Method for flexibly regulating and controlling photovoltaic power generation quality through power grid monitoring compensation

Abstract

The invention provides a method for carrying out flexible regulation and control on photovoltaic power generation quality through power grid monitoring compensation, and relates to the technical field of photovoltaic power generation regulation and control, and the method comprises the steps: carrying out the real-time monitoring of a photovoltaic power generation system through setting a photoelectric sensor, and outputting key operation parameters; performing power generation quality evaluation, and identifying a plurality of to-be-compensated devices and a plurality of flexible compensation parameters according to an obtained power generation quality evaluation index and a preset power generation quality index; establishing a communication protocol for connecting a control center with the plurality of to-be-compensated devices based on a TSN technology to obtain a TSN distributed compensation architecture; and transmitting a plurality of flexible compensation parameters through a plurality of TSN transmission channels, and carrying out end-to-end synchronous flexible regulation and control. The method and the device solve the technical problems that the prior art often depends on performance adjustment of single equipment and lacks a coordination mechanism between the equipment, so that compensation of different equipment may be asynchronous, the system operation efficiency is low, and the overall compensation effect is influenced.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation regulation, and particularly to a method for flexibly regulating the quality of photovoltaic power generation through grid monitoring compensation. Background Art

[0002] The main advantages of photovoltaic power generation lie in its low-carbon environmental protection, high efficiency, and wide applicability. However, the power generation quality of photovoltaic power generation systems is easily affected by various factors, such as weather changes, equipment aging, grid fluctuations, etc. These factors will lead to a decline in power generation efficiency, power fluctuations, and system stability problems. To ensure the efficient and stable operation of photovoltaic power generation systems, it is crucial to effectively monitor and regulate the systems. In photovoltaic power generation systems, there are various devices, and the coordination and compensation adjustment between them are relatively complex. Existing technologies often rely on the performance adjustment of a single device and lack a coordination mechanism between devices, which may cause the compensation of different devices to be out of sync, resulting in low system operation efficiency, and even may lead to conflicts between compensation measures, affecting the overall compensation effect. Summary of the Invention

[0003] This application provides a method for flexibly regulating the quality of photovoltaic power generation through grid monitoring compensation, aiming to solve the technical problems that existing technologies often rely on the performance adjustment of a single device and lack a coordination mechanism between devices, resulting in the compensation of different devices being out of sync, causing low system operation efficiency, and further affecting the overall compensation effect.

[0004] The method for flexibly regulating the quality of photovoltaic power generation through grid monitoring compensation disclosed in this application includes: setting a photovoltaic sensor to monitor the photovoltaic power generation system in real time and output key operation parameters; evaluating the power generation quality based on the key operation parameters, and identifying multiple devices to be compensated in the photovoltaic power generation system and multiple flexible compensation parameters corresponding to the multiple devices to be compensated according to the obtained power generation quality evaluation index and the preset power generation quality index; establishing a communication protocol for connecting a control center and the multiple devices to be compensated based on the TSN technology to obtain a TSN distributed compensation architecture; and invoking the TSN distributed compensation architecture to transmit the multiple flexible compensation parameters through multiple TSN transmission channels for end-to-end synchronous flexible regulation of the multiple devices to be compensated.

[0005] One or more technical solutions provided in this application have at least the following beneficial effects: By setting up optoelectronic sensors to monitor the photovoltaic power generation system in real time, key operating parameters of the photovoltaic system can be continuously tracked, and dynamic data feedback can be achieved. This enables the real-time acquisition of the operating status, providing basic data support for subsequent quality assessment and compensation decision-making. Conducting power generation quality assessment based on key operating parameters can accurately identify which equipment's performance fails to meet the standards and requires compensation adjustment. By comparing the evaluation indicators with the preset power generation quality indicators, the equipment to be compensated can be automatically identified, ensuring that the compensation work is targeted, precise, and efficient. By establishing a communication protocol between the control center and multiple devices to be compensated based on TSN technology, an efficient, low-latency, and highly real-time compensation network architecture can be constructed. The introduction of TSN technology, especially its precise time synchronization ability, ensures that compensation data can be transmitted to each device to be compensated within the specified time, improving the real-time performance and reliability of the compensation process. By invoking the TSN distributed compensation architecture, multiple flexible compensation parameters can be synchronously transmitted to each device to be compensated through multiple TSN transmission channels, achieving end-to-end synchronous flexible regulation. This synchronous regulation ensures that the compensation operations among multiple devices can be consistent, avoiding performance instability or inconsistent compensation effects caused by the compensation time difference between different devices.

[0006] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented in accordance with the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically given below. Brief Description of the Drawings

[0007] Figure 1 This is a schematic flow chart of a method for flexibly regulating the quality of photovoltaic power generation through grid monitoring and compensation provided by an embodiment of this application.

