Photovoltaic power grid power adjusting system and method based on Internet of Things

Abstract

The invention belongs to the field of photovoltaic power grid power regulation, relates to a data analysis technology, is used for solving the problem that multiple operating parameters of each inverter in a photovoltaic power station cannot be analyzed in the prior art, and particularly relates to a photovoltaic power grid power regulation system and method based on the Internet of Things. The power regulation platform is in communication connection with a deviation monitoring module, a regulation analysis module, a regulation processing module and a storage module; the deviation monitoring module is used for performing deviation analysis on the active power of the power station of the photovoltaic power grid and judging whether the photovoltaic power station needs to be subjected to power regulation or not, and generating a regulation analysis signal and sending the regulation analysis signal to the regulation analysis module through the power regulation platform when the power regulation is needed; according to the method, power regulation analysis can be carried out on the inverter of the photovoltaic power station, the power regulation necessity of the inverter is analyzed from different dimensions, and diversified data support is provided for power regulation processing of the inverter of the photovoltaic power station.

Description

Technical Field

[0001] The present invention belongs to the field of photovoltaic power grid power regulation, relates to data analysis technology, and specifically is a photovoltaic power grid power regulation system and method based on the Internet of Things. Background Art

[0002] The photovoltaic grid power regulation system connects sensors in various devices and systems with the existing Internet to achieve real-time monitoring and intelligent management of photovoltaic power stations, and realizes intelligent management and optimized control of photovoltaic power stations, thereby improving the efficiency and reliability of photovoltaic power generation.

[0003] The photovoltaic grid power regulation system in the prior art can only perform regulation analysis based on the overall photovoltaic grid power status, but cannot analyze the multiple operating parameters of each inverter in the photovoltaic power station, and thus cannot screen the inverter that most needs power regulation from multiple angles, resulting in poor power regulation effect of the photovoltaic grid.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0005] The purpose of the present invention is to provide a photovoltaic power grid power regulation system and method based on the Internet of Things, which is used to solve the problem that the prior art cannot analyze multiple operating parameters of each inverter in a photovoltaic power station;

[0006] The technical problem to be solved by the present invention is: how to provide a photovoltaic power grid power regulation system and method based on the Internet of Things that can analyze multiple operating parameters of each inverter in a photovoltaic power station.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] A photovoltaic power grid power regulation system based on the Internet of Things, comprising a power regulation platform, wherein the power regulation platform is communicatively connected with a deviation monitoring module, a regulation analysis module, a regulation processing module and a storage module;

[0009] The deviation monitoring module is used to perform deviation analysis on the active power of the photovoltaic power station and determine whether the photovoltaic power station needs power regulation. When power regulation is required, a regulation analysis signal is generated and sent to the regulation analysis module through the power regulation platform;

[0010] The regulation analysis module is used to perform power regulation analysis on the inverter of the photovoltaic power station and obtain a risk regulation sequence and an operation regulation sequence; the risk regulation sequence and the operation regulation sequence are sent to the regulation processing module through the power regulation platform;

[0011] The regulation processing module is used to perform power regulation processing on the photovoltaic power station according to the risk regulation sequence and the operation regulation sequence.

[0012] Furthermore, the specific process of performing deviation analysis on the active power of the photovoltaic power station includes: calculating the difference between the actual active power value and the target value of the photovoltaic power station grid connection point and marking it as the active power deviation value, and comparing the active power deviation value with the preset active power deviation threshold: if the active power deviation value is less than the active power deviation threshold, it is determined that the photovoltaic power station does not need to perform power regulation; if the active power deviation value is greater than or equal to the active power deviation threshold, it is determined that the photovoltaic power station needs to perform power regulation.

