard output power; Obtain the working status of historical protection devices in photovoltaic inverters during voltage drops; Performing regression analysis based on historical working status data of the protection device to obtain working status influencing factors of the protection device, wherein the working status influencing factors include fault repair data of the protection device and aging rate of the protection device; The sensitivity coefficient of the protection device is obtained by calculating the fault repair data and the aging rate of the protection device based on the preset protection device sensitivity formula; The efficiency of the power conversion unit and the sensitivity coefficient of the protection device are calculated based on a preset photovoltaic inverter stability formula to obtain a photovoltaic inverter stability coefficient.

[0040] The specific calculation formula of the sensitivity coefficient of the protection device is as follows:

[0041] The specific calculation formula of the photovoltaic inverter stability coefficient is as follows:

[0042] In the formula, is the photovoltaic inverter stability coefficient, is the efficiency of the power conversion unit, is the sensitivity coefficient of the protection device, is the number of faults of the PV inverter, is the maximum fault tolerance of the photovoltaic inverter, For fault repair data, is the aging rate of the protection device, is the weight coefficient of fault maintenance data, is the aging rate weight coefficient of the protection device.

[0043] It should be noted that voltage drops can affect the inverter's ability to track the maximum power point of the photovoltaic module's power conversion unit, preventing it from adjusting to its optimal operating state in a timely manner. This can reduce power output and affect the stability of the photovoltaic inverter. If the sensitivity of the inverter's protection settings decreases during a grid voltage drop, it can cause the system to disconnect accidentally, affecting the stability and power supply of the photovoltaic system. Therefore, analyzing the efficiency of the photovoltaic module's power conversion unit and the sensitivity coefficient of the protection device is important for analyzing the stability of the protection device during voltage drops.

[0044] Grid abnormality impact data is used to assess the impact of voltage sags on grid stability. Severe voltage sags can cause load fluctuations, dramatic changes in power demand, and imbalanced grid loads. This can lead to problems such as malfunctioning grid equipment and grid system instability.

[0045] Analyzing grid abnormality impact data has the following advantages for assessing the status of photovoltaic systems during voltage drops: Optimizing Adaptive Control of PV Systems: Inverters in PV systems typically automatically adjust their output power based on grid voltage fluctuations to ensure synchronization with the grid. Analyzing data on the impact of grid anomalies can help design more intelligent adaptive control strategies, enabling the inverter to take more appropriate responses to sudden events such as voltage drops. This optimization not only improves the system's adaptability to unstable grid environments but also reduces the PV system's negative impact on the grid.

[0046] Improving power quality and grid coordination: Analyzing grid anomaly data facilitates coordination between the PV system and the grid. By monitoring grid voltage fluctuations in real time, the PV system can more accurately schedule loads with the grid, mitigating the impact of voltage drops on PV generation. This not only improves the PV system's power generation efficiency but also helps improve the overall power quality of the grid and minimize the impact of voltage drops on other users.

[0047] Improving system stability: Grid anomalies can directly impact the performance of photovoltaic power generation systems. PV inverters may experience power outages or performance degradation during voltage drops. Analyzing grid anomaly data can proactively identify potential grid fluctuations and adjust the photovoltaic system's operating strategy, thereby reducing system downtime or power reductions caused by grid issues and enhancing overall system stability.

[0048] The specific method for obtaining the power grid abnormality impact data is as follows: Obtain the status data of power grid equipment and obtain the load data of power grid equipment based on feature analysis and extraction; Performing differential analysis on the load data of the power grid equipment based on preset stable load data to obtain a load differential value of the power grid equipment; Monitor the synchronization of different areas of the power grid system and obtain the synchronization rate of the power grid system; The grid abnormality impact data is calculated based on the preset grid abnormality impact data formula according to the load difference value of the grid equipment and the synchronization rate of the grid system.

[0049] The specific calculation formula for the power grid abnormality impact data is as follows:

[0050] In the formula, For data on abnormal impact of power grid, is the load difference value of the power grid equipment, For the The stable load of the equipment, among which, =1,2,3,...,R, R is an integer, N is the total number of devices, For the The load difference of each device is the synchronization rate of the power grid system.

[0051] It should be noted that performing differential analysis on grid device load data based on preset stable load data can be understood as follows: when voltage drops, the load stability of grid devices is affected, and a load differential value of the grid devices is obtained. A larger load differential value indicates a less stable grid stability at that time. The synchronization rate of the grid system can be understood as follows: a higher synchronization rate indicates that the transmission synchronization rates between grids are consistent, and deviations may occur, affecting grid instability.