[0008] Figure 2 This is a schematic flow chart of the TSN transmission channel test in the method for flexibly regulating the quality of photovoltaic power generation through grid monitoring and compensation provided by an embodiment of this application. Detailed Description of the Embodiments

[0009] An embodiment of this application provides a method for flexibly regulating the quality of photovoltaic power generation through grid monitoring and compensation, which solves the technical problem that the prior art often relies on the performance adjustment of a single device and lacks a coordination mechanism among devices, resulting in asynchronous compensation of different devices, low system operation efficiency, and thus affecting the overall compensation effect.

[0010] After introducing the basic principle of this application, the various non-restrictive embodiments of this application will be specifically introduced below in conjunction with the drawings in the specification.

[0011] As Figure 1As shown, the embodiments of the present application provide a method for flexibly regulating the power generation quality through power grid monitoring and compensation. The method includes: Real-time monitoring of the photovoltaic power generation system is carried out by setting optoelectronic sensors, and key operating parameters are output.

[0012] Optoelectronic sensors are set at different positions of the photovoltaic power generation system, including installing sensors on key devices such as photovoltaic modules and inverters. These sensors collect and obtain the operating data of the photovoltaic system in real time, such as key electrical parameters such as light intensity, temperature, wind speed, voltage, and current, to ensure that the operating conditions of the system can be reflected in real time.

[0013] Based on the key operating parameters, power generation quality evaluation is carried out. According to the obtained power generation quality evaluation indicators and preset power generation quality indicators, multiple devices to be compensated in the photovoltaic power generation system, as well as multiple flexible compensation parameters corresponding to the multiple devices to be compensated, are identified.

[0014] Power generation quality evaluation is carried out according to the key operating parameters monitored in real time. The main evaluation indicators include power generation efficiency, power fluctuation, equipment health, power generation stability, etc. The actually obtained power generation quality evaluation indicators are compared with the preset power generation quality indicators. The preset power generation quality indicators are ideal indicators determined by the design parameters or historical operating data of the photovoltaic power generation system. For example, the efficiency and stability under the best operating state. If the power generation quality evaluation indicator of a certain device deviates from the preset power generation quality indicator, then it is determined that the device is a device to be compensated. For example, problems such as the performance degradation of photovoltaic modules and the reduction of inverter efficiency may be identified.

[0015] Corresponding flexible compensation parameters are assigned to each device to be compensated. The flexible compensation parameters are performance adjustment values for the device, which are determined by calculating specific parameters of the device to be compensated, such as the maximum power point tracking efficiency of the inverter and the IV characteristic curve of the photovoltaic module. For example, if the maximum power point tracking efficiency of the inverter is low, the performance can be optimized by adjusting this parameter.

[0016] Based on the TSN technology, a communication protocol for connecting the control center with the multiple devices to be compensated is established to obtain a TSN distributed compensation architecture.

[0017] TSN (Time-Sensitive Networking) technology ensures the on-time transmission of critical data by guaranteeing time determinacy and low latency, allowing real-time and non-real-time data to be transmitted on the same network while ensuring their timeliness and reliability. Specifically, a communication protocol is established between the control center and the devices to be compensated. The control center, as the central management unit, is responsible for initiating the compensation operation and sending the compensation parameters to each device through the TSN protocol. Multiple devices to be compensated are connected to the control center via the TSN network. The TSN protocol can ensure that these devices can receive compensation instructions from the control center according to the preset priorities and timestamps. In the TSN network, communication between different devices is based on precise time synchronization. All devices to be compensated will receive data synchronized with a unified clock, which ensures that control commands for multiple devices can take effect at the same time point, achieving end-to-end synchronous regulation.

[0018] Invoke the TSN distributed compensation architecture to transmit the multiple flexible compensation parameters through multiple TSN transmission channels for end-to-end synchronous flexible regulation of the multiple devices to be compensated.

[0019] According to the compensation requirements of each device, transmit multiple flexible compensation parameters through multiple TSN transmission channels. Utilizing the precise clock synchronization and low latency characteristics of TSN technology, the compensation instructions are transmitted from the control center to each device through the TSN distributed network, achieving end-to-end synchronous control. This means that all devices to be compensated will receive the compensation instructions at the same time point and adjust simultaneously, which can ensure that all devices in the entire photovoltaic system work in coordination during regulation and the system performance will not be unstable due to compensation time differences. During the compensation process, the compensation parameters are dynamically adjusted according to the real-time collected operation data and evaluation metrics. Through the TSN network, the compensation parameters can be flexibly adjusted and transmitted to cope with the changes in the photovoltaic system under different working conditions.

