A method and system for power adaptive regulation of a photovoltaic assembly

Abstract

The present application relates to the field of photovoltaic regulation, and particularly relates to a power adaptive regulation method and system of a photovoltaic module, the method comprising the steps of: setting a frequency and a threshold value; collecting and outputting data according to a preset; calculating a light intensity and a wind speed working condition data change rate, if the light intensity or the wind speed change rate reaches a corresponding threshold value, then it is a sudden change state, a frequency switching instruction is triggered; otherwise, the reference frequency is maintained, and data is continuously collected; after receiving the frequency switching instruction, the frequency is increased to the highest frequency within a preset time to capture key data; after the sudden change state lasts for more than a preset time length and the working condition is stable, the frequency is decreased to the reference frequency within a preset time; in a non-sudden change state, based on the current sampling frequency data, the inverter parameters are adjusted, and the adjustment is accelerated in a sudden change state, so that the power of the photovoltaic module is continuously and adaptively regulated, and the rationality and stability of power regulation are improved.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic regulation, specifically relating to a method and system for adaptive power regulation of photovoltaic modules. Background Technology

[0002] The power adjustment of photovoltaic modules has high requirements for response to dynamic operating conditions. For example, cloud cover and gusts can cause millisecond-level changes in sunlight, requiring high sampling frequencies, such as above 100Hz. However, high frequencies bring massive amounts of raw data, increasing the hardware cost of the data acquisition unit, subsequent transmission bandwidth, and processing power. If the sampling frequency is reduced, such as to 1Hz, key data on sudden changes in operating conditions will be lost, causing algorithm lag. Therefore, it is difficult to determine an appropriate sampling frequency in the current technology, so the power adjustment of photovoltaic modules is not ideal. Furthermore, the power regulation of photovoltaic (PV) modules is powered by the PV itself, while data processing (acquisition, transmission, calculation, and storage) consumes the electrical energy generated by the PV system. To improve regulation accuracy, the data sampling rate needs to be increased, which leads to a significant increase in energy consumption for data processing (e.g., increasing the sampling rate from 1Hz to 1kHz will increase the energy consumption of the acquisition terminal several times). In practice, if the energy consumption of data processing exceeds the power gain brought by adaptive regulation, the net output power of the PV system will decrease, rendering regulation meaningless. Therefore, it is necessary to dynamically adjust the sampling rate based on the real-time output power of the PV system. Summary of the Invention

[0003] The purpose of this invention is to provide a power adaptive regulation method for photovoltaic modules, and at the same time, to provide a power adaptive regulation system for photovoltaic modules, so as to solve the problems mentioned in the background art.

[0004] To solve the above-mentioned technical problems, the present invention provides the following technical solution: A method for adaptive power regulation of a photovoltaic module includes the following steps: Set the baseline sampling frequency, light intensity change threshold, wind speed change threshold, and configure the buffer duration, algorithm adjustment step size, and response delay threshold. At the reference frequency, collect data on light intensity, wind speed, and component output power, voltage, and current, and transmit them to the buffer in real time. Calculate the rate of change of light intensity and wind speed data. If the rate of change of light intensity or wind speed reaches the corresponding threshold, it is a sudden change state, triggering a frequency switching command; otherwise, maintain the base frequency and continue to collect data. Upon receiving a frequency switching command, the frequency will be increased to the highest frequency within a preset time to capture key data; If the abnormal state continues for more than a preset time and the operating conditions stabilize, the frequency will be reduced to the reference frequency within a preset time. In a non-abrupt state, the inverter parameters are adjusted based on the current sampling frequency data; during abrupt changes, the adjustment is accelerated to achieve continuous adaptive adjustment.

[0005] Furthermore, real-time and historical parameters are collected to establish a dynamic quantitative mapping relationship between energy consumption and power gain, thereby realizing the dynamic quantitative association between the two. In the process of "raising the frequency to the highest frequency" during a sudden change, the highest frequency value is adjusted according to the calculated power gain, and the increase in energy consumption after adjustment is controlled to be no greater than the power gain.

[0006] Furthermore, the calculation of the rate of change of light intensity and wind speed operating conditions data, if the rate of change of light intensity or wind speed reaches the corresponding threshold, is a sudden change state, triggering a frequency switching command, including: using the operating condition data in the cache to calculate the rate of change of light intensity and wind speed.

[0007] Furthermore, maintaining the reference frequency and continuously collecting data includes: when the operating conditions are stable, maintaining the reference sampling frequency without triggering frequency switching commands, synchronously collecting data on sunlight, wind speed, and photovoltaic module output data, controlling the collection accuracy, and transmitting the data to the cache in real time.

[0008] Furthermore, after the mutation state lasts for more than a preset time and the operating conditions stabilize, the frequency is reduced to the reference frequency within a preset time, including: after the mutation state lasts for a preset time and the operating conditions stabilize, it is determined to be stable and a frequency reduction command is sent, and the sampling frequency is reduced from the highest frequency to the reference frequency within a preset time, while maintaining the acquisition accuracy during the process, and the reference frequency is restored to acquire data after the frequency is reduced.

