Photovoltaic inverter optimization control method considering environmental constraints

Abstract

The invention discloses a photovoltaic inverter optimization control method considering environmental constraints. The control method comprises the following steps: acquiring equipment parameters of each photovoltaic unit in a photovoltaic unit and environmental parameters of the position where the photovoltaic unit is located; obtaining a plurality of photovoltaic power generation sets and a constraint condition corresponding to each photovoltaic power generation set based on the environmental parameters of the position of each photovoltaic unit; each photovoltaic power generation set comprises a plurality of photovoltaic units; determining a target inverter corresponding to the photovoltaic power generation set and a working strategy of the target inverter based on the constraint condition corresponding to the photovoltaic power generation set and the equipment parameters of the plurality of photovoltaic units; and each target inverter is connected with each photovoltaic unit of the corresponding photovoltaic power generation set at the beginning of the next power generation cycle, and operates according to the corresponding working strategy after the beginning of the next power generation cycle. The technical problem that faults occur frequently due to the fact that the maximum power point tracking technology is directly used under the complex environment working condition in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of inverter management, and in particular to a photovoltaic inverter optimization control method taking environmental constraints into consideration. Background Art

[0002] PV inverters convert the variable DC voltage generated by photovoltaic solar panels into AC power at the mains frequency. They are a crucial component in PV array systems and can be used with AC-powered equipment.

[0003] Existing photovoltaic inverters use maximum power point tracking (MPPT) technology to ensure optimal output power from photovoltaic arrays. However, PV systems operate in complex environments. For example, in cold and high-altitude areas, despite strong sunlight, weather conditions can fluctuate rapidly, leading to extreme weather conditions that can cause the junction temperature of PV system components to rise. Using MPPT directly can lead to frequent failures, thus affecting the power generation of PV systems.

[0004] It can be seen from this that the frequent failures caused by complex environmental conditions during photovoltaic power generation are a technical problem that urgently needs to be solved. Summary of the Invention

[0005] The purpose of the present invention is to provide a photovoltaic inverter optimization control method taking environmental constraints into consideration, which solves the technical problem in the prior art that the maximum power point tracking technology directly used in complex environmental conditions leads to frequent failures.

[0006] The present invention provides a photovoltaic inverter optimization control method considering environmental constraints, the control method comprising:

[0007] Obtain the equipment parameters of each photovoltaic unit in the photovoltaic system and the environmental parameters of the location;

[0008] Based on environmental parameters of the locations of the photovoltaic units, a plurality of photovoltaic power generation sets and constraints corresponding to each photovoltaic power generation set are obtained; each photovoltaic power generation set includes a plurality of photovoltaic units;

[0009] Based on the constraints corresponding to the photovoltaic power generation set and the equipment parameters of several photovoltaic units, determine the target inverter corresponding to the photovoltaic power generation set and the working strategy of the target inverter;

[0010] Each target inverter is connected to each photovoltaic unit of the corresponding photovoltaic power generation set at the beginning of the next power generation cycle, and operates according to the corresponding working strategy after the beginning of the next power generation cycle.

[0011] Furthermore, the environmental parameters include temperature, humidity, wind speed, light intensity, altitude, air pressure, and dust concentration. Based on the environmental parameters of each photovoltaic unit, several photovoltaic power generation sets and the constraints corresponding to each photovoltaic power generation set are obtained, including:

[0012] Obtaining the photovoltaic unit junction temperature model;

[0013] Input the environmental parameters of the photovoltaic unit's location into the photovoltaic unit model to obtain the predicted junction temperature data of the photovoltaic unit in the next power generation cycle;

[0014] Based on the junction temperature prediction data of each photovoltaic unit in the next power generation cycle, multiple photovoltaic power generation sets are obtained;

[0015] Based on the junction temperature prediction data of each photovoltaic unit in the same photovoltaic power generation set, a constraint condition is obtained, where the constraint condition includes an upper limit of the set junction temperature and a lower limit of the set junction temperature.