[0020] Furthermore, as Figure 2 shown, after obtaining the TSN distributed compensation architecture, the method further includes: Obtain multiple groups of test sample signals; test the multiple TSN transmission channels of the TSN distributed compensation architecture according to the multiple groups of test sample signals, and output multiple groups of test data sets; analyze the multiple groups of test data sets to check whether the transmission quality of the multiple TSN transmission channels is greater than a preset transmission quality threshold, and update the resource configuration for the TSN transmission channels that fail the inspection until all the multiple TSN transmission channels pass the inspection.

[0021] Obtain multiple sets of test sample signals, which represent data streams under different working conditions, including but not limited to, signals under normal operating conditions, used to simulate the data transmitted when the device is operating normally; signals under perturbed conditions, used to simulate the data transmitted when the device or network is subject to external perturbations, such as network latency, interference, and load changes; signals at different traffic levels, used to test data transmission under different network loads. The selection of these signals should be representative and cover various situations that may occur during actual operation.

[0022] Input these test sample signals into the TSN distributed compensation architecture. Specifically, data testing is carried out through multiple TSN transmission channels. During the testing process, the signals will be transmitted through different paths to examine the performance of each transmission channel under different conditions, and record the responses of each transmission channel, such as transmission speed, latency, packet loss rate, etc., to form multiple sets of test data sets, which contain the performance data of each transmission channel under different test scenarios for subsequent quality analysis and optimization.

[0023] Through the analysis of multiple sets of test data sets, evaluate the transmission quality of each TSN transmission channel and compare the transmission quality with preset transmission quality thresholds, which are set according to the actual requirements and working conditions of the system. For example, the latency should be lower than a certain millisecond level, and the packet loss rate should be controlled within a certain range, etc.

[0024] If the transmission quality of some transmission channels fails to meet the preset transmission quality thresholds, then determine that these transmission channels fail the inspection, and update the resource configuration of the TSN transmission channels that fail the inspection. Specifically, load the preset resource configuration templates, which define network resource configuration parameters such as bandwidth, priority, and time slot length. For the channels that fail the inspection, use the resource configuration templates to update the resource configuration. For example, increase the bandwidth allocation, adjust the priority of data packets, and reconfigure the time slot allocation strategy, and reapply the updated resource configuration to the unqualified transmission channels for the second round of testing. This process will continue until all transmission channels can pass the quality inspection and meet the system's requirements for data transmission quality.

[0025] Furthermore, testing the multiple TSN transmission channels of the TSN distributed compensation architecture according to the multiple sets of test sample signals includes: Test the multiple TSN transmission channels of the TSN distributed compensation architecture according to the multiple groups of test sample signals in the first state, and perform perturbation tests on the multiple TSN transmission channels of the TSN distributed compensation architecture according to the multiple groups of test sample signals in the second state, to obtain multiple groups of test data sets in the first state and multiple groups of test data sets in the second state; wherein, the first state is a non-perturbation state, and the second state is a perturbation state.

[0026] In the first state, the purpose of the test is to simulate the transmission performance when the network and devices are in normal working conditions, without any artificial injection of interference or anomalies, aiming to evaluate the basic performance of the transmission channels. Specifically, in the first state, multiple groups of test sample signals are used to test through multiple TSN transmission channels, and transmission data is collected during the test, including key parameters such as delay, bandwidth, packet loss rate, and delay jitter. By analyzing this transmission data, the transmission quality of each transmission channel in the normal working state can be evaluated.

[0027] In the second state, in order to simulate abnormal working conditions, some perturbations will be artificially injected to test the performance of the TSN network in abnormal working states. Typical perturbation scenarios include: network congestion, simulating that when a large amount of data is transmitted, the network bandwidth is overloaded, resulting in increased data transmission delay and even packet loss; device clock offset, simulating the clock synchronization error between devices, resulting in inaccurate timestamps of transmission, affecting data synchronization; electromagnetic interference, simulating changes in the electromagnetic environment, resulting in instability of signal transmission. The injected perturbation parameters include: delay jitter, simulating the delay fluctuation of the network, testing the reliability of the transmission channel when the delay changes; packet loss rate gradient, by simulating different degrees of packet loss, testing the performance of the channel in the case of packet loss. For example, changing from a low packet loss rate (1%) to a high packet loss rate (10%) in a gradient manner, and observing the change in transmission quality; burst traffic, simulating the influx of a large amount of data traffic in a short period of time, resulting in an instantaneous increase in network bandwidth, which may cause problems such as congestion. In the perturbation state, the test data of multiple transmission channels are recorded. These data sets contain parameters such as delay, packet loss, bandwidth utilization rate, and transmission failure rate in the perturbation state. Compared with the data in the first state, these test data help analyze the stability and reliability of the TSN transmission channels in actual abnormal situations.

