Intelligent regulation and control system and method for distributed photovoltaic power generation cluster

Abstract

Disclosed is an intelligent regulation and control system for a distributed photovoltaic power generation cluster, relating to the technical field of distributed energy, and configured to solve the problem of low accuracy in power generation regulation and control of current photovoltaic power generation clusters. The system comprises: a data acquisition module, configured to separately acquire demand basic data, supply basic data, and fault basic data to obtain regulation and control basic data; a data analysis module, configured to analyze the regulation and control basic data to obtain regulation and control analysis data; a data processing module, configured to process the regulation and control analysis data to obtain a supply-demand balance reference value; and, an intelligent regulation and control module, configured to perform intelligent regulation and control of the photovoltaic power generation cluster on the basis of the supply-demand balance reference value. In the present invention, supply and demand for the photovoltaic power generation cluster are determined on the basis of the area value of faulty solar panels to accurately adjust the generated power of the photovoltaic power generation cluster, thereby improving energy utilization efficiency.

Description

A distributed photovoltaic power generation cluster intelligent control system and method Technical Field

[0001] This invention relates to the field of distributed energy technology, and in particular to an intelligent control system and method for distributed photovoltaic power generation clusters. Background Technology

[0002] Distributed photovoltaic (PV) power generation clusters refer to the integration and networking of multiple PV power generation systems to convert solar energy into electrical energy through distributed power generation. Such clusters include various forms such as rooftop PV power generation systems and ground-mounted PV power stations. Through clustering, PV power generation systems can work together to improve overall power generation efficiency, reduce energy loss, and better cope with grid fluctuations and demand changes.

[0003] Existing technologies for regulating distributed photovoltaic (PV) power generation clusters suffer from the following drawbacks: real-time and specific values ​​of the cluster's demand and supply cannot be obtained, resulting in a lack of targeted regulation and low energy utilization efficiency; real-time monitoring of faulty solar panels within the cluster is difficult, and the area of ​​these faulty panels is hard to obtain accurately in real time; it is difficult to determine the supply and demand of the PV power generation cluster based on the area of ​​the faulty panels, and precise regulation of the cluster's power output is impossible.

[0004] To address this, we propose an intelligent control system for distributed photovoltaic power generation clusters. Summary of the Invention

[0005] In view of the aforementioned existing problems, the present invention is proposed.

[0006] Therefore, this invention provides an intelligent control system for distributed photovoltaic power generation clusters. To achieve the above objectives, this invention acquires basic control data, analyzes the basic control data to obtain electricity demand, theoretical power supply, and real-time fault data, and defines the electricity demand, theoretical power supply, and real-time fault data as control analysis data. The control analysis data is processed to obtain a supply-demand balance reference value, and the photovoltaic power generation cluster is intelligently controlled based on the supply-demand balance reference value.

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: a distributed photovoltaic power generation cluster intelligent control system, comprising a data acquisition module that acquires demand-based data, supply-based data, and fault-based data respectively, and comprehensively obtains control-based data.

[0008] The data analysis module analyzes the basic data for regulation and control to obtain electricity demand, theoretical power generation supply, and real-time fault data, and defines these data as regulation and control analysis data.

[0009] The data processing module processes the regulatory analysis data to obtain reference values ​​for supply and demand balance.

[0010] The intelligent control module intelligently controls the photovoltaic power generation cluster based on supply and demand balance reference values.

[0011] As a preferred embodiment of the intelligent control system for distributed photovoltaic power generation clusters described in this invention, the data acquisition module includes data stored in the database, including the number of electricity-consuming units in the power supply area corresponding to the photovoltaic power generation cluster and the area of ​​the corresponding power generation panels of the power generation cluster.

[0012] The data acquisition module includes a demand unit, a supply unit, and a fault unit.

[0013] The demand unit acquires basic demand data, obtains the number of electricity-consuming units in the power supply area corresponding to the photovoltaic power generation cluster from the database, the area value of the photovoltaic panels corresponding to the power generation cluster, selects m electricity-consuming units as sample electricity-consuming units, obtains the daily baseline electricity consumption of the sample electricity-consuming units through smart meters, and calculates the average daily baseline electricity consumption of the electricity-consuming units based on the daily baseline electricity consumption of the sample electricity-consuming units:

[0014] Where Jp is the average daily baseline electricity consumption of the electricity-consuming unit, and J1, J2, J3...Jm are the daily baseline electricity consumption of m sample electricity-consuming units respectively.

[0015] Select n characteristic time points, obtain the temperature values ​​for each of the n characteristic time points from the weather forecast, and calculate the daily average temperature value using the average temperature calculation formula:

[0016] Where T1, T2, T3...Tn are the temperature values ​​at n characteristic time points, Tp is the daily average temperature value, and n is greater than 0.

[0017] The average daily baseline electricity consumption of electricity-consuming units, the number of electricity-consuming units, and the average daily temperature are defined as the basic demand data.

[0018] The supply unit acquires basic supply data, obtains the area values ​​of solar panels in the power generation cluster from the database, randomly selects p solar panels in the photovoltaic power generation cluster as characteristic solar panels, acquires the real-time power generation of each characteristic solar panel using an electrical power sensor, and acquires the area values ​​of each characteristic solar panel using an area measuring instrument. The average power generation per unit area of ​​the solar panel is calculated using the formula for power generation per unit area.

[0019] Where W1, W2, W3...Wp are the real-time power generation of the characteristic solar panels, S1, S2, S3...Sn are the area values ​​of the characteristic solar panels, and p is the number of selected characteristic solar panels, and p is greater than 0.

[0020] The daily power generation duration of each characteristic solar panel is obtained, and the average daily power generation duration of the solar panel is calculated from these values.

[0021] Where Scj is the average daily power generation duration of the solar panel, Sc1, Sc2, Sc3...Scp are the daily power generation durations of the characteristic solar panels, and p is the number of selected characteristic solar panels, with p being greater than 0.

[0022] The area of ​​solar panels in the power generation cluster, the average power generation per unit area of ​​solar panels, and the average daily power generation duration of solar panels are defined as the basic supply data.

[0023] The fault unit acquires basic fault data by using voltage sensors to acquire the open-circuit voltage of each solar panel in the photovoltaic power generation cluster in real time, using current sensors to acquire the short-circuit current of each solar panel in the photovoltaic power generation cluster in real time, and using temperature sensors to acquire the surface temperature of each solar panel in the photovoltaic power generation cluster in real time. The open-circuit voltage, short-circuit current, and surface temperature of each solar panel are defined as basic fault data.

