An adaptive learning system integrating IV detection and defect detection

Abstract

The present invention discloses an adaptive learning system that integrates IV detection and defect detection in the field of adaptive learning. S1 inputs a photovoltaic module design model step, inputs the designed photovoltaic module model and the environmental parameters of the photovoltaic module installation location into the system, and inputs initial parameters for the system algorithm. This solution adaptively learns the hyperparameters IoU and confidence in defect detection by combining small transmittance detection equipment, IV detection system and defect detection system with power data fed back by IV detection and transmittance detection technology systems and the qualified power conditions of products of this model. The intelligent adjustment model enables the model to adapt to a variety of different personalized customizations, and can make distinctions and personalized optimizations for products of different gears, thereby improving intelligence and generalization.

Description

Technical Field

[0001] The present invention relates to the technical field, and in particular to an adaptive learning system that integrates IV detection and defect detection. Background Art

[0002] With the increasing popularity of photovoltaic power generation, PV system designers increasingly require tools that can predict the amount of electricity produced by PV modules. PV module modeling not only provides designers with a reference tool for evaluating module performance to aid decision-making, but also plays a crucial role in the simulation, design, evaluation, control, and optimization of PV systems. Currently, single-diode models derived from module materials are widely used, including ideal, four-parameter, and five-parameter models. The five-parameter model offers the highest accuracy, adding equivalent series-parallel resistance compared to the ideal and four-parameter models to ensure the integrity of the single-diode model's physical structure. It is suitable for simulating PV modules composed of multiple battery cells or PV arrays composed of multiple PV modules.

[0003] The existing photovoltaic module simulation algorithms have the following problems:

[0004] 1. A larger site is required to accommodate IV testing and defect detection.

[0005] 2. When new defects appear, a new model must be trained. Model training takes a long time and cannot be used directly for new products. When new defects are discovered, a completely new model must be trained for testing. Summary of the Invention

[0006] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and in the abstract and title of the present invention to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0007] The purpose of the present invention is to address the technical problems existing in the background technology. The present invention proposes an adaptive learning system that integrates IV detection and defect detection. This solution adaptively learns the hyperparameters IoU and confidence in defect detection by combining small transmittance detection equipment, IV detection system and defect detection system with power data fed back by IV detection and transmittance detection technology systems and inputting qualified power conditions of products of this model. The intelligent adjustment model enables the model to adapt to a variety of different personalized customizations, and can make distinctions and personalized optimizations for products of different gears, thereby improving intelligence and generalization.

[0008] The present invention proposes an adaptive learning system that integrates IV detection and defect detection, including the following steps:

[0009] S1 inputs the photovoltaic module design model step, inputs the designed photovoltaic module model and the environmental parameters of the photovoltaic module installation location into the system, and inputs the initial parameters for the system algorithm;

[0010] S2 performs an IV test on the model. Based on the input initial data, the IV prediction algorithm is used to simulate the design model and installation environment to obtain the predicted daylighting efficiency of the PV module in the target installation environment.

[0011] S3 is a defect detection step for the model. The structural inspection is performed on the design model separately. The dimensions of the design model are compared with the actual installation requirements to detect design defects or unfeasible structural positions.

[0012] S4 performs a light transmittance test on the model, assembling a miniature design model from the photovoltaic module design model, performing light transmittance test on the miniature design model using a small light transmittance test device, and summarizing the light transmittance results of the miniature design model to calculate the light transmittance data of the original-sized photovoltaic module model, and then calculating the light collection efficiency of the original-sized photovoltaic module model;

[0013] S5 proposes improvement and adjustment steps for the model. By combining the structural defects detected by the model with the IV prediction algorithm for the model, the algorithm is used to propose improvement plans for the structural defects, and the daylight efficiency of the improved model is re-predicted, and the predicted daylight efficiency data is statistically analyzed.

[0014] S6 is a step of comparing IV data with test data, in which the IV prediction results are compared with the experimental data tested by a small transmittance test instrument, so as to analyze the deviation values ​​in the prediction algorithm and the reasons for the error between the algorithm and the actual test data;

[0015] S7 is a correction step for the IV algorithm. By comparing the experimental data with the predicted data, the calculation logic of the algorithm is adjusted in reverse, so that the output of the algorithm is more in line with the actual experimental data.