[0028] Furthermore, update the resource configuration for the TSN transmission channels that fail the inspection. The method includes: Load the first resource configuration template, including bandwidth ratio, priority level, and time slot length; analyze the multiple groups of test data sets in the first state to inspect the transmission quality of the multiple TSN transmission channels, and update the resource configuration of the TSN transmission channels that fail the inspection according to the first resource configuration template.

[0029] Load the first resource configuration template, including bandwidth ratio, priority level, and time slot length. Among them, the bandwidth ratio specifies the occupancy ratio of each TSN transmission channel in the total bandwidth. Bandwidth is a key factor affecting data transmission rate, and appropriate bandwidth configuration can ensure the stable transmission of data traffic. The priority level is to configure different priorities for different data streams according to the importance and real-time requirements of the data. For example, the compensation data stream may require a higher priority to ensure that the compensation instructions can be transmitted in real time, while other types of data streams, such as logs or historical data, can be configured with a lower priority. In a time-sensitive network, the time slot length determines the transmission interval of data packets. A shorter time slot means more frequent data transmission, but it may also lead to bandwidth competition. Reasonable configuration of the time slot length can balance the transmission efficiency and network resource utilization. The role of the first resource configuration template is to set a preliminary configuration scheme for each TSN transmission channel to ensure that under normal conditions, network resources can be effectively allocated and support real-time transmission requirements.

[0030] Analyze multiple groups of test data sets in the first state. Through these data, the performance of each transmission channel can be evaluated and compared with the preset transmission quality threshold. If some transmission channels fail the quality test, their resource configuration needs to be updated. Exemplarily, the binary search method is used to reallocate the bandwidth in the range of [10%, 30%]. The basic idea of the binary search method is to gradually narrow the range of bandwidth adjustment until the appropriate bandwidth ratio is found. The advantage of this method is that it can efficiently find the optimal configuration within a reasonable range. Dynamically adjust the new priority according to the formula new priority = original priority + ΔP (ΔP ≥ 1), that is, each priority adjustment is at least 1 level. The higher the priority of the data stream, the higher the resource priority it obtains in the network. Shorten the time slot length according to the traffic prediction model, where the step size of each shortening is set to be less than or equal to 10 ms. By controlling the adjustment amplitude, the network load fluctuations can be avoided and the system changes can be made smoother. The time slot length directly affects the transmission interval of data packets, and adjusting the time slot length can optimize the utilization of network resources. For the channels with unqualified transmission quality, the time slot length is dynamically adjusted according to the traffic prediction model.

[0031] The update and adjustment of resource configuration is an iterative process until all transmission channels meet the preset quality standards. By continuously adjusting the bandwidth, priority, and time slot length, the performance of each transmission channel can be gradually optimized to ensure that the entire TSN network can operate stably and reliably.

[0032] Furthermore, for the TSN transmission channels that fail the inspection, the method for updating the resource configuration further includes: Load the second resource configuration template, including bandwidth ratio, priority level, time slot length, and preemption time slot; analyze multiple sets of test data sets in the second state to test the transmission quality of the multiple TSN transmission channels, and update the resource configuration of the TSN transmission channels that fail the test according to the second resource configuration template.

[0033] Load the second resource configuration template, which is different from the first resource configuration template and is specifically for resource adjustment in the disturbance state. The second resource configuration template includes bandwidth ratio, priority level, time slot length, and preemption time slot. Among them, in the resource configuration template, the bandwidth ratio will be reallocated according to the actual network state and transmission requirements. Since there may be congestion or insufficient bandwidth in the network under the disturbance state, it is necessary to adjust the bandwidth allocation more flexibly; priority control is particularly important under disturbance conditions. High-priority data streams, such as critical compensation data, need to obtain bandwidth and resources first. At this time, the priority can be dynamically adjusted according to the importance of the data to ensure the priority transmission of critical data; the configuration of the time slot length controls the time interval for data packets to be transmitted in the network. According to the network conditions under the disturbance state, the time slot length may need to be adjusted to improve the data transmission efficiency; in TSN, the preemption time slot is a mechanism that allows some high-priority data streams to preempt network resources when needed. This is a resource configuration designed specifically for high-priority data streams, which can ensure the priority transmission of critical task data such as compensation data and prevent it from being delayed or blocked by low-priority data streams.

