Safety testing method, device, equipment and storage medium for inverter

Abstract

The present invention relates to the field of inverter testing technology, and discloses a safety testing method, device, equipment, and storage medium for an inverter. The method comprises: modeling and analyzing inverter characteristic data to obtain a non-ideal state space model of the inverter; performing harmonic analysis based on the non-ideal state space model to obtain a harmonic suppression scheme; performing conservative power theory transformation on the output current of the inverter to obtain a real-time data stream; constructing a three-layer optimization model in combination with the harmonic suppression scheme and the real-time data stream, and generating a dynamic optimization strategy based on the three-layer optimization model; performing automated testing on the inverter based on the dynamic optimization strategy to obtain a performance indicator test data set; performing fast Fourier transform and wavelet analysis on the performance indicator test data set to generate a comprehensive inverter performance diagnosis report and optimization suggestions. The present invention improves the accuracy of the inverter performance test and thereby improves the safety of the inverter.

Description

Technical Field

[0001] The present invention relates to the technical field of inverter testing, and in particular to a safety testing method, device, equipment and storage medium for an inverter. Background Art

[0002] As key equipment, inverters are playing an increasingly important role in fields such as photovoltaic and wind power generation. However, the safety and reliability of inverters have always been a focus of industry attention. Traditional inverter testing methods often suffer from long test cycles, incomplete coverage, and an inability to reflect real-time device performance. These methods struggle to meet the performance requirements of increasingly complex power systems.

[0003] Furthermore, inverters face numerous challenges in actual operation, such as harmonic interference, environmental factors, and load fluctuations, which can adversely affect the inverter's output quality and stability. Existing testing methods often struggle to fully simulate these complex operating conditions, resulting in a discrepancy between test results and actual application scenarios. Summary of the Invention

[0004] The present invention provides a safety testing method, device, equipment and storage medium for an inverter, which are used to improve the accuracy of performance testing of the inverter and thereby improve the safety of the inverter.

[0005] In a first aspect, the present invention provides a safety testing method for an inverter, the safety testing method for the inverter comprising:

[0006] Model and analyze the inverter characteristic data to obtain the non-ideal state space model of the inverter;

[0007] Performing harmonic analysis based on the non-ideal state space model to obtain a harmonic suppression solution;

[0008] Performing conservative power theory transformation on the output current of the inverter to obtain a real-time data stream;

[0009] Building a three-layer optimization model in combination with the harmonic suppression scheme and the real-time data stream, and generating a dynamic optimization strategy based on the three-layer optimization model;

[0010] Based on the dynamic optimization strategy, the inverter is automatically tested to obtain a performance indicator test data set;

[0011] Perform fast Fourier transform and wavelet analysis on the performance index test data set to generate a comprehensive inverter performance diagnosis report and optimization suggestions.

[0012] In a second aspect, the present invention provides a safety test device for an inverter, the safety test device for the inverter comprising:

[0013] A modeling module is used to perform modeling and analysis on the inverter characteristic data to obtain a non-ideal state space model of the inverter;

[0014] An analysis module, configured to perform harmonic analysis based on the non-ideal state space model to obtain a harmonic suppression solution;

[0015] a conversion module, configured to perform conservative power theory conversion on the output current of the inverter to obtain a real-time data stream;

[0016] A construction module, configured to construct a three-layer optimization model in combination with the harmonic suppression scheme and the real-time data stream, and generate a dynamic optimization strategy based on the three-layer optimization model;

[0017] A testing module, configured to perform automated testing on the inverter based on the dynamic optimization strategy to obtain a performance indicator test data set;

[0018] The generating module is used to perform fast Fourier transform and wavelet analysis on the performance index test data set to generate a comprehensive performance diagnosis report and optimization suggestions for the inverter.

[0019] A third aspect of the present invention provides a safety testing device for an inverter, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the safety testing device for the inverter executes the above-mentioned safety testing method for the inverter.

[0020] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned inverter safety testing method.

[0021] In the technical solution provided by the present invention, by modeling and analyzing the inverter characteristic data, the non-ideal state space model obtained can more accurately reflect the actual characteristics of the inverter, including the influence of parasitic parameters and environmental factors. Harmonic analysis is performed based on the non-ideal state space model, and the harmonic suppression scheme obtained can effectively reduce the harmonic content in the inverter output. The inverter output current is processed by conservative power theory transformation, and the real-time data stream obtained contains detailed harmonic and reactive current component information. The three-layer optimization model constructed by combining the harmonic suppression scheme and the real-time data stream can achieve the optimization of target performance indicators and the evaluation of comprehensive performance while ensuring basic safety standards, thereby generating a more efficient and adaptable dynamic optimization strategy. Automated testing based on the dynamic optimization strategy can efficiently execute multiple test tasks and adjust test parameters in real time, greatly improving test efficiency and the accuracy of test results. In-depth analysis of test data through fast Fourier transform and wavelet analysis can not only obtain a comprehensive performance diagnosis report, but also provide targeted optimization suggestions, improve the accuracy of inverter performance testing, and thus improve the safety of the inverter. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] 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 some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A schematic diagram of a safety test method for an inverter according to an embodiment of the present application;

[0024] Figure 2 This is a schematic block diagram of the structure of the safety testing device for the inverter provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0026] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may change based on actual circumstances.

[0027] It should also be understood that the terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0028] It should be further understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0029] The following describes some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0030] See also Figure 1 , Figure 1 A flow chart of a safety test method for an inverter provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the inverter safety testing method provided in the embodiment of the present application includes steps S100 to S600.

[0031] Step S100: Modeling and analyzing the inverter characteristic data to obtain a non-ideal state space model of the inverter;

[0032] It is understandable that the execution subject of the present invention may be a safety test device of an inverter, or may be a terminal or a server, which is not limited here. The embodiment of the present invention is described by taking a server as the execution subject as an example.

[0033] Specifically, the inverter topology is analyzed to identify key electrical components in the system, such as inductors, capacitors, and switching devices. Through this analysis, a basic circuit model incorporating these components is established. Within this basic circuit model, parameters are extracted for the non-ideal characteristic of the inductor's equivalent series resistance (ESR), resulting in a more realistic non-ideal inductor model. Inductor non-idealities can significantly impact the inverter's overall performance, especially at high frequencies, where the ESR leads to additional energy loss and thermal effects. Based on this non-ideal inductor model, a mathematical description of the inverter's switching state is developed. A switching function, including turn-on and turn-off times, is defined. Using this switching function and Kirchhoff's voltage and current laws, the relationship between the inverter's voltage and current is mathematically derived. This derivation not only considers ideal voltage and current variations but also incorporates the actual dynamic characteristics of the switching devices to derive a state equation that reflects the actual switching behavior. The state equation is Laplace transformed and discretized to obtain a discrete state-space representation of the inverter. Based on the discrete state-space representation, the inverter's input and output characteristics are simulated and analyzed to obtain performance indicators. These include steady-state performance, such as the average and effective values ​​of voltage and current, as well as dynamic performance indicators such as response time, overshoot, and steady-state error. Based on this performance indicator data, environmental factors are modeled, constructing an environmental impact model that includes temperature coefficients and electromagnetic interference coefficients. For example, temperature changes may affect the actual values ​​of inductance and capacitance, while electromagnetic interference may affect the accuracy of control signals. Based on the environmental impact model, the discrete state-space representation is modified to more accurately reflect the inverter's behavior under actual operating conditions, resulting in a non-ideal state-space model.