[0013] Furthermore, the process of obtaining the risk adjustment sequence includes: generating an analysis period, the end time of the analysis period is the current system time, and the duration of the analysis period is L1 days; marking the inverter of the photovoltaic power station as the analysis object, and obtaining the reverse risk data NF and reverse aggressive data NJ of the analysis object within the analysis period; obtaining the risk adjustment coefficient FT of the analysis object through the formula TJ=c1*NF+c2*NJ, where c1 and c2 are both proportional coefficients, and c1>c2>1; arranging all analysis objects in descending order of the risk adjustment coefficient FT values ​​to obtain the reverse flow adjustment sequence.

[0014] Furthermore, the process of acquiring the backflow risk data NF includes: acquiring the rated output power ED of the analysis object, and obtaining the risk analysis value FX of the analysis object through the formula FX=t1*ED, wherein t1 is the proportional coefficient, and 0.92≤t1≤0.97; the risk analysis value FX and the rated output power ED constitute a risk range, and the ratio of the time when the output power of the analysis object is within the risk range to the total time of the analysis period is marked as the backflow risk data NF; the process of acquiring the backflow aggressiveness data NJ includes: acquiring the maximum value of the output power of the analysis object within the analysis period and marking it as the risk monitoring value, and marking the ratio of the risk monitoring value to the rated output power as the backflow aggressiveness data NJ.

[0015] Furthermore, the process of obtaining the operation adjustment sequence includes: obtaining the stability data WD, frequency data PL and efficiency data XL of the analysis object during the analysis period; obtaining the operation adjustment coefficient YT of the analysis object during the analysis period through the formula YT=b1*XL-b2*WD-b3*PL, wherein b1, b2 and b3 are all proportional coefficients, and b1>b2>b3>1; arranging all analysis objects in order from small to large according to the value of the risk adjustment coefficient YT to obtain the operation adjustment sequence.

[0016] Furthermore, the stability data WD is the maximum value of the change in the steady-state output voltage maintained by the analysis object when the input voltage changes; the acquisition process of the frequency data PL includes: acquiring the standard frequency range of the analysis object, marking the average value of the maximum value and the minimum value of the standard frequency range as the frequency standard value, marking the absolute value of the difference between the output frequency of the analysis object and the frequency standard value as the frequency deviation value, and marking the ratio of the frequency deviation value to the frequency standard value as the frequency data PL; the efficiency data XL is the maximum value of the ratio of the output power to the input power of the analysis object within the analysis period.

[0017] Furthermore, the specific process of the adjustment processing module performing power adjustment processing on the photovoltaic power station according to the risk adjustment sequence and the operation adjustment sequence includes: comparing and analyzing the risk adjustment sequence with the operation adjustment sequence: marking the absolute value of the difference in the serial number of the analysis object in the risk adjustment sequence and the operation adjustment sequence as the adjustment unified value of the analysis object, summing and averaging the adjustment unified values ​​of all analysis objects to obtain the adjustment unified coefficient, obtaining the adjustment unified threshold through the storage module, and comparing the adjustment unified coefficient with the adjustment unified threshold: if the adjustment unified coefficient is less than the adjustment unified threshold, the risk control mode is used for adjustment processing; if the adjustment unified coefficient is greater than or equal to the adjustment unified threshold, the comprehensive control mode is used for adjustment processing.

[0018] Furthermore, the specific process of using the risk control mode for adjustment processing includes: intercepting the top L1 analysis objects in the risk adjustment sequence as control objects, and sending the control objects to the mobile phone terminal of the manager through the power adjustment platform; the specific process of using the comprehensive control mode for adjustment processing includes: marking the analysis object ranked first in the risk adjustment sequence as a risk adjustment object, and then determining whether the analysis object ranked first in the operation adjustment sequence is the same as the risk adjustment object: if they are the same, the operation adjustment object is marked as a bye; if they are not the same, the analysis object ranked first in the operation adjustment sequence is marked as an operation adjustment object; then the analysis object ranked second in the risk adjustment sequence is marked as a risk adjustment object, and so on, until the sum of the number of risk adjustment objects and operation adjustment objects reaches L1; the risk adjustment objects and the operation adjustment objects are sent to the mobile phone terminal of the manager through the power adjustment platform.