[0034] Analyze multiple sets of test data sets in the second state to test the transmission quality of each transmission channel, and find out those transmission channels that fail to meet the preset quality standards under disturbance conditions. If some transmission channels fail the quality test, it is necessary to update their resource configuration according to the second resource configuration template. Exemplarily, according to the test data under the disturbance state, dynamically adjust the bandwidth ratio. The bandwidth reallocation includes increasing the bandwidth allocated to critical data streams and reducing the bandwidth occupancy of low-priority data streams to ensure that high-priority tasks obtain sufficient bandwidth; dynamically adjust the priority of the transmission channel according to the transmission quality and traffic demand. For example, if the delay of some transmission channels is high, adjust their priority to provide higher priority for those important data streams; allow some important data streams to preempt time slots during transmission to ensure that high-priority critical data streams such as compensation parameters can be transmitted in time. For example, compensation parameters can preempt time slots in case of emergencies to avoid delaying critical data due to the transmission of other data streams; under disturbance conditions, the adjustment of the time slot length can optimize the data transmission efficiency. For example, if the network carrying capacity is insufficient or there is congestion, the time slot length can be selected to be shortened to increase the data transmission frequency, so that the network can better respond to sudden traffic demands, and dynamically adjust the time slot length according to the traffic prediction model to ensure that the transmission channel can adapt to the changing load.

[0035] After each update of the resource allocation, re-evaluate the quality of the transmission channels and conduct a second round of tests. If the updated resource allocation still does not meet the quality requirements, continue to optimize parameters such as bandwidth, priority, or time slots until all transmission channels can pass the quality inspection.

[0036] Furthermore, identify multiple devices to be compensated in the photovoltaic power generation system according to the obtained power generation quality evaluation index and the preset power generation quality index, where the multiple devices to be compensated are devices with an index difference between the power generation quality evaluation index and the preset power generation quality index greater than a preset threshold.

[0037] By comparing the power generation quality evaluation index with the preset power generation quality index, identify devices with unqualified performance. Specifically, when the difference between the power generation quality evaluation index of a certain device and the preset power generation quality index is greater than the preset threshold, the device is regarded as a target that needs to be compensated. For example, if the power generation efficiency of an inverter is more than 10% lower than the preset efficiency, or the power fluctuation exceeds ±5% of the set range, then the inverter will be marked as a device to be compensated. By comparing the difference in the evaluation index with the preset threshold, determine which devices need to be compensated and adjusted. The condition for a device to be determined as needing compensation is that the index difference is greater than the preset threshold.

[0038] Furthermore, identify multiple flexible compensation parameters corresponding to the multiple devices to be compensated. The method includes: Construct a list of candidate parameter items, and calculate the Spearman correlation coefficient between the power generation quality evaluation index and each parameter item in the list of candidate parameter items; conduct a correlation analysis on the list of candidate parameter items according to the Spearman correlation coefficient of each parameter item, obtain the identified compensation parameter items greater than the preset threshold, and identify multiple flexible compensation parameters corresponding to the multiple devices to be compensated according to the identified compensation parameter items.

[0039] Construct a list of candidate parameter items. The list of candidate parameter items includes all potential parameters that affect the power generation quality evaluation index, and these parameters are usually related to the operating state of the photovoltaic system, the health status of the equipment, and the environmental conditions. For example, it includes equipment performance parameters, operating state parameters, environmental parameters, etc.

[0040] The Spearman correlation coefficient is a method for measuring the monotonic relationship between two variables. Different from the Pearson correlation coefficient, the Spearman correlation coefficient does not require the relationship between variables to be linear and is applicable to any form of monotonic relationship. Calculate the Spearman correlation coefficient between the power generation quality evaluation index and each parameter item in the candidate parameter item list. Specifically, the power generation quality evaluation index includes indicators such as power generation efficiency, power fluctuation, and equipment health. For each candidate parameter item, such as inverter efficiency, light intensity, etc., calculate the Spearman correlation coefficient between it and each power generation quality evaluation index according to historical data or real-time data. The value range of the Spearman correlation coefficient is from -1 to 1. Among them, 1 represents a perfect positive correlation, that is, the increase in the parameter value is consistent with the increase in the evaluation index; -1 represents a perfect negative correlation, that is, the increase in the parameter value is opposite to the increase in the evaluation index; 0 represents no monotonic relationship.

[0041] According to the preset threshold, screen out those parameters that have a strong correlation with the power generation quality evaluation index. The threshold is determined through experiments, expert experience, or historical data analysis. For example, parameters with a Spearman correlation coefficient greater than 0.7 or less than -0.7 are determined to have a strong correlation. According to the correlation analysis results, identify the compensation parameter items, that is, identify all candidate parameter items that have a strong positive or negative correlation with the power generation quality evaluation index as compensation parameter items. These parameter items are determined to be the key factors affecting power generation quality, so adjustments are needed to optimize the overall performance of the system.