[0016] Furthermore, based on the junction temperature prediction data of each photovoltaic unit in the next power generation cycle, multiple photovoltaic power generation sets are obtained, including:

[0017] Based on the junction temperature prediction data of each photovoltaic unit in the next power generation cycle, obtaining junction temperature characteristic data of the photovoltaic unit in the next power generation cycle; the junction temperature characteristic data includes the maximum junction temperature, the minimum junction temperature, the average temperature change rate and the maximum temperature change rate;

[0018] Based on the junction temperature characteristic data of each photovoltaic unit, multiple photovoltaic power generation sets are obtained according to preset division conditions.

[0019] Furthermore, the preset division conditions include: a preset power generation set number condition, a set element number condition, and a set power condition; based on the junction temperature characteristic data of each photovoltaic unit, a plurality of photovoltaic power generation sets are obtained according to the preset division conditions, including:

[0020] SS1: Randomly select photovoltaic units that meet the preset power generation cluster number conditions from multiple photovoltaic units as cluster units, and the remaining photovoltaic units as non-cluster units;

[0021] SS2: Based on the junction temperature characteristic data of a non-cluster unit and each cluster unit, calculate the junction temperature distance from the non-cluster unit to each cluster unit, and associate the non-cluster unit with the cluster unit corresponding to the shortest junction temperature distance;

[0022] SS3: Each cluster unit and its associated non-cluster units form a candidate power generation combination; multiple candidate power generation sets form the obtained combination contemporary allocation result;

[0023] SS4: Obtain the cluster center based on the junction temperature characteristic data of a cluster unit and several associated non-cluster units;

[0024] SS5: Calculate the junction temperature distances of cluster units and non-cluster units to the center of each cluster, associate cluster units or non-cluster units with the cluster center with the shortest junction temperature distance, and obtain the next generation combination allocation result;

[0025] SS6: When the next generation allocation result is consistent with the current generation allocation result and each candidate power generation combination meets the conditions of the number of collective elements and the collective power, each candidate power generation combination is confirmed to be a photovoltaic power generation combination.

[0026] Furthermore, based on the junction temperature characteristic data of each photovoltaic unit, a plurality of photovoltaic power generation sets are obtained according to a preset division condition, further comprising:

[0027] SS7: When the next generation allocation result is inconsistent with the current generation allocation result, or there is a candidate generation combination that does not meet the set element quantity condition, or there is a candidate generation combination that does not meet the set power condition, update the cluster unit and return to step SS2;

[0028] SS8: Repeat SS2 to SS7 until the photovoltaic power generation set is obtained.

[0029] Furthermore, a photovoltaic unit junction temperature model is obtained, including:

[0030] Obtaining a photovoltaic unit junction temperature relationship database, the photovoltaic unit junction temperature relationship database including temperature, humidity, wind speed, light intensity, altitude, air pressure, dust concentration, junction temperature prediction data, and a mapping relationship between temperature, humidity, wind speed, light intensity, altitude, air pressure, dust concentration and junction temperature prediction data;

[0031] A neural network model is trained based on a photovoltaic unit junction temperature relationship database to obtain a photovoltaic unit junction temperature model.

[0032] Furthermore, based on the constraints corresponding to the photovoltaic power generation set and the device parameters of the plurality of photovoltaic units, a target inverter corresponding to the photovoltaic power generation set and a working strategy of the target inverter are determined, including:

[0033] Obtaining a collective power generation range based on device parameters, collective junction temperature upper limit, and collective junction temperature lower limit of several photovoltaic units in a photovoltaic power generation aggregate;

[0034] Based on the collective power generation range, confirming a target inverter, device parameters of the target inverter, and environmental parameters of a location where the target inverter is located among multiple candidate inverters;

[0035] Based on the collective power generation range and the environmental parameters of the target inverter's location, obtain the target inverter's junction temperature prediction data during the next power generation cycle;

[0036] Based on the device parameters of the target inverter and the junction temperature prediction data in the next power generation cycle, the parameter operating range of the target inverter in the next cycle is determined as the working strategy.

[0037] Furthermore, based on the device parameters, the upper limit of the collective junction temperature, and the lower limit of the collective junction temperature of the photovoltaic power generation aggregate, the collective power generation range is obtained, including:

[0038] Determine the unit power generation range of the photovoltaic unit based on the device parameters, the upper limit of the collective junction temperature, and the lower limit of the collective junction temperature of the photovoltaic unit;

[0039] Based on the unit power generation range of each photovoltaic unit, the collective power generation range is determined.

