External filtering device of photovoltaic inverter

Abstract

The invention relates to the technical field of operation and maintenance of photovoltaic inverters, and provides an external filtering device of a photovoltaic inverter, which realizes real-time monitoring and data analysis of the external filtering device by integrating an industrial camera, a sensor and an intelligent data processing module, and detects the blockage condition of an air duct by using a preset image analysis model. And a fault source is intelligently judged in combination with operation data of the cooling fan. When actual data is inconsistent with a prediction model, fault information is automatically generated, rapid positioning and repairing are facilitated, control parameters are optimized through a genetic algorithm, efficient operation of an external filtering device is ensured, and the stability and the energy utilization rate of a photovoltaic inverter are improved. Operation and maintenance cost is effectively reduced, long-term reliable operation and sustainable development of a photovoltaic system are promoted, and an innovative solution is provided for technical progress and intelligent upgrading of the photovoltaic industry.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of photovoltaic inverter operation and maintenance, and particularly relates to an external filtering device of a photovoltaic inverter. BACKGROUND

[0002] The photovoltaic inverter is also called a solar inverter or a power regulator. The photovoltaic inverter can convert a variable direct-current voltage generated by a photovoltaic solar panel into a commercial power frequency alternating current (AC) so as to be fed back to a commercial power transmission system or used for off-grid power grid. The photovoltaic inverter not only has a direct-current alternating-current conversion function, but also has a maximum power point tracking function, an islanding effect protection function, an automatic operation function and a shutdown function.

[0003] When the inverter is running, a large amount of heat is generated by internal power modules and other components. The inverter heat dissipation system circulates air to dissipate heat from the power modules and other components inside the inverter, so as to ensure the normal working temperature of each component of the inverter and ensure the service life thereof. Since the inverter is mostly used outdoors, the outdoor environment is relatively harsh. The heat dissipation air duct and the filter screen of the inverter heat dissipation system are often blocked after a long time of work, thereby affecting the heat dissipation effect. Since the photovoltaic inverters are distributed in a wide range, real-time fault information of the external filtering device of the photovoltaic inverter needs to be obtained, so as to timely maintain the external filtering device of the photovoltaic inverter. The existing external filtering device of the photovoltaic inverter cannot provide real-time fault warning information of the external filtering device of the photovoltaic inverter. To solve this technical problem, the present application provides an external filtering device of a photovoltaic inverter. SUMMARY

[0004] In view of the deficiencies in the prior art, the present application provides an external filtering device of a photovoltaic inverter, which solves the problem that the cooling fan of the external filtering device of the photovoltaic inverter fails and the air duct is blocked during operation, thereby affecting the heat dissipation effect of the photovoltaic inverter of the external filtering device of the photovoltaic inverter, and the existing external filtering device of the photovoltaic inverter cannot provide real-time fault warning information of the external filtering device of the photovoltaic inverter.

[0005] The present application provides an external filtering device of a photovoltaic inverter, which comprises an external filtering device main body, a heat dissipation fan, a sensor, an industrial camera and a control device. The heat dissipation fan, the sensor, the industrial camera and the control device are installed in the filtering device main body. The sensor and the industrial camera transmit collected data to the control device. The control device is used to control the start of the heat dissipation fan. The control device comprises:

[0006] The data acquisition module is configured to acquire basic data, including inverter operation parameters, cooling fan state data, air duct state data, environmental parameters, photovoltaic inverter historical data, and external filtering device historical data, pre-process the basic data, train a random forest model using the pre-processed basic data, and obtain a photovoltaic inverter external filtering device simulation model.

[0007] The data acquisition module is configured to acquire basic data, including inverter operation parameters, cooling fan state data, air duct state data, environmental parameters, photovoltaic inverter historical data, and external filtering device historical data, pre-process the basic data, train a random forest model using the pre-processed basic data, and obtain a photovoltaic inverter external filtering device simulation model.

[0008] The data acquisition module is configured to acquire basic data, including inverter operation parameters, cooling fan state data, air duct state data, environmental parameters, photovoltaic inverter historical data, and external filtering device historical data, pre-process the basic data, train a random forest model using the pre-processed basic data, and obtain a photovoltaic inverter external filtering device simulation model.

[0009] The data acquisition module is configured to acquire basic data, including inverter operation parameters, cooling fan state data, air duct state data, environmental parameters, photovoltaic inverter historical data, and external filtering device historical data, pre-process the basic data, train a random forest model using the pre-processed basic data, and obtain a photovoltaic inverter external filtering device simulation model.

[0010] The data acquisition module is configured to acquire basic data, including inverter operation parameters, cooling fan state data, air duct state data, environmental parameters, photovoltaic inverter historical data, and external filtering device historical data, pre-process the basic data, train a random forest model using the pre-processed basic data, and obtain a photovoltaic inverter external filtering device simulation model.

[0011] Further, the photovoltaic inverter external filtering device data processing module further comprises:

[0012] The image data of the external filtering device is collected using an industrial camera to obtain internal image data of the external filtering device, the internal image data of the external filtering device is substituted into a preset external filtering device image analysis model to obtain an external filtering device internal image analysis result, and internal calibration region image dynamic change data of the external filtering device is obtained.

[0013] Further, the photovoltaic inverter external filtering device data processing module further comprises:

[0014] If the internal calibration region image dynamic change data of the external filtering device exists wind channel blockage data, the operation data of the heat dissipation fan of the external filtering device is called, the operation data of the heat dissipation fan of the external filtering device is detected, if the operation data of the heat dissipation fan of the external filtering device is normal, channel cleaning fault information is generated.

