Inverter PWI modulation method

Abstract

The invention provides a PWI modulation method for an inverter. Belongs to the technical field of power electronics, and comprises the following steps: setting basic working parameters of an inverter, initializing an initial value and a change range of a phase weighting factor, and presetting a PWF regulation strategy according to load characteristics and waveform quality requirements; loading and initializing a control algorithm of the inverter, and generating a standard sine wave as a reference wave according to a preset output voltage and frequency; calculating a PWF value of each phase point according to the current load state and a preset PWF adjustment strategy, and multiplying the PWF value by a standard sine wave to generate a target modulation waveform; and selecting a carrier signal, comparing the target modulation waveform with the carrier signal, and generating a PWM signal according to a comparison result. By presetting the PWF adjusting strategy and dynamically adjusting the PWF value, the waveform quality of the output voltage of the inverter can be effectively improved, and different load characteristics and waveform quality requirements are met.

Description

Technical Field

[0001] The invention proposes an inverter PWI modulation method, belonging to the technical field of power electronics. Background Art

[0002] Traditional off-grid inverters mostly use methods such as sine wave pulse width modulation (SPWM) or space vector pulse width modulation (SVPWM). Although these methods can achieve the inverter function, they have limitations in dealing with complex load changes, improving the quality of output waveforms, and reducing harmonic content. Especially in the off-grid state, due to the lack of support from the power grid, the output voltage and frequency of the inverter need to be precisely controlled to ensure the quality of power supply. Therefore, it is particularly important to develop a new, efficient and stable modulation method. Summary of the invention

[0003] The present invention provides an inverter PWI modulation method to solve the problems mentioned in the above background technology:

[0004] The present invention provides an inverter PWI modulation method, the method comprising:

[0005] S1. Set the basic working parameters of the inverter, initialize the initial value of the phase weighting factor and its variation range, and preset the PWF adjustment strategy according to the load characteristics and waveform quality requirements;

[0006] S2, loading and initializing the control algorithm of the inverter, generating a standard sine wave as a reference wave according to the preset output voltage and frequency;

[0007] S3. Calculate the PWF value of each phase point according to the current load state and the preset PWF adjustment strategy, multiply the PWF value by the standard sine wave to generate a target modulation waveform; select a carrier signal, compare the target modulation waveform with the carrier signal, and generate a PWM signal according to the comparison result;

[0008] S4, input the PWM signal to the PWM drive circuit of the inverter, control the on and off of the switch tube of the inverter, and generate an AC output voltage; monitor the output current and voltage of the inverter in real time through the current sensor and the voltage sensor, and dynamically adjust the value of the PWF or the parameters of the feedback control loop according to the monitoring results;

[0009] S5. Introduce harmonic suppression mechanism in PWI modulation algorithm, reduce harmonic content in inverter output by adjusting PWF waveform or adopting other harmonic suppression strategies, evaluate inverter performance regularly, and optimize and adjust PWI modulation algorithm and feedback control strategy according to the evaluation results;

[0010] S6. Monitor the operating status of the inverter and immediately activate the protection mechanism once an abnormality or fault is detected.

[0011] Furthermore, the S1 includes:

[0012] S11, determining the expected output voltage amplitude, frequency and rated power of the inverter according to the application requirements, and presetting the initial value of the initialization phase weighting factor based on the expert knowledge base;

[0013] S12, determine the variation range of PWF during the inversion process and conduct in-depth analysis of the load characteristics in the target application;

[0014] S13. Based on load characteristics and waveform quality requirements, a PWF adjustment strategy is designed, wherein the PWF adjustment strategy includes the timing, method, and amplitude of adjustment.

[0015] Furthermore, the S2 includes:

[0016] S21, selecting a PWI modulation control algorithm from a control system of the inverter, the control algorithm including a PWI modulation algorithm and a feedback control algorithm, and loading the algorithm into a controller;

[0017] S22, generating a standard sine wave as a reference waveform of the inverter using a digital signal processor or a microcontroller according to a preset output voltage and frequency;

[0018] S23. Perform quality check on the generated reference waveform.

[0019] Furthermore, the S3 includes:

[0020] S31, preliminarily setting the initial value of PWF based on the expert knowledge base according to the design specifications, load characteristics and expected performance targets of the inverter, and automatically adjusting the value of PWF according to the real-time parameters and performance indicators of the inverter during operation through a preset PWF dynamic adjustment strategy;

[0021] S32, according to the specific requirements and application scenarios of the inverter, select a PWM modulation mode, multiply the PWF value by the reference waveform to obtain a modulated waveform; and according to the selected PWM modulation mode, convert the modulated waveform into a PWM signal;

[0022] S33, in the process of generating the PWM signal, suppressing the edge effect by appropriate measures, wherein the appropriate measures include soft switching technology and a buffer circuit;

[0023] S34, real-time monitoring of the performance indicators of the inverter, establishing a feedback control mechanism, comparing the monitored performance indicators with preset target values; if the performance indicators deviate from the target values, automatically adjusting the PWF value or the parameters of the PWM modulation method according to the degree of deviation;

[0024] S35. Introduce judgment logic into the feedback control mechanism; detect potential abnormal conditions by real-time monitoring of the operating status and performance indicators of the inverter; and perform fault diagnosis on the inverter using a fault diagnosis algorithm after detecting the abnormal condition.

[0025] Furthermore, the S34 includes:

[0026] S341. Build an inverter performance index system, use high-speed ADC to sample voltage and current, use high-precision thermometers to monitor the temperature of key components, and use spectrum analyzers to evaluate electromagnetic radiation levels;

[0027] S342, pre-processing the collected raw data, and using digital signal processing technology to smooth the signal;

[0028] S343, based on the preprocessed data, calculate the current value of each performance indicator in real time, and compare it with the preset target value or threshold;

[0029] S344. Differentiate different degrees of deviation by pre-setting multi-level thresholds for each performance indicator, use time series analysis to learn historical data of performance indicators, and predict future trends; when it is predicted that the threshold is about to be exceeded, take preventive measures in advance;

[0030] S345, combining the evaluation results of multiple performance indicators, using fuzzy logic algorithm to make comprehensive decisions, determine the optimal adjustment strategy, and automatically adjust the PWF value according to the degree of deviation of the performance indicators;

[0031] S346. Under the premise of maintaining the quality of the output voltage waveform, dynamically adjust the parameters of the PWM modulation method. When a single adjustment strategy cannot meet the performance requirements, a multi-strategy collaborative approach is used for adjustment.

[0032] Furthermore, the S445 includes:

[0033] Based on historical data analysis, weights are assigned to various performance indicators, and fuzzy set theory is used to fuzzify the performance indicators. The current value of each performance indicator is mapped to the fuzzy set, and its membership function is defined;

[0034] Based on expert knowledge and historical data, a set of fuzzy rule bases are constructed, each of which defines the adjustment measures to be taken under different performance indicator combinations;

[0035] The fuzzified performance index is input into the fuzzy inference engine, and reasoning is performed according to the fuzzy rule base to finally obtain a comprehensive decision result;

[0036] For performance indicators that need to be adjusted continuously, the gradient descent method is used to calculate the adjustment direction and step size of PWF, and the global search capability of the particle swarm optimization algorithm is combined to supplement the local search of the gradient descent method;

[0037] Under the premise of maintaining the quality of the output voltage waveform, the feasible space of PWM modulation parameters is defined, and the optimization algorithm is used to search in the parameter space to find the optimal parameter combination;

[0038] According to the real-time operating status and performance index changes of the inverter, the PWM modulation parameters are dynamically adjusted. At the same time, a feedback mechanism is established to input the adjusted performance indexes into the comprehensive decision-making system again for a new round of evaluation and adjustment.

[0039] Set priorities for different adjustment strategies based on the importance of performance indicators and the adjustment effects, and evaluate the overall effect of the coordinated adjustment of strategies;

[0040] During the adjustment process, the inverter's performance indicators are continuously monitored, and the feedback mechanism is continuously optimized based on real-time performance monitoring results.

[0041] Furthermore, the S4 includes:

[0042] S41. In the process of generating the PWM signal, based on the product result of the PWF and the reference waveform, dead time control and switch tube drive delay compensation are introduced to ensure the accuracy and reliability of the PWM signal;

[0043] S42. According to the specific requirements of the inverter, multi-level PWM modulation technology is used to improve the waveform quality and inverter efficiency of the output voltage;

[0044] S43, using historical data and machine learning algorithms to predict the load change trend; setting multiple thresholds to determine different operating states of the inverter;

[0045] S44, for multiple adjustment strategies that may be triggered simultaneously, prioritize them according to their impact on inverter performance and urgency;

[0046] S45, filtering the feedback signal and optimizing the feedback control loop by using an adaptive control algorithm; performing stability analysis on the feedback control loop, and taking measures to adjust and optimize the control loop if unstable factors are found;

[0047] S46. Establish an abnormality detection mechanism to monitor and analyze the operating status of the inverter in real time; once an abnormality is found, the corresponding processing flow is immediately triggered;

[0048] S47, after detecting an abnormal situation, using a fault diagnosis algorithm to quickly and accurately diagnose the inverter fault, and at the same time, taking isolation measures to separate the faulty part from the normal part;

[0049] S48. Develop appropriate emergency response plans based on the type and severity of the fault.

[0050] Furthermore, the S43 includes:

[0051] Based on basic current and voltage monitoring, key parameters are monitored in real time, the collected raw data is preprocessed, and key indicators that can reflect the load change characteristics are extracted from the preprocessed data.

[0052] Use machine learning algorithms to model the extracted features, learn the inherent laws and trends of load changes through model training, and use cross-validation to tune the prediction model;

[0053] Quantitatively evaluate the prediction results, adjust the model parameters according to the evaluation results, and set multiple thresholds to judge the different operating states of the inverter based on the prediction results and the performance requirements of the inverter;

[0054] Monitor the various performance indicators of the inverter in real time and compare them with the set thresholds. If the relevant parameters are detected to be beyond the threshold range, the corresponding adjustment strategy is triggered.

[0055] Furthermore, the S5 includes:

[0056] S51. Analyze the output waveform of the inverter by spectrum analysis technology to identify the main harmonic components and their sources;

[0057] S52, formulating a targeted harmonic suppression strategy based on the result of harmonic source identification, wherein the suppression strategy includes adjusting the waveform shape, phase shift, and frequency modulation of the PWF;

[0058] S53, real-time monitoring of the harmonic content in the inverter output waveform, and dynamically adjusting the PWF value through a PWF optimization algorithm based on feedback control to minimize the harmonic component;

[0059] S54. After implementing the harmonic suppression strategy, the inverter output waveform is evaluated again using spectrum analysis technology to verify whether the harmonic suppression effect reaches the expected goal; if not, the harmonic suppression strategy or PWF optimization algorithm is adjusted according to the evaluation results;

[0060] S55. Establish a comprehensive performance evaluation system, and use high-precision measurement equipment and data analysis tools to quantitatively evaluate various indicators; prioritize various indicators based on the performance evaluation results, and design special optimization strategies for indicators with higher priorities; apply the optimization strategies to the inverter control system and conduct actual tests; iterate and repeatedly adjust the optimization strategies based on the test results until all performance indicators meet the design requirements;

[0061] S56. Monitor the operating status and performance index changes of the inverter in real time through an adaptive optimization mechanism, and automatically adjust the optimization strategy according to preset rules or algorithms; use big data analysis technology to conduct in-depth analysis and mining of massive data during the operation of the inverter;

[0062] S57, establishing a simulation model of the inverter, and performing simulation verification on different optimization strategies; and predicting the impact of different strategies on the inverter performance through simulation results;

[0063] S58. Establish an inverter optimization knowledge base and experience database, and record the process data during each optimization process.

