Cluster optimization regulation and control method and system for low-voltage distributed photovoltaic access power distribution network

Abstract

The invention discloses a cluster optimization regulation and control method and system for a low-voltage distributed photovoltaic access power distribution network, and the method can automatically remove dust and pollutants on a photovoltaic panel through employing an intelligently designed cleaning assembly, effectively maintains the clean state of the photovoltaic panel, reduces the light loss, improves the power generation efficiency, and improves the power generation efficiency. Meanwhile, the requirements for water and other clean resources are remarkably reduced, the environmental influence and the operation complexity are reduced, an intelligent sensor and a data collector are installed on each photovoltaic panel, the system can collect real-time data about the generating capacity, the environmental condition and the equipment operation state, the high accuracy of the data is ensured, and the real-time performance of the system is improved. Accurate evaluation of the influence of the data processing unit on each photovoltaic panel is supported by the accurate data, and future generating capacity and power distribution network requirements are predicted through a prediction engine, so that the cluster regulation and control system can automatically regulate output according to accurate prediction, and power grid load distribution and energy utilization efficiency are optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of cluster optimization control methods, and more specifically, to a cluster optimization control method and system for low-voltage distributed photovoltaic access to a distribution network. Background Art

[0002] As the demand for renewable energy grows, low-voltage distributed photovoltaic systems have been widely deployed as an effective way to access the grid to enhance the energy diversity and reliability of the grid. These photovoltaic systems convert sunlight into electrical energy and directly connect to regional or household distribution networks to provide clean energy for residential and commercial facilities. However, in order to ensure that these low-voltage distributed photovoltaic systems can operate efficiently in the distribution network, not only the photovoltaic panels need to be continuously maintained, but also the output of the system needs to be precisely regulated and optimized.

[0003] Although low-voltage distributed photovoltaic systems have been widely deployed to improve the utilization rate of renewable energy due to their technical advantages, they still face several major challenges in actual operation. First, photovoltaic panels are easily exposed to the outdoor environment due to long-term exposure to dust and other pollutants. This accumulation not only reduces the power generation efficiency of photovoltaic panels, but also affects the accuracy of monitoring data. Accurate data is the basis for achieving effective system regulation, and inaccurate data directly threatens the effectiveness of the regulation system's decision-making, thereby affecting the performance of the entire system. Traditional photovoltaic panel cleaning and maintenance methods usually involve high resource consumption and high costs, and the cleaning efficiency varies, which not only increases the operating cost of the photovoltaic system, but also increases the environmental burden.

[0004] Therefore, in response to the above technical problems, it is necessary to provide a cluster optimization control method and system for low-voltage distributed photovoltaic access to the distribution network. Summary of the invention

[0005] The purpose of the present invention is to provide a cluster optimization control method and system for low-voltage distributed photovoltaic access to a distribution network to solve the above-mentioned problems.

[0006] In order to achieve the above purpose, the technical solution provided by an embodiment of the present invention is as follows:

[0007] The cluster optimization control method for low-voltage distributed photovoltaic access to the distribution network includes the following steps:

[0008] S1, regularly starting the automatic cleaning device integrated on multiple low-voltage distributed photovoltaic panels from low to high altitude to remove dust and other contaminants on the photovoltaic panels;

[0009] S2, uses smart sensors and data collectors installed on each low-voltage distributed photovoltaic panel to collect real-time operation data after cleaning is completed;

[0010] S3, the data processing unit of the cluster optimization control system uses data analysis technology to process the collected data to evaluate the specific impact of each photovoltaic panel on the distribution network.

[0011] S4, the forecasting engine of the same cluster optimization and control system applies statistical and machine learning algorithms to predict future power generation and distribution network demand, taking into account seasonal changes, weather forecasts and historical performance data;

[0012] S5, based on the analysis and prediction results, the cluster control system automatically adjusts the output of each photovoltaic system;

[0013] S6, the cluster optimization control system executes linear programming or genetic algorithm to achieve optimal grid load distribution and energy utilization;

[0014] S7 provides real-time feedback and control suggestions through the user interface, allowing users to manually adjust the control strategy or set priorities based on actual conditions.