[0040] Furthermore, based on the collective power generation range and the environmental parameters of the target inverter's location, the target inverter's junction temperature prediction data in the next power generation cycle is obtained, including:

[0041] Get the inverter junction temperature model;

[0042] The collective power generation range and the environmental parameters of the target inverter's location are input into the inverter junction temperature model to obtain the target inverter's junction temperature prediction data in the next power generation cycle.

[0043] Furthermore, the inverter junction temperature model is obtained, including:

[0044] Obtaining an inverter junction temperature relationship database, the inverter junction temperature relationship database including a set power generation range, temperature, humidity, wind speed, light intensity, altitude, air pressure, dust concentration, junction temperature prediction data, and a mapping relationship between the set power generation range, temperature, humidity, wind speed, light intensity, altitude, air pressure, dust concentration and the junction temperature prediction data;

[0045] A neural network model is trained based on an inverter junction temperature relationship database to obtain an inverter junction temperature model.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present invention divides the multiple photovoltaic units in a photovoltaic system into several photovoltaic power generation groups based on the environmental parameters of their locations. This ensures that each photovoltaic unit in the same photovoltaic power generation group is constrained under the same constraints during the next power generation cycle, facilitating the identification of the appropriate target inverter and its corresponding operating strategy. This avoids failures caused by direct use of maximum power point tracking technology in inverter equipment. This addresses the existing technical problem of frequent failures caused by direct use of maximum power point tracking technology in complex environmental conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1This is a method step diagram of a photovoltaic inverter optimization control method considering environmental constraints of the present invention. DETAILED DESCRIPTION

[0049] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0050] like Figure 1 As shown, the present invention provides a photovoltaic inverter optimization control method considering environmental constraints, the control method comprising:

[0051] S1: Obtain the equipment parameters of each photovoltaic unit in the photovoltaic system and the environmental parameters of the location;

[0052] In this embodiment, the photovoltaic group includes multiple photovoltaic units and multiple inverters; each photovoltaic unit can be connected to multiple inverters; the photovoltaic units are distributed in various places, and the environmental parameters of each place are different.

[0053] S2: Based on the environmental parameters of the locations of the photovoltaic units, obtain a number of photovoltaic power generation sets and the constraints corresponding to each photovoltaic power generation set; each photovoltaic power generation set includes a number of photovoltaic units;

[0054] In this embodiment, multiple photovoltaic units are divided into multiple photovoltaic power generation sets according to the environmental parameters of the locations of the photovoltaic units; multiple photovoltaic units in the same photovoltaic power generation set are subject to the same constraints to facilitate adaptation to the same inverter.

[0055] S3: Based on the constraints corresponding to the photovoltaic power generation set and the equipment parameters of the photovoltaic units, determine the target inverter corresponding to the photovoltaic power generation set and the working strategy of the target inverter;

[0056] In this embodiment, the photovoltaic power generation set composed of several photovoltaic units affected by the same constraints is convenient for adaptation to the target inverter, and it is also convenient for the target inverter to set a corresponding working strategy to avoid individual photovoltaic units in the photovoltaic power generation set being affected by environmental parameters and thus affecting the overall operation.

[0057] S4: Each target inverter is connected to each photovoltaic unit of the corresponding photovoltaic power generation set at the beginning of the next power generation cycle, and operates according to the corresponding working strategy after the beginning of the next power generation cycle.

[0058] In this embodiment, the next power generation cycle includes 15 minutes, 30 minutes, 45 minutes and 60 minutes.

[0059] The specific implementation process of this embodiment includes:

[0060] In this embodiment, the multiple photovoltaic units in a photovoltaic system are divided into several photovoltaic power generation groups based on the environmental parameters of their respective locations. This ensures that each photovoltaic unit in the same photovoltaic power generation group is constrained under the same constraints during the next power generation cycle, facilitating the identification of the appropriate target inverter and its corresponding operating strategy. This avoids failures caused by direct use of maximum power point tracking technology in inverter equipment. This addresses the technical issue in existing technologies where direct use of maximum power point tracking technology in complex environmental conditions leads to frequent failures.