[0019] A photovoltaic power grid power regulation method based on the Internet of Things comprises the following steps:

[0020] Step 1: Analyze the deviation of the active power of the photovoltaic power station. When the photovoltaic power station needs to adjust the power, execute step 2.

[0021] Step 2: Perform power regulation analysis on the inverter of the photovoltaic power station and obtain the risk regulation sequence and operation regulation sequence;

[0022] Step 3: Screen the inverters that need power adjustment in the photovoltaic power station according to the risk adjustment sequence and the operation adjustment sequence.

[0023] The present invention has the following beneficial effects:

[0024] 1. The deviation monitoring module can be used to analyze the active power of the photovoltaic power station, evaluate the necessity of power regulation of the photovoltaic power station as a whole, and conduct regulation analysis from a local perspective when the overall power needs to be adjusted;

[0025] 2. The adjustment analysis module can be used to analyze the power adjustment of the inverter of the photovoltaic power station, analyze the necessity of power adjustment of the inverter from different dimensions, and then generate risk adjustment sequence and operation adjustment sequence according to the corresponding basis, providing diversified data support for the power adjustment processing of the photovoltaic power station inverter;

[0026] 3. Through the adjustment processing module, the photovoltaic power station can be regulated according to the risk adjustment sequence and the operation adjustment sequence. The control mode is marked according to the value of the calculated unified adjustment coefficient. Targeted control modes are used for power regulation of photovoltaic power stations under different operating conditions. After completing the power adjustment, both the operation safety and efficiency of the power station are taken into account. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 is a system block diagram of Embodiment 1 of the present invention;

[0029] Figure 2 This is a flow chart of the method of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0030] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] Embodiment 1: Figure 1As shown, a photovoltaic power grid power regulation system based on the Internet of Things includes a power regulation platform, and the power regulation platform is communicatively connected with a deviation monitoring module, a regulation analysis module, a regulation processing module and a storage module.

[0032] The deviation monitoring module is used to perform deviation analysis on the active power of the photovoltaic power station: calculate the difference between the actual active power value and the target value of the photovoltaic power station grid connection point and mark it as the active deviation value, and compare the active deviation value with the preset active deviation threshold: if the active deviation value is less than the active deviation threshold, it is determined that the photovoltaic power station does not need power regulation; if the active deviation value is greater than or equal to the active deviation threshold, it is determined that the photovoltaic power station needs power regulation, generate an adjustment analysis signal and send the adjustment analysis signal to the adjustment analysis module through the power regulation platform; perform deviation analysis on the active power of the photovoltaic power station, evaluate the necessity of power regulation of the photovoltaic power station as a whole, and perform adjustment analysis from a local perspective when the overall power needs to be adjusted.

[0033] The regulation analysis module is used to perform power regulation analysis on the inverter of the photovoltaic power station: generate an analysis period, the end time of the analysis period is the current system time, and the duration of the analysis period is L1 days; mark the inverter of the photovoltaic power station as the analysis object, and obtain the reverse flow risk data NF and reverse flow aggressiveness data NJ of the analysis object within the analysis period; the acquisition process of the reverse flow risk data NF includes: obtaining the rated output power ED of the analysis object, and obtaining the risk analysis value FX of the analysis object through the formula FX=t1*ED, where t1 is the proportionality coefficient, and 0.92≤t1≤0.97; the risk analysis value FX and the rated output power ED constitute a risk range, The ratio of the duration of the output power of the analysis object being within the risk range to the total duration of the analysis period is marked as the backflow risk data NF; the process of obtaining the backflow aggressiveness data NJ includes: obtaining the maximum value of the output power of the analysis object within the analysis period and marking it as the risk monitoring value, and marking the ratio of the risk monitoring value to the rated output power as the backflow aggressiveness data NJ; obtaining the risk adjustment coefficient FT of the analysis object by the formula TJ=c1*NF+c2*NJ, wherein c1 and c2 are both proportional coefficients, and c1>c2>1; arranging all the analysis objects in descending order of the value of the risk adjustment coefficient FT to obtain the backflow adjustment sequence;