[0015] As a further improvement of the present invention, the cleaning device includes multiple cleaning components, which include: a collecting component, a recycling component and a collision component. The collecting component includes a collecting box, which is installed on the photovoltaic panel, and a filter plate and a collecting plate are respectively installed on the top and side ends of the collecting box, and a driving pump is installed in the collecting box; the recycling component includes a recycling plate, an inflow groove is provided on the recycling plate, the inflow groove is inclined, one end of the inflow groove is fixedly connected to a diversion pipe connected thereto, and the bottom end of the recycling plate is fixedly connected to a release block; the collision component includes a collision block, which is installed on the photovoltaic panel, and a pair of mutually symmetrical diversion ports are provided on the collision block, a blocking frame is fixedly connected to the inner wall of the diversion port, an abutment rod is fixedly connected to the blocking frame, and a pair of mutually symmetrical transfer pipes are installed on one end of the collision block, the transfer pipe is connected to the diversion port, and the diversion port is connected to an adjacent collection box.

[0016] As a further improvement of the present invention, one end of the collection box is fixedly connected to a conduit connected thereto, and the conduit is arranged on the upper side of the recovery plate. One end of the collection box is fixedly connected to a recovery frame, a rotating shaft is installed in the recovery frame, a coil spring is installed outside the rotating shaft, and a pull rope is installed outside the rotating shaft, the pull rope is connected to one end of the recovery plate, and a magnetic block and an electromagnet are respectively installed at one end of the recovery plate and the collision block close to each other.

[0017] As a further improvement of the present invention, one end of the release block is fixedly connected to a contact block, the top of the photovoltaic panel is fixedly connected to a friction pad, the friction pad abuts against the contact block, a protective groove and a cleaning groove are provided on the release block, an antistatic agent is provided in the protective groove, and the cleaning groove is connected to the inflow groove.

[0018] As a further improvement of the present invention, a damper is fixedly connected to the bottom end of the release block, and a cleaning block is fixedly connected to the bottom end of the damper. The cleaning block consists of a friction cleaning block, an absorption block and a protection block, and extrusion components are installed in the protection groove and the cleaning groove.

[0019] As a further improvement of the present invention, the extrusion assembly includes a pair of first support frames, the pair of first support frames are respectively installed in the protective groove and the cleaning groove, a first limit strip is installed at the bottom end of the first support frame, a first sealing ball is fixedly connected to the bottom end of the first limit strip, a pair of first sealing balls are respectively abutted against the inner walls of the protective groove and the cleaning groove, and a pair of first sealing balls are respectively installed with diverter blocks at the bottom ends, and a pair of diverter blocks are respectively connected to the friction cleaning block and the protective block.

[0020] As a further improvement of the present invention, a second support frame is fixedly connected to the inner wall of the guide pipe, one end of the second support frame is fixedly connected to a second limit strip, one end of the second limit strip is fixedly connected to a second sealing ball, and the second sealing ball abuts against the inner wall of the guide pipe.

[0021] As a further improvement of the present invention, the collecting of real-time operation data in S2 includes:

[0022] Power generation: measured by current and voltage sensors;

[0023] Load demand: measured by energy metering equipment connected to the grid;

[0024] Environmental conditions: measured by environmental sensors, including temperature, light intensity, wind speed, and humidity data;

[0025] Equipment operating status: Determined by performance monitoring sensors integrated with the inverter and battery management system (BMS).

[0026] As a further improvement of the present invention, in S4, the statistical and machine learning algorithms used by the prediction engine of the cluster optimization control system include:

[0027] Time series analysis: used to process and predict time-based generation and grid load demand, taking into account trends and cyclical changes in historical data;

[0028] Regression analysis: used to predict the relationship between power generation and environmental factors, and their impact on grid load;

[0029] Neural networks: used for complex pattern recognition and prediction problems, learning and modeling nonlinear relationships based on large historical data sets;

[0030] Support Vector Machine (SVM): used for classification and regression analysis to optimize the decision-making process of regulatory strategies.