[0061] In this embodiment, the environmental parameters include temperature, humidity, wind speed, light intensity, altitude, air pressure, and dust concentration. Based on the environmental parameters of each photovoltaic unit, several photovoltaic power generation sets and the constraints corresponding to each photovoltaic power generation set are obtained, including:

[0062] S21: Obtaining the photovoltaic unit junction temperature model;

[0063] In this embodiment, obtaining a photovoltaic unit junction temperature model includes: obtaining a photovoltaic unit junction temperature relationship database, the photovoltaic unit junction temperature relationship database including temperature, humidity, wind speed, light intensity, altitude, air pressure, dust concentration, junction temperature prediction data, and mapping relationships between temperature, humidity, wind speed, light intensity, altitude, air pressure, dust concentration, and junction temperature prediction data; and training a neural network model based on the photovoltaic unit junction temperature relationship database to obtain the photovoltaic unit junction temperature model. In this embodiment, the neural network model includes a convolutional neural network model.

[0064] S22: Inputting environmental parameters of the location of the photovoltaic unit into the photovoltaic unit model to obtain junction temperature prediction data of the photovoltaic unit in the next power generation cycle;

[0065] In this embodiment, the junction temperature prediction data of the photovoltaic unit includes the predicted junction temperatures at multiple moments. In this embodiment, the interval between adjacent moments is 1 minute.

[0066] S23: Based on the junction temperature prediction data of each photovoltaic unit in the next power generation cycle, multiple photovoltaic power generation sets are obtained, including:

[0067] Based on the junction temperature prediction data of each photovoltaic unit in the next power generation cycle, the junction temperature characteristic data of the photovoltaic unit in the next power generation cycle is obtained; the junction temperature characteristic data includes the highest junction temperature, the lowest junction temperature, the average temperature change rate and the maximum temperature change rate; based on the junction temperature characteristic data of each photovoltaic unit, multiple photovoltaic power generation sets are obtained according to preset division conditions.

[0068] S24: Obtaining constraint conditions based on the junction temperature prediction data of each photovoltaic unit in the same photovoltaic power generation set, where the constraint conditions include an upper limit of the set junction temperature and a lower limit of the set junction temperature.

[0069] In this embodiment, based on the junction temperature prediction data of each photovoltaic unit in the next power generation cycle, multiple photovoltaic power generation sets are obtained, including:

[0070] In this embodiment, the preset division conditions include: a preset power generation set number condition, a set element number condition, and a set power condition; based on the junction temperature characteristic data of each photovoltaic unit, multiple photovoltaic power generation sets are obtained according to the preset division conditions, including:

[0071] SS1: Randomly select photovoltaic units that meet the preset power generation cluster number conditions from multiple photovoltaic units as cluster units, and the remaining photovoltaic units as non-cluster units;

[0072] In this embodiment, the preset generation cluster number condition includes [a, b], where a and b are fixed values, b is greater than a, and b is not greater than the number of inverters configured in the photovoltaic array. It should be noted that in this embodiment, at the beginning, cluster units are selected in ascending order.

[0073] SS2: Based on the junction temperature characteristic data of a non-cluster unit and each cluster unit, calculate the junction temperature distance from the non-cluster unit to each cluster unit, and associate the non-cluster unit with the cluster unit corresponding to the shortest junction temperature distance;

[0074] In this embodiment, the calculation formula of the junction temperature distance is as follows:

[0075]

[0076] Where JL is the junction temperature distance; α i is the proportional coefficient corresponding to the i-th junction temperature characteristic data; x i is the i-th junction temperature characteristic data corresponding to a photovoltaic unit; y i is the i-th junction temperature characteristic data corresponding to another photovoltaic unit.

[0077] It should be noted that each junction temperature characteristic data needs to be normalized before calculation to remove its corresponding dimension.

[0078] In this embodiment, the shorter the junction temperature distance is, the closer the junction temperatures of the two photovoltaic units are, so the non-clustered units are associated with the clustered units corresponding to the shortest junction temperature distance for clustering.

