Multi-algorithm fused photovoltaic power generation data preprocessing system
Through the photovoltaic power generation data preprocessing system integrated with multiple algorithms, abnormal data is automatically identified and processed, solving the problem of manual handling of photovoltaic power generation equipment failures, reducing the burden on staff, and improving the accuracy of fault determination.
Patent Information
- Application Number
- CN202411348335.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-26
- Publication Date
- 2025-02-14
AI Technical Summary
The existing photovoltaic power generation system needs to be manually dealt with when equipment failures, which increases the work intensity and burden of staff.
Design a photovoltaic power generation data preprocessing system that integrates multiple algorithms, and automatically identify and process abnormal data through the coordinated work of data acquisition, integration, analysis and processing, conclusion prediction and preprocessing modules to reduce manual intervention.
The system can more effectively identify and process abnormal data, reduce manual processing needs, reduce work intensity and burden on staff, and improve the accuracy of fault determination.
Smart Images
Figure CN119441714A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to a photovoltaic power generation data preprocessing system integrating multiple algorithms. Background Art
[0002] Photovoltaic power generation systems are widely used in a variety of occasions in the process of converting solar energy into electrical energy, including residential, commercial, industrial and remote areas far away from the power grid. With the widespread application of photovoltaic technology, data management and processing of photovoltaic systems have become the key to improving system efficiency and reducing operation and maintenance costs.
[0003] With the widespread deployment of photovoltaic systems, it is particularly important to collect and analyze photovoltaic power generation data. These data not only include power output, but also involve multiple environmental and equipment parameters, such as temperature, light intensity, module angle, etc. An effective data processing system can extract valuable information from these data, such as power generation efficiency, equipment operating status and maintenance requirements, thereby optimizing power generation and reducing operation and maintenance costs.
[0004] Existing photovoltaic systems usually use a single algorithm for calculations, which often makes them unable to cope with the complex and changeable data environment of photovoltaic power generation systems. Especially when photovoltaic power generation equipment fails, the system will promptly remind the staff to handle it manually. However, when considering some minor faults, the staff still needs to perform manual control and processing, which increases the workload of the staff. Summary of the invention
[0005] In view of the deficiencies in the prior art, the present invention provides a photovoltaic power generation data preprocessing system integrating multiple algorithms, which solves the problem that when photovoltaic power generation equipment fails, the system will promptly remind the staff to perform manual processing, thereby increasing the work intensity.
[0006] To achieve the above objectives, the present invention provides a multi-algorithm fusion photovoltaic power generation data preprocessing system, including a data acquisition module, the data acquisition module collects various data of photovoltaic power generation, and transmits the collected data to a data integration module, the data acquisition module is connected to a data integration module, the data integration module receives various data collected from the data acquisition module, and integrates the collected data, the data integration module is connected to an analysis and processing module, the analysis and processing module receives the integrated data, and uses a variety of different algorithms to substitute the data for calculation according to the data, the analysis and processing module is connected to a conclusion estimation module, the conclusion estimation module receives the calculation results processed by the analysis and processing module, and estimates according to the calculation results, the conclusion estimation module is connected to a preprocessing module, the preprocessing module receives the conclusion obtained from the conclusion estimation module, and is enabled when a minor fault occurs in the photovoltaic power generation equipment, and the preprocessing module is connected to a timely feedback module, the timely feedback module receives the results obtained from the conclusion estimation module, and feeds the results back to the staff.
[0007] Preferably, the photovoltaic power generation data include voltage, current and temperature, wherein the equipment for collecting the data includes: a voltage sensor, a current sensor and a temperature sensor.
[0008] Preferably, the data integration module integrates the collected data including: data denoising, data normalization, and deletion of duplicate data.
[0009] Preferably, the estimation of the conclusion estimation module includes: photovoltaic power generation equipment performance evaluation, photovoltaic power generation equipment failure prediction, and photovoltaic power generation equipment maintenance requirements.
