Balanced control method, system and equipment of photovoltaic power generation system and medium

Abstract

The invention belongs to the field of photovoltaic power station control, and discloses a balance control method, system and device of a photovoltaic power generation system and a medium. The control method comprises the following steps: acquiring electrical data, temperature data and illumination data of each assembly and string in a photovoltaic array, calculating a comprehensive unbalance degree by using a multi-dimensional unbalance degree formula, and further matching a corresponding equalization mode (such as a distributed mode, a centralized mode or a mixed mode) based on a preset equalization decision strategy and in combination with the illumination data and the comprehensive unbalance degree, so as to realize the control of the photovoltaic array. And finally, precise equalization control is carried out on the photovoltaic array. The problem of component imbalance caused by factors such as illumination, temperature and component aging in a traditional photovoltaic power generation system is solved, balance lag or excessive balance is avoided, energy transfer efficiency is improved, energy loss is reduced, and therefore the overall operation stability and energy utilization efficiency of the photovoltaic power generation system are improved.

Description

Technical Field

[0001] This application belongs to the field of photovoltaic power plant control, and specifically relates to a method, system, equipment and medium for equalization control of a photovoltaic power generation system. Background Technology

[0002] With the popularization of photovoltaic power generation technology, photovoltaic arrays are composed of a large number of photovoltaic modules connected in series or in parallel, and the output voltage, current and power characteristics of each photovoltaic module are significantly different.

[0003] In traditional photovoltaic power generation systems, most systems only adopt simple passive balancing or active balancing with a fixed topology. They cannot dynamically adjust the balancing strategy according to the degree of array imbalance, resulting in large energy loss or low balancing efficiency. It is difficult to accurately identify the source of imbalance, and excess energy is directly dissipated during the balancing process. Without coordination with the energy storage system, energy is wasted.

[0004] Therefore, there is an urgent need for a photovoltaic power generation system and method based on a photovoltaic equalizer to solve the power loss problem caused by component imbalance and improve the system's operational stability and energy utilization rate. Summary of the Invention

[0005] To address the aforementioned issues, this application provides a method, system, equipment, and medium for equalization control of a photovoltaic power generation system. This solution avoids equalization lag or over-equalization, improves energy transfer efficiency, and reduces energy loss.

[0006] To address the aforementioned technical problems, the first aspect of this disclosure proposes a method for equalization control of a photovoltaic power generation system, the control method comprising: Collect electrical, temperature, and illumination data for each photovoltaic module and string in the photovoltaic array; The overall imbalance of the photovoltaic array is calculated based on the electrical data and the temperature data using a multidimensional imbalance formula. Based on the preset equalization decision strategy, the corresponding equalization mode is matched according to the illumination data and the overall imbalance degree. The photovoltaic modules and strings in the photovoltaic array are balanced according to the balance mode.

[0007] According to a preferred embodiment of this disclosure, the step of calculating the comprehensive imbalance of the photovoltaic array based on the electrical data and the temperature data using a multidimensional imbalance formula includes: Calculate the first relative deviation between the electrical data of each photovoltaic module and the string and the average value of the electrical data; Calculate the second relative deviation between the temperature data of each photovoltaic module and the string and the average value of the temperature data; Based on the preset balance weights of the electrical data and the temperature data, the first relative deviation and the second relative deviation are weighted and summed to calculate the overall imbalance degree.

[0008] According to a preferred embodiment of this disclosure, the electrical data includes: current data and voltage data; The calculation of the first relative deviation between the electrical data of each of the photovoltaic modules and the string and the average value of the electrical data includes: Calculate the relative voltage deviation between the voltage data of each of the voltage components and the string and the average voltage data; Calculate the relative deviation of the current data of each of the said volt components and the said string from the average value of the current data; The relative deviations of each voltage and each relative deviation of each current are taken as the first relative deviation.

[0009] According to a preferred embodiment of this disclosure, the control method further includes: An optimization objective function for the balancing effect is constructed based on the balancing efficiency and energy loss of the photovoltaic array; Based on reinforcement learning algorithms, the optimization objective function is trained using historical data from the photovoltaic array to determine the optimization objective value corresponding to different preset equilibrium weights.

[0010] According to a preferred embodiment of this disclosure, the step of matching a corresponding equalization mode based on a preset equalization decision strategy, according to the illumination data and the overall imbalance degree, includes: Based on the preset equilibrium decision strategy, the degree of imbalance to which the comprehensive imbalance belongs is determined; When the overall imbalance degree belongs to a preset first imbalance degree, a first balancing mode is matched. The first balancing mode includes: determining whether the overall imbalance degree is caused by a local shadow area based on the illumination data; if it is caused by the local shadow area, then performing centralized balancing and distributed balancing on the photovoltaic modules and strings within the local shadow area, and then performing centralized balancing control and distributed balancing control on the photovoltaic modules and strings outside the local shadow area; if it is not caused by the local shadow area, then performing centralized balancing control and distributed balancing control on the photovoltaic modules and strings. When the overall imbalance degree belongs to the preset second imbalance degree, a second balance mode is matched. The second balance mode includes: performing distributed balance control on the photovoltaic module and the photovoltaic modules in the string. When the overall imbalance degree belongs to the preset third imbalance degree, the third balance mode is matched. The third balance mode includes: not performing balance control and recording the generated data.

[0011] According to a preferred embodiment of this disclosure, the control method further includes: When the output power of the photovoltaic array exceeds the sum of the load power and the energy transfer power of the equalization control, the photovoltaic array charges the energy storage battery according to the state of charge of the energy storage battery. When the output power of the photovoltaic array is less than the sum of the load power and the energy transfer power of the equalization control, the energy storage battery discharges to the photovoltaic array.

