Control parameter identification method and system based on photovoltaic grid-connected inverter in filtering process

Abstract

The invention relates to the technical field of photovoltaic grid-connected inverters, in particular to a control parameter identification method and system based on a photovoltaic grid-connected inverter in a filtering process. The method comprises the following steps: acquiring key operation data of a specified photovoltaic grid-connected inverter in a current sampling period, acquiring a photovoltaic panel input voltage, a grid-connected output voltage, a grid-connected output current and a power factor, and constructing a mathematical model; monitoring the change conditions of the output voltage waveform and the current waveform of the specified photovoltaic grid-connected inverter in real time to obtain an output waveform interference score, and judging whether to execute filtering operation or not according to the output waveform interference score; and performing linearization processing on the controller according to the to-be-identified control parameters to obtain a linearized observation matrix, and performing optimization state estimation on the observation matrix. According to the method, the corresponding mathematical model is constructed through the obtained key operation data, and the state optimization estimation value is obtained more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic grid-connected inverters, and in particular to a control parameter identification method and system based on a photovoltaic grid-connected inverter in a filtering process. Background Art

[0002] With the global energy structure transformation and the emphasis on renewable energy, the performance of photovoltaic grid-connected inverters, as the interface device between photovoltaic power stations and power grids, directly affects the power generation efficiency of photovoltaic power stations and the stability of power grids. During the filtering operation, accurate identification of control parameters is crucial to ensure the quality and stability of the inverter output power. However, traditional parameter identification methods often face challenges when dealing with changes in photovoltaic system and grid conditions, resulting in reduced identification accuracy and affecting system performance. In this context, Kalman filtering, as a classic parameter estimation method, has gradually been introduced into the control parameter identification of photovoltaic inverters. Although the standard Kalman filter performs well in dealing with linear system state estimation problems, the applicability of traditional methods is limited due to the nonlinear characteristics of photovoltaic inverters. Therefore, an improved Kalman filter algorithm for these nonlinear dynamic characteristics (the key is how to effectively deal with the nonlinear characteristics of the system) is particularly important.

[0003] The existing technology establishes a mathematical model of the photovoltaic grid-connected inverter, inputs the actual operating data of the photovoltaic inverter during operation into the mathematical model, and identifies the control parameters based on data analysis and machine learning algorithms.

[0004] For example, the invention patent with announcement number: CN106992551B discloses a photovoltaic inverter controller parameter identification method based on a fuzzy C-mean and differential evolution hybrid algorithm, which includes: after a three-phase short-circuit fault disturbance is set at the grid-connected point of the photovoltaic inverter, the active power output of the photovoltaic inverter is sampled at equal time intervals according to a sampling time period; the five control parameters of the photovoltaic inverter controller are identified using a fuzzy C-mean and differential evolution hybrid algorithm, and the identification results of the voltage outer loop proportional coefficient, the voltage outer loop integral coefficient, and the current inner loop proportional coefficient are used as the final results.

[0005] For example, the patent application with publication number: CN117833327A discloses a method and system for determining inverter current and voltage multi-loop control parameters in a weak power grid, including: obtaining filter parameters and grid inductance in the weak power grid, and calculating inverter control parameters that meet the capacitor voltage inner loop design indicators and the grid current outer loop design indicators. Summary of the invention

[0006] In view of the problems existing in the prior art, the present invention is proposed.

[0007] Therefore, the problem to be solved by the present invention is how to solve the problem that the mathematical model of the photovoltaic grid-connected inverter does not take into account the influence of nonlinear factors in the denoising process, and it is difficult to fully and accurately reflect the actual operating characteristics of the inverter, resulting in poor adaptability of the existing mathematical model. Secondly, the existing parameter identification algorithms (such as fuzzy C-means and differential evolution hybrid algorithms), although they can solve the parameter identification problem to a certain extent, have high computational complexity and insufficient convergence speed during use, resulting in deviations in the parameter identification results, and there is a problem of low accuracy in identifying control parameters of the photovoltaic grid-connected inverter during the filtering operation.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0009] In a first aspect, an embodiment of the present invention provides a control parameter identification method based on a photovoltaic grid-connected inverter in a filtering process, which includes obtaining key operating data of a specified photovoltaic grid-connected inverter in a current sampling period, obtaining a photovoltaic panel input voltage, a grid-connected output voltage, a grid-connected output current and a power factor, and constructing a mathematical model;

[0010] Monitor the output voltage waveform and current waveform changes of the specified photovoltaic grid-connected inverter in real time to obtain the output waveform interference score, and determine whether to perform filtering operation according to the output waveform interference score;

[0011] The controller is linearized according to the control parameters to be identified to obtain a linearized observation matrix, and the observation matrix is ​​optimized for state estimation.

[0012] As a preferred solution of the control parameter identification method based on the photovoltaic grid-connected inverter in the filtering process described in the present invention, the control parameters to be identified include: state variables, including inverter output voltage and controller integral variables; observed variables, including output current and voltage waveforms; initial parameters, including proportional gain, integral gain, filtering parameters and modulation methods, wherein the modulation methods include SPWM and SVPWM.

[0013] As a preferred solution of the control parameter identification method based on the photovoltaic grid-connected inverter in the filtering process described in the present invention, the specific process of judging whether to perform the filtering operation based on the output waveform interference score is as follows: judging whether the obtained output waveform interference score is within the output waveform interference threshold range: if it is within the output waveform interference threshold range, the filtering operation is not performed; if it is not within the output waveform interference threshold range, the filtering operation is performed through the improved Kalman filter.

[0014] As a preferred solution of the control parameter identification method based on the photovoltaic grid-connected inverter in the filtering process described in the present invention, wherein: after executing the filtering operation, it also includes: real-time monitoring of the suppression effect of the harmonic situation during the filtering operation to obtain a harmonic suppression score, the harmonic suppression score includes a voltage harmonic suppression score and a current harmonic suppression score; the voltage harmonic suppression score represents the change ratio of the voltage harmonic component before and after filtering; the current harmonic suppression score represents the change ratio of the current harmonic component before and after filtering.

[0015] As a preferred solution of the control parameter identification method based on the photovoltaic grid-connected inverter in the filtering process described in the present invention, after obtaining the harmonic suppression score, it also includes: when the voltage harmonic suppression score is less than a preset value, increasing the voltage harmonic suppression gain and continuing filtering; when the current harmonic suppression score is less than a preset value, increasing the current harmonic suppression gain and continuing filtering; when both the voltage and current harmonic suppression scores are greater than the preset values, completing the filtering operation.

[0016] As a preferred solution of the control parameter identification method based on the photovoltaic grid-connected inverter in the filtering process described in the present invention, it also includes: weighting the actual output value and the state optimization estimate value to obtain a weighted value; and using the identified control parameter and the weighted value together as the input signal of the controller.

