Method for enhancing anti-disturbance capability of off-grid photovoltaic based on intelligent filtering technology

Abstract

The present application relates to off-grid photovoltaic anti-disturbance technical field, specifically for the method for enhancing off-grid photovoltaic anti-disturbance ability based on intelligent filtering technology. First, the known disturbances such as light, temperature, load mutation and the like in the system are analyzed and a model is established. Then, an intelligent filter is designed, an adaptive algorithm is used to monitor and estimate unknown disturbances, and the observation values of DC and harmonic disturbances are obtained and fused. Then, a self-disturbance control controller based on intelligent filtering is constructed, the unknown and known disturbances are added to obtain the centralized disturbance observation value, and the parameters are designed to respond to and suppress the disturbances. Finally, the voltage preliminary control instruction is obtained, the filter observation value is combined to construct the instruction, and the current harmonic disturbance is suppressed. Through these steps, the stability and reliability of the off-grid photovoltaic system are improved.

Description

Technical Field

[0001] This invention relates to the field of off-grid photovoltaic (PV) anti-disturbance technology, specifically a method for enhancing the anti-disturbance capability of off-grid PV based on intelligent filtering technology. Background Technology

[0002] Off-grid photovoltaic (PV) systems, as a renewable energy utilization method, have gained widespread attention and application worldwide due to their clean and environmentally friendly characteristics. These systems typically consist of photovoltaic panels, batteries, controllers, and inverters, converting solar energy into electricity for use in homes or small power grids. However, off-grid PV systems face several challenges in practical operation, particularly the stability of system output. The stability of system output is affected by various factors, with changes in sunlight intensity, temperature, and sudden load fluctuations being the most significant disturbances.

[0003] To improve the stability and disturbance immunity of off-grid photovoltaic (PV) systems, it is necessary to effectively monitor and control these disturbance factors. Traditional control methods often struggle to cope with these complex and ever-changing disturbances. Therefore, developing new control technologies, especially those based on intelligent filtering technology, is of great significance for improving the stability and reliability of off-grid PV systems. Intelligent filtering technology can monitor and estimate disturbances in real time, and adjust filter parameters through adaptive algorithms to improve the accuracy of disturbance estimation. This provides the system with more precise control commands, achieving effective suppression of disturbances. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention proposes a method for enhancing the anti-disturbance capability of off-grid photovoltaic systems based on intelligent filtering technology. By comprehensively considering factors such as changes in light intensity, temperature, and sudden load changes, an intelligent filter and an active disturbance rejection controller are designed, and precise control commands are constructed to suppress current harmonic disturbances in off-grid photovoltaic systems and improve the stability of system output voltage and current.

[0005] The specific technical solution of the present invention is as follows:

[0006] Methods for enhancing the disturbance immunity of off-grid photovoltaic systems based on intelligent filtering technology include:

[0007] Establish perturbation models, including perturbation models for changes in light intensity, temperature changes, and sudden load changes;

[0008] A smart filter is designed, and an adaptive filtering algorithm is used to monitor and calculate the DC disturbance component and harmonic disturbance component to obtain the observed values. The observed values ​​of the DC disturbance component and harmonic disturbance component are then fused to obtain the unknown disturbance observed value.

[0009] The active disturbance rejection controller is constructed, and the active disturbance rejection controller includes a tracking differentiator, an extended state observer and a nonlinear state error feedback control law; the observation value of the centralized disturbance is calculated by using the observation value of the unknown disturbance obtained by the intelligent filter and the known disturbance model, and the parameters of the tracking differentiator, the extended state observer and the nonlinear state error feedback control law of the active disturbance rejection controller are adjusted;

[0010] The control instruction is constructed, the observation values of the direct current disturbance and the harmonic disturbance provided by the intelligent filter are combined, the accurate control instruction is constructed, and the active disturbance rejection controller is controlled through the control instruction to realize the suppression of the current harmonic disturbance of the off-grid photovoltaic system.