[0015] Further, the photovoltaic inverter external filtering device data processing module further comprises:

[0016] If the internal calibration region image dynamic change data of the external filtering device exists wind channel blockage data, the operation data of the heat dissipation fan of the external filtering device is called, the operation data of the heat dissipation fan of the external filtering device is detected, if the operation data of the heat dissipation fan of the external filtering device is abnormal, heat dissipation fan and channel cleaning fault information are generated.

[0017] Further, the photovoltaic inverter external filtering device detection optimization module is further used for:

[0018] The genetic algorithm is initialized, and basic parameters of the genetic algorithm are determined, such as population size, genetic generation number, crossover probability, and mutation probability.

[0019] The population is initialized, that is, a group of initial external filtering device control model parameters are generated as the starting point of the genetic algorithm; encoding and decoding:

[0020] The parameters of the external filtering device control model are encoded into the genotype of the genetic algorithm using binary encoding;

[0021] The fitness function is received and set, and the fitness function is used to evaluate the advantages and disadvantages of each individual, that is, a group of external filtering device control model parameters;

[0022] The selection, crossover, and mutation operations are repeatedly executed until the preset genetic generation number stopping condition is reached.

[0023] Decode the optimal individual in the final population obtained by the genetic algorithm to obtain the optimized external filtering device control model parameter.

[0024] Further, the photovoltaic inverter external filtering device of the application, the detection optimization module is also used for:

[0025] Receive the data feedback after the external filtering device control parameters corresponding to the photovoltaic inverter task executed by the heat dissipation fan, the sensor and the industrial camera are received, obtain the to-be-verified data, compare the to-be-verified data with the photovoltaic inverter external filtering prediction data, if the to-be-verified data is consistent with the photovoltaic inverter external filtering prediction data, the control parameter optimization is successful, if the to-be-verified data is inconsistent with the photovoltaic inverter external filtering prediction data, generate external filtering device control parameter fault information.

[0026] The beneficial effects of the application are as follows:

[0027] The application can accurately grasp the actual working state of the external filtering device by real-time collection and analysis of image data, operation data of the heat dissipation fan and other related sensor data. Combined with advanced prediction model and genetic algorithm optimization technology, the application can dynamically adjust and optimize the control parameters, so that the external filtering device always runs in the best state, thereby significantly improving the overall operation efficiency and stability of the photovoltaic inverter.

[0028] The intelligent detection optimization module built-in the application can automatically compare the actual operation data with the prediction data, and when an abnormality or deviation is found, immediately generate fault information and notify the operation and maintenance personnel. The intelligent fault detection and early warning mechanism can shorten the time of fault discovery and processing, reduce the energy waste and economic loss caused by faults, and improve the reliability and availability of the photovoltaic system. Through intelligent data analysis and fault warning, the application enables the operation and maintenance personnel to more targetedly carry out maintenance and repair work, avoiding unnecessary inspection and maintenance, thereby reducing the operation and maintenance cost. At the same time, the optimized control parameters can reduce the energy consumption and wear of the external filtering device, prolong its service life, and further reduce the long-term operation cost.

[0029] The efficient operation of the external filtering device is crucial for the heat dissipation performance of the photovoltaic inverter. The application optimizes the control parameters to ensure that the external filtering device can more effectively remove impurities and dust in the air, maintain the cleanliness and heat dissipation performance of the photovoltaic inverter, thereby improving the energy utilization rate and power generation efficiency of the photovoltaic system.

[0030] The implementation of the present application not only improves the technical level of the external filtering device of the photovoltaic inverter, but also provides strong support for the intelligentization, automation and sustainable development of the photovoltaic industry. By improving the operation efficiency and reliability of the photovoltaic system, the present application helps to reduce the cost of photovoltaic power generation, promotes the wide application and popularization of photovoltaic energy, and contributes to the construction of a green, low-carbon and sustainable energy system.

[0031] In summary, the external filtering device of the photovoltaic inverter and the detection optimization module thereof described in the present application exhibit significant beneficial effects in improving operation efficiency, reducing operation and maintenance costs, improving energy utilization rate, and promoting sustainable development of the photovoltaic industry, and have broad application prospects and important popularization value. BRIEF DESCRIPTION OF DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. Obviously, for those skilled in the art, other drawings can also be obtained without creative labor based on the drawings.

[0033] Figure 1 The functional module schematic diagram of the control device in the external filtering device of the photovoltaic inverter provided by the embodiments of the present application. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the present application more clear, the following will combine the specific embodiments of the present application and the corresponding drawings to clearly and completely describe the technical solutions of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application. The following will combine the drawings to specifically describe the technical solutions provided by the embodiments of the present application.

[0035] In order to better understand the purpose of the present application, the present application will be further described in detail as follows.

[0036] The present application provides an external filtering device of a photovoltaic inverter, comprising: an external filtering device main body, a cooling fan, a sensor, an industrial camera and a control device, the cooling fan, the sensor, the industrial camera and the control device are installed in the filtering device main body, the sensor and the industrial camera transmit the collected data to the control device, and the control device is used for controlling the start of the cooling fan, and the control device comprises:

[0037] The data acquisition module is used for acquiring basic data, the basic data including inverter operation parameters, cooling fan state data, air duct state data, and environmental parameters, photovoltaic inverter historical data, and external filtering device historical data, preprocessing the basic data, training a random forest model using the preprocessed basic data, and obtaining a photovoltaic inverter external filtering device simulation model.