[0009] Furthermore, the method of adjusting inverter parameters based on the current sampling frequency data in a non-abrupt state and accelerating adjustment during abrupt changes to achieve continuous adaptive adjustment includes: in a non-abrupt state, collecting data at the current sampling frequency and adjusting inverter parameters accordingly, while controlling energy consumption according to the dynamic quantization mapping relationship between energy consumption and power gain; when the operating condition changes abruptly, immediately switching to the accelerated adjustment mode, capturing data based on a high sampling frequency, and quickly adjusting inverter parameters.

[0010] Furthermore, the process of collecting real-time and historical parameters and establishing a dynamic quantitative mapping relationship between energy consumption and power gain to achieve dynamic quantitative correlation between the two includes: collecting real-time and historical parameters during the photovoltaic power adaptive adjustment process, preprocessing the parameters to remove abnormal data, classifying and statistically processing energy consumption and power gain according to sampling frequency, and establishing a dynamic quantitative mapping relationship between the two through data fitting.

[0011] Furthermore, during the process of "raising the frequency to the highest frequency" in the abrupt change state, the highest frequency value is adjusted according to the calculated power gain, and the energy consumption increase after adjustment is controlled to be no greater than the power gain. This includes: when the sampling frequency is raised in the abrupt change state, the real-time output power of the photovoltaic module is collected simultaneously, the real-time energy consumption parameters are processed, and the current real-time power gain is calculated based on the established dynamic quantitative mapping relationship between energy consumption and power gain. Based on this, the upper limit of the allowable energy consumption increase is calculated in reverse, and then the specific value of the highest frequency is adjusted.

[0012] This application also discloses an electronic device, including: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the power adaptive adjustment method for a photovoltaic module described above.

[0013] This application also discloses a power adaptive regulation system for photovoltaic modules, including: The data acquisition module is used to collect data on light intensity, wind speed, and component output operating conditions at a reference frequency, and transmit them to the buffer in real time. The mutation detection module is used to calculate the rate of change of light intensity and wind speed data, determine whether the corresponding mutation threshold has been reached, and trigger or cancel the frequency switching command. The frequency adjustment module is used to raise the sampling frequency to the highest frequency within a preset time after receiving the frequency switching command, and lower it to the reference frequency after the sudden change state stabilizes. The parameter adjustment module is used to adjust the inverter parameters based on sampled data in a non-abrupt state, and to accelerate the adjustment in the event of an abrupt change to achieve adaptive adjustment. The cache management module is used to store real-time collected operating condition data and historical parameters, and manage the data lifecycle according to the configured cache duration. The energy consumption and power mapping module is used to establish a dynamic quantitative mapping relationship between energy consumption and power gain. When the frequency rises to the highest level, the highest frequency is adjusted according to the power gain to control the increase in energy consumption to be no greater than the power gain.

[0014] Beneficial effects: This application can effectively solve the problem of difficulty in determining the sampling frequency when adjusting the power of photovoltaic modules in the prior art. By setting a reference sampling frequency and a threshold for sudden changes in operating conditions, the reference frequency is maintained to collect data when the operating conditions are stable, avoiding the increase in hardware cost, transmission bandwidth and processing power burden of the collector caused by the massive amount of data from high-frequency sampling. At the same time, the sampling frequency is increased to capture key data when the operating conditions change, avoiding the algorithm lag caused by low-frequency sampling, so that the power adjustment of photovoltaic modules is more in line with the actual operating conditions.

[0015] Meanwhile, by establishing a dynamic quantitative mapping relationship between energy consumption and power gain, the energy consumption of data processing can be reasonably controlled during sampling frequency adjustment and power regulation, ensuring that the increase in energy consumption is not greater than the power gain, avoiding a decrease in the net output power of the photovoltaic system due to excessive energy consumption, ensuring the practical significance of power regulation, realizing continuous adaptive adjustment of photovoltaic module power, improving the rationality and stability of power regulation, and taking into account both regulation effect and energy consumption control requirements. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the overall process of this application. Figure 2 A flowchart establishing the mapping relationship for this application; Figure 3 A flowchart illustrating the mapping relationship applied in this application; Figure 4 This is a schematic diagram illustrating a specific mapping relationship in this application; Figure 5 This is a comparison chart showing the application effects of this application and the fixed frequency scheme. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0018] This invention provides a method for adaptive power regulation of photovoltaic modules, such as... Figure 1 As shown, the steps include: Set the baseline sampling frequency, light intensity change threshold, wind speed change threshold, and configure the buffer duration, algorithm adjustment step size, and response delay threshold. At the reference frequency, collect data on light intensity, wind speed, and component output power, voltage, and current, and transmit them to the buffer in real time. Calculate the rate of change of light intensity and wind speed data. If the rate of change of light intensity or wind speed reaches the corresponding threshold, it is a sudden change state, triggering a frequency switching command; otherwise, maintain the base frequency and continue to collect data. Upon receiving a frequency switching command, the frequency will be increased to the highest frequency within a preset time to capture key data; If the abnormal state continues for more than a preset time and the operating conditions stabilize, the frequency will be reduced to the reference frequency within a preset time. In a non-abrupt state, the inverter parameters are adjusted based on the current sampling frequency data; during abrupt changes, the adjustment is accelerated to achieve continuous adaptive adjustment. It also includes collecting real-time and historical parameters, establishing a dynamic quantitative mapping relationship between energy consumption and power gain, realizing the dynamic quantitative association between the two, and adjusting the highest frequency value according to the calculated power gain during the process of "raising the frequency to the highest frequency" in the sudden change state, so as to control the increase in energy consumption after adjustment to be no greater than the power gain.