[0042] Associate each device to be compensated with one or more identified compensation parameter items. For example, if the Spearman correlation coefficient between the maximum power point tracking efficiency of a certain inverter and the power generation efficiency is high, the maximum power point tracking efficiency of the inverter can be used as the compensation parameter for this device, and the power generation quality can be optimized by adjusting its efficiency. These identified compensation parameters are used for adjustment and optimization in subsequent compensation steps. For example, for some devices with low performance, their flexible compensation parameters can be dynamically adjusted to improve their performance to meet the expected power generation quality standards.

[0043] Furthermore, the multiple flexible compensation parameters at least include the maximum power point tracking efficiency of the inverter and the offset of the IV characteristic curve of the photovoltaic module; among them, the compensation value of the maximum power point tracking efficiency of the inverter and the compensation value of the offset of the IV characteristic curve of the photovoltaic module are obtained from historical healthy operation parameters.

[0044] Multiple flexible compensation parameters at least include the maximum power point tracking efficiency of the inverter and the offset of the PV module's I-V characteristic curve. Among them, the maximum power point tracking efficiency of the inverter refers to the efficiency of the inverter in converting the direct current generated by the PV module into alternating current under different light conditions. The improvement of the maximum power point tracking efficiency means that more electricity can be effectively converted from the PV module into usable electricity. The maximum power point tracking efficiency in the flexible compensation parameters is a key parameter in the photovoltaic power generation system because it directly affects the overall power generation efficiency of the system. If the maximum power point tracking efficiency of the inverter is lower than expected, it may be due to equipment aging, environmental factors, or improper operation, and compensation is required to adjust this efficiency to improve the overall power generation efficiency. During the compensation process, the maximum power point tracking efficiency of the inverter will be adjusted through real-time monitoring data and historical health parameters so that the system can restore or optimize its performance.

[0045] The I-V characteristic curve of the PV module describes the relationship between the current and voltage of the PV module under different working conditions, usually showing a non-linear relationship between current and voltage. The offset refers to the degree of deviation of the I-V curve, usually caused by component aging, temperature change, or light intensity change. If the I-V characteristic curve is offset, it may lead to a decrease in the output power of the PV module. The offset of the I-V characteristic curve in the flexible compensation parameters is another important adjustment parameter. When the I-V characteristic curve of the PV module is offset, it is necessary to compensate for this offset to restore the best output characteristics of the component.

[0046] Historical healthy operation parameters refer to the operation data of the photovoltaic power generation system over a period of time in the past. These data include the long-term performance of the equipment, operation efficiency, fault records, environmental conditions, etc. By monitoring the maximum power point tracking efficiency of the inverter over a long period, its performance under different environmental conditions can be obtained. For example, under different light intensities and temperatures, the maximum power point tracking efficiency of the inverter will change. Historical data can help identify whether it has been inefficient in past operations; by regularly monitoring the I-V characteristics of the PV module, the performance changes of the module during long-term operation can be obtained. If the component ages or other faults occur, the I-V characteristic curve may shift, and historical data can help accurately identify these changes.

[0047] By comparing the historical health data with the preset normal working range, the compensation value for the maximum power point tracking efficiency of the inverter can be determined. Specifically, if the historical data indicates that the maximum power point tracking efficiency of the inverter is lower than the ideal value, the compensation value will be used to increase the efficiency to achieve the expected performance. For example, if the inverter showed a lower-than-normal maximum power point tracking efficiency during certain periods in the past, the compensation value will be calculated based on the historical data to increase the output power of the inverter after compensation.

[0048] By analyzing the historical monitoring data of the IV curve of the photovoltaic module, the degree of deviation of the IV characteristic curve can be identified, and the compensation value is calculated based on the health data of the module and the preset IV curve model. For example, if the historical data indicates that the deviation of the IV curve of the photovoltaic module is greater than a certain threshold, the working state of the photovoltaic module is adjusted according to the compensation model to restore its optimal output characteristics.

[0049] The compensation values of the inverter and the photovoltaic module are applied to the control system of the inverter and the operation management of the photovoltaic module in real time. By adjusting the maximum power point tracking efficiency of the inverter and the deviation of the IV characteristic curve of the photovoltaic module, the power generation quality can be effectively improved, and it is ensured that the photovoltaic power generation system can maintain efficient operation under different working environments.

[0050] Furthermore, after obtaining the multiple devices to be compensated, the method further includes: Grouping the multiple devices to be compensated according to the compensation order to obtain a synchronous compensation device group; establishing a communication protocol for connecting the control center with each group in the synchronous compensation device group based on the TSN technology, and updating the TSN distributed compensation architecture, where the TSN transmission channels within each synchronous compensation device group transmit synchronously.