[0034] The stable data WD, frequency data PL and efficiency data XL of the analysis object within the analysis period are obtained, wherein the stable data WD is the maximum value of the change in the steady-state output voltage maintained by the analysis object when the input voltage changes; the acquisition process of the frequency data PL includes: obtaining the standard frequency range of the analysis object, marking the average value of the maximum value and the minimum value of the standard frequency range as the frequency standard value, marking the absolute value of the difference between the output frequency of the analysis object and the frequency standard value as the frequency deviation value, and marking the ratio of the frequency deviation value to the frequency standard value as the frequency data PL; the efficiency data XL is the maximum value of the ratio of the output power to the input power of the analysis object within the analysis period; the operation adjustment coefficient YT of the analysis object within the analysis period is obtained by the formula YT=b1*XL-b2*WD-b3*PL, wherein b1, b2 and b3 are all proportional coefficients, and b1>b2>b3>1; all the analysis objects are arranged in the order of the risk adjustment coefficient YT values ​​from small to large to obtain the operation adjustment sequence;

[0035] The risk adjustment sequence and the operation adjustment sequence are sent to the adjustment processing module through the power adjustment platform; the power adjustment analysis of the inverter of the photovoltaic power station is carried out, and the necessity of power adjustment of the inverter is analyzed from different dimensions, and then the risk adjustment sequence and the operation adjustment sequence are generated according to the corresponding basis, providing diversified data support for the power adjustment processing of the photovoltaic power station inverter.

[0036] The regulation processing module is used to perform power regulation processing on the photovoltaic power station according to the risk regulation sequence and the operation regulation sequence: compare and analyze the risk regulation sequence with the operation regulation sequence: mark the absolute value of the difference between the sequence numbers of the analysis object in the risk regulation sequence and the operation regulation sequence as the unified regulation value of the analysis object, sum and average the unified regulation values ​​of all analysis objects to obtain the unified regulation coefficient, obtain the unified regulation threshold through the storage module, and compare the unified regulation coefficient with the unified regulation threshold: if the unified regulation coefficient is less than the unified regulation threshold, the risk control mode is used for regulation processing; if the unified regulation coefficient is greater than or equal to the unified regulation threshold, the comprehensive control mode is used for regulation processing; the specific process of using the risk control mode for regulation processing includes: intercepting the top L1 analysis objects in the risk adjustment sequence as control objects, L1 is a numerical constant, and the specific value of L1 is set by the management personnel; sending the control object to the management personnel's mobile terminal through the power regulation platform; using The specific process of the comprehensive control mode for adjustment processing includes: marking the analysis object ranked first in the risk adjustment sequence as the risk adjustment object, and then determining whether the analysis object ranked first in the operation adjustment sequence is the same as the risk adjustment object: if they are the same, the operation adjustment object is marked as a bye; if they are not the same, the analysis object ranked first in the operation adjustment sequence is marked as the operation adjustment object; then the analysis object ranked second in the risk adjustment sequence is marked as the risk adjustment object, and so on, until the sum of the number of risk adjustment objects and operation adjustment objects reaches L1; the risk adjustment objects and the operation adjustment objects are sent to the mobile phone terminal of the manager through the power adjustment platform; power adjustment processing is performed on the photovoltaic power station according to the risk adjustment sequence and the operation adjustment sequence, and the control mode is marked according to the calculated value of the unified adjustment coefficient, and targeted control modes are used for power adjustment processing for photovoltaic power stations under different operating conditions, and after completing the power adjustment, both the operation safety and operation efficiency of the power station are taken into account.

[0037] Embodiment 2: Figure 2 As shown, a photovoltaic power grid power regulation method based on the Internet of Things includes the following steps:

[0038] Step 1: Analyze the deviation of the active power of the photovoltaic power station. When the photovoltaic power station needs to adjust the power, execute step 2.