[0079] SS3: Each cluster unit and its associated non-cluster units form a candidate power generation combination; multiple candidate power generation sets form the obtained combination contemporary allocation result;

[0080] SS4: Obtain the cluster center based on the junction temperature characteristic data of a cluster unit and several associated non-cluster units;

[0081] In this embodiment, the cluster center is obtained by:

[0082]

[0083] Where C(λ1, λ2, ..., λ3) represents the cluster center; i represents that there are i items of junction temperature characteristic data in total; and n represents the number of photovoltaic units in the selected power generation set.

[0084] SS5: Calculate the junction temperature distances of cluster units and non-cluster units to the center of each cluster, associate cluster units or non-cluster units with the cluster center with the shortest junction temperature distance, and obtain the next generation combination allocation result;

[0085] SS6: When the next generation allocation result is consistent with the current generation allocation result and each candidate power generation combination meets the conditions of the number of collective elements and the collective power, each candidate power generation combination is confirmed to be a photovoltaic power generation combination.

[0086] The condition for the number of elements in the set includes: the number of elements in the photovoltaic power generation set is within [c, d]; the condition for the power of the set includes: the sum of the power of each photovoltaic unit in the photovoltaic power generation set is between [s, t].

[0087] SS7: When the next generation allocation result is inconsistent with the current generation allocation result, or there is a candidate generation combination that does not meet the set element quantity condition, or there is a candidate generation combination that does not meet the set power condition, update the cluster unit and return to step SS2;

[0088] In this embodiment, updating the cluster units includes reselecting multiple cluster units from the multiple photovoltaic units, or increasing the number of selected cluster units.

[0089] SS8: Repeat SS2 to SS7 until the photovoltaic power generation set is obtained.

[0090] In this embodiment, based on the constraints corresponding to the photovoltaic power generation set and the device parameters of the photovoltaic units, determining the target inverter corresponding to the photovoltaic power generation set and the working strategy of the target inverter includes:

[0091] S31: obtaining a collective power generation range based on device parameters, a collective junction temperature upper limit, and a collective junction temperature lower limit of a plurality of photovoltaic units in the photovoltaic power generation aggregate;

[0092] In this embodiment, the unit power generation range of a photovoltaic unit is determined based on the device parameters of a photovoltaic unit, the upper and lower collective junction temperatures, and the lower collective junction temperature. The collective power generation range is determined based on the unit power generation ranges of each photovoltaic unit. In this embodiment, the upper collective junction temperature limit includes the maximum of multiple highest junction temperatures of each photovoltaic unit in the photovoltaic power generation assembly during the next power generation cycle; the lower collective junction temperature limit includes the lowest of multiple lowest junction temperatures of each photovoltaic unit in the photovoltaic power generation assembly during the next power generation cycle.

[0093] The photovoltaic unit power generation range is obtained based on the photovoltaic unit device parameters, the collective junction temperature upper limit, and the collective junction temperature lower limit. In this embodiment, the photovoltaic unit device parameters include a mapping relationship between junction temperature and power under multiple environmental parameters.

[0094] The sum of the lower limits of the unit power generation ranges of the multiple photovoltaic units constitutes the lower limit of the collective power generation range;

[0095] The sum of the upper limits of the unit power generation ranges of the plurality of photovoltaic units constitutes the upper limit of the collective power generation range.

[0096] S32: Based on the collective power generation range, confirming a target inverter, device parameters of the target inverter, and environmental parameters of the location of the target inverter from among multiple candidate inverters;

[0097] In this embodiment, each of the inverters to be selected is provided with a corresponding inversion range; the inversion range of the target inverter includes the collective power generation range of the corresponding photovoltaic power generation set.

[0098] S33: Based on the collective power generation range and the environmental parameters of the target inverter's location, obtain the target inverter's junction temperature prediction data in the next power generation cycle, including:

[0099] Obtaining an inverter junction temperature model; inputting the collective power generation range and the environmental parameters of the target inverter's location into the inverter junction temperature model to obtain predicted junction temperature data of the target inverter in the next power generation cycle;

[0100] Obtaining the inverter junction temperature model includes: obtaining an inverter junction temperature relationship database, the inverter junction temperature relationship database including a collection of power generation range, temperature, humidity, wind speed, light intensity, altitude, air pressure, dust concentration, junction temperature prediction data, and a mapping relationship between the collection of power generation range, temperature, humidity, wind speed, light intensity, altitude, air pressure, dust concentration, and the junction temperature prediction data; and training a neural network model based on the inverter junction temperature relationship database to obtain the inverter junction temperature model. In this embodiment, the neural network model includes a convolutional neural network model.