[0038] The data acquisition module in the application is used for collecting a series of basic data, including inverter operation parameters, cooling fan state data, air duct state data, environmental parameters, photovoltaic inverter historical data, and external filtering device historical data.

[0039] Firstly, the inverter operation parameters are the key to understanding the current working state of the inverter, which reflects the output power, voltage, and current indicators of the inverter. The cooling fan state data reveals the speed and working state of the fan, which is crucial for determining whether the fan is running normally. The air duct state data provides information on whether the air duct is unobstructed or blocked. Environmental parameters such as temperature and humidity also have an important impact on the operating efficiency and heat dissipation effect of the inverter.

[0040] In addition to real-time data, photovoltaic inverter historical data and external filtering device historical data record past operation and fault records, which help to identify potential problems and trends.

[0041] After collecting the data, the data acquisition module will preprocess it, including data cleaning, format conversion, missing value filling, and other steps to ensure the accuracy and consistency of the data.

[0042] Next, the preprocessed basic data will be used to train a random forest model. Random forest is an ensemble learning method that improves the accuracy and robustness of predictions by constructing multiple decision trees and combining their results. In the application, the random forest model is used to simulate the operating state of the photovoltaic inverter external filtering device, thereby realizing the prediction and fault warning of the future state.

[0043] Through training, the random forest model can learn the rules and patterns in the data and generate a photovoltaic inverter external filtering device simulation model. This model can simulate the operating state of the device under different conditions, providing strong support for subsequent real-time monitoring and fault warning.

[0044] In summary, the data acquisition module collects and preprocesses basic data, and uses these data to train a random forest model, providing a technical means for fault warning and state monitoring of the photovoltaic inverter external filtering device.

[0045] The data acquisition module is configured to receive photovoltaic inverter tasks, analyze the photovoltaic inverter tasks, obtain photovoltaic inverter task monitoring data, and collect data according to the type of photovoltaic inverter task monitoring data to obtain photovoltaic inverter real-time monitoring data.

[0046] The data acquisition module in the external filtering device of the photovoltaic inverter receives photovoltaic inverter tasks, analyzes these tasks, determines the type of data to be monitored, and then collects real-time data according to these data types to obtain real-time monitoring data of the photovoltaic inverter.

[0047] Specifically, when the photovoltaic inverter has a running or monitoring requirement, the data acquisition module will first receive these tasks. Then, the data acquisition module will analyze the tasks in detail, extract the data items that need to be focused on and monitored from the tasks, and the monitored data items are usually related to the running state, performance parameters and fault modes of the photovoltaic inverter.

[0048] After determining the type of data to be monitored, the data acquisition module will start the corresponding data collection process. The data collection process includes reading data from sensors, obtaining data from inverter control systems, or collecting information from other related data sources. The data collection process is real-time, and the data acquisition module can continuously obtain the latest data to ensure accurate and timely monitoring of the photovoltaic inverter state.

[0049] Finally, the data acquisition module outputs the real-time monitoring data of the photovoltaic inverter. Real-time monitoring data is an important basis for subsequent analysis and decision-making, which can be used to determine whether the inverter is running normally, whether there is a potential risk of failure, and what measures need to be taken to ensure the safe, stable and efficient operation of the inverter.

[0050] In summary, the data acquisition module provides comprehensive, accurate and timely real-time monitoring data for the external filtering device of the photovoltaic inverter through the steps of receiving tasks, analyzing tasks, determining monitoring data types, and collecting real-time data.

[0051] The data comparison module is configured to match the photovoltaic inverter task with the preset external control parameter knowledge base, obtain the photovoltaic inverter task corresponding external filtering device control parameter, substitute the photovoltaic inverter task corresponding external filtering device control parameter into the photovoltaic inverter external filtering device simulation model, obtain the photovoltaic inverter external filtering prediction data, classify the photovoltaic inverter external filtering prediction data and the photovoltaic inverter real-time monitoring data obtained by data acquisition according to the preset time segmentation, and obtain the photovoltaic inverter external filtering segmented prediction data and real-time monitoring segmented data.

[0052] The data comparison module is responsible for matching the photovoltaic inverter task with the preset external control parameter knowledge base in the technical scheme of the present application, so as to obtain the corresponding external filtering device control parameters. This process ensures that the external filtering device can be accurately regulated according to the actual needs of the photovoltaic inverter.

[0053] The data comparison module receives the task request from the photovoltaic inverter, which contains the current working state and the required monitoring data of the photovoltaic inverter. Then, the module searches and matches in the preset external control parameter knowledge base to find the external filtering device control parameters corresponding to the current photovoltaic inverter task. These control parameters are obtained through a large number of experiments and data training, which can ensure that the external filtering device can achieve the best filtering and heat dissipation effect under various working conditions.

[0054] After obtaining the corresponding external filtering device control parameters, the data comparison module substitutes these parameters into the previously trained photovoltaic inverter external filtering device simulation model. This simulation model can predict the running state and performance of the external filtering device in the future period of time based on the current photovoltaic inverter task and external filtering device control parameters, including filtering efficiency and heat dissipation effect indicators.

