Self-adaptive online monitoring system and method for single-phase photovoltaic grid-connected inverter capacitor

Abstract

According to the self-adaptive online monitoring system and method for the single-phase photovoltaic grid-connected inverter capacitor, intelligent identification is achieved by judging the MPPT locking state and the power stability, and monitoring is triggered only in a stable golden window; actively injecting a high-frequency micro-disturbance signal, and synchronously collecting voltage and current of a capacitor; the method comprises the following steps: accurately extracting complex impedance of a capacitor at an injection frequency from strong background noise by using an FFT (Fast Fourier Transform) equal frequency domain analysis technology, and calculating equivalent series resistance (ESR) and a capacitance value (C); the measured value is compared with a health reference value dynamically inquired based on the current working condition (voltage, current and temperature), a normalized aging deviation index is calculated, and accurate health state diagnosis is achieved; according to the method, high-signal-to-noise-ratio, non-inductive and low-cost online monitoring is achieved, the method can adapt to working condition changes, normal drifting and aging degradation of parameters are effectively distinguished, and a reliable basis is provided for predictive maintenance.

Description

Technical Field

[0001] This invention belongs to the fields of power electronics and new energy technology, specifically relating to a condition monitoring and fault diagnosis technology for key components in power electronic converters, and in particular a system and method for online assessment of the health status of DC link support capacitors in photovoltaic grid-connected inverters. Background Technology

[0002] The DC-Link Capacitor is a core passive component in power electronic converters such as single-phase photovoltaic grid-connected inverters. In a single-phase photovoltaic grid-connected inverter, its main function is to buffer power fluctuations; that is, its DC side needs to absorb and release an energy difference with a frequency twice the grid frequency (100Hz). This is a low-frequency energy fluctuation with a fixed frequency and known characteristics. Based on the power and voltage ripple coefficient, the minimum DC-Link capacitor size can be calculated.

[0003] The performance and reliability of the DC link capacitor directly determine the stability and lifespan of the entire photovoltaic grid-connected inverter and even the photovoltaic power generation system.

[0004] During long-term operation, capacitors undergo electrochemical aging due to high ripple current, high voltage stress, and periodic temperature changes. The macroscopic manifestations are primarily an increase in equivalent series resistance (ESR) and a decrease in capacitance (C). The decrease in capacitance leads to increased bus voltage ripple, thereby reducing the converter's key performance indicators. Furthermore, the increase in ESR exacerbates the capacitor's power loss and heat generation, creating a vicious cycle that accelerates the aging process. In extreme cases, this can cause capacitor bulging, leakage, or even explosion, resulting in equipment shutdown, system failure, or even serious safety accidents.

[0005] To ensure system reliability, real-time monitoring and early warning of the State of Health (SOH) of DC link capacitors are crucial.

[0006] Currently, methods for monitoring the health status of capacitors are mainly divided into two categories: offline detection and online detection. 1. Offline testing: This method requires removing the capacitor from the system after the equipment is shut down, and then using specialized precision instruments such as an LCR bridge for measurement. Although offline testing has high accuracy, its drawbacks are obvious: it not only interrupts the normal operation of the equipment, significantly increasing maintenance costs and complexity, but more importantly, it cannot provide continuous status data, thus failing to achieve status-based predictive maintenance.

[0007] 2. Online detection: To overcome the shortcomings of offline detection, various online detection technologies have emerged.

[0008] One mainstream technical approach is the passive monitoring method, such as the patent document "Capacitor Capacitance Detection Circuit in Solar Inverter" (Publication No.: CN202886481U). This method can only automatically detect the capacity of the bus capacitor C when the solar inverter starts or stops each day. It can also be used to manually start the capacitor capacity test using the solar inverter. By judging the capacitance decay, the capacitor's lifespan can be effectively determined. However, this method has limited applications, cannot monitor the capacitor's operating status in real time and across the entire range, and the algorithm is relatively simple, only able to calculate capacity and unable to estimate the most important indicator, ESR.

[0009] Other methods attempt to indirectly infer capacitor state by analyzing macroscopic electrical quantities of the system using artificial intelligence or advanced signal processing algorithms. These methods often require massive amounts of data for model training and involve extremely high computational demands, making real-time and efficient monitoring difficult to achieve on resource-constrained embedded platforms. This is illustrated in the patent document "Online Monitoring Method and System for Capacitor Equipment Based on Artificial Intelligence" (Publication No. CN117741517A).

[0010] Another approach is based on active signal injection: this method measures capacitive impedance by injecting a specific excitation signal into the system. For example, patent document "A Method, Device, System and Auxiliary Discharge Network for Online Monitoring of Capacitor Value" (Publication No.: CN120177893A). This method: when the power converter is in online operation, during the phase where the bipolar junction transistor is off and the resistor is in operation, the DC power supply voltage is repeatedly sampled and recorded at specific times; by repeatedly switching the load, significant signal changes are caused. Its main problem is the introduction of electromagnetic compatibility (EMC) risks, accelerating equipment aging. It is not an ideal method.