[0101] When the environmental parameters change, the junction temperature data of the inverter is affected by the input side power generation power and the environmental parameters in the next power generation cycle.

[0102] S34: Based on the device parameters of the target inverter and the junction temperature prediction data in the next power generation cycle, the parameter operating range of the target inverter in the next cycle is determined as the working strategy.

[0103] In this embodiment, the predicted junction temperature data for the target inverter during the next power generation cycle includes junction temperatures at several moments, with one minute between each moment. When the junction temperature at multiple consecutive moments exceeds a preset upper temperature limit, derating protection is triggered. In this embodiment, a mapping between the over-limit range and the derating range is pre-set. The derating range in this embodiment includes the voltage reduction range of the highest power point in the maximum power point tracking technology.

[0104] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0105] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A photovoltaic inverter optimization control method considering environmental constraints, characterized by: The control method includes: Obtain the equipment parameters of each photovoltaic unit in the photovoltaic system and the environmental parameters of the location; Based on environmental parameters of the locations of the photovoltaic units, a plurality of photovoltaic power generation sets and constraints corresponding to each photovoltaic power generation set are obtained; each photovoltaic power generation set includes a plurality of photovoltaic units; Based on the constraints corresponding to the photovoltaic power generation set and the equipment parameters of several photovoltaic units, determine the target inverter corresponding to the photovoltaic power generation set and the working strategy of the target inverter; Each target inverter is connected to each photovoltaic unit of the corresponding photovoltaic power generation set at the beginning of the next power generation cycle, and operates according to the corresponding working strategy after the beginning of the next power generation cycle.

2. The photovoltaic inverter optimization control method considering environmental constraints according to claim 1, characterized in that: The environmental parameters include temperature, humidity, wind speed, light intensity, altitude, air pressure, and dust concentration. Based on the environmental parameters of each photovoltaic unit, several photovoltaic power generation sets and the constraints corresponding to each photovoltaic power generation set are obtained, including: Obtaining the photovoltaic unit junction temperature model; Input the environmental parameters of the photovoltaic unit's location into the photovoltaic unit model to obtain the predicted junction temperature data of the photovoltaic unit in the next power generation cycle; Based on the junction temperature prediction data of each photovoltaic unit in the next power generation cycle, multiple photovoltaic power generation sets are obtained; Based on the junction temperature prediction data of each photovoltaic unit in the same photovoltaic power generation set, a constraint condition is obtained, where the constraint condition includes an upper limit of the set junction temperature and a lower limit of the set junction temperature.

3. The photovoltaic inverter optimization control method considering environmental constraints according to claim 2, characterized in that: Based on the junction temperature prediction data of each photovoltaic unit in the next power generation cycle, multiple photovoltaic power generation sets are obtained, including: Based on the junction temperature prediction data of each photovoltaic unit in the next power generation cycle, obtaining junction temperature characteristic data of the photovoltaic unit in the next power generation cycle; the junction temperature characteristic data includes the maximum junction temperature, the minimum junction temperature, the average temperature change rate and the maximum temperature change rate; Based on the junction temperature characteristic data of each photovoltaic unit, multiple photovoltaic power generation sets are obtained according to preset division conditions.

4. The photovoltaic inverter optimization control method considering environmental constraints according to claim 3, characterized in that: The preset division conditions include: a preset power generation set number condition, a set element number condition, and a set power condition; based on the junction temperature characteristic data of each photovoltaic unit, multiple photovoltaic power generation sets are obtained according to the preset division conditions, including: SS1: Randomly select photovoltaic units that meet the preset power generation cluster number conditions from multiple photovoltaic units as cluster units, and the remaining photovoltaic units as non-cluster units; SS2: Based on the junction temperature characteristic data of a non-cluster unit and each cluster unit, calculate the junction temperature distance from the non-cluster unit to each cluster unit, and associate the non-cluster unit with the cluster unit corresponding to the shortest junction temperature distance; SS3: Each cluster unit and its associated non-cluster units form a candidate power generation combination; multiple candidate power generation sets form the obtained combination contemporary allocation result; SS4: Obtain the cluster center based on the junction temperature characteristic data of a cluster unit and several associated non-cluster units; SS5: Calculate the junction temperature distances of cluster units and non-cluster units to the center of each cluster, associate cluster units or non-cluster units with the cluster center with the shortest junction temperature distance, and obtain the next generation combination allocation result; SS6: When the next generation allocation result is consistent with the current generation allocation result and each candidate power generation combination meets the conditions of the number of collective elements and the collective power, each candidate power generation combination is confirmed to be a photovoltaic power generation combination.