[0016] S8 adaptive model correction step, by re-entering the algorithm-corrected design model as initial data into the system for further calculation and analysis, the system achieves the adaptive iterative correction effect. By repeatedly analyzing and correcting the design model, the design defects in the design model can be gradually reduced;

[0017] In the step S9 of adaptive algorithm correction, the algorithm that has been compared and corrected is used again as the IV prediction algorithm of the system, so that the prediction algorithm can be iteratively corrected during the process of repeated iteration of the design model.

[0018] By adopting the above technical solution, the present solution can achieve the effect of adaptive adjustment through the automatic iterative correction algorithm and correction model.

[0019] Preferably, in step S1, the installation environment parameters include the environment height and the sunshine time and angle, and the installation environment parameters are improved by adding the specific installation direction and installation method of the photovoltaic components.

[0020] By adopting the above technical solution, this solution can correct and supplement the prediction results by supplementing the environmental parameters, and more complete environmental parameters can ensure the accuracy of the prediction results.

[0021] Preferably, in step S2, the estimated light conversion efficiency of the photovoltaic module design model is obtained in the IV prediction algorithm by performing a prediction operation on the design model. The prediction algorithm combines the size model of the photovoltaic module model with the installation environment parameters to derive the prediction data.

[0022] By adopting the above technical solution, this solution corrects the detailed parameters of the photovoltaic components through the prediction algorithm, thereby avoiding the influence of the position parameters on the prediction results, so that the system has a hyperparameter effect.

[0023] Preferably, in the S4 step, a miniature model of the photovoltaic module design model is produced in equal proportion according to the parameters of the photovoltaic module design model, and then the photovoltaic module design model is tested by a non-predictive algorithm. By setting a small transmittance detection device at different distances from the photovoltaic module miniature model, the effect of the photovoltaic module light transmittance under different lighting environments can be simulated.

[0024] By adopting the above technical solution, the present solution corrects the algorithm through data obtained by the non-predictive algorithm, thereby avoiding repeated iterations of erroneous parameters.

[0025] Preferably, in step S3, the algorithm for performing defect detection on the structure of the photovoltaic module is independent of the IV prediction algorithm, the defect detection algorithm does not change during the iterative use process, and the defect detection algorithm only includes the detection of the basic logic of the photovoltaic module structure.

[0026] By adopting the above technical solution, this solution can ensure the logical smoothness of the basic structure of the photovoltaic module through an independent defect detection algorithm.

[0027] Preferably, in step S5, the defective part of the structure is analyzed specifically by using the IV prediction algorithm, and an improved structure corresponding to the defective part can be found from the previously predicted structural data model, thereby correcting the defective structure.

[0028] By adopting the above technical solution, this solution summarizes the previously predicted design model and corrects the defective parts.

[0029] Preferably, in the step S7, the prediction algorithm is corrected by a reverse recursive method, with the measurement data as a fixed result and the design model as the input initial data, so that the algorithm can adjust and fit itself according to the fixed input and output results, and adopt multiple iterations. Through multiple fittings, the internal parameters of the algorithm can gradually fit the actual situation.

[0030] By adopting the above technical solution, this solution avoids the error existing in the single correction prediction through the effect of multiple iterations, so that the final result is more consistent with the actual situation.

[0031] Preferably, in step S8, the revised design model is re-entered as input data into the internal algorithm of the system, thereby achieving an iterative effect. During the iterative process, both the design model and the prediction algorithm are adaptively adjusted.

[0032] By adopting the above technical solution, the present solution can change the algorithm at the same time as the design model changes through adaptive adjustment, thereby achieving synchronous progress.

[0033] In summary, the present invention has at least one of the following beneficial effects:

[0034] The design method of this system can improve the accuracy of the prediction of the lighting efficiency of photovoltaic modules after actual installation. The system has the function of automatically improving the design scheme, so that it can automatically correct the design defects in the design model, thereby improving the lighting efficiency of the final model. The system is corrected by using a miniature model, thereby improving the accuracy of the algorithm while reducing the actual production cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0036] Figure 1 This is a front view of an embodiment of the present invention that integrates IV detection and defect detection to implement an adaptive learning system; DETAILED DESCRIPTION

[0037] The following is combined with Figure 1 The present invention is described in further detail.

[0038] Example 1

[0039] like Figure 1 As shown, in this embodiment, in order to solve the existing problems, the present invention discloses an adaptive learning system that integrates IV detection and defect detection, including the following steps:

[0040] S1 inputs the photovoltaic module design model step, inputs the designed photovoltaic module model and the environmental parameters of the photovoltaic module installation location into the system, and inputs the initial parameters for the system algorithm;

[0041] S2 performs an IV test on the model. Based on the input initial data, the IV prediction algorithm is used to simulate the design model and installation environment to obtain the predicted daylighting efficiency of the PV module in the target installation environment.