[0012] According to a preferred embodiment of this disclosure, the control method further includes: When the power grid is in a low-price period, the energy storage battery is charged through the power grid; When the power grid is in a peak electricity price period, the photovoltaic array and the energy storage battery discharge to the power grid.

[0013] To address the aforementioned technical problems, a second aspect of this disclosure proposes a balance control system for a photovoltaic power generation system, the control system comprising: The photovoltaic array status sensing module is used to collect electrical data, temperature data, and light data of each photovoltaic module and string in the photovoltaic array; The imbalance calculation module is used to calculate the overall imbalance of the photovoltaic array based on the electrical data and the temperature data using a multi-dimensional imbalance formula. The equilibrium decision module is used to match the corresponding equilibrium mode based on the preset equilibrium decision strategy, the illumination data, and the overall imbalance degree. A multi-mode photovoltaic balancing execution module is used to perform balancing control on photovoltaic modules and strings in the photovoltaic array according to the balancing mode.

[0014] To address the aforementioned technical problems, a third aspect of this disclosure provides an electronic device, comprising: Processor; and A memory storing computer-executable instructions, which, when executed, cause the processor to perform the method described in any of the above embodiments.

[0015] To address the aforementioned technical problems, a fourth aspect of this disclosure provides a computer storage medium that stores one or more programs, which, when executed by a processor, implement the method described in any of the above embodiments.

[0016] Compared with existing technologies, this application has the following advantages: By collecting electrical, temperature, and illumination data from each component and string in the photovoltaic array, the comprehensive imbalance degree is calculated using a multi-dimensional imbalance degree formula. Then, based on a preset balance decision strategy, the corresponding balance mode (such as distributed, centralized, or hybrid mode) is matched by combining illumination data and the comprehensive imbalance degree, ultimately achieving precise balance control of the photovoltaic array. This solves the component imbalance problem caused by factors such as illumination, temperature, and component aging in traditional photovoltaic power generation systems, avoids balance lag or over-balancing, improves energy transfer efficiency, reduces energy loss, and thus enhances the overall operational stability and energy utilization efficiency of the photovoltaic power generation system.

[0017] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A schematic flowchart of a method for equalization control of a photovoltaic power generation system according to an embodiment of the present disclosure is shown. Figure 2 A second schematic flowchart of a photovoltaic power generation system equalization control method according to an embodiment of the present disclosure is shown. Figure 3 A third schematic flowchart of a method for equalization control of a photovoltaic power generation system according to an embodiment of the present disclosure is shown. Figure 4 A schematic flowchart of a method for equalization control of a photovoltaic power generation system according to an embodiment of the present disclosure is shown in part four. Figure 5 A schematic diagram of the equalization control system structure of a photovoltaic power generation system according to an embodiment of the present disclosure is shown; Figure 6 A schematic diagram of an electronic device structure according to an embodiment of the present disclosure is shown. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] The same reference numerals in the accompanying drawings denote the same or similar elements, components, or parts, and therefore repeated descriptions of the same or similar elements, components, or parts may be omitted below. It should also be understood that although qualifiers such as first, second, third, etc., indicating numbers may be used herein to describe various devices, elements, components, or parts, these devices, elements, components, or parts should not be limited by these qualifiers. That is, these qualifiers are only used to distinguish one from another. For example, a first device may also be referred to as a second device, without departing from the essence of the technical solution of this disclosure. Furthermore, the terms "and / or" and "and / or" refer to all combinations including the first or more of the listed items.

[0022] Please see Figure 1 , Figure 1 This is one of the flowcharts of a balance control method for a photovoltaic power generation system provided in this disclosure, such as... Figure 1 As shown, the control methods include: S11. Collect electrical data, temperature data, and light data of each photovoltaic module and string in the photovoltaic array.

[0023] In this embodiment, the photovoltaic array is composed of photovoltaic modules connected in series or parallel, with a string being a series unit. A photovoltaic module is the smallest unit of a photovoltaic power generation system, consisting of multiple photovoltaic cells encapsulated together, directly converting solar energy into direct current (DC). A string (photovoltaic module string) is a DC output unit formed by multiple modules connected in series, typically consisting of 20-24 modules, used to boost the output voltage to meet the inverter's requirements.

[0024] In this embodiment, by deploying distributed acquisition units and light intensity acquisition units in the photovoltaic array, electrical data such as voltage and current of each photovoltaic module and string, temperature data, and light intensity data of different areas are acquired in real time, providing a comprehensive and accurate data foundation for subsequent imbalance analysis. The technical effect is that it can accurately capture the actual operating status and environmental conditions of each component and string in the photovoltaic array, providing a reliable basis for dynamic decision-making on the balance mode and optimization of energy distribution, thereby effectively improving the operational stability and power generation efficiency of the photovoltaic power generation system.

[0025] Specifically, relevant data from the photovoltaic array can be obtained by setting up a photovoltaic array status sensing module, which includes a distributed acquisition unit, a light intensity acquisition unit, and a data synchronization and transmission unit. This module is used to collect the voltage, current, temperature, and light intensity of the photovoltaic modules or strings, and then upload the data after filtering and synchronization.

[0026] In this embodiment, the distributed acquisition unit is configured as follows: each photovoltaic module or every 2-3 series modules is a acquisition unit with 1 voltage sampling circuit, 1 current sampling circuit and 1 temperature sampling unit; the voltage sampling adopts a differential amplifier circuit to avoid common-mode interference; the current sampling adopts a Hall current sensor to prevent series loss; the temperature sampling adopts an NTC thermistor to collect the surface temperature in close contact with the back of the module.

[0027] In this embodiment, the light intensity acquisition unit deploys multi-channel light sensors in different areas of the photovoltaic array (such as the array edge, center, and easily shaded areas) to collect the real-time irradiance of each area, which is used to distinguish between the unevenness caused by the aging of the components themselves and the unevenness caused by external light.