[0011] The illumination intensity change disturbance model is: , wherein represents the influence of the illumination intensity disturbance on the output power of the photovoltaic system, is a function describing the change of the illumination intensity with time, is the rated output power of the photovoltaic panel under the standard illumination intensity;

[0012] The temperature change disturbance model is: , wherein represents the temperature of the photovoltaic panel, represents the solar radiation intensity, represents the ambient temperature, and a, b and c are coefficients obtained by experiment fitting;

[0013] The temperature change disturbance model on the output power is: , wherein represents the influence of the temperature change on the output power of the photovoltaic system, k is the temperature coefficient of the photovoltaic panel, is the reference temperature of the photovoltaic panel, is the rated output power of the photovoltaic panel under the reference temperature;

[0014] The load mutation disturbance model is: , wherein is the influence of the load change on the output power of the photovoltaic system, and are the load power values before and after the mutation, is the time when the mutation occurs;

[0015] The disturbance model of the off-grid photovoltaic system is: .

[0016] The intelligent filter includes a low-pass filter and a band-pass filter, wherein the low-pass filter is used for monitoring and calculating the direct current disturbance component, and the band-pass filter is used for monitoring and calculating the harmonic disturbance component.

[0017] The low-pass filter is used for monitoring and calculating the DC disturbance component, and the method comprises the following steps: according to the property of discrete Fourier transform, designing a frequency response function, setting the spectral coefficients of high-frequency components to 0, and reserving the spectral coefficients of low-frequency components; then obtaining the filtered time-domain signal, i.e. the preliminary calculated value of the DC disturbance component, through inverse discrete Fourier transform; and finally continuously adjusting the parameters of the low-pass filter through an adaptive algorithm;

[0018] The method for continuously adjusting the parameters of the low-pass filter through the adaptive algorithm comprises the following steps:

[0019] Supposing that the input signal is , the output of the filter is , the expected output is , and the weight coefficient of the filter is , according to the LMS algorithm, the update formula of the weight coefficient is: wherein is a step factor, is an error signal.

[0020] The band-pass filter is used for monitoring and calculating the harmonic disturbance component, and the method comprises the following steps: designing a group of frequency response functions, each of which corresponds to the frequency range of a band-pass filter; converting the input signal into a frequency-domain signal through discrete Fourier transform, then extracting the harmonic signal of the corresponding frequency according to the frequency response function, obtaining the time-domain harmonic disturbance component through inverse discrete Fourier transform, and finally adjusting the parameters of the band-pass filter through an adaptive algorithm;

[0021] The method for adjusting the parameters of the band-pass filter through the adaptive algorithm comprises the following steps:

[0022] Supposing that the input signal of each band-pass filter is , the output is , the expected output is , and the weight coefficient of the filter is , the update formula of the weight coefficient is: wherein is a step factor, is an error signal.

[0023] The observation values of the DC disturbance component and the harmonic disturbance component are fused through weighted average, supposing that the observation value of the DC disturbance component is , the observation value of the harmonic disturbance component is , and the fused unknown disturbance observation value is , then wherein is a weighted coefficient.

[0024] The tracking differentiator is realized through the following formula:

[0025]

[0026] where and are the output of the tracking differentiator and its derivative, respectively, is the sampling step, is a velocity factor, is a nonlinear function;

[0027] The extended state observer is implemented by the following equation:

[0028] where and are the system state and unknown disturbance calculated by the extended state observer, respectively, is the output signal of the system, is the input signal of the system, is the parameter of the observer;

[0029] The nonlinear state error feedback control law is implemented by the following equation: ,

[0030] where is the state error of the system, is the derivative of the state error, is the parameter of the controller, is a compensation term.

[0031] The control command is constructed by the following equation:

[0032] Let the preliminary control command of the voltage be , the DC disturbance observation value be , and the harmonic disturbance observation value be .

[0033] For the compensation of the DC disturbance, the new control command is constructed by the following equation: where is the DC disturbance compensation coefficient, which is determined according to experiments and simulations.

