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

Abstract

The invention relates to the technical field of off-grid photovoltaic disturbance rejection, in particular to a method for enhancing off-grid photovoltaic disturbance rejection capability based on an intelligent filtering technology. Firstly, known disturbances such as illumination, temperature and load abrupt change in the system are analyzed, and a model is established. Designing an intelligent filter, monitoring and estimating unknown disturbance by using a self-adaptive algorithm, obtaining direct current and harmonic disturbance observation values, and fusing the direct current and harmonic disturbance observation values; and then an active disturbance rejection controller based on intelligent filtering is constructed, unknown disturbance and known disturbance are added to obtain a concentrated disturbance observation value, and parameters are designed to respond to suppression disturbance. And finally, obtaining a voltage preliminary control instruction, and combining with a filter observation value construction instruction to suppress current harmonic disturbance. Through the steps, the stability and reliability of the off-grid photovoltaic system are improved.

Description

Technical Field

[0001] The present invention relates to the field of off-grid photovoltaic anti-disturbance technology, and in particular to a method for enhancing off-grid photovoltaic anti-disturbance capability based on intelligent filtering technology. Background Art

[0002] As a renewable energy utilization method, off-grid photovoltaic systems have received extensive attention and application worldwide due to their clean and environmentally friendly characteristics. Such systems are usually composed of photovoltaic panels, batteries, controllers and inverters, which can convert solar energy into electrical energy for use in homes or small power grids. However, off-grid photovoltaic systems face many challenges in actual operation, especially the stability of system output. The stability of system output is affected by many factors, among which changes in light intensity, temperature and load mutations are the main disturbance factors.

[0003] In order to improve the stability and anti-disturbance ability of off-grid photovoltaic systems, it is necessary to effectively monitor and control these disturbance factors. Traditional control methods are often difficult to cope with these complex and changeable disturbances. Therefore, the development of new control technologies, especially control methods based on intelligent filtering technology, is of great significance to improving the stability and reliability of off-grid photovoltaic systems. Intelligent filtering technology can monitor and estimate disturbances in real time, adjust filter parameters through adaptive algorithms to improve the estimation accuracy of disturbances, thereby providing the system with more accurate control instructions and achieving effective suppression of disturbances. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention proposes a method for enhancing the anti-disturbance capability of off-grid photovoltaics based on intelligent filtering technology. By comprehensively considering factors such as changes in light intensity, temperature changes, and sudden load changes, an intelligent filter and an anti-disturbance controller are designed, and precise control instructions are constructed to suppress current harmonic disturbances in the off-grid photovoltaic system and improve the stability of the system output voltage and current.

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

[0006] The method of enhancing the anti-disturbance capability of off-grid photovoltaic based on intelligent filtering technology includes:

[0007] Establish disturbance models, including light intensity change disturbance model, temperature change disturbance model and load mutation disturbance model;

[0008] Design intelligent filters, use adaptive filtering algorithms to monitor and calculate DC disturbance components and harmonic disturbance components, obtain observation values, fuse the observation values ​​of DC disturbance components and harmonic disturbance components, and obtain unknown disturbance observation values;

[0009] Construct an active disturbance rejection controller, which includes a tracking differentiator, an extended state observer, and a nonlinear state error feedback control law; use the unknown disturbance observation value obtained by the intelligent filter and the known disturbance model to calculate the observation value of the concentrated disturbance, and adjust the parameters of the tracking differentiator, the extended state observer, and the nonlinear state error feedback control law of the active disturbance rejection controller;

[0010] Construct control instructions, combine the observed values ​​of DC disturbance and harmonic disturbance provided by the intelligent filter, construct accurate control instructions, and control the self-disturbance rejection controller through control instructions to achieve the suppression of current harmonic disturbance of off-grid photovoltaic system.