[0028] Data synchronization and transmission unit: The SPI bus is used to realize the synchronous transmission of data between each acquisition unit and the module main controller. The synchronization accuracy is controlled at the millisecond level to avoid imbalance judgment errors caused by data delay. The main controller filters the acquired data to remove pulse interference and then uploads it to the imbalance analysis module via Ethernet.

[0029] S12. Calculate the overall imbalance of the photovoltaic array based on electrical and temperature data using the multidimensional imbalance formula.

[0030] In this embodiment, an imbalance calculation formula is constructed that includes multiple parameters such as voltage, current, and temperature. By quantifying and weighting the deviation of each component / string from the average value, a comprehensive imbalance index with a value range of 0-1 is generated. The technical effect is that it can accurately assess the overall imbalance state of the photovoltaic array, overcome the limitations of single parameter judgment, provide a scientific basis for dynamically selecting distributed or centralized balance mode, and thus effectively reduce power loss caused by component mismatch.

[0031] S13. Based on the preset equilibrium decision strategy, match the corresponding equilibrium mode according to the illumination data and the overall imbalance degree.

[0032] In this embodiment, a three-level equalization decision logic is established. An imbalance threshold is preset and combined with illumination data. When the overall imbalance is lower than the first threshold, equalization is not initiated. For moderate imbalance, a distributed equalization mode is used to adjust individual components. For severe imbalance, a hybrid mode of centralized and distributed equalization is initiated to prioritize adjusting the power between strings before optimizing component equalization. At the same time, if the imbalance is caused by local shading, equalization is initiated for components in the shaded area first. By dynamically matching the equalization mode, the problem of equalization lag or over-equalization caused by traditional single judgment is effectively solved, improving energy transfer efficiency and reducing power loss.

[0033] S14. Perform equalization control on the photovoltaic modules and strings in the photovoltaic array according to the equalization mode.

[0034] In this embodiment, based on the aforementioned decision-making equilibrium mode (distributed / centralized / hybrid mode), the energy transfer direction and power between modules or strings are adjusted in real time through Buck-Boost converters and bidirectional DC-DC converters. A PID algorithm is used to control the converter duty cycle to ensure stable output voltage, achieving precise voltage matching at the module or string level. This eliminates output power imbalance caused by uneven illumination, temperature differences, or aging, improves the overall power generation efficiency of the photovoltaic array, and avoids the energy dissipation problem of traditional passive equilibrium.

[0035] In this embodiment, the monitored parameters include: the open-circuit voltage and short-circuit current of the photovoltaic module; the switching transistor temperature, capacitor voltage, and inductor current of the equalizer; the individual cell voltage, total voltage, and charging / discharging current of the energy storage battery; and the output voltage, frequency, and power factor of the inverter. The monitoring data is uploaded to the module main controller in real time via the RS485 bus.

[0036] In this embodiment, a diagnostic method based on threshold comparison and feature analysis is adopted: Threshold-type faults include, but are not limited to, the open-circuit voltage of the component being lower than 80% of the rated value, which is determined to be component aging; the temperature of the equalizer switch tube being higher than 85°C, which is determined to be switch tube overheating; the voltage difference of the energy storage cell being greater than 0.2V, which is determined to be battery imbalance; Feature-type faults include, but are not limited to, the equalizer output current exhibiting high-frequency fluctuations, which is determined to be inductor fault; the photovoltaic current suddenly dropping to 0 while the illumination is normal, which is determined to be component short circuit.

[0037] In this embodiment, different measures are triggered according to the fault level: minor fault (such as slight overheating of the switching transistor): an alarm signal is issued, and the duty cycle of the equalizer is adjusted to reduce power; moderate fault (such as component aging): the equalizer of the faulty component is disconnected to avoid affecting other components; severe fault (such as component short circuit or inverter failure): the relay is triggered to disconnect the faulty circuit, and the backup equalizer or inverter is started at the same time to ensure that some functions of the system operate normally.

[0038] In this embodiment, an SD card or cloud server is used to store daily photovoltaic array status data, balancing strategies, balancing effects, and fault records. The photovoltaic array status data includes voltage, current, temperature, and irradiance.

[0039] In this embodiment, electrical, temperature, and illumination data of each component and string in the photovoltaic array are collected. A multi-dimensional imbalance formula is used to calculate the overall imbalance degree. Then, based on a preset balancing decision strategy, the appropriate balancing mode (such as distributed, centralized, or hybrid mode) is matched by combining illumination data and the overall imbalance degree. Finally, precise balancing control is implemented on the photovoltaic array. This solves the component imbalance problem caused by factors such as illumination, temperature, and component aging in traditional photovoltaic power generation systems, avoids balancing lag or over-balancing, improves energy transfer efficiency, reduces energy loss, and thus enhances the overall operational stability and energy utilization efficiency of the photovoltaic power generation system.

[0040] Please see Figure 2 , Figure 2 This is the second schematic diagram of a balance control method for a photovoltaic power generation system provided in this disclosure, such as... Figure 2 As shown, the control method includes the following steps: S21. Calculate the first relative deviation between the electrical data of each photovoltaic module and string and the average value of the electrical data.

[0041] In this embodiment, by calculating the relative deviation (such as voltage deviation rate and current deviation rate) of the electrical data such as voltage and current of each photovoltaic module or string from the average value of its array, the degree of difference between the performance of each unit and the overall performance is quantified. This provides key quantitative indicators for subsequent imbalance analysis, accurately locates modules or strings whose output characteristics deviate from the average level, provides data support for dynamic balance decision-making, and thus effectively improves the energy conversion consistency of the photovoltaic array.

