A PWI modulation method for inverter

Abstract

The present invention proposes a PWI modulation method for an inverter. This method belongs to the field of power electronics technology and includes: setting basic operating parameters of the inverter, initializing the initial value of the phase weighting factor and its variation range, and presetting the PWF adjustment strategy based on load characteristics and waveform quality requirements; loading and initializing the inverter control algorithm, generating a standard sine wave as a reference wave based on a preset output voltage and frequency; calculating the PWF value of each phase point based on the current load state and the preset PWF adjustment strategy, multiplying the PWF value by the standard sine wave to generate a target modulation waveform; selecting a carrier signal, comparing the target modulation waveform with the carrier signal, and generating a PWM signal based on the comparison result. By presetting the PWF adjustment 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.

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 often use methods such as sinusoidal pulse width modulation (SPWM) or space vector pulse width modulation (SVPWM). While these methods can achieve inverter functionality, they have limitations in handling complex load variations, improving output waveform quality, and reducing harmonic content. Particularly in off-grid conditions, due to the lack of grid support, the inverter's output voltage and frequency must be precisely controlled to ensure power quality. Therefore, the development of a new, efficient, and stable modulation method is crucial. 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 proposes an inverter PWI modulation method, the method comprising:

[0005] S1. Set the basic operating 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. Load and initialize the inverter control algorithm, and generate a standard sine wave as a reference wave according to the preset output voltage and frequency;

[0007] S3. Calculate the PWF value at each phase point based on 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 based on the comparison result;

[0008] S4. Input the PWM signal to the PWM drive circuit of the inverter to control the on / off of the inverter switch to generate an AC output voltage. Monitor the output current and voltage of the inverter in real time through the current sensor and voltage sensor, and dynamically adjust the PWF value or the parameters of the feedback control loop based on the monitoring results.

[0009] S5. Introduce a harmonic suppression mechanism into the PWI modulation algorithm. Reduce the harmonic content in the inverter output by adjusting the PWF waveform or adopting other harmonic suppression strategies. Regularly evaluate the inverter performance and optimize the PWI modulation algorithm and feedback control strategy based on 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. Determine the expected output voltage amplitude, frequency, and rated power of the inverter according to application requirements, and preset a starting value of the initialization phase weighting factor based on an expert knowledge base;

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

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

[0015] Furthermore, the S2 includes:

[0016] S21. Select a PWI modulation control algorithm from the control system of the inverter, the control algorithm including a PWI modulation algorithm and a feedback control algorithm, and load it into the controller;

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

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

[0019] Furthermore, the S3 includes:

[0020] S31. Preliminarily setting an initial value of the 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 PWF value according to the real-time parameters and performance indicators of the inverter during operation through a preset PWF dynamic adjustment strategy;

[0021] S32. Select a PWM modulation method based on the specific requirements and application scenarios of the inverter, multiply the PWF value by the reference waveform to obtain a modulated waveform; and convert the modulated waveform into a PWM signal based on the selected PWM modulation method;

[0022] S33. During the PWM signal generation process, suppressing edge effects through 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 PWM modulation parameters 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 inverter's operating status and performance indicators; and perform fault diagnosis on the inverter using a fault diagnosis algorithm after detecting an abnormal condition.

[0025] Furthermore, the S34 includes:

[0026] S341. Build an inverter performance indicator system, use high-speed ADCs for voltage and current sampling, use high-precision thermometers to monitor the temperature of key components, and use spectrum analyzers to assess electromagnetic radiation levels.

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

[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 between different levels of deviations using pre-set multi-level thresholds for each performance indicator. Use time series analysis to study historical data on performance indicators and predict future trends. When a threshold is predicted to be exceeded, preventive measures are taken in advance.

[0030] S345. Combining the evaluation results of multiple performance indicators, a fuzzy logic algorithm is used to make a comprehensive decision to determine the optimal adjustment strategy, and the PWF value is automatically adjusted 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 adopted for adjustment.

[0032] Furthermore, the S345 includes:

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

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

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

[0036] For performance indicators that require continuous adjustment, the gradient descent method is used to calculate the adjustment direction and step size of the 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] Dynamically adjust PWM modulation parameters based on the inverter's real-time operating status and performance indicator changes. At the same time, establish a feedback mechanism to input the adjusted performance indicators back into the comprehensive decision-making system for a new round of evaluation and adjustment.

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

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

[0041] Furthermore, the S4 includes:

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

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

[0044] S43. Use historical data and machine learning algorithms to predict load change trends; set multiple thresholds to determine different operating states of the inverter;

[0045] S44. Prioritize multiple adjustment strategies that may be triggered simultaneously according to their impact on inverter performance and urgency.

[0046] S45. Filter the feedback signal and use an adaptive control algorithm to optimize the feedback control loop; perform stability analysis on the feedback control loop, and if unstable factors are found in the control loop, take measures to adjust and optimize it;

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

[0048] S47. After detecting an abnormality, use a fault diagnosis algorithm to quickly and accurately diagnose the inverter fault, and at the same time, take 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 are 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. By training the model, learn the inherent laws and trends of load changes, and use cross-validation to tune the prediction model.

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

[0054] Monitor the 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 using spectrum analysis technology to identify the main harmonic components and their sources;

[0057] S52. Based on the result of harmonic source identification, formulate a targeted harmonic suppression strategy, wherein the suppression strategy includes adjusting the waveform shape, phase offset, 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 content;

[0059] S54. After implementing the harmonic suppression strategy, use spectrum analysis technology to evaluate the inverter output waveform again to verify whether the harmonic suppression effect has achieved the expected goal; if not, adjust the harmonic suppression strategy or PWF optimization algorithm based on the evaluation results;

[0060] S55. Establish a comprehensive performance evaluation system, using high-precision measurement equipment and data analysis tools to quantitatively evaluate various indicators. Based on the performance evaluation results, prioritize various indicators and design specialized optimization strategies for higher-priority indicators. 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 inverter's operating status and performance indicator changes in real time through an adaptive optimization mechanism, and automatically adjust the optimization strategy based on preset rules or algorithms; use big data analysis technology to conduct in-depth analysis and mining of massive data during the inverter's operation;

[0062] S57. Establish a simulation model of the inverter and perform simulation verification on different optimization strategies; predict the impact of different strategies on inverter performance based on the simulation results;

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

[0064] Furthermore, the S6 includes:

[0065] S61: Monitor the operating status of the inverter in real time and use anomaly detection algorithms to quickly analyze the monitored data to identify potential faults or abnormalities.

[0066] S62. If an abnormality or fault is detected, immediately activate a corresponding protection mechanism, which includes power off, alarm prompt, and automatic restart;

[0067] S63. Record the information of the fault, including the time, type and cause, analyze the pattern 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 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 utilizing the expert knowledge base and machine learning algorithm, the inverter control strategy can be optimized to improve the overall performance of the inverter; the operating status of the inverter can be monitored in real time, and once an abnormality or fault is detected, the protection mechanism can be immediately activated to ensure the safe operation of the system; the use of multi-level PWM modulation technology can improve the output voltage waveform quality and 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 adaptability of the system can be improved; an inverter optimization knowledge base and experience database are established to facilitate maintenance, upgrading and optimization of the system. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0070] The preferred embodiments of the present invention are described below with reference to 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, a method for modulating the PWI of an inverter, the method comprising:

[0072] S1. Set the basic operating 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. Load and initialize the inverter control algorithm, including the PWI modulation algorithm and feedback control algorithm, and generate a standard sine wave as a reference wave based on the preset output voltage and frequency;

[0074] S3. Calculate the PWF value at each phase point based on 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 an appropriate carrier signal, compare the target modulation waveform with the carrier signal, and generate a PWM signal based on the comparison result;

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

[0076] S5. Introduce a harmonic suppression mechanism into the PWI modulation algorithm. Reduce the harmonic content in the inverter output by adjusting the PWF waveform or adopting other harmonic suppression strategies. Regularly evaluate the inverter performance, including indicators such as inverter efficiency, output voltage waveform quality, and harmonic content. Optimize and adjust the PWI modulation algorithm and feedback control strategy based on 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 and issuing an alarm.