[0019] The setting of the reference sampling frequency, light intensity change threshold, and wind speed change threshold specifically includes: reasonably setting each core parameter based on the actual needs of photovoltaic module power adjustment, data processing energy consumption control targets, and operating condition change response requirements. The reference sampling frequency is set to 20Hz as the initial sampling frequency under stable operating conditions, balancing data acquisition accuracy and data processing energy consumption. This avoids excessively high sampling frequencies that would increase the hardware cost, transmission bandwidth, and processing computing power burden of the acquisition unit due to massive amounts of raw data, while also avoiding excessively low sampling frequencies that would lead to the loss of key data and algorithm lag due to operating condition changes.

[0020] The threshold for sudden changes in light intensity is set to 50 W / (m²). The wind speed mutation threshold is set to 3 m / s. This threshold is based on the actual intensity range of common operating condition mutations such as cloud cover and gusts. It can accurately identify millisecond-level changes in sunlight and wind speed, providing an accurate basis for subsequent sampling frequency switching. This ensures that the set parameters can meet the key data capture requirements during operating condition mutations, while also reducing data processing energy consumption when the operating conditions are stable. This prevents the increase in energy consumption from exceeding the power gain, ensuring the stability of the net output power of the photovoltaic system. At the same time, it works in synergy with the system initialization configuration, operating condition mutation identification, and subsequent sampling frequency switching processes to ensure the consistency and feasibility of the entire power adaptive adjustment scheme.

[0021] The configuration of cache duration, algorithm adjustment step size, and adjustment response delay threshold specifically includes the following in implementation: Based on the overall requirements of photovoltaic module power adaptive adjustment, the reference sampling frequency (20Hz), and the dynamic quantitative mapping relationship between energy consumption and power gain, each parameter is reasonably configured to achieve process coordination and efficiency balance. The cache duration is set to 100ms, and the cache capacity matches the amount of data per unit time corresponding to the reference sampling frequency. This ensures that when the operating conditions are stable, real-time collected data such as irradiance, wind speed, and module output power can be temporarily stored. At the same time, when the operating conditions change and the sampling frequency is switched, the key data before and after the change can be completely retained, providing data support for identifying the characteristics of the change and accurately switching the sampling frequency. This also avoids the increase in storage energy consumption due to excessively long cache duration and the loss of key data due to excessively short cache duration.

[0022] The algorithm adjustment step size is set to 0.01V, which balances power adjustment accuracy and data processing energy consumption. It can meet the adjustment requirements of photovoltaic module power stabilization near the maximum power point, avoid adjustment deviation caused by excessive step size, and increase computing power and energy consumption by excessive step size. At the same time, it is compatible with the subsequent feedback calibration process, which facilitates small-scale fine-tuning when the adjustment error exceeds the standard, and ensures that the adjustment accuracy continues to meet the standard.

[0023] The response delay threshold is set to 5ms to accommodate the urgent adjustment needs during sudden changes in operating conditions. This ensures that after the sampling frequency is increased to the highest frequency (120Hz) to capture key data, the power adjustment module can respond to the adjustment command in a timely manner, avoiding excessive delay that could lead to algorithm lag and untimely power adjustment. Furthermore, this threshold is coordinated with the preset time for sampling frequency switching (1ms) and the threshold for sudden changes in operating conditions, and matches the dynamic quantization mapping relationship between energy consumption and power gain. This ensures that the configured parameters can support the smooth execution of core processes such as adaptive switching of sampling frequency and dynamic power adjustment, while also controlling data processing energy consumption to prevent the increase in energy consumption from exceeding the power gain.

[0024] The collected real-time and historical parameters are used to establish a dynamic quantitative mapping relationship between energy consumption and power gain, realizing a dynamic quantitative correlation between the two. Figure 2 In practice, this includes: based on the overall process and core parameters of the photovoltaic module power adaptive adjustment of this application, collecting various key parameters under the current operating conditions in real time, including the current sampling frequency, light intensity, wind speed, module output power, voltage, and current, such as a sampling frequency of 20Hz as the base or 120Hz as the maximum, as well as real-time energy consumption data of the entire data processing process, such as real-time energy consumption data of the entire process of collection, transmission, calculation, and storage. At the same time, extracting historical parameters, including the above real-time parameters within the past buffering time (100ms) and longer periods, sampling frequency switching records, power adjustment step size, adjustment response time, power adjustment error, and energy consumption data and power gain data of the corresponding time period, such as the difference between the module output power after adjustment and before adjustment. Preprocessing the collected real-time and historical parameters, removing abnormal fluctuation data in the early stage of sudden changes in operating conditions and the transition stage of adjustment and calibration, to ensure the accuracy and effectiveness of the parameters.