[0024] Demand-based data, supply-based data, and fault-based data are defined as regulatory-based data, and the data acquisition module acquires the regulatory-based data.

[0025] As a preferred embodiment of the intelligent control system for distributed photovoltaic power generation clusters described in this invention, the data analysis module includes analyzing basic control data to obtain control analysis data, and the data analysis module includes a demand analysis unit, a supply analysis unit, and a fault analysis unit.

[0026] The database also stores data such as the open-circuit reference voltage, short-circuit reference current, and surface reference temperature of the solar panel, as well as the open-circuit voltage fault error, short-circuit current fault error, and surface temperature fault error of the solar panel.

[0027] The demand analysis unit analyzes the basic demand data, and obtains the average daily baseline electricity consumption, the number of electricity-consuming units, and the average daily temperature based on the basic demand data. The electricity demand is then calculated using the electricity demand calculation formula: Xd=Jp*Ds*1+|Tp-25|

[0028] Where Xd is the electricity demand, Jp is the average daily base electricity consumption of the electricity user, Ds is the number of electricity users, and Tp is the average daily temperature.

[0029] The supply analysis unit analyzes the basic supply data, and obtains the area of ​​the power generation cluster's solar panels, the average power generation per unit area of ​​the solar panels, and the average daily power generation duration of the solar panels based on the basic supply data. It then calculates the theoretical power supply using these data: Fd=Mj*Dw*Scj

[0030] Where Fd is the theoretical power supply, Mj is the area data of the solar panels in the power generation cluster, Dw is the average power generation per unit area of ​​the solar panels, and Scj is the average power generation duration of the solar panels on that day.

[0031] The fault analysis unit analyzes the basic fault data to obtain real-time fault data.

[0032] Electricity demand, theoretical power generation supply, and real-time fault data are defined as control and analysis data, and the data analysis module acquires the control and analysis data.

[0033] As a preferred embodiment of the intelligent control system for distributed photovoltaic power generation clusters described in this invention, the fault analysis unit analyzes the basic fault data, including obtaining the open-circuit voltage, short-circuit current, and real-time surface temperature of each solar panel based on the basic fault data.

[0034] The open-circuit reference voltage, short-circuit reference current, and surface reference temperature of the solar panel are obtained from the database.

[0035] The following values ​​are used to calculate the reference values ​​for solar panel fault diagnosis: Tp = |Vk - Vkj| * |Id - Idj| + |Bw - Bwj| * a1

[0036] Where Tp is the reference value for judging solar panel faults, Vk is the open circuit voltage value, Id is the short circuit current value, Bw is the real-time surface temperature value, Vkj is the open circuit reference voltage value, Idj is the short circuit reference current value, Bwj is the surface reference temperature value, and a1 is the set proportional coefficient and a1 is greater than 0.

[0037] The open-circuit voltage fault error value, short-circuit current fault error value, and surface temperature fault error value of the solar panel are obtained from the database, and the fault judgment reference threshold of the solar panel is calculated. Fault judgment is then performed on the solar panel: Tp1=Vk1*Id1+Bw1*a1

[0038] Wherein, Tp1 is the reference threshold for judging solar panel faults, Vk1 is the open circuit voltage fault error value, Id1 is the short circuit current fault error value, Bw1 is the surface temperature fault error value, and a1 is the set proportional coefficient and a1 is greater than 0.

[0039] When Tp≥Tp1, the solar panel is determined to be a faulty solar panel.

[0040] When Tp1 > Tp, the solar panel is judged to be a normal solar panel.

[0041] Real-time area statistics are performed on normal solar panels to obtain the real-time area values ​​of faulty solar panels.

[0042] The real-time area value of a faulty solar panel is defined as real-time fault data.

[0043] As a preferred embodiment of the intelligent control system for distributed photovoltaic power generation clusters described in this invention, the data processing module includes processing control analysis data to obtain supply and demand control data, and the data processing module obtains electricity demand, theoretical power generation supply and real-time fault data based on the control analysis data.

[0044] The data processing module includes a supply processing unit and a supply-demand balancing unit.

[0045] The supply processing unit obtains the actual power generation supply, obtains the theoretical power generation supply based on the control analysis data, obtains the real-time area value of the faulty solar panels based on the real-time fault data, and obtains the average power generation per unit area of ​​the solar panels and the average power generation duration of the solar panels on the same day based on the control basic data.

[0046] The actual power supply is calculated using the following formula: Sg = Fd - (Gb * Dw * Scj) based on the theoretical power supply, the real-time area of ​​the faulty solar panels, the average power output per unit area of ​​the solar panels, and the average daily power generation duration of the solar panels.

[0047] Where Sg is the actual power generation supply, Fd is the theoretical power generation supply, Dw is the average power generation per unit area of ​​the solar panel, Scj is the average power generation duration of the solar panel on the same day, and Gb is the real-time area of ​​the faulty solar panel.

[0048] The supply and demand balancing unit obtains supply and demand balancing reference values, specifically the actual power generation supply and electricity demand. It then uses these values, along with the obtained supply and demand balancing reference values, to calculate the final supply and demand balancing reference value using the supply and demand balancing reference value calculation formula.

[0049] Where Ph is the reference value for supply and demand balance, Xd is the electricity demand, and Sg is the actual power generation supply.

[0050] The data processing module acquires the supply and demand balance reference values ​​and transmits them to the intelligent control module.

[0051] As a preferred embodiment of the intelligent control system for distributed photovoltaic power generation clusters described in this invention, the intelligent control module includes a first control interval when the supply and demand balance reference value Ph is greater than 1, at which point the electricity demand is greater than the actual power generation supply.

[0052] When the supply and demand balance reference value Ph equals 1, the electricity demand equals the actual power generation supply, and this is set as the second control range.

[0053] When the supply and demand balance reference value Ph is less than 1, the electricity demand is less than the actual power generation supply, and this is set as the third control range.

[0054] For the first control range, switch to high-efficiency working mode. The photovoltaic power generation cluster increases the power generation of the photovoltaic power generation cluster through the collaborative control system, and temporarily supplies the stored electrical energy to the power consumption unit. Increase the tilt angle of the photovoltaic panels to increase the solar radiation received and improve the power generation efficiency of the photovoltaic cells. Optimize the operating point of the photovoltaic cells through the maximum power point tracking algorithm to increase the power generation of the single operating point.

[0055] For the second control range, the photovoltaic power generation cluster maintains its current power generation capacity through a collaborative control system.