[0078] The working principle of the above technical solution is as follows: according to the application requirements of the inverter, the desired output voltage amplitude, frequency, and rated power of the inverter are set; the initial value and variation range of the phase weighting factor (PWF) are initialized, and these values are dynamically adjusted according to the load characteristics and waveform quality requirements; the PWF adjustment strategy is preset so that the PWF value can be adjusted according to load changes and system status during inverter operation; the inverter control algorithm, including the PWI modulation algorithm and the feedback control algorithm, is loaded and initialized; based on the preset output voltage and frequency, a standard sine wave is generated as a reference wave, which represents the desired output voltage waveform; the PWF value at each phase point is calculated based on the current load status and the preset PWF adjustment strategy; the PWF value is multiplied by 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, which can more flexibly control the output voltage waveform; and a suitable high-frequency carrier signal (such as a triangle wave or sawtooth wave) is selected and the target modulation waveform is compared with the carrier signal to generate the PWM signal. The duty cycle of the PWM signal reflects the instantaneous value of the target modulation waveform, thereby controlling the on / off switching of the inverter's switches. The PWM signal is input into the inverter's PWM drive circuit, controlling the on / off switching of the inverter's switches to generate the desired AC output voltage. Current and voltage sensors monitor the inverter's output current and voltage in real time, and the monitoring data is analyzed to determine whether the load has changed or whether the system is stable. Based on the monitoring results, the PWF value or feedback control loop parameters are dynamically adjusted to maintain output voltage stability and waveform quality. A harmonic suppression mechanism is introduced into the PWI modulation algorithm to reduce the harmonic content in the inverter output by adjusting the PWF waveform or adopting other harmonic suppression strategies. The inverter's performance is regularly evaluated, including indicators such as inverter efficiency, output voltage waveform quality, and harmonic content. Based on the evaluation results, the PWI modulation algorithm and feedback control strategy are optimized to further improve inverter performance. The inverter's operating status is monitored, including key parameters such as temperature, current, and voltage. Upon detecting an abnormality or fault, protection mechanisms are immediately activated, such as power cutoff and alarm prompts, to ensure safe and stable operation of the inverter and system.

[0079] The above technical solution achieves the following: By dynamically adjusting the phase weighting factor (PWF) value, the PWI modulation method can more flexibly control the output voltage waveform, making it closer to an ideal sine wave. This helps reduce waveform distortion and improve the output voltage waveform quality, meeting high-quality waveform requirements in applications. The real-time feedback control algorithm monitors the inverter's output current and voltage and dynamically adjusts the PWF value or feedback control loop parameters based on the monitoring results. This closed-loop control mechanism helps quickly respond to load changes and system disturbances, maintaining output voltage stability and overall system stability. Introducing a harmonic suppression mechanism into the PWI modulation algorithm significantly reduces the harmonic content in the inverter output by adjusting the PWF waveform or employing other harmonic suppression strategies. Harmonic suppression is important for protecting grid equipment and improving power quality, especially in harmonic-sensitive applications. By optimizing the inverter control algorithm, including the PWI modulation algorithm and feedback control strategy, the inverter's conversion efficiency can be improved. Efficient inverters can reduce energy loss, improve energy utilization, and lower operating costs. Monitoring the inverter's operating status, including key parameters such as temperature, current, and voltage, can promptly detect potential faults or abnormalities. Once an abnormality or fault is detected, protection mechanisms (such as power cuts and alarms) are immediately activated, helping to prevent equipment damage and safety incidents, thereby improving system reliability and safety. The PWI modulation method offers high flexibility and scalability. By adjusting the PWF regulation strategy and feedback control algorithm, it can adapt to different load characteristics and application requirements. Furthermore, with continued technological advancement, 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. Determine the expected output voltage amplitude, frequency, and rated power of the inverter according to application requirements, and preset an initial value of the initial phase weighting factor (PWF) based on an expert knowledge base;

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

[0083] S13. Based on the load characteristics and waveform quality requirements, design a set of refined PWF adjustment strategies, wherein the PWF adjustment strategies include the timing, method, and amplitude of adjustment.

[0084] The working principle of the above technical solution is as follows: Based on the specific application requirements of the inverter (such as the type of power supply equipment and grid requirements), the expected output voltage amplitude, frequency, and rated power of the inverter are determined. These parameters are the basis for inverter design and operation. A starting value for the phase weighting factor (PWF) is preset based on expert knowledge or historical experience. This starting value should be a reasonable estimate that provides good output voltage waveform quality in the initial state. The expert knowledge base may include recommended PWF values for different load types and waveform quality requirements. To address the various load conditions and waveform quality requirements encountered during inverter operation, the possible range of PWF variation during the inverter operation needs to be determined. This range should be wide enough to cover all possible situations, while avoiding being too large to increase control complexity and computational effort. In-depth analysis of the load characteristics of the target application is performed, including both static and dynamic characteristics. Static characteristics refer to the load's electrical characteristics in a steady state, such as resistance, inductance, and capacitance. Dynamic characteristics refer to the load's response characteristics during a changing state, such as starting current and transient response. These analysis results provide an important basis for developing a PWF regulation strategy. Based on load characteristics and waveform quality requirements, a refined PWF regulation strategy should be designed. This strategy should include the timing, method, and amplitude of regulation. The strategy should determine when to perform PWF regulation. This is typically associated with events such as load changes, system stability assessments, or output voltage waveform quality testing. An appropriate regulation method should be selected, such as continuous regulation, step regulation, or algorithm-based intelligent regulation. The regulation method should be able to quickly respond to load changes while maintaining system stability. The PWF regulation amplitude should be determined based on the magnitude of the load change and the stringency of the waveform quality requirements. The amplitude should be moderate, meeting waveform quality requirements while avoiding system impacts caused by excessive regulation.

[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 application requirements, the inverter is ensured to meet the requirements of specific application scenarios; the initial value of the initialization phase weighting factor (PWF) is preset based on the expert knowledge base, so that the inverter can have a more reasonable initial working state during the startup phase, thereby improving the adaptability and response speed of the system; the possible variation range of the PWF during the inverter process is determined, and all possible situations under different load conditions and waveform quality requirements are covered, ensuring that the inverter can maintain a stable output voltage waveform when facing complex and changing load environments; in-depth analysis of the load characteristics in the target application, including static characteristics and dynamic change characteristics, provides a scientific basis for the formulation of the PWF adjustment strategy, and further enhances the stability and reliability of the system; a sophisticated PWF adjustment strategy is designed, 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 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; because the system has high stability and reliability, it reduces losses and costs caused by fault downtime or maintenance; refined PWF regulation strategies also help to reduce potential damage to the power grid and equipment due to poor waveform quality, further reducing maintenance costs; high-quality output voltage waveforms and stable power supply performance provide users with a better power experience; especially in application scenarios with high requirements for power quality (such as precision instruments, medical equipment, etc.), this improvement is particularly obvious.

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

[0087] S21. Select a PWI modulation control algorithm from the control system of the inverter, the control algorithm including a PWI modulation algorithm and a feedback control algorithm, and load it into the controller;

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

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

[0090] The working principle of the above technical solution is as follows: a control algorithm suitable for PWI modulation is selected from the inverter control system. These algorithms typically include a PWI modulation algorithm and a feedback control algorithm. The PWI modulation algorithm generates the target modulation waveform based on the phase weighting factor (PWF), while the feedback control algorithm monitors and adjusts the inverter output in real time to ensure its stability and accuracy. These algorithms are loaded into the inverter controller, such as a digital signal processor (DSP) or microcontroller (MCU), for execution during the inverter process. Based on the preset output voltage and frequency, the waveform generation function within the DSP or MCU is used to generate a standard sine wave as the inverter's reference waveform. This reference waveform serves as a reference for the inverter's output voltage 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 design requirements. Waveform distortion refers to the degree of difference between the actual waveform and the ideal waveform, which affects the quality of the inverter's output voltage waveform. Phase error refers to the phase difference between the actual waveform and the reference waveform, which affects the synchronization of the inverter's output voltage with the grid or other devices. Through quality verification, problems in the reference waveform can be discovered and corrected in a timely manner, ensuring the normal operation of the inverter and the accuracy of the output voltage.