[0017] As a preferred solution of the control parameter identification method based on the photovoltaic grid-connected inverter in the filtering process described in the present invention, it also includes: adjusting the voltage outer loop parameters according to the weighted value; inputting the adjusted voltage outer loop parameters into the photovoltaic grid-connected inverter for controlling the input voltage and input current.

[0018] In a second aspect, an embodiment of the present invention provides a control parameter identification system based on a photovoltaic grid-connected inverter in a filtering process, which includes an operation data acquisition module, which acquires key operation data of a specified photovoltaic grid-connected inverter in a current sampling period, acquires photovoltaic panel input voltage, grid-connected output voltage, grid-connected output current and power factor, and constructs a mathematical model;

[0019] The monitoring module monitors the output voltage waveform and current waveform changes of the specified photovoltaic grid-connected inverter in real time to obtain the output waveform interference score, and determines whether to perform the filtering operation according to the output waveform interference score;

[0020] The estimation and identification module linearizes the controller according to the control parameters to be identified to obtain the linearized observation matrix, and performs optimized state estimation on the observation matrix.

[0021] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the control parameter identification method based on the photovoltaic grid-connected inverter in the filtering process as described in the first aspect of the present invention are implemented.

[0022] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the control parameter identification method based on the photovoltaic grid-connected inverter in the filtering process as described in the first aspect of the present invention are implemented.

[0023] The beneficial effects of the present invention are as follows: the present invention constructs a corresponding mathematical model through the acquired key operating data, and then linearizes the controller according to the acquired control parameters to be identified to obtain a linearized observation matrix and optimizes the state estimation to obtain a state optimization estimate, and finally identifies the control parameters to be identified according to the acquired state optimization estimate, thereby achieving more accurate acquisition of the state optimization estimate, and then achieving improved control parameter identification accuracy during the filtering operation of the photovoltaic grid-connected inverter, effectively solving the problem of low control parameter identification accuracy during the filtering operation of the photovoltaic grid-connected inverter in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 It is a flow chart of a control parameter identification method based on a photovoltaic grid-connected inverter in a filtering process;

[0026] Figure 2 A computer device diagram for a control parameter identification method based on a photovoltaic grid-connected inverter in a filtering process;

[0027] Figure 3 A specific identification flow chart of the control parameters to be identified based on the control parameter identification method of the photovoltaic grid-connected inverter in the filtering process;

[0028] Figure 4 A schematic diagram of waveform results of weighted values ​​of a control parameter identification method based on a photovoltaic grid-connected inverter in a filtering process;

[0029] Figure 5 The structure diagram of the control principle of the photovoltaic grid-connected inverter is based on the control parameter identification method of the photovoltaic grid-connected inverter in the filtering process. DETAILED DESCRIPTION

[0030] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0031] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0032] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0033] Example 1

[0034] Reference Figure 1-2 , which is the first embodiment of the present invention, and provides a control parameter identification method based on a photovoltaic grid-connected inverter in a filtering process, comprising:

[0035] S100: Obtain key operating data of the specified photovoltaic grid-connected inverter in the current sampling period, obtain the photovoltaic panel input voltage, grid-connected output voltage, grid-connected output current and power factor, and build a mathematical model;

[0036] Specifically, the photovoltaic panel input voltage data can be the open-circuit voltage of the photovoltaic array, the maximum power point voltage, the output voltage corresponding to the light intensity and other data; the grid-connected output voltage data can be the three-phase voltage effective value, phase difference, voltage imbalance and other data; the grid-connected output current data can be the three-phase current effective value, harmonic content, power factor and other data; the power factor data can be reactive power compensation parameters, active power utilization, power fluctuation index and other data.

[0037] In an optional embodiment, the photovoltaic panel input voltage, grid-connected output voltage, grid-connected output current and power factor may be any combination of data acquired by detection equipment such as a voltage detection circuit, a current detection circuit, a power analyzer, an oscilloscope, etc. For example, when the photovoltaic panel input voltage is the output voltage data corresponding to the light intensity, the power factor may be reactive power compensation parameter data.

[0038] In an optional embodiment, the photovoltaic panel input voltage, grid-connected output voltage, grid-connected output current and power factor can also add or reduce other operating data according to different control objectives. For example, when more refined filtering control is required, environmental data (i.e., temperature, humidity and other data) can be added to capture the impact of environmental factors on system operation, thereby further improving the accuracy and comprehensiveness of control parameter identification. In addition, considering the impact of the diversity of operating environments on data quality, an adaptive data smoothing algorithm can also be introduced to dynamically adjust the collected key operating data to ensure that high-quality system operation data can be obtained under various operating environments.

[0039] In an optional embodiment, if the power factor does not need to be considered, the power factor data can be omitted, thereby reducing the workload of data collection and processing. However, in this application, in order to ensure the comprehensiveness of the control, such data is still retained.

[0040] It should be noted that the acquisition of the above-mentioned key operating data can fully cover all aspects of the operation of the photovoltaic grid-connected inverter, including but not limited to DC side characteristics, AC side characteristics, system stability, power quality, etc., so as to ensure the accuracy and comprehensiveness of the control parameter identification results. At the same time, by introducing multiple detection devices and data sources, such as voltage detection circuits, current detection circuits, power analyzers and oscilloscopes, the advantages of various types of data can be fully utilized, complement each other, and improve the efficiency and accuracy of identification. In addition, according to the needs of different control objectives, the type and quantity of data can be flexibly adjusted to meet the needs of actual control work. This multi-source data fusion strategy not only improves the flexibility and adaptability of the control system, but also provides a rich data source for subsequent intelligent analysis.

[0041] S200: monitor the output voltage waveform and current waveform changes of the specified photovoltaic grid-connected inverter in real time to obtain the output waveform interference score, and determine whether to perform the filtering operation according to the output waveform interference score;

[0042] S201: The specific process of determining whether to perform a filtering operation based on the output waveform interference score is as follows: determining whether the obtained output waveform interference score is within the output waveform interference threshold range: if it is within the output waveform interference threshold range, no filtering operation is performed; if it is not within the output waveform interference threshold range, a filtering operation is performed using an improved Kalman filter.

[0043] S202: After performing the filtering operation, it also includes: real-time monitoring of the suppression effect of the harmonic situation during the filtering operation to obtain a harmonic suppression score, the harmonic suppression score includes a voltage harmonic suppression score and a current harmonic suppression score; the voltage harmonic suppression score represents the change ratio of the voltage harmonic component before and after filtering; the current harmonic suppression score represents the change ratio of the current harmonic component before and after filtering.

[0044] Specifically, the waveform interference score includes the voltage waveform interference score and the current waveform interference score. The voltage waveform interference score can be data such as voltage peak interference, voltage harmonic content, and voltage fluctuation rate. The current waveform interference score can be data such as current peak interference, current harmonic content, and current fluctuation rate. These interference scores are obtained through real-time sampling and calculation, and the sampling frequency can reach 50kHz, which ensures the real-time and accuracy of the data.