[0051] In a photovoltaic power generation system, not all devices to be compensated need to be compensated simultaneously. The compensation order of the devices is usually determined according to factors such as their importance in the system, the severity of the fault, and the compensation requirements. According to the compensation order, multiple devices to be compensated are divided into multiple groups, and the devices within each group will receive compensation at the same time point. The principle of device grouping is to ensure that the compensation work of the devices within the same group can be carried out synchronously to reduce the complexity and delay of system scheduling. For example, the devices are grouped according to their functions or types, such as an inverter group, a photovoltaic module group, etc.; or grouped according to the compensation requirements of the devices, such as the devices with priority compensation are divided into one group, and the devices with sub-optimal compensation are divided into another group.

[0052] The TSN distributed compensation architecture is a communication architecture designed based on TSN technology. It can support real-time data transmission between multiple synchronous compensation device groups. By updating the existing compensation architecture, it can ensure more efficient and stable communication between device groups, strengthen the coordination and synchronization between different device groups by updating the communication architecture, so that each device can receive compensation instructions at the correct time point. Specifically, for the devices within each synchronous compensation device group, the TSN technology ensures the synchronous transmission of compensation parameters through the TSN transmission channels of these devices. All devices within the group will perform compensation according to unified clock synchronization, ensuring collaborative work among devices without the confusion or delay of compensation instructions. Within each synchronous compensation device group, the compensation operations of all devices will be synchronized through the TSN transmission channels. Through an accurate clock synchronization mechanism, it can ensure that all devices within the group receive compensation parameters at the same time point and immediately start the compensation operation.

[0053] Furthermore, power generation quality assessment is carried out based on the key operating parameters. Among them, the indicators of power generation quality assessment include power generation efficiency, power fluctuation, device health, and power generation stability and persistence.

[0054] Power generation quality assessment is carried out according to the key operating parameters. The indicators of power generation quality assessment include power generation efficiency, power fluctuation, device health, and power generation stability and persistence. Specifically, calculate the power generation efficiency and evaluate the conversion efficiency of photovoltaic modules under the existing light conditions, usually by comparing the output power of the system with the theoretical maximum power; calculate the degree of power output fluctuation and evaluate the stability of the system. If the power output fluctuates greatly in different time periods, it indicates that the stability of the system is poor and compensation may be required; by monitoring the historical data and current status of the device, evaluate whether there are problems such as aging, faults or other issues with the device. If the device health is low, it may affect the overall power generation quality of the system; by evaluating the continuous power generation capacity of the system over a period of time, judge whether it can maintain a stable output during long-term operation. Power generation stability is an important indicator to measure the long-term operation ability of a photovoltaic power generation system. Through quantitative analysis of the above power generation quality assessment indicators, a power generation quality assessment result is generated to provide a basis for subsequent compensation operations.

[0055] In summary, the method for flexibly regulating the power generation quality of photovoltaic power generation through grid monitoring compensation provided by the embodiments of the present application has the following technical effects: By setting up optoelectronic sensors to monitor the photovoltaic power generation system in real time, the key operating parameters of the photovoltaic system can be continuously tracked, and dynamic data feedback can be achieved. This enables the real-time acquisition of the operating status and provides basic data support for subsequent quality assessment and compensation decision-making. By evaluating the power generation quality based on the key operating parameters, it is possible to accurately identify which equipment's performance fails to meet the standards and requires compensation adjustment. By comparing the evaluation indicators with the preset power generation quality indicators, the equipment to be compensated can be automatically identified, ensuring that the compensation work is targeted, accurate, and efficient. By establishing a communication protocol between the control center and multiple devices to be compensated based on TSN technology, an efficient, low-latency, and highly real-time compensation network architecture can be constructed. The introduction of TSN technology, especially its precise time synchronization ability, ensures that the compensation data can be transmitted to each device to be compensated within the specified time, improving the real-time performance and reliability of the compensation process. By invoking the TSN distributed compensation architecture, multiple flexible compensation parameters can be synchronously transmitted to each device to be compensated through multiple TSN transmission channels, realizing end-to-end synchronous flexible regulation. This synchronous regulation ensures that the compensation operations among multiple devices can be consistent, avoiding performance instability or inconsistent compensation effects caused by the compensation time difference of different devices.

[0056] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for flexibly regulating the quality of photovoltaic power generation through grid monitoring and compensation, characterized in that: The method comprises: By setting up photoelectric sensors to monitor the photovoltaic power generation system in real time and output key operating parameters; Performing power generation quality assessment based on the key operating parameters, identifying multiple devices to be compensated in the photovoltaic power generation system and multiple flexible compensation parameters corresponding to the multiple devices to be compensated according to the obtained power generation quality assessment index and the preset power generation quality index; Establishing a communication protocol between the control center and the plurality of devices to be compensated based on the TSN technology to obtain a TSN distributed compensation architecture; The TSN distributed compensation architecture is called to transmit the multiple flexible compensation parameters through multiple TSN transmission channels, so as to perform end-to-end synchronous flexible regulation on the multiple devices to be compensated.