[0091] The above technical solution achieves the following: By selecting and loading the PWI modulation algorithm and feedback control algorithm into the controller, the inverter can more precisely control the output voltage waveform and parameters. The PWI modulation algorithm generates the target modulation waveform based on the phase weighting factor (PWF), while the feedback control algorithm monitors and adjusts the inverter output in real time to ensure it remains consistent with preset values. This dual control mechanism significantly improves the inverter's control accuracy. The application of the feedback control algorithm enables the inverter to quickly respond to load changes and external disturbances, maintaining system stability by adjusting output parameters. Simultaneously, the reference waveform is quality-checked to ensure that parameters such as waveform distortion and phase error meet design requirements, further enhancing system stability and reliability. Using a high-precision controller such as a DSP or MCU to generate a standard sine wave as the reference waveform and ensuring its accuracy through quality verification helps optimize the inverter's output voltage waveform quality. A low-distortion, low-phase-error reference waveform reduces waveform distortion and harmonic content during the inverter process, improving 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 operating conditions. High-precision control algorithms and real-time feedback adjustment mechanisms reduce the risk of inverter failure and damage due to poor output waveform quality. This helps reduce inverter maintenance costs and downtime. The optimized output voltage waveform quality and stable power supply performance provide users with a better power experience. This improvement is particularly evident in application scenarios with high requirements for power quality (such as precision instruments and medical equipment). 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. Preliminarily set an initial value of the PWF based on the expert knowledge base according to the design specifications, load characteristics, and expected performance targets of the inverter. Automatically adjust the PWF value based on 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, and optimization algorithms (such as genetic algorithms and particle swarm optimization).

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

[0095] S33. During the PWM signal generation process, suppressing edge effects through appropriate measures, wherein the appropriate measures include soft switching technology and a buffer circuit;

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

[0097] S35. Introduce complex judgment logic into the feedback control mechanism. For example, multiple thresholds can be set to determine the degree 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 changing trends of the inverter's operating status can also be considered to predict future performance changes and take preemptive measures. Potential abnormalities can be detected by real-time monitoring of the inverter's operating status and performance indicators. For example, a sudden increase or decrease in output voltage or a sharp increase in current harmonics may indicate an inverter abnormality. After detecting an abnormality, a fault diagnosis algorithm can be used to quickly and accurately diagnose the inverter fault.

[0098] The working principle of the above technical solution is as follows: Based on the inverter's design specifications, load characteristics, and expected performance targets, the initial value of the PWF is preliminarily set based on an expert knowledge base. A preset PWF dynamic adjustment strategy is used to automatically adjust the PWF value based on the inverter's real-time operating parameters (such as output voltage, current, and temperature) and performance indicators (such as efficiency, harmonic content, and stability). Adjustment strategies can include rule-based adjustment, fuzzy logic control, and optimization algorithms (such as genetic algorithms and particle swarm optimization) to achieve precise control of inverter performance. The appropriate PWM modulation method is selected based on the inverter's specific requirements and application scenarios. In addition to traditional sinusoidal PWM (SPWM), advanced modulation methods such as space vector PWM (SVPWM) and specific harmonic elimination PWM (SHEPWM) can also be considered to improve inverter performance and efficiency. The PWF value is multiplied by a reference waveform (such as a sine wave) to obtain the modulated waveform. Then, based on the selected PWM modulation method, the modulated waveform is converted into a PWM signal to control the inverter's switching elements. During the PWM signal generation process, appropriate measures (such as soft switching technology and snubber circuits) are used to suppress edge effects and reduce losses and electromagnetic interference during the switching process. Inverter performance indicators such as output voltage waveform quality (THD, voltage fluctuation rate, etc.), inverter efficiency, and load response speed are monitored in real time. A feedback control mechanism is established to compare the monitored performance indicators with preset target values. If the performance indicators deviate from the target values, the PWF value or PWM modulation parameters are automatically adjusted based on the degree of deviation to restore the inverter's normal performance. Complex judgment logic is introduced into the feedback control mechanism, such as setting multiple thresholds to judge the different degrees of deviation of performance indicators. Intelligent algorithms such as fuzzy logic or neural networks are used to simulate the human decision-making process, considering historical data and changing trends of the inverter's operating status to predict future performance changes and take preemptive measures. Potential abnormalities are detected by real-time monitoring of the inverter's operating status and performance indicators. For example, a sudden increase or decrease in output voltage or a sharp rise in current harmonics could indicate an inverter anomaly. Upon detecting an anomaly, the fault diagnosis algorithm quickly and accurately diagnoses the inverter. Once an inverter fault is confirmed, emergency measures are immediately implemented to prevent further escalation and protect the inverter. These measures can include shutting off the inverter's power output, switching to a backup power source, or activating a fault alarm system.

[0099] The effects of the above technical solution are as follows: by dynamically adjusting the PWF value, the inverter can automatically optimize its operating state according to real-time operating parameters and performance indicators, thereby improving the inverter's efficiency, reducing harmonic content, enhancing stability and other key performance indicators; selecting appropriate PWM modulation methods, 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; this technical solution can flexibly adjust the parameters of the 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 the 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 inverter's performance indicators and establishing a feedback control mechanism can promptly detect and correct system deviations to maintain stable operation of the inverter. At the same time, by incorporating complex judgment logic and intelligent algorithms, it is possible to more accurately predict and respond to system changes, improving system stability and reliability. The incorporation of anomaly detection within the feedback control mechanism enables real-time monitoring of the inverter's operating status and performance indicators, promptly identifying potential anomalies. Once a fault is detected, a fault diagnosis algorithm is used to quickly and accurately diagnose it, and emergency measures are immediately implemented to prevent further escalation and protect the inverter. This helps reduce system downtime and maintenance costs, improving system availability and cost-effectiveness. By employing 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 uncertainties, enhancing the system's intelligence and adaptability.

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

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

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

[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 between different levels of deviation using pre-set multi-level thresholds for each performance indicator. For example, for output voltage THD, set warning and fault thresholds to trigger different adjustment measures. Use time series analysis to study historical data of performance indicators and predict future trends. When a threshold is predicted to be exceeded, preventive measures are taken in advance.

[0105] S345. Combining the evaluation results of multiple performance indicators, a fuzzy logic algorithm is 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 indicators. Algorithms such as gradient descent and particle swarm optimization are used to achieve fine-tuning of the 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 as follows: construct a comprehensive set of inverter performance indicators, 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. This data provides the basis for subsequent performance evaluation and adjustments. The collected raw data undergoes preprocessing, including denoising, filtering, and outlier removal, to reduce the impact of noise and interference on performance evaluation. Digital signal processing (DSP) techniques, such as sliding average, median filtering, and Kalman filtering, are used to smooth the signal and improve data accuracy and reliability. Based on the preprocessed data, the current values of various performance indicators are calculated in real time. The calculated performance indicator values are compared with preset target values or thresholds to determine whether the inverter is operating normally or requires adjustment. Multiple thresholds are set to distinguish between different levels of deviation. For example, for output voltage THD, warning and fault thresholds are set to trigger different adjustment measures. Time series analysis is used to learn from historical performance indicator data and predict future trends. When a threshold is predicted to be exceeded, preventive measures are taken to avoid system performance deterioration. Fuzzy logic algorithms are used to make comprehensive decisions based on the evaluation results of multiple performance indicators to determine the optimal adjustment strategy. The PWF value is automatically adjusted based on the degree of deviation of the performance indicator. Algorithms such as gradient descent and particle swarm optimization are used to fine-tune the PWF to minimize performance deviations. PWM modulation parameters (such as carrier frequency and modulation ratio) are dynamically adjusted while maintaining the quality of the output voltage waveform. When a single adjustment strategy fails to meet performance requirements, a coordinated approach using multiple strategies is employed. For example, PWF and PWM modulation parameters can be adjusted simultaneously to achieve comprehensive optimization of performance indicators.