[0034] Step S200: Perform harmonic analysis based on a non-ideal state space model to obtain a harmonic suppression solution;

[0035] Specifically, the non-ideal state-space model is transformed into the frequency domain to obtain the frequency response function of the inverter output. The frequency response function is a mathematical expression that describes how a system responds to input signals of varying frequencies. Based on the frequency response function, the inverter output is subjected to harmonic decomposition, extracting the amplitude and phase information of each harmonic. The presence of harmonics can lead to equipment overheating, reduced efficiency, and grid interference. Statistical analysis of the amplitude and phase information of each harmonic is performed to determine the harmonic distribution characteristics and initial values ​​of total harmonic distortion (THD), reflecting the overall harmonic distribution. Based on the harmonic distribution characteristics, the transfer function of the proportional-integral controller is designed, and a fundamental wave control algorithm is developed. The fundamental wave control algorithm ensures the stability and accuracy of the fundamental wave component of the inverter output. Furthermore, based on the initial value of total harmonic distortion, the resonant portion of the proportional resonant controller is constructed, forming a selective control algorithm for subharmonics. The proportional resonant controller can effectively suppress harmonics of specific frequencies, thereby reducing the negative impact of harmonics on the system. The proportional-integral controller and the proportional resonant controller are combined in parallel to form a composite controller structure. The composite controller not only regulates the fundamental harmonic but also selectively suppresses specific subharmonics. Based on the composite controller structure, a state feedback matrix is ​​designed to obtain the state equation of the closed-loop control system, which describes the dynamic behavior of the system under the control of the controller. A stability analysis of the state equation of the closed-loop control system is performed to determine the stability constraints of the controller parameters, ensuring that the system does not become unstable during operation. The constraints provide the necessary boundaries for parameter optimization. Based on the stability constraints, the controller parameters are optimized and adjusted to obtain a harmonic suppression solution. This harmonic suppression solution can effectively reduce the harmonic content of the inverter output, improve power quality, and ensure stable and efficient operation of the inverter in various operating environments.

[0036] Step S300: Perform conservative power theory transformation on the output current of the inverter to obtain a real-time data stream;

[0037] Specifically, the inverter's output current is sampled to obtain raw current data in a discrete time series. Based on the raw current data, the sampling frequency is adaptively adjusted to obtain optimized sampling parameters. Based on the optimized sampling parameters, the raw current data is resampled to obtain an evenly spaced current sampling sequence, ensuring temporal uniformity of the data. The evenly spaced current sampling sequence is orthogonally decomposed to extract the instantaneous active and reactive components of the current. Based on the instantaneous active and reactive components, the instantaneous active and reactive powers are calculated to form a time-domain representation of power. Sliding window processing is performed on the time-domain representation of power to obtain a frequency-domain representation of power. Sliding window processing captures the frequency characteristics of power variations, enabling the system to analyze power distribution and variations in the frequency domain. Based on the frequency-domain representation of power, the fundamental and harmonic components are extracted to obtain the harmonic power spectrum. The harmonic power spectrum is an important tool for describing the contribution of each harmonic to the total power and can be used to identify which harmonics have a significant impact on the system. Based on the harmonic power spectrum, the active and reactive power of each harmonic are calculated to obtain a harmonic power distribution matrix. The harmonic power distribution matrix is ​​then separated into even and odd harmonics to generate eigenvectors for the odd and even harmonics. These eigenvectors describe the power characteristics of the odd and even harmonics, respectively, and facilitate identification and control of different types of harmonics. Based on the eigenvectors of the odd and even harmonics, a feature description model for the real-time data stream is constructed, generating a real-time data stream containing information about harmonic and reactive current components.

[0038] Step S400: constructing a three-layer optimization model by combining the harmonic suppression scheme and the real-time data stream, and generating a dynamic optimization strategy based on the three-layer optimization model;

[0039] Specifically, the harmonic suppression scheme is parameterized to obtain a set of harmonic suppression control parameters. This parameter set includes key factors such as the gains of various controllers and the cutoff frequency of filters, which directly impact the effectiveness of harmonic suppression. Furthermore, a set of inverter performance evaluation metrics, such as harmonic distortion, power factor, and efficiency, is extracted from the real-time data stream. These metrics reflect the actual performance of the inverter under different operating conditions. Based on the harmonic suppression control parameter set and the performance evaluation metric set, a first-level optimization objective function is constructed to form a basic safety standard optimization model. This ensures that the inverter operates within minimum safety standards, i.e., under basic conditions, safety thresholds such as harmonic distortion and temperature rise are not exceeded. Constraints are set on this basic safety standard optimization model to determine the parameter range that meets the minimum safety requirements. These constraints ensure that the inverter operates within the safe range and prevent any operations that could cause equipment damage or performance degradation. Based on the determined parameter range and the real-time data stream, a second-level optimization objective function is constructed to obtain a target performance indicator optimization model. This model optimizes key inverter performance indicators, such as improving efficiency and reducing harmonic distortion. To achieve multi-objective optimization, the target performance indicator optimization model assigns weights to multiple objectives, forming a performance indicator priority matrix. This determines the relative importance of each performance indicator in the optimization process, ensuring that, given limited resources, the indicators with the greatest impact on overall system performance are prioritized. Based on the priority matrix and real-time data streams, a third-level optimization objective function is constructed to form a comprehensive performance evaluation optimization model. This model considers the balance between multiple optimization objectives, such as maximizing efficiency while reducing harmonic distortion, or maintaining system stability while improving power factor. The comprehensive performance evaluation optimization model integrates multiple optimization objectives to ensure the inverter performs optimally across the board in complex real-world operating environments. The basic safety standard optimization model, the target performance indicator optimization model, and the comprehensive performance evaluation optimization model are integrated to create a unified optimization objective function. This function represents a comprehensive representation of all optimization objectives, ensuring consistency among optimization objectives at different levels. Parameter optimization is performed based on this unified optimization objective function to obtain the optimal control parameter combination. By creating a dynamic adjustment strategy for this optimal control parameter combination, a dynamic optimization strategy that can automatically adapt to inverters of different power levels is generated. This strategy can automatically adjust control parameters according to the real-time operating status to cope with actual operating conditions such as load changes and grid fluctuations, ensuring that the inverter always operates in the best state.