[0055] At the same time, the data comparison module also compares the photovoltaic inverter external filtering prediction data with the photovoltaic inverter monitoring data collected in real time by the data acquisition module. In order to ensure the accuracy and effectiveness of the comparison, the module will classify the prediction data and real-time monitoring data according to the same time period according to the preset time segmentation rule, to obtain photovoltaic inverter external filtering segmented prediction data and real-time monitoring segmented data.

[0056] Through the comparison and analysis of these segmented data, the data comparison module can timely find out the problems and hidden dangers existing in the running process of the photovoltaic inverter external filtering device. For example, if it is found that there is a large deviation between the prediction data and the real-time monitoring data in a certain period of time, the data comparison module will trigger the corresponding alarm mechanism to prompt the operation and maintenance personnel to check and maintain, so as to ensure that the photovoltaic inverter external filtering device can run continuously, stably and efficiently.

[0057] The data processing module is used to substitute the photovoltaic inverter task into the photovoltaic inverter external filtering device simulation model to obtain photovoltaic inverter prediction data, compare the photovoltaic inverter prediction data with the photovoltaic inverter real-time monitoring data, and obtain photovoltaic inverter data comparison results. The photovoltaic inverter data comparison results include photovoltaic inverter data comparison consistency and photovoltaic inverter data comparison inconsistency. If the photovoltaic inverter data comparison is inconsistent, the real-time monitoring segmented data corresponding to the inconsistency is retrieved from the database, and the real-time external filtering device control parameters of the real-time monitoring segmented data are retrieved.

[0058] The data processing module functions in the external filter device of the photovoltaic inverter by substituting the task of the photovoltaic inverter into the simulation model of the external filter device of the photovoltaic inverter, thereby obtaining the predicted data of the photovoltaic inverter. The predicted data of the photovoltaic inverter is then compared with the real-time monitoring data of the photovoltaic inverter to evaluate the performance and operating state of the external filter device.

[0059] Firstly, the data processing module receives the task information from the photovoltaic inverter, which includes the operating state, load condition, and environmental temperature parameters of the photovoltaic inverter. Then, the module substitutes these parameters into the pre-trained simulation model of the external filter device of the photovoltaic inverter. Based on a large amount of historical and experimental data, this model can simulate the operating state and performance of the external filter device of the photovoltaic inverter under different working conditions.

[0060] Through the calculation of the simulation model, the data processing module obtains the predicted data of the photovoltaic inverter, including current, voltage, and power parameters. Then, the module compares these predicted data with the real-time monitoring data of the photovoltaic inverter to check the differences between them.

[0061] The comparison results of the photovoltaic inverter data mainly include two cases: consistent data comparison and inconsistent data comparison. If the predicted data is similar to the real-time monitoring data, it means that the operating state of the external filter device is good, and it can accurately work according to the preset control strategy. At this time, the system will continue to run without additional intervention.

[0062] However, if there is a large difference between the predicted data and the real-time monitoring data, i.e., inconsistent data comparison, the data processing module will immediately trigger the alarm mechanism. The module will search for the real-time monitoring segment data corresponding to the inconsistent comparison period in the database and retrieve the control parameters of the real-time external filter device in these time periods. Through detailed analysis of these data, the operation and maintenance personnel can locate the root cause of the problem and take appropriate measures to repair it.

[0063] This data processing method not only improves the stability of the external filter device of the photovoltaic inverter, but also enables the operation and maintenance personnel to timely discover and solve problems, thereby avoiding energy waste and economic losses caused by faults. At the same time, this module also provides strong support for the intelligent operation and maintenance of the external filter device of the photovoltaic inverter.

[0064] The detection optimization module is configured to retrieve real-time external filtering device control parameters corresponding to external filtering device standard control parameters from the external filtering device knowledge base based on the time information, compare the real-time external filtering device control parameters of the real-time monitoring segmented data with the real-time external filtering device control parameters corresponding to the external filtering device standard control parameters, obtain external filtering device control parameter error data, optimize the preset external filtering device control model using the external filtering device control parameter error data and the external filtering device control parameters, obtain an optimized external filtering device control model, substitute the external filtering device control parameters corresponding to the photovoltaic inverter task into the external filtering device control model for execution, obtain corrected external filtering device control parameters corresponding to the photovoltaic inverter task, and send the corrected external filtering device control parameters corresponding to the photovoltaic inverter task to the cooling fan, the sensor, and the industrial camera.

[0065] The detection optimization module functions in the photovoltaic inverter external filtering device to compare real-time monitoring data with standard control parameters, optimize the control model of the external filtering device through a genetic algorithm, and thus achieve precise control of the cooling fan, the sensor, and the industrial camera.

[0066] First, the detection optimization module retrieves standard control parameters corresponding to real-time monitoring data from the external filtering device knowledge base based on time information. These standard control parameters are based on a large amount of experimental data and operational experience, ensuring that the external filtering device operates in an optimal state.

[0067] Next, the detection optimization module compares real-time external filtering device control parameters of real-time monitoring segmented data with standard control parameters, calculating error data of the control parameters. These error data reflect the gap between the current operational state of the external filtering device and the ideal state.

[0068] To narrow this gap and improve the operational efficiency of the external filtering device, the detection optimization module uses a genetic algorithm to optimize the preset external filtering device control model. Genetic algorithms are search algorithms that simulate natural selection and genetic mechanisms, enabling them to find optimal solutions in complex search spaces.