[0056] For the third control range, the photovoltaic power generation cluster switches to a low-power operating mode. The photovoltaic power generation cluster reduces the power generation of the photovoltaic power generation cluster through the collaborative control system, stores the excess electrical energy, reduces the tilt angle of the photovoltaic panels, reduces the solar radiation receiving area, and reduces the power generation.

[0057] Another objective of this invention is to provide an intelligent control method for distributed photovoltaic (PV) power generation clusters. Through intelligent management and control, this method improves the overall performance and economic efficiency of PV power generation systems. Specifically, it aims to adjust the operating point of PV panels to their maximum power point by real-time monitoring and analysis of the operating status and environmental conditions, thereby maximizing the power output of each panel. Simultaneously, the system automatically adjusts the output of the PV power generation cluster based on real-time demand and power generation capacity, ensuring supply-demand balance and reducing energy waste. Furthermore, through real-time monitoring and analysis of fault data, the intelligent control system can promptly identify and address potential problems, reducing system failures and downtime. This makes distributed PV power generation systems more efficient, stable, and reliable, and allows for automatic adjustments under different environmental and demand conditions to achieve optimal power generation and economic benefits, while promoting sustainable development.

[0058] The method of the intelligent control system for distributed photovoltaic power generation clusters described in this invention is characterized by: acquiring basic control data.

[0059] By analyzing the basic data on regulation, we obtain regulation analysis data.

[0060] The regulatory analysis data is processed to obtain reference values ​​for supply and demand balance.

[0061] The photovoltaic power generation cluster is intelligently controlled based on the supply and demand balance reference value, and the operating point of the photovoltaic cells is optimized through the maximum power point tracking algorithm.

[0062] As a preferred embodiment of the intelligent control method for distributed photovoltaic power generation clusters described in this invention, the maximum power point tracking algorithm includes optimizing the operating point of the photovoltaic cells, and the specific model is as follows:

[0063] Among them, Popt θ represents the optimal power output of the photovoltaic panel after optimization using the maximum power point tracking algorithm. min and θ max These are the minimum and maximum values ​​of the solar panel tilt angle, I. SC It is the short-circuit current of the solar panel, I pgc It is photocurrent, P mpp (θ) is the maximum power point power at a tilt angle of θ, N is the total number of photovoltaic panels, and P mpp,i (θ) is the maximum power point power of the i-th solar panel at an angle of θ.

[0064] When P opt Close to P mpp When P(θ) reaches its maximum value, it indicates that the photovoltaic panel has reached its optimal power generation state after optimization by the maximum power point tracking algorithm; when P opt A value close to 0 indicates extremely low photovoltaic panel power generation efficiency, due to improper panel tilt angle settings. Considering the impact of panel tilt angle and azimuth angle on power generation efficiency, the appropriate tilt angle for the panel is calculated:

[0065] Considering the impact of the Maximum Power Point Tracking (MPPT) algorithm on power generation efficiency, the output power of the MPPT algorithm is expressed as follows: P PV (t)=I PV (t)×V PV (t)

[0066] The optimized calculation of the appropriate angle for the solar panel tilt angle: P PV,opt (t)=f MPPT (I PV (t)×V PV (t))

[0067] Among them, I PV (t) represents the current of the photovoltaic panel at time t, V. PV (t) Voltage of the photovoltaic panel at time t, P PV (t) represents the power of the photovoltaic panel at time t, α opt Let β represent the tilt angle of the photovoltaic panel relative to the ground, β represent the azimuth angle of the photovoltaic panel, and θ(t) represent the solar altitude angle. G(t) represents the azimuth angle of the sun, G(t) represents the solar radiation intensity, and f represents the solar azimuth angle of the sun. MPPT (P PV P represents the output power of the maximum power point tracking algorithm. PV,max It is the maximum power point power of the photovoltaic panel.

[0068] A computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement the steps of a distributed photovoltaic power generation cluster intelligent control system.

[0069] A computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of an intelligent control system for a distributed photovoltaic power generation cluster.

[0070] The beneficial effects of this invention are: This invention can accurately obtain the area value of faulty solar panels in real time, realizing real-time fault monitoring of solar panels in the power generation cluster; This invention can make supply and demand judgments of the photovoltaic power generation cluster based on the area value of faulty solar panels, and accurately regulate the power generation of the photovoltaic power generation cluster. Attached Figure Description

[0071] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0072] Figure 1 is a flowchart of an intelligent control system for a distributed photovoltaic power generation cluster provided in an embodiment of the present invention.

[0073] Figure 2 is a schematic diagram of the operation of a solar panel in a distributed photovoltaic power generation cluster intelligent control system according to an embodiment of the present invention.

[0074] Figure 3 is a flowchart of an intelligent control method for distributed photovoltaic power generation clusters according to an embodiment of the present invention. Detailed Implementation

[0075] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0076] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0077] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0078] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0079] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0080] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0081] Example 1

[0082] Referring to Figure 1, which illustrates the first embodiment of the present invention, this embodiment provides an intelligent control system for distributed photovoltaic power generation clusters, including a data acquisition module that acquires basic demand data, basic supply data, and basic fault data, and synthesizes them to obtain basic control data.

[0083] The data analysis module analyzes the basic data for regulation and control to obtain electricity demand, theoretical power generation supply, and real-time fault data, and defines these data as regulation and control analysis data.

[0084] The data processing module processes the regulatory analysis data to obtain reference values ​​for supply and demand balance.

[0085] The intelligent control module intelligently controls the photovoltaic power generation cluster based on supply and demand balance reference values.

[0086] Preferably, the data acquisition module, data analysis module, data processing module, and intelligent control module are each connected to the server.

[0087] It should be noted that the data acquisition module includes data stored in the database, including the number of electricity-consuming units in the power supply area corresponding to the photovoltaic power generation cluster and the area of ​​the corresponding power generation panels of the power generation cluster.

[0088] The data acquisition module includes a demand unit, a supply unit, and a fault unit.

[0089] The demand unit acquires basic demand data, obtains the number of electricity-consuming units in the power supply area corresponding to the photovoltaic power generation cluster from the database, the area value of the photovoltaic panels corresponding to the power generation cluster, selects m electricity-consuming units as sample electricity-consuming units, obtains the daily baseline electricity consumption of the sample electricity-consuming units through smart meters, and calculates the average daily baseline electricity consumption of the electricity-consuming units based on the daily baseline electricity consumption of the sample electricity-consuming units:

[0090] Where Jp is the average daily baseline electricity consumption of the electricity-consuming unit, and J1, J2, J3...Jm are the daily baseline electricity consumption of m sample electricity-consuming units respectively.