[0011] The disturbance model of light intensity change is: P light ( t )=f(t)×I0,where P light (t) represents the impact of light intensity disturbance on the output power of the photovoltaic system, f(t) is a function that describes the change of light intensity over time, and I0 is the rated output power of the photovoltaic panel under standard light intensity;

[0012] Temperature change disturbance model: T(t) = a×S(t)+b×T env (t)+c, where T(t) represents the temperature of the photovoltaic panel, S(t) represents the solar radiation intensity, and T env (t) represents the ambient temperature, a, b, and c are coefficients obtained by experimental fitting;

[0013] The disturbance model of temperature change on output power is: P temp (t) = k × (T (t) - T ref )×P rated , where P temp (t) represents the effect of temperature change on the output power of the photovoltaic system, k is the temperature coefficient of the photovoltaic panel, T ref is the reference temperature of the photovoltaic panel, P rated is the rated output power of the photovoltaic panel at the reference temperature;

[0014] The load mutation disturbance model is established as: Among them, P load (t) is the impact of load change on the output power of the photovoltaic system, P load1 and P load2 The load power value before and after the mutation, t0 is the time when the mutation occurs;

[0015] The disturbance model of the off-grid photovoltaic system is: P total (t) = P light (t)+P termp (t)+P load (t).

[0016] The intelligent filter comprises 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 steps of using the low-pass filter to monitor and calculate the DC disturbance component are as follows: according to the properties of discrete Fourier transform, a frequency response function is designed, the spectrum coefficients of the high-frequency component are set to 0, and the spectrum coefficients of the low-frequency component are retained; then, a filtered time domain signal, i.e., a preliminary calculated value of the DC disturbance component, is obtained by inverse discrete Fourier transform, and finally, the parameters of the low-pass filter are continuously adjusted by an adaptive algorithm;

[0018] Among them, the adaptive algorithm continuously adjusts the parameters of the low-pass filter as follows:

[0019] Assume the input signal is x(n), the output of the filter is y(n), the expected output is d(n), and the weight coefficient of the filter is w(n). According to the LMs algorithm, the update formula of the weight coefficient is: w(n+1)=w(n)+μ×e(n)×x(n), where μ is the step size factor, e(n)=d(n)y(n) is the error signal.

[0020] The steps of monitoring and calculating the harmonic disturbance component of the bandpass filter are as follows: designing a set of frequency response functions, each frequency response function corresponds to a frequency range of the bandpass filter; converting the input signal into a spectrum domain signal by discrete Fourier transform, then extracting the harmonic signal of the corresponding frequency according to the frequency response function, then obtaining the harmonic disturbance component in the time domain by inverse discrete Fourier transform, and finally adjusting the parameters of the bandpass filter by an adaptive algorithm;

[0021] Wherein, the method for adjusting the parameters of the bandpass filter by an adaptive algorithm is:

[0022] Assume that the input signal of each bandpass filter is x i (n), the output is y i (n), the expected output is d i (n), the weight coefficient of the filter is w i (n). The updating formula of weight coefficient is: i (n+1)=w i (n)+μ×e i (n)×x i (n), where μ is the step size factor, e i (n) = d i (n)-y i (n) is the error signal.

[0023] The observed values ​​of the DC disturbance component and the harmonic disturbance component are fused by weighted average. Suppose the observed value of the DC disturbance component is uDC , the observed value of the harmonic disturbance component is uH armonic , the unknown disturbance observation value after fusion is u, then u=α×u DC +(1-α)×uH armonic , where α is the weighting coefficient.

[0024] The tracking differentiator is implemented using the following formula:

[0025]

[0026] Where v1(k) and v2(k) are the output of the tracking differentiator and its derivative, h is the sampling step, r is a speed factor, and f(v1(k), v2(k), r, h) is a nonlinear function;

[0027] The extended state observer is implemented using the following formula:

[0028]

[0029] Where z1(k) and z2(k) are the system state and unknown disturbance calculated by the extended state observer, y(k) is the output signal of the system, u(k) is the input signal of the system, and β1, β2, and β3 are the parameters of the observer:

[0030] The nonlinear state error feedback control law is implemented using the following formula:

[0031]

[0032] Where e(k) is the state error of the system, e′(k) is the derivative of the state error, and k p , k d , k i are the parameters of the controller and u0(k) is a compensation term.

[0033] 8. The method for enhancing the anti-disturbance capability of off-grid photovoltaic based on intelligent filtering technology according to claim 1, characterized in that the control instruction construction method includes:

[0034] Assume that the initial control command of voltage is U0(t) and the DC disturbance observation value is u DC (t), the observed value of harmonic disturbance is u Harmonic (n).