[0069] During optimization, the module generates a set of initial control model parameters based on error data and current external filtering device control parameters as the starting point for the genetic algorithm. Then, through encoding and decoding operations, these parameters are converted into the genotype of the genetic algorithm.

[0070] Next, the detection optimization module sets a fitness function to evaluate the quality of each individual (i.e., a set of control model parameters). The fitness function is usually designed based on error data and the stability of control parameters and other factors.

[0071] Subsequently, the detection optimization module repeatedly performs selection, crossover and mutation operations to constantly screen out better individuals by simulating natural selection and genetic processes. This process will continue until a preset genetic generation stopping condition is reached.

[0072] Finally, the detection optimization module decodes the optimal individual in the final population obtained by the genetic algorithm to obtain the optimized external filter device control model parameters. These parameters can more accurately reflect the actual operating state of the external filter device and achieve precise control of the cooling fan, sensor and industrial camera.

[0073] Finally, the detection optimization module sends the corrected photovoltaic inverter task corresponding external filter device control parameters to the cooling fan, sensor and industrial camera execution mechanism to ensure that they can work according to the optimal control strategy.

[0074] Through this detection optimization mechanism, the application not only improves the reliability and stability of the photovoltaic inverter external filter device, but also realizes real-time adjustment and optimization of the control parameters, thereby further improving the operating efficiency and energy utilization rate of the entire photovoltaic system.

[0075] Specifically, the photovoltaic inverter external filter device of the application, the data processing module further comprises:

[0076] The industrial camera is used to collect image data of the external filter device to obtain internal image data of the external filter device. The internal image data of the external filter device is substituted into a preset external filter device image analysis model to obtain internal image analysis results of the external filter device, and to obtain calibration region image dynamic change data inside the external filter device.

[0077] Specifically, the data processing module controls the industrial camera to perform real-time image acquisition of the external filter device to obtain internal image data of the external filter device. The image data includes the working state of the filter device, the cleanliness of the filter screen, and whether there is foreign matter blocking information.

[0078] After obtaining the image data, the data processing module substitutes the data into a preset external filter device image analysis model. This model is constructed based on machine learning and image processing technology and can automatically identify and analyze key features in the image to obtain analysis results of the internal image of the external filter device.

[0079] Through the analysis results, the data processing module can further extract image dynamic change data of the calibration region inside the external filter device. These data reflect the changes in the internal state of the filter device during operation, such as the wear degree of the filter screen and the change in the blocking condition.

[0080] The image dynamic change data is of great significance for evaluating the performance of the external filtering device and predicting its service life. They can provide intuitive visual information for the operation and maintenance personnel, help them discover and handle problems in the filtering device in time, and ensure that the photovoltaic inverter can continue to operate stably.

[0081] Meanwhile, these image data and analysis results can also serve as an important part of the intelligent operation and maintenance system of the external filtering device of the photovoltaic inverter, providing strong data support for the decision-making and optimization of the system. By combining other sensor data and historical operation data, the system can more comprehensively understand the operation state of the external filtering device, and realize more accurate control and management.

[0082] Specifically, the data processing module of the external filtering device of the photovoltaic inverter further comprises:

[0083] If there is wind channel blockage data in the image dynamic change data of the calibration area in the external filtering device, the running data of the cooling fan of the external filtering device is called, the running data of the cooling fan of the external filtering device is detected, and if the running data of the cooling fan of the external filtering device is normal, a channel to be cleaned fault information is generated.

[0084] Specifically, when the data processing module analyzes the image dynamic change data of the calibration area in the external filtering device and finds that there is a wind channel blockage, it will immediately start a series of subsequent processing procedures. First, the module will actively call the running data of the cooling fan of the external filtering device. These running data include but are not limited to the speed, working time, current and voltage indicators of the fan, which can comprehensively reflect the working state of the cooling fan.

[0085] Next, the data processing module will conduct detailed detection and analysis on these running data of the cooling fan. By comparing the normal working parameters of the fan with the current actual running parameters, the module can quickly judge whether the cooling fan has any abnormality or fault.

[0086] If it is found through detection that the running data of the cooling fan is normal and there is no fault or abnormality, the data processing module will further deduce that the wind channel blockage is caused by excessive dust accumulation and serious blockage of the filter screen or other components inside the external filtering device. At this time, the module will generate a "channel to be cleaned" fault information, and timely notify the operation and maintenance personnel through the system interface or alarm mechanism.

[0087] After receiving the fault information, the operation and maintenance personnel can quickly locate the problem and take appropriate cleaning measures, such as replacing the filter screen, cleaning the air duct, etc., to ensure that the external filtering device can resume normal operation and avoid adversely affecting the heat dissipation performance of the photovoltaic inverter.

[0088] Through the intelligent fault detection and alarm mechanism, the photovoltaic inverter external filtering device provided by the application not only improves the operation and maintenance efficiency, but also reduces the energy waste and economic loss caused by faults, and provides a strong guarantee for the stable and efficient operation of the photovoltaic system.

[0089] Specifically, the photovoltaic inverter external filtering device provided by the application, the data processing module further comprises:

[0090] If the wind channel blocking data exists in the image dynamic change data of the calibration area in the external filtering device, the operation data of the cooling fan of the external filtering device is called, the operation data of the cooling fan of the external filtering device is detected, and if the operation data of the cooling fan of the external filtering device is abnormal, the cooling fan and the channel to be cleaned fault information is generated.