[0042] Specifically, the electrical data includes: current data and voltage data; calculating the voltage relative deviation of each voltage component and string from the average voltage data; calculating the current relative deviation of each voltage component and string from the average current data; and using each voltage relative deviation and each current relative deviation as the first relative deviation.

[0043] S22. Calculate the second relative deviation between the temperature data of each photovoltaic module and string and the average temperature data.

[0044] In this embodiment, by calculating the relative deviation between the backsheet temperature data of each photovoltaic module or string and the average temperature of its array, the degree of difference between the temperature distribution of each unit and the overall average is quantified. This enables accurate identification of modules whose performance has degraded due to local overheating or poor heat dissipation, providing key data support for judging the imbalance caused by temperature factors, thereby optimizing the balancing strategy to reduce the impact of high temperature on power generation efficiency.

[0045] S23. Based on the preset balance weights of electrical data and temperature data, the first relative deviation and the second relative deviation are weighted and summed to calculate the overall imbalance degree.

[0046] In this embodiment, the electrical and temperature deviations of each component / string are weighted and summed according to preset voltage, current, and temperature imbalance weights (such as 0.5, 0.3, and 0.2) to generate a comprehensive imbalance index in the range of 0-1. By quantifying the coupling effect of multiple parameters, the overall imbalance state of the system is accurately assessed, providing a scientific basis for dynamically selecting distributed or centralized balancing modes and avoiding the failure of balancing strategies caused by single parameter judgment.

[0047] Specifically, a multi-dimensional imbalance evaluation index is constructed, comprehensively considering differences in voltage, current, and temperature. The calculation formula is as follows:

[0048] in: The overall imbalance of the photovoltaic array is represented by D, which ranges from 0 to 1. The larger the value of D, the more severe the imbalance. Let i be the output voltage of the i-th photovoltaic module or string. The average voltage of all photovoltaic modules or strings; Let i be the output current of the i-th photovoltaic module or string. This represents the average current across all photovoltaic modules or strings. Let be the backsheet temperature of the i-th photovoltaic module. The average temperature of all photovoltaic modules; , , The unbalanced weights for voltage, current, and temperature are respectively, satisfying... The initial value is set to =0.5、 =0.3、 =0.2; Let n be the number of photovoltaic modules and strings in the photovoltaic array. Specifically, the photovoltaic modules and strings connected in parallel in the photovoltaic array can be numbered sequentially, and the total number of photovoltaic modules and strings is n.

[0049] In this embodiment, the unbalance weight values ​​for voltage, current, and temperature in this scheme can be optimized based on the unbalance adjustment of the photovoltaic array. An optimization objective function for the balancing effect can be constructed based on the balancing efficiency and energy loss of the photovoltaic array. Based on the reinforcement learning algorithm, the optimization objective function is trained using historical data of the photovoltaic array to determine the optimization target value of the optimization objective function corresponding to different preset balancing weights.

[0050] Specifically, an optimization objective function for the equilibrium effect is constructed, aiming at maximizing equilibrium efficiency and minimizing energy loss: The formula for the optimization objective function is:

[0051] in: To optimize the target value, the range is 0-1. A smaller value indicates a better optimization effect; The time consumed for a single balanced run, The total duration of a single imbalance; Energy losses during the equalization process include equalizer switching losses and line losses. This represents the total output power of the photovoltaic array; For weighting coefficients, 0 < <1, prioritizing shortening the equilibrium time will increase Prioritizing reducing losses will decrease ; Reinforcement learning optimization unit: Employing the Q-Learning algorithm, it treats imbalanced scenarios under different environments as states and adjusts the weights accordingly. , , As an action, optimize the target value. As a reward; the model is trained using daily data, for example: if a certain adjustment After increasing from 0.5 to 0.6, If the weight is reduced by 10%, the model will remember the effectiveness of the action in the corresponding scenario and will prioritize using this weight configuration when encountering similar scenarios in the future. At the same time, the weight is dynamically adjusted according to seasonal changes (such as the greater impact of temperature in summer and the greater impact of sunlight in winter) to make the imbalance calculation more accurate.

[0052] Please see Figure 3 , Figure 3 This is the third schematic diagram of a balance control method for a photovoltaic power generation system provided in this disclosure, such as... Figure 3 As shown, the control method includes the following steps: S31. Based on the preset equilibrium decision strategy, determine the degree of imbalance to which the comprehensive imbalance belongs.

[0053] In this embodiment, the preset equilibrium decision strategy can be preset imbalance thresholds Dth1 and Dth2 to form a three-level decision logic. When D < Dth1: it is judged as "slight imbalance", when Dth1 ≤ D < Dth2: it is judged as "moderate imbalance", and when D ≥ Dth2: it is judged as "severe imbalance".

[0054] S32. When the overall imbalance degree belongs to the preset first imbalance degree, the first balance mode is matched. The first balance mode includes: determining whether the overall imbalance degree is caused by a local shaded area based on the illumination data. If it is caused by a local shaded area, then the photovoltaic modules and strings within the local shaded area are first balanced by centralized and distributed balance, and then the photovoltaic modules and strings outside the local shaded area are balanced by centralized and distributed balance control. If it is not caused by a local shaded area, then the photovoltaic modules and strings are balanced by centralized and distributed balance control.

[0055] In this embodiment, a first degree of imbalance is preset, namely "severe imbalance". The decision adopts a hybrid equalization mode of centralized and distributed equalization, that is, first adjust the equalization between strings, and then optimize the equalization of components within strings. At the same time, combined with the light intensity data, if the imbalance is caused by local shadows, equalization is initiated first for components in the shadow area.