[0010] Preferably, the minor faults of the photovoltaic power generation equipment mentioned in the preprocessing module include: excessive temperature, unstable output voltage, low performance of the photovoltaic panel, abnormal sensor data and low battery storage capacity.
[0011] Preferably, the analysis and processing module includes an algorithm selection module, which reads the content in the algorithm storage module and selects an algorithm to evaluate photovoltaic power generation. The algorithm selection module is connected to an algorithm storage module, which stores a variety of different algorithms and provides a variety of different options for the algorithm selection module. The algorithm storage module is connected to an algorithm calculation module, which executes the selected algorithm, processes input data and generates results. The algorithm calculation module is connected to a conclusion prediction module, which compares and predicts the results calculated by the algorithm calculation module.
[0012] Preferably, the timely feedback module is connected to a correction module, and the correction module makes appropriate adjustments based on the selection problem of the feedback adjustment algorithm. The correction module is connected to a storage solution module, and the storage solution module reads the specific content and steps of the algorithm used in the analysis and processing module, and extracts and stores them externally. The storage solution module is connected to the analysis and processing module. The storage solution module is connected to an evaluation collection module, and the evaluation collection module collects the staff's usage evaluation after each system evaluation. The evaluation collection module is connected to a corresponding module, and the corresponding module matches the evaluation content of the time with the algorithm selection content one by one. The corresponding module is connected to a bad review extraction module, and the bad review extraction module selects the corresponding content of the bad review separately. The bad review extraction module is connected to a simulation evaluation module, and the simulation evaluation module uses AI simulation inference technology to substitute the algorithm selection content corresponding to the bad review. The simulation evaluation module is connected to an adjustment algorithm module. The correction module adjusts the selection method of the selection algorithm module according to the inference result, and the correction module is connected to the selection algorithm module.
[0013] Preferably, the pre-processing module includes a corresponding solution module, and the corresponding solution module records a plurality of solutions for solving minor faults.
[0014] Preferably, the corresponding solution module is connected to an automatic adjustment module, and the automatic adjustment module receives the adjustment solution given by the corresponding solution module and enables the system to be implemented according to the adjustment solution.
[0015] Preferably, the specific solutions to the minor faults include: too high temperature: adjust the cooling system settings, increase the fan speed, and adjust the placement angle of the solar panels to reduce the impact of direct sunlight and lower the temperature; unstable output voltage: adjust the inverter parameter settings to ensure stable voltage output; low photovoltaic panel performance: adjust the position of the photovoltaic panel to avoid blocked areas; abnormal sensor data: calibrate the sensor data to ensure data accuracy; low battery storage capacity: check the parameters of the battery pack, optimize the battery charge and discharge cycle, and extend battery life.
[0016] The photovoltaic power generation data preprocessing system of the present invention with multi-algorithm fusion has the following beneficial effects: 1. By integrating multiple anomaly detection algorithms, the present invention enables the system to more effectively identify and process abnormal data and classify faults into two categories. When minor faults occur, the system performs preprocessing without reminding the staff to manually process them, thereby reducing the workload and burden of the staff.
[0017] 2. The present invention uses multi-algorithm calculation to select the most appropriate algorithm according to the data type, data quality and specific needs, or dynamically adjust the algorithm configuration, so as to improve the adaptability and flexibility of the system, comprehensively analyze the problems existing in photovoltaic power generation from multiple algorithms, and further improve the accuracy of determining photovoltaic power generation failures.
[0018] 3. The present invention collects user comments and stores the current algorithm calculation plan, extracts the current algorithm calculation plan based on negative comments, uses AI simulation technology to make a secondary guess on the current plan, and modifies the algorithm to determine the result. When the result is determined to be better than the previous plan, the algorithm selection plan is appropriately adjusted to ensure the accuracy of the next judgment. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] Figure 1 It is a system flow chart of the present invention.