[0039] Step 2: Perform power regulation analysis on the inverter of the photovoltaic power station and obtain the risk regulation sequence and operation regulation sequence;

[0040] Step 3: Screen the inverters that need power adjustment in the photovoltaic power station according to the risk adjustment sequence and the operation adjustment sequence.

[0041] A photovoltaic power grid power regulation system and method based on the Internet of Things, when working, performs deviation analysis on the active power of the photovoltaic power station. When the photovoltaic power station needs to perform power regulation, the power regulation analysis is performed on the inverter of the photovoltaic power station to obtain a risk regulation sequence and an operation regulation sequence; the inverters that need to be power regulated in the photovoltaic power station are screened according to the risk regulation sequence and the operation regulation sequence.

[0042] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0043] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula close to the real value. The coefficients in the formula are set by technicians in this field according to the actual situation; for example: formula YT = b1*XL-b2*WD-b3*PL; technicians in this field collect multiple groups of sample data and set corresponding operation adjustment coefficients for each group of sample data; substitute the set operation adjustment coefficients and the collected sample data into the formula, any three formulas constitute a three-variable linear equation group, screen the calculated coefficients and take the average, and obtain the values ​​of b1, b2 and b3 as 3.18, 2.65 and 2.07 respectively;

[0044] The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the initial setting of the corresponding operation adjustment coefficient for each set of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantified value, such as the operation adjustment coefficient is proportional to the value of the efficiency data.

[0045] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0046] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A photovoltaic power grid power regulation system based on the Internet of Things, characterized in that: It includes a power regulation platform, which is communicatively connected with a deviation monitoring module, a regulation analysis module, a regulation processing module and a storage module; The deviation monitoring module is used to perform deviation analysis on the active power of the photovoltaic power station and determine whether the photovoltaic power station needs power regulation. When power regulation is required, a regulation analysis signal is generated and sent to the regulation analysis module through the power regulation platform; The regulation analysis module is used to perform power regulation analysis on the inverter of the photovoltaic power station and obtain a risk regulation sequence and an operation regulation sequence; the risk regulation sequence and the operation regulation sequence are sent to the regulation processing module through the power regulation platform; The regulation processing module is used to perform power regulation processing on the photovoltaic power station according to the risk regulation sequence and the operation regulation sequence.

2. A photovoltaic power grid power regulation system based on the Internet of Things according to claim 1, characterized in that: The specific process of performing deviation analysis on the active power of the photovoltaic power station includes: calculating the difference between the actual active power value and the target value of the photovoltaic power station grid connection point and marking it as the active power deviation value, and comparing the active power deviation value with the preset active power deviation threshold: if the active power deviation value is less than the active power deviation threshold, it is determined that the photovoltaic power station does not need to perform power regulation; if the active power deviation value is greater than or equal to the active power deviation threshold, it is determined that the photovoltaic power station needs to perform power regulation.

3. A photovoltaic power grid power regulation system based on the Internet of Things according to claim 2, characterized in that: The process of obtaining the risk adjustment sequence includes: generating an analysis period, the end time of the analysis period is the current system time, and the duration of the analysis period is L1 days; marking the inverter of the photovoltaic power station as the analysis object, and obtaining the reverse risk data NF and reverse aggressive data NJ of the analysis object within the analysis period; obtaining the risk adjustment coefficient FT of the analysis object through the formula TJ=c1*NF+c2*NJ, where c1 and c2 are both proportional coefficients, and c1>c2>1; arranging all the analysis objects in descending order according to the value of the risk adjustment coefficient FT to obtain the reverse flow adjustment sequence.

4. A photovoltaic power grid power regulation system based on the Internet of Things according to claim 3, characterized in that: The process of obtaining the backflow risk data NF includes: obtaining the rated output power ED of the analysis object, and obtaining the risk analysis value FX of the analysis object through the formula FX=t1*ED, where t1 is the proportional coefficient, and 0.92≤t1≤0.97; the risk analysis value FX and the rated output power ED constitute a risk range, and the ratio of the time when the output power of the analysis object is within the risk range to the total time of the analysis period is marked as the backflow risk data NF; the process of obtaining the backflow aggressiveness data NJ includes: obtaining the maximum value of the output power of the analysis object within the analysis period and marking it as the risk monitoring value, and marking the ratio of the risk monitoring value to the rated output power as the backflow aggressiveness data NJ.