[0091] Select n characteristic time points, obtain the temperature values ​​for each of the n characteristic time points from the weather forecast, and calculate the daily average temperature value using the average temperature calculation formula:

[0092] Where T1, T2, T3...Tn are the temperature values ​​at n characteristic time points, Tp is the daily average temperature value, and n is greater than 0.

[0093] The average daily baseline electricity consumption of electricity-consuming units, the number of electricity-consuming units, and the average daily temperature are defined as the basic demand data.

[0094] It should be noted here that: daily baseline electricity consumption refers to the normal electricity consumption of a certain electricity-consuming unit within a specific time period (usually one day); the sample electricity-consuming units include different electricity-consuming units such as hospitals, shopping malls, and residential communities.

[0095] The supply unit acquires basic supply data, obtains the area values ​​of solar panels in the power generation cluster from the database, randomly selects p solar panels in the photovoltaic power generation cluster as characteristic solar panels, acquires the real-time power generation of each characteristic solar panel using an electrical power sensor, and acquires the area values ​​of each characteristic solar panel using an area measuring instrument. The average power generation per unit area of ​​the solar panel is calculated using the formula for power generation per unit area.

[0096] Where W1, W2, W3...Wp are the real-time power generation of the characteristic solar panels, S1, S2, S3...Sn are the area values ​​of the characteristic solar panels, and p is the number of selected characteristic solar panels, and p is greater than 0.

[0097] It should be noted that the unit area mentioned here is specifically set to 1 square meter.

[0098] Please refer to Figure 2. The solar panels selected in this embodiment include solar panels under different lighting conditions.

[0099] The daily power generation duration of each characteristic solar panel is obtained, and the average daily power generation duration of the solar panel is calculated from these values.

[0100] Where Scj is the average daily power generation duration of the solar panel, Sc1, Sc2, Sc3...Scp are the daily power generation durations of the characteristic solar panels, and p is the number of selected characteristic solar panels, with p being greater than 0.

[0101] The area of ​​solar panels in the power generation cluster, the average power generation per unit area of ​​solar panels, and the average daily power generation duration of solar panels are defined as the basic supply data.

[0102] The fault unit acquires basic fault data and includes a voltage sensor, a current sensor, and a temperature sensor.

[0103] The open-circuit voltage of each solar panel in the photovoltaic power generation cluster is acquired in real time using voltage sensors; the short-circuit current of each solar panel in the photovoltaic power generation cluster is acquired in real time using current sensors; and the surface temperature of each solar panel in the photovoltaic power generation cluster is acquired in real time using temperature sensors. The open-circuit voltage, short-circuit current, and surface temperature of each solar panel are defined as the basic fault data.

[0104] It should be noted here that the open-circuit voltage (Voc) of a solar panel refers to the maximum voltage generated by the solar panel when no load is connected. It is the voltage output of the solar panel under standard test conditions (STC). The open-circuit voltage can be used to evaluate the voltage performance of the solar panel.

[0105] Short-circuit current (Isc) refers to the maximum current generated by a solar panel when a short circuit occurs in the circuit. It is the current output of the solar panel under standard test conditions and can be used to evaluate the current performance of the solar panel.

[0106] Demand-based data, supply-based data, and fault-based data are defined as regulatory-based data, and the data acquisition module acquires the regulatory-based data.

[0107] Furthermore, the data acquisition module acquires the basic regulatory data and sends it to the data analysis module and the data processing module; the data analysis module analyzes the basic regulatory data to obtain regulatory analysis data; the data analysis module acquires demand basic data, supply basic data, and fault basic data based on the basic regulatory data; the data analysis module includes a demand analysis unit, a supply analysis unit, and a fault analysis unit.

[0108] The database also stores data such as the open-circuit reference voltage, short-circuit reference current, and surface reference temperature of the solar panel, as well as the open-circuit voltage fault error, short-circuit current fault error, and surface temperature fault error of the solar panel.

[0109] The demand analysis unit analyzes the basic demand data, and obtains the average daily baseline electricity consumption, the number of electricity-consuming units, and the average daily temperature based on the basic demand data. The electricity demand is then calculated using the electricity demand calculation formula: Xd=Jp*Ds*1+|Tp-25|

[0110] Where Xd is the electricity demand, Jp is the average daily base electricity consumption of the electricity user, Ds is the number of electricity users, and Tp is the average daily temperature value, with 25 degrees Celsius set as the base temperature value.

[0111] The supply analysis unit analyzes the basic supply data, and obtains the area of ​​the power generation cluster's solar panels, the average power generation per unit area of ​​the solar panels, and the average daily power generation duration of the solar panels based on the basic supply data. It then calculates the theoretical power supply using these data: Fd=Mj*Dw*Scj

[0112] Where Fd is the theoretical power supply, Mj is the area data of the solar panels in the power generation cluster, Dw is the average power generation per unit area of ​​the solar panels, and Scj is the average power generation duration of the solar panels on that day.

[0113] The fault analysis unit analyzes the basic fault data to obtain real-time fault data.

[0114] Electricity demand, theoretical power generation supply, and real-time fault data are defined as control and analysis data, and the data analysis module acquires the control and analysis data.

[0115] It should also be noted that the data stored in the database also includes the open-circuit reference voltage value, short-circuit reference current value, and surface reference temperature value of the solar panel, as well as the open-circuit voltage fault error value, short-circuit current fault error value, and surface temperature fault error value of the solar panel.

[0116] The fault analysis unit analyzes the basic fault data, including obtaining the open-circuit voltage, short-circuit current, and real-time surface temperature of each solar panel based on the basic fault data.

[0117] The open-circuit reference voltage, short-circuit reference current, and surface reference temperature of the solar panel are obtained from the database.

[0118] The following values ​​are used to calculate the reference values ​​for solar panel fault diagnosis: Tp = |Vk - Vkj| * |Id - Idj| + |Bw - Bwj| * a1

[0119] Where Tp is the reference value for judging solar panel faults, Vk is the open circuit voltage value, Id is the short circuit current value, Bw is the real-time surface temperature value, Vkj is the open circuit reference voltage value, Idj is the short circuit reference current value, Bwj is the surface reference temperature value, and a1 is the set proportional coefficient and a1 is greater than 0.

[0120] The open-circuit voltage fault error value, short-circuit current fault error value, and surface temperature fault error value of the solar panel are obtained from the database, and the fault judgment reference threshold of the solar panel is calculated. Fault judgment is then performed on the solar panel: Tp1=Vk1*Id1+Bw1*a1

[0121] Wherein, Tp1 is the reference threshold for judging solar panel faults, Vk1 is the open circuit voltage fault error value, Id1 is the short circuit current fault error value, Bw1 is the surface temperature fault error value, and a1 is the set proportional coefficient and a1 is greater than 0.