[0064] Furthermore, the S6 includes:

[0065] S61, monitor the operating status of the inverter in real time, and quickly analyze the monitored data through anomaly detection algorithms to detect potential faults or abnormal conditions;

[0066] S62, if an abnormality or failure is detected, immediately start a corresponding protection mechanism, the protection mechanism including cutting off power, alarm prompting and automatic restart;

[0067] S63. Record the information of the fault, including the time, type and cause, analyze the law and trend of the fault, and continuously optimize and improve the system based on the analysis results.

[0068] The beneficial effects of the present invention are as follows: by presetting the PWF regulation strategy and dynamically adjusting the PWF value, the waveform quality of the inverter output voltage can be effectively improved to meet different load characteristics and waveform quality requirements; by introducing the harmonic suppression mechanism and the PWF optimization algorithm, the harmonic content in the inverter output can be significantly reduced and the power quality can be improved; by real-time monitoring and dynamic adjustment of the PWF value or the parameters of the feedback control loop, the stability and reliability of the inverter can be enhanced; by using the expert knowledge base and the machine learning algorithm, the inverter control strategy can be optimized to improve the overall performance of the inverter; the operating status of the inverter is monitored in real time, and once an abnormality or fault is detected, the protection mechanism can be immediately started to ensure the safe operation of the system; the multi-level PWM modulation technology can be used to improve the output voltage waveform quality and the inverter efficiency of the inverter; through the adaptive optimization mechanism, the operating status and performance index changes of the inverter can be monitored in real time, the optimization strategy can be automatically adjusted, and the adaptive ability of the system can be improved; an inverter optimization knowledge base and an experience database are established to facilitate the maintenance, upgrade and optimization of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is a step diagram of the method described in the present invention. DETAILED DESCRIPTION

[0070] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0071] One embodiment of the present invention, as Figure 1 As shown, an inverter PWI modulation method, the method comprising:

[0072] S1. Set the basic working parameters of the inverter, initialize the initial value of the phase weighting factor (PWF) and its variation range, and preset the PWF adjustment strategy according to the load characteristics and waveform quality requirements;

[0073] S2, loading and initializing the control algorithm of the inverter, including the PWI modulation algorithm and the feedback control algorithm, and generating a standard sine wave as a reference wave according to the preset output voltage and frequency;

[0074] S3. Calculate the PWF value of each phase point according to the current load state and the preset PWF adjustment strategy, multiply the PWF value by the standard sine wave to generate a target modulation waveform; select a suitable carrier signal, compare the target modulation waveform with the carrier signal, and generate a PWM signal according to the comparison result;

[0075] S4, input the PWM signal to the PWM drive circuit of the inverter, control the on and off of the switch tube of the inverter, and generate the desired AC output voltage; monitor the output current and voltage of the inverter in real time through the current sensor and the voltage sensor, analyze the monitoring data, and determine whether the load has changed or whether the system is in a stable state; dynamically adjust the value of PWF or the parameters of the feedback control loop according to the monitoring results;

[0076] S5. Introduce harmonic suppression mechanism in PWI modulation algorithm, reduce harmonic content in inverter output by adjusting PWF waveform or adopting other harmonic suppression strategies, regularly evaluate inverter performance, including indicators such as inverter efficiency, output voltage waveform quality, harmonic content, etc., and optimize and adjust PWI modulation algorithm and feedback control strategy according to the evaluation results;

[0077] S6. Monitor the operating status of the inverter, including key parameters such as temperature, current, and voltage. Once an abnormality or fault is detected, immediately activate the protection mechanism, such as cutting off the power supply, alarming, etc.

[0078] The working principle of the above technical solution is as follows: according to the application requirements of the inverter, set the expected output voltage amplitude, frequency and rated power of the inverter and other basic parameters; initialize the initial value of the phase weighting factor (PWF) and its range of variation, which will be dynamically adjusted according to the load characteristics and waveform quality requirements; preset the PWF adjustment strategy so that the PWF value can be adjusted according to the load changes and system status during the operation of the inverter; load and initialize the control algorithm of the inverter, including the PWI modulation algorithm and the feedback control algorithm; according to the preset output voltage and frequency, generate a standard sine wave as a reference wave, which represents the desired output voltage waveform; according to the current load state and the preset PWF adjustment strategy, calculate the PWF value of each phase point; multiply the PWF value with the standard sine wave to generate the target modulation waveform. This waveform combines the shape of the sine wave and the weighting effect of the PWF, and can more flexibly control the waveform of the output voltage; select a suitable high-frequency carrier signal (such as a triangle wave or a sawtooth wave), compare the target modulation waveform with the carrier signal, and generate a PWM signal. The duty cycle of the PWM signal reflects the instantaneous value of the target modulation waveform, thereby controlling the on and off of the switch tube of the inverter; the PWM signal is input into the PWM drive circuit of the inverter to control the on and off of the switch tube of the inverter to generate the desired AC output voltage; the output current and voltage of the inverter are monitored in real time through current sensors and voltage sensors, and the monitoring data are analyzed to determine whether the load has changed or whether the system is in a stable state; the value of PWF or the parameters of the feedback control loop are dynamically adjusted according to the monitoring results to maintain the stability and waveform quality of the output voltage; a harmonic suppression mechanism is introduced into the PWI modulation algorithm to reduce the harmonic content in the inverter output by adjusting the waveform of PWF or adopting other harmonic suppression strategies; the performance of the inverter is regularly evaluated, including indicators such as inverter efficiency, output voltage waveform quality, and harmonic content; the PWI modulation algorithm and feedback control strategy are optimized and adjusted according to the evaluation results to further improve the performance of the inverter; the operating status of the inverter is monitored, including key parameters such as temperature, current, and voltage; once an abnormal or fault condition is detected, the protection mechanism is immediately activated, such as cutting off the power supply and alarm prompts, to ensure the safe and stable operation of the inverter and the system.

[0079] The effect of the above technical solution is: by dynamically adjusting the value of the phase weighting factor (PWF), the PWI modulation method can more flexibly control the waveform of the output voltage to make it closer to the ideal sine wave. This helps to reduce waveform distortion, improve the waveform quality of the output voltage, and meet the application scenarios with high requirements for waveform quality; the real-time feedback control algorithm can monitor the output current and voltage of the inverter, and dynamically adjust the value of the PWF or the parameters of the feedback control loop according to the monitoring results. This closed-loop control mechanism helps to quickly respond to load changes and system disturbances, maintain the stability of the output voltage and the overall stability of the system; the harmonic suppression mechanism is introduced into the PWI modulation algorithm, and the harmonic content in the inverter output can be significantly reduced by adjusting the waveform of the PWF or adopting other harmonic suppression strategies. Harmonic suppression is of great significance for protecting power grid equipment and improving power quality, especially in applications that are sensitive to harmonics; the conversion efficiency of the inverter can be improved by optimizing and adjusting the inverter control algorithm, including the PWI modulation algorithm and the feedback control strategy. Efficient inverters can reduce energy loss, improve energy utilization, and reduce operating costs; monitoring the operating status of the inverter, including key parameters such as temperature, current, and voltage, can promptly detect potential faults or abnormal conditions. Once an abnormality or fault is detected, the protection mechanism (such as power cut-off, alarm prompt, etc.) is immediately activated, which helps prevent equipment damage and safety accidents, and improves the reliability and safety of the system; the PWI modulation method has high flexibility and scalability. By adjusting the PWF regulation strategy and feedback control algorithm, it can adapt to different load characteristics and application requirements. In addition, with the continuous development of technology, the PWI modulation algorithm can be further optimized and improved to meet higher performance requirements.

[0080] In one embodiment of the present invention, the S1 includes:

[0081] S11, determining the expected output voltage amplitude, frequency and rated power of the inverter according to the application requirements, and presetting the initial value of the initialization phase weighting factor (PWF) based on the expert knowledge base;

[0082] S12, determine the possible variation range of PWF during the inversion process and conduct in-depth analysis of the load characteristics in the target application;

[0083] S13. Based on the load characteristics and waveform quality requirements, a set of sophisticated PWF adjustment strategies is designed, wherein the PWF adjustment strategy includes the timing, method and amplitude of adjustment.

[0084] The working principle of the above technical solution is: according to the specific application requirements of the inverter (such as the type of power supply equipment, grid requirements, etc.), determine the expected output voltage amplitude, frequency and rated power of the inverter. These parameters are the basis for the design and operation of the inverter; based on the expert knowledge base or historical experience, preset the starting value of the phase weighting factor (PWF). This starting value should be a reasonable estimate that can provide a good output voltage waveform quality in the initial state. The expert knowledge base may contain recommended PWF values ​​for different load types and waveform quality requirements; in order to cope with various load conditions and waveform quality requirements that may be encountered during the operation of the inverter, it is necessary to determine the possible range of changes of PWF during the inverter process. This range should be wide enough to cover all possible situations, while avoiding excessive increase in control complexity and calculation; in-depth analysis of the load characteristics in the target application, including the static characteristics and dynamic change characteristics of the load. Static characteristics refer to the electrical characteristics of the load in a stable state, such as resistance, inductance, capacitance, etc.; dynamic characteristics refer to the response characteristics of the load during the change process, such as starting current, transient response, etc. These analysis results provide an important basis for the formulation of PWF regulation strategy; based on load characteristics and waveform quality requirements, a set of sophisticated PWF regulation strategies is designed. This strategy should include the timing, method and amplitude of regulation; determine when to adjust the PWF. This is usually associated with events such as load changes, system stability assessment or output voltage waveform quality detection; select an appropriate regulation method, such as continuous regulation, step regulation or intelligent regulation based on a certain algorithm. The regulation method should be able to respond quickly to load changes while maintaining system stability; determine the PWF regulation amplitude based on the size of the load change and the strictness of the waveform quality requirements. The regulation amplitude should be moderate, which can not only meet the waveform quality requirements, but also avoid the impact of excessive regulation on the system.

