CN116596517A - Photovoltaic module cleaning decision method and system based on neural network model - Google Patents

Photovoltaic module cleaning decision method and system based on neural network model Download PDF

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CN116596517A
CN116596517A CN202310650761.0A CN202310650761A CN116596517A CN 116596517 A CN116596517 A CN 116596517A CN 202310650761 A CN202310650761 A CN 202310650761A CN 116596517 A CN116596517 A CN 116596517A
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neural network
cleaning
photovoltaic module
network model
data
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王钢
刘林
郑诚
贾春宇
刘波
于良
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First Construction and Installation Co Ltd of China Construction Third Engineering Bureau Co Ltd
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Abstract

The invention discloses a photovoltaic module cleaning decision method and a system based on a neural network model, wherein the method comprises the following steps: collecting performance data and environmental indexes of the photovoltaic module; preprocessing the collected data; dividing the preprocessed data into a training set and a testing set; designing a network architecture based on a neural network algorithm; training the neural network model by using the training set; the performance of the trained model is evaluated using the test set. According to the photovoltaic module cleaning decision method and system based on the neural network model, the cleaning demand model can be established by collecting performance data and environmental indexes of the photovoltaic module and combining the historical cleaning data, the model can accurately predict when the photovoltaic module needs cleaning based on analysis and pattern recognition of the historical data, and the historical data can be utilized to determine the cleaning frequency and the optimal time of the photovoltaic module, so that the cleaning efficiency is improved, and excessive or insufficient cleaning operation is avoided.