[0122] When Tp≥Tp1, the solar panel is determined to be a faulty solar panel.

[0123] When Tp1 > Tp, the solar panel is judged to be a normal solar panel.

[0124] Real-time area statistics are performed on normal solar panels to obtain the real-time area values ​​of faulty solar panels.

[0125] The real-time area value of a faulty solar panel is defined as real-time fault data.

[0126] Furthermore, the data processing module includes processing the control and analysis data to obtain supply and demand control data. The data processing module obtains electricity demand, theoretical power generation supply, and real-time fault data based on the control and analysis data.

[0127] The data processing module includes a supply processing unit and a supply-demand balancing unit.

[0128] The supply processing unit obtains the actual power generation supply, obtains the theoretical power generation supply based on the control analysis data, obtains the real-time area value of the faulty solar panels based on the real-time fault data, and obtains the average power generation per unit area of ​​the solar panels and the average power generation duration of the solar panels on the same day based on the control basic data.

[0129] The actual power supply is calculated using the following formula: Sg = Fd - (Gb * Dw * Scj) based on the theoretical power supply, the real-time area of ​​the faulty solar panels, the average power output per unit area of ​​the solar panels, and the average daily power generation duration of the solar panels.

[0130] Where Sg is the actual power generation supply, Fd is the theoretical power generation supply, Dw is the average power generation per unit area of ​​the solar panel, Scj is the average power generation duration of the solar panel on the same day, and Gb is the real-time area of ​​the faulty solar panel.

[0131] The supply and demand balancing unit obtains supply and demand balancing reference values, specifically the actual power generation supply and electricity demand. It then uses these values, along with the obtained supply and demand balancing reference values, to calculate the final supply and demand balancing reference value using the supply and demand balancing reference value calculation formula.

[0132] Where Ph is the reference value for supply and demand balance, Xd is the electricity demand, and Sg is the actual power generation supply.

[0133] The data processing module acquires the supply and demand balance reference values ​​and transmits them to the intelligent control module.

[0134] It should also be noted that the intelligent control module includes a first control range when the supply and demand balance reference value Ph is greater than 1, at which point the electricity demand is greater than the actual power generation supply.

[0135] When the supply and demand balance reference value Ph equals 1, the electricity demand equals the actual power generation supply, and this is set as the second control range.

[0136] When the supply and demand balance reference value Ph is less than 1, the electricity demand is less than the actual power generation supply, and this is set as the third control range.

[0137] For the first control range, switch to high-efficiency working mode. The photovoltaic power generation cluster increases the power generation of the photovoltaic power generation cluster through the collaborative control system, and temporarily supplies the stored electrical energy to the power consumption unit. Increase the tilt angle of the photovoltaic panels to increase the solar radiation received and improve the power generation efficiency of the photovoltaic cells. Optimize the operating point of the photovoltaic cells through the maximum power point tracking algorithm to increase the power generation of the single operating point.

[0138] For the second control range, the photovoltaic power generation cluster maintains its current power generation capacity through a collaborative control system.

[0139] For the third control range, the photovoltaic power generation cluster switches to a low-power operating mode. The photovoltaic power generation cluster reduces the power generation of the photovoltaic power generation cluster through the collaborative control system, stores the excess electrical energy, reduces the tilt angle of the photovoltaic panels, reduces the solar radiation receiving area, and reduces the power generation.

[0140] In this embodiment, the communication protocol of the collaborative control system conforms to the MQTT protocol.

[0141] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0142] Example 2

[0143] One embodiment of the present invention provides an intelligent control system for distributed photovoltaic power generation clusters. To verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0144] The database was used to extract the number of electricity-consuming units and the area of ​​photovoltaic panels corresponding to the power supply area of ​​the photovoltaic power generation cluster. m electricity-consuming units were randomly selected as samples to record the daily baseline electricity consumption; n characteristic time points were selected to record temperature values.

[0145] Using the collected data, the electricity demand and theoretical power generation supply are calculated, and real-time fault data is obtained through fault data analysis. Combining the supply and demand data, the actual power generation supply is calculated, and a supply and demand balance reference value is obtained through the supply and demand balance calculation formula. Based on the supply and demand balance reference value, the photovoltaic power generation cluster is intelligently controlled, including high-efficiency working mode, current power generation mode, and low-power working mode.

[0146] The effects of the intelligent control system for distributed photovoltaic power generation clusters are analyzed in the table below:

[0147] Table 1

[0148] As shown in Table 1, the actual power generation supply after implementing intelligent control is slightly lower than the theoretical power generation supply, mainly due to the existence of real-time fault data. However, the supply-demand balance reference value is generally greater than 1, indicating that in most cases, the actual power generation can meet the electricity demand.

[0149] Compared to traditional photovoltaic power generation systems, the intelligent control system in this invention can manage supply and demand balance more effectively, especially during peak demand periods. For example, in power supply area 1, although the actual power generation is slightly lower than the theoretical value, the supply and demand balance reference value is 1.2, indicating that the system can still effectively meet the electricity demand.

[0150] Furthermore, the system's adaptability to temperature changes and different power demand patterns was also demonstrated. For example, in power supply area 3, despite the high daily average temperature, the actual power generation supply was close to the theoretical value, showing the system's stability under high-temperature conditions.

[0151] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0152] Example 3

[0153] The third embodiment of the present invention differs from the first two embodiments in that:

[0154] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0155] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0156] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0157] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0158] Example 4

[0159] Referring to Figure 3, one embodiment of the present invention provides a method for intelligent control of distributed photovoltaic power generation clusters, characterized in that it includes:

[0160] Step S11: Obtain the basic data for the requirements, as follows:

[0161] The system obtains the number of electricity-consuming units in the power supply area corresponding to the photovoltaic power generation cluster from the database; selects m electricity-consuming units as sample electricity-consuming units, and obtains the daily baseline electricity consumption of the sample electricity-consuming units through smart meters; calculates the average daily baseline electricity consumption of the electricity-consuming units using the average daily baseline electricity consumption calculation formula; selects n characteristic time points, and obtains the temperature values ​​of the n characteristic time points through weather forecasts; calculates the daily average temperature value using the average temperature calculation formula for the n characteristic time points; and defines the average daily baseline electricity consumption of the electricity-consuming units, the number of electricity-consuming units, and the daily average temperature value as the basic demand data.