[0085] The effects of the above technical solution are as follows: by determining the expected output voltage amplitude, frequency and rated power of the inverter according to the application requirements, it is ensured that the inverter can meet the requirements of specific application scenarios; by presetting the initial value of the initialization phase weighting factor (PWF), based on the expert knowledge base, the inverter can have a more reasonable initial working state during the startup phase, thereby improving the adaptability and response speed of the system; determining the possible range of changes of the PWF during the inversion process, and covering all possible situations under different load conditions and waveform quality requirements, ensuring that the inverter can maintain a stable output voltage waveform when facing a complex and changeable load environment; conducting an in-depth analysis of the load characteristics in the target application, including static characteristics and dynamic change characteristics, providing a scientific basis for the formulation of the PWF adjustment strategy, and further enhancing the stability and reliability of the system; designing a sophisticated PWF adjustment strategy, including the timing, method and amplitude of adjustment, so that the inverter can dynamically adjust the PWF value according to load changes and waveform quality requirements, thereby optimizing the waveform quality of the output voltage. This helps to reduce waveform distortion and harmonic content and improve power quality. Through a reasonable PWF regulation strategy, the inverter can convert electricity more efficiently, reduce energy loss and improve inverter efficiency. At the same time, the optimized output voltage waveform quality also helps to improve the operating efficiency and performance of electrical equipment and extend the service life of equipment. Since the system has high stability and reliability, it reduces losses and costs caused by downtime or maintenance. The refined PWF regulation strategy also helps to reduce potential damage to the power grid and equipment due to poor waveform quality, further reducing maintenance costs. High-quality output voltage waveform and stable power supply performance provide users with a better power experience. This improvement is particularly obvious in application scenarios with high requirements for power quality (such as precision instruments, medical equipment, etc.).

[0086] In one embodiment of the present invention, the S2 includes:

[0087] S21, selecting a PWI modulation control algorithm from a control system of the inverter, the control algorithm including a PWI modulation algorithm and a feedback control algorithm, and loading the algorithm into a controller;

[0088] S22, according to the preset output voltage and frequency, using a digital signal processor (DSP) or a microcontroller (MCU) to generate a standard sine wave as a reference waveform of the inverter;

[0089] S23. Perform quality check on the generated reference waveform.

[0090] The working principle of the above technical solution is: select a control algorithm suitable for PWI modulation from the control system of the inverter, which usually includes a PWI modulation algorithm and a feedback control algorithm. The PWI modulation algorithm is responsible for generating a target modulation waveform according to a phase weighting factor (PWF), while the feedback control algorithm is used to monitor and adjust the output of the inverter in real time to ensure its stability and accuracy. These algorithms are loaded into the controller of the inverter, such as a digital signal processor (DSP) or a microcontroller (MCU), for execution during the inverter process; according to the preset output voltage and frequency, a standard sine wave is generated as the reference waveform of the inverter by using the waveform generation function inside the controller such as the DSP or MCU. This reference waveform is a reference for the output voltage of the inverter, and is used for comparison and modulation with the target modulation waveform generated by the PWI modulation algorithm; the generated reference waveform is quality checked to ensure that its waveform distortion, phase error and other parameters meet the design requirements. Waveform distortion refers to the degree of difference between the actual waveform and the ideal waveform, which affects the quality of the output voltage waveform of the inverter. Phase error refers to the phase difference between the actual waveform and the reference waveform, which affects the synchronization of the output voltage of the inverter with the power grid or other equipment. Through quality verification, problems in the reference waveform can be discovered and corrected in a timely manner to ensure the normal operation of the inverter and the accuracy of the output voltage.

[0091] The effect of the above technical solution is: by selecting and loading the PWI modulation algorithm and the feedback control algorithm into the controller, the inverter can more accurately control the waveform and parameters of the output voltage. The PWI modulation algorithm generates the target modulation waveform according to the phase weighting factor (PWF), while the feedback control algorithm monitors and adjusts the output of the inverter in real time to ensure that it is consistent with the preset value. This dual control mechanism greatly improves the control accuracy of the inverter; the application of the feedback control algorithm enables the inverter to respond quickly to load changes and external interference, and maintain the stability of the system by adjusting the output parameters. At the same time, the reference waveform is quality-checked to ensure that its waveform distortion, phase error and other parameters meet the design requirements, further enhancing the stability and reliability of the system; using a high-precision controller such as DSP or MCU to generate a standard sine wave as a reference waveform, and ensuring its accuracy through quality verification, it helps to optimize the output voltage waveform quality of the inverter. The reference waveform with low distortion and low phase error can reduce the waveform distortion and harmonic content in the inverter process and improve the power quality; by loading different control algorithms and adjusting preset parameters, the inverter can adapt to different application scenarios and load conditions. This flexibility enables the inverter to maintain efficient and stable operation under a variety of working conditions; the high-precision control algorithm and real-time feedback adjustment mechanism reduce the risk of failure and damage to the inverter due to poor output waveform quality. This helps reduce the maintenance cost and downtime of the inverter; the optimized output voltage waveform quality and stable power supply performance provide users with a better power experience. This improvement is particularly obvious in application scenarios with high requirements for power quality (such as precision instruments, medical equipment, etc.); by improving the efficiency and stability of the inverter, energy waste and environmental pollution are reduced. Efficient power conversion and stable power supply help reduce energy consumption and carbon emissions, in line with the requirements of sustainable development.

[0092] In one embodiment of the present invention, S3 includes:

[0093] S31. According to the design specifications, load characteristics and expected performance targets of the inverter, the initial value of the PWF is preliminarily set based on the expert knowledge base, and the value of the PWF is automatically adjusted according to the real-time parameters (such as output voltage, current, temperature, etc.) and performance indicators (such as efficiency, harmonic content, stability, etc.) of the inverter during operation through a preset PWF dynamic adjustment strategy; the adjustment strategy includes rule-based adjustment, fuzzy logic control, optimization algorithm (such as genetic algorithm, particle swarm optimization), etc.;

[0094] S32, according to the specific requirements and application scenarios of the inverter, select a suitable PWM modulation method, multiply the PWF value by a reference waveform (such as a sine wave) to obtain a modulated waveform; and according to the selected PWM modulation method, convert the modulated waveform into a PWM signal;

[0095] S33, in the process of generating the PWM signal, suppressing the edge effect by appropriate measures, wherein the appropriate measures include soft switching technology and a buffer circuit;

[0096] S34, real-time monitoring of the performance indicators of the inverter, such as output voltage waveform quality (THD, voltage fluctuation rate, etc.), inverter efficiency, load response speed, etc., establishing a feedback control mechanism, and comparing the monitored performance indicators with preset target values; if the performance indicators deviate from the target values, the PWF value or the parameters of the PWM modulation method are automatically adjusted according to the degree of deviation;

[0097] S35. Introduce complex judgment logic in the feedback control mechanism; for example, multiple thresholds can be set to judge different degrees of deviation of performance indicators; intelligent algorithms such as fuzzy logic or neural networks can be used to simulate the human decision-making process; historical data and change trends of the inverter's operating status can also be considered to predict future performance changes and take measures in advance; detect potential abnormalities by real-time monitoring of the inverter's operating status and performance indicators; for example, if the output voltage suddenly increases or decreases, the current harmonic content increases sharply, etc., it may indicate that the inverter has an abnormality. After detecting the abnormality, use the fault diagnosis algorithm to quickly and accurately diagnose the inverter.

[0098] The working principle of the above technical solution is as follows: according to the design specifications, load characteristics and expected performance targets of the inverter, the initial value of PWF is preliminarily set based on the expert knowledge base; through the preset PWF dynamic adjustment strategy, the value of PWF is automatically adjusted according to the real-time parameters (such as output voltage, current, temperature, etc.) and performance indicators (such as efficiency, harmonic content, stability, etc.) of the inverter during operation. The adjustment strategy can include rule-based adjustment, fuzzy logic control, optimization algorithms (such as genetic algorithms, particle swarm optimization), etc., to achieve precise control of inverter performance; according to the specific needs and application scenarios of the inverter, select the appropriate PWM modulation method. In addition to the traditional sine wave PWM (SPWM), advanced modulation methods such as space vector PWM (SVPWM) and specific harmonic elimination PWM (SHEPWM) can also be considered to improve the performance and efficiency of the inverter; the value of PWF is multiplied by the reference waveform (such as a sine wave) to obtain the modulated waveform. Then, according to the selected PWM modulation mode, the modulated waveform is converted into a PWM signal to control the switching elements of the inverter; in the process of generating the PWM signal, appropriate measures (such as soft switching technology and buffer circuit) are used to suppress the edge effect and reduce the loss and electromagnetic interference in the switching process; real-time monitoring of the inverter's performance indicators, such as output voltage waveform quality (THD, voltage fluctuation rate, etc.), inverter efficiency, load response speed, etc.; a feedback control mechanism is established to compare the monitored performance indicators with the preset target values. If the performance indicator deviates from the target value, the value of the PWF or the parameters of the PWM modulation mode are automatically adjusted according to the degree of deviation to restore the normal performance of the inverter; complex judgment logic is introduced in the feedback control mechanism, such as setting multiple thresholds to judge the different deviation degrees of the performance indicators, using intelligent algorithms such as fuzzy logic or neural networks to simulate the human decision-making process, considering the historical data and change trends of the inverter's operating status to predict future performance changes and take measures in advance; potential abnormalities are detected by real-time monitoring of the inverter's operating status and performance indicators. For example, if the output voltage suddenly increases or decreases, or the current harmonic content increases sharply, it may indicate that the inverter has an abnormal condition. After detecting the abnormal condition, the fault diagnosis algorithm is used to quickly and accurately diagnose the inverter. After confirming that the inverter has a fault, emergency measures are immediately taken to prevent the fault from expanding and protect the safety of the inverter. For example, the inverter power output can be cut off, switched to a backup power source, or a fault alarm system can be activated.

[0099] The effects of the above technical solution are as follows: by dynamically adjusting the value of PWF, the inverter can automatically optimize its working state according to the real-time operating parameters and performance indicators, thereby improving the efficiency of the inverter, reducing the harmonic content, enhancing stability and other key performance indicators; selecting a suitable PWM modulation method, such as advanced modulation methods such as SVPWM and SHEPWM, can further improve the performance and efficiency of the inverter and reduce energy loss and electromagnetic interference; the technical solution can flexibly adjust the parameters of PWF and PWM modulation methods according to different load characteristics and application scenarios, so that the inverter can adapt to various complex working environments and improve the adaptability and flexibility of the system; by multiplying PWF with the reference waveform and converting it into a PWM signal, the inverter can generate a high-quality modulated waveform, reduce the waveform distortion (THD) and voltage fluctuation rate of the output voltage, and improve the power quality; real-time monitoring of the performance indicators of the inverter and establishing a feedback control mechanism can timely detect and correct system deviations and maintain stable operation of the inverter. At the same time, by introducing complex judgment logic and intelligent algorithms, it is possible to more accurately predict and respond to system changes, improve the stability and reliability of the system; by introducing anomaly detection function in the feedback control mechanism, it is possible to monitor the operating status and performance indicators of the inverter in real time, and detect potential abnormalities in a timely manner. Once a fault is detected, the fault diagnosis algorithm is used for rapid and accurate diagnosis, and emergency treatment measures are taken immediately to prevent the fault from expanding and protect the safety of the inverter. This helps to reduce system downtime and maintenance costs, improve system availability and economy; by using intelligent algorithms such as fuzzy logic and neural networks to simulate the human decision-making process, this technical solution promotes the intelligent development of inverter control systems. These intelligent algorithms can handle complex nonlinear problems and uncertain factors, and improve the intelligence level and adaptive ability of the system.