[0045] In an optional embodiment, the waveform interference score can be calculated using different evaluation methods. For example, the harmonic content can be analyzed based on the Fast Fourier Transform (FFT), the transient characteristics can be analyzed using the wavelet transform, or the waveform stability can be evaluated using statistical methods. These methods can be flexibly combined according to actual needs to obtain a more comprehensive waveform quality evaluation result.

[0046] In an optional embodiment, the threshold setting of the waveform interference score can be dynamically adjusted. The system can adaptively adjust the threshold of the waveform interference score based on historical operating data, load characteristics, environmental conditions and other factors. For example, when the light intensity changes dramatically, the threshold standard can be appropriately relaxed; when the power grid fluctuates greatly, stricter threshold control is required.

[0047] It should be noted that the monitoring and calculation process of the waveform interference score fully considers the characteristics of the photovoltaic grid-connected system. By monitoring the changes in the output waveform in real time, the system can promptly detect potential power quality problems and automatically decide whether to start the filtering operation. This adaptive control strategy based on waveform quality effectively improves the operating stability and reliability of the system.

[0048] S203: After obtaining the harmonic suppression score, it also includes: when the voltage harmonic suppression score is less than the preset value, increasing the voltage harmonic suppression gain and continuing filtering; when the current harmonic suppression score is less than the preset value, increasing the current harmonic suppression gain and continuing filtering; when both the voltage and current harmonic suppression scores are greater than the preset values, completing the filtering operation.

[0049] S300: linearizing the controller according to the control parameters to be identified to obtain a linearized observation matrix, and performing optimized state estimation on the observation matrix.

[0050] Specifically, the control parameters to be identified include: state parameters (such as output voltage, current value, etc.), control parameters (such as PI controller parameters, PWM modulation parameters, etc.), and filter parameters. The linearization process mainly involves Taylor expansion of these nonlinear filter parameters near the operating point to obtain linearized state equations and observation equations.

[0051] In an optional embodiment, the linearization process can be performed using different methods. For example, the system can be linearized using a small signal analysis method, or a feedback linearization method can be used to eliminate the nonlinear characteristics of the system, or a fuzzy linearization method can be used to process a strong nonlinear region. These methods have their own characteristics, and the most suitable linearization strategy can be selected according to actual control requirements.

[0052] In an optional embodiment, the construction of the linearized observation matrix can take into account the influence of multiple factors, such as the uncertainty of filter parameters, the existence of measurement noise, the influence of external interference, etc. By establishing a suitable mathematical model, the system can more accurately describe the influence of these factors on the control effect.

[0053] It should be noted that linearization is a key step in achieving accurate parameter identification. Through reasonable linearization methods, complex nonlinear problems can be transformed into relatively simple linear problems, which is convenient for subsequent state estimation and parameter identification. At the same time, the linearization process also needs to consider the balance between model accuracy and computational complexity to ensure that the system can effectively complete the parameter identification task in real time.

[0054] S301: The control parameters to be identified include: state variables, including inverter output voltage and controller integral variables; observed variables, including output current and voltage waveforms; initial parameters, including proportional gain, integral gain, filter parameters and modulation methods, wherein the modulation methods include SPWM and SVPWM.

[0055] S302: Also includes: weighting the actual output value and the state optimization estimation value to obtain a weighted value; and using the identified control parameter and the weighted value together as an input signal of the controller.

[0056] Specifically, the optimization state estimation includes three main steps: state prediction, measurement update, and gain optimization. State prediction is based on the system model to estimate the state value at the next moment, measurement update uses actual measurement data to correct the prediction result, and gain optimization balances the weight of the predicted value and the measured value by adjusting the Kalman filter gain.

[0057] In an optional embodiment, the state estimation algorithm can be flexibly selected according to the characteristics of the system. For example, an extended Kalman filter (EKF) can be used to handle slightly nonlinear problems, an unscented Kalman filter (UKF) can be used to handle strongly nonlinear systems, or a particle filter (PF) can be used to handle non-Gaussian distribution problems. These algorithms can be combined or improved according to actual needs.

[0058] In an optional embodiment, the optimization objectives of state estimation can be various, such as minimizing the estimation error variance, maximizing the convergence speed, optimizing the computational efficiency, etc. The system can select a suitable optimization criterion according to the actual application scenario, and achieve the optimization objective through parameter adjustment.

[0059] It should be noted that the process of optimizing state estimation fully considers the dynamic characteristics of the photovoltaic grid-connected system. By updating the state estimation value in real time, the system can accurately track the changes in the system state and provide a reliable basis for subsequent parameter identification. This optimization-based state estimation method significantly improves the control accuracy and dynamic response capability of the system.

[0060] S303: Also includes: adjusting the voltage outer loop parameters according to the weighted values; inputting the adjusted voltage outer loop parameters into the photovoltaic grid-connected inverter for controlling the input voltage and input current.

[0061] Specifically, the parameter identification process includes three main steps: model construction, parameter estimation and result verification. Model construction establishes a mathematical model based on the physical characteristics of the system, parameter estimation uses an improved Kalman filter algorithm to update parameter values ​​in real time, and result verification evaluates the identification effect by comparing simulation and measured data.

[0062] In an optional embodiment, the parameter identification method can adopt different improvement strategies. For example, an adaptive factor can be introduced to improve the robustness of the algorithm, a forgetting factor can be used to deal with time-varying parameter problems, or a multi-model switching mechanism can be used to cope with changes in working conditions. These improvement measures can be flexibly combined and used according to actual needs.

[0063] In an optional embodiment, the evaluation of the identification result can be based on multiple performance indicators, such as convergence speed of parameter estimation, steady-state accuracy, anti-interference ability, etc. By comprehensively evaluating these indicators, the system can objectively judge the effect of parameter identification and optimize the control strategy accordingly.

[0064] It should be noted that the improved Kalman filter algorithm plays a core role in the parameter identification process. Through reasonable algorithm improvement and parameter optimization, the system can accurately identify key control parameters and provide a strong guarantee for high-quality grid-connected control. This optimization-based parameter identification method not only improves the control accuracy of the system, but also enhances the adaptability and reliability of the system.

[0065] Furthermore, this embodiment also provides a control parameter identification system based on a photovoltaic grid-connected inverter in a filtering process, comprising:

[0066] The operation data acquisition module obtains the key operation data of the specified photovoltaic grid-connected inverter in the current sampling period, obtains the photovoltaic panel input voltage, grid-connected output voltage, grid-connected output current and power factor, and constructs a mathematical model;

[0067] The monitoring module monitors the output voltage waveform and current waveform changes of the specified photovoltaic grid-connected inverter in real time to obtain the output waveform interference score, and determines whether to perform the filtering operation according to the output waveform interference score;

[0068] The estimation and identification module linearizes the controller according to the control parameters to be identified to obtain the linearized observation matrix, and performs optimized state estimation on the observation matrix.