[0020] Figure 2 It is a flow chart of the analysis and processing module of the present invention.
[0021] Figure 3 It is a flow chart of the preprocessing module of the present invention.
[0022] Figure 4 It is a flow chart of the timely feedback module of the present invention. DETAILED DESCRIPTION
[0023] The following will be combined with the drawings of the present invention specification to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0024] Example: Please see attached Figure 1 - Attachment Figure 4 The embodiment of the present invention provides a photovoltaic power generation data preprocessing system with multi-algorithm fusion, including a data acquisition module, the data acquisition module collects various data of photovoltaic power generation, and transmits the collected data to a data integration module, the various data of photovoltaic power generation include voltage, current, and temperature; wherein the equipment for collecting data includes: a voltage sensor, a current sensor, and a temperature sensor The data collection module is connected to a data integration module, which receives various data collected from the data collection module, integrates the collected data, and transmits the integrated data to the analysis and processing module. The specific integration includes: data denoising, data normalization, and deletion of duplicate data; The data integration module is connected to an analysis and processing module, which receives the integrated data and uses a variety of different algorithms to calculate the data, obtains the calculation results, and transmits the calculation results downward to the conclusion estimation module; The analysis and processing module is connected to a conclusion estimation module, which receives the calculation results processed by the analysis and processing module, and estimates based on the calculation results to obtain specific photovoltaic power generation equipment performance parameters, thereby determining whether the photovoltaic power generation equipment has a fault. The estimation includes photovoltaic power generation equipment performance evaluation, photovoltaic power generation equipment fault prediction, and photovoltaic power generation equipment maintenance requirements: The conclusion estimation module is connected to a preprocessing module, which receives the conclusions obtained from the conclusion estimation module, is enabled when a minor fault occurs in the photovoltaic power generation equipment, and uses the automatic adjustment module to adjust various states of the system and equipment to solve the problem, thereby avoiding the situation where the staff is reminded as soon as a fault occurs. The minor faults of the photovoltaic power generation equipment include: too high temperature, unstable output voltage, low performance of the photovoltaic panel, abnormal sensor data and low battery storage capacity; The preprocessing module is connected to a timely feedback module, which receives the results from the conclusion estimation module and feeds back the results to the staff, so that the staff can view and understand the status in time. Usually, when a fault occurs that the preprocessing module cannot handle, the staff needs to be notified as soon as possible to handle it. The faults that the preprocessing module cannot handle include: circuit short circuit and structural damage.
[0025] The analysis and processing module includes an algorithm selection module, which reads the content in the algorithm storage module and selects an algorithm to evaluate photovoltaic power generation; The algorithm selection module is connected to an algorithm storage module, which stores a variety of different algorithms and provides a variety of different options for the algorithm selection module, including: Prediction algorithm: Use historical weather data and photovoltaic production data to predict future power output; Performance evaluation algorithm: Calculate and analyze the performance coefficient of photovoltaic modules and evaluate the efficiency of modules through statistical algorithms; Abnormal detection algorithm: Calculate various data of photovoltaic power generation and compare them to comprehensively determine abnormalities or faults in photovoltaic power generation; The algorithm storage module is connected to an algorithm calculation module, which executes the selected algorithm, processes the input data and generates results, and transmits the conclusions to the decision path module; The algorithm calculation module is connected to a conclusion estimation module, and the conclusion estimation module compares and predicts the results calculated by the algorithm calculation module to determine the specific fault of the photovoltaic power generation equipment; The timely feedback module is connected to a correction module, which makes appropriate adjustments based on the selection problem of the feedback adjustment algorithm to improve the accuracy of the next use. The correction module is connected to a storage solution module, which reads the specific content and steps of the algorithm used in the analysis and processing module, and extracts and stores them externally. The storage solution module is connected to the analysis and processing module, and is connected to an evaluation collection module. The evaluation collection module collects the staff's usage evaluation after each system evaluation, and divides the evaluation into good, medium and bad reviews; The evaluation collection module is connected to a corresponding module, which makes a one-to-one correspondence between the current evaluation content and the algorithm-selected content, so as to facilitate subsequent viewing and selection by subsequent staff. The corresponding module is connected to a negative review extraction module, which selects the corresponding content of the negative review separately. The negative review extraction module is connected to a simulation evaluation module, which uses AI simulation inference technology to substitute the algorithm-selected content corresponding to the negative review, and uses different algorithms to calculate and infer the results. The simulation evaluation module is connected to an adjustment algorithm module, and the correction module adjusts the selection method of the selection algorithm module according to the inferred results, so as to improve the accuracy of the next selection algorithm calculation. The correction module is connected to the selection algorithm module.