Description

Photovoltaic module cleaning decision method and system based on neural network model
Technical Field
The invention relates to the technical field of photovoltaic modules, in particular to a photovoltaic module cleaning decision method and system based on a neural network model.
Background
The photovoltaic module (also called a solar panel) is a core part in a solar power generation system, and is also the most important part in the solar power generation system, and is a combination of photovoltaic modules with different specifications cut by a photovoltaic module sheet or a laser cutting machine or a steel wire cutting machine.
While the cleaning of the photovoltaic module plays a crucial role in ensuring efficient power generation, the conventional cleaning method of the photovoltaic module is generally performed based on a fixed time interval, and individual differences and environmental changes of the cleaning requirements of the photovoltaic module are ignored. This method tends to cause unnecessary cleaning or delayed cleaning, resulting in a situation where resources are wasted and the power generation efficiency is lowered. Therefore, a method capable of predicting according to actual cleaning requirements is needed to improve cleaning efficiency and reduce cost.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a photovoltaic module cleaning decision method and a system based on a neural network model, and aims to solve the problems.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a photovoltaic module cleaning decision system based on a neural network model, comprising:
the sensor is used for monitoring performance data and environmental indexes of the photovoltaic module in real time;
the data processing module is used for receiving the data acquired by the sensor and preprocessing the data;
the neural network model predicts the cleaning time based on the training data;
and the cleaning time decision module is used for making cleaning time decisions by utilizing the neural network model and the real-time monitoring data.
Preferably, the sensor includes an illumination intensity sensor, a temperature sensor, and a humidity sensor.
Preferably, the data processing module comprises a data cleaning module, a denoising module and a feature extraction module.
Preferably, the neural network model adopts a multi-layer perceptron network structure.
Preferably, the neural network model adopts a convolutional neural network structure.
A photovoltaic module cleaning decision method based on a neural network model comprises the following steps:
step one: collecting performance data and environmental indexes of the photovoltaic module;
step two: preprocessing the collected data, including cleaning, denoising and feature extraction;
step three: dividing the preprocessed data into a training set and a testing set;
step four: designing a network architecture based on a neural network algorithm;
step five: training the neural network model by using the training set;
step six: evaluating the performance of the trained model using the test set;
step seven: and predicting the cleaning time of the photovoltaic module by utilizing the trained neural network model in combination with the real-time monitoring data.
Preferably, the neural network model adopts a multi-layer perceptron network structure.
Preferably, the neural network model adopts a convolutional neural network structure.
Compared with the prior art, the invention has the following beneficial effects:
according to the photovoltaic module cleaning decision method and system based on the neural network model, the cleaning demand model can be established by collecting performance data and environmental indexes of the photovoltaic module and combining the historical cleaning data, the model can accurately predict when the photovoltaic module needs cleaning based on analysis and pattern recognition of the historical data, and the historical data can be utilized to determine the cleaning frequency and the optimal time of the photovoltaic module, so that the cleaning efficiency is improved, and excessive or insufficient cleaning operation is avoided.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention;
FIG. 2 is a schematic flow chart of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a photovoltaic module cleaning decision system based on a neural network model includes the following components:
a sensor:
the system mainly comprises a plurality of illumination intensity sensors, temperature sensors and humidity sensors, and is used for monitoring performance data and environmental indexes of the photovoltaic module in real time; the sensor can be used for measuring key parameters such as illumination intensity, temperature, humidity and the like, and parameters including current, voltage, temperature and the like of the component and representing the power generation efficiency, and transmitting data to the data processing module.
Among them, the parameter of common characterization photovoltaic module generating efficiency:
monitoring of the current output of the photovoltaic module: monitoring the current output current intensity; a lower current output may mean that dirt on the surface of the component builds up and requires cleaning.
Monitoring of the voltage output of the photovoltaic module: monitoring the current stability of the output voltage; abnormal voltage fluctuations may be associated with fouling of the component surfaces, requiring cleaning.
Regarding the power generation efficiency of the photovoltaic module: calculating the actual power generation efficiency of the photovoltaic module, namely the efficiency of converting light energy into electric energy; over time, the accumulation of fouling can reduce the power generation efficiency of the assembly, and monitoring changes in power generation efficiency can therefore be an indicator of the cleaning time.
Temperature change for photovoltaic modules: observing the change condition of the surface temperature of the photovoltaic module; build-up of dirt may cause the temperature of the assembly to rise, so that abnormal changes in temperature may indicate whether cleaning is required.
And a data processing module:
the device mainly comprises a data cleaning module, a denoising module and a feature extraction module, wherein the data processing module receives data acquired by a sensor and performs data preprocessing; the module performs cleaning, denoising, feature extraction and other processes on the data so as to ensure the accuracy and usability of the data.
The preprocessed data will be used for training and prediction of neural network models.
Neural network model:
the system integrates a model based on a neural network algorithm and is used for deciding the cleaning time of the photovoltaic module; the model trains and predicts the data preprocessed by the data processing module, and can predict the optimal cleaning time by learning the relation between the performance data of the photovoltaic module and the environmental index.
The neural network model may select a suitable network architecture according to specific requirements, such as a multi-layer perceptron (MLP), a Convolutional Neural Network (CNN), and the like.
Referring to fig. 2, a method for cleaning and deciding a photovoltaic module based on a neural network model comprises the following steps:
step 1:
collecting data; through sensor and monitoring facilities, the performance data and the environmental index of photovoltaic module such as illumination intensity, temperature, humidity and the parameter etc. that the module itself represents generating efficiency are collected regularly to record cleaning time and performance data after the washing, form historical cleaning dataset.
Step 2:
preprocessing data; preprocessing the collected data, wherein the preprocessing comprises data cleaning, denoising, feature extraction and the like, so that the accuracy and usability of the data are ensured, and preparation is made for subsequent model establishment.
Step 3:
dividing data; the historical data set is divided into a training set and a testing set, wherein the training set is used for building a neural network model, and the testing set is used for evaluating the performance and the prediction accuracy of the model.
Step 4:
designing a network architecture; based on a neural network algorithm, network structures such as a multi-layer perceptron (MLP) or a Convolutional Neural Network (CNN) can be selected, and a network architecture suitable for cleaning time decision of the photovoltaic module is calculated and designed.
Step 5:
training a model; the neural network model is trained using a training set, with the network parameters being continually adjusted by a back-propagation algorithm and optimizer to minimize the prediction error.
Step 6:
evaluating a model; evaluating the trained model by using a test set, and calculating prediction accuracy and error indexes; and optimizing the model according to the evaluation result, and ensuring the accuracy and reliability of the model in the cleaning time decision.
Step 7:
deciding the cleaning time; and predicting the cleaning time of the photovoltaic module by combining the trained neural network model with the real-time monitoring data.
According to the prediction result, the cleaning time can be decided so as to improve the power generation efficiency of the photovoltaic module to the greatest extent.
In this embodiment, the photovoltaic module of a certain photovoltaic power station is in an operating state, and the illumination intensity sensor, the temperature sensor and the humidity sensor in the monitoring device collect the following data in real time: the illumination intensity is 800W/m 2 The temperature is 30 ℃, the humidity is 60%, meanwhile, the current output is 5A, the voltage output is stabilized at 30V, the power generation efficiency is 85%, the surface temperature of the assembly is 40 ℃, and the data are transmitted to a data processing module for processing.
And then, the data processing module cleans the acquired data, removes possible abnormal values and noise, and ensures the accuracy of the data.
And then extracting features from the cleaned data, and extracting the light intensity, temperature, humidity, current output, voltage output, power generation efficiency and surface temperature as input features, for example:
illumination intensity: 800W/m 2
Temperature: 30 DEG C
Humidity: 60 percent of
Current output: 5A
Voltage output: 30V
Generating efficiency: 85%
Surface temperature: 40 DEG C
The preprocessed data is divided into a training set and a testing set, and is used for training and evaluating the performance of the neural network model and perfecting the establishment of the model.
And then selecting a proper neural network architecture, such as a multi-layer perceptron (MLP) or a Convolutional Neural Network (CNN), according to specific requirements, and calculating and designing the network architecture suitable for the cleaning time decision of the photovoltaic module.
And then training the neural network model by using the training set, and continuously adjusting model parameters to enable the neural network model to learn the relation between the performance data of the photovoltaic module and the environmental indexes.
In the hypothesis training process, the neural network model learns the following relationship:
cleaning time = 0.8 illumination intensity +0.5 temperature-0.3 humidity +0.6 current output +0.4 voltage output +0.7 power generation efficiency-0.2 surface temperature +1.2
Meanwhile, the cleaning time decision module monitors data in real time: continuously monitoring performance data and environmental indexes of the photovoltaic module through a sensor, and transmitting the data to a cleaning time decision module;
based on the data monitored in real time, the following is possible:
illumination intensity: 800W/m 2
Temperature: 30 DEG C
Humidity: 60 percent of
Current output: 5A
Voltage output: 30V
Generating efficiency: 85%
Surface temperature: 40 DEG C
Prediction using a neural network model:
the cleaning time is as follows according to the predictive formula of the neural network model:
cleaning time = 0.8 illumination intensity +0.5 temperature-0.3 humidity +0.6 current output +0.4 voltage output +0.7 power generation efficiency-0.2 surface temperature +1.2
Substituting the real-time monitored data:
time for cleaning = 0.8 x 800+0.5 x 30-0.3 x 60+0.6 x 5+0.4 x 30+0.7 x 85-0.2 x 40+1.2
＝640+15-18+3+12+59.5-8+1.2
＝704.7
And according to the prediction result, the cleaning time decision module judges that the optimal cleaning time of the photovoltaic module is 704.7 days.
Finally, the cleaning task is arranged according to the judged cleaning time, and the process of executing the cleaning task comprises the following steps:
cleaning instructions: the cleaning time decision module sends out a cleaning instruction to inform an operator or an automation device to execute a cleaning task.
Cleaning task arrangement: according to the cleaning instructions, the operator or the automation equipment arranges to perform the cleaning tasks of the photovoltaic module after 704.7 days.
The cleaning time of the photovoltaic module can be accurately predicted by comprehensively considering a plurality of indexes such as illumination intensity, temperature, humidity, current output, voltage output, power generation efficiency, surface temperature and the like and combining with training and prediction of a neural network model, and cleaning operation can be performed when appropriate, so that the normal operation and power generation efficiency of the module are ensured.
The illumination intensity sensor, the temperature sensor, the humidity sensor, the multi-layer perceptron (MLP) and the Convolutional Neural Network (CNN) are all related to the prior art, so the description is omitted
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (8)