[0100] In one embodiment of the present invention, the S34 includes:

[0101] S341. Build a comprehensive set of inverter performance indicators, use high-speed ADC for voltage and current sampling, combine high-precision thermometers to monitor the temperature of key components, and use spectrum analyzers to evaluate electromagnetic radiation levels;

[0102] S342, pre-processing the collected raw data, and using digital signal processing (DSP) technology to smooth the signal;

[0103] S343, based on the preprocessed data, calculate the current value of each performance indicator in real time, and compare it with the preset target value or threshold;

[0104] S344. Differentiate different degrees of deviation by presetting multi-level thresholds for each performance indicator. For example, for output voltage THD, set a warning threshold and a fault threshold to trigger different adjustment measures respectively. Use time series analysis to learn historical data of performance indicators and predict future change trends. When it is predicted that the threshold is about to be exceeded, take preventive measures in advance.

[0105] S345. Combining the evaluation results of multiple performance indicators, a fuzzy logic algorithm is used to make a comprehensive decision, determine the optimal adjustment strategy, and automatically adjust the PWF value according to the degree of deviation of the performance indicators; gradient descent, particle swarm optimization and other algorithms are used to achieve fine adjustment of PWF to minimize performance deviation.

[0106] S346. Under the premise of maintaining the quality of the output voltage waveform, dynamically adjust the parameters of the PWM modulation method (such as carrier frequency, modulation ratio, etc.). When a single adjustment strategy cannot meet the performance requirements, a multi-strategy collaborative approach is used for adjustment.

[0107] The working principle of the above technical solution is: to build a comprehensive inverter performance indicator system, including output voltage waveform quality (such as THD, voltage imbalance), inverter efficiency (distinguishing efficiency under different load types and load rates), load response speed (response time, overshoot, steady-state error, etc.), thermal management efficiency (temperature distribution uniformity, heat dissipation efficiency) and electromagnetic compatibility (EMI / EMC level), etc.; use high-speed ADC to sample voltage and current, combine with high-precision thermometer to monitor the temperature of key components, and use spectrum analyzer to evaluate electromagnetic radiation level. These data provide a basis for subsequent performance evaluation and adjustment; the collected raw data is preprocessed, including denoising, filtering and outlier removal to reduce the impact of noise and interference on performance evaluation; digital signal processing (DSP) technology, such as sliding average, median filtering, Kalman filtering, etc., is used to smooth the signal to improve the accuracy and reliability of the data; based on the preprocessed data, the current value of each performance indicator is calculated in real time; the calculated performance indicator value is compared with the preset target value or threshold to determine whether the inverter is in normal working state or needs adjustment; multi-level thresholds are set to distinguish different degrees of deviation. For example, for the output voltage THD, a warning threshold and a fault threshold are set to trigger different adjustment measures respectively; the historical data of the performance indicator is learned by time series analysis to predict future change trends. When it is predicted that the threshold is about to be exceeded, preventive measures are taken in advance to avoid system performance deterioration; combined with the evaluation results of multiple performance indicators, fuzzy logic algorithms are used to make comprehensive decisions and determine the optimal adjustment strategy; the PWF value is automatically adjusted according to the degree of deviation of the performance indicator. Gradient descent, particle swarm optimization and other algorithms are used to achieve fine adjustment of PWF to minimize performance deviation; the parameters of PWM modulation (such as carrier frequency, modulation ratio, etc.) are dynamically adjusted while maintaining the quality of the output voltage waveform; when a single adjustment strategy cannot meet the performance requirements, multiple strategies are used for adjustment in a coordinated manner. For example, PWF and PWM modulation parameters are adjusted simultaneously to achieve comprehensive optimization of performance indicators.

[0108] The effect of the above technical solution is: by building a comprehensive set of inverter performance indicator system, multiple key performance indicators of the inverter can be monitored in real time, including but not limited to output voltage waveform quality, inverter efficiency, load response speed, thermal management efficiency and electromagnetic compatibility. This comprehensive monitoring helps to fully understand the operating status and performance of the inverter; the use of high-speed ADC for voltage and current sampling, combined with high-precision thermometers and spectrum analyzers, ensures the accuracy and reliability of data acquisition. At the same time, through data preprocessing technology (such as denoising, filtering, outlier removal, etc.) and digital signal processing (DSP) technology, the accuracy and reliability of the data are further improved, providing a solid foundation for subsequent performance evaluation and adjustment; the current value of each performance indicator is calculated in real time and compared with the preset target value or threshold, so that the performance deviation of the inverter can be discovered in time. Through multi-level threshold setting, different degrees of deviation can be distinguished and corresponding adjustment measures can be triggered. In addition, by using time series analysis to predict future change trends, preventive measures can be taken in advance before problems occur to avoid performance deterioration; combined with the evaluation results of multiple performance indicators, fuzzy logic algorithms are used for comprehensive decision-making to determine the optimal adjustment strategy. This intelligent decision-making method can simulate the human decision-making process and deal with complex nonlinear problems and uncertain factors. At the same time, the use of algorithms such as gradient descent and particle swarm optimization to achieve fine adjustment of PWF can minimize performance deviations and improve the operating efficiency and stability of the inverter; dynamically adjust the parameters of the PWM modulation method (such as carrier frequency, modulation ratio, etc.) while maintaining the quality of the output voltage waveform. When a single adjustment strategy cannot meet the performance requirements, a multi-strategy collaborative approach is adopted for adjustment, such as adjusting the PWF and PWM modulation parameters at the same time. This multi-strategy collaborative adjustment method can more comprehensively optimize the performance indicators of the inverter and improve the overall performance of the system; through real-time monitoring and dynamic adjustment of the inverter's performance indicators, system deviations can be discovered and corrected in a timely manner to maintain the stable operation of the inverter. At the same time, through intelligent comprehensive decision-making and multi-strategy collaborative adjustment, the reliability and stability of the system can be further improved and the possibility of failure can be reduced; through early warning and intelligent adjustment, the downtime and maintenance costs of the inverter caused by performance problems can be reduced. At the same time, optimizing the operating efficiency and performance of the inverter can improve the economic benefits and energy utilization of the system.

[0109] In one embodiment of the present invention, the S445 includes:

[0110] Based on historical data analysis, reasonable weights are assigned to each performance indicator, and fuzzy set theory is used to fuzzify the performance indicators. The current value of each performance indicator is mapped to the fuzzy set, and its membership function is defined;

[0111] Based on expert knowledge and historical data, a set of fuzzy rule bases are constructed, each of which defines the adjustment measures to be taken under different performance indicator combinations;

[0112] The fuzzified performance index is input into the fuzzy inference engine, and reasoning is performed according to the fuzzy rule base to finally obtain a comprehensive decision result;

[0113] For performance indicators that need to be continuously adjusted (such as THD), the gradient descent method is used to calculate the adjustment direction and step size of PWF. Through iterative calculation, the optimal PWF value is gradually approached to minimize the performance deviation. The global search capability of the particle swarm optimization algorithm is combined to supplement the local search of the gradient descent method; through information sharing and collaboration between particles, the convergence to the global optimal solution is accelerated.

[0114] Under the premise of maintaining the quality of the output voltage waveform, the feasible space of PWM modulation parameters is defined, and the optimization algorithm (such as genetic algorithm, simulated annealing, etc.) is used to search in the parameter space to find the optimal parameter combination;

[0115] According to the real-time operating status and performance index changes of the inverter, the PWM modulation parameters are dynamically adjusted. At the same time, a feedback mechanism is established to input the adjusted performance indexes into the comprehensive decision-making system again for a new round of evaluation and adjustment.

[0116] According to the importance of performance indicators and the effect of adjustment, set priorities for different adjustment strategies. When multiple parameters need to be adjusted at the same time, perform adjustment measures in order of priority. And evaluate the overall effect of the coordinated adjustment of strategies.

[0117] During the adjustment process, the inverter's performance indicators are continuously monitored, and real-time data analysis technology is used to quickly respond to performance changes to ensure the effectiveness of adjustment measures. The feedback mechanism is continuously optimized based on real-time performance monitoring results.

[0118] The working principle of the above technical solution is as follows: Based on historical data analysis, reasonable weights are assigned to each performance indicator to reflect their importance to the overall performance of the inverter; Fuzzy set theory is used to map the current value of each performance indicator to a fuzzy set and define its membership function. The membership function describes the degree to which the performance indicator value belongs to a fuzzy set, thereby realizing the fuzzification of the performance indicator; Based on expert knowledge and historical data, a set of fuzzy rule bases are constructed. Each rule defines the adjustment measures to be taken under different performance indicator combinations. For example, "If the output voltage THD is high and the inverter efficiency is low, increase the PWF value and optimize the PWM modulation parameters"; the fuzzified performance indicators are input into the fuzzy inference engine; the fuzzy inference engine performs inference according to the fuzzy rule base. The inference process includes matching rules, calculating the activation degree of rules (i.e., the degree to which the rules are satisfied), and aggregating rule outputs. Finally, by synthesizing the outputs of all rules, a comprehensive decision result is obtained; for performance indicators that need to be adjusted continuously (such as THD), the gradient descent method is used to calculate the adjustment direction and step size of PWF. Through iterative calculation, the optimal PWF value is gradually approached to minimize the performance deviation; combined with the global search capability of the particle swarm optimization algorithm, the local search of the gradient descent method is supplemented. The particle swarm algorithm accelerates convergence to the global optimal solution through information sharing and collaboration between particles; under the premise of maintaining the quality of the output voltage waveform, the feasible space of PWM modulation parameters is defined. Optimization algorithms (such as genetic algorithms, simulated annealing, etc.) are used to search in the parameter space to find the optimal parameter combination; according to the real-time operating status of the inverter and the changes in performance indicators, the PWM modulation parameters are dynamically adjusted; a feedback mechanism is established to input the adjusted performance indicators into the comprehensive decision-making system again for a new round of evaluation and adjustment. This ensures that the system can continuously adapt to changes in inverter performance; set priorities for different adjustment strategies, and when multiple parameters need to be adjusted at the same time, the adjustment measures are executed in sequence according to the priority order; evaluate the overall effect of the coordinated adjustment of the strategy, and verify the effectiveness and superiority of the coordinated adjustment by comparing the changes in performance indicators before and after the adjustment; during the adjustment process, the various performance indicators of the inverter are continuously monitored; use real-time data analysis technology to quickly respond to performance changes and ensure the effectiveness of adjustment measures; and continuously optimize the feedback mechanism based on real-time performance monitoring results. Adjust threshold settings, fuzzy rule bases, and optimization algorithm parameters to improve the accuracy and robustness of the comprehensive decision-making system.