[0011] In summary, existing capacitor health status monitoring technologies suffer from inherent drawbacks: offline methods are costly and not real-time, while online passive methods suffer from low accuracy and poor resistance to operating conditions. Online methods also have unsatisfactory performance. Therefore, there is an urgent need in this field for a novel online monitoring technology. This technology must not only possess high accuracy and a high signal-to-noise ratio, but more importantly, it must be able to effectively overcome the impact of dynamic changes in operating conditions caused by MPPT control and environmental variations, achieving reliable and adaptive assessment of capacitor health status. Summary of the Invention

[0012] The purpose of this invention is to address the limitations of existing technologies by proposing an adaptive online monitoring system and method for the capacitors of single-phase photovoltaic grid-connected inverters.

[0013] The present invention adopts the following technical solution: An adaptive online monitoring method for the capacitor of a single-phase photovoltaic grid-connected inverter, characterized by comprising the following steps: Step 1: In the digital controller of the inverter, a dual-condition judgment mechanism is set up to identify the unique golden window for health status assessment. The golden window is used to determine whether the inverter is currently operating at a stable maximum power point. When the golden window is identified, the digital controller immediately generates a monitoring trigger signal. Step 2: After receiving the monitoring trigger signal, the digital controller generates a first disturbance signal with a frequency F_inject; this disturbance signal is superimposed with the PWM modulation wave output by the inverter digital controller to form a PWM signal, which drives the power switching devices of the inverter. Step 3: While superimposing the disturbance signal, the voltage signal v(t) across the DC side capacitor and the current signal i(t) flowing through the capacitor are acquired through the sensor and analog-to-digital converter. Step 4: Perform digital signal processing on the time-domain signal sequence of the acquired voltage and current signals, calculate the complex impedance of the capacitor at frequency F_inject, and calculate the real-time equivalent series resistance ESR_measured and capacitance value C_measured of the capacitor. Step 5: a) When the monitoring trigger signal is generated in Step 1, the controller synchronously latches the current DC bus voltage V_dc and DC input current I_dc through the MPPT control algorithm; reads the current capacitor temperature T_c; V_dc, I_dc and T_c together constitute the instantaneous operating condition vector; b) The controller uses the instantaneous operating condition vector as input to determine the dynamic health baseline values ​​ESR_base and C_base corresponding to the current instantaneous operating condition vector through an adaptive baseline model; c) Compare ESR_measured and C_measured with ESR_base and C_base to obtain the aging deviation index, and diagnose the health status of the capacitor based on the aging deviation index.

[0014] Preferably, the dual-condition judgment mechanism includes: The first condition for MPPT status determination is as follows: The controller checks the status flags of its internal MPPT control algorithm in real time; the first condition is met only when the MPPT control algorithm is in a locked or successfully tracked state and is not in a search, scan, or start state; this ensures that the evaluation is performed under the premise that the inverter is providing effective power output.

[0015] Second condition for power stability judgment: On the basis of satisfying the first condition, the controller monitors the rate of change of DC side input power P within a preset time window |ΔP / Δt|; the second condition is satisfied only when the rate of change is continuously less than a preset stability threshold ε_P. Once the second condition is met, the controller determines that the inverter is currently operating within the golden window and simultaneously generates a monitoring trigger signal.

[0016] Preferably, the aging deviation index includes the equivalent series resistance health index ESR_Health_Index and the capacitance health index C_Health_Index; ESR_Health_Index = ESR_measured / ESR_base; C_Health_Index = C_measured / C_base; If ESR_Health_Index ≈ 1.0 and C_Health_Index ≈ 1.0, then the capacitor is considered to be in good health. If ESR_Health_Index > 1.8 or C_Health_Index < 0.85, the system will issue a warning indicating severe aging and recommend replacement. If 1.8 ≥ ESR_Health_Index > 1.5 or 0.85 ≤ C_Health_Index < 0.90, the system will issue a warning indicating moderate aging.

[0017] Preferably, the first disturbance signal is a first sinusoidal disturbance signal.

[0018] Preferably, the controller performs lookup or multidimensional interpolation operations in the adaptive baseline model to determine the dynamic health baseline values ​​ESR_base and C_base corresponding to the current instantaneous operating condition vector.

[0019] An adaptive online monitoring system for the capacitor of a single-phase photovoltaic grid-connected inverter, comprising hardware and software components, is characterized by: The hardware components include: a sensor unit, a photovoltaic grid-connected inverter power stage, and a digital controller; The software component runs on the digital controller and includes: a judgment and monitoring trigger module, used to generate a monitoring trigger signal when the controller determines that the inverter is currently operating within the golden window; a harmonic injection module, used to superimpose a second sinusoidal disturbance signal into a first sinusoidal disturbance signal and then superimpose it onto the PWM modulation wave; a signal processing module, used to process the signals collected by the sensor unit, perform frequency domain transformation, and calculate the real-time ESR_measured and C_measured values; and an adaptive evaluation module, used to obtain real-time operating conditions from the MPPT control algorithm, query the model to determine the dynamic benchmark, and perform the final capacitor health status diagnosis.

[0020] Preferably, the frequency of the first sinusoidal disturbance signal is greater than the frequency of the second sinusoidal disturbance signal.