[0108] The above technical solution achieves the following: By building a comprehensive set of inverter performance indicators, it is possible to monitor multiple key inverter performance indicators 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 provide a comprehensive understanding of the inverter's operating status and performance. High-speed ADCs are used for voltage and current sampling, combined with high-precision thermometers and spectrum analyzers, to ensure the accuracy and reliability of data acquisition. Furthermore, data preprocessing techniques (such as denoising, filtering, and outlier removal) and digital signal processing (DSP) technologies further improve data accuracy and reliability, 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 preset target values or thresholds, enabling timely detection of inverter performance deviations. Multi-level threshold settings can distinguish between different levels of deviation and trigger appropriate adjustment measures. Furthermore, by using time series analysis to predict future trends, preventive measures can be taken before problems occur to avoid performance deterioration. Combining the evaluation results of multiple performance indicators and using fuzzy logic algorithms for comprehensive decision-making, the optimal adjustment strategy can be determined. This intelligent decision-making approach can mimic the human decision-making process and handle complex nonlinear problems and uncertainties. Furthermore, algorithms such as gradient descent and particle swarm optimization are used to fine-tune the PWF, minimizing performance deviations and improving inverter efficiency and stability. PWM modulation parameters (such as carrier frequency and modulation ratio) are dynamically adjusted while maintaining output voltage waveform quality. When a single adjustment strategy fails to meet performance requirements, a multi-strategy coordinated approach is employed, such as simultaneous adjustment of PWF and PWM modulation parameters. This multi-strategy coordinated adjustment approach can more comprehensively optimize inverter performance indicators and improve overall system performance. By real-time monitoring and dynamic adjustment of inverter performance indicators, system deviations can be promptly detected and corrected, maintaining stable inverter operation. Furthermore, intelligent comprehensive decision-making and multi-strategy coordinated adjustment can further improve system reliability and stability, reducing the likelihood of failures. Early warning and intelligent adjustments can reduce inverter downtime and repair costs caused by performance issues. Furthermore, optimizing inverter efficiency and performance can improve system economic benefits and energy utilization.

[0109] In one embodiment of the present invention, the step S345 includes:

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

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

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

[0113] For performance indicators that require continuous adjustment (such as THD), the gradient descent method is used to calculate the adjustment direction and step size of the PWF. Through iterative calculation, the optimal PWF value is gradually approached to minimize 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] Dynamically adjust PWM modulation parameters based on the inverter's real-time operating status and performance indicator changes. At the same time, establish a feedback mechanism to input the adjusted performance indicators back into the comprehensive decision-making system for a new round of evaluation and adjustment.

[0116] Prioritize different adjustment strategies based on the importance of performance indicators and the effect of adjustments. When multiple parameters need to be adjusted simultaneously, execute the adjustment measures in order of priority. Evaluate the overall effect of the coordinated adjustment 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, each performance indicator is assigned a reasonable weight to reflect its importance to the overall performance of the inverter. Using fuzzy set theory, the current value of each performance indicator is mapped to a fuzzy set and its membership function is defined. The membership function describes the degree to which the performance indicator value belongs to a fuzzy set, thereby achieving fuzzification of the performance indicators. Based on expert knowledge and historical data, a fuzzy rule base is constructed. Each rule defines the adjustment measures to be taken for 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, which then performs inference based on the fuzzy rule base. The inference process includes matching rules, calculating the rule activation degree (i.e., the degree to which the rules are satisfied), and aggregating the rule outputs. Finally, the outputs of all rules are combined to obtain a comprehensive decision result. For performance indicators that require continuous adjustment (such as THD), the gradient descent method is used to calculate the PWF adjustment direction and step size. Through iterative calculations, the optimal PWF value is gradually approached to minimize performance deviations. The global search capabilities of the particle swarm optimization algorithm complement the local search of the gradient descent method. The particle swarm optimization algorithm accelerates convergence to the global optimal solution through information sharing and collaboration among particles. While maintaining the quality of the output voltage waveform, the feasible space of PWM modulation parameters is defined. Optimization algorithms (such as genetic algorithms and simulated annealing) are used to search within the parameter space to find the optimal parameter combination. PWM modulation parameters are dynamically adjusted based on the real-time operating status and performance changes of the inverter. A feedback mechanism is established to input the adjusted performance indicators back into the integrated decision-making system for a new round of evaluation and adjustment. This ensures that the system can continuously adapt to changes in inverter performance. Priorities are set for different adjustment strategies. When multiple parameters need to be adjusted simultaneously, adjustment measures are implemented sequentially according to priority. The overall effect of the coordinated adjustment strategies is evaluated, and the effectiveness and superiority of the coordinated adjustment are verified by comparing the performance changes before and after the adjustments. During the adjustment process, various inverter performance indicators are continuously monitored. Real-time data analysis techniques are used to quickly respond to performance changes and ensure the effectiveness of adjustment measures. The feedback mechanism is continuously optimized 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 integrated decision-making system.

[0119] The above technical solution achieves the following: By assigning appropriate weights to each performance indicator and employing fuzzy set theory for fuzzification, it comprehensively considers multiple inverter performance indicators and flexibly addresses the complex relationships between them. A fuzzy rule base built based on expert knowledge and historical data enables the system to automatically infer appropriate adjustment measures based on different combinations of performance indicators. This intelligent decision-making capability improves the system's automation level and responsiveness. For performance indicators that require continuous adjustment, a gradient descent method combined with a particle swarm optimization algorithm is used for fine-tuning. Gradient descent ensures local search accuracy, while the particle swarm optimization algorithm provides global search capabilities. This combination accelerates convergence to the global optimal solution, minimizing performance deviations. The system dynamically adjusts PWM modulation parameters based on the inverter's real-time operating status and performance indicator changes, and continuously evaluates and adjusts the adjustment results through a feedback mechanism. This dynamic adaptability ensures that the system maintains optimal operating conditions. Priorities are set for different adjustment strategies, and adjustment measures are executed sequentially according to priority when necessary. This coordinated strategy mechanism ensures that when multiple parameters require simultaneous adjustment, the system can execute adjustment tasks in an orderly and efficient manner. The effectiveness and superiority of coordinated adjustment were verified by evaluating the overall effect of the coordinated strategy adjustments. During the adjustment process, various inverter performance indicators were continuously monitored, and real-time data analysis technology was used to rapidly respond to performance changes. This real-time monitoring capability ensured the effectiveness of adjustment measures and enabled the timely identification and resolution of potential issues. Furthermore, based on real-time performance monitoring results, feedback mechanisms, threshold settings, fuzzy rule bases, and algorithm parameters were continuously optimized, enhancing the accuracy and robustness of the integrated decision-making system. Through the combined application of these technical approaches, the S445 solution significantly improved the operational stability and reliability of the inverter. It automatically adjusted parameters under different operating conditions to optimize performance indicators, reducing the likelihood of failures and extending the service life of the equipment. Because the system automatically monitored and adjusted the inverter's operating status, manual intervention and maintenance downtime were reduced. This not only reduced maintenance costs but also improved equipment operational efficiency and economic benefits. Furthermore, by optimizing inverter performance, energy utilization and emission reduction efforts were also improved.