[0119] The effects of the above technical solution are as follows: by assigning reasonable weights to various performance indicators and using fuzzy set theory for fuzzification, the technical solution can comprehensively consider multiple performance indicators of the inverter and flexibly deal with the complex relationship between different performance indicators; the fuzzy rule base built based on expert knowledge and historical data enables the system to automatically infer corresponding adjustment measures according to different performance indicator combinations. This intelligent decision-making ability improves the automation level and response speed of the system; for performance indicators that need to be adjusted continuously, the gradient descent method combined with the particle swarm optimization algorithm is used for fine adjustment. The gradient descent method ensures the accuracy of local search, while the particle swarm optimization algorithm provides global search capabilities. The combination of the two can accelerate convergence to the global optimal solution, thereby minimizing performance deviations; the system can dynamically adjust the PWM modulation parameters according to the real-time operating status of the inverter and the changes in performance indicators, and continuously evaluate and adjust the adjustment effect through a feedback mechanism. This dynamic adaptability enables the system to continuously maintain the best operating state; set priorities for different adjustment strategies, and execute adjustment measures in order of priority when necessary. This strategy coordination mechanism ensures that when multiple parameters need to be adjusted at the same time, the system can perform adjustment tasks in an orderly and efficient manner. At the same time, the effectiveness and superiority of the coordinated adjustment were verified by evaluating the overall effect of the coordinated adjustment of the strategy; the various performance indicators of the inverter were continuously monitored during the adjustment process, and the real-time data analysis technology was used to quickly respond to performance changes. This real-time monitoring capability ensures the effectiveness of the adjustment measures and can promptly discover and solve potential problems. At the same time, the feedback mechanism, threshold settings, fuzzy rule base and algorithm parameters are continuously optimized according to the real-time performance monitoring results, which improves the accuracy and robustness of the comprehensive decision-making system; through the comprehensive application of the above technical means, the S445 technical solution can significantly improve the operating stability and reliability of the inverter. It can automatically adjust parameters under different working conditions to optimize performance indicators, reduce the possibility of failures, and extend the service life of the equipment; because the system can automatically monitor and adjust the operating status of the inverter, it reduces the number of manual interventions and shutdown maintenance. This not only reduces maintenance costs, but also improves the operating efficiency and economic benefits of the equipment. At the same time, by optimizing the performance of the inverter, it can also improve energy utilization and energy saving and emission reduction effects.

[0120] In one embodiment of the present invention, the S4 includes:

[0121] S41. In the process of generating the PWM signal, based on the product result of the PWF and the reference waveform, dead time control and switch tube drive delay compensation are introduced to ensure the accuracy and reliability of the PWM signal;

[0122] S42. According to the specific requirements of the inverter, multi-level PWM modulation technology, including space vector PWM (SVPWM), specific harmonic elimination PWM (SHEPWM), etc., is used to improve the waveform quality of the output voltage and the inverter efficiency;

[0123] S43. Use historical data and machine learning algorithms to predict the changing trend of the load. The prediction results can be used to adjust the PWF value or feedback control loop parameters in advance to cope with the upcoming load changes, and set multiple thresholds to judge the different operating states of the inverter. For example, different thresholds are set according to the output voltage fluctuation range, current harmonic content, etc. Once the relevant parameters are monitored to exceed the threshold range, the corresponding adjustment strategy is triggered.

[0124] S44. For multiple adjustment strategies that may be triggered at the same time, prioritize them according to their impact on the inverter performance and urgency; prioritize the adjustment strategy that has the greatest impact on the inverter performance or is the most urgent. When adjusting the PWF value or the feedback control loop parameters, use the method of gradual approximation, first make a small-scale adjustment, observe the adjustment effect, and if the expected goal is not achieved, gradually increase the adjustment range according to the adjustment effect until the performance requirements are met;

[0125] S45, filtering the feedback signal and optimizing the feedback control loop by using an adaptive control algorithm; performing stability analysis on the feedback control loop, and taking timely measures to adjust and optimize if unstable factors are found in the control loop;

[0126] S46. Establish a comprehensive abnormality detection mechanism to monitor and analyze the operating status of the inverter in real time; once an abnormality (such as overcurrent, overvoltage, overheating, etc.) is found, the corresponding processing flow is immediately triggered;

[0127] S47, after detecting an abnormal situation, using a fault diagnosis algorithm to quickly and accurately diagnose the inverter fault, and at the same time, taking isolation measures to separate the faulty part from the normal part;

[0128] S48. Develop appropriate emergency response plans based on the type and severity of the fault.

[0129] The working principle of the above technical solution is as follows: in the process of generating PWM signals, a PWM waveform is generated based on the product of PWF (pulse width factor) and the reference waveform; in order to ensure the accuracy and reliability of the PWM signal, dead time control is introduced to avoid the short circuit problem caused by the simultaneous conduction of the switch tubes, and the switch tube drive delay compensation is performed to eliminate the PWM waveform distortion caused by the drive circuit delay; according to the specific needs of the inverter, multi-level PWM modulation technology (such as SVPWM, SHEPWM, etc.) is used to optimize the waveform quality and inverter efficiency of the output voltage; by precisely controlling the shape and phase of the PWM waveform, the harmonic content of the output voltage is reduced and the waveform quality is improved; the PWM modulation strategy is optimized to reduce switching losses and improve the overall efficiency of the inverter; historical data and machine learning algorithms are used to predict the change trend of the load; according to the output voltage fluctuation range, current Multiple thresholds are set based on harmonic content, etc. to judge different operating states of the inverter; the PWF value or feedback control loop parameters are adjusted in advance according to the prediction results to cope with the upcoming load changes; for multiple adjustment strategies that may be triggered at the same time, they are prioritized according to their impact on the inverter performance and urgency; when adjusting the PWF value or feedback control loop parameters, the method of stepwise approximation is adopted, first making small-scale adjustments and observing the effects, and if the expected goals are not achieved, the adjustment amplitude is gradually increased until the performance requirements are met; the feedback signal is filtered to eliminate noise and interference, and to improve the accuracy and reliability of the feedback signal; an adaptive control algorithm is used to monitor the operating state and performance index changes of the inverter in real time, and the control parameters and strategies are automatically adjusted to achieve the optimal control effect; stability analysis is performed on the feedback control loop to ensure that the control loop can maintain stable operation under various working conditions. If unstable factors are found, timely measures will be taken to adjust and optimize; a comprehensive abnormality detection mechanism will be established to monitor and analyze the operating status of the inverter in real time; once an abnormal situation (such as overcurrent, overvoltage, overheating, etc.) is found, the corresponding processing flow will be triggered immediately; the fault diagnosis algorithm will be used to quickly and accurately diagnose the inverter fault; isolation measures will be taken to separate the faulty part from the normal part to prevent the fault from spreading and affecting the stable operation of the entire system; corresponding emergency response plans will be formulated according to the type and severity of the fault to ensure the safe and stable operation of the system.

[0130] The effect of the above technical solution is: by introducing dead time control and switch tube drive delay compensation in the process of PWM signal generation, the signal distortion and error caused by simultaneous conduction or drive delay of the switch tube are effectively reduced, thereby improving the accuracy and reliability of the PWM signal. This is crucial for the stable operation and performance optimization of the inverter; the use of multi-level PWM modulation technology (such as SVPWM, SHEPWM, etc.) can finely control the waveform of the output voltage according to the specific needs of the inverter, reduce the harmonic content, and improve the waveform quality. At the same time, these modulation technologies can also optimize the switching strategy of the inverter, reduce switching losses, and improve the inverter efficiency; use historical data and machine learning algorithms to predict the change trend of the load, so that the system can adjust the PWF value or feedback control loop parameters in advance to cope with the upcoming load changes. This prediction and adjustment mechanism enhances the load adaptability of the inverter, enabling it to maintain stable performance and efficient operation under different load conditions; for multiple adjustment strategies that may be triggered at the same time, the system can prioritize them according to their impact and urgency on the inverter performance, and give priority to the adjustment strategy that has the greatest or most urgent impact on performance. This intelligent strategy adjustment mechanism ensures that the inverter can respond quickly and accurately when facing complex working conditions; the feedback signal is filtered to eliminate noise and interference, and improve the accuracy and reliability of the feedback signal. At the same time, an adaptive control algorithm is used to optimize the feedback control loop, so that it can monitor the operating status and performance index changes of the inverter in real time, and automatically adjust the control parameters and strategies to achieve the optimal control effect. In addition, the stability analysis of the feedback control loop ensures its stable operation under various working conditions; a comprehensive abnormality detection mechanism is established to monitor and analyze the operating status of the inverter in real time, and the processing flow is triggered immediately once an abnormal situation is found. At the same time, the fault diagnosis algorithm is used to quickly and accurately diagnose the inverter fault, and isolation measures are taken to prevent the fault from spreading. This mechanism ensures that the inverter can be quickly processed and restored to normal operation when a fault occurs; the corresponding emergency treatment plan is formulated according to the type and severity of the fault, providing clear guidance and support for the fault handling of the inverter. This helps to reduce the impact of faults on the system and reduce maintenance costs.

[0131] In one embodiment of the present invention, the S43 includes:

[0132] Based on basic current and voltage monitoring, real-time monitoring of key parameters is performed, including power factor, load impedance, temperature change, input voltage stability, and system frequency fluctuation. The collected raw data is preprocessed, and key indicators that can reflect the load change characteristics are extracted from the preprocessed data, such as the time series change of load power, the fluctuation pattern of load impedance, the trend of temperature change, etc.

[0133] Use machine learning algorithms (such as long short-term memory network LSTM, support vector machine SVM, random forest, etc.) to model the extracted features, learn the inherent laws and trends of load changes through model training, and use cross-validation to tune the prediction model;

[0134] Quantitatively evaluate the prediction results, including prediction error, confidence interval, etc.; adjust the model parameters according to the evaluation results, and set multiple thresholds to judge the different operating states of the inverter based on the prediction results and the performance requirements of the inverter;

[0135] Monitor the various performance indicators of the inverter in real time and compare them with the set thresholds. If the relevant parameters are detected to be beyond the threshold range, the corresponding adjustment strategy is triggered.

[0136] The working principle of the above technical solution is: on the basis of basic current and voltage monitoring, add real-time monitoring of key parameters such as power factor, load impedance, temperature change, input voltage stability and system frequency fluctuation. These parameters can fully reflect the operating environment and load status of the inverter; pre-process the collected raw data, including data cleaning (removing noise, outliers, etc.), data conversion (such as normalization, standardization, etc.) and data compression, etc., to improve the efficiency and accuracy of subsequent processing; extract key indicators that can reflect the characteristics of load changes from the pre-processed data, such as the time series change of load power, the fluctuation pattern of load impedance, the trend of temperature change, etc. These characteristic indicators are the basis for subsequent model prediction; machine learning algorithms (such as long short-term memory network LSTM, support vector machine SVM, random forest, etc.) are used to model the extracted features. These algorithms have strong nonlinear fitting and generalization capabilities, and can learn the inherent laws and trends of load changes; by training the model, it can accurately predict future load changes. During the training process, the model can be tuned using cross-validation and other techniques to improve the prediction accuracy and generalization ability of the model; the prediction results can be quantitatively evaluated, including prediction error, confidence interval, etc. These evaluation indicators can reflect the prediction performance of the model and provide a basis for subsequent model adjustments; based on the prediction results and the performance requirements of the inverter, multiple thresholds are set to judge the different operating states of the inverter. For example, different thresholds can be set according to the output voltage fluctuation range, current harmonic content, etc.; various performance indicators of the inverter, such as output voltage, current, temperature, etc., are monitored in real time and compared with the set thresholds; if the relevant parameters are monitored to exceed the threshold range, the corresponding adjustment strategy is triggered. These adjustment strategies may include adjusting the PWF value, optimizing the feedback control loop parameters, switching the working mode, etc., to cope with the impact of load changes on the inverter performance.