[0040] Step S500: Based on the dynamic optimization strategy, perform automated testing on the inverter to obtain a performance indicator test data set;

[0041] Specifically, based on the dynamic optimization strategy, an automated test sequence is generated, including detailed test items, parameter settings, and execution order, forming a complete test instruction set. This test instruction set covers all necessary test steps, including startup, monitoring, and data acquisition, ensuring a comprehensive and systematic testing process. The test instruction set is prioritized to determine the importance of test items and the execution order, resulting in a test execution queue. Based on this prioritized test execution queue, the inverter system is initialized and configured, including device power settings, connection checks, and initial state settings. Once the initial configuration is complete, the current test item is initiated and the raw test data stream is collected. During data acquisition, the raw test data stream is processed in real time, applying a previously defined harmonic mitigation scheme to eliminate or reduce harmonic content in the data, generating harmonically mitigated processed data. Based on this harmonically mitigated processed data, performance metrics for the current test item, such as efficiency, power factor, and harmonic distortion, are calculated to generate individual test results. Based on these individual test results, individual test data is generated, recording the specific performance of each test item. As each test item is completed, the dynamic optimization strategy parameters are updated based on the individual test data. Parameter updates are based on real-time data feedback and aim to optimize the execution plan for subsequent tests to adapt to changing test conditions and inverter performance. Dynamic adjustments ensure that each test item is performed under optimal conditions, improving test accuracy and effectiveness. The next test item in the test execution queue is executed using the updated optimized control parameters. This process is repeated until all test items are completed, forming a complete test dataset. This complete test dataset is then consolidated and standardized. This includes data cleaning, outlier processing, and unified unit and scale conversion to ensure data comparability and consistency, ultimately forming a performance indicator test dataset.

[0042] Step S600: Perform fast Fourier transform and wavelet analysis on the performance index test data set to generate a comprehensive inverter performance diagnosis report and optimization suggestions.

[0043] Specifically, the performance indicator test data set is preprocessed, including removing noise and outliers and normalizing the data to obtain a standardized test data matrix. A fast Fourier transform is then performed on the standardized test data matrix, converting the data from the time domain to the frequency domain to obtain a frequency domain characteristic spectrum. The frequency domain characteristic spectrum displays the inverter's response characteristics at different frequencies, specifically the amplitude and phase information of the fundamental and harmonic components. By extracting this information, a harmonic distribution diagram is generated, showing the proportion and characteristics of each harmonic in the total output. Wavelet decomposition is performed on the standardized test data matrix to extract the multi-scale time-frequency characteristics of the data. Decomposing the data using a wavelet transform captures characteristic variations at different time scales, providing finer time and frequency resolution than traditional Fourier transforms. Singular value decomposition is performed based on the multi-scale time-frequency characteristics to extract the target characteristic components of the data. A feature vector space is constructed based on the target characteristic components, forming a multi-dimensional representation of the inverter's performance, including various performance indicators such as efficiency, harmonic distortion, and thermal effects. Cluster analysis is performed on the multi-dimensional representation to classify the inverter's performance data into different categories. Each category represents the inverter's performance under specific operating conditions, such as normal, overloaded, or abnormal conditions. Based on the classification results and preset performance indicator thresholds, pattern recognition is performed to evaluate the inverter's operating status. Pattern recognition helps determine whether the inverter is in optimal operating condition and whether there are potential problems, such as equipment aging or deteriorating environmental conditions. A comprehensive inverter performance diagnostic report is generated based on the operating status assessment information. The report describes the inverter's current performance status, potential problems, and the possibility of performance improvement. Based on the comprehensive performance diagnostic report, intelligent analysis is performed to provide specific optimization suggestions. These suggestions may include adjusting control parameters, optimizing the cooling system, or recommending equipment maintenance.

[0044] In an embodiment of the present invention, by modeling and analyzing inverter characteristic data, the resulting non-ideal state-space model can more accurately reflect the actual characteristics of the inverter, including the influence of parasitic parameters and environmental factors. Harmonic analysis based on the non-ideal state-space model yields a harmonic suppression scheme that effectively reduces the harmonic content in the inverter output. The inverter output current is processed using conservative power theory transformation, resulting in a real-time data stream containing detailed information on harmonic and reactive current components. A three-layer optimization model constructed by combining the harmonic suppression scheme and the real-time data stream can optimize target performance indicators and evaluate comprehensive performance while ensuring basic safety standards, thereby generating a more efficient and adaptable dynamic optimization strategy. Automated testing based on the dynamic optimization strategy can efficiently execute multiple test tasks and adjust test parameters in real time, significantly improving test efficiency and the accuracy of test results. In-depth analysis of test data using fast Fourier transform and wavelet analysis not only produces a comprehensive performance diagnostic report but also provides targeted optimization recommendations, improving the accuracy of inverter performance testing and, in turn, the safety of the inverter.

[0045] In a specific embodiment, the process of executing step S100 may specifically include the following steps:

[0046] The inverter topology is analyzed to obtain a basic circuit model consisting of inductors, capacitors, and switching devices. Based on this basic circuit model, the equivalent series resistance of the inductor is parameterized to obtain a non-ideal inductor model.

[0047] Based on a non-ideal inductor model, the switching state of the inverter is mathematically described to obtain a switching function that includes the turn-on time and turn-off time. Based on the switching function and Kirchhoff's law, the relationship between the voltage and current of the inverter is mathematically derived to obtain the state equation of the inverter.

[0048] Perform Laplace transform and discretization on the state equation to obtain the discrete state space expression of the inverter. Based on the discrete state space expression, simulate and analyze the input and output characteristics of the inverter to obtain performance index data.

[0049] Based on the performance index data, the environmental factors are modeled to obtain an environmental impact model including the temperature coefficient and the electromagnetic interference coefficient. According to the environmental impact model, the discrete state space expression is modified to obtain a non-ideal state space model.

[0050] Specifically, for a typical single-phase full-bridge inverter, its topology includes four power switching elements (such as IGBTs or MOSFETs) and two symmetrical DC capacitors. These switching elements are turned on or off at different times to control the waveform of the AC output. The control signal of each switching element determines its on and off time, thereby affecting the waveform of the output voltage and current. When establishing a basic circuit model, all electrical components are first idealized, ignoring actual non-ideal characteristics such as the resistance of the inductor and the ESR (equivalent series resistance) of the capacitor. However, in order to more accurately simulate and analyze the behavior of the inverter, non-ideal factors need to be considered. For example, the equivalent series resistance (ESR) of the inductor is the resistance of the inductor winding, which will generate energy loss as the frequency increases. These parameters are obtained through experimental measurement or from the device data sheet, added to the model, and a non-ideal inductor model is constructed. The equivalent series resistance of the inductor L is set to R s , then the actual inductance can be expressed as L and R s Based on the non-ideal inductor model, the switching state of the inverter is mathematically described. The switching function S(t) defines the state of each switching element at different times. S(t) = 1 indicates that the switch is on, and S(t) = 0 indicates that the switch is off. Using the switching function and Kirchhoff's law, the relationship between the inverter voltage and current is derived. Assume that the DC input voltage is V dc , the load current is I L , the output voltage is V out , the output voltage can be expressed as:

[0051] V out (t)=S1(t)·V dc -S2(t)·V dc ;

[0052] Where S1(t) and S2(t) represent the states of the two switches respectively. The relationship between current and voltage needs to take into account the change of inductor current, that is:

[0053]

[0054] Using these relationships, the inverter's state equation is derived, describing the system's dynamic behavior under different switching states. A Laplace transform is performed on the state equation, converting it from the time domain to the s-domain. This transform simplifies solving the differential equation and facilitates analysis of the system's frequency response. To facilitate application in practical digital control systems, the Laplace-transformed equation is discretized, representing the continuous system as a discrete-time state-space model. The discretized state-space expression describes the system's state changes within each sampling period. Based on the discrete state-space expression, the inverter's input and output characteristics are simulated and analyzed to evaluate the inverter's performance. For example, by varying the input voltage or load conditions, performance indicators such as the inverter's response time, steady-state error, and harmonic distortion are observed. Based on this performance indicator data, environmental factors such as temperature and electromagnetic interference are modeled. Environmental factors can significantly affect inverter performance. For example, temperature changes can cause changes in semiconductor device characteristics, while electromagnetic interference can affect the accuracy of control signals. Using experimental or literature data, models for the temperature coefficient and electromagnetic interference coefficient are established. These coefficients are used to modify the inverter's discrete state-space model. For example, the effect of temperature on resistance can be expressed as:

[0055] R(T)=R0(1+α(T-T0));

[0056] Where R0 is the resistance value at the reference temperature T0, and α is the temperature coefficient of resistance. Based on the modified model, a non-ideal state space model is obtained.

[0057] In a specific embodiment, the process of executing step S200 may specifically include the following steps:

[0058] Perform frequency domain transformation on the non-ideal state space model to obtain the frequency response function of the inverter output. Based on the frequency response function, perform harmonic decomposition on the inverter output to obtain the amplitude and phase information of each harmonic.

[0059] Perform statistical analysis on the amplitude and phase information of each harmonic to obtain the harmonic distribution characteristics and the initial value of total harmonic distortion;

[0060] According to the harmonic distribution characteristics, the transfer function of the proportional-integral controller is designed to obtain the fundamental wave control algorithm. Based on the initial value of the total harmonic distortion, the resonant part of the proportional resonant controller is constructed to obtain a selective control algorithm for subharmonics.

[0061] The proportional-integral controller and the proportional-resonant controller are combined in parallel to obtain a composite controller structure. Based on the composite controller structure, a state feedback matrix is ​​designed to obtain the state equation of the closed-loop control system.

[0062] The stability analysis of the state equation of the closed-loop control system is performed to obtain the stability constraints of the controller parameters. Based on the stability constraints, the controller parameters are optimized to obtain a harmonic suppression solution.

[0063] Specifically, the state space equation is Laplace transformed to convert it from the time domain to the frequency domain to obtain the response characteristics of the system at different frequencies. The frequency response function H(s) describes the output response of the system at the input signal frequency s, and is usually in the form of:

[0064] H(s)=C(sI-A) -1 B+D;

[0065] Where A, B, C, and D are the matrices of the state-space model, s is the complex frequency variable, and I is the identity matrix. By solving H(s), the response characteristics of the inverter at various frequencies are obtained. Based on the frequency response function, the inverter output is harmonically decomposed to extract the amplitude and phase information of each harmonic. Assuming the output voltage or current signal is y(t), its harmonic components can be expressed as:

[0066]

[0067] Among them, A n is the amplitude of the nth harmonic, ω0 is the fundamental frequency, φ n is the phase angle. Through Fourier transform, the specific values ​​of these harmonic components are extracted to form a harmonic spectrum. Statistical analysis of the amplitude and phase information of each harmonic is performed to obtain the harmonic distribution characteristics and the initial value of total harmonic distortion (THD). Total harmonic distortion can be calculated using the following formula:

[0068]

[0069] Where A1 is the amplitude of the fundamental wave. THD provides a comprehensive indicator for measuring the impact of harmonics on the overall system performance. Based on the harmonic distribution characteristics, the transfer function of the proportional-integral controller (PI controller) is designed to control the characteristics of the fundamental wave. The transfer function of the PI controller is usually expressed as:

[0070]

[0071] Among them, K p is the proportional gain, K i is the integral gain. The purpose of the PI controller is to eliminate steady-state errors and improve system stability. Based on the initial value of total harmonic distortion, the resonant part of the proportional resonant controller (PR controller) is constructed to selectively suppress specific subharmonics. The transfer function of the PR controller can be expressed as:

[0072]

[0073] Among them, K r is the resonant gain, ω c is the resonant frequency. The PR controller sets different resonant frequencies ω c Targeted harmonic suppression of specific frequencies. A proportional-integral controller and a proportional-resonant controller are combined in parallel to form a composite controller structure. This structure not only controls fundamental wave characteristics but also suppresses subharmonics, improving overall system performance. Based on the composite controller structure, a state feedback matrix is ​​designed to form the state equation of the closed-loop control system. The design of the state feedback matrix must consider the system's dynamic response characteristics to ensure the stability of the closed-loop system. Stability analysis of the closed-loop control system's state equation is performed to ensure system stability under various operating conditions. Stability analysis can be performed using methods such as the root locus method or the Nyquist stability criterion, which assess system stability by analyzing the position of system eigenvalues. Based on stability constraints, controller parameters are optimized to ensure optimal system performance. The optimization process requires balancing harmonic suppression effectiveness with control system response speed to avoid excessive harmonic suppression resulting in sluggish system response. An optimized harmonic suppression scheme is obtained, which significantly reduces the harmonic content of the inverter output, improves power quality, and protects power equipment from harmonic interference. For example, in practical applications, if the output of an inverter mainly contains the 5th and 7th harmonics, the resonant frequencies of the PR controller can be set to the frequencies of these harmonics for targeted suppression.

[0074] In a specific embodiment, the process of executing step S300 may specifically include the following steps:

[0075] The output current of the inverter is sampled to obtain the original current data of the discrete time series, and the sampling frequency is adaptively adjusted according to the original current data to obtain the optimized sampling parameters;

[0076] Based on the optimized sampling parameters, the original current data is resampled to obtain an equally spaced current sampling sequence, and the equally spaced current sampling sequence is orthogonally decomposed to obtain the instantaneous active and reactive components of the current;

[0077] Based on the instantaneous active and reactive components, the instantaneous active power and reactive power are calculated to obtain the time domain representation of the power, and the time domain representation of the power is processed by sliding window to obtain the frequency domain representation of the power;

[0078] Based on the frequency domain representation of power, the fundamental and harmonic components are extracted to obtain the harmonic power spectrum. Based on the harmonic power spectrum, the active power and reactive power of each harmonic are calculated to obtain the harmonic power distribution matrix.