[0042] S3 is a defect detection step for the model. The structural inspection is performed on the design model separately. The dimensions of the design model are compared with the actual installation requirements to detect design defects or unfeasible structural positions.

[0043] S4 performs a light transmittance test on the model, assembling a miniature design model from the photovoltaic module design model, performing light transmittance test on the miniature design model using a small light transmittance test device, and summarizing the light transmittance results of the miniature design model to calculate the light transmittance data of the original-sized photovoltaic module model, and then calculating the light collection efficiency of the original-sized photovoltaic module model;

[0044] S5 proposes improvement and adjustment steps for the model. By combining the structural defects detected by the model with the IV prediction algorithm for the model, the algorithm is used to propose improvement plans for the structural defects, and the daylight efficiency of the improved model is re-predicted, and the predicted daylight efficiency data is statistically analyzed.

[0045] S6 is a step of comparing IV data with test data, in which the IV prediction results are compared with the experimental data tested by a small transmittance test instrument, so as to analyze the deviation values ​​in the prediction algorithm and the reasons for the error between the algorithm and the actual test data;

[0046] S7 is a correction step for the IV algorithm. By comparing the experimental data with the predicted data, the calculation logic of the algorithm is adjusted in reverse, so that the output of the algorithm is more in line with the actual experimental data.

[0047] S8 adaptive model correction step, by re-entering the algorithm-corrected design model as initial data into the system for further calculation and analysis, the system achieves the adaptive iterative correction effect. By repeatedly analyzing and correcting the design model, the design defects in the design model can be gradually reduced;

[0048] In the step S9 of adaptive algorithm correction, the algorithm that has been compared and corrected is used again as the IV prediction algorithm of the system, so that the prediction algorithm can be iteratively corrected during the process of repeated iteration of the design model.

[0049] This system can predict the daylight conversion rate of photovoltaic modules and can also correct and improve its own prediction algorithm. Therefore, compared with traditional prediction algorithms, this system has a higher degree of fitting and more accurate prediction results. The combination of algorithm prediction and transmittance experimental instrument detection can effectively avoid the cyclic iteration of erroneous data, thereby ensuring that the data is always supported by real experimental data during multiple iterations.

[0050] Example 2

[0051] like Figure 1 As shown, in order to solve the existing problems, in this embodiment, based on the same concept as the above-mentioned embodiment 1, the adaptive learning system implemented by integrating IV detection and defect detection also includes: in the S1 step, the installation environment parameters include the ambient height and the sunshine time and angle, and the installation environment parameters are improved by adding the specific installation direction and installation method of the photovoltaic components.

[0052] In step S2, the estimated light conversion efficiency of the photovoltaic module design model is obtained in the IV prediction algorithm by performing a prediction operation on the design model. The prediction algorithm combines the size model of the photovoltaic module model with the installation environment parameters to derive the prediction data.

[0053] In the step S4, a miniature model of the photovoltaic module design model is produced in proportion to the parameters of the photovoltaic module design model, and then the photovoltaic module design model is tested using a non-predictive algorithm. By setting a small transmittance detection device at different distances from the photovoltaic module miniature model, the effect of the photovoltaic module's light transmittance under different lighting environments can be simulated.

[0054] In step S3, the algorithm for performing defect detection on the structure of the photovoltaic module is independent of the IV prediction algorithm. The defect detection algorithm does not change during the iterative use process. The defect detection algorithm only includes the detection of the basic logic of the photovoltaic module structure.

[0055] In step S5, the defective part of the structure is analyzed specifically by the IV prediction algorithm, and the improved structure corresponding to the defective part can be found from the previously predicted structural data model, thereby correcting the defective structure.

[0056] In the step S7, the prediction algorithm is corrected by a reverse recursive method, with the measured data as a fixed result and the design model as the input initial data, so that the algorithm can adjust and fit itself according to the fixed input and output results. Multiple iterations are used, and through multiple fittings, the internal parameters of the algorithm can gradually fit the actual situation.

[0057] In step S8, the revised design model is re-entered as input data into the internal algorithm of the system to achieve an iterative effect. During the iterative process, both the design model and the prediction algorithm are adaptively adjusted.