[0035] For compensation of DC disturbance, a new control instruction is constructed as: U1(t)=U0(t)+k DC ×u DC (t), where k DC is the DC disturbance compensation coefficient, which is determined based on experiments and simulations.

[0036] For the suppression of harmonic disturbances, a new control instruction is constructed as follows: where k Harmonic is the harmonic disturbance suppression coefficient, N is the number of harmonics considered, and ω is the fundamental frequency

[0037] The beneficial effects brought about by the technical solution provided in the embodiments of the present application include at least the following beneficial effects:

[0038] First, in terms of disturbance analysis and modeling, through the analysis and modeling of a variety of known disturbances, we can gain a deep understanding of system behavior and improve adaptability. In terms of intelligent filter design, the disturbance perception capability is enhanced and the filtering accuracy is improved. In the construction of the anti-disturbance controller, disturbances can be effectively suppressed, and the system stability and dynamic performance can be improved. In terms of control instruction construction and disturbance suppression, precise control and disturbance compensation are achieved to meet user needs and ensure stable system operation.

[0039] Secondly, overall, the present invention comprehensively improves the anti-disturbance capability of off-grid photovoltaic systems. From accurate analysis and perception of disturbances to effective suppression and compensation, each link works closely together to ensure the stability of the system output voltage and current, improve system efficiency and reliability, and lay a solid foundation for the widespread application and sustainable development of off-grid photovoltaic systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The overall flow chart of the method for enhancing the anti-disturbance capability of off-grid photovoltaics based on intelligent filtering technology is shown; DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0042] See also Figure 1 , which shows a method for enhancing the anti-disturbance capability of off-grid photovoltaic based on intelligent filtering technology provided by an embodiment of the present invention, including:

[0043] Establish disturbance models, including light intensity change disturbance model, temperature change disturbance model and load mutation disturbance model;

[0044] Design intelligent filters, use adaptive filtering algorithms to monitor and calculate DC disturbance components and harmonic disturbance components, obtain observation values, fuse the observation values ​​of DC disturbance components and harmonic disturbance components, and obtain unknown disturbance observation values;

[0045] Construct an active disturbance rejection controller, which includes a tracking differentiator, an extended state observer, and a nonlinear state error feedback control law; use the unknown disturbance observation value obtained by the intelligent filter and the known disturbance model to calculate the observation value of the concentrated disturbance, and adjust the parameters of the tracking differentiator, the extended state observer, and the nonlinear state error feedback control law of the active disturbance rejection controller;

[0046] Construct control instructions, combine the observed values ​​of DC disturbance and harmonic disturbance provided by the intelligent filter, construct accurate control instructions, and control the self-disturbance rejection controller through control instructions to achieve the suppression of current harmonic disturbance of off-grid photovoltaic system.

[0047] 1. Perturbation Analysis and Model Building

[0048] 1. Analysis of light intensity change disturbance

[0049] Light intensity is one of the key factors affecting the output of off-grid photovoltaic systems. In actual environments, light intensity varies with time, weather conditions, geographic location, seasons, and other factors. For example, during a day, light intensity gradually increases from early morning to noon, reaches a maximum at noon, and then gradually decreases. Different weather conditions such as sunny, cloudy, and overcast days can also lead to large differences in light intensity.

[0050] In order to establish a disturbance model of light intensity changes, light intensity sensors are installed near the photovoltaic panels. These sensors can collect light intensity data in real time. Through the analysis of a large amount of collected data, it is found that the change of light intensity can be approximately described by a time series function. Exemplarily, a piecewise linear function or a combination of trigonometric functions is used to fit the curve of light intensity change over time.

[0051] Taking into account the influence of different geographical locations and seasons, the collected data needs to be normalized. The collected light intensity value is divided by the maximum average light intensity of the area in a specific season to obtain a dimensionless relative light intensity value. This makes the established model more universal and applicable to off-grid photovoltaic systems in different regions and seasons.

[0052] According to the above analysis, the established light intensity change disturbance model is: P light (t) = f(t) × I0, where F light (t) represents the impact of light intensity disturbance on the output power of the photovoltaic system, f(t) is a function that describes the change of light intensity over time, and I0 is the rated output power of the photovoltaic panel under standard light intensity.