[0031] A cluster optimization control system for low-voltage distributed photovoltaic access to a distribution network, the cluster optimization control system comprising:

[0032] Automatic cleaning modules: Multiple cleaning components deployed on each low-voltage distributed photovoltaic panel to regularly remove dust and other contaminants from the photovoltaic panels;

[0033] Data collection module: smart sensors and data collectors installed on photovoltaic panels, responsible for collecting key performance indicators;

[0034] Data processing and analysis module: Use data analysis technology to process the collected data and evaluate the specific impact of each photovoltaic panel on the distribution network;

[0035] Control and regulation module: automatically adjusts the output of each photovoltaic system based on data processing and prediction results;

[0036] User interface module: provides real-time feedback and control suggestions, allowing users to manually adjust control strategies or set priorities based on actual conditions.

[0037] Compared with the prior art, the advantages of the present invention are:

[0038] The cluster optimization control system of low-voltage distributed photovoltaic access to the distribution network of this solution realizes the efficient operation and low-cost maintenance of the photovoltaic system through its innovative structure and function. The system integrates an automatic cleaning device and adopts intelligently designed collection components, recycling components and collision components. These components work together to automatically remove dust and pollutants on the photovoltaic panels, effectively keep the photovoltaic panels clean, reduce light loss and improve power generation efficiency, while significantly reducing the demand for water and other cleaning resources, reducing environmental impact and operational complexity. By installing intelligent sensors and data collectors on each photovoltaic panel, the system can collect real-time data on power generation, environmental conditions and equipment operating status, ensuring high data accuracy. Accurate data supports the data processing unit to accurately evaluate the impact of each photovoltaic panel, and predicts future power generation and distribution network demand through the prediction engine, so that the cluster control system can automatically adjust the output according to accurate predictions, optimize the grid load distribution and energy utilization efficiency, not only ensuring the optimal performance of photovoltaic panels, but also significantly reducing operating costs by reducing the need for manual cleaning and maintenance, providing important technical support for the widespread deployment of photovoltaic power generation systems, and achieving the optimization of both economic and environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a schematic diagram of the cluster optimization and control method of the present invention;

[0040] Figure 2It is a schematic diagram of the three-dimensional structure of the automatic cleaning device of the present invention;

[0041] Figure 3 It is a schematic diagram of the three-dimensional structure of the collecting component and the recycling component of the present invention;

[0042] Figure 4 It is a schematic diagram of the three-dimensional structure of the collision assembly of the present invention;

[0043] Figure 5 It is a schematic diagram of the side cross-sectional structure of the recovery component of the present invention.

[0044] Description of the numbers in the figure:

[0045] 1. Photovoltaic panel; 2. Cleaning component; 3. Collecting component; 4. Recycling component; 5. Collision component; 31. Collecting box; 32. Collecting plate; 33. Filter plate; 34. Recycling frame; 35. Conduit; 41. Recycling plate; 42. Inflow slot; 43. Diversion pipe; 44. Protection slot; 45. Release block; 46. Abutment block; 47. Damper; 48. Friction cleaning block; 49. Absorption block; 50. Protection block; 51. Collision block; 52. Diversion port; 53. Transfer pipe; DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention; it is obvious that the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments, and all other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making creative work are within the scope of protection of the present invention.

[0047] Example:

[0048] See also Figure 1-5 The cluster optimization control method of low-voltage distributed photovoltaic access to the distribution network includes the following steps:

[0049] S1, regularly starting the automatic cleaning device integrated on the plurality of low-voltage distributed photovoltaic panels 1 from low to high altitude to remove dust and other contaminants on the photovoltaic panels;

[0050] S2, using smart sensors and data collectors installed on each low-voltage distributed photovoltaic panel 1 to collect real-time operation data after cleaning is completed;

[0051] S3, the data processing unit of the cluster optimization control system processes the collected data using data analysis technology to evaluate the specific impact of each photovoltaic panel 1 on the distribution network.

[0052] S4, the forecasting engine of the same cluster optimization and control system applies statistical and machine learning algorithms to predict future power generation and distribution network demand, taking into account seasonal changes, weather forecasts and historical performance data;

[0053] S5, based on the analysis and prediction results, the cluster control system automatically adjusts the output of each photovoltaic system;

[0054] S6, the cluster optimization control system executes linear programming or genetic algorithm to achieve optimal grid load distribution and energy utilization;

[0055] S7 provides real-time feedback and control suggestions through the user interface, allowing users to manually adjust the control strategy or set priorities based on actual conditions.