[0052] S3, builds a photovoltaic system voltage drop status assessment model based on machine learning according to the performance impact data and grid abnormality impact data.

[0053] Performance impact data and grid anomaly impact data are obtained and trained based on machine learning to determine the weighted proportions of their impact on the entire photovoltaic system during voltage sags. The weights are automatically adjusted and updated to construct a photovoltaic system voltage sag assessment model. It should be noted that machine learning is currently available and will not be elaborated on in detail.

[0054] The specific calculation formula of the voltage drop state assessment model is as follows:

[0055] In the formula, is the voltage drop status assessment coefficient, is the grid abnormality impact data, S is the photovoltaic inverter sensitivity coefficient, is the photovoltaic inverter stability coefficient, is the weight coefficient of the power grid abnormality impact data, is the sensitivity coefficient of the photovoltaic inverter, is the weight coefficient of the PV inverter stability factor.

[0056] It should be noted that when the grid abnormality impact data becomes smaller and smaller, the photovoltaic inverter sensitivity coefficient becomes larger and larger, and the photovoltaic inverter stability coefficient becomes larger and larger, the voltage drop state assessment coefficient also becomes larger and larger.

[0057] S4, performing preliminary screening and secondary screening on the output of the photovoltaic system voltage drop state assessment model to obtain a voltage drop preset value, the specific steps are as follows: Obtaining a voltage sag state assessment coefficient according to an output of a photovoltaic system voltage sag state assessment model; Monitor the status of protective devices in the photovoltaic system and obtain the voltage drop value that causes the photovoltaic system to frequently perform protective actions due to the voltage drop being too small; Taking the voltage drop state assessment coefficient corresponding to the voltage drop value at which the protection action is frequently performed as the limit minimum value; Obtain the voltage drop value of the photovoltaic system that causes the inverter to disconnect due to excessive voltage drop; Taking the voltage drop state assessment coefficient corresponding to the voltage drop value causing the inverter to disconnect as the maximum limit value; Obtain all voltage drop status assessment coefficients and perform preliminary screening based on the minimum and maximum limits; Eliminate the voltage drop state assessment coefficients that are less than the minimum limit and greater than the maximum limit to obtain a preliminary screened voltage drop state assessment data set; Calculating an average of the voltage drop state assessment data set, and setting the average as a voltage drop state assessment stable value; Based on the voltage drop state assessment stability value, the voltage drop state assessment data set is screened twice by comparison, and the optimal voltage drop state assessment coefficient is obtained based on the gradient downward method; The voltage drop value corresponding to the optimal voltage drop state evaluation coefficient is set as a voltage drop preset value.

[0058] S5, judging and controlling the PV system to perform low voltage ride-through operation according to the preset voltage drop value, the specific steps are as follows: If the voltage drop value is less than the voltage drop preset value, the photovoltaic system does not need to perform low voltage ride-through operation, and the system performs real-time monitoring; If the voltage drop value is greater than or equal to the voltage drop preset value, the system automatically schedules the photovoltaic system to perform low voltage ride-through operation. The inverter maintains the grid voltage level through reactive power compensation to reduce the risk of further voltage drop. When the voltage returns to the standard operating voltage, the low voltage ride-through operation of the photovoltaic system is stopped and the photovoltaic inverter resumes normal operation.

[0059] Example 2, Figure 2 A schematic diagram of the structure of a photovoltaic power grid low voltage ride-through coordination system provided in an embodiment of the present application includes a signal transmission module, a data acquisition module, a model building module, a data screening module, and an instruction generation module: A signal transmission module is used to monitor voltage fluctuations in the photovoltaic system in real time, obtain a voltage steady-state coefficient based on the voltage fluctuations, and send a voltage drop signal to the photovoltaic system if the voltage is lower than the voltage steady-state coefficient; A data acquisition module is used to obtain multiple voltage drop values ​​based on the voltage drop signal, and obtain performance impact data of the photovoltaic inverter and grid abnormality impact data under each voltage drop value; A model building module is used to build a photovoltaic system voltage drop status assessment model based on machine learning according to performance impact data and grid abnormality impact data; A data screening module, configured to perform preliminary screening and secondary screening on the output of the photovoltaic system voltage drop state assessment model to obtain a voltage drop preset value; The instruction generation module is used to determine the corresponding instruction set for controlling the photovoltaic system to perform low voltage ride-through operation based on the preset voltage drop value.