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

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

[0122] S42. Based on the specific requirements of the inverter, multi-level PWM modulation technology, including space vector PWM (SVPWM) and specific harmonic elimination PWM (SHEPWM), 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 load change trends. The prediction results can be used to adjust the PWF value or feedback control loop parameters in advance to cope with upcoming load changes. Set multiple thresholds to judge different operating states of the inverter. For example, different thresholds can be 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 simultaneously, prioritize them based on their impact on inverter performance and urgency; prioritize the adjustment strategy that has the greatest impact on inverter performance or is the most urgent. When adjusting the PWF value or feedback control loop parameters, use a stepwise approach, first making small adjustments and observing the effect. If the expected target is not achieved, gradually increase the adjustment range based on the effect until the performance requirements are met.

[0125] S45. Filter the feedback signal and use an adaptive control algorithm to optimize the feedback control loop. Perform stability analysis on the feedback control loop. If any unstable factors are found in the control loop, take timely measures to adjust and optimize it.

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

[0127] S47. After detecting an abnormality, use a fault diagnosis algorithm to quickly and accurately diagnose the inverter fault, and at the same time, take 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 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 and current harmonic content Multiple thresholds are set to judge the 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 the degree of impact and urgency on the inverter performance; when adjusting the PWF value or feedback control loop parameters, a step-by-step approximation method is adopted, first making small-scale adjustments and observing the effects. If the expected target is 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; a stability analysis of the feedback control loop is performed 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 is found (such as overcurrent, overvoltage, overheating, etc.), 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 above technical solution achieves the following benefits: By introducing dead-time control and switch drive delay compensation into the PWM signal generation process, signal distortion and errors caused by simultaneous switch conduction or drive delays are effectively reduced, thereby improving the accuracy and reliability of the PWM signal. This is crucial for stable inverter operation and performance optimization. Multi-level PWM modulation techniques (such as SVPWM and SHEPWM) enable precise control of the output voltage waveform based on the specific needs of the inverter, reducing harmonic content and improving waveform quality. These modulation techniques also optimize the inverter's switching strategy, reducing switching losses and improving inverter efficiency. By leveraging historical data and machine learning algorithms to predict load trends, the system can proactively adjust the PWF value or feedback control loop parameters to address upcoming load changes. This prediction and adjustment mechanism enhances the inverter's load adaptability, enabling it to maintain stable performance and efficient operation under varying load conditions. For multiple adjustment strategies that may be triggered simultaneously, the system prioritizes them based on their impact on inverter performance and urgency, prioritizing the strategy with the greatest performance impact or the most urgent need. This intelligent strategy adjustment mechanism ensures the inverter can respond quickly and accurately under complex operating conditions. Feedback signals are filtered to eliminate noise and interference, improving their accuracy and reliability. Furthermore, an adaptive control algorithm is employed to optimize the feedback control loop, enabling real-time monitoring of the inverter's operating status and performance changes, and automatically adjusting control parameters and strategies to achieve optimal control results. Furthermore, stability analysis of the feedback control loop ensures stable operation under various operating conditions. A comprehensive anomaly detection mechanism monitors and analyzes the inverter's operating status in real time, immediately triggering action upon detection of an anomaly. Furthermore, a fault diagnosis algorithm is employed to quickly and accurately diagnose inverter faults and implement isolation measures to prevent the spread of the fault. This mechanism ensures that inverter faults can be quickly addressed and restored to normal operation. Emergency response plans are developed based on the fault type and severity, providing clear guidance and support for inverter fault handling. This helps minimize the impact of faults on the system and lowers maintenance costs.

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

[0132] Based on basic current and voltage monitoring, key parameters are monitored in real time, including power factor, load impedance, temperature change, input voltage stability, and system frequency fluctuation. The collected raw data is preprocessed to extract key indicators that can reflect the characteristics of load changes, such as time series changes in load power, fluctuation patterns of load impedance, and temperature change trends.

[0133] Use machine learning algorithms (such as long short-term memory networks (LSTMs), support vector machines (SVMs), and random forests) to model the extracted features. By training the model, the inherent patterns and trends of load changes are learned, and cross-validation is used to tune the prediction model.

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

[0135] Monitor the 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 as follows: In addition to basic current and voltage monitoring, real-time monitoring of key parameters such as power factor, load impedance, temperature variation, input voltage stability, and system frequency fluctuation is added. These parameters can comprehensively reflect the inverter's operating environment and load status. The collected raw data is preprocessed, including data cleaning (removing noise and outliers), data conversion (such as normalization and standardization), and data compression to improve the efficiency and accuracy of subsequent processing. Key indicators that reflect load variation characteristics are extracted from the preprocessed data, such as time series changes in load power, fluctuation patterns in load impedance, and temperature trends. These characteristic indicators form the basis for subsequent model prediction. Machine learning algorithms (such as long short-term memory networks (LSTMs), support vector machines (SVMs), and random forests) are used to model the extracted features. These algorithms have strong nonlinear fitting and generalization capabilities, capable of learning the inherent patterns and trends of load variations. By training the model, it can accurately predict future load changes. During the training process, the model can be fine-tuned using techniques such as cross-validation to improve its prediction accuracy and generalization capabilities. Prediction results are quantitatively evaluated, including prediction error and confidence intervals. These evaluation metrics reflect the model's predictive performance and provide a basis for subsequent model adjustments. Based on the prediction results and the inverter's performance requirements, multiple thresholds can be set to determine different inverter operating states. For example, different thresholds can be set based on factors such as the output voltage fluctuation range and current harmonic content. Inverter performance indicators such as output voltage, current, and temperature are monitored in real time and compared with set thresholds. If a parameter is detected to be outside the threshold range, appropriate adjustment strategies are triggered. These adjustment strategies may include adjusting the PWF value, optimizing feedback control loop parameters, switching operating modes, and other factors to address the impact of load changes on inverter performance.

[0137] The above technical solution achieves the following: By real-time monitoring of key parameters such as power factor, load impedance, temperature variation, input voltage stability, and system frequency fluctuations, and combining them with advanced machine learning algorithms (such as LSTM, SVM, and random forest), this solution can more accurately predict the inherent patterns and trends of load fluctuations. This high-precision prediction helps the inverter prepare for possible load fluctuations, thereby improving system stability and reliability. By extracting and modeling load variation characteristics, the system can better understand the dynamic characteristics of the load and adjust the control strategy accordingly. This enables the inverter to adapt to operating requirements under different load conditions, improving the system's adaptability and flexibility. Based on the prediction results and the inverter's performance requirements, multiple thresholds are set to determine different inverter operating states. This threshold setting mechanism enables the system to monitor inverter performance indicators in real time and immediately trigger appropriate adjustment strategies if relevant parameters exceed the threshold range. This immediate feedback and adjustment helps optimize inverter performance parameters such as output voltage quality and current harmonic content, improving the overall operating efficiency of the system. By real-time monitoring and prediction of load changes, the system can proactively identify potential risks and problems and take appropriate preventive measures. This forward-looking control strategy helps reduce the probability of system failures and improves system stability and reliability. This technical solution integrates machine learning and real-time monitoring technologies to achieve intelligent inverter operation and maintenance. The system automatically learns patterns in load fluctuations and adjusts control strategies accordingly, reducing the need for manual intervention. This not only improves operation and maintenance efficiency but also reduces costs. By precisely controlling and optimizing the inverter's operating status, this technical solution helps reduce energy waste and emissions. For example, it automatically adjusts the inverter's output power when the load is low, avoiding unnecessary energy consumption. When a system fault is detected, timely measures are taken to prevent further energy losses and environmental pollution from escalating.

[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 feedback control loop parameters, use the method of gradual approximation, 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 will be set to prevent excessive adjustment from causing system instability.