[0025] Based on the preprocessed parameters, statistics are categorized and statistically analyzed according to different sampling frequencies. The energy consumption per unit time for data processing at each sampling frequency is calculated, along with the power gain under the corresponding power adjustment mode. Power adjustment modes include regular adjustment and expedited adjustment. Through data fitting and error calibration, a quantized functional relationship between energy consumption and power gain is established, forming a dynamic quantized mapping relationship. Figure 4 This allows for the identification of reasonable energy consumption thresholds corresponding to different power gain ranges, enabling precise quantitative correlation between the two.

[0026] This mapping relationship is not fixed. It is continuously and dynamically optimized based on the new parameters collected in real time and the historical parameters that are updated on a rolling basis. For example, after each sampling frequency switch and power adjustment feedback calibration, new energy consumption data and power gain data are added to the relationship, and the quantization function parameters are fine-tuned to ensure that the mapping relationship always conforms to the actual operating conditions.

[0027] The process involves collecting data on light intensity, wind speed, and component output power, voltage, and current at a reference frequency, and transmitting this data to a buffer in real time. Specifically, this includes continuously collecting data on light intensity and wind speed, as well as core output data on component output power, voltage, and current, based on a set reference sampling frequency (20Hz). Strict control over data accuracy is maintained during the collection process, with light intensity acquisition accuracy controlled within ±1W / m² and output power acquisition accuracy controlled within ±0.1%, ensuring the accuracy and completeness of the collected data to meet the needs of subsequent identification of sudden changes in operating conditions and power adjustment.

[0028] After each data acquisition is completed, the real-time data is immediately transmitted to the preset cache module for temporary storage. The caching process strictly matches the configured cache duration (100ms) and corresponding cache capacity to ensure that the data can be retained in an orderly manner. This provides complete data support for subsequent calculation of operating condition change rate and identification of sudden changes in operating conditions, while also avoiding cache redundancy that increases energy consumption.

[0029] Simultaneously, the real-time nature of data transmission is taken into account to ensure timely synchronization of collected data, guarantee the timeliness of sampling frequency switching and power adjustment, and the entire acquisition and transmission process takes into account energy consumption control requirements to avoid excessive consumption of photovoltaic power due to data acquisition and transmission. It is compatible with the dynamic quantitative mapping relationship between energy consumption and power gain established in this application to ensure that the increase in energy consumption in the acquisition stage does not exceed the power gain. At the same time, it is coordinated and connected with the system initialization configuration, operating condition change identification, and sampling frequency switching process to ensure the continuity of the entire adaptive adjustment scheme. It achieves efficient and low-power operation of data acquisition under stable operating conditions, avoiding the hardware cost, bandwidth and computing power burden caused by high-frequency acquisition, and preventing data loss from affecting the progress of subsequent processes.

[0030] The calculation of the rate of change of light intensity and wind speed data is performed. If the rate of change of light intensity or wind speed reaches the corresponding threshold, it is considered a sudden change state, triggering a frequency switching command. Specifically, this includes: based on the reference sampling frequency (20Hz) and the light intensity sudden change threshold (50W / (m²)) set in this application... The system calculates the difference between the real-time operating data stored in the cache and the operating data from the previous collection period, using the data set as follows: ms), wind speed change threshold (3m / s), and buffer duration (100ms). Then, it divides the difference by the sampling period (0.05s) to accurately calculate the rate of change of light intensity and wind speed. During the calculation process, the calculation error is strictly controlled to be ≤0.1% to ensure the accuracy of the rate of change data to support the judgment of sudden changes.

[0031] After the calculation is completed, the real-time light intensity change rate is compared with the preset threshold, and the wind speed change rate is also compared with the corresponding preset threshold. If any change rate reaches or exceeds the corresponding threshold, it is immediately determined to be a sudden change in the working condition, such as a millisecond-level change in light intensity caused by cloud cover or gusts. The sampling frequency switching command is immediately generated, and the command transmission delay is controlled within 1ms to meet the adjustment response delay threshold (5ms) requirement set in this application.

[0032] The mutation identification and command triggering process strictly adapts to the dynamic quantification mapping relationship between energy consumption and power gain, controls the energy consumption of the data processing stage, ensures that its increase value does not exceed the power gain, guarantees the stability of the net output power of the photovoltaic system, and coordinates with the operating condition data acquisition and sampling frequency switching process.

[0033] Maintaining the reference frequency and continuously collecting data specifically includes, in practice: based on the reference sampling frequency (20Hz), buffer duration (100ms), and dynamic quantization mapping relationship between energy consumption and power gain set in this application, determining that the rate of change in illuminance is <50W / (m²) When the wind speed change rate is <3m / s and the operating condition is stable, the reference sampling frequency of 20Hz is maintained and the sampling frequency switching command is not triggered.

[0034] During the data collection process, data on two operating conditions—light intensity and wind speed—as well as three core output data—output power, output voltage, and output current of the photovoltaic modules—were collected simultaneously. The accuracy of the data collection was strictly controlled, with the light intensity data collection accuracy maintained at ±1W / m² and the output power data collection accuracy maintained at ±0.1%. This ensured the accuracy and completeness of the collected data, providing continuous and reliable data support for subsequent real-time identification of sudden changes in operating conditions, dynamic power adjustment, and feedback calibration.