[0069] In summary, the present invention constructs a corresponding mathematical model by acquiring key operating data, and then linearizes the controller according to the acquired control parameters to be identified to obtain a linearized observation matrix and optimizes the state estimation to obtain a state optimization estimate value, and finally identifies the control parameters to be identified according to the acquired state optimization estimate value, thereby achieving more accurate acquisition of the state optimization estimate value, and then achieving improved control parameter identification accuracy during the filtering operation of the photovoltaic grid-connected inverter, effectively solving the problem of low control parameter identification accuracy during the filtering operation of the photovoltaic grid-connected inverter in the prior art.

[0070] The present invention obtains the voltage total harmonic ratio and the current total harmonic ratio according to the total amplitude of the voltage harmonic components and the total amplitude of the current harmonic components when the output voltage peak value and the output current peak value are both less than the corresponding reference values, and simultaneously obtains the output waveform interference score in combination with the obtained voltage peak interference score and current peak interference score, thereby improving the accuracy of obtaining the output waveform interference score, and further achieving a more accurate evaluation of the stability of the output voltage waveform and the output current waveform.

[0071] The present invention obtains the predicted value of the state variable at the next moment by inputting the state variable into the linearized observation matrix and monitors the change of the predicted value of the state variable in the current acquisition cycle in real time to obtain the state error covariance, then updates the state covariance matrix according to the obtained Kalman gain and performs state estimation according to the observed variables at the current moment, and finally optimizes the state estimation based on the state estimation according to the EKF algorithm to obtain the state optimized estimated value, thereby improving the accuracy of obtaining the state optimized estimated value, and further achieving more accurate acquisition of the predicted value corresponding to the control parameter to be identified.

[0072] Example 2

[0073] Reference Figure 2 - Figure 5, which is the second embodiment of the present invention, and this embodiment provides a control parameter identification method based on the filtering process of the photovoltaic grid-connected inverter.

[0074] like Figure 1 As shown, it is a flow chart of a control parameter identification method based on a photovoltaic grid-connected inverter in a filtering operation process provided by an embodiment of the present application, and the method includes the following steps: S1, obtaining key operating data of a specified photovoltaic grid-connected inverter in a current sampling period, and constructing a corresponding mathematical model according to the obtained key operating data; S2, real-time monitoring of the changes in the output voltage waveform and the output current waveform of the specified photovoltaic grid-connected inverter in the current sampling period to obtain an output waveform interference score, and judging whether to perform a filtering operation based on the obtained output waveform interference score, the output waveform interference score is used to evaluate the influence of the resonance situation on the stability of the voltage waveform and the current waveform; S3, if the filtering operation is performed, the controller of the specified photovoltaic grid-connected inverter is linearized according to the obtained control parameters to be identified to obtain a linearized observation matrix, and the linearized observation matrix is ​​optimized for state estimation to obtain a state optimization estimate, and the state optimization estimate represents the optimal estimate of the current operating state of the specified photovoltaic grid-connected inverter; S4, the obtained state optimization estimate is input into the improved Kalman filter to identify the control parameters to be identified, and the identified control parameters are used as input signals of the controller of the specified photovoltaic grid-connected inverter and the real-time control of the converter is achieved through modulation.

[0075] like Figure 3 As shown, it is a specific identification flow chart of the control parameters to be identified provided in the embodiment of the present application, wherein the key operation data obtains the photovoltaic panel input voltage (measured in real time by the voltage sensor deployed on the photovoltaic panel), the grid-connected output voltage (measured in real time by the voltage sensor deployed at the input port of the power grid in the designated photovoltaic grid-connected inverter), the grid-connected output current (measured in real time by the current sensor deployed at the input port of the power grid in the designated photovoltaic grid-connected inverter) and the inverter power factor (measured in real time by the power sensor deployed on the input port of the designated photovoltaic grid-connected inverter); the control parameters to be identified include state variables, observation variables and initial parameters of the controller of the specified photovoltaic grid-connected inverter; state variables include the output voltage of the specified photovoltaic grid-connected inverter and the integral variables of the controller of the specified photovoltaic grid-connected inverter; observed variables include output current and voltage waveforms; initial parameters include proportional gain, integral gain, filtering parameters and modulation mode; filtering parameters include type (L type, LC type), order (used to reflect the stability of the filtering effect) and cutoff frequency (determines the ability of the improved Kalman filter to suppress harmonic signals and high-frequency signals); the mathematical model is a mathematical expression of the output voltage and output current of the specified photovoltaic grid-connected inverter.

[0076] In this embodiment, modulation modes include SPWM and SVPWM; PWM (Pulse Width Modulation) is pulse width modulation, which generally includes sinusoidal pulse width modulation (SPWM) and space vector pulse width modulation (SVPWM); filtering operation is performed by an improved Kalman filter; linearization processing is used to simplify the nonlinear characteristics in the controller of the specified photovoltaic grid-connected inverter into linear characteristics; the linearized observation matrix is ​​used to visualize the relationship between the actual operating state of the specified photovoltaic grid-connected inverter and the observed value; the input signal is used to adjust the control signal of the converter in the specified photovoltaic grid-connected inverter.

[0077] It should be added that the key operating data and control parameters to be identified of the specified photovoltaic grid-connected inverter are obtained, and then a photovoltaic array model is established based on the solar radiation intensity and temperature changes in the area where the photovoltaic panels in the specified photovoltaic grid-connected inverter are located; the photovoltaic array model is a mathematical expression that visualizes the output parameters of the power grid in the specified photovoltaic grid-connected inverter; the power grid output parameters include the output current, DC bus voltage, active power and DC side capacitance of the photovoltaic array.

[0078] Specifically, the mathematical expression of the photovoltaic array model is:

[0079]

[0080] In the formula, C represents the DC side capacitance, U dc Indicates the DC bus voltage, I pv Represents the output current of the photovoltaic array, P oc Indicates active power, It represents the rate of change of the DC bus voltage with the grid operation time t in the specified PV grid-connected inverter, where the grid operation time t is the duration of the current sampling period of the grid in the specified PV grid-connected inverter.

[0081] This example can more accurately identify control parameters by comprehensively considering key operating data, control parameters to be identified, mathematical models and photovoltaic array models (which can reflect the impact of solar radiation intensity and temperature changes in the area where the photovoltaic panels are located on the grid output parameters). In addition, by real-time monitoring of the output waveform of the specified photovoltaic grid-connected inverter, calculating the output waveform interference score and filtering operation judgment, it can respond to changes in the power grid and photovoltaic panels in a timely manner, thereby achieving improved control parameter identification accuracy of the photovoltaic grid-connected inverter during the filtering operation process.