1. A photovoltaic module cleaning decision system based on a neural network model is characterized by comprising:
the sensor is used for monitoring performance data and environmental indexes of the photovoltaic module in real time;
the data processing module is used for receiving the data acquired by the sensor and preprocessing the data;
the neural network model predicts the cleaning time based on the training data;
and the cleaning time decision module is used for making cleaning time decisions by utilizing the neural network model and the real-time monitoring data.
2. The neural network model-based photovoltaic module cleaning decision system of claim 1, wherein the sensors comprise an illumination intensity sensor, a temperature sensor, and a humidity sensor.
3. The photovoltaic module cleaning decision system based on the neural network model of claim 1, wherein the data processing module comprises a data cleaning module, a denoising module and a feature extraction module.
4. The photovoltaic module cleaning decision system based on the neural network model according to claim 1, wherein the neural network model adopts a multi-layer perceptron network structure.
5. The photovoltaic module cleaning decision system based on a neural network model according to claim 1, wherein the neural network model adopts a convolutional neural network structure.
6. The photovoltaic module cleaning decision-making method based on the neural network model is characterized by comprising the following steps of:
step one: collecting performance data and environmental indexes of the photovoltaic module;
step two: preprocessing the collected data, including cleaning, denoising and feature extraction;
step three: dividing the preprocessed data into a training set and a testing set;
step four: designing a network architecture based on a neural network algorithm;
step five: training the neural network model by using the training set;
step six: evaluating the performance of the trained model using the test set;
step seven: and predicting the cleaning time of the photovoltaic module by utilizing the trained neural network model in combination with the real-time monitoring data.
7. The method for cleaning and deciding a photovoltaic module based on a neural network model according to claim 6, wherein the neural network model adopts a multi-layer perceptron network structure.
8. The method for cleaning and deciding a photovoltaic module based on a neural network model according to claim 6, wherein the neural network model adopts a convolutional neural network structure.
CN202310650761.0A 2023-06-05 2023-06-05 Photovoltaic module cleaning decision method and system based on neural network model Pending CN116596517A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117169449A (en) * 2023-10-31 2023-12-05 江苏权正检验检测有限公司 Food decomposition and cleaning method and system for food detection

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117169449A (en) * 2023-10-31 2023-12-05 江苏权正检验检测有限公司 Food decomposition and cleaning method and system for food detection
CN117169449B (en) * 2023-10-31 2023-12-29 江苏权正检验检测有限公司 Food decomposition and cleaning method and system for food detection

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