[0143] The working principle of the above technical solution is as follows: When the inverter detects a situation requiring adjustment, multiple adjustment strategies may be triggered simultaneously. To effectively respond, the system first prioritizes these strategies based on their impact on inverter performance and urgency. Strategies with greater impact and higher urgency are given higher priority. Based on this prioritization, 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 constructs a hierarchical model to decompose complex decision-making factors into their constituent components. It then determines the relative importance of each factor through pairwise comparison, ultimately calculating ranking weights that reflect the strengths and weaknesses of each strategy. The system then selects the optimal adjustment strategy based on these weights. After determining the optimal adjustment strategy, it uses a successive approximation method to adjust the PWF value or feedback control loop parameters. The system first performs small adjustments and monitors the inverter performance changes in real time. After the adjustments are made, the system observes whether the inverter's performance indicators have improved and whether the degree of improvement meets the expected target. 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 will be required; if the initial small-scale adjustment does not achieve the expected goal, the system will gradually increase the adjustment range based on the adjustment effect. During this process, the system will continuously monitor the performance indicators of the inverter and dynamically adjust the adjustment range based on the monitoring results to ensure that the adjustment process is neither too radical nor too conservative; to prevent system instability due to excessive adjustment range during the adjustment process, the system will set upper and lower limits for 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 a backup control strategy, to prevent the system from being damaged by excessive adjustment.

[0144] The above technical solution achieves the following: By prioritizing multiple adjustment strategies that may be triggered simultaneously, the S44 solution ensures that the most urgent or impactful adjustments to inverter performance are prioritized in resource-limited or time-sensitive situations. This prioritization mechanism helps optimize the decision-making process and improve decision-making efficiency. By using the Analytic Hierarchy Process (AHP) to comprehensively consider the advantages and disadvantages of different strategies and the current inverter status, the S44 solution can more comprehensively evaluate the applicability of each adjustment strategy. By combining quantitative and qualitative analysis, the AHP method provides a scientific basis for selecting the optimal adjustment strategy, thereby improving decision-making accuracy and reliability. By employing a stepwise approximation approach when adjusting the PWF value or feedback control loop parameters, the S44 solution avoids the risk of system instability that may be caused by large, one-time adjustments. By making small adjustments, observing the effects, and then gradually increasing the adjustments, the system can transition more smoothly to the new operating state, thereby enhancing system stability and reliability. Setting upper and lower adjustment limits is a key feature of the S44 solution. These limits help prevent system instability or performance degradation caused by excessive adjustments during the adjustment process. By limiting the adjustment range, the system can ensure that performance requirements are met while avoiding unnecessary damage to the system. Because the S44 technical solution can more accurately predict and respond to various issues in inverter operation, it can reduce the inconvenience caused to users by system failures or performance degradation. At the same time, by optimizing adjustment strategies and processes, the system can recover to its optimal operating state more quickly, thereby improving the overall user experience. The S44 technical solution integrates multiple intelligent technologies such as priority sorting, hierarchical analysis method, and stepwise 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 inverters, but also provides strong support for the construction of future smart grids and energy management systems.

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

[0146] S51. Analyze the inverter output waveform in detail using spectrum analysis technology (such as FFT) to identify the main harmonic components and their sources;

[0147] S52. Based on the results of harmonic source identification, formulate a targeted harmonic suppression strategy, wherein the suppression strategy includes adjusting the waveform shape of the PWF (e.g., 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 resonance points);

[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 content;

[0149] S54. After implementing the harmonic suppression strategy, use spectrum analysis technology to evaluate the inverter output waveform again to verify whether the harmonic suppression effect has achieved the expected goal; if not, adjust the harmonic suppression strategy or PWF optimization algorithm based on 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, and system stability; 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 poor, it may be necessary to further refine the PWF regulation 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 inverter's operating status and performance indicator changes in real time through an adaptive optimization mechanism, and automatically adjust the optimization strategy based on preset rules or algorithms; use big data analysis technology to conduct in-depth analysis and mining of massive data during the inverter's operation;

[0152] S57. Establish a simulation model of the inverter and perform simulation verification on different optimization strategies; predict the impact of different strategies on inverter performance based on the simulation results;

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

[0154] The working principle of the above technical solution is as follows: Spectral analysis technology (such as fast Fourier transform (FFT)) is used to perform a detailed analysis of the inverter's output waveform to identify the main harmonic components and their sources. FFT technology can convert time-domain signals into frequency-domain signals, clearly displaying the amplitude and phase of each frequency component, facilitating harmonic identification. Based on the results of harmonic source identification, targeted harmonic suppression strategies are formulated. These strategies may include adjusting the PWF waveform shape (such as introducing nonlinear factors to offset specific harmonics), phase shifting (reducing harmonic superposition by precisely controlling the phase of the PWM signal), and frequency modulation (dynamically adjusting the inverter's output frequency to avoid resonant points and reduce harmonic resonance). The harmonic content in the inverter output waveform is monitored in real time, and the PWF value is dynamically adjusted using a PWF optimization algorithm based on feedback control. Based on real-time feedback on the harmonic content, the algorithm automatically adjusts the PWF parameters to minimize the harmonic content. This closed-loop control mechanism ensures consistently high quality of the inverter's output waveform. After implementing the harmonic suppression strategy, spectrum analysis is used to evaluate the inverter output waveform to verify whether the harmonic suppression effect meets expectations. If not, the harmonic suppression strategy or PWF optimization algorithm is adjusted based on the evaluation results until it meets the requirements. A comprehensive performance evaluation system is established to quantitatively assess multiple key performance indicators of the inverter. Based on the evaluation results, each indicator is prioritized, and specialized optimization strategies are designed for high-priority indicators. These strategies may involve optimizing the PWM signal generation algorithm, adjusting the inverter switching frequency, refining the PWF regulation strategy, or introducing more advanced harmonic suppression technologies. The optimization strategy is verified and adjusted in actual testing until all performance indicators meet design requirements. An adaptive optimization mechanism monitors the inverter's operating status and performance indicator changes in real time and automatically adjusts the optimization strategy based on pre-set rules or algorithms. Big data analytics technologies are used to deeply analyze and mine the massive amounts of data generated during inverter operation to identify potential performance improvements and directions. This mechanism ensures the inverter maintains optimal operating conditions under various operating conditions. An inverter simulation model is established to simulate and verify different optimization strategies. The simulation results predict the impact of different strategies on inverter performance, providing a theoretical basis and reference for actual optimization. An inverter optimization knowledge base and experience database are established to record key data, strategies, and results from each optimization process. This data and experience provide valuable reference and inspiration for subsequent optimization efforts, helping to improve optimization efficiency and effectiveness.

[0155] The above technical solution achieves the following: Detailed analysis of the inverter output waveform using spectrum analysis techniques (such as FFT) allows precise identification of major harmonic components and their sources. This provides reliable data support for developing targeted harmonic mitigation strategies, helping to reduce the adverse effects of harmonics on the grid and loads. Based on the results of harmonic source identification, harmonic mitigation strategies are developed, including adjustments to the PWF waveform shape, phase offset, and frequency modulation. These strategies effectively suppress harmonic generation, improve the quality of the inverter output waveform, and reduce total harmonic distortion (THD) and voltage fluctuation. The harmonic content of the inverter output waveform is monitored in real time, and a PWF optimization algorithm based on feedback control dynamically adjusts the PWF value to minimize harmonic content. This closed-loop control mechanism ensures the inverter maintains optimal operating conditions under various operating conditions, improving system stability and reliability. A comprehensive performance evaluation system is established to quantitatively assess multiple key performance indicators of the inverter and design specialized optimization strategies based on the evaluation results. This comprehensive evaluation and optimization approach ensures that all inverter performance indicators meet design requirements, improving overall performance. An adaptive optimization mechanism monitors the inverter's operating status and performance changes in real time, automatically adjusting the optimization strategy based on pre-set rules or algorithms. Furthermore, big data analytics techniques are used to deeply analyze and mine the massive amounts of data generated during inverter operation, identifying potential performance enhancements and areas for improvement. This adaptive and intelligent optimization approach can further improve the inverter's operating efficiency and performance stability. An inverter simulation model is established to simulate and validate different optimization strategies and predict their impact on inverter performance. This helps evaluate the feasibility and effectiveness of strategies before actual implementation, reducing trial-and-error costs. Furthermore, an inverter optimization knowledge base and experience database are established to record key data, strategies, and results from each optimization process, providing valuable reference and learning for subsequent optimization efforts. Through the implementation of this technical solution, key performance indicators such as the inverter's output waveform quality, inverter efficiency, and load response speed have been significantly improved. This not only improves the user experience but also enhances system reliability and stability, reducing failure rates and maintenance costs.