[0082] Furthermore, the specific steps for obtaining the output waveform interference score are as follows: E1, obtaining the output voltage peak value and output current peak value of the photovoltaic grid-connected inverter at the current moment, and judging whether the output voltage peak value and the output current peak value are both less than the corresponding reference value. If so, execute E2, otherwise it indicates that the voltage waveform and the current waveform are distorted at the current moment. At this time, the preset personnel need to be prompted to reduce the initial input voltage and the initial input current on the control panel of the photovoltaic grid-connected inverter and re-obtain the output voltage peak value and the output current peak value until the obtained output voltage peak value is less than the reference average output voltage peak value and the output current peak value is less than the reference average output current peak value; E2, obtaining the output voltage waveform and the output current waveform of the photovoltaic grid-connected inverter in the current sampling period to obtain the voltage signal and the current signal, and obtaining the voltage signal spectrum and the current signal spectrum and the corresponding voltage harmonic component and the current harmonic component in the current signal spectrum based on the fast Fourier transform; E3, obtaining the voltage harmonic component amplitude of the voltage harmonic component at the current moment in the current acquisition period and judging whether it is less than the maximum allowable voltage harmonic component. Component amplitude, if yes, then count the total amplitude of the voltage harmonic component that is less than the maximum allowable voltage harmonic component amplitude in the current sampling period and process it to obtain the total voltage harmonic proportion (i.e., X in the limiting expression of the output waveform interference score), otherwise it is considered that the output voltage waveform at the current moment is distorted and prompts the preset personnel to perform a shutdown operation; E4, obtain the current harmonic component amplitude of the current harmonic component at the current moment and determine whether it is less than the maximum allowable current harmonic component amplitude. If yes, count the total amplitude of the current harmonic component that is less than the maximum allowable current harmonic component amplitude in the current sampling period and process it to obtain the total current harmonic proportion (i.e., N in the limiting expression of the output waveform interference score), otherwise it is considered that the output current waveform at the current moment is distorted and prompts the preset personnel to perform a shutdown operation; E5, obtain the voltage peak interference score (i.e., Y in the limiting expression of the output waveform interference score) and the current peak interference score (i.e., L in the limiting expression of the output waveform interference score) and combine the obtained voltage total harmonic proportion and current total harmonic proportion to obtain the output waveform interference score.

[0083] The specific limiting expression of the output waveform interference fraction is:

[0084]

[0085] ZX1=(X1) 2 +(X2) 2 +,+(X A ) 2 ;

[0086] ZN1=(N1) 2 +(N2) 2 +,+(N B ) 2 ;

[0087] Wherein, a is the number of the voltage harmonic component amplitude in the current sampling period, a=1,2,...,A, A is the total number of voltage harmonic component amplitudes in the current sampling period, b is the number of the current harmonic component amplitude in the current sampling period, b=1,2,...,B, B is the total number of current harmonic component amplitudes in the current sampling period, e is a natural constant, SHU represents the output waveform interference fraction of the photovoltaic grid-connected inverter in the current sampling period, Y represents the voltage peak interference fraction of the photovoltaic grid-connected inverter in the current sampling period, Y1 represents the output voltage peak value of the photovoltaic grid-connected inverter at the current moment in the current sampling period, represents the reference average output voltage peak value, L represents the current peak interference fraction of the photovoltaic grid-connected inverter in the current sampling period, L1 represents the output current peak value of the photovoltaic grid-connected inverter at the current moment in the current sampling period, represents the reference average output current peak value, X represents the voltage total harmonic ratio of the photovoltaic grid-connected inverter in the current sampling period, ZX1 represents the total amplitude of the voltage harmonic component of the current harmonic component in the current sampling period, X0 represents the reference voltage harmonic component amplitude, X a Indicates the amplitude of the voltage harmonic component at the current moment in the current sampling period, ΔX max Indicates the maximum allowable voltage harmonic component amplitude, N indicates the current total harmonic proportion of the photovoltaic grid-connected inverter in the current sampling period, ZN1 indicates the total current harmonic component amplitude in the current sampling period, N0 indicates the reference current harmonic component amplitude, N b Indicates the amplitude of the current harmonic component at the current moment in the current sampling period, ΔN max Indicates the maximum allowable current harmonic component amplitude.

[0088] Among them, the voltage total harmonic ratio represents the square root of the ratio of the total amplitude of the voltage harmonic components to the square of the amplitude of the voltage fundamental component; the current total harmonic ratio represents the square root of the ratio of the total amplitude of the current harmonic components to the square of the amplitude of the current fundamental component; the voltage peak interference fraction represents the ratio of the output voltage peak to the reference average output voltage peak; the current peak interference fraction represents the ratio of the output current peak to the reference average output current peak.

[0089] In this embodiment, the output voltage peak value, the output current peak value, the output voltage waveform and the output current waveform are monitored in real time by an oscilloscope, the voltage harmonic component (amplitude) and the current harmonic component (amplitude) are monitored in real time by a spectrum analyzer, the reference average output voltage peak value is represented by the result of summing and averaging the historical output voltage peak values ​​of the photovoltaic grid-connected inverters in the historical sampling period in the preset database, the reference average output current peak value is represented by the result of summing and averaging the historical output current peak values ​​of the photovoltaic grid-connected inverters in the historical sampling period in the preset database, the maximum allowable voltage harmonic component amplitude is the maximum value of the historical current harmonic component amplitude in the output voltage waveform of the photovoltaic grid-connected inverter in the preset database, and the maximum allowable current harmonic component amplitude is the maximum value of the historical current harmonic component amplitude in the output current waveform of the photovoltaic grid-connected inverter in the preset database, thereby improving the accuracy and reliability of obtaining the output waveform interference score.

[0090] In practical applications, for a 220V or 230V low-voltage power grid, the reference average output voltage peak is 311V, the reference average output current peak is 25A, the maximum allowable voltage harmonic component amplitude is 5% of the reference voltage harmonic component, and the maximum allowable current harmonic component amplitude is 3% of the reference current harmonic component. The output waveform interference score change statistics are shown in Table 1:

[0091] Table 1 Statistics of changes in output waveform interference scores

[0092]

[0093]

[0094] It should be understood that, as shown in Table 1, the output waveform interference score (the smaller the better) increases with the increase of the voltage peak interference score, the current peak interference score, the voltage total harmonic ratio and the current total harmonic ratio. It should be noted that the voltage peak interference score also indirectly affects the values ​​of the current peak interference score, the voltage total harmonic ratio and the current total harmonic ratio. When the voltage peak interference score increases, it means that the voltage waveform output by the inverter fluctuates or is distorted at the peak value. This fluctuation may be caused by the nonlinear components inside the inverter or the interference of the external power grid. Since the power grid is an interconnected system, the voltage fluctuation will directly affect the current. When the voltage waveform is distorted at the peak value, the current waveform will also be affected accordingly, resulting in an increase in the current peak interference score.