[0058] Compared with traditional algorithms, this system has the function of adaptive correction. Compared with traditional fixed prediction algorithms, which only have fixed output results for single input data, this system can self-correct after multiple uses, thereby improving the accuracy of the system.

[0059] The above are all preferred embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. An adaptive learning system that integrates IV detection and defect detection, characterized in that: The following steps are involved: S1 inputs the photovoltaic module design model step, inputs the designed photovoltaic module model and the environmental parameters of the photovoltaic module installation location into the system, and inputs the initial parameters for the system algorithm; S2 performs an IV test on the model. Based on the input initial data, the IV prediction algorithm is used to simulate the design model and installation environment to obtain the predicted daylighting efficiency of the PV module in the target installation environment. S3 is a defect detection step for the model. The structural inspection is performed on the design model separately. The dimensions of the design model are compared with the actual installation requirements to detect design defects or unfeasible structural positions. S4 performs a light transmittance test on the model, assembling a miniature design model from the photovoltaic module design model, performing light transmittance test on the miniature design model using a small light transmittance test device, and summarizing the light transmittance results of the miniature design model to calculate the light transmittance data of the original-sized photovoltaic module model, and then calculating the light collection efficiency of the original-sized photovoltaic module model; S5 proposes improvement and adjustment steps for the model. By combining the structural defects detected by the model with the IV prediction algorithm for the model, the algorithm is used to propose improvement plans for the structural defects, and the daylight efficiency of the improved model is re-predicted, and the predicted daylight efficiency data is statistically analyzed. S6 is a step of comparing IV data with test data, in which the IV prediction results are compared with the experimental data tested by a small transmittance test instrument, so as to analyze the deviation values ​​in the prediction algorithm and the reasons for the error between the algorithm and the actual test data; S7 is a correction step for the IV algorithm. By comparing the experimental data with the predicted data, the calculation logic of the algorithm is adjusted in reverse, so that the output of the algorithm is more in line with the actual experimental data. S8 adaptive model correction step, by re-entering the algorithm-corrected design model as initial data into the system for further calculation and analysis, the system achieves the adaptive iterative correction effect. By repeatedly analyzing and correcting the design model, the design defects in the design model can be gradually reduced; S9 adaptive algorithm correction step, by using the algorithm after comparison and correction as the IV prediction algorithm of this system, it is possible to iteratively correct the prediction algorithm during the repeated iteration of the design model; In step S1, the installation environment parameters include the environment height, sunshine time and angle, and the installation environment parameters are improved by adding the specific installation direction and installation method of the photovoltaic module; In step S2, a predicted light conversion efficiency of the photovoltaic module design model is obtained by performing a prediction operation on the design model in an IV prediction algorithm. The prediction algorithm combines the size model of the photovoltaic module model with the installation environment parameters to derive the predicted data. In the step S4, a miniature model of the photovoltaic module design model is produced in proportion to the parameters of the photovoltaic module design model, and then the photovoltaic module design model is tested using a non-predictive algorithm. By setting a small transmittance detection device at different distances from the photovoltaic module miniature model, the effect of the photovoltaic module's light transmittance under different lighting environments can be simulated.

2. The adaptive learning system integrating IV detection and defect detection according to claim 1 is characterized in that: In step S3, the algorithm for performing defect detection on the structure of the photovoltaic module is independent of the IV prediction algorithm. The defect detection algorithm does not change during the iterative use process. The defect detection algorithm only includes the detection of the basic logic of the photovoltaic module structure.

3. The adaptive learning system integrating IV detection and defect detection according to claim 2 is characterized in that: In step S5, the defective part of the structure is analyzed specifically by the IV prediction algorithm, and the improved structure corresponding to the defective part can be found from the previously predicted structural data model, thereby correcting the defective structure.

4. The adaptive learning system achieved by integrating IV detection and defect detection according to claim 3 is characterized in that: In the step S7, the prediction algorithm is corrected by a reverse recursive method, with the measured data as a fixed result and the design model as the input initial data, so that the algorithm can adjust and fit itself according to the fixed input and output results. Multiple iterations are used, and through multiple fittings, the internal parameters of the algorithm can gradually fit the actual situation.

5. The adaptive learning system integrating IV detection and defect detection according to claim 4 is characterized in that: In step S8, the revised design model is re-entered as input data into the internal algorithm of the system to achieve an iterative effect. During the iterative process, both the design model and the prediction algorithm are adaptively adjusted.

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