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

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

[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 ensure that the simulation results are highly consistent with the actual operation data;

[0159] The optimization strategies to be verified (such as PWF waveform adjustment, PWM phase offset, frequency modulation, etc.) are parameterized, and the specific implementation methods and adjustable parameter ranges of each strategy are clearly defined. Based on the actual operating environment of the inverter, multiple simulation experiment scenarios are set up, including different load types, load variations, 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, recording 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 the simulation data to extract the changing trends and statistical characteristics of key performance indicators; By comparing the simulation results under different optimization strategies, evaluate the impact of each strategy on inverter performance;

[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 results to evaluate the accuracy and reliability of the prediction model; based on the evaluation results, tune and improve the prediction model;

[0163] Based on the simulation results and prediction model, optimization strategies that significantly improve inverter performance and have low implementation costs are screened out. The screened optimization strategies are applied to the inverter control system and actually tested. Based on 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 appropriate 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; by adjusting the model parameters and edge The simulation results are highly consistent with the actual operating data, improving the model's prediction accuracy. The optimization strategies to be verified (such as PWF waveform adjustment, PWM phase offset, and frequency modulation) are parameterized to clarify the specific implementation methods and adjustable parameter ranges of each strategy. Based on the actual operating environment of the inverter, various simulation experiment scenarios are set up, including different load types, load changes, input voltage fluctuations, and temperature changes, 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 during the simulation process, such as output voltage waveform, current waveform, harmonic content, inverter efficiency, and load response speed, are recorded. Data analysis tools are used to process and analyze the simulation data, extracting the changing trends and statistical characteristics of key performance indicators. By comparing the simulation results under different optimization strategies, the impact of each strategy on inverter performance is evaluated, and it is determined which strategies have a significant effect on performance improvement. Based on the simulation data, an inverter performance prediction model is constructed. Using machine learning or statistical learning methods, a mapping relationship between performance indicators and optimization strategy parameters is established to achieve rapid performance prediction. The prediction results are compared with simulation results to verify the accuracy and reliability of the prediction model. Based on the evaluation results, the prediction model is tuned and improved. Based on the simulation results and the prediction model, optimization strategies that significantly improve inverter performance and have low implementation costs are selected. The selected optimization strategies are applied to the inverter control system and tested in practice. Based on the test results and performance evaluation results, the optimization strategy is iterated and adjusted until all performance indicators meet the design requirements.

[0165] The above technical solution achieves the following: By customizing a detailed simulation model of the inverter and validating and calibrating it using actual operating data or standard test data, the accuracy and reliability of the simulation results are ensured. This helps identify potential issues encountered by the inverter during actual operation during the simulation phase, allowing for early optimization and adjustment. A variety of simulation experiment scenarios are set up, including different load types, load variations, input voltage fluctuations, and temperature changes, to comprehensively evaluate the performance of the optimization strategy under different conditions. This comprehensive evaluation approach ensures that the optimization strategy has better adaptability and stability in actual applications. The optimization strategies to be verified are parameterized, and based on simulation data and prediction models, optimization strategies that significantly improve inverter performance and have low implementation costs are selected. This approach significantly improves the efficiency of optimization strategy screening, reduces trial-and-error costs, and quickly finds the optimal solution. An inverter performance prediction model is constructed, and machine learning or statistical learning methods are used to establish a mapping between performance indicators and optimization strategy parameters, enabling rapid performance prediction. This helps predict performance before implementing optimization strategies, reducing the number and time of actual testing and accelerating iteration and adjustment during the optimization process. Through simulation verification and actual testing, the inverter control strategy can be continuously optimized to reduce harmonic content, improve output voltage waveform quality, enhance inverter efficiency, and accelerate load response. These performance improvements will directly improve the overall performance and reliability of the inverter, reducing failure rates and maintenance costs. The simulation model can simulate inverter operation under different operating conditions, helping engineers proactively identify and resolve potential issues. This helps enhance inverter system stability and reduce downtime and losses caused by system instability. Through simulation verification and optimization strategy screening, inverter performance can be optimized and adjusted multiple times without increasing actual hardware investment. This significantly reduces development costs and shortens product time-to-market. The establishment of simulation models and the research on optimization strategies provide strong support for inverter technological innovation. Engineers can experiment with new control algorithms and optimization strategies in the simulation environment, driving the continuous advancement and development of inverter technology.

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

[0167] S61. Real-time monitoring of the inverter's operating status, including key parameters such as temperature, current, and voltage, to ensure these parameters fluctuate within the permitted safety range. Through anomaly detection algorithms, rapid analysis of monitored data can be performed to identify potential faults or abnormalities.

[0168] S62. If an abnormality or fault is detected, immediately activate a corresponding protection mechanism, which includes power off, alarm prompt, and automatic restart;

[0169] S63. Record the information of the fault, including the time, type and cause, analyze the pattern 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 uses sensors and monitoring equipment to collect real-time operating status data from the inverter, including key parameters such as temperature, current, and voltage. This data is an important basis for assessing the health and operational safety of the inverter. The collected data is fed into a data processing unit for rapid analysis using predefined anomaly detection algorithms. These algorithms can identify abnormal patterns in the data or values outside of preset safety ranges, thereby determining whether the inverter has potential faults or abnormal conditions. When the anomaly detection algorithm detects abnormal operating data or a fault, it immediately issues a warning signal. Based on the type and severity of the warning signal, the system automatically triggers appropriate protection mechanisms. These protection mechanisms may include powering off to prevent further damage, issuing an alarm to notify maintenance personnel, and attempting to automatically restart the inverter to restore normal operation. When a protection mechanism is triggered, the system prioritizes the action based on the severity and urgency of the fault, ensuring that the most important protection actions are executed first. The system also records the time, type, and possible cause of each fault in detail. This information is crucial for subsequent analysis and improvement. By statistically analyzing the recorded fault information, patterns and trends in fault occurrence can be identified. For example, the time distribution and type distribution of faults can be analyzed, as well as their correlation with other operating parameters. Based on the results of this fault analysis, the inverter system can be continuously optimized and improved. This may include adjusting monitoring parameter thresholds, optimizing anomaly detection algorithms, and improving the design of protection mechanisms. These measures can improve the reliability and stability of the inverter and reduce the frequency and impact of faults.