[0035] After a set of data is collected, the real-time data is immediately transmitted to the cache module for temporary storage. The caching process matches the preset cache duration and corresponding cache capacity to avoid cache redundancy and increase storage energy consumption. At the same time, it ensures real-time data transmission, ensures that the operating status can be continuously monitored, and promptly captures potential sudden changes in operating conditions.

[0036] The entire continuous data acquisition process strictly adheres to energy consumption control requirements, balancing data acquisition efficiency with data processing energy consumption.

[0037] Upon receiving the frequency switching command, the frequency is increased to the highest frequency within a preset time to capture key data. In practice, this specifically includes: after receiving the sent sampling frequency switching command, strictly following the adjustment response delay threshold requirements set in this application, completing the sampling frequency increase operation within 1ms, increasing the sampling frequency from the reference sampling frequency of 20Hz to the highest frequency of 120Hz, thereby meeting the high sampling frequency requirement during sudden changes in operating conditions and ensuring that every set of key data during the sudden change in operating conditions can be accurately and completely captured.

[0038] Key data include real-time light intensity, wind speed change data, and dynamic fluctuation data of photovoltaic module output power, voltage, and current. In addition, during the frequency increase process, the dynamic quantitative mapping relationship between energy consumption and power gain is established based on this application.

[0039] After the abrupt change in the operating condition persists for more than a preset time and the operating condition stabilizes, the frequency is reduced to a reference frequency within a preset time. Specifically, this includes: setting a reference sampling frequency of 20Hz, a maximum sampling frequency of 120Hz, an operating condition abrupt change threshold, and a preset time based on the criteria set in this application. The operating condition abrupt change threshold may be, for example, a light intensity abrupt change threshold of 50W / (m²). The system monitors real-time operating data under abrupt changes (ms) and a wind speed change threshold of 3 m / s. The preset time length is adapted to the buffer duration and stable operating condition judgment requirements. For example, if set to 100 ms, it continuously monitors real-time operating data under abrupt changes, calculating the rate of change of light intensity and wind speed in real time. If the duration of the abrupt change exceeds 100 ms, and the rate of change of light intensity is <50 W / (m²) for multiple consecutive collection cycles, the system will take action. If the wind speed change rate is less than 3 m / s, the operating condition is considered to have returned to stability, and a frequency reduction command is immediately sent. The acquisition cycle is calculated based on the highest sampling frequency of 120 Hz, and is at least 3 acquisition cycles.

[0040] Upon receiving the instruction, strictly following the requirement of adjusting the response delay threshold of 5ms set in this application, the sampling frequency is reduced from the highest frequency of 120Hz to the reference sampling frequency of 20Hz. During the reduction process, the data acquisition accuracy remains unchanged, the light intensity acquisition accuracy is maintained at ±1W / m², and the output power acquisition accuracy is maintained at ±0.1%, ensuring that the data acquisition after the operating conditions stabilize can still meet the needs of subsequent dynamic power adjustment and feedback calibration.

[0041] Simultaneously, during the frequency reduction process, the energy consumption changes of data processing, such as the energy consumption changes of acquisition, transmission, calculation and storage, are monitored in real time based on the dynamic quantitative mapping relationship between energy consumption and power gain established in this application. This ensures that energy consumption can be reduced synchronously after the frequency decreases, strictly controls the increase in energy consumption to not exceed the power gain, ensures the stability of the net output power of the photovoltaic system, and avoids energy waste caused by untimely frequency reduction.

[0042] After the frequency drops to the reference frequency, it resumes continuous acquisition of light intensity, wind speed, and component output power, voltage, and current data at the reference frequency of 20Hz. The data is transmitted to the buffer module in real time and coordinated with subsequent dynamic power adjustment and feedback calibration processes to ensure the continuity and feasibility of the entire adaptive power adjustment process. This not only achieves complete capture of key data after sudden changes in operating conditions but also allows for timely reduction of the sampling frequency after the operating conditions stabilize.

[0043] In the non-abrupt state, the inverter parameters are adjusted based on the current sampling frequency data. In the event of an abrupt change, the adjustment is accelerated to achieve continuous adaptive adjustment. Specifically, in the implementation, under stable non-abrupt operating conditions, a reference sampling frequency of 20Hz is maintained to continuously collect data on light intensity, wind speed, and the output power, voltage, and current of the photovoltaic modules. Based on the real-time data at this sampling frequency, the optimal output voltage and current parameters of the photovoltaic modules are calculated according to the preset MPPT (Maximum Power Point Tracking) algorithm. The adjustment step size is adjusted according to the set 0.01V algorithm, and adjustment commands are sent to the inverter to fine-tune the inverter output parameters, ensuring that the photovoltaic module power is stable near the maximum power point. At the same time, the dynamic quantitative mapping relationship between energy consumption and power gain is strictly followed, and the data processing and energy consumption adjustment during this process are controlled to ensure that the increase in energy consumption does not exceed the power gain, thus ensuring the stability of the net output power of the photovoltaic system.