[0095] An increase in the voltage peak interference fraction usually means that there are more high-frequency components in the waveform (i.e., harmonics, a general term for all frequency components except the fundamental wave, which will lead to a decrease in waveform quality). These high-frequency components will increase the voltage total harmonic ratio. Similar to the voltage total harmonic ratio, the current total harmonic ratio will also be affected by the voltage waveform distortion. When there are harmonics in the voltage waveform, these harmonics will be transmitted to the current waveform through the load, resulting in an increase in the current total harmonic ratio.

[0096] By considering the above-mentioned indirect influence mechanism, the stability and efficiency of the output waveform of the specified photovoltaic grid-connected inverter are improved, thereby achieving an improvement in the accuracy of control parameter identification during the filtering operation of the photovoltaic grid-connected inverter, and effectively solving the problem of low accuracy of control parameter identification during the filtering operation of the photovoltaic grid-connected inverter in the prior art.

[0097] Furthermore, the specific process of determining whether to perform a filtering operation based on the obtained output waveform interference score is as follows: determining whether the obtained output waveform interference score is within the output waveform interference threshold range: if the obtained output waveform interference score is within the output waveform interference threshold range (including the maximum and minimum values ​​of the historical output waveform interference scores), it indicates that the degree of influence of the resonance condition on the stability of the voltage waveform and the current waveform meets the preset requirements, and at this time, the filtering operation is not performed through the improved Kalman filter; if the obtained output waveform interference score is not within the output waveform interference threshold range, it indicates that the voltage waveform and the current waveform are distorted, and at this time, the filtering operation is performed through the improved Kalman filter.

[0098] In this embodiment, the output waveform interference threshold range represents a range corresponding to the maximum and minimum values ​​of the historical output waveform interference scores of the photovoltaic grid-connected inverter in the historical sampling period in the preset database.

[0099] It should be added that the filtering operation is performed, and then the real-time monitoring of the suppression effect of the harmonic situation during the filtering operation is included to obtain the harmonic suppression score, wherein the harmonic suppression score is composed of the output waveform interference score, the voltage harmonic suppression score and the current harmonic suppression score; the voltage harmonic suppression score represents the ratio of the difference between the voltage harmonic component in the voltage signal spectrum before the filtering operation and the voltage harmonic component in the voltage signal spectrum after the filtering operation to the voltage harmonic component in the voltage signal spectrum before the filtering operation; the current harmonic suppression score represents the ratio of the difference between the current harmonic component in the current signal spectrum before the filtering operation and the current harmonic component in the current signal spectrum after the filtering operation to the current harmonic component in the current signal spectrum before the filtering operation.

[0100] Specifically, when the obtained output waveform interference score is not within the output waveform interference threshold range, the specific limiting expression of the harmonic suppression score is:

[0101]

[0102] Wherein, e is a natural constant, YI represents the harmonic suppression score of the photovoltaic grid-connected inverter in the current sampling period, SHU represents the output waveform interference score of the photovoltaic grid-connected inverter in the current sampling period, LIU represents the current harmonic suppression score of the photovoltaic grid-connected inverter in the current sampling period, LIU1 represents the voltage harmonic component in the current signal spectrum before filtering operation, LIU2 represents the current harmonic component in the voltage signal spectrum after filtering operation, YA represents the voltage harmonic suppression score of the photovoltaic grid-connected inverter in the current sampling period, YA1 represents the voltage harmonic component in the voltage signal spectrum before filtering operation, YA2 represents the voltage harmonic component in the voltage signal spectrum after filtering operation, and ΔSHU represents the output waveform interference threshold range.

[0103] It should be understood that the harmonic suppression score increases with the increase of the output waveform interference score, the voltage harmonic suppression score and the current harmonic suppression score. Among them, the output waveform interference score also indirectly affects the values ​​of the voltage harmonic suppression score and the current harmonic suppression score. When the output waveform interference score increases, this may be due to changes in grid conditions or aging of internal components of the photovoltaic grid-connected inverter. In order to cope with this situation, the inverter needs to increase the order of the filter or adjust the cutoff frequency of the filter to more effectively remove harmonics. That is, the increase in the output waveform interference score increases the corresponding voltage and current harmonic suppression degree (that is, the voltage harmonic suppression score and the current harmonic suppression score increase).

[0104] The above-mentioned influence mechanism enables the inverter to more accurately identify and control relevant parameters during the filtering operation, thereby improving the accuracy of identifying control parameters of the photovoltaic grid-connected inverter during the filtering operation, and effectively solves the problem of low accuracy of identifying control parameters of the photovoltaic grid-connected inverter during the filtering operation in the prior art.

[0105] It should be added that the harmonic suppression score is obtained, and then it is also included to determine whether to perform linearization processing based on the obtained harmonic suppression score. The specific process is: M1, determine whether the obtained voltage harmonic suppression score is greater than the preset voltage harmonic suppression score. If so, it indicates that the suppression effect of the voltage harmonics meets the expected demand and M2 is executed, otherwise the voltage harmonic suppression gain is increased and the filtering operation continues; M2, determine whether the obtained current harmonic suppression score is greater than the preset current harmonic suppression score. If so, it indicates that the suppression effect of the current harmonics meets the expected demand and the filtering operation is completed, otherwise the current harmonic suppression gain is increased and the filtering operation is continued.

[0106] In this embodiment, the preset voltage harmonic suppression score (set by the preset personnel) is represented by the result of summing and averaging the historical voltage harmonic suppression scores of the photovoltaic grid-connected inverter in the historical sampling period in the preset database, and the preset current harmonic suppression score (set by the preset personnel) is represented by the result of summing and averaging the historical current harmonic suppression scores of the photovoltaic grid-connected inverter in the historical sampling period in the preset database; in actual applications, the preset personnel can increase the voltage harmonic suppression gain and the current harmonic suppression gain by increasing the order or cutoff frequency of the improved Kalman filter. It should be noted that the increased order or cutoff frequency cannot be greater than the maximum allowable order or the maximum allowable cutoff frequency, so as to avoid increasing the complexity of the Coleman filter and introducing phase delay, wherein the maximum allowable order is the maximum value of the historical order of the Kalman filter in the preset database, and the maximum allowable cutoff frequency is the maximum value of the historical cutoff frequency of the Kalman filter in the preset database, thereby achieving more accurate suppression of harmonic components.

[0107] Furthermore, the specific steps of optimizing the state estimation of the linearized observation matrix to obtain the state optimization estimate value include: when the obtained output waveform interference score is not within the output waveform interference threshold range and the obtained harmonic suppression score is greater than the preset harmonic suppression score, the state variable is input into the linearized observation matrix to obtain the state variable prediction value at the next moment, and the change of the state variable prediction value in the current acquisition cycle is monitored in real time to obtain the state error covariance; the Kalman gain is obtained according to the obtained state error covariance, and the state covariance matrix is ​​updated according to the Kalman gain and the state estimation is performed according to the observed variables at the current moment; the state estimation is optimized based on the state estimation according to the EKF algorithm to obtain the state optimization estimate value.