[0056] In this embodiment, the cause of the overall imbalance is determined by combining illumination data. If it is caused by local shadows, centralized and distributed hybrid equalization is initiated on the components in the shadow area to quickly eliminate the occlusion effect, and then equalization is performed on the non-shadow area. If it is not caused by shadows, centralized and distributed equalization control is directly implemented on the entire array. Through differentiated equalization strategies, shadow occlusion and inherent imbalance problems are accurately solved, energy transfer efficiency is improved, power loss is reduced, and unnecessary equalization operations on non-shadow areas are avoided.

[0057] S33. When the overall imbalance degree belongs to the preset second imbalance degree, the second balance mode is matched. The second balance mode includes: distributed balance control of photovoltaic modules and photovoltaic modules in the string.

[0058] In this embodiment, a second degree of imbalance, namely "moderate imbalance", is preset, and the decision adopts a distributed equilibrium mode, that is, local equilibrium for a single component.

[0059] In this embodiment, for each individual photovoltaic module in the photovoltaic array, a module-level DC-DC converter or power optimizer is deployed to monitor and adjust its output voltage / current in real time, so that each module operates near its maximum power point. This can effectively compensate for output inconsistencies caused by local shading, module aging, or differences in installation angle, improve the overall power generation efficiency of the array, and avoid the over-adjustment of non-faulty modules by traditional centralized balancing, thus achieving refined energy management.

[0060] S34. When the overall imbalance degree belongs to the preset third imbalance degree, match the third balance mode. The third balance mode includes: not performing balance control and recording the generated data.

[0061] In this embodiment, a third degree of imbalance, namely "slight imbalance", is preset, so there is no need to start the balancing execution module; only data is recorded.

[0062] In this scheme, centralized and distributed equalization control are implemented for photovoltaic modules and strings. This includes: firstly, centralized equalization control is implemented for each string to enable bidirectional energy transfer between strings; then, distributed equalization control is implemented for each photovoltaic module and the photovoltaic modules within the modules to ensure that the output voltage of each photovoltaic module reaches the average output voltage of all photovoltaic modules and strings.

[0063] Specifically, the implementation of distributed equalization control includes: each photovoltaic module is connected in series with one Buck-Boost converter as a sub-equalizer; the input of the converter is connected to the output of the module, and the output is connected in parallel to the common bus of the array; when the module voltage Ui > When Ui < 0, the converter operates in Buck mode, stepping down excess energy from the components and delivering it to the bus; when Ui < 0.05 At this time, the converter operates in Boost mode, drawing power from the bus, boosting it, and then supplying it to the modules, causing the module output voltage to approach the voltage level. .

[0064] The implementation of centralized equalization control includes: configuring a bidirectional DC-DC converter between photovoltaic strings as a string equalizer, with its input connected to the output of the high-power string and its output connected to the input of the low-power string; and realizing bidirectional energy transfer between strings by controlling the duty cycle of the switching transistor through PWM. For example, when the total power of string A is higher than that of string B, the centralized equalizer transfers part of the energy of string A to string B to balance the output power of the two strings.

[0065] Please see Figure 4 , Figure 4 This is the fourth flowchart of a photovoltaic power generation system equilibrium control method disclosed herein, as shown in Figure 4. Figure 4 As shown, the control method includes the following steps: S41. When the output power of the photovoltaic array exceeds the sum of the load power and the energy transfer power of the equalization control, the photovoltaic array charges the energy storage battery according to the state of charge of the energy storage battery.

[0066] In this embodiment, a dynamic power allocation mechanism is constructed. When the real-time output power of the photovoltaic array is greater than the sum of the load demand and the energy transfer power of the equalization system itself, the system automatically detects the state of charge (SOC) of the energy storage battery. If the SOC does not reach the upper limit, charging control is activated to efficiently store the excess power into the battery through the DC / DC converter. The technical effect is to maximize the utilization of photovoltaic energy, avoid curtailment, prevent battery overcharging, and provide a guarantee for power supply to the load at night or on cloudy or rainy days, thereby improving the stability and economy of the entire photovoltaic energy storage system.

[0067] Specifically, the output power P based on the photovoltaic array PV The energy transfer power P of the equalizer bal And formulate scheduling strategies based on the SOC of energy storage batteries: When P PV >P load +P bal At that time, where P load For load power: Excess energy is used to charge the energy storage battery through the inverter. The charging current is adjusted according to the SOC. When SOC < 20%, a large current is used for fast charging, and when SOC > 80%, a small current is used for slow charging.

[0068] S42. When the output power of the photovoltaic array is less than the sum of the load power and the energy transfer power of the equalization control, the photovoltaic array is discharged through the energy storage battery.

[0069] In this embodiment, when the output power of the photovoltaic array cannot meet the sum of the load demand and the energy transfer power of the balancing control, the system detects the state of charge (SOC) of the energy storage battery. If the SOC is sufficient, the battery discharges, and the electrical energy is injected back into the insufficient power components or strings in the array through a bidirectional DC / DC converter to assist in power supply. The technical effect is that it can effectively solve the problem of array output fluctuations caused by partial shading, component failure, or sudden power changes, maintain system power balance, avoid the risk of load power outages, and improve the stable operation capability of the photovoltaic array under complex operating conditions.

[0070] That is when At this time, the energy storage battery discharges to replenish the energy gap and avoid system power fluctuations caused by insufficient photovoltaic output during the balancing process.

[0071] When the output power of the photovoltaic array equals the sum of the load power and the energy transfer power of the equalization control, the supply and demand are balanced, and no other operation is required.

[0072] Furthermore, when the power grid is in a low-price period, the energy storage battery is charged through the power grid; when the power grid is in a high-price period, the photovoltaic array and energy storage battery discharge to the power grid.

[0073] In this embodiment, the voltage, current, and temperature of the energy storage battery are collected in real time, and the State of Charge (SOC) is calculated using the ampere-hour integration method to avoid the SOC being below 10% (over-discharge) or above 90% (overcharge), thus extending the battery life.