[0026] The pre-processing module includes a corresponding solution module, which records a variety of solutions to minor faults. Specific solutions to minor faults include: Temperature is too high: Adjust the cooling system settings, increase the fan speed, and adjust the placement angle of the solar panels to reduce the impact of direct sunlight and lower the temperature; Unstable output voltage: Adjust the inverter parameter settings to ensure stable voltage output; The performance of photovoltaic panels is low: adjust the position of photovoltaic panels to avoid blocked areas; Sensor data anomaly: calibrate sensor data to ensure data accuracy; Low battery storage capacity: Check various parameters of the battery pack, optimize the battery charge and discharge cycle, and extend battery life; The corresponding solution module is connected to an automatic adjustment module, which receives the adjustment solution given by the corresponding solution module and enables the system to implement the adjustment solution, thereby solving the minor fault problem of the photovoltaic power generation equipment.
[0027] Experimental design: Experimental subjects: Two groups of photovoltaic power generation systems were selected, one using a multi-algorithm fusion system and the other using a traditional single algorithm system.
[0028] Experimental period: The experiment lasted 30 days, which was enough to cover various weather changes and equipment status changes.
[0029] Data collection: Both systems were installed with the same sensor equipment (voltage, current, and temperature sensors) to collect data daily.
[0030] Data processing: Multi-algorithm fusion system: using prediction algorithms, performance evaluation algorithms and anomaly detection algorithms.
[0031] Experimental monitoring indicators: Accuracy: the accuracy of evaluation and prediction, including equipment performance parameter evaluation and fault prediction accuracy; Fault detection time: the time from fault occurrence to detection; System response time: the speed at which the system processes data and feeds back results.
[0032] Data analysis: Use statistical methods to compare the performance differences between the two groups of systems; calculate the accuracy of fault prediction and response time.
[0033] Experimental results show: index Multi-algorithm fusion system Traditional systems Percent Improvement Fault prediction accuracy (%) 95% 85% 11.76% Equipment performance evaluation accuracy (%) 90% 80% 12.5% Fault detection time (hours) 2 hours 4 hours 50% System response time (seconds) 30 seconds 45 seconds 33.33%
Claims
1. A photovoltaic power generation data preprocessing system with multi-algorithm fusion, characterized in that: It includes a data acquisition module, which collects various data of photovoltaic power generation and transmits the collected data to a data integration module. The data acquisition module is connected to a data integration module, which receives various data collected from the data acquisition module and integrates the collected data. The data integration module is connected to an analysis and processing module, which receives the integrated data and uses a variety of different algorithms to substitute the data for calculation according to the data. The analysis and processing module is connected to a conclusion prediction module, which receives the calculation results processed by the analysis and processing module and makes predictions according to the calculation results. The conclusion prediction module is connected to a preprocessing module, which receives the conclusions obtained from the conclusion prediction module and is enabled when a minor fault occurs in the photovoltaic power generation equipment. The preprocessing module is connected to a timely feedback module, which receives the results obtained from the conclusion prediction module and feeds the results back to the staff.