[0137] The effect of the above technical solution is: by real-time monitoring of key parameters such as power factor, load impedance, temperature change, input voltage stability, and system frequency fluctuation, and combining advanced machine learning algorithms (such as LSTM, SVM, random forest, etc.), the technical solution can more accurately predict the inherent laws and trends of load changes. This high-precision prediction helps the inverter to prepare in advance to deal with possible load fluctuations, thereby improving the stability and reliability of the system; by extracting and modeling the characteristics of load changes, the system can better understand the dynamic characteristics of the load and adjust the control strategy accordingly. This enables the inverter to adapt to the operating requirements under different load conditions and improve the adaptability and flexibility of the system; based on the prediction results and the performance requirements of the inverter, multiple thresholds are set to judge the different operating states of the inverter. This threshold setting mechanism enables the system to monitor the performance indicators of the inverter in real time, and once the relevant parameters are monitored to exceed the threshold range, the corresponding adjustment strategy is immediately triggered. This instant feedback and adjustment helps to optimize the performance parameters of the inverter, such as output voltage quality, current harmonic content, etc., and improve the overall operating efficiency of the system; by real-time monitoring and prediction of load changes, the system can predict potential risks and problems in advance and take corresponding preventive measures. This forward-looking control strategy helps reduce the probability of system failures and improves system stability and reliability. The technical solution combines machine learning and real-time monitoring technologies to achieve intelligent operation and maintenance of the inverter. The system can automatically learn the law of load changes and adjust the control strategy based on the learning results, reducing the need for manual intervention. This not only improves operation and maintenance efficiency, but also reduces operation and maintenance costs. Through precise control and optimization of the inverter's operating status, the technical solution helps reduce energy waste and emissions. For example, the output power of the inverter is automatically adjusted when the load is low to avoid unnecessary energy consumption; when a system fault is detected, timely measures are taken to prevent the fault from expanding and causing greater energy loss and environmental pollution.

[0138] In one embodiment of the present invention, the S44 includes:

[0139] For multiple adjustment strategies that may be triggered simultaneously, prioritize them according to their impact on inverter performance and urgency;

[0140] The analytic hierarchy process (AHP) is used to comprehensively consider the advantages and disadvantages of different strategies and the current status of the inverter to select the optimal adjustment strategy;

[0141] When adjusting the PWF value or the feedback control loop parameters, use the stepwise approximation method, first make small adjustments and observe the adjustment effect;

[0142] If the expected target is not achieved, the adjustment range will be gradually increased according to the adjustment effect until the performance requirements are met. At the same time, the upper and lower limits of the adjustment are set to prevent excessive adjustment from causing system instability.

[0143] The working principle of the above technical solution is: when the inverter detects a situation that needs adjustment, multiple adjustment strategies may be triggered at the same time. In order to respond effectively, the system will first prioritize these strategies according to the degree of impact and urgency of the inverter performance. Strategies with greater impact and higher urgency will be given higher priority; based on the priority ranking, the system uses the analytic hierarchy process (AHP) to comprehensively consider the advantages and disadvantages of different strategies and the current operating status of the inverter. AHP decomposes complex decision factors into different components by constructing a hierarchical model, and determines the relative importance of each factor by comparing them two by two, and finally calculates the ranking weights that reflect the advantages and disadvantages of the strategy. The system selects the optimal adjustment strategy based on these weights; after determining the optimal adjustment strategy, the system uses the stepwise approximation method to adjust the PWF value or feedback control loop parameters. First, the system will make a small range of adjustments and monitor the performance changes of the adjusted inverter in real time; after the adjustment, the system will observe whether the performance indicators of the inverter have improved and whether the degree of improvement has reached the expected goal. If the adjustment effect is significant and in line with expectations, the adjustment process may be terminated; if the effect is not ideal, further adjustments are required; if the first small-scale adjustment does not achieve the expected goal, the system will gradually increase the adjustment range according to the adjustment effect. During this process, the system will continuously monitor the performance indicators of the inverter and dynamically adjust the adjustment range according to the monitoring results to ensure that the adjustment process is neither too radical nor too conservative; in order to prevent the system from being unstable due to excessive adjustment during the adjustment process, the system will set the upper and lower limits of the adjustment. These limit values ​​are determined based on the design parameters and performance requirements of the inverter to ensure that the adjustment process is always carried out within a safe range; when the adjustment approaches or reaches the upper / lower limit, the system will trigger the corresponding safety protection mechanism, such as issuing an alarm, suspending the adjustment, or switching to the backup control strategy, etc., to prevent the system from being damaged due to excessive adjustment.

[0144] The effects of the above technical solutions are as follows: by prioritizing multiple adjustment strategies that may be triggered at the same time, the S44 technical solution can ensure that the adjustment requirements that have the greatest or most urgent impact on the inverter performance are handled first when resources are limited or time is tight. This priority sorting mechanism helps to optimize the decision-making process and improve decision-making efficiency; the S44 technical solution can more comprehensively evaluate the applicability of each adjustment strategy by adopting the analytic hierarchy process (AHP) to comprehensively consider the advantages and disadvantages of different strategies and the current state of the inverter. The AHP method provides a scientific basis for selecting the optimal adjustment strategy by combining quantitative and qualitative analysis, thereby improving the accuracy and reliability of decision-making; when adjusting the PWF value or the feedback control loop parameters, the S44 technical solution adopts the stepwise approximation method, which can avoid the risk of system instability that may be caused by a one-time large-scale adjustment. By adjusting in a small range and observing the effect, and then gradually increasing the adjustment range, the system can transition to a new operating state more smoothly, thereby enhancing the stability and reliability of the system; setting the upper and lower limits of the adjustment is an important feature of the S44 technical solution. These limit values ​​help prevent system instability or performance degradation due to excessive adjustment during the adjustment process. By limiting the adjustment range, the system can avoid unnecessary damage to the system while ensuring that performance requirements are met; because the S44 technical solution can more accurately predict and respond to various problems in the operation of the inverter, it can reduce the inconvenience caused to users by system failures or performance degradation. At the same time, by optimizing the adjustment strategy and process, the system can recover to the optimal operating state more quickly, thereby improving the overall user experience; the S44 technical solution integrates a variety of intelligent technologies such as priority sorting, hierarchical analysis method, and step-by-step approximation adjustment to achieve intelligent selection and execution of inverter adjustment strategies. This intelligent development trend not only improves the operating efficiency and performance of the inverter, but also provides strong support for the construction of future smart grids and energy management systems.

[0145] In one embodiment of the present invention, S5 includes:

[0146] S51, analyze the output waveform of the inverter in detail by spectrum analysis technology (such as FFT) to identify the main harmonic components and their sources;

[0147] S52. Based on the result of harmonic source identification, formulate a targeted harmonic suppression strategy, wherein the suppression strategy includes adjusting the waveform shape of the PWF (such as introducing nonlinear factors), phase shift (by precisely controlling the phase of the PWM signal), and frequency modulation (dynamically adjusting the output frequency of the inverter to avoid the resonance point);

[0148] S53, real-time monitoring of the harmonic content in the inverter output waveform, and dynamically adjusting the PWF value through a PWF optimization algorithm based on feedback control to minimize the harmonic component;

[0149] S54. After implementing the harmonic suppression strategy, the inverter output waveform is evaluated again using spectrum analysis technology to verify whether the harmonic suppression effect reaches the expected goal; if not, the harmonic suppression strategy or PWF optimization algorithm is adjusted according to the evaluation results;

[0150] S55. Establish a comprehensive performance evaluation system, including multiple dimensions such as inverter efficiency, output voltage waveform quality (such as total harmonic distortion THD, voltage fluctuation rate, etc.), load response speed, system stability, etc.; use high-precision measurement equipment and data analysis tools to quantitatively evaluate various indicators; prioritize various indicators based on the performance evaluation results, and design special optimization strategies for indicators with higher priority; for example, if the inverter efficiency is low, it may be necessary to optimize the PWM signal generation algorithm or adjust the inverter switching frequency; if the output voltage waveform quality is not good, it may be necessary to further refine the PWF adjustment strategy or introduce more advanced harmonic suppression technology. Apply the optimization strategy to the inverter control system and conduct actual tests; iterate and repeatedly adjust the optimization strategy based on the test results until all performance indicators meet the design requirements;

[0151] S56. Monitor the operating status and performance index changes of the inverter in real time through an adaptive optimization mechanism, and automatically adjust the optimization strategy according to preset rules or algorithms; use big data analysis technology to conduct in-depth analysis and mining of massive data during the operation of the inverter;

[0152] S57, establishing a simulation model of the inverter, and performing simulation verification on different optimization strategies; and predicting the impact of different strategies on the inverter performance through simulation results;

[0153] S58. Establish an inverter optimization knowledge base and experience database, and record the process data during each optimization process.

[0154] The working principle of the above technical solution is: the output waveform of the inverter is finely analyzed through spectrum analysis technology (such as fast Fourier transform FFT) to identify the main harmonic components and their sources. FFT technology can convert time domain signals into frequency domain signals, so as to clearly display the amplitude and phase of each frequency component, which is convenient for identifying harmonics; based on the results of harmonic source identification, targeted harmonic suppression strategies are formulated. These strategies may include adjusting the waveform shape of PWF (pulse width modulation waveform) (such as introducing nonlinear factors to offset specific harmonics), phase shift (reducing harmonic superposition by precisely controlling the phase of PWM signal) and frequency modulation (dynamically adjusting the output frequency of the inverter to avoid resonance points and reduce harmonic resonance); real-time monitoring of the harmonic content in the inverter output waveform, and dynamically adjusting the value of PWF through a PWF optimization algorithm based on feedback control. The algorithm automatically adjusts the PWF parameters based on real-time feedback of the harmonic content to minimize the harmonic components. This closed-loop control mechanism can ensure that the quality of the inverter output waveform is always maintained at a high level; after implementing the harmonic suppression strategy, the spectrum analysis technology is used again to evaluate the inverter output waveform to verify whether the harmonic suppression effect reaches the expected goal. If the expected effect is not achieved, the harmonic suppression strategy or PWF optimization algorithm is adjusted according to the evaluation results until the requirements are met; a comprehensive performance evaluation system is established to quantitatively evaluate multiple key performance indicators of the inverter. According to the evaluation results, the indicators are prioritized and special optimization strategies are designed for indicators with higher priority. These strategies may involve the optimization of the PWM signal generation algorithm, the adjustment of the inverter switching frequency, the refinement of the PWF adjustment strategy, or the introduction of more advanced harmonic suppression technology. The optimization strategy is verified and adjusted in actual tests until all performance indicators meet the design requirements; the operating status and performance indicator changes of the inverter are monitored in real time through the adaptive optimization mechanism, and the optimization strategy is automatically adjusted according to the preset rules or algorithms. The massive data during the operation of the inverter is deeply analyzed and mined using big data analysis technology to discover potential performance improvement points and improvement directions. This mechanism can ensure that the inverter can maintain the best operating state under different working conditions; establish a simulation model of the inverter and simulate and verify different optimization strategies. The simulation results predict the impact of different strategies on the inverter performance, providing a theoretical basis and reference for actual optimization; establish an inverter optimization knowledge base and experience database to record the key data, strategies and results of each optimization process. These data and experience provide valuable reference and reference for subsequent optimization work, which helps to improve the optimization efficiency and effect.