[0091] When the data processing module detects that the wind channel is blocked by analyzing the image dynamic change data of the calibration area in the external filtering device, it will take action quickly. First, the module will call the operation data of the cooling fan in the external filtering device, which is an important basis for evaluating the working state of the fan, including but not limited to the speed, working current, voltage and running time of the fan.

[0092] The data processing module will comprehensively and carefully detect these operation data. By comparing the normal working parameters of the fan with the current actual operation parameters, the module can sensitively capture any abnormality or deviation. This comparison analysis helps to accurately judge whether the cooling fan is in normal working state.

[0093] If the detection result shows that the operation data of the cooling fan is abnormal, the data processing module will immediately generate two fault information: one is the fault information about the cooling fan itself, indicating that the fan has a problem and needs to be repaired or replaced; the other is the fault information about the channel to be cleaned, prompting that the wind channel is blocked and needs to be cleaned to ensure smooth air flow.

[0094] The two fault information will be sent to the operation and maintenance personnel in time by the data processing module through the system interface, SMS, email or other alarm methods, so that the operation and maintenance personnel can quickly know and respond. After receiving the fault information, the operation and maintenance personnel can repair and clean the external filtering device according to the information content, so as to quickly restore its normal working state.

[0095] The intelligent fault detection and alarm mechanism not only greatly improves the operation and maintenance efficiency, reduces the energy waste and economic loss caused by faults, but also provides a solid guarantee for the long-term stable operation of the photovoltaic system. Through the photovoltaic inverter external filtering device and its data processing module provided by the application, more fine and efficient management and maintenance of the photovoltaic system can be realized.

[0096] Specifically, the photovoltaic inverter external filtering device detection optimization module is further used for:

[0097] Initializing the genetic algorithm, determining the basic parameters of the genetic algorithm, such as population size, genetic generation number, crossover probability, and mutation probability;

[0098] Initializing the population, that is, generating a set of initial external filtering device control model parameters as the starting point of the genetic algorithm; encoding and decoding:

[0099] Encoding the parameters of the external filtering device control model into the genotype of the genetic algorithm using binary encoding;

[0100] Receiving and setting the fitness function, which is used to evaluate the advantages and disadvantages of each individual, that is, a set of external filtering device control model parameters;

[0101] Repeating the selection, crossover, and mutation operations until the preset genetic generation number stopping condition is reached;

[0102] Decoding the optimal individual in the final population obtained by the genetic algorithm to obtain the optimized external filtering device control model parameters.

[0103] Using the genetic algorithm to optimize the preset external filtering device control model with the external filtering device control parameter error data and the external filtering device control parameters, the specific steps of obtaining the optimized external filtering device control model are as follows:

[0104] Initializing the genetic algorithm:

[0105] Determining the basic parameters of the genetic algorithm, such as population size, genetic generation number, crossover probability, and mutation probability.

[0106] Initializing the population, that is, generating a set of initial external filtering device control model parameters as the starting point of the genetic algorithm. These parameters can be random or based on prior knowledge.

[0107] Encoding and decoding:

[0108] Encoding the parameters of the external filtering device control model into the genotype of the genetic algorithm, usually using binary encoding or real number encoding.

[0109] Correspondingly, a decoding function also needs to be defined to decode the genotype back to the parameters of the control model.

[0110] Fitness function design:

[0111] Designing a fitness function, which is used to evaluate the advantages and disadvantages of each individual (that is, a set of control model parameters).

[0112] In this example, the fitness function can be calculated based on the error data of the external filter control parameters. Specifically, the error between the control model parameters corresponding to each individual and the actual control parameters can be calculated, and the inverse of the error (or some transformed form of the error) is taken as the fitness value. The smaller the error, the larger the fitness value, indicating that the individual is better.

[0113] Selection operation:

[0114] Evaluate the fitness value of each individual according to the fitness function, and select the excellent individuals as parents for subsequent crossover and mutation operations according to a certain selection strategy (such as roulette selection, tournament selection, etc.).

[0115] Crossover operation:

[0116] The selected parent individuals are subjected to crossover operation, i.e. exchanging part of the genes of the parent individuals in a certain way (such as single-point crossover, double-point crossover, etc.) to generate new offspring individuals.

[0117] Crossover operation helps to spread excellent genes in the population and promote the evolution of the population.

[0118] Mutation operation:

[0119] The offspring individuals are subjected to mutation operation, i.e. randomly changing part of the gene values of the offspring individuals according to a certain probability.

[0120] Mutation operation helps to increase the diversity of the population and avoid the algorithm falling into local optimal solution.

[0121] Update population and iteration:

[0122] Replace part or all of the parent individuals with the generated offspring individuals to form a new population.

[0123] Repeat the selection, crossover and mutation operations until the preset number of generations of genetic algorithm is reached or other stopping conditions are met.

[0124] Decoding and output:

[0125] Decode the optimal individual in the final population obtained by the genetic algorithm to obtain the optimized external filter control model parameters.

[0126] Substitute these parameters into the external filter control model to obtain the optimized control model.

[0127] Verification and optimization results:

[0128] Use the optimized control model to predict and correct the external control parameters of the photovoltaic inverter task.

[0129] The effect of the optimized control model, such as control accuracy and response speed, is verified through actual operation or simulation.

[0130] If the effect is not satisfactory, the parameters or fitness function of the genetic algorithm can be adjusted, and the above optimization process is repeated.