[0056] Among them, the cluster optimization control system of the low-voltage distributed photovoltaic access distribution network of this scheme realizes the efficient operation and low-cost maintenance of the photovoltaic system through its innovative structure and function. The system integrates an automatic cleaning device and adopts an intelligently designed collection component 3, a recycling component 4 and a collision component 5. These components work together to automatically remove dust and pollutants on the photovoltaic panels, effectively keep the photovoltaic panels clean, reduce light loss and improve power generation efficiency, while significantly reducing the demand for water and other cleaning resources, reducing environmental impact and operational complexity. By installing intelligent sensors and data collectors on each photovoltaic panel 1, the system can collect real-time data on power generation, environmental conditions and equipment operating status, ensuring high accuracy of the data. Accurate data supports the data processing unit to accurately evaluate the impact of each photovoltaic panel, and predicts future power generation and distribution network demand through a prediction engine, so that the cluster control system can automatically adjust the output according to accurate predictions, optimize the grid load distribution and energy utilization efficiency, not only ensuring the optimal performance of the photovoltaic panels, but also significantly reducing operating costs by reducing the need for manual cleaning and maintenance, providing important technical support for the widespread deployment of photovoltaic power generation systems, and achieving optimization of both economic and environmental benefits.

[0057] The cleaning device includes a plurality of cleaning components 2, and the cleaning component 2 includes: a collecting component 3, a recycling component 4 and a collision component 5. The collecting component 3 includes a collecting box 31, which is installed on the photovoltaic panel 1, and a filter plate 33 and a collecting plate 32 are installed on the top and side ends of the collecting box 31 respectively, and a driving pump is installed in the collecting box 31; the recycling component 4 includes a recycling plate 41, and an inflow groove 42 is provided on the recycling plate 41. The inflow groove 42 is inclined, and one end of the inflow groove 42 is fixedly connected to a guide pipe 43 connected thereto, and a release block 45 is fixedly connected to the bottom end of the recycling plate 41; the collision component 5 includes a collision block 51, which is installed on the photovoltaic panel 1, and a pair of mutually symmetrical guide ports 52 are provided on the collision block 51, and a blocking frame is fixedly connected to the inner wall of the guide port 52, and an abutment rod is fixedly connected to the blocking frame, and a pair of mutually symmetrical transfer pipes 53 are installed on one end of the collision block 51, and the transfer pipe 53 is connected to the guide port 52, and the guide port 52 is connected to the adjacent collecting box 31.

[0058] One end of the collecting box 31 is fixedly connected to a conduit 35 which is in communication therewith, and the conduit 35 is arranged on the upper side of the recovery plate 41. One end of the collecting box 31 is fixedly connected to a recovery frame 34, and a rotating shaft is installed in the recovery frame 34. A coil spring is installed on the outer periphery of the rotating shaft, and a pull rope is installed on the outer periphery of the rotating shaft. The pull rope is connected to one end of the recovery plate 41, and a magnetic block and an electromagnet are respectively installed at one end of the recovery plate 41 and the collision block 51 which are close to each other.

[0059] One end of the release block 45 is fixedly connected to a contact block 46, and the top of the photovoltaic panel 1 is fixedly connected to a friction pad, which abuts against the contact block 46. A protective groove 44 and a cleaning groove are provided on the release block 45. An antistatic agent is provided in the protective groove 44, and the cleaning groove is connected to the inflow groove 42.

[0060] The bottom end of the release block 45 is fixedly connected with a damper 47, and the bottom end of the damper 47 is fixedly connected with a cleaning block. The cleaning block is composed of a friction cleaning block 48, an absorption block 49 and a protection block 50. Extrusion components are installed in the protection groove 44 and the cleaning groove.

[0061] The extrusion assembly includes a pair of first support frames, which are respectively installed in the protective groove 44 and the cleaning groove. A first limit strip is installed at the bottom end of the first support frame, and a first sealing ball is fixedly connected to the bottom end of the first limit strip. A pair of first sealing balls are respectively abutted against the inner walls of the protective groove 44 and the cleaning groove, and a pair of diverter blocks are installed at the bottom ends of the pair of first sealing balls, and the pair of diverter blocks are respectively connected to the friction cleaning block 48 and the protective block 50.