[0044] Upon detecting a sudden change in operating conditions and triggering a switch to the sampling frequency of 120Hz, the system immediately switches to expedited adjustment mode. The power regulation module prioritizes responding to adjustment commands, strictly controlling the adjustment response time within a 5ms adjustment response delay threshold. Based on key data captured at the 120Hz high sampling frequency, it quickly calculates the optimal adjustment parameters and expedited fine-tunes the inverter output parameters, avoiding untimely power adjustment and increased power loss due to algorithm lag. Whether it is regular adjustment in a non-sudden change state or expedited adjustment in a sudden change state, it is coordinated with real-time calibration by the feedback module. Based on the adjusted power output error, the adjustment step size and inverter adjustment parameters are dynamically fine-tuned to form a closed-loop adjustment mechanism. This mechanism continuously cycles through acquisition, analysis, adjustment, and calibration operations to achieve continuous adaptive adjustment of photovoltaic module power. Simultaneously, it coordinates with the overall scheme of adaptive switching of sampling frequency and dynamic balance of energy consumption and power gain to ensure that the adjustment effect is consistent with the energy consumption control target.

[0045] Preferably, in the abrupt change state, during the process of "raising the frequency to the highest frequency," the highest frequency value is adjusted according to the calculated power gain, and the increase in energy consumption after adjustment is controlled to be no greater than the power gain, such as... Figure 3 In practice, this includes: upon receiving the sampling frequency switching command and initiating the frequency boosting operation, simultaneously collecting the real-time output power of the photovoltaic module and the real-time energy consumption key parameters of the data processing, and based on the previously established dynamic quantitative mapping relationship between energy consumption and power gain, quickly extracting the historical power gain data under the corresponding operating conditions, and calculating the real-time power gain under the current sudden change in operating conditions, i.e., the expected increase in module output power after the sampling frequency is boosted.

[0046] Based on this real-time power gain value, the upper limit of the allowable increase in data processing energy consumption is calculated in reverse through the dynamic quantitative mapping relationship between energy consumption and power gain. Then, according to the unit time data processing energy consumption standard corresponding to different sampling frequencies, if the sampling frequency is between the reference sampling frequency of 20Hz and the default maximum sampling frequency of 120Hz, the specific value of the maximum frequency is precisely adjusted. If the calculated power gain is large, the maximum frequency can be adjusted to be close to or reach 120Hz to fully capture key data of sudden changes in operating conditions and maximize power gain. If the power gain is small, the maximum frequency is appropriately reduced, but not lower than the reference sampling frequency of 20Hz, to avoid the increase in data processing energy consumption exceeding the power gain due to excessively high frequency. The entire process of adjusting the highest frequency is carried out simultaneously with the frequency increase operation to ensure that the capture of key data on sudden changes in operating conditions is not affected. At the same time, the accuracy of data acquisition is strictly controlled, maintaining the accuracy of light intensity acquisition at ±1W / m² and output power acquisition at ±0.1%. The key data acquired after adjustment is transmitted to the buffer module in real time, with a matching 100ms buffering time to provide data support for subsequent emergency power adjustment. Furthermore, the energy consumption of the entire data processing stage after adjustment must be strictly controlled within the upper limit of the calculated energy consumption increase value to ensure that the energy consumption increase value does not exceed the real-time power gain, thereby ensuring the stability of the net output power of the photovoltaic system and avoiding the loss of the meaning of power regulation.

[0047] like Figure 5The superiority of this application's solution can be verified through a comparison of three operating conditions. In the non-abrupt phase (0-20s): the patented solution uses a 20Hz reference frequency, with data processing energy consumption of only 500W and a net output power of 10.0kW, which is on par with the fixed low frequency and significantly higher than the fixed high frequency (9.2kW), verifying the "low energy consumption during non-abrupt" design of this application's patent. In the abrupt phase (20-40s): the patented solution dynamically increases to 120Hz, accurately capturing key data, and the component output power increases to 14.0kW. After deducting 2800W of energy consumption, the net power reaches 11.2kW, which is higher than the fixed high frequency (10.5kW) and the fixed low frequency (8.5kW), respectively, solving the pain points of energy waste at the fixed high frequency and data loss at the fixed low frequency. In the recovery phase (40-60s): the patented solution quickly reduces the frequency to 20Hz, and the net power returns to 10.0kW, remaining stable. The application directly verified the adaptability of the dynamic adjustment mechanism of this patent under different operating conditions, ensuring optimal and stable net output power, which is significantly better than the traditional fixed frequency solution.

[0048] The dynamic quantitative mapping relationship between energy consumption and power gain in this application is established based on historical data fitting, which has a certain degree of data lag and cannot accurately respond to instantaneous power fluctuations caused by sudden changes in operating conditions.