[0131] Through the above steps, the genetic algorithm can be used to optimize the control model of the external filtering device, and more accurate and efficient control model parameters can be obtained. This will help improve the control performance of the external filtering device of the photovoltaic inverter.

[0132] Specifically, the photovoltaic inverter external filtering device detection optimization module is also used for:

[0133] Receiving data feedback from the heat dissipation fan, sensor and industrial camera after executing the modified photovoltaic inverter task corresponding external filtering device control parameters, obtaining the to-be-verified data, comparing the to-be-verified data with the photovoltaic inverter external filtering prediction data, if the to-be-verified data is consistent with the photovoltaic inverter external filtering prediction data, the control parameter optimization is successful, if the to-be-verified data is inconsistent with the photovoltaic inverter external filtering prediction data, generating external filtering device control parameter fault information.

[0134] In the photovoltaic inverter external filtering device, the detection optimization module undertakes the important task of ensuring accurate and continuous optimization of control parameters, not only responsible for sending the modified control parameters to the heat dissipation fan, sensor and industrial camera and other execution mechanisms, but also responsible for receiving the feedback data from these execution mechanisms and performing strict verification and optimization.

[0135] Specifically, after the detection optimization module sends the modified photovoltaic inverter task corresponding external filtering device control parameters, it waits for and receives feedback data from the heat dissipation fan, sensor and industrial camera and other execution mechanisms. These data are referred to as to-be-verified data. The to-be-verified data contains the actual running state and performance of the execution mechanism after receiving the new control parameters, and is the key basis for verifying whether the control parameters are accurate and effective.

[0136] The detection optimization module compares the to-be-verified data with the photovoltaic inverter external filtering prediction data obtained by the simulation model. This comparison process aims to check whether the actual running effect is consistent with the expectation, so as to evaluate the optimization effect of the control parameters.

[0137] If the to-be-verified data is consistent with the photovoltaic inverter external filtering prediction data, it means that the modified control parameters have successfully improved the running state of the external filtering device and achieved the expected optimization effect. At this time, the detection optimization module will record this successful case and further fine-tune the control parameters according to the actual situation to pursue better performance.

[0138] However, if the to-be-verified data is inconsistent with the photovoltaic inverter external filtering prediction data, the detection optimization module will immediately generate external filtering device control parameter fault information. This fault information will record the inconsistency in detail, including which parameters deviate, how much the deviation is, etc., so that the operation and maintenance personnel can quickly locate the problem and take corresponding measures to repair.

[0139] Through the verification and optimization mechanism, the photovoltaic inverter external filtering device of the present application can ensure the accuracy and effectiveness of the control parameters, continuously improve the operation efficiency and stability of the external filtering device, and thus provide strong support for the long-term stable operation of the photovoltaic system. At the same time, this mechanism also provides a convenient way for the operation and maintenance personnel to diagnose and solve problems, reducing the difficulty and cost of operation and maintenance.

[0140] The technical scheme of the present application solves the problems of the photovoltaic inverter external filtering device, such as the failure of the cooling fan, the blockage of the air duct, the influence on the heat dissipation effect of the photovoltaic inverter external filtering device, and the inability of the existing photovoltaic inverter external filtering device to provide real-time fault warning information of the photovoltaic inverter external filtering device, in the following ways:

[0141] The photovoltaic inverter external filtering device provided by the present application integrates the external filtering device main body, the cooling fan, the sensor, the industrial camera and the control device. These components work together to ensure the effective operation and monitoring of the device.

[0142] The data acquisition module in the control device is responsible for collecting basic data such as inverter operation parameters, cooling fan state data, air duct state data, environmental parameters, photovoltaic inverter historical data and external filtering device historical data. These data are preprocessed and used to train a random forest model to obtain a photovoltaic inverter external filtering device simulation model.

[0143] The data acquisition module receives the photovoltaic inverter task, parses the task and collects real-time monitoring data. The data comparison module matches the task in the preset external control parameter knowledge base to obtain the corresponding control parameters, and substitutes them into the simulation model to obtain the prediction data. The real-time monitoring data and the prediction data are classified according to the preset time segmentation for subsequent comparison.

[0144] The data processing module compares the prediction data and the real-time monitoring data. If the data is inconsistent, the inconsistent time period data and its real-time external filtering device control parameters are retrieved. Through further analysis, if the air duct is blocked and the cooling fan is running normally, a channel to be cleaned fault information is generated; if the cooling fan is running abnormally, a cooling fan and channel to be cleaned fault information is generated.

[0145] The detection optimization module retrieves standard control parameters in the external filter device knowledge base according to the time information, and compares them with the real-time monitored control parameters to obtain error data. The genetic algorithm is used to optimize the control model of the external filter device to obtain the optimized model parameters. These corrected parameters are sent to the cooling fan, sensor and industrial camera to ensure the normal operation of the device.

[0146] The genetic algorithm finds the optimal control model parameters of the external filter device by initializing the population, encoding and decoding, setting the fitness function, and repeatedly executing selection, crossover and mutation operations. This helps to improve the efficiency and reliability of the device. The control device receives feedback data after executing the corrected control parameters, and compares them with the predicted data. If the data are consistent, the control parameter optimization is successful. If the data are inconsistent, the external filter device control parameter fault information is generated to take timely measures.