[0062] A second support frame is fixedly connected to the inner wall of the guide pipe 43 , one end of the second support frame is fixedly connected to a second limit bar, one end of the second limit bar is fixedly connected to a second sealing ball, and the second sealing ball abuts against the inner wall of the guide pipe 43 .

[0063] Among them, there is dust on its surface for a long time when collecting data, so it is necessary to clean the dust when adjusting. At this time, the rainwater is collected by the collecting plate 32 installed on the collecting box 31, and then flows into the collecting box 31 through the filter plate 33. The filter plate 33 can block large particles to prevent blockage. When targeting multiple photovoltaic panels 1 in the same column, the cleaning device on the lowest photovoltaic panel 1 can be started first, and then the driving pump in the collecting box 31 is started, so that the driving pump can drive the collected rainwater in the collecting box 31 to be injected into the inflow groove 42 opened in the recovery plate 41 through the conduit 35. Because the shape of the inflow groove 42 is inclined, as more and more water is injected, its downward trend becomes greater and greater, thereby overcoming the friction between the abutment block 46 and the friction pad, so that the recovery plate 41 can drive the inflow groove 42 to slide.

[0064] As the recovery plate 41 slides downward, the shaft installed in the recovery frame 34 is pulled, and a coil spring is installed around the shaft to store its energy in the coil spring. Through the downward sliding process of the recovery plate 41, the protective groove 44 and the cleaning groove are both installed with extrusion components, so that the cleaning block is squeezed with the photovoltaic panel 1, so that the diverter block squeezes the first sealing ball, and the first sealing ball squeezes the first limit strip. Through the first sealing ball squeezing the first limit strip, the rainwater collected in the flow groove 42 can be released to the friction cleaning block 48, and the friction cleaning block 48 is in contact with the dust to achieve the purpose of cleaning, and the absorption block 49 is in contact with the photovoltaic panel 1 to achieve the purpose of drying the cleaned photovoltaic panel 1, and the antistatic agent in the protective groove 44 can be released through the protective block 50 later, so as to achieve better protection effect.

[0065] The cleaning time can be put in sunny weather. Later, when the recovery plate 41 slides to the bottom and contacts the collision block 51, the electromagnet installed on the collision block 51 is activated, which can generate attraction for the magnetic block on the recovery plate 41, so that it can be fixed. At the same time, the downward movement of the recovery plate 41 can simultaneously pull the rotating shaft in the recovery frame 34 and store its energy in the coil spring. When the recovery plate 41 and the collision block 51 abut, the diversion pipe 43 can be combined with the diversion port 52 opened on the collision block 51, and the second sealing ball in the diversion pipe 43 is squeezed by the abutment rod on the blocking frame fixedly connected in the diversion port 52, so that the excess rainwater in the inflow groove 42 can be released and discharged through the transfer pipe 53. At the same time, the friction cleaning block 48 and the absorption block 49 can be dried in the sun during the discharge process, so that the applied antistatic agent can be wiped off easily during the subsequent reset.

[0066] The device operation here is the operation of the automatic cleaning device on the photovoltaic panel 1 with a lower height, and the operation sequence is from low to high. When the automatic cleaning device on the upper side discharges the water in the inflow groove 42, it is discharged into the collection box 31 of the automatic cleaning device with a lower height through the transfer pipe 53, thereby saving water and facilitating subsequent use. Subsequently, it is only necessary to disconnect the electromagnetic switch and contact the restriction of the recovery plate 41 and the collision block 51. It can easily reset the collision block 51 under the action of the coil spring on the rotating shaft, that is, complete the cleaning work.

[0067] Through this highly integrated and automated cleaning system, the maintenance of low-voltage distributed photovoltaic panels becomes simpler and more cost-effective. By keeping the photovoltaic panels continuously clean, the data collected from the photovoltaic panels is ensured to be continuously accurate, which not only improves the efficiency of photovoltaic power generation, but also significantly reduces long-term operation and maintenance costs, providing important technical support for the widespread deployment of photovoltaic power generation systems.