[0053] 2. Temperature change disturbance analysis

[0054] 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.

[0055] Temperature sensors are installed on photovoltaic panels 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 photovoltaic panels and the intensity of solar radiation and ambient temperature. The following temperature change disturbance model is established: T(t) = a×S(t) + b×T env (t)+c, where T(t) represents the temperature of the photovoltaic panel, S(t) represents the solar radiation intensity, and T env (t) represents the ambient temperature, and a, b, and c are coefficients obtained through experimental fitting.

[0056] Further analysis of the impact of temperature on the output power of photovoltaic panels, based on the temperature coefficient characteristics of photovoltaic panels, the disturbance model of temperature change on output power is obtained: P temp (t) = k × (T (t) - T ref )×P rated , where P temp (t) represents the effect of temperature change on the output power of the photovoltaic system, k is the temperature coefficient of the photovoltaic panel, T ref is the reference temperature of the photovoltaic panel, P rated is the rated output power of the photovoltaic panel at the reference temperature.

[0057] 3. Load Sudden Change Disturbance Analysis

[0058] In off-grid photovoltaic systems, the load changes are complex. The load may increase or decrease suddenly. For example, when a user turns on or off a high-power electrical device, the load mutation will cause the output voltage and current of the photovoltaic system to change dramatically, affecting the stability of the system.

[0059] In order to analyze the disturbance caused by load mutation, current and voltage sensors are installed at the load end. By monitoring the changes in load current and voltage, the load power change curve is obtained. When the load mutates, the load power value P before and after the mutation is recorded. load1 and P load2 , and the time t0 when the mutation occurs.

[0060] The load mutation disturbance model is established as:

[0061] Taking into account factors such as light intensity change, temperature change and load mutation, the disturbance model of the off-grid photovoltaic system is established as follows: total (t) = P light (t)+P temp(t)+P load (t).

[0062] 2. Intelligent filter design

[0063] 1. Principle of intelligent filter

[0064] The design purpose of the intelligent filter is to monitor and calculate the unknown disturbance in the disturbance model in real time, obtain the observation values ​​of the DC disturbance component and the harmonic disturbance component, and fuse them to obtain the observation value of the unknown disturbance.

[0065] Adaptive filtering algorithm is used to realize the function of intelligent filter. 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.

[0066] For the monitoring and calculation of DC disturbance components, the principle of low-pass filter is used. A digital low-pass filter with a low cut-off frequency is designed to filter out the high-frequency components in the input signal and obtain the preliminary calculated value of the DC 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 DC disturbance component.

[0067] For the monitoring and calculation of harmonic disturbance components, a method combining bandpass filters and adaptive algorithms is used. First, a set of bandpass filters is designed to cover the possible harmonic frequency range. For example, for off-grid photovoltaic systems, it may be necessary to consider the 5th, 7th, 11th and other harmonic frequencies. Each bandpass filter can extract the harmonic signal of the corresponding frequency, and then the parameters of the bandpass filter are adjusted through the adaptive algorithm to accurately calculate the harmonic disturbance components.

[0068] 2. Smart filter implementation details

[0069] In digital signal processing, discrete Fourier transform is used to realize the function of filter. For low-pass filter, according to the properties of discrete Fourier transform, a suitable frequency response function is designed to set the spectrum coefficient of high-frequency component to 0 and retain the spectrum coefficient of low-frequency component. Then, the filtered time domain signal, i.e. the preliminary calculated value of DC disturbance component, is obtained by inverse discrete Fourier transform.

[0070] For the bandpass filter, a set of frequency response functions are designed according to the properties of discrete Fourier transform, and each frequency response function corresponds to the frequency range of a bandpass filter. The input signal is converted into a spectrum domain signal through discrete Fourier transform, and then the harmonic signal of the corresponding frequency is extracted according to the frequency response function, and then the harmonic disturbance component in the time domain is obtained through inverse discrete Fourier transform.

[0071] The implementation of the adaptive algorithm adopts the least mean square (LMS) algorithm. Taking the low-pass filter as an example, let the input signal be x(n), the output of the filter be y(n), the expected output be d(n), and the weight coefficient of the filter be w(n). According to the LMS algorithm, the update formula of the weight coefficient is: w(n+1)=w(n)+μ×e(n)×x(n), where μ is the step size factor and e(n)=d(n)-y(n) is the error signal. By continuously updating the weight coefficient, the output of the filter gradually approaches the expected output, thereby improving the accuracy of the calculation of the DC disturbance component.