[0079] The harmonic power distribution matrix is ​​separated into odd and even harmonics to obtain the eigenvectors of odd and even harmonics. Based on the eigenvectors of odd and even harmonics, a feature description model of the real-time data stream is constructed to obtain a real-time data stream containing harmonic and reactive current component information.

[0080] Specifically, a suitable sampling frequency is selected to sample the output current of the inverter to obtain the original current data of the discrete time series. By analyzing the initial sampling data, the sampling frequency is adaptively adjusted to optimize the data quality. For example, if the initial sampling data contains high-frequency components that exceed the Nyquist frequency of the current sampling rate, the sampling frequency needs to be increased to avoid aliasing. Adaptive adjustment is usually achieved by analyzing the spectrum of the sampled data to determine whether there is a phenomenon in which frequency components are incorrectly mapped to a lower frequency region. Based on the optimized sampling parameters, the original current data is resampled to obtain an equally spaced current sampling sequence. The resampling process is implemented by interpolation or other signal processing techniques to ensure that all data points are evenly distributed on the time axis. The equally spaced current sampling sequence is orthogonally decomposed. This decomposition method is often used to analyze the active and reactive components in non-sinusoidal waveforms, usually using Clarke or Park transform. The results of the orthogonal decomposition include the instantaneous active component I of the current d and reactive component I q , where I d represents the active part, which is related to the actual power, and I q represents the reactive component, which is related to the ineffective transfer of energy. The instantaneous active power P(t) and reactive power Q(t) are calculated from the instantaneous active and reactive components. These power values ​​can be expressed by the following formula:

[0081] P(t)=V d (t)·I d (t);

[0082] Q(t)=V q (t)·I q (t);

[0083] Among them, V d and V q They correspond to I d and I qThe resulting time-domain representation of power provides a detailed view of power variations over time. Sliding window processing is performed on the time-domain representation of power to obtain a frequency-domain representation of power. By moving a fixed-length time window and performing a fast Fourier transform on the data within each window, time information is converted to frequency information. This captures the frequency variations of power over different time periods and reveals the dynamic characteristics of harmonic components over time. Based on the frequency-domain representation of power, the fundamental and harmonic components are extracted to form a harmonic power spectrum. This spectrum displays the power magnitude of each frequency component, specifically the magnitude and location of the harmonic components. Based on the harmonic power spectrum, the active and reactive power of each harmonic is calculated. The results are summarized into a harmonic power distribution matrix, where each element corresponds to the active or reactive power value of a specific harmonic component. The harmonic power distribution matrix is ​​then subjected to odd- and even-order harmonic separation. Harmonic components are classified into odd and even harmonics, representing harmonics caused by odd and even multiples of the fundamental frequency, respectively. The eigenvector provides a structured representation of different types of harmonic components, facilitating the identification and classification of harmonic sources. For example, odd harmonics are often caused by nonlinear loads, while even harmonics can stem from asymmetric faults in the power system. Based on the eigenvectors of odd and even harmonics, a characterization model for the real-time data stream is constructed. This model integrates information about harmonics and reactive current components, providing a comprehensive system condition monitoring tool. The real-time data stream not only displays the current system status but can also be used to predict and prevent potential problems, such as overloads or power quality deterioration.

[0084] In a specific embodiment, the process of executing step S400 may specifically include the following steps:

[0085] Parameterize the harmonic suppression scheme to obtain a harmonic suppression control parameter set, and extract the inverter performance evaluation index set based on the real-time data stream;

[0086] Based on the harmonic suppression control parameter set and performance evaluation index set, the first-level optimization objective function is constructed to obtain the basic safety standard optimization model;

[0087] Constraints are set for the basic safety standard optimization model to obtain the parameter range that meets the minimum safety requirements. Based on the parameter range and real-time data flow, a second-level optimization objective function is constructed to obtain the target performance indicator optimization model.

[0088] Perform multi-objective weight assignment on the target performance indicator optimization model to obtain the priority matrix of performance indicators. Based on the priority matrix and real-time data stream, construct the third-level optimization objective function to obtain a comprehensive performance evaluation optimization model.

[0089] Integrate the basic safety standard optimization model, target performance indicator optimization model, and comprehensive performance evaluation optimization model to obtain a unified optimization objective function;

[0090] Parameter optimization is performed according to a unified optimization objective function to obtain the optimal control parameter combination, and a dynamic adjustment strategy is created for the optimal control parameter combination to obtain a dynamic optimization strategy that can automatically adapt to inverters of different power levels.

[0091] Specifically, the harmonic suppression scheme is parameterized to obtain a harmonic suppression control parameter set. All relevant control parameters in the system are determined. These parameters may include the gain K of the proportional integral (PI) controller. p and K i , and the resonant frequency f of the proportional resonant (PR) controller r and resonant gain K r . For example, the PI controller is mainly used to control the fundamental wave output of the inverter, while the PR controller selectively suppresses specific subharmonics. Therefore, the selection of the harmonic suppression control parameter set should be carefully adjusted according to the operating environment and load characteristics of the inverter. The performance evaluation index set of the inverter is extracted through the real-time data stream. These indicators may include total harmonic distortion (THD), power factor, efficiency, and distortion of the current waveform. The real-time data stream provides the latest information on the current operating status of the system, so that the evaluation indicators can reflect the actual performance of the system under various operating conditions. Based on the harmonic suppression control parameter set and the performance evaluation index set, the first-level optimization objective function is constructed to form a basic safety standard optimization model. The goal of the optimization model is to ensure that the system operates under the most basic safety requirements and avoid problems such as overheating and overload that endanger the stability of the system. The first-level optimization objective function can be expressed as:

[0092]

[0093] Among them, P i is the i-th performance indicator, is the corresponding target value, w iis a weight coefficient used to indicate the importance of each indicator. Constraints are set for the basic safety standard optimization model to determine the parameter range that meets minimum safety requirements. Constraints ensure that the system operates within a safe range. For example, the gain parameters of the PI and PR controllers should be within a range that does not cause system oscillation. These constraints are determined based on the system's physical characteristics and operating environment, ensuring stable system operation even under extreme conditions. Based on the parameter range and real-time data stream, a second-level optimization objective function is constructed to obtain a target performance indicator optimization model. This model aims to optimize a specific performance indicator, such as improving power factor or reducing harmonic distortion. The second-level optimization objective function can be more complex because it not only considers basic safety requirements but also requires optimizing the balance between multiple performance indicators. For example, reducing harmonic distortion may increase system losses, necessitating a trade-off between the two. To appropriately allocate the importance of each performance indicator, a multi-objective weighting is assigned to the target performance indicator optimization model, forming a performance indicator priority matrix. This matrix helps decision makers clarify the relative priority of each indicator in the overall optimization process, enabling them to prioritize their performance during multi-objective optimization. Based on the priority matrix and real-time data streams, a third-layer optimization objective function is constructed to form a comprehensive performance evaluation optimization model. This model considers the overall performance of the system, such as stability and efficiency under different load conditions. The basic safety standard optimization model, the target performance indicator optimization model, and the comprehensive performance evaluation optimization model are integrated to obtain a unified optimization objective function. The optimization objective function integrates all optimization goals to ensure that the needs of various aspects are taken into account during the optimization process. The unified optimization objective function can be expressed as:

[0094]

[0095] Among them, J k is the optimization objective function of the kth layer, λ k are the corresponding weight coefficients, used to balance the relative importance of each objective. Parameter optimization is performed based on a unified optimization objective function to obtain the optimal control parameter combination, including the optimal settings for all control parameters, such as the gains and frequency settings of the PI and PR controllers. A dynamic adjustment strategy is then created for this optimal control parameter combination. This dynamic adjustment strategy enables the control system to automatically adjust control parameters based on real-time monitored data streams, adapting to inverters with different power levels and load conditions.