[0162] Step S12: Obtain basic supply data, as follows:

[0163] The system obtains the area values ​​of solar panels corresponding to the power generation cluster from the database, and randomly selects p solar panels from the photovoltaic power generation cluster as characteristic solar panels. It then obtains the real-time power generation of each characteristic solar panel using an electrical power sensor and the area values ​​of each characteristic solar panel using an area measuring instrument. Finally, it calculates the average power generation per unit area of ​​the solar panel using the formula for power generation per unit area, based on the real-time power generation and area values ​​of the characteristic solar panels. The system also obtains the daily power generation duration of each characteristic solar panel and calculates the average daily power generation duration using the formula for average daily power generation duration. These values ​​are defined as the supply base data.

[0164] Step S13: Obtain basic fault data, as follows:

[0165] The open-circuit voltage of each solar panel in the photovoltaic power generation cluster is acquired in real time using voltage sensors; the short-circuit current of each solar panel in the photovoltaic power generation cluster is acquired in real time using current sensors; and the surface temperature of each solar panel in the photovoltaic power generation cluster is acquired in real time using temperature sensors. The open-circuit voltage, short-circuit current, and surface temperature of each solar panel are defined as the basic fault data.

[0166] Step S14: Define the demand base data, supply base data, and fault base data as the regulation base data.

[0167] Step S2: Analyze the basic data of regulation to obtain regulation analysis data.

[0168] Step S21: Analyze the basic demand data, as follows:

[0169] Based on the demand data, obtain the average daily baseline electricity consumption, the number of electricity-consuming units, and the average daily temperature. Calculate the electricity demand using the electricity demand calculation formula.

[0170] Step S22: Analyze the supply-side data, as follows:

[0171] Based on the supply-based data, obtain the area of ​​the solar panels corresponding to the power generation cluster, the average power generation per unit area of ​​the solar panels, and the average daily power generation duration of the solar panels; then calculate the theoretical power supply using the theoretical power supply calculation formula.

[0172] Step S23: Analyze the basic fault data, as follows:

[0173] Based on the fault baseline data, obtain the open-circuit voltage, short-circuit current, and real-time surface temperature of each solar panel. Obtain the open-circuit reference voltage, short-circuit reference current, and surface reference temperature of the solar panel from the database. Calculate the solar panel fault judgment reference values ​​using the formula for calculating solar panel fault judgment reference values. Obtain the open-circuit voltage fault error value, short-circuit current fault error value, and surface temperature fault error value of the solar panel from the database. Calculate the solar panel fault judgment reference threshold using the formula for calculating solar panel fault judgment reference thresholds. Perform fault judgment on the solar panel based on the solar panel fault judgment reference values ​​and the solar panel fault judgment reference thresholds, as follows:

[0174] When Tp≥Tp1, the solar panel is determined to be a faulty solar panel.

[0175] When Tp1 > Tp, the solar panel is considered a normal solar panel.

[0176] Real-time area statistics are performed on normal solar panels to obtain the real-time area value of faulty solar panels; the real-time area value of faulty solar panels is defined as real-time fault data.

[0177] Step S24: Define the electricity demand, theoretical power generation supply, and real-time fault data as control and analysis data.

[0178] Step S3: Process the regulation and control analysis data to obtain supply and demand regulation and control data.

[0179] Step S31: Obtain the actual power generation supply, as follows:

[0180] Theoretical power generation supply is obtained based on regulation and control analysis data; real-time area value of faulty solar panels is obtained based on real-time fault data; and average power generation per unit area and average daily power generation duration of solar panels are obtained based on regulation and control basic data.

[0181] The actual power supply is calculated using the formula for calculating the actual power supply, which includes the theoretical power supply, the real-time area of ​​the faulty solar panels, the average power output per unit area of ​​the solar panels, and the average daily power generation duration of the solar panels.

[0182] Step S32: Obtain the supply and demand balance reference value, as follows:

[0183] The actual power generation supply and power demand are obtained separately.

[0184] The supply and demand balance reference values ​​are obtained by taking the actual power generation supply and power demand as the reference values, and then calculated using the supply and demand balance reference value calculation formula.

[0185] Step S4: Perform intelligent control of the photovoltaic power generation cluster, as follows:

[0186] The adjustment range is divided based on the supply and demand balance reference value.

[0187] When Ph is greater than 1, the electricity demand exceeds the actual power generation supply, and this is set as the first control range.

[0188] When Ph equals 1, the electricity demand equals the actual power generation supply, and this is set as the second control range.

[0189] When Ph is less than 1, the electricity demand is less than the actual power generation supply, and this is set as the third control range.

[0190] Intelligent regulation is implemented based on the control range.

[0191] For the first control period, the photovoltaic power generation cluster increases its power generation capacity through a collaborative control system and temporarily supplies the stored electricity to electricity users.

[0192] For the second control range, the photovoltaic power generation cluster maintains its current power generation capacity through a collaborative control system.

[0193] For the third control range, the photovoltaic power generation cluster reduces the power generation of the photovoltaic power generation cluster through the collaborative control system and stores the excess electrical energy.

[0194] The maximum power point tracking algorithm includes optimizing the operating point of photovoltaic cells. The specific model is as follows:

[0195] Among them, P opt θ represents the optimal power output of the photovoltaic panel after optimization using the maximum power point tracking algorithm. min and θ max These are the minimum and maximum values ​​of the solar panel tilt angle, I. SC It is the short-circuit current of the solar panel, I pgc It is photocurrent, P mpp (θ) is the maximum power point power at a tilt angle of θ, N is the total number of photovoltaic panels, and P mpp,i (θ) is the maximum power point power of the i-th solar panel at an angle of θ.

[0196] When P opt Close to P mpp When P(θ) reaches its maximum value, it indicates that the photovoltaic panel has reached its optimal power generation state after optimization by the maximum power point tracking algorithm; when P opt A value close to 0 indicates extremely low photovoltaic panel power generation efficiency, due to improper panel tilt angle settings. Considering the impact of panel tilt angle and azimuth angle on power generation efficiency, the appropriate tilt angle for the panel is calculated:

[0197] Considering the impact of the Maximum Power Point Tracking (MPPT) algorithm on power generation efficiency, the output power of the MPPT algorithm is expressed as follows: P PV (t)=I PV (t)×V PV (t)

[0198] The optimized calculation of the appropriate angle for the solar panel tilt angle: P PV,opt (t)=f MPPT (I PV (t)×V PV (t))

[0199] Among them, I PV (t) represents the current of the photovoltaic panel at time t, V. PV (t) Voltage of the photovoltaic panel at time t, P PV (t) represents the power of the photovoltaic panel at time t, α optLet β represent the tilt angle of the photovoltaic panel relative to the ground, β represent the azimuth angle of the photovoltaic panel, and θ(t) represent the solar altitude angle. G(t) represents the azimuth angle of the sun, G(t) represents the solar radiation intensity, and f represents the solar azimuth angle of the sun. MPPT (P PV P represents the output power of the maximum power point tracking algorithm. PV,max It is the maximum power point power of the photovoltaic panel.