[0072] For the calculation of harmonic disturbance components, the LMS algorithm is applied on the basis of each bandpass filter. Let the input signal of each bandpass filter be x i (n), the output is y i (n), the expected output is d i (n), the weight coefficient of the filter is w i (n). The updating formula of weight coefficient is: i (n+1)=w i (n)+μ×e i (n)×x i (n), where μ is the step size factor, e i (n) = d i (n)-y i (n) is the error signal. By continuously updating the weight coefficients, the output of the bandpass filter gradually approaches the expected output, thereby improving the accuracy of the calculation of the harmonic disturbance component.

[0073] Finally, the observed values ​​of the DC disturbance component and the harmonic disturbance component are fused. Using the weighted average method, the observed value of the DC disturbance component is set to u DC , the observed value of the harmonic disturbance component is u Harmonic , the unknown disturbance observation value after fusion is u, then u=α×u DC +(1-α)×u Harmonic , where α is the weighting coefficient, which is adjusted according to actual conditions.

[0074] 3. Construction of Active Disturbance Rejection Controller

[0075] 1. Principle of Active Disturbance Rejection Controller

[0076] The purpose of constructing an auto-disturbance rejection controller based on intelligent filtering is to add the observed values ​​of unknown disturbances to the known disturbances to obtain the observed values ​​of concentrated disturbances, and to design the controller parameters so that the controller can respond to and suppress disturbances.

[0077] The core idea of ​​the ADRC is to eliminate the influence of disturbances on the system by calculating and compensating the system state. It mainly consists of a tracking differentiator, an extended state observer, and a nonlinear state error feedback control law.

[0078] The function of the tracking differentiator is to filter and differentiate the input signal of the system to obtain a smooth reference trajectory and its derivative, which helps to reduce the overshoot of the system and improve the response speed of the system.

[0079] The function of the extended state observer is to calculate the state and unknown disturbance of the system. It calculates the state variables and unknown disturbance variables of the system through a certain algorithm based on the input and output signals of the system. 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.

[0080] The role of the nonlinear state error feedback control law is to design a suitable control law based on the system state error 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 impact of the disturbance on the system.

[0081] 2. Implementation details of ADRC

[0082] For the tracking differentiator, the following formula is used to implement it:

[0083]

[0084] where u1(k) and v2(k) are the output of the tracking differentiator and its derivative, respectively, h is the sampling step, r is a speed factor, and f(v1(k), v2(k), r, h) is a nonlinear function whose specific form is adjusted according to the characteristics of the system and the control requirements.

[0085] For the extended state observer (ESO), the following formula is used to implement it:

[0086]

[0087] Where z1(k) and z2(k) are the system state and unknown disturbance calculated by the extended state observer, y(k) is the output signal of the system, u(k) is the input signal of the system, β1, β2, β3 are the parameters of the observer, which are adjusted according to the characteristics of the system and the control requirements.

[0088] For the nonlinear state error feedback control law (NLSEF), the following formula is used to implement it:

[0089]

[0090] Where e(k) is the state error of the system, e′(k) is the derivative of the state error, and k p , k d , k i is a parameter of the controller, u0(k) is a compensation term, and its specific form is adjusted according to the characteristics of the system and the control requirements.

[0091] When constructing an ADRC, the observed value of the unknown disturbance is first added to the known disturbance to obtain the observed value of the concentrated disturbance. Then, the parameters of the tracking differentiator, extended state observer, and nonlinear state error feedback control law are adjusted according to the dynamic characteristics and control requirements of the system.

[0092] 4. Control Instruction Construction and Disturbance Suppression

[0093] 1. Control instruction construction principle

[0094] The preliminary control instructions for the voltage output by the off-grid photovoltaic system are obtained, and combined with the observation values ​​of DC disturbance and harmonic disturbance provided by the intelligent filter, more accurate control instructions are constructed to suppress the current harmonic disturbance of the off-grid photovoltaic system.