2. The method for flexibly regulating the quality of photovoltaic power generation through grid monitoring compensation as claimed in claim 1, characterized in that: After obtaining the TSN distributed compensation architecture, the method further includes: Acquire multiple groups of test sample signals; Testing the multiple TSN transmission channels of the TSN distributed compensation architecture according to the multiple groups of test sample signals, and outputting multiple groups of test data sets; The multiple test data sets are analyzed to check whether the transmission quality of the multiple TSN transmission channels is greater than a preset transmission quality threshold, and resource configuration is updated for the TSN transmission channels that fail the test until all the multiple TSN transmission channels pass the test.

3. The method for flexibly regulating the quality of photovoltaic power generation through grid monitoring compensation as claimed in claim 2, characterized in that: Testing the multiple TSN transmission channels of the TSN distributed compensation architecture according to the multiple groups of test sample signals includes: Testing the multiple TSN transmission channels of the TSN distributed compensation architecture according to the multiple groups of test sample signals in a first state, and performing disturbance tests on the multiple TSN transmission channels of the TSN distributed compensation architecture according to the multiple groups of test sample signals in a second state, to obtain multiple groups of test data sets in the first state and multiple groups of test data sets in the second state; The first state is a non-disturbance state, and the second state is a disturbance state.

4. The method for flexibly regulating the quality of photovoltaic power generation through grid monitoring compensation as claimed in claim 3, characterized in that: Update resource configuration for TSN transmission channels that fail the inspection. The methods include: Loading a first resource configuration template, including bandwidth proportion, priority level, and time slot length; Analyze the multiple test data sets in the first state to verify the transmission quality of the multiple TSN transmission channels, and update the resource configuration of the TSN transmission channels that fail the verification according to the first resource configuration template.

5. The method for flexibly regulating the quality of photovoltaic power generation through grid monitoring compensation as claimed in claim 3, characterized in that: The resource configuration of the TSN transmission channel that fails the inspection is updated, and the method further includes: Loading the second resource configuration template, including bandwidth proportion, priority level, time slot length and preemption time slot; Analyze the multiple test data sets in the second state to verify the transmission quality of the multiple TSN transmission channels, and update the resource configuration of the TSN transmission channels that fail the inspection according to the second resource configuration template.

6. The method for flexibly regulating the quality of photovoltaic power generation through grid monitoring compensation as claimed in claim 1, characterized in that: According to the obtained power generation quality assessment index and the preset power generation quality index, multiple devices to be compensated in the photovoltaic power generation system are identified, wherein the multiple devices to be compensated are devices whose index difference between the power generation quality assessment index and the preset power generation quality index is greater than a preset threshold.

7. The method for flexibly regulating the quality of photovoltaic power generation through grid monitoring compensation as claimed in claim 1, characterized in that: Identifying a plurality of flexible compensation parameters corresponding to the plurality of devices to be compensated, the method comprising: Constructing a list of candidate parameter items, and calculating the Spearman correlation coefficient between the power generation quality assessment index and each parameter item in the list of candidate parameter items; The candidate parameter item list is subjected to correlation analysis according to the Spearman correlation coefficient of each parameter item, an identification compensation parameter item greater than a preset threshold is obtained, and a plurality of flexible compensation parameters corresponding to the plurality of devices to be compensated are identified according to the identification compensation parameter.

8. The method for flexibly regulating the quality of photovoltaic power generation through grid monitoring compensation as claimed in claim 7, characterized in that: The multiple flexible compensation parameters include at least the inverter maximum power point tracking efficiency and the photovoltaic module IV characteristic curve offset; The compensation value of the inverter maximum power point tracking efficiency and the compensation value of the photovoltaic module IV characteristic curve offset are obtained through historical healthy operation parameters.

9. The method for flexibly regulating the quality of photovoltaic power generation through grid monitoring compensation as claimed in claim 1, characterized in that: After obtaining the plurality of devices to be compensated, the method further includes: Grouping the multiple devices to be compensated according to a compensation order to obtain a synchronous compensation device group; A communication protocol is established based on the TSN technology to connect the control center with each group in the synchronous compensation device group, and the TSN distributed compensation architecture is updated, wherein the TSN transmission channels in each synchronous compensation device group are transmitted synchronously.

10. The method for flexibly regulating the quality of photovoltaic power generation through grid monitoring compensation as claimed in claim 1, characterized in that: The power generation quality is evaluated based on the key operating parameters, wherein the indicators of the power generation quality evaluation include power generation efficiency, power fluctuation, equipment health and power generation stability and continuity.

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