[0155] The effect of the above technical solution is: by using spectrum analysis technology (such as FFT) to analyze the inverter output waveform in detail, the main harmonic components and their sources can be accurately identified. This provides reliable data support for the formulation of targeted harmonic suppression strategies, which helps to reduce the adverse effects of harmonics on the power grid and loads; based on the results of harmonic source identification, the harmonic suppression strategies formulated include adjusting the PWF waveform shape, phase shift and frequency modulation and other means. These strategies can effectively suppress harmonic generation, improve the quality of the inverter output waveform, and reduce total harmonic distortion (THD) and voltage fluctuation rate; monitor the harmonic content in the inverter output waveform in real time, and dynamically adjust the PWF value through the PWF optimization algorithm based on feedback control to minimize the harmonic components. This closed-loop control mechanism can ensure that the inverter can maintain the best operating state under different working conditions and improve the stability and reliability of the system; establish a comprehensive performance evaluation system, quantitatively evaluate multiple key performance indicators of the inverter, and design a special optimization strategy based on the evaluation results. This comprehensive evaluation and optimization method can ensure that all performance indicators of the inverter meet the design requirements and improve the overall performance; the operating status and performance indicator changes of the inverter are monitored in real time through the adaptive optimization mechanism, and the optimization strategy is automatically adjusted according to the preset rules or algorithms. At the same time, the massive data in the inverter operation process is deeply analyzed and mined by big data analysis technology to find potential performance improvement points and improvement directions. This adaptive and intelligent optimization method can further improve the operating efficiency and performance stability of the inverter; establish a simulation model of the inverter, simulate and verify different optimization strategies, and predict the impact of different strategies on the performance of the inverter. This helps to evaluate the feasibility and effect of the strategy before actual application and reduce the cost of trial and error. At the same time, establish an inverter optimization knowledge base and experience database to record the key data, strategies and results of each optimization process, and provide valuable reference and reference for subsequent optimization work; through the implementation of the above technical solutions, the output waveform quality, inverter efficiency, load response speed and other key performance indicators of the inverter have been significantly improved. This not only improves the user's power experience, but also enhances the reliability and stability of the system, and reduces the failure rate and maintenance cost.

[0156] In one embodiment of the present invention, the S57 includes:

[0157] According to the specific type of inverter (such as single-phase, three-phase, voltage source type, current source type, etc.) and application scenario, select appropriate simulation software (such as MATLAB / Simulink, PSIM, PLECS, etc.) for modeling; according to the electrical parameters, control strategy and external environmental conditions of the inverter, customize and build a detailed simulation model of the inverter;

[0158] Use actual inverter operation data or standard test data to verify and calibrate the simulation model, and adjust model parameters and boundary conditions to make the simulation results highly consistent with the actual operation data;

[0159] Define the optimization strategies to be verified (such as PWF waveform adjustment, PWM phase shift, frequency modulation, etc.) in a parameterized manner, clarify the specific implementation methods and adjustable parameter ranges of each strategy, and set up a variety of simulation experiment scenarios according to the actual operating environment of the inverter, including different load types, load changes, input voltage fluctuations, temperature changes, etc., to comprehensively evaluate the performance of the optimization strategies under different conditions;

[0160] Implement the defined optimization strategy in the simulation model and run simulation experiments. Record key data during the simulation process, such as output voltage waveform, current waveform, harmonic content, inverter efficiency, load response speed, etc. Use data analysis tools to process and analyze simulation data and extract the changing trends and statistical characteristics of key performance indicators. Evaluate the impact of each strategy on inverter performance by comparing simulation results under different optimization strategies.

[0161] Based on simulation data, an inverter performance prediction model is constructed. By using machine learning or statistical learning methods, a mapping relationship between performance indicators and optimization strategy parameters is established to achieve rapid prediction of performance indicators.

[0162] Compare and verify the prediction results with the simulation experiment results to evaluate the accuracy and reliability of the prediction model; according to the evaluation results, tune and improve the prediction model;

[0163] Based on the simulation results and prediction model, the optimization strategies that significantly improve the inverter performance and have low implementation costs are screened out, and the screened optimization strategies are applied to the inverter control system and actual tests are carried out. According to the test results and performance evaluation results, the optimization strategies are iterated and adjusted until all performance indicators meet the design requirements.

[0164] The working principle of the above technical solution is: according to the specific type of inverter (such as single-phase, three-phase, voltage source type, current source type, etc.) and application scenario, select suitable simulation software (such as MATLAB / Simulink, PSIM, PLECS, etc.) for modeling; according to the electrical parameters of the inverter (such as inductance, capacitance, resistance, etc.), control strategy (such as PWM control, PID control, etc.) and external environmental conditions (such as temperature, humidity, grid voltage fluctuation, etc.), customize and build a detailed simulation model of the inverter; use actual inverter operation data or standard test data to verify and calibrate the simulation model to ensure the accuracy and reliability of the simulation results; adjust the model parameters and edge boundary conditions, so that the simulation results are highly consistent with the actual operation data, and the prediction accuracy of the model is improved; the optimization strategies to be verified (such as PWF waveform adjustment, PWM phase offset, frequency modulation, etc.) are parameterized and defined, and the specific implementation methods and adjustable parameter ranges of each strategy are clarified; according to the actual operating environment of the inverter, a variety of simulation experiment scenarios are set, including different load types, load changes, input voltage fluctuations, temperature changes, etc., to comprehensively evaluate the performance of the optimization strategies under different conditions; the defined optimization strategies are implemented in the simulation model, and simulation experiments are run; key data in the simulation process are recorded, such as output voltage waveform, current waveform, harmonic content, inverter efficiency, load response speed, etc. The simulation data are processed and analyzed using data analysis tools to extract the change trends and statistical characteristics of key performance indicators; by comparing the simulation results under different optimization strategies, the impact of each strategy on the inverter performance is evaluated, and which strategies have a significant effect on performance improvement is determined; based on the simulation data, an inverter performance prediction model is constructed. Use machine learning or statistical learning methods to establish a mapping relationship between performance indicators and optimization strategy parameters to achieve rapid prediction of performance indicators; compare and verify the prediction results with the simulation experiment results to evaluate the accuracy and reliability of the prediction model. According to the evaluation results, tune and improve the prediction model; based on the simulation results and the prediction model, select optimization strategies that significantly improve inverter performance and have low implementation costs; apply the selected optimization strategies to the inverter control system and conduct actual tests. According to the test results and performance evaluation results, iterate and adjust the optimization strategy until all performance indicators meet the design requirements.

[0165] The effects of the above technical solution are: by customizing the detailed simulation model of the inverter, and verifying and calibrating it using actual operation data or standard test data, the accuracy and reliability of the simulation results are ensured. This helps to capture the problems that the inverter may encounter in actual operation during the simulation stage, so as to optimize and adjust it in advance; a variety of simulation experiment scenarios are set up, including different load types, load changes, input voltage fluctuations, temperature changes, etc., to comprehensively evaluate the performance of the optimization strategy under different conditions. This comprehensive evaluation method can ensure that the optimization strategy has better adaptability and stability in practical applications; the optimization strategy to be verified is parameterized, and the optimization strategy that significantly improves the inverter performance and has a low implementation cost is selected based on the simulation data and prediction model. This method can significantly improve the efficiency of optimization strategy screening, reduce the cost of trial and error, and quickly find the optimal solution; build an inverter performance prediction model, use machine learning or statistical learning methods to establish a mapping relationship between performance indicators and optimization strategy parameters, and realize rapid prediction of performance indicators. This helps to predict performance before the optimization strategy is implemented, reduce the number and time of actual tests, and accelerate the iteration and adjustment of the optimization process; through simulation verification and actual testing, the control strategy of the inverter is continuously optimized, the harmonic content is reduced, the output voltage waveform quality is improved, the inverter efficiency is improved, and the load response speed is accelerated. These performance improvements will directly improve the overall performance and reliability of the inverter, reduce the failure rate and maintenance cost; the simulation model can simulate the operation of the inverter under different working conditions, helping engineers to discover and solve potential problems in advance. This helps to enhance the stability of the inverter system and reduce downtime and losses caused by system instability; through simulation verification and optimization strategy screening, the performance of the inverter can be optimized and adjusted multiple times without increasing the actual hardware investment. This greatly reduces the development cost and shortens the product launch cycle; the establishment of the simulation model and the research on the optimization strategy provide strong support for the technical innovation of the inverter. Engineers can try new control algorithms and optimization strategies in the simulation environment to promote the continuous progress and development of inverter technology.

[0166] In one embodiment of the present invention, S6 includes:

[0167] S61. Real-time monitoring of the inverter's operating status, including key parameters such as temperature, current, and voltage, to ensure that these parameters fluctuate within the permitted safety range. Through anomaly detection algorithms, the monitored data is quickly analyzed to detect potential faults or abnormal conditions.

[0168] S62, if an abnormality or failure is detected, immediately start a corresponding protection mechanism, the protection mechanism including cutting off power, alarm prompting and automatic restart;

[0169] S63. Record the information of the fault, including the time, type and cause, analyze the law and trend of the fault, and continuously optimize and improve the system based on the analysis results.

[0170] The working principle of the above technical solution is as follows: the system collects the operating status data of the inverter in real time through sensors and monitoring equipment, including key parameters such as temperature, current, and voltage. These data are important bases for evaluating the health status and operating safety of the inverter; the collected data are sent to the data processing unit and quickly analyzed by predefined anomaly detection algorithms. These algorithms can identify abnormal patterns in the data or values ​​that exceed the preset safety range, so as to determine whether there are potential faults or abnormal conditions in the inverter; when the anomaly detection algorithm finds that the inverter operating data is abnormal or there is a fault, it will immediately issue a warning signal; according to the type and severity of the warning signal, the system will automatically trigger the corresponding protection mechanism. These protection mechanisms may include cutting off the power supply to prevent further damage, issuing an alarm to notify the operation and maintenance personnel, and trying to automatically restart the inverter to resume normal operation; when the protection mechanism is triggered, the system will determine the priority of processing according to the severity and urgency of the fault to ensure that the most important protection action can be executed first; the system will record the time, type and possible cause of each fault in detail. This information is an important basis for subsequent analysis and improvement; by statistically analyzing the recorded fault information, the laws and trends of fault occurrence can be identified. For example, the time distribution and type distribution of faults and their correlation with other operating parameters can be analyzed; based on the results of fault analysis, the inverter system can be continuously optimized and improved. This may include adjusting the thresholds of monitoring parameters, optimizing anomaly detection algorithms, improving the design of protection mechanisms, etc. Through these measures, the reliability and stability of the inverter can be improved, and the frequency and scope of faults can be reduced.