[0171] The above technical solution achieves the following benefits: By real-time monitoring of key inverter parameters such as temperature, current, and voltage, these parameters can be ensured to fluctuate within acceptable safety limits. Once a parameter anomaly is detected, the system can react quickly and promptly identify potential faults or abnormalities. Real-time monitoring and early warning mechanisms facilitate early intervention, preventing escalation and reducing losses. When an abnormality or fault is detected, the system immediately activates appropriate protection mechanisms, such as power cuts, alarms, and automatic restarts. These measures help prevent further damage to the inverter and ensure equipment safety. Rapidly responding protection mechanisms reduce inverter downtime and improve system reliability and availability. In power systems, stable inverter operation is crucial for ensuring power supply. The system records the time, type, and cause of faults in detail, providing a robust basis for subsequent fault analysis and resolution. By analyzing and analyzing fault records, patterns and trends can be identified, enabling continuous system optimization and improvement. This helps improve the overall performance and reliability of the inverter and reduce the probability of failure. Based on the results of fault analysis, targeted optimization and improvements can be made to the inverter system. For example, adjusting monitoring parameter thresholds, optimizing anomaly detection algorithms, and improving the design of protection mechanisms can improve system performance and stability. Continuous optimization and improvement can reduce inverter operation and maintenance costs. For example, reducing downtime due to faults and the frequency of repairs and component replacements can save users money. Features such as real-time monitoring, rapid response, and fault logging can enhance user confidence in the inverter system, improving user satisfaction and loyalty. 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 may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for modulating the PWI of an inverter, characterized in that: The method comprises: S1. Set the basic operating 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; the PWF is also the phase weighting factor; S2. Load and initialize the inverter control algorithm, and generate a standard sine wave as a reference wave according to the preset output voltage and frequency; S3. Calculate the PWF value at each phase point based on 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 based on the comparison result; S4. Input the PWM signal to the PWM drive circuit of the inverter to control the on / off of the inverter switch to generate an AC output voltage; monitor the output current and voltage of the inverter in real time, and dynamically adjust the PWF value or the parameters of the feedback control loop based on the monitoring results; S5. Introduce a harmonic suppression mechanism into the PWI modulation algorithm. Reduce the harmonic content in the inverter output by adjusting the PWF waveform or adopting other harmonic suppression strategies. Regularly evaluate the inverter performance and optimize the PWI modulation algorithm and feedback control strategy based on 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. Determine the expected output voltage amplitude, frequency, and rated power of the inverter according to application requirements, and preset a starting value of the initialization phase weighting factor based on an expert knowledge base; S12. Determine the range of PWF variation during the inversion process and conduct an in-depth analysis of the load characteristics in the target application; S13. Based on the load characteristics and waveform quality requirements, a PWF adjustment strategy is designed. 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: Said S2 comprises: S21. Select a PWI modulation control algorithm from the control system of the inverter, the control algorithm including a PWI modulation algorithm and a feedback control algorithm, and load it into the controller; S22. Generate a standard sine wave as a reference waveform for the inverter using a digital signal processor or a microcontroller according to a preset output voltage and frequency; S23. Perform quality verification on the generated reference waveform.

4. The inverter PWI modulation method according to claim 1, characterized in that: Said S3 comprises: S31. Preliminarily setting an initial value of the 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 PWF value according to the real-time parameters and performance indicators of the inverter during operation through a preset PWF dynamic adjustment strategy; S32. Select a PWM modulation method based on the specific requirements and application scenarios of the inverter, multiply the PWF value by the reference waveform to obtain a modulated waveform; and convert the modulated waveform into a PWM signal based on the selected PWM modulation method; S33. During the PWM signal generation process, suppressing edge effects through 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 PWM modulation parameters 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 inverter's operating status and performance indicators; and perform fault diagnosis on the inverter using a fault diagnosis algorithm after detecting an abnormal condition.

5. The inverter PWI modulation method according to claim 4, characterized in that: Said S34 comprises: S341. Build an inverter performance indicator system, use high-speed ADCs for voltage and current sampling, use high-precision thermometers to monitor the temperature of key components, and use spectrum analyzers to assess electromagnetic radiation levels. S342, pre-processing the collected raw data and smoothing the signal using digital signal processing technology; 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 between different levels of deviations using pre-set multi-level thresholds for each performance indicator. Use time series analysis to study historical data on performance indicators and predict future trends. When a threshold is predicted to be exceeded, preventive measures are taken in advance. S345. Combining the evaluation results of multiple performance indicators, a fuzzy logic algorithm is used to make a comprehensive decision to determine the optimal adjustment strategy, and the PWF value is automatically adjusted 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 adopted for adjustment.

6. The inverter PWI modulation method according to claim 5, characterized in that: The S345 includes: Based on historical data analysis, weights are assigned to various performance indicators. Fuzzy set theory is used to fuzzify the performance indicators. The current value of each performance indicator is mapped to a fuzzy set, and its membership function is defined. Based on expert knowledge and historical data, a set of fuzzy rule bases is constructed, each rule defining the adjustment measures to be taken under different performance indicator combinations; The fuzzified performance indicators are input into the fuzzy inference engine, and reasoning is performed according to the fuzzy rule base to finally obtain the comprehensive decision result; For performance indicators that require continuous adjustment, the gradient descent method is used to calculate the adjustment direction and step size of the 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; Dynamically adjust PWM modulation parameters based on the inverter's real-time operating status and performance indicator changes. At the same time, establish a feedback mechanism to input the adjusted performance indicators back into the comprehensive decision-making system for a new round of evaluation and adjustment. Prioritize different adjustment strategies based on the importance of performance indicators and the adjustment effect, and evaluate the overall effect of coordinated adjustment of strategies; During the adjustment process, the various performance indicators of the inverter are continuously monitored, and the feedback mechanism is continuously optimized based on the real-time performance monitoring results.

7. The inverter PWI modulation method according to claim 1, characterized in that: Said S4 comprises: S41. During the PWM signal generation process, based on the product of the PWF and the reference waveform, dead time control and switch drive delay compensation are introduced to ensure the accuracy and reliability of the PWM signal. S42. Based on the specific requirements of the inverter, multi-level PWM modulation technology is used to improve the waveform quality of the output voltage and the inverter efficiency; S43. Use historical data and machine learning algorithms to predict load change trends; set multiple thresholds to determine different operating states of the inverter; S44. Prioritize multiple adjustment strategies that may be triggered simultaneously according to their impact on inverter performance and urgency. S45. Filter the feedback signal and use an adaptive control algorithm to optimize the feedback control loop; perform stability analysis on the feedback control loop, and if unstable factors are found in the control loop, take measures to adjust and optimize it; S46. Establish an abnormality detection mechanism to monitor and analyze the inverter's operating status in real time; once an abnormality is detected, the corresponding processing flow is immediately triggered; S47. After detecting an abnormality, use a fault diagnosis algorithm to quickly and accurately diagnose the inverter fault, and at the same time, take 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 are 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. By training the model, learn the inherent laws and trends of load changes, and use cross-validation to tune the prediction model. Quantitatively evaluate the prediction results, adjust the model parameters based on the evaluation results, and set multiple thresholds to judge different operating states of the inverter based on the prediction results and the performance requirements of the inverter; Monitor the 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: Said S5 comprises: S51. Analyze the output waveform of the inverter using spectrum analysis technology to identify the main harmonic components and their sources; S52. Based on the result of harmonic source identification, formulate a targeted harmonic suppression strategy, wherein the suppression strategy includes adjusting the waveform shape, phase offset, 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 content; S54. After implementing the harmonic suppression strategy, use spectrum analysis technology to evaluate the inverter output waveform again to verify whether the harmonic suppression effect has achieved the expected goal; if not, adjust the harmonic suppression strategy or PWF optimization algorithm based on the evaluation results; S55. Establish a comprehensive performance evaluation system, using high-precision measurement equipment and data analysis tools to quantitatively evaluate various indicators. Based on the performance evaluation results, prioritize various indicators and design specialized optimization strategies for higher-priority indicators. 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 inverter's operating status and performance indicator changes in real time through an adaptive optimization mechanism, and automatically adjust the optimization strategy based on preset rules or algorithms; use big data analysis technology to conduct in-depth analysis and mining of massive data during the inverter's operation; S57. Establish a simulation model of the inverter and perform simulation verification on different optimization strategies; predict the impact of different strategies on inverter performance based on the simulation results; S58. Establish an inverter optimization knowledge base and experience database, and record process data during each optimization process.

10. The inverter PWI modulation method according to claim 1, characterized in that: Said S6 comprises: S61: Monitor the operating status of the inverter in real time and use anomaly detection algorithms to quickly analyze the monitored data to identify potential faults or abnormalities. S62. If an abnormality or fault is detected, immediately activate a corresponding protection mechanism, which includes power off, alarm prompt, and automatic restart; S63. Record the information of the fault, including the time, type and cause, analyze the pattern and trend of the fault, and continuously optimize and improve the system based on the analysis results.

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