[0095] Firstly, according to the basic principle and control target of the off-grid photovoltaic system, the preliminary control instruction of the voltage is obtained through the control method (such as PID control).

[0096] Then, the observation values ​​of DC disturbance and harmonic disturbance provided by the intelligent filter are integrated into the control instructions. For DC disturbance, the influence of DC disturbance on the system is compensated by adjusting the DC component in the control instruction. For harmonic disturbance, the influence of harmonic disturbance on the system is suppressed by adding corresponding harmonic suppression items in the control instruction.

[0097] 2. Control instruction construction implementation details

[0098] Assume that the initial control command of voltage is U0(t) and the DC disturbance observation value is u DC (t), the observed value of harmonic disturbance is u Harmonic (n).

[0099] For compensation of DC disturbance, a new control instruction is constructed as: U1(t)=U0(t)+k DC ×u DC (t), where k DC is the DC disturbance compensation coefficient, which is determined based on experiments and simulations.

[0100] For the suppression of harmonic disturbances, a new control instruction is constructed as follows: where k Harmonicis the harmonic disturbance suppression coefficient, N is the harmonic number considered, and ω is the fundamental frequency. Through the control instruction construction method, the influence of DC disturbance and harmonic disturbance on the off-grid photovoltaic system is effectively suppressed, and the stability of the system output voltage and current is improved.

[0101] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present application. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, rather than limitation, and the above details do not limit the present application to being implemented by adopting the above specific details.

[0102] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems are connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0103] It should also be noted that in the apparatus, device and method of the present application, each component or each step is decomposed and / or recombined, and these decompositions and / or recombinations should be regarded as equivalent solutions of the present application.

[0104] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein apply 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 rather to the widest scope consistent with the principles and novel features of the present invention.

[0105] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, etc. 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 photovoltaics based on intelligent filtering technology, characterized in that: include: Establish disturbance models, including light intensity change disturbance model, temperature change disturbance model and load mutation disturbance model; Design intelligent filters, use adaptive filtering algorithms to monitor and calculate DC disturbance components and harmonic disturbance components, obtain observation values, fuse the observation values ​​of DC disturbance components and harmonic disturbance components, and obtain unknown disturbance observation values; Construct an active disturbance rejection controller, which includes a tracking differentiator, an extended state observer, and a nonlinear state error feedback control law; use the unknown disturbance observation value obtained by the intelligent filter and the known disturbance model to calculate the observation value of the concentrated disturbance, and adjust the parameters of the tracking differentiator, the extended state observer, and the nonlinear state error feedback control law of the active disturbance rejection controller; Construct control instructions, combine the observed values ​​of DC disturbance and harmonic disturbance provided by the intelligent filter, construct accurate control instructions, and control the self-disturbance rejection controller through control instructions to achieve the suppression of current harmonic disturbance of off-grid photovoltaic system.

2. The method for enhancing the anti-disturbance capability of off-grid photovoltaic based on intelligent filtering technology according to claim 1 is characterized in that: The light intensity variation disturbance model is: light (t) = f(t) × I0, where P light (t) represents the impact of light intensity disturbance on the output power of the photovoltaic system, f(t) is a function that describes the change of light intensity over time, and I0 is the rated output power of the photovoltaic panel under standard light intensity; The temperature change disturbance model: T(t) = a×S(t)+b×T env (t)+c, where T(t) represents the temperature of the photovoltaic panel, S(t) represents the solar radiation intensity, and T env (t) represents the ambient temperature, a, b, and c are coefficients obtained by experimental fitting; The disturbance model of temperature change on output power is: P temp (t) = k × (T (t) - T ref )×P rated , where P temp (t) represents the effect of temperature change on the output power of the photovoltaic system, k is the temperature coefficient of the photovoltaic panel, T ref is the reference temperature of the photovoltaic panel, P rated is the rated output power of the photovoltaic panel at the reference temperature; The load mutation disturbance model is established as: Among them, P load (t) is the impact of load change on the output power of the photovoltaic system, P load1 and P load2 The load power value before and after the mutation, t0 is the time when the mutation occurs; The disturbance model of the off-grid photovoltaic system is: P total (t) = P light (t)+P temp (t)+P load (t).

