Dynamic maximum power point tracking method for photovoltaic power generation system

Abstract

The invention relates to the technical field of photovoltaic power generation, in particular to a dynamic maximum power point tracking method for a photovoltaic power generation system. According to the photovoltaic power generation system dynamic maximum power point tracking method based on the random forest algorithm, the output voltage and current of a photovoltaic module are monitored in real time, a data set containing power change and other characteristics is constructed, a prediction model is trained through the random forest algorithm to predict the optimal disturbance voltage step length, and the given voltage is dynamically adjusted according to the optimal disturbance voltage step length. The real-time adjustment of the working state of the photovoltaic module is realized, so that the maximum power point output is approached and maintained, and the overall efficiency and stability of a photovoltaic power generation system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to a method for tracking a dynamic maximum power point of a photovoltaic power generation system. Background Art

[0002] The output power of a photovoltaic power generation system is closely related to the output characteristics of photovoltaic cells, which are affected by many factors such as load resistance, solar radiation intensity and temperature. Therefore, in order to achieve maximum power generation efficiency, maximum power point tracking technology is required. Existing dynamic maximum power point tracking methods include constant voltage tracking method, interference observation method, incremental conductance method, etc., but these methods have problems such as low tracking accuracy and slow response speed when facing complex and changing environmental conditions. Therefore, a more efficient and accurate dynamic maximum power point tracking method is needed. Summary of the invention

[0003] The technical problem to be solved by the present invention is: to overcome the shortcomings of the prior art and provide a method for dynamic maximum power point tracking of a photovoltaic power generation system. The method is based on a random forest algorithm and can track the maximum power point of a photovoltaic array in real time and accurately, thereby improving the power generation efficiency and stability of the photovoltaic power generation system.

[0004] The technical solution of the present invention is:

[0005] A method for tracking a dynamic maximum power point of a photovoltaic power generation system comprises the following steps:

[0006] a. Data collection: During the operation of the photovoltaic power generation system, the output voltage and output current values ​​of the photovoltaic modules are collected regularly as initial data;

[0007] b. Feature construction: Based on the collected data, the output power of the photovoltaic module is calculated, and a data set containing the historical output power, the current output power, and the power change caused by the disturbance is constructed;

[0008] c. Model training: using the random forest algorithm and the data set as training samples, a prediction model for predicting the optimal disturbance voltage step size for the next disturbance cycle is constructed;

[0009] d. Disturbance voltage prediction and adjustment: Calculate the disturbance voltage value of the next disturbance cycle according to the actual situation of the current disturbance cycle and the optimal disturbance voltage step size output by the prediction model;

[0010] e. Given voltage adjustment: According to the disturbance voltage value and the current working state of the photovoltaic module, the given voltage is adjusted using an appropriate control strategy to approach the maximum power point;

[0011] f. Loop iteration: Repeat steps a to e to achieve dynamic maximum power point tracking of the photovoltaic power generation system.

[0012] Preferably, the data collection step also includes synchronously recording environmental parameters, including light intensity and temperature, for eliminating the influence of environmental factors during subsequent feature construction.

[0013] Preferably, the feature construction step further includes: calculating and storing power response curves of photovoltaic modules under different disturbance voltages as auxiliary features for model training.

[0014] Preferably, the model training step also includes optimizing parameters of the random forest model, adjusting the number of trees, maximum depth, and minimum number of sample splits to improve prediction accuracy.

[0015] Preferably, the disturbance voltage prediction and adjustment step further includes: introducing a feedback mechanism to fine-tune the prediction model according to the actual effect after adjustment in the previous cycle to improve the dynamic response capability of tracking.

[0016] Preferably, the given voltage adjustment step further includes: using a fuzzy logic control algorithm to adjust the given voltage to improve tracking accuracy and stability.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] Through the above method, the present invention can realize the dynamic maximum power point tracking of the photovoltaic power generation system, and improve the power generation efficiency and stability of the photovoltaic power generation system. At the same time, the method has the advantages of strong adaptability, fast response speed, and high tracking accuracy, and is suitable for various complex and changeable environmental conditions. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention are described clearly and completely. 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 should fall within the scope of protection of the present invention.