[0074] Please see Figure 5 , Figure 5 This disclosure provides a photovoltaic power generation system equilibrium control system, which includes: a photovoltaic array state sensing module 11, an imbalance calculation module 12, an equilibrium decision module 13, and a multi-mode photovoltaic equilibrium execution module 14.

[0075] In this embodiment, the photovoltaic array status sensing module 11 is used to collect electrical data, temperature data and light data of each photovoltaic module and string in the photovoltaic array.

[0076] In this embodiment, the photovoltaic array status sensing module 11 includes: a distributed acquisition unit: each photovoltaic module or every 2-3 series modules is configured as an acquisition unit with 1 voltage sampling circuit, 1 current sampling circuit and 1 temperature sampling unit; the voltage sampling adopts a differential amplifier circuit to avoid common-mode interference; the current sampling adopts a Hall current sensor to prevent series loss; the temperature sampling adopts an NTC thermistor to collect the surface temperature in close contact with the back of the module.

[0077] In this embodiment, the photovoltaic array state sensing module 11 includes: a light intensity acquisition unit: multi-channel light sensors are deployed in different areas of the photovoltaic array (such as the array edge, center, and easily shaded areas) to collect the real-time irradiance of each area, which is used to distinguish between the unevenness caused by the aging of the components themselves and the unevenness caused by external light.

[0078] In this embodiment, the photovoltaic array state sensing module 11 includes: a data synchronization and transmission unit: using an SPI bus to realize the synchronous transmission of data between each acquisition unit and the module's main controller, with synchronization accuracy controlled at the millisecond level to avoid imbalance judgment errors caused by data delay; the main controller filters the acquired data to remove pulse interference, and then uploads it to the imbalance analysis module via Ethernet.

[0079] In this embodiment, the imbalance calculation module 12 is used to calculate the overall imbalance of the photovoltaic array based on electrical data and temperature data using a multidimensional imbalance formula.

[0080] In this embodiment, the equilibrium decision module 13 is used to match the corresponding equilibrium mode based on the preset equilibrium decision strategy, illumination data and overall imbalance degree.

[0081] In this embodiment, the multi-mode photovoltaic equalization execution module 14 is used to perform equalization control on the photovoltaic modules and strings in the photovoltaic array according to the equalization mode.

[0082] In this embodiment, the imbalance calculation module 12 is specifically used to calculate the first relative deviation between the electrical data of each photovoltaic module and string and the average value of the electrical data; calculate the second relative deviation between the temperature data of each photovoltaic module and string and the average value of the temperature data; and calculate the comprehensive imbalance by weighting and summing the first relative deviation and the second relative deviation according to the preset balance weights of the electrical data and the temperature data.

[0083] In this embodiment, the imbalance calculation module 12 is specifically used to calculate the voltage relative deviation between the voltage data of each voltage component and string and the average voltage data; calculate the current relative deviation between the current data of each voltage component and string and the average current data; and use each voltage relative deviation and each current relative deviation as the first relative deviation.

[0084] In this embodiment, the control system further includes: an adaptive optimization and learning module, used to construct an optimization objective function for the equalization effect based on the equalization efficiency and energy loss of the photovoltaic array; and to train the optimization objective function using historical data of the photovoltaic array based on a reinforcement learning algorithm, thereby determining the optimization target value of the optimization objective function corresponding to different preset equalization weights.

[0085] In this embodiment, the adaptive optimization and learning module includes: a historical data storage unit: which uses an SD card or cloud server to store daily photovoltaic array status data, balancing strategies, balancing effects and fault records. The photovoltaic array status data includes voltage, current, temperature and irradiance.

[0086] In this embodiment, the adaptive optimization and learning module includes: an optimization objective calculation unit, used to construct an optimization objective function for the equilibrium effect, with the goal of maximizing equilibrium efficiency and minimizing energy loss; the optimization objective function formula is:

[0087] in: To optimize the target value, the range is 0-1. A smaller value indicates a better optimization effect; The time consumed for a single balanced run, The total duration of a single imbalance; Energy losses during the equalization process include equalizer switching losses and line losses. This represents the total output power of the photovoltaic array; For weighting coefficients, 0 < <1, prioritizing shortening the equilibrium time will increase Prioritizing reducing losses will decrease .

[0088] In this embodiment, the adaptive optimization and learning module includes: a reinforcement learning optimization unit, used to adjust weights by taking imbalanced scenarios under different environments as states using the Q-Learning algorithm. , , As an action, optimize the target value. As a reward; the model is trained using daily data, for example: if a certain adjustment After increasing from 0.5 to 0.6, If the weight is reduced by 10%, the model will remember the effectiveness of the action in the corresponding scenario and will prioritize using this weight configuration when encountering similar scenarios in the future. At the same time, the weight is dynamically adjusted according to seasonal changes (such as the greater impact of temperature in summer and the greater impact of sunlight in winter) to make the imbalance calculation more accurate.

[0089] In this embodiment, the equilibrium decision module 13 is specifically used to determine the degree of imbalance of the overall imbalance based on a preset equilibrium decision strategy. When the overall imbalance belongs to a preset first degree of imbalance, a first equilibrium mode is matched. The first equilibrium mode includes: determining whether the overall imbalance is caused by a local shaded area based on the illumination data. If it is caused by a local shaded area, then centralized equilibrium and distributed equilibrium are first performed on the photovoltaic modules and strings within the local shaded area, and then centralized equilibrium control and distributed equilibrium control are performed on the photovoltaic modules and strings outside the local shaded area. If it is not caused by a local shaded area, then centralized equilibrium control and distributed equilibrium control are performed on the photovoltaic modules and strings. When the overall imbalance belongs to a preset second degree of imbalance, a second equilibrium mode is matched. The second equilibrium mode includes: distributed equilibrium control is performed on the photovoltaic modules and strings. When the overall imbalance belongs to a preset third degree of imbalance, a third equilibrium mode is matched. The third equilibrium mode includes: no equilibrium control is performed, and the generated data is recorded.