3. The method for enhancing the anti-disturbance capability of off-grid photovoltaic based on intelligent filtering technology according to claim 1 is characterized in that: The intelligent filter comprises 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.

4. The method for enhancing the anti-disturbance capability of off-grid photovoltaic based on intelligent filtering technology according to claim 3 is characterized in that: The steps of using the low-pass filter to monitor and calculate the DC disturbance component are as follows: according to the properties of discrete Fourier transform, a frequency response function is designed, the spectrum coefficients of the high-frequency component are set to 0, and the spectrum coefficients of the low-frequency component are retained; then, a filtered time domain signal, i.e., a preliminary calculated value of the DC disturbance component, is obtained by inverse discrete Fourier transform, and finally, the parameters of the low-pass filter are continuously adjusted by an adaptive algorithm; Among them, the adaptive algorithm continuously adjusts the parameters of the low-pass filter as follows: Suppose the input signal is x(n), the output of the filter is y(n), the expected output is d(n), and the weight coefficient of the filter is w(n). According to the LMS algorithm, the update formula of the weight coefficient is: w(n+1)=w(n)+μ×e(n)×x(n), where μ is the step factor and e(n)=d(n)-y(n) is the error signal.

5. The method for enhancing the anti-disturbance capability of off-grid photovoltaic based on intelligent filtering technology according to claim 3 is characterized in that: The steps of monitoring and calculating the harmonic disturbance component of the bandpass filter are as follows: designing a set of frequency response functions, each frequency response function corresponds to a frequency range of the bandpass filter; converting the input signal into a spectrum domain signal by discrete Fourier transform, then extracting the harmonic signal of the corresponding frequency according to the frequency response function, then obtaining the harmonic disturbance component in the time domain by inverse discrete Fourier transform, and finally adjusting the parameters of the bandpass filter by an adaptive algorithm; Wherein, the method for adjusting the parameters of the bandpass filter by an adaptive algorithm is: Assume that the input signal of each bandpass filter is x i (n), the output is y i (n), the expected output is d i (n), the weight coefficient of the filter is w i (n), the update formula of the weight coefficient is: ω i (n+1)=w i (n)+μ×e i (n)×x i (n), where μ is the step size factor, e i (n) = d i (n)-y i (n) is the error signal.

6. The method for enhancing the anti-disturbance capability of off-grid photovoltaic based on intelligent filtering technology according to claim 1, 4 or 5, characterized in that: The observed values ​​of the DC disturbance component and the harmonic disturbance component are fused by weighted average. Suppose the observed value of the DC disturbance component is u DC , the observed value of the harmonic disturbance component is u Harmonic , the unknown disturbance observation value after fusion is u, then u=α×u DC +(1-α)×u Harmoic , where α is the weighting coefficient.

7. The method for enhancing the anti-disturbance capability of off-grid photovoltaic based on intelligent filtering technology according to claim 1, characterized in that: The tracking differentiator is implemented using the following formula: Where v1(k) and v2(k) are the output of the tracking differentiator and its derivative, h is the sampling step, r is a speed factor, and f(v1(k), v2(k), r, h) is a nonlinear function; The extended state observer is implemented using the following formula: Where z1(k) and z2(k) are the system state and unknown disturbance calculated by the extended state observer, y(k) is the output signal of the system, u(k) is the input signal of the system, and β1, β2, and β3 are the parameters of the observer; The nonlinear state error feedback control law is implemented using the following formula: Where e(k) is the state error of the system, e′(k) is the derivative of the state error, and k p ,,k d , k i are the parameters of the controller and u0(k) is a compensation term.

8. The method for enhancing the anti-disturbance capability of off-grid photovoltaic based on intelligent filtering technology according to claim 1, characterized in that: The control instruction construction method includes: Assume that the initial control command of voltage is U0(t) and the DC disturbance observation value is u DC (t), the harmonic disturbance observation value is u Harmonic (n); For compensation of DC disturbance, a new control instruction is constructed as: U1(t)=U0(t)+k DC ×u DC (t), where k DC is the DC disturbance compensation coefficient, determined based on experiments and simulations; For the suppression of harmonic disturbances, a new control instruction is constructed as follows: where k Harmonic is the harmonic disturbance suppression coefficient, N is the number of harmonics considered, and ω is the fundamental frequency.

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