[0108] Among them, the state variable prediction value includes the output voltage prediction value and the integral variable prediction value; the state error covariance is obtained by the prediction covariance matrix, the photovoltaic grid-connected inverter noise covariance matrix and the discretized state transfer matrix; the Kalman gain represents the weight of the weighted observation value and the state variable prediction value in the state estimation process; the state estimation represents the process of estimating the state variables at the current moment; the optimized state estimation represents the process of optimizing the state estimation result at the current moment based on the state estimation.

[0109] In this embodiment, in the Kalman filter filtering process, the linearized observation matrix is ​​inherent and is used to observe the predicted value of the state variable in the current sampling period (i.e., the predicted value of the state variable); as the Kalman gain increases, the corresponding observation value and the predicted value of the state variable of the specified photovoltaic grid-connected inverter are more accurate. By increasing the size of the Kalman gain, the influence between prediction and measurement can be better balanced in the state estimation, thereby improving the accuracy and stability of the filtering. The extended Kalman filter (EKF) algorithm is a state estimation algorithm suitable for nonlinear systems. It approximates the actual operating state of the specified photovoltaic grid-connected inverter through a linearized nonlinear model, thereby effectively processing system and observation noise, improving the accuracy of state estimation, and thereby achieving an improvement in the accuracy and reliability of obtaining state optimization estimation values.

[0110] Specifically, the specific restriction expression of the predicted value of the state variable is:

[0111]

[0112] Where i is the number of the current moment in the current sampling period, i = 1, 2, ..., J, J is the total number of moments in the current sampling period, represents the predicted value of the state variable at the next moment i+1, represents the state variable at the current time i, represents the input variable at the current time i, It represents the state variable corresponding to the input variable at the current moment i, T represents the current sampling period, SHU represents the output waveform interference score of the photovoltaic grid-connected inverter in the current sampling period, ΔSHU represents the output waveform interference threshold range, YI represents the harmonic suppression score of the photovoltaic grid-connected inverter in the current sampling period, and YI0 represents the preset harmonic suppression score (representing the sum of the preset voltage harmonic suppression score and the preset current harmonic suppression score).

[0113] The specific limiting expression of Kalman gain is:

[0114]

[0115] Where i is the number of the current moment in the current sampling period, i = 1, 2, ..., J, J is the total number of moments in the current sampling period, represents the predicted value of the state variable at the next moment i+1, represents the state variable at the current time i, K i+1 represents the Kalman gain at the next moment i+1, P i+1 represents the updated state covariance matrix at the next moment i+1, It represents the linearized observation matrix corresponding to the next time i+1 in the next sampling period, H i+1 represents the linearized observation matrix at the next moment i+1, R represents the measurement noise covariance matrix, F i+1 represents the discretized state transfer matrix at the next moment i+1, P i represents the state covariance matrix at the current time i, represents the discretized state transfer matrix corresponding to the next moment i+1 in the next sampling period, and Q represents the noise covariance matrix of the photovoltaic grid-connected inverter.

[0116] Furthermore, the identified control parameters are used as input signals of a controller of a specified photovoltaic grid-connected inverter. The actual output value of the photovoltaic grid-connected inverter is weighted with the state optimization estimate to obtain a weighted value. The identified control parameters and the obtained weighted value are used as input signals of a controller of the specified photovoltaic grid-connected inverter. The weighted value is used to further optimize the output performance of the controller of the specified photovoltaic grid-connected inverter.

[0117] Among them, the weighted value is the sum of the actual output value coefficient and the state optimization estimate value coefficient, the actual output value coefficient is the product of the actual output value and the actual output value weight factor, and the state optimization estimate value coefficient is the product of the state optimization estimate value and the state optimization estimate value weight factor.

[0118] Specifically, the actual output value weight factor is the weight factor corresponding to the weight value acquisition process in the preset database, which represents the numerical value corresponding to the degree of influence of the actual output value on the weight value. When used, the weight factor corresponding to the actual output value can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the actual output value corresponding to the actual output value in the weight value acquisition process and the weight factor corresponding to the actual output value in the preset database form a mapping set, and the real-time actual output value is input into the mapping set to obtain the corresponding actual output value weight factor, wherein the mapping relationship can be one-to-one or many-to-one. The value range of the actual output value weight factor in this example is [0,1].

[0119] The state optimization estimate weight factor is the corresponding weight factor in the process of obtaining the weighted value in the preset database, which indicates the numerical value corresponding to the influence of the state optimization estimate on the weighted value. When used, the weight factor corresponding to the state optimization estimate can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the state optimization estimate corresponding to the state optimization estimate in the process of obtaining the weighted value and the weight factor corresponding to the state optimization estimate in the preset database form a mapping set, and the real-time state optimization estimate is input into the mapping set to obtain the corresponding state optimization estimate weight factor, wherein the mapping relationship can be one-to-one or many-to-one. In this example, the value range of the state optimization estimate weight factor is [0,1].

[0120] It should be noted that the sum of the actual output value weight factor and the state optimization estimate weight factor in this example is 1.

[0121] like Figure 4 , which is a schematic diagram of a waveform result of a weighted value provided in an embodiment of the present application (i.e., a waveform result after weighting the actual output value of the photovoltaic grid-connected inverter and the state optimization estimated value), Figure 4 Two graphs, labeled (a) and (b), are shown for comparing the differences between measured values, predicted values, and output values, each having an x-axis and a y-axis, the x-axis representing time t / s and the y-axis representing the measured or predicted value.

[0122] In chart (a), as time goes by (the x-axis represents time t / s), the measured value gradually increases from 0 to 2.5. At the same time, the predicted value also shows a similar growth trend and finally stabilizes at 2.4. After weighted processing, the output value is between the measured value and the predicted value, and finally increases to 2.3. This result shows that the weighted processing effectively integrates the information of the measured value and the predicted value, making the output value more stable and close to the actual state.

[0123] In chart (b), the measured value, predicted value, and output value all start from 0 and gradually increase over time. It is worth noting that in this chart, the predicted value almost completely overlaps with the measured value, and finally reaches 9.5, which shows that during this time period, the accuracy of the prediction model is very high and can accurately reflect the actual state of the system. Therefore, the output value after weighted processing also closely follows the measured value and the predicted value, and finally reaches 9.5.

[0124] Both charts show the changing trends of measured values, predicted values, and output values ​​over time. By comparing the two charts, we can conclude that weighted processing can comprehensively consider the information of measured values ​​and predicted values, making the output value more accurate and stable, thereby improving the accuracy and reliability of control parameter identification.

[0125] It should be added that the weighted value is obtained, and then the voltage outer loop parameters are adjusted according to the obtained weighted value, and the adjusted voltage outer loop parameters are input into the photovoltaic grid-connected inverter to control the input voltage and input current of the controller of the specified photovoltaic grid-connected inverter. The voltage outer loop parameters include a voltage set value and a voltage regulation gain.