[0068] The real-time operation data collected in S2 includes:

[0069] Power generation: measured by current and voltage sensors;

[0070] Load demand: measured by energy metering equipment connected to the grid;

[0071] Environmental conditions: measured by environmental sensors, including temperature, light intensity, wind speed, and humidity data;

[0072] Equipment operating status: Determined by performance monitoring sensors integrated with the inverter and battery management system (BMS).

[0073] The statistical and machine learning algorithms used by the prediction engine of the cluster optimization control system in S4 include:

[0074] Time series analysis: used to process and predict time-based generation and grid load demand, taking into account trends and cyclical changes in historical data;

[0075] Regression analysis: used to predict the relationship between power generation and environmental factors, and their impact on grid load;

[0076] Neural networks: used for complex pattern recognition and prediction problems, learning and modeling nonlinear relationships based on large historical data sets;

[0077] Support Vector Machine (SVM): used for classification and regression analysis to optimize the decision-making process of regulatory strategies.

[0078] Among them, real-time data collection and advanced analysis and processing are the core of system performance optimization and decision-making. The system uses highly integrated sensor technology and advanced data analysis methods to ensure comprehensive monitoring and control of photovoltaic system performance at all levels.

[0079] The data collection function in the system is based on intelligent sensors and data collectors, which are distributed on each low-voltage distributed photovoltaic panel. These devices continuously monitor and record key performance indicators:

[0080] Power generation: measured in real time by current and voltage sensors, which accurately capture power output and provide basic data for system regulation;

[0081] Load demand: measured by energy metering equipment connected to the grid to ensure a balance between grid demand and supply;

[0082] Environmental conditions: Environmental sensors measure temperature, light intensity, wind speed, and humidity, which are critical to predicting PV panel performance because these environmental factors directly affect the efficiency of PV panels in generating electricity.

[0083] Equipment operating status: Performance monitoring sensors integrated with the inverter and battery management system (BMS) help identify any potential equipment failure or performance degradation, allowing timely maintenance or adjustments.

[0084] The prediction engine of the cluster optimization control system uses a variety of statistical and machine learning algorithms to analyze data to achieve accurate prediction and optimization:

[0085] Time series analysis: Data with time dependence, such as power generation and grid load demand, is used to predict future changes by taking into account trends and cyclical changes in historical data;

[0086] Regression analysis: used to establish a relationship model between power generation and environmental factors such as light intensity and temperature, and predict their direct impact on grid load;

[0087] Neural networks: Deep learning tools can process large data sets, learn complex patterns and relationships, provide nonlinear data analysis capabilities, and make more accurate predictions of future power generation and load demand;

[0088] Support Vector Machine (SVM): A powerful machine learning algorithm used to solve classification and regression problems. In the control strategy, SVM helps the system identify the optimal operating point to achieve the best energy efficiency and load distribution under different operating conditions;

[0089] The above analysis tools enable the PV system to achieve self-optimization, automatically adjust output based on prediction results and real-time data, optimize energy utilization, and ensure the stable operation of the power grid. This intelligent data-driven approach not only improves the economic benefits and reliability of the PV system, but also contributes to the sustainable development of the power grid.

[0090] The cluster optimization control system for low-voltage distributed photovoltaic access to the distribution network includes:

[0091] Automatic cleaning module: multiple cleaning components 2 deployed on each low-voltage distributed photovoltaic panel 1 to regularly remove dust and other contaminants on the photovoltaic panel;

[0092] Data collection module: smart sensors and data collectors installed on photovoltaic panels 1, responsible for collecting key performance indicators;

[0093] Data processing and analysis module: using data analysis technology to process the collected data and evaluate the specific impact of each photovoltaic panel 1 on the distribution network;

[0094] Control and regulation module: automatically adjusts the output of each photovoltaic system based on data processing and prediction results;

[0095] User interface module: provides real-time feedback and control suggestions, allowing users to manually adjust control strategies or set priorities based on actual conditions.