[0060] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0061] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0062] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0063] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0064] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0065] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A photovoltaic power grid low voltage ride through coordination method, characterized in that: The steps include: Real-time monitoring of voltage fluctuations in the photovoltaic system, obtaining a voltage steady-state coefficient based on the voltage fluctuations, and sending a voltage drop signal to the photovoltaic system if the voltage is lower than the voltage steady-state coefficient; Based on the voltage drop signal, multiple voltage drop values ​​are obtained, and the performance impact data of the photovoltaic inverter and the grid abnormality impact data under each voltage drop value are obtained; Build a photovoltaic system voltage drop status assessment model based on machine learning according to performance impact data and grid abnormality impact data; Performing a preliminary screening and a secondary screening on the output of the photovoltaic system voltage drop state assessment model to obtain a voltage drop preset value; The corresponding instruction set is used to judge and control the photovoltaic system to perform low voltage ride-through operation according to the preset voltage drop value.

2. A photovoltaic power grid low voltage ride through coordination method according to claim 1, characterized in that: The performance influencing data of the photovoltaic inverter includes the photovoltaic inverter sensitivity coefficient. The specific method for obtaining the photovoltaic inverter sensitivity coefficient is as follows: Obtain the input signal of the photovoltaic inverter and the light radiation and temperature data around the photovoltaic inverter under the current voltage drop; Performing data analysis and feature extraction on the light radiation and temperature data around the photovoltaic inverter to obtain a light radiation influencing factor and a temperature influencing factor that affect the propagation of the voltage signal; Acquiring an output signal waveform of the photovoltaic inverter based on the photovoltaic inverter input signal; The output signal waveform is analyzed to obtain the response time interval between when the photovoltaic inverter receives the input signal and when it outputs the signal; The photovoltaic inverter sensitivity coefficient is calculated based on the response time interval combined with the light radiation influence factor and the temperature influence factor based on the preset photovoltaic inverter sensitivity formula.

3. A photovoltaic power grid low voltage ride through coordination method according to claim 2, characterized in that: The specific steps of performing data analysis and feature extraction on the light radiation and temperature data around the photovoltaic inverter to obtain the light radiation influencing factor and the temperature influencing factor that affect the propagation of the voltage signal are as follows: Collect light radiation and temperature data for feature extraction, obtain weight coefficients of light radiation and temperature data, and remove random fluctuations in the data based on a smoothing algorithm; The processed light radiation and temperature data are used to draw a scatter diagram of the influence of light radiation and temperature data on the signal of the photovoltaic inverter; Obtain the changing trend of the influence of light radiation and temperature data on the signal propagation of photovoltaic inverters based on the scatter plot of signal influence; The change trend is quantified based on regression analysis to obtain the light radiation influence factor and the temperature influence factor respectively.

4. A photovoltaic power grid low voltage ride through coordination method according to claim 1, characterized in that: The performance influencing data of the photovoltaic inverter includes a photovoltaic inverter stability coefficient. The photovoltaic inverter stability coefficient is specifically obtained in the following manner: Get the waveform of the photovoltaic inverter output power; Based on the output power waveform, the efficiency of the power conversion unit is obtained by analyzing the change trend of the output power before and after the voltage drop according to the preset standard output power; Obtain the working status of the historical protection device in the photovoltaic inverter during voltage drop; Performing regression analysis based on historical working status data of the protection device to obtain working status influencing factors of the protection device, wherein the working status influencing factors include fault repair data of the protection device and an aging rate of the protection device; The fault repair data and the aging rate of the protection device are calculated based on the preset protection device sensitivity formula to obtain the protection device sensitivity coefficient; The efficiency of the power conversion unit and the sensitivity coefficient of the protection device are calculated based on a preset photovoltaic inverter stability formula to obtain a photovoltaic inverter stability coefficient.

5. A photovoltaic power grid low voltage ride through coordination method according to claim 1, characterized in that: The specific method for obtaining the power grid abnormality impact data is as follows: Obtain the status data of power grid equipment, and obtain the load data of power grid equipment based on feature analysis and extraction; Performing differential analysis on the load data of the power grid equipment based on preset stable load data to obtain a load differential value of the power grid equipment; Monitor the synchronization of different areas of the power grid system and obtain the synchronization rate of the power grid system; The grid abnormality impact data is calculated based on the preset grid abnormality impact data formula according to the load difference value of the grid equipment and the synchronization rate of the grid system.