[0200] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A distributed photovoltaic power generation cluster intelligent control system, characterized in that: include, The data acquisition module acquires basic demand data, basic supply data, and basic fault data respectively, and combines them to obtain basic regulation data. The data analysis module analyzes the basic data of regulation and control to obtain electricity demand, theoretical power generation supply and real-time fault data, and defines electricity demand, theoretical power generation supply and real-time fault data as regulation and control analysis data; The data processing module processes the regulatory analysis data to obtain reference values ​​for supply and demand balance; The intelligent control module intelligently controls the photovoltaic power generation cluster based on supply and demand balance reference values.

2. The intelligent control system for distributed photovoltaic power generation clusters as described in claim 1, characterized in that: The data acquisition module includes data stored in the database, including the number of electricity-consuming units in the power supply area corresponding to the photovoltaic power generation cluster and the area value of the power generation panels corresponding to the power generation cluster. The data acquisition module includes a demand unit, a supply unit, and a fault unit; The demand unit acquires basic demand data, obtains the number of electricity-consuming units in the power supply area corresponding to the photovoltaic power generation cluster from the database, the area value of the photovoltaic panels corresponding to the power generation cluster, selects m electricity-consuming units as sample electricity-consuming units, obtains the daily baseline electricity consumption of the sample electricity-consuming units through smart meters, and calculates the average daily baseline electricity consumption of the electricity-consuming units based on the daily baseline electricity consumption of the sample electricity-consuming units: Wherein, Jp is the average daily baseline electricity consumption of the electricity-consuming unit, and J1, J2, J3...Jm are the daily baseline electricity consumption of m sample electricity-consuming units respectively; Select n characteristic time points, obtain the temperature values ​​for each of the n characteristic time points from the weather forecast, and calculate the daily average temperature value using the average temperature calculation formula: Where T1, T2, T3...Tn are the temperature values ​​at n characteristic time points, Tp is the daily average temperature value, and n is greater than 0; The average daily baseline electricity consumption of electricity-consuming units, the number of electricity-consuming units, and the average daily temperature are defined as the basic demand data. The supply unit acquires basic supply data, obtains the area values ​​of solar panels in the power generation cluster from the database, randomly selects p solar panels in the photovoltaic power generation cluster as characteristic solar panels, acquires the real-time power generation of each characteristic solar panel using an electrical power sensor, and acquires the area values ​​of each characteristic solar panel using an area measuring instrument. The average power generation per unit area of ​​the solar panel is calculated using the formula for power generation per unit area. Where W1, W2, W3...Wp are the real-time power generation of the characteristic solar panels, S1, S2, S3...Sn are the area values ​​of the characteristic solar panels, and p is the number of selected characteristic solar panels, and p is greater than 0. The daily power generation duration of each characteristic solar panel is obtained, and the average daily power generation duration of the solar panel is calculated from these values. Where Scj is the average daily power generation duration of the solar panel, Sc1, Sc2, Sc3...Scp are the daily power generation durations of the characteristic solar panels, and p is the number of selected characteristic solar panels, with p being greater than 0. The area of ​​solar panels in the power generation cluster, the average power generation per unit area of ​​solar panels, and the average daily power generation duration of solar panels are defined as the basic supply data. The fault unit acquires basic fault data by using a voltage sensor to acquire the open-circuit voltage of each solar panel in the photovoltaic power generation cluster in real time, using a current sensor to acquire the short-circuit current of each solar panel in the photovoltaic power generation cluster in real time, and using a temperature sensor to acquire the surface temperature of each solar panel in the photovoltaic power generation cluster in real time. The open-circuit voltage, short-circuit current, and surface temperature of each solar panel are defined as basic fault data. Demand-based data, supply-based data, and fault-based data are defined as regulatory-based data, and the data acquisition module acquires the regulatory-based data.

3. The intelligent control system for distributed photovoltaic power generation clusters as described in claim 2, characterized in that: The data analysis module includes analyzing basic regulatory data to obtain regulatory analysis data. The data analysis module includes a demand analysis unit, a supply analysis unit, and a fault analysis unit. The database also stores data such as the open-circuit reference voltage, short-circuit reference current, and surface reference temperature of the solar panel, as well as the open-circuit voltage fault error, short-circuit current fault error, and surface temperature fault error of the solar panel. The demand analysis unit analyzes the basic demand data, obtains the average daily baseline electricity consumption of electricity-consuming units, the number of electricity-consuming units, and the average daily temperature based on the basic demand data, and calculates the electricity demand using the electricity demand calculation formula: Xd = Jp * Ds * 1 + |Tp - 25| Where Xd is the electricity demand, Jp is the average daily base electricity consumption of the electricity user, Ds is the number of electricity users, and Tp is the average daily temperature. The supply analysis unit analyzes the basic supply data, and obtains the area of ​​the power generation cluster's solar panels, the average power generation per unit area of ​​the solar panels, and the average daily power generation duration of the solar panels based on the basic supply data. It then calculates the theoretical power supply from these data: Fd=Mj*Dw*Scj Where Fd is the theoretical power supply, Mj is the area data of the power generation cluster panels, Dw is the average power generation per unit area of ​​the solar panels, and Scj is the average power generation duration of the solar panels on that day. The fault analysis unit analyzes the basic fault data to obtain real-time fault data; Electricity demand, theoretical power generation supply, and real-time fault data are defined as control and analysis data, and the data analysis module acquires the control and analysis data.