[0049] Therefore, in further implementation, this application introduces an energy consumption elasticity coefficient, which can quantify the sensitivity of energy consumption to frequency changes. Compared with the static threshold judgment of the original technology, it has stronger adaptability to different photovoltaic modules. In the process of "increasing the frequency to the highest frequency in a sudden change state", a dynamic matching algorithm of real-time marginal power gain and energy consumption elasticity coefficient is used to adjust the highest frequency value, and control the increase in energy consumption after adjustment to not be greater than the power gain. The specific process is as follows: After the frequency switching command is triggered, the following instantaneous data are collected synchronously: The instantaneous output power of the component, specifically the time when the frequency switching command is triggered. Real-time output power of photovoltaic modules; Instantaneous energy consumption for data processing, specifically the time when the frequency switching command is triggered. Energy consumption throughout the entire process of data acquisition, transmission, and computation; : Current sampling frequency, specifically the reference sampling frequency before frequency switching (e.g., 20Hz); : Rate of change of light intensity, specifically the instantaneous rate of change of light intensity at the moment of abrupt change; : Rate of change of wind speed, specifically the instantaneous rate of change of wind speed at the moment of abrupt change; : Lower limit of the reference sampling frequency, specifically the minimum sampling frequency (fixed value) that is not lower than this value; : The default maximum sampling frequency limit, which is the maximum sampling frequency supported by the hardware (fixed value); Calculate instantaneous marginal power gain Marginal power gain represents the instantaneous increase in the output power of a photovoltaic module for every 1Hz increase in the sampling frequency. It reflects the contribution of frequency increase to power gain, and the calculation formula is as follows: ; for Instantaneous marginal power gain at time t, in units The first derivative of the output power with respect to the sampling frequency is obtained by... The power and frequency discrete data from three sampling points before and after time step 1 are calculated using the three-point numerical differentiation method: ;in For a small frequency increment, a value of 1Hz is used to ensure calculation accuracy.

[0050] Calculate the energy consumption elasticity coefficient The energy consumption elasticity coefficient represents the ratio of the rate of change of energy consumption to the rate of change of frequency, reflecting the sensitivity of energy consumption to changes in frequency. The calculation formula is: ; The coefficient is dimensionless. The instantaneous rate of change of energy consumption, in units , For frequency adjustment rate, in units (Preset to 100Hz / ms to match the requirements of sudden change response).

[0051] This indicates that energy consumption is sensitive to frequency changes; a small increase in frequency can lead to a significant increase in energy consumption. This indicates that energy consumption is not sensitive to frequency changes.

[0052] The adjusted optimal maximum frequency is set as The frequency increase range is The increase in energy consumption within this range and the total increment of marginal power gain They are respectively: ; Derived from the energy consumption elasticity coefficient :Will Deformation yields Substituting into the formula for the increase in energy consumption, we get: ; To ensure that the increase in energy consumption does not exceed the power gain, the following must be met: .

[0053] Solving for the optimal highest frequency Based on boundary conditions Solving the inequality equation yields ; Output optimal highest frequency Instruct it to change the sampling frequency from within a preset time period. Rise to .

[0054] This application also provides an embodiment of an electronic device. The electronic device is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: one or more processors or processing units, memory, and buses connecting different components (including memory and processing units).

[0055] A bus refers to one or more of several bus architectures, including memory buses or memory controllers, peripheral buses, graphics acceleration ports, processors, or local buses using any of the various bus architectures. Examples of these architectures include, but are not limited to, Industry Standard Architecture (ISA) buses, Micro Channel Architecture (MCA) buses, Enhanced ISA buses, Video Electronics Standards Association (VESA) local buses, and Peripheral Component Interconnect (PCI) buses.

[0056] Electronic devices typically include a variety of computer-readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, and removable and non-removable media.

[0057] The memory may include computer-readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. Electronic devices may further include other removable / non-removable, volatile / non-volatile computer device storage media. By way of example only, the storage system may be used to read and write non-removable, non-volatile magnetic media.

[0058] The electronic device can also communicate with one or more external devices (e.g., keyboard, pointing device, camera, etc.), may include a display, and may communicate with one or more devices that enable a user to interact with the electronic device, and / or with any device that enables the electronic device to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via an input / output (I / O) interface. Furthermore, the electronic device can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN)) and / or public networks, such as the Internet) via a network adapter. The network adapter communicates with other modules of the electronic device via a bus. The processor executes various functional applications and data processing by running programs stored in memory, such as implementing the power adaptive regulation method for a photovoltaic module provided in the above embodiments of the present invention.

[0059] This application also discloses a power adaptive regulation system for photovoltaic modules, including: The data acquisition module is used to collect data on light intensity, wind speed, and component output operating conditions at a reference frequency, and transmit them to the buffer in real time. The mutation detection module is used to calculate the rate of change of light intensity and wind speed data, determine whether the corresponding mutation threshold has been reached, and trigger or cancel the frequency switching command. The frequency adjustment module is used to raise the sampling frequency to the highest frequency within a preset time after receiving the frequency switching command, and lower it to the reference frequency after the sudden change state stabilizes. The parameter adjustment module is used to adjust the inverter parameters based on sampled data in a non-abrupt state, and to accelerate the adjustment in the event of an abrupt change to achieve adaptive adjustment. The cache management module is used to store real-time collected operating condition data and historical parameters, and manage the data lifecycle according to the configured cache duration. The energy consumption and power mapping module is used to establish a dynamic quantitative mapping relationship between energy consumption and power gain. When the frequency rises to the highest level, the highest frequency is adjusted according to the power gain to control the increase in energy consumption to be no greater than the power gain.