[0096] Working principle:

[0097] First, the system automatically removes dust and pollutants using an automatic cleaning device integrated on each photovoltaic panel 1. This process involves the use of a collection component 3, a recovery component 4, and a collision component 5. Specifically, the collection component 3 collects rainwater through a driving pump of a collection box 31, and filters out large particles through a filter plate 33 and a collection plate 32, and then transfers it to an inclined inflow trough 42 through a conduit 35. As the amount of water increases, the water flow activates the recovery plate 41, and through the release block 45 connected thereto and the guide port 52 and transfer pipe 53 of the collision block 51, deep cleaning of the photovoltaic panel is achieved.

[0098] After cleaning, the system collects real-time data on power generation, environmental conditions, and equipment operating status through smart sensors and data collectors installed on the photovoltaic panels. These data are then transmitted to the data processing unit, which analyzes the impact of each photovoltaic panel's output on the distribution network. Based on these analysis results, the cluster optimization control system's prediction engine uses algorithms such as time series analysis, regression analysis, neural networks, and support vector machines (SVM) to predict future power generation and grid demand. These predictions help the control system automatically adjust the output of each photovoltaic system to ensure the optimization of grid load distribution and energy utilization;

[0099] In addition, the system provides real-time feedback and control suggestions through the user interface, allowing users to manually adjust the control strategy or set priorities based on the current operating conditions and system performance. This integrated approach not only improves the operating efficiency and responsiveness of the PV system, but also simplifies maintenance procedures and reduces operating costs.

[0100] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0101] In addition, it should be understood that although the present specification is described according to embodiments, not every embodiment contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation methods that those skilled in the art can understand.

Claims

1. A cluster optimization control method for low-voltage distributed photovoltaic access to a distribution network, characterized by: The following steps are involved: S1, regularly starting the automatic cleaning device integrated on the plurality of low-voltage distributed photovoltaic panels (1) from low to high altitude to remove dust and other contaminants on the photovoltaic panels; S2, using smart sensors and data collectors installed on each low-voltage distributed photovoltaic panel (1) to collect real-time operating data after cleaning is completed; S3, the data processing unit of the cluster optimization control system processes the collected data using data analysis technology to evaluate the specific impact of each photovoltaic panel (1) on the distribution network. S4, the forecasting engine of the same cluster optimization and control system applies statistical and machine learning algorithms to predict future power generation and distribution network demand, taking into account seasonal changes, weather forecasts and historical performance data; S5, based on the analysis and prediction results, the cluster control system automatically adjusts the output of each photovoltaic system; S6, the cluster optimization control system executes linear programming or genetic algorithm to achieve optimal grid load distribution and energy utilization; S7 provides real-time feedback and control suggestions through the user interface, allowing users to manually adjust the control strategy or set priorities based on actual conditions.

2. The cluster optimization control method for low-voltage distributed photovoltaic access to the distribution network according to claim 1 is characterized by: The cleaning device comprises a plurality of cleaning components (2), wherein the cleaning components (2) comprise: A collecting assembly (3), the collecting assembly (3) comprising a collecting box (31), the collecting box (31) being mounted on the photovoltaic panel (1), and a filter plate (33) and a collecting plate (32) being mounted on the top and side ends of the collecting box (31), respectively, and a driving pump being mounted in the collecting box (31); A recovery component (4), the recovery component (4) comprising a recovery plate (41), an inflow groove (42) being provided on the recovery plate (41), the inflow groove (42) being inclined, one end of the inflow groove (42) being fixedly connected to a guide pipe (43) communicating therewith, and a release block (45) being fixedly connected to the bottom end of the recovery plate (41); A collision assembly (5), the collision assembly (5) comprising a collision block (51), the collision block (51) being mounted on a photovoltaic panel (1), and the collision block (51) being provided with a pair of mutually symmetrical flow guide ports (52), the inner wall of the flow guide port (52) being fixedly connected to a blocking frame, the blocking frame being fixedly connected to an abutment rod, a pair of mutually symmetrical transfer pipes (53) being mounted at one end of the collision block (51), the transfer pipes (53) being connected to the flow guide port (52), and the flow guide port (52) being connected to an adjacent collection box (31).