5. A photovoltaic power grid power regulation system based on the Internet of Things according to claim 4, characterized in that: The process of obtaining the operation adjustment sequence includes: obtaining the stable data WD, frequency data PL and efficiency data XL of the analysis object during the analysis period; obtaining the operation adjustment coefficient YT of the analysis object during the analysis period through the formula YT=b1*XL-b2*WD-b3*PL, wherein b1, b2 and b3 are all proportional coefficients, and b1>b2>b3>1; arranging all the analysis objects in the order of the risk adjustment coefficient YT value from small to large to obtain the operation adjustment sequence.

6. A photovoltaic power grid power regulation system based on the Internet of Things according to claim 5, characterized in that: The stability data WD is the maximum value of the change in the steady-state output voltage maintained by the analysis object when the input voltage changes; the acquisition process of the frequency data PL includes: acquiring the standard frequency range of the analysis object, marking the average value of the maximum value and the minimum value of the standard frequency range as the frequency standard value, marking the absolute value of the difference between the output frequency of the analysis object and the frequency standard value as the frequency deviation value, and marking the ratio of the frequency deviation value to the frequency standard value as the frequency data PL; the efficiency data XL is the maximum value of the ratio of the output power to the input power of the analysis object within the analysis period.

7. A photovoltaic power grid power regulation system based on the Internet of Things according to claim 6, characterized in that: The specific process of the adjustment processing module performing power adjustment processing on the photovoltaic power station according to the risk adjustment sequence and the operation adjustment sequence includes: comparing and analyzing the risk adjustment sequence with the operation adjustment sequence: marking the absolute value of the difference between the sequence numbers of the analysis object in the risk adjustment sequence and the operation adjustment sequence as the adjustment unified value of the analysis object, summing and averaging the adjustment unified values ​​of all analysis objects to obtain the adjustment unified coefficient, obtaining the adjustment unified threshold through the storage module, and comparing the adjustment unified coefficient with the adjustment unified threshold: if the adjustment unified coefficient is less than the adjustment unified threshold, the risk control mode is used for adjustment processing; if the adjustment unified coefficient is greater than or equal to the adjustment unified threshold, the comprehensive control mode is used for adjustment processing.

8. The photovoltaic power grid power regulation system based on the Internet of Things according to claim 7 is characterized in that: The specific process of using the risk control mode for adjustment processing includes: intercepting the L1 analysis objects ranked first in the risk adjustment sequence as control objects, and sending the control objects to the mobile phone terminal of the manager through the power adjustment platform; the specific process of using the comprehensive control mode for adjustment processing includes: marking the analysis object ranked first in the risk adjustment sequence as a risk adjustment object, and then determining whether the analysis object ranked first in the operation adjustment sequence is the same as the risk adjustment object: if they are the same, the operation adjustment object is marked as a bye; if they are not the same, the analysis object ranked first in the operation adjustment sequence is marked as an operation adjustment object; then the analysis object ranked second in the risk adjustment sequence is marked as a risk adjustment object, and so on, until the sum of the number of risk adjustment objects and operation adjustment objects reaches L1; the risk adjustment objects and the operation adjustment objects are sent to the mobile phone terminal of the manager through the power adjustment platform.

9. A photovoltaic power grid power regulation method based on the Internet of Things, characterized in that: The following steps are involved: Step 1: Analyze the deviation of the active power of the photovoltaic power station. When the photovoltaic power station needs to adjust the power, execute step 2. Step 2: Perform power regulation analysis on the inverter of the photovoltaic power station and obtain the risk regulation sequence and operation regulation sequence; Step 3: Screen the inverters that need power adjustment in the photovoltaic power station according to the risk adjustment sequence and the operation adjustment sequence.

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