[0126] In this embodiment, the voltage setting value and the voltage regulation gain are usually adjusted by a preset person. When the output voltage peak value of the designated photovoltaic grid-connected inverter during operation is not less than the reference average output voltage peak value and the weighted value is less than the preset weighted value (represented by the result of summing and averaging the historical weighted values ​​in the preset database), it indicates that the corresponding output voltage waveform may be distorted or deformed. At this time, the controller in the designated photovoltaic grid-connected inverter receives the corresponding reduction instruction and automatically reduces the voltage regulation gain of the converter through PWM, such as Figure 5 As shown, it is a structural block diagram of the control principle of the photovoltaic grid-connected inverter provided in the embodiment of the present application, wherein U dc It represents the input voltage on the DC side of the photovoltaic grid-connected inverter (i.e. the potential difference in the circuit), I dc Represents the input current of the DC side in the photovoltaic grid-connected inverter, I PV Indicates the current generated by the photovoltaic (PV) module in the photovoltaic grid-connected inverter, U dc ref Indicates the reference value of the DC side voltage, I dc ref Represents the reference value of the DC side current, PI represents the proportional-integral controller, which is used to adjust the output of the photovoltaic grid-connected inverter to achieve the desired value, e d In the dq coordinate system (a rotating coordinate system used for power electronic system analysis), this usually represents the error or component on the d-axis, WL represents the winding parameters in the photovoltaic grid-connected inverter, and I d represents the current component on the d-axis (in the dq coordinate system), I q represents the current component on the q axis (in the dq coordinate system), I q ref represents the reference value of the q-axis current, that is, the q-axis current value expected or controlled by the system, e q It represents the error or component on the q-axis (in the dq coordinate system), abc represents the three phases of the three-phase system, namely, phase A, phase B and phase C, and dq represents the conversion relationship between the dq coordinate systems.

[0127] If the output voltage peak is still not less than the reference average output voltage peak in the next acquisition cycle, the preset personnel will be notified to perform the shutdown operation and reduce the voltage setting value. After the voltage setting value is reduced, the designated photovoltaic grid-connected inverter will be restarted and the output voltage peak and weighted value obtained during the operation will be monitored in real time until the re-acquired output voltage peak is less than the reference average output voltage peak and the weighted value is not less than the preset weighted value, thereby achieving stable operation and efficient power generation of the designated photovoltaic grid-connected inverter, thereby achieving improved accuracy in identifying control parameters of the photovoltaic grid-connected inverter during filtering operation.

[0128] Example 3

[0129] This embodiment also provides a computer device, which is applicable to a control parameter identification method based on a photovoltaic grid-connected inverter in a filtering process, and includes a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a forced oscillation detection and positioning method for a distribution network as proposed in the above embodiment.

[0130] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, a forced oscillation detection and positioning method for a distribution network is implemented as proposed in the above embodiment.

[0131] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0132] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0133] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0134] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0135] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0136] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A control parameter identification method based on a photovoltaic grid-connected inverter in a filtering process, characterized in that: It includes obtaining key operating data of the specified photovoltaic grid-connected inverter in the current sampling period, obtaining the photovoltaic panel input voltage, grid-connected output voltage, grid-connected output current and power factor, and building a mathematical model; Monitor the output voltage waveform and current waveform changes of the specified photovoltaic grid-connected inverter in real time to obtain the output waveform interference score, and determine whether to perform filtering operation according to the output waveform interference score; The controller is linearized according to the control parameters to be identified to obtain a linearized observation matrix, and the observation matrix is ​​optimized for state estimation.

2. The control parameter identification method based on the photovoltaic grid-connected inverter in the filtering process according to claim 1, characterized in that: The control parameters to be identified include: state variables, including inverter output voltage and controller integral variables; observed variables, including output current and voltage waveforms; initial parameters, including proportional gain, integral gain, filter parameters and modulation methods, wherein the modulation methods include SPWM and SVPWM.

3. The control parameter identification method based on the photovoltaic grid-connected inverter in the filtering process according to claim 2, characterized in that: The specific process of determining whether to perform the filtering operation based on the output waveform interference score is as follows: determining whether the obtained output waveform interference score is within the output waveform interference threshold range: if it is within the output waveform interference threshold range, then not performing the filtering operation; If it is not within the output waveform interference threshold range, the improved Kalman filter is used for filtering.

4. The method for identifying parameters of a photovoltaic grid-connected inverter in a filtering process according to claim 3, characterized in that: After performing the filtering operation, the method further includes: real-time monitoring of the suppression effect of the harmonic situation during the filtering operation to obtain a harmonic suppression score, wherein the harmonic suppression score includes a voltage harmonic suppression score and a current harmonic suppression score; the voltage harmonic suppression score represents the change ratio of the voltage harmonic component before and after filtering; the current harmonic suppression score represents the change ratio of the current harmonic component before and after filtering.

5. The control parameter identification method based on the photovoltaic grid-connected inverter in the filtering process according to claim 4, characterized in that: After obtaining the harmonic suppression score, the method also includes: when the voltage harmonic suppression score is less than a preset value, increasing the voltage harmonic suppression gain and continuing filtering; when the current harmonic suppression score is less than a preset value, increasing the current harmonic suppression gain and continuing filtering; when both the voltage and current harmonic suppression scores are greater than the preset values, completing the filtering operation.

6. The control parameter identification method based on the photovoltaic grid-connected inverter in the filtering process according to claim 5, characterized in that: Also includes: weighting the actual output value and the state optimization estimate value to obtain a weighted value; The identified control parameters and weighted values ​​are used together as the input signals of the controller.

7. The control parameter identification method based on the photovoltaic grid-connected inverter in the filtering process according to claim 6, characterized in that: Also includes: Adjust the voltage outer loop parameters according to the weighted values; The adjusted voltage outer loop parameters are input into the photovoltaic grid-connected inverter to control the input voltage and input current.

8. A control parameter identification system based on a photovoltaic grid-connected inverter in a filtering process, based on the control parameter identification method based on a photovoltaic grid-connected inverter in a filtering process according to any one of claims 1 to 7, characterized in that: It also includes an operation data acquisition module, which acquires key operation data of the specified photovoltaic grid-connected inverter in the current sampling period, acquires the photovoltaic panel input voltage, grid-connected output voltage, grid-connected output current and power factor, and constructs a mathematical model; The monitoring module monitors the output voltage waveform and current waveform changes of the specified photovoltaic grid-connected inverter in real time to obtain the output waveform interference score, and determines whether to perform the filtering operation according to the output waveform interference score; The estimation and identification module linearizes the controller according to the control parameters to be identified to obtain the linearized observation matrix, and performs optimized state estimation on the observation matrix.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the control parameter identification method based on the photovoltaic grid-connected inverter in the filtering process according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the control parameter identification method based on the filtering process of the photovoltaic grid-connected inverter according to any one of claims 1 to 7 are implemented.

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