[0060] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for adaptive power regulation of a photovoltaic module, characterized in that, Includes the following steps: Set the baseline sampling frequency, light intensity change threshold, wind speed change threshold, and configure the buffer duration, algorithm adjustment step size, and response delay threshold. At the reference frequency, collect data on light intensity, wind speed, and component output power, voltage, and current, and transmit them to the buffer in real time. Calculate the rate of change of light intensity and wind speed data. If the rate of change of light intensity or wind speed reaches the corresponding threshold, it is a sudden change state, triggering a frequency switching command; otherwise, maintain the base frequency and continue to collect data. Upon receiving a frequency switching command, the frequency will be increased to the highest frequency within a preset time to capture key data; If the abnormal state continues for more than a preset time and the operating conditions stabilize, the frequency will be reduced to the reference frequency within a preset time. In a non-abrupt state, the inverter parameters are adjusted based on the current sampling frequency data; during abrupt changes, the adjustment is accelerated to achieve continuous adaptive adjustment. Collect real-time and historical parameters, establish a dynamic quantitative mapping relationship between energy consumption and power gain, realize the dynamic quantitative association between the two, and in the process of "raising the frequency to the highest frequency" in the sudden change state, adjust the highest frequency value according to the calculated power gain, and control the increase in energy consumption after adjustment to not be greater than the power gain.

2. The method for adaptive power adjustment of a photovoltaic module according to claim 1, characterized in that, The calculation of the rate of change of light intensity and wind speed operating conditions data, if the rate of change of light intensity or wind speed reaches the corresponding threshold, is a sudden change state, triggering a frequency switching command, including: using the operating condition data in the cache to calculate the rate of change of light intensity and wind speed.

3. The method for adaptive power adjustment of a photovoltaic module according to claim 2, characterized in that, Maintaining the reference frequency and continuously collecting data includes: when the operating conditions are stable, maintaining the reference sampling frequency without triggering frequency switching commands, synchronously collecting data on sunlight, wind speed, and photovoltaic module output data, controlling the acquisition accuracy, and transmitting the data to the buffer in real time.

4. The power adaptive adjustment method for a photovoltaic module according to claim 3, characterized in that, After the mutation state lasts for more than a preset time and the operating conditions stabilize, the frequency is reduced to the reference frequency within a preset time, including: after the mutation state lasts for a preset time and the operating conditions stabilize, the system determines that it is stable and sends a frequency reduction command, and the sampling frequency is reduced from the highest frequency to the reference frequency within a preset time, while maintaining the acquisition accuracy during the process, and the reference frequency is restored to acquire data after the frequency is reduced.

5. The power adaptive adjustment method for a photovoltaic module according to claim 4, characterized in that, In the non-abrupt state, the inverter parameters are adjusted based on the current sampling frequency data, and in the event of an abrupt change, the adjustment is accelerated to achieve continuous adaptive adjustment. This includes: in the non-abrupt state, data is collected at the current sampling frequency, and the inverter parameters are adjusted accordingly, while energy consumption is controlled in accordance with the dynamic quantization mapping relationship between energy consumption and power gain; in the event of an abrupt change in operating conditions, the system immediately switches to the accelerated adjustment mode, and the inverter parameters are quickly adjusted based on data captured at a high sampling frequency.

6. The method for adaptive power adjustment of a photovoltaic module according to claim 5, characterized in that, The process of collecting real-time and historical parameters and establishing a dynamic quantitative mapping relationship between energy consumption and power gain to achieve dynamic quantitative correlation between the two includes: collecting real-time and historical parameters during the photovoltaic power adaptive adjustment process, preprocessing the parameters to remove abnormal data, classifying and statistically processing energy consumption and power gain according to sampling frequency, and establishing a dynamic quantitative mapping relationship between the two through data fitting.

7. The power adaptive adjustment method for a photovoltaic module according to claim 6, characterized in that, In the process of "raising the frequency to the highest frequency" during the sudden change state, the highest frequency value is adjusted according to the calculated power gain, and the energy consumption increase after adjustment is controlled to be no greater than the power gain. This includes: when the sampling frequency is raised in the sudden change state, the real-time output power of the photovoltaic module is collected simultaneously, the real-time energy consumption parameters are processed, and the current real-time power gain is calculated based on the established dynamic quantitative mapping relationship between energy consumption and power gain. Based on this, the upper limit of the allowable energy consumption increase is calculated in reverse, and then the specific value of the highest frequency is adjusted.

8. A system utilizing the power adaptive adjustment method for a photovoltaic module as described in claim 7, characterized in that, include: The data acquisition module is used to collect data on light intensity, wind speed, and component output operating conditions at a reference frequency, and transmit them to the buffer in real time. The mutation detection module is used to calculate the rate of change of light intensity and wind speed data, determine whether the corresponding mutation threshold has been reached, and trigger or cancel the frequency switching command. The frequency adjustment module is used to raise the sampling frequency to the highest frequency within a preset time after receiving the frequency switching command, and lower it to the reference frequency after the sudden change state stabilizes. The parameter adjustment module is used to adjust the inverter parameters based on sampled data in a non-abrupt state, and to accelerate the adjustment in the event of an abrupt change to achieve adaptive adjustment. The cache management module is used to store real-time collected operating condition data and historical parameters, and manage the data lifecycle according to the configured cache duration. The energy consumption and power mapping module is used to establish a dynamic quantitative mapping relationship between energy consumption and power gain. When the frequency rises to the highest level, the highest frequency is adjusted according to the power gain to control the increase in energy consumption to be no greater than the power gain.

Images