[0034] For the suppression of the harmonic disturbance, the new control command is constructed by the following equation: , where is the harmonic disturbance suppression coefficient, is the considered harmonic order, is the fundamental frequency

[0035] The technical scheme provided by the embodiments of the present application brings at least the following beneficial effects:

[0036] Firstly, in the disturbance analysis and modeling, through the analysis and modeling of multiple known disturbances, the system behavior can be deeply understood and the adaptability can be improved. In the intelligent filter design aspect, the disturbance perception ability is enhanced and the filtering accuracy is improved. In the construction of the active disturbance rejection controller, the disturbance can be effectively suppressed, and the system stability and dynamic performance can be improved. In the control instruction construction and disturbance suppression aspect, accurate control and disturbance compensation are realized, and the user demand and system stable operation are met.

[0037] Secondly, overall, the present application comprehensively improves the anti-disturbance ability of the off-grid photovoltaic system. From accurate analysis and perception of disturbances to effective suppression and compensation, each link closely cooperates to ensure the stability of the system output voltage and current, improve the system efficiency and reliability, and lay a solid foundation for the wide application and sustainable development of the off-grid photovoltaic system. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 The overall flowchart of the method for enhancing the anti-disturbance ability of the off-grid photovoltaic system based on the intelligent filtering technology is shown; DETAILED DESCRIPTION

[0039] The technical scheme in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a 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 fall within the scope of protection of the present application.

[0040] Please refer to Figure 1 which shows the method for enhancing the anti-disturbance ability of the off-grid photovoltaic system based on the intelligent filtering technology provided by an embodiment of the present application, which comprises:

[0041] A disturbance model is established, including a light intensity change disturbance model, a temperature change disturbance model and a load mutation disturbance model;

[0042] An intelligent filter is designed, and an adaptive filtering algorithm is used to realize the monitoring and calculation of the direct current disturbance component and the harmonic disturbance component, to obtain observation values. The observation values of the direct current disturbance component and the harmonic disturbance component are fused to obtain unknown disturbance observation values;

[0043] The active disturbance rejection controller is constructed, and the active disturbance rejection controller includes a tracking differentiator, an extended state observer, and a nonlinear state error feedback control law; the unknown disturbance observation value obtained by the intelligent filter and the known disturbance model are used to calculate the observation value of the concentrated disturbance, and the parameters of the tracking differentiator, the extended state observer, and the nonlinear state error feedback control law of the active disturbance rejection controller are adjusted;

[0044] The control instruction is constructed, the observation values of the direct current disturbance and the harmonic disturbance provided by the intelligent filter are combined to construct an accurate control instruction, and the active disturbance rejection controller is controlled through the control instruction to realize the suppression of the harmonic disturbance of the off-grid photovoltaic system current.

[0045] I. Disturbance analysis and model establishment

[0046] 1. Analysis of illumination intensity change disturbance

[0047] Illumination intensity is one of the key factors affecting the output of off-grid photovoltaic systems. In the actual environment, the illumination intensity will change with time, weather conditions, geographical location, and season. For example, during the day, the illumination intensity gradually increases from morning to noon, reaches a maximum at noon, and then gradually decreases. Different weather conditions such as sunny, cloudy, and overcast will also cause great differences in illumination intensity.

[0048] In order to establish a disturbance model of illumination intensity change, illumination intensity sensors are installed near the photovoltaic panels. These sensors can collect illumination intensity data in real time. Through analysis of a large amount of collected data, it is found that the change of illumination intensity can be approximately described by a time series function. For example, a piecewise linear function or a combination of triangular functions is used to fit the curve of illumination intensity change over time.

[0049] Considering the influence of different geographical locations and seasons, the collected data also needs to be normalized. Divide the collected illumination intensity value by the average maximum illumination intensity of the region in a particular season to obtain a dimensionless relative illumination intensity value. This makes the established model more universal and applicable to off-grid photovoltaic systems in different regions and seasons.

[0050] According to the above analysis, the disturbance model of illumination intensity change is established as follows: wherein represents the influence of illumination intensity disturbance on the output power of the photovoltaic system, is a function describing the change of illumination intensity over time, is the rated output power of the photovoltaic panel under standard illumination intensity.

[0051] 2. Analysis of temperature change disturbance

[0052] Temperature also has a significant impact on the performance of photovoltaic panels. As the temperature rises, the open-circuit voltage of the photovoltaic panel will decrease, the short-circuit current will increase slightly, but the overall output power will decrease. The change in temperature is mainly related to the ambient temperature, the heat dissipation conditions of the photovoltaic panel, and the intensity of solar radiation.