[0096] In a specific embodiment, the process of executing step S500 may specifically include the following steps:

[0097] Generate an automated test sequence based on the dynamic optimization strategy to obtain a test instruction set including test items, parameter settings and execution order;

[0098] Prioritize the test instruction set to obtain a test execution queue, perform initialization configuration based on the test execution queue, start the current test project, and obtain the original test data stream;

[0099] Perform real-time processing on the original test data stream and apply the harmonic suppression scheme to obtain harmonically suppressed processed data;

[0100] Based on the processed data after harmonic suppression, the performance index of the current test item is calculated to obtain the single test result, and the single test data is generated according to the single test result;

[0101] Based on the single test data, the dynamic optimization strategy parameters are updated to obtain the optimized control parameters for the next test item;

[0102] Based on the optimized control parameters, the next test item in the test execution queue is executed until all test items are completed and a complete test data set is obtained;

[0103] The complete test data set is integrated and standardized to obtain the performance index test data set.

[0104] Specifically, the inverter's operating conditions, control parameters, and performance indicators are analyzed. The dynamic optimization strategy determines which tests are required based on the system's real-time operating status and historical data. For example, if the system exhibits abnormal behavior at a specific harmonic frequency, the test program should include detailed testing of this harmonic frequency. The test program selection typically includes basic performance tests, such as efficiency and harmonic distortion, as well as stability tests under specific conditions. After the test program is generated, a detailed test instruction set is developed based on the dynamic optimization strategy. The instruction set includes specific parameter settings for each test item, such as test voltage, test current, frequency range, and the order in which the tests should be executed. For example, when performing an efficiency test, different load conditions are set to observe the inverter's performance under light, medium, and full load conditions. The test instruction set is prioritized to determine which test items should be executed first to maximize testing efficiency and data validity. Prioritization can be based on various factors, including test item complexity, impact on system performance, and urgency. For example, if real-time data streams show that the system exhibits high harmonic distortion under high load, tests related to high load should be executed first. Initial configuration is performed based on the test execution queue, including setting up measurement instruments, configuring the data acquisition system, and adjusting the test environment. After initial configuration is complete, the current test project is started and a raw test data stream is collected. This raw data stream contains unprocessed basic electrical parameters such as current and voltage. Real-time processing is performed on the raw test data stream, applying the previously defined harmonic mitigation strategy. This mitigation strategy may include using filters or adjusting controller parameters to reduce harmonic interference on test results, resulting in processed data with harmonic mitigation. Based on this processed data, performance metrics for the current test project are calculated. These metrics may include total harmonic distortion (THD), power factor, and efficiency. These metrics help evaluate inverter performance under different test conditions. The results of each test project are recorded as individual test results and corresponding individual test data. For example, an individual test result might show a THD of 5% and a power factor of 0.98 under specific load conditions. Based on this individual test data, the dynamic optimization strategy parameters are updated. This parameter update may involve adjusting controller gains or modifying filter characteristics to adapt to the new test data. Through adjustments, the optimized control parameters are updated, providing a more accurate control basis for the next test item. For example, if a harmonic component exceeded expectations in the previous test item, the resonant gain of the PR controller may need to be increased to better suppress that harmonic. Based on the optimized control parameters, the next test item in the test execution queue is executed. This process continues until all test items are completed, forming a complete test data set. The test data set includes the raw and processed data from all test items, as well as the comprehensive analysis results of various performance indicators.The complete test data set is integrated and standardized, including data normalization, outlier processing, and data unit unification to ensure data comparability and consistency, and obtain a performance indicator test data set.

[0105] In a specific embodiment, the process of executing step S600 may specifically include the following steps:

[0106] Perform data preprocessing on the performance index test data set to obtain a standardized test data matrix, and perform fast Fourier transform on the standardized test data matrix to obtain a frequency domain characteristic spectrum;

[0107] According to the frequency domain characteristic spectrum, the fundamental wave and each harmonic component are extracted to obtain the harmonic distribution map, and the standardized test data matrix is ​​decomposed by wavelet to obtain the multi-scale time-frequency characteristics;

[0108] Perform singular value decomposition based on multi-scale time-frequency features to obtain the target characteristic components of the data. Based on the target characteristic components, a feature vector space is constructed to obtain a multi-dimensional representation of the inverter performance.

[0109] Perform cluster analysis on the multi-dimensional representation to obtain the classification results of the inverter performance, and perform pattern recognition based on the classification results and preset performance indicator thresholds to obtain the working status evaluation information of the inverter;

[0110] Generate a comprehensive inverter performance diagnosis report based on the working status evaluation information, and perform intelligent analysis on the comprehensive inverter performance diagnosis report to obtain optimization suggestions.

[0111] Specifically, the performance index test data set is first cleaned and standardized. Data cleaning includes removing outliers and processing missing data to ensure the integrity and accuracy of the data set. The purpose of standardization is to convert data of different dimensions into a unified scale to avoid analytical errors caused by magnitude differences. The standardized test data matrix is ​​fast Fourier transformed to obtain the frequency domain characteristic spectrum. The time domain signal is converted to the frequency domain so that the amplitude and phase of each frequency component are clearly presented. The fast Fourier transform formula is expressed as:

[0112]

[0113] Among them, X(f) is the frequency domain feature, x[n] is the time domain signal, N is the number of sampling points, and f is the frequency. Through fast Fourier transform, detailed information of each frequency component in the inverter output signal is obtained, especially the amplitude and phase information of the fundamental wave and each harmonic. According to the frequency domain characteristic spectrum, the fundamental wave and each harmonic component are extracted, and a harmonic distribution diagram is drawn to show the relative strength and position of each harmonic, which helps to identify the main harmonic source. Wavelet decomposition is performed on the standardized test data matrix. Wavelet decomposition can provide information about the signal at different time scales. The wavelet transform formula is:

[0114]

[0115] Where W(a,b) is the wavelet coefficient, a and b are the scale and translation parameters, respectively, ψ(t) is the mother wavelet function, and x(t) is the original signal. Through multi-scale analysis, the wavelet transform can capture the frequency changes of the signal in different time periods and obtain multi-scale time-frequency features. Based on the multi-scale time-frequency features, the data is subjected to singular value decomposition to extract the main characteristic components of the data. The basic form of singular value decomposition is:

[0116] A=UΣV * ;

[0117] Where A is the matrix to be decomposed, U and V are orthogonal matrices, and Σ is a diagonal matrix containing singular values. These singular values ​​reflect the importance of each feature in the data. By selecting the top few largest singular values ​​and their corresponding vectors, a low-dimensional feature vector space is constructed. Cluster analysis is performed on the multidimensional representation to identify different operating states. Cluster analysis helps find natural groupings in the data, which can be used to classify the inverter's performance under different operating conditions. Based on the classification results and preset performance indicator thresholds, pattern recognition is performed to provide operating status assessment information. For example, if the clustering results indicate that certain data points correspond to fault conditions, the system can identify potential fault risks. Based on the operating status assessment information, a comprehensive inverter performance diagnostic report is generated. The report contains issues related to the current system status and possible improvement areas. For example, if cluster analysis shows a significant increase in harmonic distortion under specific load conditions, the diagnostic report may recommend adding a harmonic filter or adjusting control parameters.

[0118] See also Figure 2 , Figure 2 The schematic block diagram of the structure of the safety test device 200 for the inverter provided in the embodiment of the present application is as follows: Figure 2 As shown, the inverter safety test device 200 includes:

[0119] A modeling module 210 is configured to perform modeling analysis on the inverter characteristic data to obtain a non-ideal state space model of the inverter;

[0120] An analysis module 220 is configured to perform harmonic analysis based on a non-ideal state space model to obtain a harmonic suppression solution;

[0121] The conversion module 230 is used to perform conservative power theory conversion on the output current of the inverter to obtain a real-time data stream;

[0122] A construction module 240 is configured to construct a three-layer optimization model in combination with the harmonic suppression scheme and the real-time data stream, and to generate a dynamic optimization strategy based on the three-layer optimization model;

[0123] The testing module 250 is used to perform automated testing on the inverter based on a dynamic optimization strategy to obtain a performance indicator test data set;

[0124] The generation module 260 is used to perform fast Fourier transform and wavelet analysis on the performance index test data set to generate a comprehensive inverter performance diagnosis report and optimization suggestions.

[0125] Through the collaborative efforts of these components, the non-ideal state-space model derived from inverter characteristic data modeling and analysis more accurately reflects the actual inverter characteristics, including the influence of parasitic parameters and environmental factors. Harmonic analysis based on the non-ideal state-space model yields a harmonic mitigation scheme that effectively reduces the harmonic content in the inverter output. The inverter output current is processed using conservative power theory, resulting in a real-time data stream containing detailed information on harmonic and reactive current components. A three-layer optimization model, combining the harmonic mitigation scheme and the real-time data stream, optimizes target performance indicators and evaluates comprehensive performance while ensuring basic safety standards, thereby generating a more efficient and adaptable dynamic optimization strategy. Automated testing based on this dynamic optimization strategy efficiently executes multiple test tasks and adjusts test parameters in real time, significantly improving test efficiency and the accuracy of test results. In-depth analysis of test data using fast Fourier transform and wavelet analysis not only generates a comprehensive performance diagnostic report but also provides targeted optimization recommendations, improving the accuracy of inverter performance testing and, consequently, inverter safety.

[0126] The present application also provides a safety testing device for an inverter, which includes a memory and a processor. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor executes the steps of the inverter safety testing method in the above-mentioned embodiments.

[0127] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the steps of the inverter safety testing method.

[0128] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0129] If the integrated unit 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 application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0130] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A safety testing method for an inverter, characterized in that: include: Modeling and analyzing the characteristic data of the inverter to obtain a non-ideal state space model of the inverter; specifically comprising: analyzing the topological structure of the inverter to obtain a basic circuit model including an inductor, a capacitor and a switching device, and extracting parameters of the equivalent series resistance of the inductor based on the basic circuit model to obtain a non-ideal inductor model; mathematically describing the switching state of the inverter based on the non-ideal inductor model to obtain a switching function including a turn-on time and a turn-off time, and mathematically deriving the relationship between the voltage and current of the inverter based on the switching function and Kirchhoff's law to obtain a state equation of the inverter; performing Laplace transformation and discretization on the state equation to obtain a discrete state space expression of the inverter, and simulating and analyzing the input and output characteristics of the inverter based on the discrete state space expression to obtain performance index data; modeling environmental factors based on the performance index data to obtain an environmental impact model including a temperature coefficient and an electromagnetic interference coefficient, and correcting the discrete state space expression based on the environmental impact model to obtain a non-ideal state space model; Performing harmonic analysis based on the non-ideal state space model to obtain a harmonic suppression scheme; specifically comprising: performing frequency domain transformation on the non-ideal state space model to obtain a frequency response function of the inverter output, and performing harmonic decomposition on the inverter output based on the frequency response function to obtain the amplitude and phase information of each harmonic; performing statistical analysis on the amplitude and phase information of each harmonic to obtain harmonic distribution characteristics and an initial value of total harmonic distortion; designing a transfer function of a proportional-integral controller based on the harmonic distribution characteristics to obtain a fundamental wave control algorithm, and constructing a resonant part of a proportional resonant controller based on the initial value of total harmonic distortion to obtain a selective control algorithm for subharmonics; combining the proportional-integral controller and the proportional resonant controller in parallel to obtain a composite controller structure, and designing a state feedback matrix based on the composite controller structure to obtain a state equation of a closed-loop control system; performing stability analysis on the state equation of the closed-loop control system to obtain stability constraints of controller parameters, and optimizing the controller parameters based on the stability constraints to obtain a harmonic suppression scheme; Performing conservative power theory transformation on the output current of the inverter to obtain a real-time data stream; Building a three-layer optimization model in combination with the harmonic suppression scheme and the real-time data stream, and generating a dynamic optimization strategy based on the three-layer optimization model; Based on the dynamic optimization strategy, the inverter is automatically tested to obtain a performance indicator test data set; Perform fast Fourier transform and wavelet analysis on the performance index test data set to generate a comprehensive inverter performance diagnosis report and optimization suggestions.

2. The inverter safety testing method according to claim 1, characterized in that: The step of performing conservative power theory transformation on the output current of the inverter to obtain a real-time data stream includes: Sampling the output current of the inverter to obtain original current data of a discrete time series, and adaptively adjusting the sampling frequency according to the original current data to obtain optimized sampling parameters; Based on the optimized sampling parameters, the original current data is resampled to obtain an equally spaced current sampling sequence, and the equally spaced current sampling sequence is orthogonally decomposed to obtain the instantaneous active component and reactive component of the current; Calculating instantaneous active power and reactive power based on the instantaneous active component and reactive component to obtain a time domain representation of power, and performing sliding window processing on the time domain representation of power to obtain a frequency domain representation of power; Based on the frequency domain representation of the power, the fundamental wave and harmonic components are extracted to obtain a harmonic power spectrum, and the active power and reactive power of each harmonic are calculated according to the harmonic power spectrum to obtain a harmonic power distribution matrix; The harmonic power distribution matrix is ​​subjected to odd and even harmonic separation to obtain characteristic vectors of odd harmonics and even harmonics, and a characteristic description model of a real-time data stream is constructed based on the characteristic vectors of the odd harmonics and even harmonics to obtain a real-time data stream containing harmonic and reactive current component information.