6. A photovoltaic power grid low voltage ride through coordination method according to claim 1, characterized in that: The output of the photovoltaic system voltage drop state assessment model is preliminarily screened and secondarily screened to obtain a voltage drop preset value, and the specific steps are as follows: Obtaining a voltage drop state assessment coefficient according to an output of a photovoltaic system voltage drop state assessment model; Monitor the status of the protection device in the photovoltaic system and obtain the voltage drop value that causes the photovoltaic system to frequently perform protection actions due to the voltage drop being too small; The voltage drop state assessment coefficient corresponding to the voltage drop value for which the protection action is frequently performed is taken as the limit minimum value; Get the voltage drop value of the photovoltaic system when the voltage drop is too large, causing the inverter to disconnect; Taking the voltage drop state assessment coefficient corresponding to the voltage drop value causing the inverter to disconnect as the maximum limit value; Obtain all voltage drop status assessment coefficients and perform preliminary screening based on the minimum and maximum limits; The voltage drop state assessment coefficients that are less than the limit minimum value and greater than the limit maximum value are excluded to obtain a preliminary screened voltage drop state assessment data set; Calculate an average of the voltage drop state assessment data set, and set the average as a voltage drop state assessment stable value; Based on the voltage drop state assessment stability value, the voltage drop state assessment data set is screened twice by comparison, and the optimal voltage drop state assessment coefficient is obtained based on the gradient downward method; The voltage drop value corresponding to the optimal voltage drop state evaluation coefficient is set as a voltage drop preset value.

7. A photovoltaic power grid low voltage ride through coordination method according to claim 1, characterized in that: The corresponding instruction set for judging and controlling the photovoltaic system to perform low voltage ride-through operation according to the voltage drop preset value has the following specific steps: If the voltage drop value is less than the voltage drop preset value, the photovoltaic system does not perform low voltage ride-through operation, and the system performs real-time monitoring; If the voltage drop value is greater than or equal to the voltage drop preset value, the system automatically schedules the photovoltaic system to perform low voltage ride-through operation.

8. A photovoltaic power grid low voltage ride through coordination method according to claim 2, characterized in that: The specific calculation formulas of the light radiation influence factor and the temperature influence factor are as follows: The specific calculation formula of the photovoltaic inverter sensitivity coefficient is as follows: In the formula, S is the sensitivity coefficient of the photovoltaic inverter, is the response time interval, is the light radiation influencing factor, is the temperature influence factor, is the standard data of photovoltaic inverter sensitivity, is an adjustable constant, is the light radiation, is the light radiation weight coefficient, is the temperature data, is the temperature data weight coefficient.

9. A photovoltaic power grid low voltage ride through coordination method according to claim 1, characterized in that: The specific calculation formula of the voltage drop state assessment model is as follows: In the formula, is the voltage drop status assessment coefficient, is the grid abnormality impact data, S is the photovoltaic inverter sensitivity coefficient, is the photovoltaic inverter stability factor, is the weight coefficient of power grid abnormality impact data, is the sensitivity coefficient of the photovoltaic inverter, is the weight coefficient of the PV inverter stability factor.

10. A system using a photovoltaic power grid low voltage ride through coordination method according to any one of claims 1 to 9, characterized in that: It includes signal transmission module, data acquisition module, model building module, data screening module and instruction generation module: A signal transmission module is used to monitor voltage fluctuations in the photovoltaic system in real time, obtain a voltage steady-state coefficient based on the voltage fluctuations, and send a voltage drop signal to the photovoltaic system if the voltage is lower than the voltage steady-state coefficient; A data acquisition module is used to obtain multiple voltage drop values ​​based on the voltage drop signal, and obtain performance impact data of the photovoltaic inverter and power grid abnormality impact data under each voltage drop value; A model building module is used to build a photovoltaic system voltage drop state assessment model based on machine learning according to performance impact data and grid abnormality impact data; A data screening module, used for performing preliminary screening and secondary screening on the output of the photovoltaic system voltage drop state assessment model to obtain a voltage drop preset value; The instruction generation module is used to judge and control the corresponding instruction set of the photovoltaic system to perform low voltage ride-through operation according to the preset value of voltage drop.

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