[0053] A temperature sensor is installed on the photovoltaic panel to monitor temperature changes in real time. Through experiments and data analysis, it is found that there is a certain linear relationship between the temperature change of the photovoltaic panel and the intensity of solar radiation and the ambient temperature. The following temperature change disturbance model is established: , where represents the temperature of the photovoltaic panel, represents the intensity of solar radiation, represents the ambient temperature, and a, b, c are coefficients obtained by experimental fitting.

[0054] Further analysis of the impact of temperature on the output power of the photovoltaic panel, according to the temperature coefficient characteristics of the photovoltaic panel, the disturbance model of temperature change on the output power is: , where represents the impact of temperature change on the output power of the photovoltaic system, k is the temperature coefficient of the photovoltaic panel, is the reference temperature of the photovoltaic panel, is the rated output power of the photovoltaic panel at the reference temperature.

[0055] 3. Load mutation disturbance analysis

[0056] In off-grid photovoltaic systems, the load change is relatively complex. The load may suddenly increase or decrease. When the user turns on or off a high-power electrical device, for example, the load mutation will cause the output voltage and current of the photovoltaic system to change dramatically, affecting the stability of the system.

[0057] In order to analyze the disturbance of load mutation, current and voltage sensors are installed at the load end. By monitoring the changes in load current and voltage, the power change curve of the load is obtained. When the load mutates, the load power values before and after the mutation are recorded and , and the time of mutation .

[0058] The load mutation disturbance model is established as: .

[0059] Considering factors such as changes in light intensity, temperature changes, and load mutations, the disturbance model of the off-grid photovoltaic system is established as: .

[0060] II. Intelligent filter design

[0061] 1. Principle of intelligent filter

[0062] The design purpose of the intelligent filter is to monitor and calculate unknown disturbances in the disturbance model in real time, obtain observation values of the direct current disturbance component and the harmonic disturbance component, and fuse them to obtain observation values of the unknown disturbance.

[0063] An adaptive filtering algorithm is used to realize the function of the intelligent filter. The adaptive filtering algorithm can automatically adjust the parameters of the filter according to the statistical characteristics of the input signal to achieve the best filtering effect.

[0064] For the monitoring and calculation of the direct current disturbance component, the principle of a low-pass filter is used. A digital low-pass filter with a relatively low cutoff frequency is designed to filter out the high-frequency components in the input signal to obtain the preliminary calculation value of the direct current disturbance component. Then, the parameters of the low-pass filter are continuously adjusted through the adaptive algorithm to improve the accuracy of the calculation of the direct current disturbance component.

[0065] For the monitoring and calculation of the harmonic disturbance component, a method combining a band-pass filter and an adaptive algorithm is used. First, a set of band-pass filters is designed to cover the harmonic frequency range that may occur. For example, for an off-grid photovoltaic system, the 5th, 7th, 11th, etc. harmonic frequencies may need to be considered. Each band-pass filter can extract the harmonic signal of the corresponding frequency, and then the parameters of the band-pass filter are adjusted through the adaptive algorithm to accurately calculate the harmonic disturbance component.

[0066] 2. Implementation details of the intelligent filter

[0067] In digital signal processing, the discrete Fourier transform is used to realize the function of the filter. For a low-pass filter, according to the properties of the discrete Fourier transform, a suitable frequency response function is designed, the spectral coefficients of high-frequency components are set to 0, and the spectral coefficients of low-frequency components are retained. Then, the filtered time-domain signal, i.e. the preliminary calculation value of the direct current disturbance component, is obtained through inverse discrete Fourier transform.

[0068] For a band-pass filter, according to the properties of the discrete Fourier transform, a set of frequency response functions is designed, each corresponding to the frequency range of a band-pass filter. The input signal is converted to a frequency domain signal through discrete Fourier transform, then the harmonic signal of the corresponding frequency is extracted according to the frequency response function, and finally the time-domain harmonic disturbance component is obtained through inverse discrete Fourier transform.