3. The cluster optimization control method for low-voltage distributed photovoltaic access to the distribution network according to claim 2 is characterized by: One end of the collection box (31) is fixedly connected to a conduit (35) in communication therewith, and the conduit (35) is arranged on the upper side of the recovery plate (41). One end of the collection box (31) is fixedly connected to a recovery frame (34), and a rotating shaft is installed in the recovery frame (34). A coil spring is installed outside the rotating shaft, and a pull rope is installed outside the rotating shaft. The pull rope is connected to one end of the recovery plate (41). The recovery plate (41) and the collision block (51) are respectively installed with a magnetic block and an electromagnet at one end close to each other.

4. The cluster optimization control method for low-voltage distributed photovoltaic access to the distribution network according to claim 2 is characterized in that: One end of the release block (45) is fixedly connected to an abutment block (46), the top of the photovoltaic panel (1) is fixedly connected to a friction pad, the friction pad abuts against the abutment block (46), the release block (45) is provided with a protection groove (44) and a cleaning groove, an antistatic agent is arranged in the protection groove (44), and the cleaning groove is connected to the inflow groove (42).

5. The cluster optimization control method for low-voltage distributed photovoltaic access to the distribution network according to claim 4 is characterized in that: The bottom end of the release block (45) is fixedly connected to a damper (47), and the bottom end of the damper (47) is fixedly connected to a cleaning block, the cleaning block is composed of a friction cleaning block (48), an absorption block (49) and a protection block (50), and an extrusion assembly is installed in both the protection groove (44) and the cleaning groove.

6. The cluster optimization control method for low-voltage distributed photovoltaic access to the distribution network according to claim 5 is characterized in that: The extrusion assembly comprises a pair of first support frames, the pair of first support frames are respectively installed in the protection groove (44) and the cleaning groove, a first limit strip is installed at the bottom end of the first support frame, a first sealing ball is fixedly connected to the bottom end of the first limit strip, the pair of first sealing balls are respectively abutted against the inner wall of the protection groove (44) and the cleaning groove, and a shunt block is installed at the bottom end of the pair of first sealing balls, and the pair of shunt blocks are respectively connected to the friction cleaning block (48) and the protection block (50).

7. The cluster optimization control method for low-voltage distributed photovoltaic access to the distribution network according to claim 2 is characterized by: A second support frame is fixedly connected to the inner wall of the guide pipe (43); one end of the second support frame is fixedly connected to a second limit strip; one end of the second limit strip is fixedly connected to a second sealing ball; the second sealing ball abuts against the inner wall of the guide pipe (43).

8. The cluster optimization control method for low-voltage distributed photovoltaic access to the distribution network according to claim 1 is characterized by: The real-time operation data collected in S2 includes: Power generation: measured by current and voltage sensors; Load demand: measured by energy metering equipment connected to the grid; Environmental conditions: measured by environmental sensors, including temperature, light intensity, wind speed, and humidity data; Equipment operating status: Determined by performance monitoring sensors integrated with the inverter and battery management system (BMS).

9. The cluster optimization control method for low-voltage distributed photovoltaic access to the distribution network according to claim 1 is characterized by: In S4, the statistical and machine learning algorithms used by the prediction engine of the cluster optimization control system include: Time series analysis: used to process and predict time-based generation and grid load demand, taking into account trends and cyclical changes in historical data; Regression analysis: used to predict the relationship between power generation and environmental factors, and their impact on grid load; Neural networks: used for complex pattern recognition and prediction problems, learning and modeling nonlinear relationships based on large historical data sets; Support Vector Machine (SVM): used for classification and regression analysis to optimize the decision-making process of regulatory strategies.

10. Cluster optimization control system for low-voltage distributed photovoltaic access to distribution network, characterized by: The cluster optimization control system comprises: Automatic cleaning module: multiple cleaning components (2) deployed on each low-voltage distributed photovoltaic panel (1) to regularly remove dust and other contaminants on the photovoltaic panel; Data collection module: smart sensors and data collectors installed on the photovoltaic panels (1), responsible for collecting key performance indicators; Data processing and analysis module: using data analysis technology to process the collected data and evaluate the specific impact of each photovoltaic panel (1) on the distribution network; Control and regulation module: automatically adjusts the output of each photovoltaic system based on data processing and prediction results; User interface module: provides real-time feedback and control suggestions, allowing users to manually adjust control strategies or set priorities based on actual conditions.

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