[0090] In this embodiment, the multi-mode photovoltaic equalization execution module 14 includes: a distributed equalization unit, used for connecting one Buck-Boost converter in series with each photovoltaic module as a sub-equalizer, the input of the converter being connected to the output of the module, and the output being connected in parallel to the common bus of the array; when the module voltage... > When the converter operates in Buck mode, it steps down excess energy from the components and delivers it to the bus; when < At this time, the converter operates in Boost mode, drawing power from the bus, boosting it, and then supplying it to the modules, causing the module output voltage to approach the voltage level. .

[0091] In this embodiment, the multi-mode photovoltaic equalization execution module 14 includes: a centralized equalization unit: a bidirectional DC-DC converter is configured between photovoltaic strings as a string equalizer, with its input connected to the output terminal of the high-power string and its output connected to the input terminal of the low-power string; by controlling the duty cycle of the switching transistor through PWM, bidirectional energy transfer between strings is realized. For example, when the total power of string A is higher than that of string B, the centralized equalizer transfers part of the energy of string A to string B to balance the output power of the two strings.

[0092] In this embodiment, the equalization control unit uses an STM32G474 as the main controller of the equalizer. It receives instructions from the decision module (such as the target voltage and current for equalization), adjusts the duty cycle of the Buck-Boost converter through a PID algorithm, and monitors the output voltage and current of the equalizer in real time to ensure the stability of the energy transfer process and avoid damage to components due to overcurrent or overvoltage.

[0093] In this embodiment, the control system further includes an energy scheduling and storage coordination module, which is used to charge the energy storage battery through the photovoltaic array according to the state of charge of the energy storage battery when the output power of the photovoltaic array exceeds the sum of the load power and the energy transfer power of the equalization control; and to discharge the photovoltaic array through the energy storage battery when the output power of the photovoltaic array is less than the sum of the load power and the energy transfer power of the equalization control.

[0094] In this embodiment, the energy dispatch and storage coordination module is also used to charge the energy storage battery through the grid when the grid is in a valley electricity price period; and to discharge to the grid through the photovoltaic array and the energy storage battery when the grid is in a peak electricity price period.

[0095] In this embodiment, the energy dispatch and storage coordination module includes: an energy storage interface unit: a bidirectional DC-AC inverter is used to connect the energy storage battery to the photovoltaic array bus or the power grid. The inverter supports four-quadrant operation and realizes three modes: photovoltaic charging energy storage, energy storage replenishing photovoltaic power, and energy storage discharging to the grid.

[0096] In this embodiment, the energy dispatch and storage coordination module includes: an energy dispatch unit: based on the output power of the photovoltaic array. Energy transfer power of equalizer And the SOC of energy storage batteries to formulate scheduling strategies: when At that time, among them For load power: Excess energy charges the energy storage battery through the inverter. The charging current is adjusted according to the SOC: high current for fast charging when SOC < 20%, and low current for slow charging when SOC > 80%. When the energy storage battery discharges, it replenishes the energy gap and avoids system power fluctuations caused by insufficient photovoltaic output during the balancing process. When the grid is in the peak-valley electricity price period: during the valley period, the grid is used to charge the energy storage first, and during the peak period, photovoltaic and energy storage discharge to the grid to improve economic efficiency.

[0097] In this embodiment, the energy scheduling and storage coordination module includes: a SOC management unit: which collects the voltage, current and temperature of the energy storage battery in real time, calculates the SOC by the ampere-hour integration method, and avoids the SOC from being below 10% (over-discharge) or above 90% (overcharge), thereby extending the battery life.

[0098] In this embodiment, the control system further includes a system status monitoring and fault diagnosis module.

[0099] In this embodiment, the system status monitoring and fault diagnosis module includes: a status monitoring unit; monitoring parameters including: open-circuit voltage and short-circuit current of the photovoltaic module; switching transistor temperature, capacitor voltage, and inductor current of the equalizer; individual cell voltage, total voltage, and charging / discharging current of the energy storage battery; output voltage, frequency, and power factor of the inverter; monitoring data is uploaded to the module's main controller in real time via an RS485 bus. In this embodiment, the system status monitoring and fault diagnosis module includes: a fault diagnosis unit: employing a diagnostic method based on threshold comparison and feature analysis: threshold-type faults include, but are not limited to, component open-circuit voltage below 80% of rated value, indicating component aging; equalizer switch tube temperature above 85°C, indicating switch tube overheating; energy storage cell voltage difference exceeding 0.2V, indicating battery imbalance; feature-type faults include, but are not limited to, high-frequency fluctuations in equalizer output current, indicating inductor fault; photovoltaic current suddenly dropping to 0 while illumination is normal, indicating component short circuit.

[0100] In this embodiment, the system status monitoring and fault diagnosis module includes: a fault handling unit: triggering different measures according to the fault level: minor fault (such as slight overheating of the switching transistor): issuing an alarm signal and adjusting the duty cycle of the equalizer to reduce power; moderate fault (such as component aging): disconnecting the equalizer of the faulty component to avoid affecting other components; severe fault (such as component short circuit or inverter fault): triggering a relay to disconnect the faulty circuit and simultaneously starting a backup equalizer or inverter to ensure the normal operation of some system functions.

[0101] like Figure 6 As shown, this embodiment of the present disclosure provides an electronic device, including a processor 1110, a communication interface 1120, a memory 1130, and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other through the communication bus 1140. Memory 1130 is used to store computer programs; When the processor 1110 executes the program stored in the memory 1130, it implements any of the above methods.