[0069] The implementation of the adaptive algorithm uses the least mean square (LMS) algorithm. Taking a low-pass filter as an example, let the input signal be , the output of the filter be , the expected output be , and the weight coefficient of the filter be . According to the LMS algorithm, the update formula of the weight coefficient is: where is a step factor, is an error signal. By constantly updating the weight coefficient, the output of the filter gradually approaches the desired output, thereby improving the accuracy of the direct current disturbance component calculation.

[0070] For the calculation of the harmonic disturbance component, the LMS algorithm is applied on the basis of each band-pass filter. Let the input signal of each band-pass filter be , the output be , the desired output be , and the weight coefficient of the filter be . The update formula of the weight coefficient is: , where is a step factor, is an error signal. By constantly updating the weight coefficient, the output of the band-pass filter gradually approaches the desired output, thereby improving the accuracy of the harmonic disturbance component calculation.

[0071] Finally, the observed values of the direct current disturbance component and the harmonic disturbance component are fused. Using the weighted average method, let the observed value of the direct current disturbance component be , the observed value of the harmonic disturbance component be , and the fused unknown disturbance observed value be , then , where is a weighting coefficient, which is adjusted according to the actual situation.

[0072] III. Construction of active disturbance rejection controller

[0073] 1. Principle of active disturbance rejection controller

[0074] The purpose of constructing an active disturbance rejection controller based on intelligent filtering is to add the observed value of the unknown disturbance to the known disturbance to obtain the observed value of the concentrated disturbance, and to design the controller parameters so that the controller can respond to and suppress the disturbance.

[0075] The core idea of the active disturbance rejection controller is to eliminate the influence of the disturbance on the system by calculating and compensating the state of the system. It mainly consists of a tracking differentiator, an extended state observer, and a nonlinear state error feedback control law.

[0076] The role of the tracking differentiator is to filter and differentiate the input signal of the system to obtain a smooth reference trajectory and its derivative. It helps to reduce the overshoot of the system and improve the response speed of the system.

[0077] The role of the extended state observer is to calculate the state of the system and the unknown disturbance. It calculates the state variables and unknown disturbance variables of the system according to the input and output signals of the system through a certain algorithm. In the application, the extended state observer uses the unknown disturbance observation value obtained by the intelligent filter and the known disturbance model to calculate the concentrated disturbance of the system.

[0078] The role of the nonlinear state error feedback control law is to design a suitable control law according to the state error of the system and the concentrated disturbance calculated by the extended state observer, so that the system can quickly and accurately track the reference trajectory and suppress the influence of the disturbance on the system.

[0079] 2. Details of the active disturbance rejection controller

[0080] For the tracking differentiator, the following formula is used to implement it:

[0081]

[0082] where and are the output of the tracking differentiator and its derivative, respectively, is the sampling step, is a speed factor, is a nonlinear function, the specific form of which is adjusted according to the characteristics of the system and the control requirements.

[0083] For the extended state observer (ESO), the following formula is used to implement it:

[0084] where and are the system state and unknown disturbance calculated by the extended state observer, respectively, is the output signal of the system, is the input signal of the system, is the parameter of the observer, which is adjusted according to the characteristics of the system and the control requirements.

[0085] For the nonlinear state error feedback control law (NLSEF), the following formula is used to implement it: ,

[0086] where is the state error of the system, is the derivative of the state error, is the parameter of the controller, is a compensation term, the specific form of which is adjusted according to the characteristics of the system and the control requirements.

[0087] In the construction of the active disturbance rejection controller, the observation value of the unknown disturbance is first added to the known disturbance to obtain the observation value of the centralized disturbance. Then, according to the dynamic characteristics of the system and the control requirements, the parameters of the tracking differentiator, the extended state observer and the nonlinear state error feedback control law are adjusted.

[0088] Four, control instruction construction and disturbance suppression

[0089] 1. Control instruction construction principle

[0090] The preliminary control instruction of the voltage output by the off-grid photovoltaic system is obtained, and the observation values of the DC disturbance and harmonic disturbance provided by the intelligent filter are combined to construct a more accurate control instruction to realize the suppression of the harmonic disturbance of the off-grid photovoltaic system current.