[0171] The effect of the above technical solution is: by real-time monitoring of the key parameters of the inverter, such as temperature, current, and voltage, it can be ensured that these parameters fluctuate within the allowable safety range. Once a parameter abnormality is found, the system can respond quickly and detect potential faults or abnormal conditions in time; the real-time monitoring and early warning mechanism helps to intervene in the early stage of the fault, thereby avoiding the expansion of the fault and reducing the losses caused by the fault; when an abnormality or fault is detected, the system can immediately start the corresponding protection mechanism, such as cutting off the power supply, alarming, and automatic restarting. These measures help prevent the fault from causing further damage to the inverter and ensure the safety of the equipment; the fast-response protection mechanism can reduce the downtime of the inverter and improve the reliability and availability of the system. In the power system, the stable operation of the inverter is crucial to ensure the power supply; the system records the time, type, and cause of the fault in detail, which provides a strong basis for subsequent fault analysis and processing; through the statistics and analysis of the fault records, the laws and trends of the fault can be identified, and then the system can be continuously optimized and improved. This helps to improve the overall performance and reliability of the inverter and reduce the probability of faults; based on the results of the fault analysis, the inverter system can be optimized and improved in a targeted manner. For example, the performance and stability of the system can be improved by adjusting the threshold of monitoring parameters, optimizing the anomaly detection algorithm, improving the design of the protection mechanism, etc.; through continuous optimization and improvement, the operation and maintenance costs of the inverter can be reduced. For example, the downtime caused by faults can be reduced, and the frequency of repairs and replacement of parts can be reduced, saving expenses for users; functions such as real-time monitoring, rapid response and fault recording can enhance users' confidence in the inverter system and improve user satisfaction and loyalty; providing detailed fault records and trend analysis results can help users better manage the inverter system and reduce the difficulty and workload of operation and maintenance.

[0172] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An inverter PWI modulation method, characterized in that: The method comprises: S1. Set the basic working parameters of the inverter, initialize the initial value of the phase weighting factor and its variation range, and preset the PWF adjustment strategy according to the load characteristics and waveform quality requirements; S2, loading and initializing the control algorithm of the inverter, generating a standard sine wave as a reference wave according to the preset output voltage and frequency; S3. Calculate the PWF value of each phase point according to the current load state and the preset PWF adjustment strategy, multiply the PWF value by the standard sine wave to generate a target modulation waveform; select a carrier signal, compare the target modulation waveform with the carrier signal, and generate a PWM signal according to the comparison result; S4, input the PWM signal to the PWM drive circuit of the inverter, control the on and off of the switch tube of the inverter, and generate an AC output voltage; monitor the output current and voltage of the inverter in real time, and dynamically adjust the value of the PWF or the parameters of the feedback control loop according to the monitoring results; S5. Introduce harmonic suppression mechanism in PWI modulation algorithm; regularly evaluate the performance of inverter, and optimize and adjust PWI modulation algorithm and feedback control strategy according to the evaluation results; S6. Monitor the operating status of the inverter and immediately activate the protection mechanism once an abnormality or fault is detected.

2. The inverter PWI modulation method according to claim 1, characterized in that: Said S1 comprises: S11, determining the expected output voltage amplitude, frequency and rated power of the inverter according to the application requirements, and presetting the initial value of the initialization phase weighting factor based on the expert knowledge base; S12, determine the variation range of PWF during the inversion process and conduct in-depth analysis of the load characteristics in the target application; S13. Based on load characteristics and waveform quality requirements, a PWF adjustment strategy is designed, wherein the PWF adjustment strategy includes the timing, method, and amplitude of adjustment.

3. The inverter PWI modulation method according to claim 1, characterized in that: The S2 comprises: S21, selecting a PWI modulation control algorithm from a control system of the inverter, the control algorithm including a PWI modulation algorithm and a feedback control algorithm, and loading the algorithm into a controller; S22, generating a standard sine wave as a reference waveform of the inverter using a digital signal processor or a microcontroller according to a preset output voltage and frequency; S23. Perform quality check on the generated reference waveform.

4. The inverter PWI modulation method according to claim 1, characterized in that: The S3 includes: S31, preliminarily setting the initial value of PWF based on the expert knowledge base according to the design specifications, load characteristics and expected performance targets of the inverter, and automatically adjusting the value of PWF according to the real-time parameters and performance indicators of the inverter during operation through a preset PWF dynamic adjustment strategy; S32, according to the specific requirements and application scenarios of the inverter, select a PWM modulation mode, multiply the PWF value by the reference waveform to obtain a modulated waveform; and according to the selected PWM modulation mode, convert the modulated waveform into a PWM signal; S33, in the process of generating the PWM signal, suppressing the edge effect by appropriate measures, wherein the appropriate measures include soft switching technology and a buffer circuit; S34, real-time monitoring of the performance indicators of the inverter, establishing a feedback control mechanism, comparing the monitored performance indicators with preset target values; if the performance indicators deviate from the target values, automatically adjusting the PWF value or the parameters of the PWM modulation method according to the degree of deviation; S35. Introduce judgment logic into the feedback control mechanism; detect potential abnormal conditions by real-time monitoring of the operating status and performance indicators of the inverter; and perform fault diagnosis on the inverter using a fault diagnosis algorithm after detecting the abnormal condition.

5. The inverter PWI modulation method according to claim 4, characterized in that: The S34 comprises: S341. Build an inverter performance index system, use high-speed ADC to sample voltage and current, use high-precision thermometers to monitor the temperature of key components, and use spectrum analyzers to evaluate electromagnetic radiation levels; S342, pre-processing the collected raw data, and using digital signal processing technology to smooth the signal; S343, based on the preprocessed data, calculate the current value of each performance indicator in real time, and compare it with the preset target value or threshold; S344. Differentiate different degrees of deviation by pre-setting multi-level thresholds for each performance indicator, use time series analysis to learn historical data of performance indicators, and predict future trends; when it is predicted that the threshold is about to be exceeded, take preventive measures in advance; S345, combining the evaluation results of multiple performance indicators, using fuzzy logic algorithm to make comprehensive decisions, determine the optimal adjustment strategy, and automatically adjust the PWF value according to the degree of deviation of the performance indicators; S346. Under the premise of maintaining the quality of the output voltage waveform, dynamically adjust the parameters of the PWM modulation method. When a single adjustment strategy cannot meet the performance requirements, a multi-strategy collaborative approach is used for adjustment.

6. The inverter PWI modulation method according to claim 5, characterized in that: The S445 includes: Based on historical data analysis, weights are assigned to various performance indicators, and fuzzy set theory is used to fuzzify the performance indicators. The current value of each performance indicator is mapped to the fuzzy set, and its membership function is defined; Based on expert knowledge and historical data, a set of fuzzy rule bases are constructed, each of which defines the adjustment measures to be taken under different performance indicator combinations; The fuzzified performance index is input into the fuzzy inference engine, and reasoning is performed according to the fuzzy rule base to finally obtain a comprehensive decision result; For performance indicators that need to be adjusted continuously, the gradient descent method is used to calculate the adjustment direction and step size of PWF, and the global search capability of the particle swarm optimization algorithm is combined to supplement the local search of the gradient descent method; Under the premise of maintaining the quality of the output voltage waveform, the feasible space of PWM modulation parameters is defined, and the optimization algorithm is used to search in the parameter space to find the optimal parameter combination; According to the real-time operating status and performance index changes of the inverter, the PWM modulation parameters are dynamically adjusted. At the same time, a feedback mechanism is established to input the adjusted performance indexes into the comprehensive decision-making system again for a new round of evaluation and adjustment. Set priorities for different adjustment strategies based on the importance of performance indicators and the adjustment effects, and evaluate the overall effect of the coordinated adjustment of strategies; During the adjustment process, the inverter's performance indicators are continuously monitored, and the feedback mechanism is continuously optimized based on real-time performance monitoring results.

7. The inverter PWI modulation method according to claim 1, characterized in that: The S4 comprises: S41. In the process of generating the PWM signal, based on the product result of the PWF and the reference waveform, dead time control and switch tube drive delay compensation are introduced to ensure the accuracy and reliability of the PWM signal; S42. According to the specific requirements of the inverter, multi-level PWM modulation technology is used to improve the waveform quality and inverter efficiency of the output voltage; S43, using historical data and machine learning algorithms to predict the load change trend; setting multiple thresholds to determine different operating states of the inverter; S44, for multiple adjustment strategies that may be triggered simultaneously, prioritize them according to their impact on inverter performance and urgency; S45, filtering the feedback signal and optimizing the feedback control loop by using an adaptive control algorithm; performing stability analysis on the feedback control loop, and taking measures to adjust and optimize the control loop if unstable factors are found; S46. Establish an abnormality detection mechanism to monitor and analyze the operating status of the inverter in real time; once an abnormality is found, the corresponding processing flow is immediately triggered; S47, after detecting an abnormal situation, using a fault diagnosis algorithm to quickly and accurately diagnose the inverter fault, and at the same time, taking isolation measures to separate the faulty part from the normal part; S48. Develop appropriate emergency response plans based on the type and severity of the fault.

8. The inverter PWI modulation method according to claim 7, characterized in that: The S43 includes: Based on basic current and voltage monitoring, key parameters are monitored in real time, the collected raw data is preprocessed, and key indicators that can reflect the load change characteristics are extracted from the preprocessed data. Use machine learning algorithms to model the extracted features, learn the inherent laws and trends of load changes through model training, and use cross-validation to tune the prediction model; Quantitatively evaluate the prediction results, adjust the model parameters according to the evaluation results, and set multiple thresholds to judge the different operating states of the inverter based on the prediction results and the performance requirements of the inverter; Monitor the various performance indicators of the inverter in real time and compare them with the set thresholds. If the relevant parameters are detected to be beyond the threshold range, the corresponding adjustment strategy is triggered.

9. The inverter PWI modulation method according to claim 1, characterized in that: The S5 comprises: S51. Analyze the output waveform of the inverter by spectrum analysis technology to identify the main harmonic components and their sources; S52, formulating a targeted harmonic suppression strategy based on the result of harmonic source identification, wherein the suppression strategy includes adjusting the waveform shape, phase shift, and frequency modulation of the PWF; S53, real-time monitoring of the harmonic content in the inverter output waveform, and dynamically adjusting the PWF value through a PWF optimization algorithm based on feedback control to minimize the harmonic component; S54. After implementing the harmonic suppression strategy, the inverter output waveform is evaluated again using spectrum analysis technology to verify whether the harmonic suppression effect reaches the expected goal; if not, the harmonic suppression strategy or PWF optimization algorithm is adjusted according to the evaluation results; S55. Establish a comprehensive performance evaluation system, and use high-precision measurement equipment and data analysis tools to quantitatively evaluate various indicators; prioritize various indicators based on the performance evaluation results, and design special optimization strategies for indicators with higher priorities; apply the optimization strategies to the inverter control system and conduct actual tests; iterate and repeatedly adjust the optimization strategies based on the test results until all performance indicators meet the design requirements; S56. Monitor the operating status and performance index changes of the inverter in real time through an adaptive optimization mechanism, and automatically adjust the optimization strategy according to preset rules or algorithms; use big data analysis technology to conduct in-depth analysis and mining of massive data during the operation of the inverter; S57, establishing a simulation model of the inverter, and performing simulation verification on different optimization strategies; and predicting the impact of different strategies on the inverter performance through simulation results; S58. Establish an inverter optimization knowledge base and experience database, and record the process data during each optimization process.

10. The inverter PWI modulation method according to claim 1, characterized in that: The S6 comprises: S61, monitor the operating status of the inverter in real time, and quickly analyze the monitored data through anomaly detection algorithms to detect potential faults or abnormal conditions; S62, if an abnormality or failure is detected, immediately start a corresponding protection mechanism, the protection mechanism including cutting off power, alarm prompting and automatic restart; S63. Record the information of the fault, including the time, type and cause, analyze the law and trend of the fault, and continuously optimize and improve the system based on the analysis results.

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