[0147] In summary, the present application effectively solves the problems of cooling fan failure, air duct blockage and lack of real-time fault warning information of the external filter device of the photovoltaic inverter by integrating multiple functional modules, data acquisition and simulation, real-time monitoring and comparison, data processing and fault warning, detection optimization and parameter correction, and genetic algorithm optimization.

Claims

1. A photovoltaic inverter external filtering device, characterized in that, The utility model relates to an external filtering device for photovoltaic inverter, comprising: The utility model relates to an external filtering device for photovoltaic inverter, comprising: Data acquisition module is used to receive photovoltaic inverter task, and the photovoltaic inverter task is analyzed, and the photovoltaic inverter task data to be monitored are obtained, and the data acquisition is carried out according to the photovoltaic inverter task data type, and the photovoltaic inverter real -time monitoring data are obtained; Data comparison module is used to match photovoltaic inverter task in the preset external control parameter knowledge base, and the photovoltaic inverter task corresponding external filtering device control parameter is obtained, and the photovoltaic inverter task corresponding external filtering device control parameter is substituted into photovoltaic inverter external filtering device simulation model, and the photovoltaic inverter external filtering forecast data are obtained, and the photovoltaic inverter external filtering forecast data and the photovoltaic inverter real -time monitoring data obtained by data acquisition are classified according to the preset time segmentation, and the photovoltaic inverter external filtering segmented forecast data and real -time monitoring segmented data are obtained; Data processing module is used to substitute photovoltaic inverter task into photovoltaic inverter external filtering device simulation model, and the photovoltaic inverter forecast data are obtained, and the photovoltaic inverter forecast data are compared with the photovoltaic inverter real -time monitoring data, and the photovoltaic inverter data comparison result is obtained, and the photovoltaic inverter data comparison result includes photovoltaic inverter data comparison consistent and photovoltaic inverter data comparison inconsistency, and if the photovoltaic inverter data comparison inconsistency, then the real -time monitoring segmented data corresponding to the comparison inconsistency in the database is retrieved, and the real -time external filtering device control parameter of real -time monitoring segmented data is retrieved; Detection optimization module is used to retrieve real -time external filtering device control parameter corresponding external filtering device standard control parameter in external filtering device knowledge base according to time information, and the real -time external filtering device control parameter of real -time monitoring segmented data is compared with real -time external filtering device control parameter corresponding external filtering device standard control parameter, and the external filtering device control parameter error data are obtained, and the external filtering device control parameter error data and external filtering device control parameter are used Genetic algorithm is used to optimize the preset external filtering device control model, and the optimized external filtering device control model is obtained, and the photovoltaic inverter task corresponding external filtering device control parameter is substituted into external filtering device control model and is executed, and the corrected photovoltaic inverter task corresponding external filtering device control parameter is obtained, and the corrected photovoltaic inverter task corresponding external filtering device control parameter is sent to the cooling fan, sensor and industrial camera. ​ 2. The photovoltaic inverter off-filter device of claim 1, wherein, The data processing module further comprises: The image data of the external filtering device is collected by using the industrial camera, and the image data inside the external filtering device is obtained. The image data inside the external filtering device is substituted into the preset image analysis model of the external filtering device to obtain the image analysis result inside the external filtering device, and the calibration region image dynamic change data inside the external filtering device is obtained.

3. The photovoltaic inverter external filtering device of claim 2, wherein, The data processing module further comprises: If the calibration region image dynamic change data inside the external filtering device has air duct blockage data, the operation data of the cooling fan of the external filtering device is called, and the operation data of the cooling fan of the external filtering device is detected. If the operation data of the cooling fan of the external filtering device is normal, the channel to be cleaned fault information is generated.

4. The photovoltaic inverter external filtering device of claim 3, wherein, The data processing module further comprises: If the calibration region image dynamic change data inside the external filtering device has air duct blockage data, the operation data of the cooling fan of the external filtering device is called, and the operation data of the cooling fan of the external filtering device is detected. If the operation data of the cooling fan of the external filtering device is abnormal, the cooling fan and the channel to be cleaned fault information are generated.

5. The photovoltaic inverter external filtering device of claim 1, wherein, The detection optimization module is further used for: Initializing the genetic algorithm to determine the basic parameters of the genetic algorithm, such as population size, genetic generation number, crossover probability, and mutation probability; Initializing the population, that is, generating a set of initial external filtering device control model parameters as the starting point of the genetic algorithm; encoding and decoding: The parameters of the external filtering device control model are encoded into the genotype of the genetic algorithm using binary encoding; Receiving and setting the fitness function, which is used to evaluate the advantages and disadvantages of each individual, that is, a set of external filtering device control model parameters; Repeat the selection, crossover and mutation operations until the preset genetic generation number stopping condition is reached; Decode the optimal individual in the final population obtained by the genetic algorithm to obtain the optimized external filtering device control model parameters.

6. The photovoltaic inverter external filtering device of claim 5, wherein, The detection optimization module is further used for: Receiving the data feedback after the modified photovoltaic inverter task corresponding external filtering device control parameters are executed by the cooling fan, the sensor and the industrial camera, obtaining the to-be-verified data, comparing the to-be-verified data with the photovoltaic inverter external filtering prediction data, if the to-be-verified data is consistent with the photovoltaic inverter external filtering prediction data, the control parameter optimization is successful, if the to-be-verified data is inconsistent with the photovoltaic inverter external filtering prediction data, the external filtering device control parameter fault information is generated.

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