[0091] First, according to the basic principles and control objectives of the off-grid photovoltaic system, the preliminary control instruction of the voltage is obtained through the control method (such as PID control).

[0092] Then, the observation values of the DC disturbance and harmonic disturbance provided by the intelligent filter are integrated into the control instruction. For the DC disturbance, the influence of the DC disturbance on the system is compensated by adjusting the DC component in the control instruction. For the harmonic disturbance, the influence of the harmonic disturbance on the system is suppressed by adding the corresponding harmonic suppression term in the control instruction.

[0093] 2. Control instruction construction implementation details

[0094] Let the preliminary control instruction of the voltage be , the observation value of the DC disturbance be , and the observation value of the harmonic disturbance be .

[0095] For the compensation of the DC disturbance, the new control instruction is constructed as: , where is the DC disturbance compensation coefficient, which is determined according to experiments and simulations.

[0096] For the suppression of the harmonic disturbance, the new control instruction is constructed as: , where is the harmonic disturbance suppression coefficient, is the considered harmonic number, is the fundamental frequency. Through the control instruction construction method, the influence of the DC disturbance and the harmonic disturbance on the off-grid photovoltaic system is effectively suppressed, and the stability of the system output voltage and current is improved.

[0097] The above describes the basic principles of the present application with reference to specific embodiments, but it should be noted that the advantages, benefits, effects and the like mentioned in the present application are only examples and are not limiting, and these advantages, benefits, effects and the like cannot be considered as necessary for each embodiment of the present application. In addition, the above-mentioned specific details are only for the purpose of example and understanding, and are not limiting, and the above-mentioned details do not limit the present application to be necessarily implemented with the above-mentioned specific details.

[0098] The block diagrams of the devices, apparatuses, equipment, systems involved in the present application are only illustrative examples and are not intended to require or imply the connection, arrangement, configuration shown in the block diagram. As those skilled in the art will recognize, these devices, apparatuses, equipment, systems are connected, arranged, configured in any manner. Words such as "include", "contain", "have" and the like are open-ended words, mean "include but not limited to", and can be used interchangeably. The words "or" and "and" used herein mean the word "and / or", and can be used interchangeably unless the context clearly indicates otherwise. The word "such as" used herein means the phrase "such as but not limited to", and can be used interchangeably.

[0099] It should also be noted that in the devices, equipment and methods of the present application, each component or each step is decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions of the present application.

[0100] The above description of the disclosed aspects is provided so that any person skilled in the art can make or use the present application. Various modifications to these aspects will be apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but is intended to be consistent with the widest scope consistent with the principles and novel features disclosed herein.