3. The inverter safety testing method according to claim 2, characterized in that: The step of constructing a three-layer optimization model by combining the harmonic suppression scheme and the real-time data stream, and generating a dynamic optimization strategy based on the three-layer optimization model, includes: Parameterizing the harmonic suppression scheme to obtain a harmonic suppression control parameter set, and extracting a performance evaluation index set of the inverter based on the real-time data stream; Based on the harmonic suppression control parameter set and the performance evaluation index set, a first-level optimization objective function is constructed to obtain a basic safety standard optimization model; Setting constraints on the basic safety standard optimization model to obtain a parameter range that meets the minimum safety requirements, and constructing a second-level optimization objective function based on the parameter range and the real-time data stream to obtain a target performance indicator optimization model; Performing multi-objective weight assignment on the target performance indicator optimization model to obtain a priority matrix of performance indicators, and constructing a third-layer optimization objective function based on the priority matrix and the real-time data stream to obtain a comprehensive performance evaluation optimization model; Integrating the basic safety standard optimization model, the target performance indicator optimization model, and the comprehensive performance evaluation optimization model to obtain a unified optimization objective function; Parameter optimization is performed according to the unified optimization objective function to obtain an optimal control parameter combination, and a dynamic adjustment strategy is created for the optimal control parameter combination to obtain a dynamic optimization strategy that can automatically adapt to inverters of different power levels.

4. The inverter safety testing method according to claim 1, characterized in that: The step of performing automated testing on the inverter based on the dynamic optimization strategy to obtain a performance indicator test data set includes: Generate an automated test sequence based on the dynamic optimization strategy to obtain a test instruction set including test items, parameter settings and execution order; Prioritizing the test instruction set to obtain a test execution queue, performing initialization configuration based on the test execution queue, starting a current test project, and obtaining an original test data stream; Processing the original test data stream in real time, applying the harmonic suppression scheme to obtain harmonically suppressed processed data; Calculating the performance index of the current test item based on the processed data after harmonic suppression to obtain a single test result, and generating single test data according to the single test result; According to the single test data, the dynamic optimization strategy parameters are updated to obtain the optimized control parameters for the next test item; Based on the optimized control parameters, executing the next test item in the test execution queue until all test items are completed to obtain a complete test data set; The complete test data set is integrated and standardized to obtain a performance index test data set.

5. The inverter safety testing method according to claim 1, characterized in that: The fast Fourier transform and wavelet analysis are performed on the performance index test data set to generate a comprehensive inverter performance diagnosis report and optimization suggestions, including: Performing data preprocessing on the performance index test data set to obtain a standardized test data matrix, and performing fast Fourier transform on the standardized test data matrix to obtain a frequency domain characteristic spectrum; Extracting the fundamental wave and each harmonic component according to the frequency domain characteristic spectrum to obtain a harmonic distribution diagram, and performing wavelet decomposition on the standardized test data matrix to obtain multi-scale time-frequency features; Performing singular value decomposition based on the multi-scale time-frequency features to obtain target characteristic components of the data, and constructing a feature vector space based on the target characteristic components to obtain a multi-dimensional representation of the inverter performance; Performing cluster analysis on the multidimensional representation to obtain a classification result of the inverter performance, and performing pattern recognition based on the classification result and a preset performance indicator threshold to obtain working status evaluation information of the inverter; An inverter comprehensive performance diagnosis report is generated based on the working status evaluation information, and the inverter comprehensive performance diagnosis report is intelligently analyzed to obtain optimization suggestions.

6. A safety test device for an inverter, characterized in that: A device for performing a safety test method for an inverter according to any one of claims 1 to 5, wherein the safety test device for the inverter comprises: A modeling module is used to model and analyze the characteristic data of the inverter to obtain a non-ideal state space model of the inverter; specifically comprising: analyzing the topological structure of the inverter to obtain a basic circuit model including inductors, capacitors and switching devices, and extracting parameters of the equivalent series resistance of the inductor based on the basic circuit model to obtain a non-ideal inductor model; mathematically describing the switching state of the inverter based on the non-ideal inductor model to obtain a switching function including turn-on time and turn-off time, and mathematically deriving the relationship between the voltage and current of the inverter based on the switching function and Kirchhoff's law to obtain a state equation of the inverter; performing Laplace transform and discretization on the state equation to obtain a discrete state space expression of the inverter, and simulating and analyzing the input and output characteristics of the inverter based on the discrete state space expression to obtain performance index data; modeling environmental factors based on the performance index data to obtain an environmental impact model including a temperature coefficient and an electromagnetic interference coefficient, and correcting the discrete state space expression based on the environmental impact model to obtain a non-ideal state space model; An analysis module is used to perform harmonic analysis based on the non-ideal state space model to obtain a harmonic suppression scheme; specifically comprising: performing frequency domain transformation on the non-ideal state space model to obtain a frequency response function of the inverter output, and performing harmonic decomposition on the inverter output based on the frequency response function to obtain the amplitude and phase information of each harmonic; performing statistical analysis on the amplitude and phase information of each harmonic to obtain harmonic distribution characteristics and an initial value of total harmonic distortion; designing a transfer function of a proportional-integral controller based on the harmonic distribution characteristics to obtain a fundamental wave control algorithm, and constructing a resonant part of a proportional resonant controller based on the initial value of total harmonic distortion to obtain a selective control algorithm for subharmonics; combining the proportional-integral controller and the proportional resonant controller in parallel to obtain a composite controller structure, and designing a state feedback matrix based on the composite controller structure to obtain a state equation of a closed-loop control system; performing stability analysis on the state equation of the closed-loop control system to obtain stability constraints of controller parameters, and optimizing the controller parameters based on the stability constraints to obtain a harmonic suppression scheme; a conversion module, configured to perform conservative power theory conversion on the output current of the inverter to obtain a real-time data stream; A construction module, configured to construct a three-layer optimization model in combination with the harmonic suppression scheme and the real-time data stream, and generate a dynamic optimization strategy based on the three-layer optimization model; A testing module, configured to perform automated testing on the inverter based on the dynamic optimization strategy to obtain a performance indicator test data set; The generating module is used to perform fast Fourier transform and wavelet analysis on the performance index test data set to generate a comprehensive performance diagnosis report and optimization suggestions for the inverter.

7. A safety test device for an inverter, characterized in that: The inverter safety test device includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory to enable the inverter safety testing device to execute the inverter safety testing method according to any one of claims 1 to 5.

8. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by a processor, the safety testing method for the inverter according to any one of claims 1 to 5 is implemented.

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