[0102] The electronic device provided in this embodiment of the present disclosure includes a processor 1110 that collects electrical data, temperature data, and illumination data of each photovoltaic module and string in a photovoltaic array by executing a program stored in a memory 1130; calculates the comprehensive imbalance of the photovoltaic array based on the electrical data and temperature data using a multi-dimensional imbalance formula; matches a corresponding balance mode based on a preset balance decision strategy and the illumination data and comprehensive imbalance; and performs balance control on the photovoltaic modules and strings in the photovoltaic array according to the balance mode.

[0103] The communication bus 1140 mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus 1140 can be divided into an address bus, a data bus, and a component bus, etc. For ease of illustration, it is represented by only one thick line in the figure, but this does not indicate that there is only one bus or one type of bus.

[0104] The communication interface 1120 is used for communication between the above-mentioned electronic device and other devices.

[0105] The memory 1130 may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory 1130 may also be at least one storage device located remotely from the aforementioned processor 1110.

[0106] The processor 1110 mentioned above can be a general-purpose processor 1110, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0107] This disclosure provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors 1110 to implement the methods of any of the above embodiments.

[0108] In the above embodiments, implementation can be achieved, in whole or in part, by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this disclosure is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0109] Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for equalization control of a photovoltaic power generation system, characterized in that, The control method includes: Collect electrical, temperature, and illumination data for each photovoltaic module and string in the photovoltaic array; The overall imbalance of the photovoltaic array is calculated based on the electrical data and the temperature data using a multidimensional imbalance formula. Based on the preset equalization decision strategy, the corresponding equalization mode is matched according to the illumination data and the overall imbalance degree. The photovoltaic modules and strings in the photovoltaic array are balanced according to the balance mode.

2. The control method according to claim 1, characterized in that, The calculation of the comprehensive imbalance of the photovoltaic array using a multidimensional imbalance formula, based on the electrical data and the temperature data, includes: Calculate the first relative deviation between the electrical data of each photovoltaic module and the string and the average value of the electrical data; Calculate the second relative deviation between the temperature data of each photovoltaic module and the string and the average value of the temperature data; Based on the preset balance weights of the electrical data and the temperature data, the first relative deviation and the second relative deviation are weighted and summed to calculate the overall imbalance degree.

3. The control method according to claim 2, characterized in that, The electrical data includes: current data and voltage data; The calculation of the first relative deviation between the electrical data of each of the photovoltaic modules and the string and the average value of the electrical data includes: Calculate the relative voltage deviation between the voltage data of each of the voltage components and the string and the average voltage data; Calculate the relative deviation of the current data of each of the said volt components and the said string from the average value of the current data; The relative deviations of each voltage and each relative deviation of each current are taken as the first relative deviation.

4. The control method according to claim 2, characterized in that, The control method further includes: An optimization objective function for the balancing effect is constructed based on the balancing efficiency and energy loss of the photovoltaic array; Based on reinforcement learning algorithms, the optimization objective function is trained using historical data from the photovoltaic array to determine the optimization objective value corresponding to different preset equilibrium weights.

5. The control method according to claim 1, characterized in that, The method based on a preset equalization decision strategy, which matches a corresponding equalization mode according to the illumination data and the overall imbalance, includes: Based on the preset equilibrium decision strategy, the degree of imbalance to which the comprehensive imbalance belongs is determined; When the overall imbalance degree belongs to a preset first imbalance degree, a first balancing mode is matched. The first balancing mode includes: determining whether the overall imbalance degree is caused by a local shadow area based on the illumination data; if it is caused by the local shadow area, then performing centralized balancing and distributed balancing on the photovoltaic modules and strings within the local shadow area, and then performing centralized balancing control and distributed balancing control on the photovoltaic modules and strings outside the local shadow area; if it is not caused by the local shadow area, then performing centralized balancing control and distributed balancing control on the photovoltaic modules and strings. When the overall imbalance degree belongs to the preset second imbalance degree, a second balance mode is matched. The second balance mode includes: performing distributed balance control on the photovoltaic module and the photovoltaic modules in the string. When the overall imbalance degree belongs to the preset third imbalance degree, the third balance mode is matched. The third balance mode includes: not performing balance control and recording the generated data.

6. The control method according to any one of claims 1 to 5, characterized in that, The control method further includes: When the output power of the photovoltaic array exceeds the sum of the load power and the energy transfer power of the equalization control, the photovoltaic array charges the energy storage battery according to the state of charge of the energy storage battery. When the output power of the photovoltaic array is less than the sum of the load power and the energy transfer power of the equalization control, the energy storage battery discharges to the photovoltaic array.

7. The control method according to claim 6, characterized in that, The control method further includes: When the power grid is in a low-price period, the energy storage battery is charged through the power grid; When the power grid is in a peak electricity price period, the photovoltaic array and the energy storage battery discharge to the power grid.

8. A balance control system for a photovoltaic power generation system, characterized in that, The control system includes: The photovoltaic array status sensing module is used to collect electrical data, temperature data, and light data of each photovoltaic module and string in the photovoltaic array; The imbalance calculation module is used to calculate the overall imbalance of the photovoltaic array based on the electrical data and the temperature data using a multi-dimensional imbalance formula. The equilibrium decision module is used to match the corresponding equilibrium mode based on the preset equilibrium decision strategy, the illumination data, and the overall imbalance degree. A multi-mode photovoltaic balancing execution module is used to perform balancing control on photovoltaic modules and strings in the photovoltaic array according to the balancing mode.

9. An electronic device, characterized in that, include: processor; as well as A memory storing computer-executable instructions, which, when executed, cause the processor to perform the method according to any one of claims 1-7.

10. A computer storage medium, characterized in that, in, The computer storage medium stores one or more programs that, when executed by a processor, implement the method of any one of claims 1-7.

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