[0101] The above is only the preferred embodiment of the present application, and does not limit the present application, and any modification, equivalent replacement and the like made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for enhancing the anti-disturbance capability of off-grid photovoltaic systems based on intelligent filtering technology, characterized in that, include: Establish perturbation models, including perturbation models for changes in light intensity, temperature changes, and sudden load changes; A smart filter is designed, and an adaptive filtering algorithm is used to monitor and calculate the DC disturbance component and harmonic disturbance component to obtain the observed values. The observed values ​​of the DC disturbance component and harmonic disturbance component are then fused to obtain the unknown disturbance observed value. An active disturbance rejection controller (ADRC) is constructed, which includes a tracking differentiator, an extended state observer, and a nonlinear state error feedback control law. Using the unknown disturbance observations obtained from the intelligent filter and the known disturbance model, the observed values ​​of lumped disturbances are calculated, and the parameters of the tracking differentiator, extended state observer, and nonlinear state error feedback control law of the ADRC are adjusted. Construct control commands and combine them with the observed values ​​of DC disturbance and harmonic disturbance provided by the intelligent filter to construct precise control commands. Use these control commands to control the active disturbance rejection controller to suppress the current harmonic disturbance of the off-grid photovoltaic system. The light intensity variation perturbation model is as follows: ,in This indicates the impact of light intensity disturbances on the output power of the photovoltaic system. It is a function describing the change of light intensity over time. This is the rated output power of the photovoltaic panel under standard light intensity; The temperature change perturbation model: ,in Indicates the temperature of the photovoltaic panel. Indicates the intensity of solar radiation. The ambient temperature is represented by coefficients a, b, and c, which are obtained through experimental fitting. The disturbance model of temperature change on output power is as follows: ,in This indicates the effect of temperature changes on the output power of a photovoltaic system, where k is the temperature coefficient of the photovoltaic panel. This is the reference temperature for the photovoltaic panel. This is the rated output power of the photovoltaic panel at the reference temperature; The load mutation disturbance model is established as follows: ,in, To explain the impact of load changes on the output power of photovoltaic systems. and Load power values ​​before and after the mutation. The time when the mutation occurred; The disturbance model for off-grid photovoltaic systems is as follows: ; The intelligent filter includes a low-pass filter and a band-pass filter, wherein the low-pass filter is used for monitoring and calculating DC disturbance components, and the band-pass filter is used for monitoring and calculating harmonic disturbance components. The steps for monitoring and calculating the DC disturbance component using the low-pass filter are as follows: Based on the properties of the discrete Fourier transform, a frequency response function is designed, the spectral coefficients of the high-frequency components are set to 0, and the spectral coefficients of the low-frequency components are retained; then, the filtered time-domain signal, i.e., the preliminary calculated value of the DC disturbance component, is obtained through the inverse discrete Fourier transform; finally, the parameters of the low-pass filter are continuously adjusted through an adaptive algorithm. The adaptive algorithm continuously adjusts the parameters of the low-pass filter as follows: Let the input signal be The output of the filter is The expected output is The filter's weight coefficients are According to the LMS algorithm, the update formula for the weight coefficients is: ,in It is the step size factor. It is an error signal.

2. The method for enhancing the anti-disturbance capability of off-grid photovoltaic systems based on intelligent filtering technology according to claim 1, characterized in that, The bandpass filter is used for monitoring and calculating harmonic disturbance components. The steps are as follows: design a set of frequency response functions, each frequency response function corresponding to the frequency range of a bandpass filter; convert the input signal into a frequency domain signal through discrete Fourier transform; extract the harmonic signal of the corresponding frequency according to the frequency response function; obtain the harmonic disturbance component in the time domain through inverse discrete Fourier transform; and finally adjust the parameters of the bandpass filter through an adaptive algorithm. The method for adjusting the parameters of the bandpass filter using an adaptive algorithm is as follows: Let the input signal of each bandpass filter be... The output is The expected output is The filter's weight coefficients are The formula for updating the weight coefficients is: ,in It is the step size factor. It is an error signal.

3. The method for enhancing the anti-disturbance capability of off-grid photovoltaic systems based on intelligent filtering technology according to claim 1 or 2, characterized in that, The observed values ​​of the DC disturbance component and the harmonic disturbance component are fused using a weighted average. Let the observed value of the DC disturbance component be... The observed values ​​of the harmonic disturbance components are The merged unknown perturbation observation value ,but ,in These are weighting coefficients.

4. The method for enhancing the anti-disturbance capability of off-grid photovoltaic systems based on intelligent filtering technology according to claim 1, characterized in that, The tracking differentiator is implemented using the following formula: ； in and These are the output of the tracking differentiator and its derivative, respectively. It is the sampling step size. It is a velocity factor. It is a non-linear function; The extended state observer is implemented using the following formula: ； in and These are the system state calculated by the extended state observer and the unknown disturbance, respectively. It is the system's output signal. It is the system's input signal. These are the parameters of the observer; The nonlinear state error feedback control law is implemented using the following formula: , in It is the system's state error. It is the derivative of the state error. These are the controller parameters. It is a compensation item.

5. The method for enhancing the anti-disturbance capability of off-grid photovoltaic systems based on intelligent filtering technology according to claim 1, characterized in that, The control command construction method includes: Let the initial control command for the voltage be: DC disturbance observation value The observed value of harmonic disturbance is ; For compensation of DC disturbances, the new control command is constructed as follows: ,in It is the DC disturbance compensation coefficient, determined based on experiments and simulations; To suppress harmonic disturbances, a new control command is constructed as follows: ， in It is the harmonic disturbance suppression coefficient. It considers the harmonic order. It is the fundamental frequency.

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