5. The photovoltaic inverter optimization control method considering environmental constraints according to claim 4, characterized in that: Based on the junction temperature characteristic data of each photovoltaic unit, multiple photovoltaic power generation sets are obtained according to preset division conditions, which also includes: SS7: When the next generation allocation result is inconsistent with the current generation allocation result, or there is a candidate generation combination that does not meet the set element quantity condition, or there is a candidate generation combination that does not meet the set power condition, update the cluster unit and return to step SS2; SS8: Repeat SS2 to SS7 until the photovoltaic power generation set is obtained.

6. The photovoltaic inverter optimization control method considering environmental constraints according to claim 2, characterized in that: Obtain the PV unit junction temperature model, including: Obtaining a photovoltaic unit junction temperature relationship database, the photovoltaic unit junction temperature relationship database including temperature, humidity, wind speed, light intensity, altitude, air pressure, dust concentration, junction temperature prediction data, and a mapping relationship between temperature, humidity, wind speed, light intensity, altitude, air pressure, dust concentration and junction temperature prediction data; A neural network model is trained based on a photovoltaic unit junction temperature relationship database to obtain a photovoltaic unit junction temperature model.

7. The photovoltaic inverter optimization control method considering environmental constraints according to claim 6, characterized in that: Based on the constraints corresponding to the photovoltaic power generation set and the equipment parameters of several photovoltaic units, the target inverter corresponding to the photovoltaic power generation set and the working strategy of the target inverter are determined, including: Obtaining a collective power generation range based on device parameters, collective junction temperature upper limit, and collective junction temperature lower limit of several photovoltaic units in a photovoltaic power generation aggregate; Based on the collective power generation range, confirming a target inverter, device parameters of the target inverter, and environmental parameters of a location where the target inverter is located among multiple candidate inverters; Based on the collective power generation range and the environmental parameters of the target inverter's location, obtain the target inverter's junction temperature prediction data during the next power generation cycle; Based on the device parameters of the target inverter and the junction temperature prediction data in the next power generation cycle, the parameter operating range of the target inverter in the next cycle is determined as the working strategy.

8. The photovoltaic inverter optimization control method considering environmental constraints according to claim 7, characterized in that: Based on the equipment parameters, upper and lower junction temperature limits of several photovoltaic units in the photovoltaic power generation group, the collective power generation range is obtained, including: Determine the unit power generation range of the photovoltaic unit based on the device parameters, the upper limit of the collective junction temperature, and the lower limit of the collective junction temperature of the photovoltaic unit; Based on the unit power generation range of each photovoltaic unit, the collective power generation range is determined.

9. The photovoltaic inverter optimization control method considering environmental constraints according to claim 7, characterized in that: Based on the collective power generation range and the environmental parameters of the target inverter's location, the target inverter's junction temperature prediction data for the next power generation cycle is obtained, including: Get the inverter junction temperature model; The collective power generation range and the environmental parameters of the target inverter's location are input into the inverter junction temperature model to obtain the target inverter's junction temperature prediction data in the next power generation cycle.

10. The photovoltaic inverter optimization control method considering environmental constraints according to claim 9, characterized in that: Get the inverter junction temperature model, including: Obtaining an inverter junction temperature relationship database, the inverter junction temperature relationship database including a set power generation range, temperature, humidity, wind speed, light intensity, altitude, air pressure, dust concentration, junction temperature prediction data, and a mapping relationship between the set power generation range, temperature, humidity, wind speed, light intensity, altitude, air pressure, dust concentration and the junction temperature prediction data; A neural network model is trained based on an inverter junction temperature relationship database to obtain an inverter junction temperature model.

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