4. The intelligent control system for distributed photovoltaic power generation clusters as described in claim 3, characterized in that: The fault analysis unit analyzes the basic fault data, including obtaining the open-circuit voltage, short-circuit current, and real-time surface temperature of each solar panel based on the basic fault data. Obtain the open-circuit reference voltage, short-circuit reference current, and surface reference temperature values ​​of the solar panel from the database. The reference values ​​for diagnosing solar panel faults are calculated by combining the open-circuit voltage, short-circuit current, real-time surface temperature, open-circuit reference voltage, short-circuit reference current, and surface reference temperature of the solar panel. Tp=|Vk-Vkj|*|Id-Idj|+|Bw-Bwj|*a1 Where Tp is the reference value for judging solar panel faults, Vk is the open circuit voltage value, Id is the short circuit current value, Bw is the real-time surface temperature value, Vkj is the open circuit reference voltage value, Idj is the short circuit reference current value, Bwj is the surface reference temperature value, and a1 is the set proportional coefficient and a1 is greater than 0. The open-circuit voltage fault error value, short-circuit current fault error value, and surface temperature fault error value of the solar panel are obtained from the database, and a fault judgment reference threshold is calculated to determine the fault of the solar panel. Tp1=Vk1*Id1+Bw1*a1 Wherein, Tp1 is the reference threshold for judging solar panel faults, Vk1 is the open circuit voltage fault error value, Id1 is the short circuit current fault error value, Bw1 is the surface temperature fault error value, and a1 is the set proportional coefficient and a1 is greater than 0. When Tp≥Tp1, the solar panel is determined to be a faulty solar panel; When Tp1 > Tp, the solar panel is judged to be a normal solar panel; Real-time area statistics are performed on normal solar panels to obtain the real-time area values ​​of faulty solar panels. The real-time area value of a faulty solar panel is defined as real-time fault data.

5. The intelligent control system for distributed photovoltaic power generation clusters as described in claim 4, characterized in that: The data processing module includes processing the control and analysis data to obtain supply and demand control data. The data processing module obtains electricity demand, theoretical power generation supply and real-time fault data based on the control and analysis data. The data processing module includes a supply processing unit and a supply-demand balancing unit; The supply processing unit obtains the actual power generation supply, obtains the theoretical power generation supply based on the control analysis data, obtains the real-time area value of the faulty solar panels based on the real-time fault data, and obtains the average power generation per unit area of ​​the solar panels and the average power generation duration of the solar panels on the same day based on the control basic data. The actual power supply is calculated using the formula for calculating the actual power supply, which includes the theoretical power supply, the real-time area of ​​the faulty solar panels, the average power output per unit area of ​​the solar panels, and the average daily power generation duration of the solar panels. Sg = Fd - (Gb * Dw * Scj) Where Sg is the actual power generation supply, Fd is the theoretical power generation supply, Dw is the average power generation per unit area of ​​the solar panel, Scj is the average power generation duration of the solar panel on the same day, and Gb is the real-time area of ​​the faulty solar panel. The supply and demand balancing unit obtains supply and demand balancing reference values, specifically the actual power generation supply and electricity demand. It then uses these values, along with the obtained supply and demand balancing reference values, to calculate the final supply and demand balancing reference value using the supply and demand balancing reference value calculation formula. Where Ph is the supply and demand balance reference value, Xd is the electricity demand, and Sg is the actual power generation supply. The data processing module acquires the supply and demand balance reference values ​​and transmits them to the intelligent control module.

6. The intelligent control system for distributed photovoltaic power generation clusters as described in claim 5, characterized in that: The intelligent control module includes a first control interval when the supply and demand balance reference value Ph is greater than 1, at which point the electricity demand is greater than the actual power generation supply. When the supply and demand balance reference value Ph equals 1, the electricity demand is equal to the actual power generation supply, and this is set as the second control range. When the supply and demand balance reference value Ph is less than 1, the electricity demand is less than the actual power generation supply, and this is set as the third control range. For the first control range, switch to high-efficiency working mode. The photovoltaic power generation cluster increases the power generation of the photovoltaic power generation cluster through the collaborative control system, and temporarily supplies the stored electrical energy to the power consumption unit. Increase the tilt angle of the photovoltaic panels to increase the solar radiation received and improve the power generation efficiency of the photovoltaic cells. Optimize the operating point of the photovoltaic cells through the maximum power point tracking algorithm to increase the power generation of a single operating point. For the second control period, the photovoltaic power generation cluster maintains its current power generation capacity through a collaborative control system; For the third control range, the photovoltaic power generation cluster switches to a low-power operating mode. The photovoltaic power generation cluster reduces the power generation of the photovoltaic power generation cluster through the collaborative control system, stores the excess electrical energy, reduces the tilt angle of the photovoltaic panels, reduces the solar radiation receiving area, and reduces the power generation.

7. A method for using a distributed photovoltaic power generation cluster intelligent control system as described in any one of claims 1 to 6, characterized in that: Obtain basic data for regulation; By analyzing the basic data on regulation, we obtain regulation analysis data; The regulatory analysis data is processed to obtain a reference value for supply and demand balance; The photovoltaic power generation cluster is intelligently controlled based on the supply and demand balance reference value, and the operating point of the photovoltaic cells is optimized through the maximum power point tracking algorithm.

8. The intelligent control method for distributed photovoltaic power generation clusters as described in claim 7, characterized in that: The maximum power point tracking algorithm includes optimizing the operating point of photovoltaic cells, and the specific model is as follows: Among them, P opt θ represents the optimal power output of the photovoltaic panel after optimization using the maximum power point tracking algorithm. min and θ max These are the minimum and maximum values ​​of the solar panel tilt angle, I. SC It is the short-circuit current of the solar panel, I pgc It is photocurrent, P mpp (θ) is the maximum power point power at a tilt angle of θ, N is the total number of photovoltaic panels, and P mpp,i (θ) is the maximum power point power of the i-th solar panel at an angle of θ. When P opt Close to P mpp When P(θ) reaches its maximum value, it indicates that the photovoltaic panel has reached its optimal power generation state after optimization by the maximum power point tracking algorithm; when P opt A value close to 0 indicates extremely low photovoltaic panel power generation efficiency, due to improper panel tilt angle settings. Considering the impact of panel tilt angle and azimuth angle on power generation efficiency, the appropriate tilt angle for the panel is calculated: Considering the impact of the Maximum Power Point Tracking (MPPT) algorithm on power generation efficiency, the output power of the MPPT algorithm is expressed as follows: P PV (t)=I PV (t)×V PV (t) The optimized calculation of the appropriate angle for the solar panel tilt angle is as follows: P PV,opt (t)=f MPPT (I PV (t)×V PV (t)) Among them, I PV (t) represents the current of the photovoltaic panel at time t, V. PV (t) Voltage of the photovoltaic panel at time t, P PV (t) represents the power of the photovoltaic panel at time t, α opt Let β represent the tilt angle of the photovoltaic panel relative to the ground, β represent the azimuth angle of the photovoltaic panel, and θ(t) represent the solar altitude angle. G(t) represents the azimuth angle of the sun, G(t) represents the solar radiation intensity, and f represents the solar azimuth angle of the sun. MPPT (P PV P represents the output power of the maximum power point tracking algorithm. PV,max It is the maximum power point power of the photovoltaic panel.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 6 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 6 to 7.

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