2. A photovoltaic power generation data preprocessing system with multi-algorithm fusion according to claim 1, characterized in that: The photovoltaic power generation data include voltage, current, and temperature, and the equipment used to collect the data includes: a voltage sensor, a current sensor, and a temperature sensor.
3. The photovoltaic power generation data preprocessing system with multi-algorithm fusion according to claim 1 is characterized in that: The data integration module integrates the collected data including: data denoising, data normalization, and deletion of duplicate data.
4. The photovoltaic power generation data preprocessing system with multi-algorithm fusion according to claim 1 is characterized in that: The estimation of the conclusion estimation module includes: photovoltaic power generation equipment performance evaluation, photovoltaic power generation equipment failure prediction, and photovoltaic power generation equipment maintenance requirements.
5. The photovoltaic power generation data preprocessing system with multi-algorithm fusion according to claim 1 is characterized in that: The photovoltaic power generation equipment minor faults mentioned in the preprocessing module include: excessive temperature, unstable output voltage, low photovoltaic panel performance, abnormal sensor data and low battery storage capacity.
6. The photovoltaic power generation data preprocessing system with multi-algorithm fusion according to claim 1 is characterized in that: The analysis and processing module includes an algorithm selection module, which reads the content in the algorithm storage module and selects an algorithm to evaluate photovoltaic power generation. The algorithm selection module is connected to an algorithm storage module, which stores a variety of different algorithms and provides a variety of different options for the algorithm selection module. The algorithm storage module is connected to an algorithm calculation module, which executes the selected algorithm, processes input data and generates results. The algorithm calculation module is connected to a conclusion prediction module, which compares and predicts the results calculated by the algorithm calculation module.
7. The photovoltaic power generation data preprocessing system with multi-algorithm fusion according to claim 1 is characterized in that: The timely feedback module is connected to a correction module, and the correction module makes appropriate adjustments based on the selection problem of the feedback adjustment algorithm. The correction module is connected to a storage solution module, and the storage solution module reads the specific content and steps of the algorithm used in the analysis and processing module, and extracts and stores them externally. The storage solution module is connected to the analysis and processing module. The storage solution module is connected to an evaluation collection module, and the evaluation collection module collects the staff's usage evaluation after each system evaluation. The evaluation collection module is connected to a corresponding module, and the corresponding module matches the evaluation content of the time with the algorithm selection content one by one. The corresponding module is connected to a bad review extraction module, and the bad review extraction module selects the corresponding content of the bad review separately. The bad review extraction module is connected to a simulation evaluation module, and the simulation evaluation module uses AI simulation inference technology to substitute the algorithm selection content corresponding to the bad review. The simulation evaluation module is connected to an adjustment algorithm module. The correction module adjusts the selection method of the selection algorithm module according to the inference result, and the correction module is connected to the selection algorithm module.
8. The photovoltaic power generation data preprocessing system with multi-algorithm fusion according to claim 1 is characterized in that: The pre-processing module includes a corresponding solution module, and the corresponding solution module records a plurality of solutions for solving minor faults.
9. The photovoltaic power generation data preprocessing system with multi-algorithm fusion according to claim 8, characterized in that: The corresponding solution module is connected to an automatic adjustment module, and the automatic adjustment module receives the adjustment solution given by the corresponding solution module and enables the system to implement the adjustment solution.
10. A photovoltaic power generation data preprocessing system with multi-algorithm fusion according to claim 9, characterized in that: The specific solutions to the minor faults include: too high temperature: adjust the cooling system settings, increase the fan speed, and adjust the angle of the solar panels to reduce the impact of direct sunlight and lower the temperature; unstable output voltage: adjust the inverter parameter settings to ensure stable voltage output; low photovoltaic panel performance: adjust the position of the photovoltaic panel to avoid blocked areas; abnormal sensor data: calibrate the sensor data to ensure data accuracy; low battery storage capacity: check the parameters of the battery pack, optimize the battery charge and discharge cycle, and extend battery life.