[0020] Example

[0021] This embodiment provides a method for dynamic maximum power point tracking of a photovoltaic power generation system, comprising the following steps:

[0022] a. Data collection: During the operation of the photovoltaic power generation system, the output voltage and output current values ​​of the photovoltaic modules are collected regularly as initial data;

[0023] b. Feature construction: Based on the collected data, the output power of the photovoltaic module is calculated, and a data set containing the historical output power, the current output power, and the power change caused by the disturbance is constructed;

[0024] c. Model training: using the random forest algorithm and the data set as training samples, a prediction model for predicting the optimal disturbance voltage step size for the next disturbance cycle is constructed;

[0025] d. Disturbance voltage prediction and adjustment: Calculate the disturbance voltage value of the next disturbance cycle according to the actual situation of the current disturbance cycle and the optimal disturbance voltage step size output by the prediction model;

[0026] e. Given voltage adjustment: According to the disturbance voltage value and the current working state of the photovoltaic module, the given voltage is adjusted using an appropriate control strategy to approach the maximum power point;

[0027] f. Loop iteration: Repeat steps a to e to achieve dynamic maximum power point tracking of the photovoltaic power generation system.

[0028] Preferably, the data collection step also includes synchronously recording environmental parameters, including light intensity and temperature, for eliminating the influence of environmental factors during subsequent feature construction.

[0029] Preferably, the feature construction step further includes: calculating and storing power response curves of photovoltaic modules under different disturbance voltages as auxiliary features for model training.

[0030] Preferably, the model training step also includes optimizing parameters of the random forest model, adjusting the number of trees, maximum depth, and minimum number of sample splits to improve prediction accuracy.

[0031] Preferably, the disturbance voltage prediction and adjustment step further includes: introducing a feedback mechanism to fine-tune the prediction model according to the actual effect after adjustment in the previous cycle to improve the dynamic response capability of tracking.

[0032] Preferably, the given voltage adjustment step further includes: using a fuzzy logic control algorithm to adjust the given voltage to improve tracking accuracy and stability.

[0033] Working principle:

[0034] The above-mentioned dynamic maximum power point tracking method of the photovoltaic power generation system based on the random forest algorithm monitors the output voltage and current of the photovoltaic components in real time, constructs a data set containing characteristics such as power changes, and uses the random forest algorithm to train the prediction model to predict the optimal disturbance voltage step size. The given voltage is dynamically adjusted accordingly to achieve real-time adjustment of the working state of the photovoltaic components, thereby approaching and maintaining the maximum power point output, and improving the overall efficiency and stability of the photovoltaic power generation system.

[0035] Although the present invention has been described in detail by way of preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. Any person of ordinary skill in the art may easily think of changes or substitutions within the technical scope disclosed by the present invention, and these shall be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A method for tracking a dynamic maximum power point of a photovoltaic power generation system, characterized in that: The following steps are involved: a. Data collection: During the operation of the photovoltaic power generation system, the output voltage and output current values ​​of the photovoltaic modules are collected regularly as initial data; b. Feature construction: Based on the collected data, the output power of the photovoltaic module is calculated, and a data set containing the historical output power, the current output power, and the power change caused by the disturbance is constructed; c. Model training: using the random forest algorithm and the data set as training samples, a prediction model for predicting the optimal disturbance voltage step size for the next disturbance cycle is constructed; d. Disturbance voltage prediction and adjustment: Calculate the disturbance voltage value of the next disturbance cycle according to the actual situation of the current disturbance cycle and the optimal disturbance voltage step size output by the prediction model; e. Given voltage adjustment: According to the disturbance voltage value and the current working state of the photovoltaic module, the given voltage is adjusted using an appropriate control strategy to approach the maximum power point; f. Loop iteration: Repeat steps a to e to achieve dynamic maximum power point tracking of the photovoltaic power generation system.

2. The method for tracking the dynamic maximum power point of a photovoltaic power generation system according to claim 1, wherein: The data collection step also includes synchronously recording environmental parameters, including light intensity and temperature, for eliminating the influence of environmental factors during subsequent feature construction.

3. The method for tracking the dynamic maximum power point of a photovoltaic power generation system according to claim 1, wherein: The feature construction step also includes: calculating and storing the power response curve of the photovoltaic module under different disturbance voltages as an auxiliary feature for model training.

4. The method for dynamic maximum power point tracking of a photovoltaic power generation system according to claim 1, characterized in that: The model training step also includes optimizing the parameters of the random forest model, adjusting the number of trees, the maximum depth, and the minimum number of sample splits to improve prediction accuracy.

5. The method for dynamic maximum power point tracking of a photovoltaic power generation system according to claim 1, characterized in that: The disturbance voltage prediction and adjustment step also includes: introducing a feedback mechanism to fine-tune the prediction model according to the actual effect after adjustment in the previous cycle to improve the dynamic response capability of tracking.

6. The method for dynamic maximum power point tracking of a photovoltaic power generation system according to claim 1, characterized in that: The given voltage adjustment step also includes: using a fuzzy logic control algorithm to adjust the given voltage to improve the accuracy and stability of tracking.