[0021] The beneficial effects achieved by this invention are: 1. Extremely high measurement accuracy and signal-to-noise ratio: By actively injecting a probe signal with known characteristics into the system and performing targeted frequency domain analysis, the shortcomings of passive methods, such as reliance on weak natural ripples, low signal-to-noise ratio, and susceptibility to noise interference, are fundamentally overcome.

[0022] By combining the intelligent triggering mechanism of "stable maximum power point", the measurement process is ensured to avoid strong noise interference from all transient operating conditions, further improving the signal-to-noise ratio to the optimal level, and making the accuracy and repeatability of the measurement results reach a level that is difficult to achieve with existing technologies.

[0023] 2. Completely eliminates the risk of misjudgment due to operating condition drift: Existing technologies generally face the challenge of normal drift in capacitor parameters due to changes in operating conditions such as temperature and voltage, which can be confused with aging and deterioration. This invention achieves perfect synchronization between measurement conditions and evaluation benchmarks through the synergistic effect of two core technologies: "stable operating condition triggering" and "instantaneous operating condition adaptive benchmark".

[0024] It can accurately distinguish between normal parameter drift and irreversible aging, completely solving the problem of false alarms or missed alarms caused by operating condition dependence, and ensuring the uniqueness and certainty of the diagnostic conclusion.

[0025] 3. Achieved truly online, seamless, and low-cost monitoring: All monitoring operations are implemented by the inverter controller software, eliminating the need for additional hardware costs and making it highly economical.

[0026] The injected disturbance signal has a weak amplitude and a specially selected frequency. Under the combined action of the inverter's closed-loop control and the filter circuit, it has no perceptible impact on the inverter's efficiency, output power quality, and system stability, achieving non-intrusive monitoring that is completely "unobtrusive" to users and the power grid.

[0027] 4. It has achieved a leap from "passive diagnosis" to "proactive predictive maintenance": Due to its high measurement accuracy and elimination of operating condition interference, this invention can reliably track the evolution curves of capacitor health status, which show the evolution trends of ESR_Health_Index and C_Health_Index over time.

[0028] This provides a solid data foundation for accurately predicting the remaining useful life (RUL) of capacitors, enabling system maintenance strategies to shift from a passive "repair after failure" model to a proactive predictive maintenance model of "early warning of failure and planned replacement." This has significant economic and technical value for improving the long-term reliability of unattended photovoltaic power plants, reducing unexpected downtime losses, and optimizing spare parts management.

[0029] To further understand the features and technical content of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are for reference and illustration only and are not intended to limit the present invention. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the hardware block diagram of the monitoring system of the present invention; Figure 2 This is a schematic diagram showing the installation location of the temperature sensor on the capacitor; Figure 3 This is the overall flowchart of the monitoring algorithm of this invention; Figure 4 yes Figure 3 Detailed flowchart of the stable maximum power point determination and monitoring trigger algorithm; Figure 5 yes Figure 3 Detailed flowchart of active signal injection measurement and data acquisition; Figure 6 yes Figure 3 Detailed flowchart of mid-frequency domain parameter calculation; Figure 7 yes Figure 3 A detailed flowchart of adaptive benchmark assessment and diagnosis.

[0031] Figure 8 This is a simulation result diagram of Example 3.

[0032] Figure 9 This is a simulation result diagram of Example 4.

[0033] Figure 10 This is a simulation result diagram of Example 5. Detailed Implementation

[0034] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated in advance. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0035] Example 1: As Figure 3 An adaptive online monitoring method for the capacitor of a single-phase photovoltaic grid-connected inverter is shown, characterized by comprising the following steps: Step 1: Determination and Monitoring Triggering of Stable Maximum Power Point (MPPT): A dual-condition judgment mechanism is set in the inverter digital controller to identify the unique golden window for health status assessment. This golden window is used to determine whether the inverter is currently operating at a stable maximum power point. When the golden window is identified, the inverter digital controller immediately generates a monitoring trigger signal. Step Two: Active Signal Injection: Upon receiving the monitoring trigger signal generated in Step One, the inverter digital controller generates a first sinusoidal disturbance signal (i.e., a high-frequency sinusoidal disturbance signal) with a known frequency F_inject and a weak amplitude A_inject through software. This first disturbance signal is then superimposed on the PWM modulation wave output by the inverter digital controller to form a PWM signal, which drives the inverter's power switching devices. Specifically, upon receiving the Monitor_Trigger == 1 signal, the superposition of the first sinusoidal disturbance signal is immediately executed.

[0036] Step 3: Synchronous data acquisition: While injecting the disturbance signal, the voltage signal v(t) across the DC side capacitor and the current signal i(t) flowing through the capacitor are simultaneously acquired through the sensor and analog-to-digital converter (ADC). Step 4: Frequency Domain Parameter Calculation: Perform digital signal processing (such as Fast Fourier Transform, FFT) on the acquired voltage and current time-domain signal sequences to extract the complex voltage V_inject and complex current I_inject (including amplitude and phase information) at the injection frequency F_inject point; calculate the complex impedance Z_c = V_inject / I_inject at this frequency according to Ohm's law in the frequency domain, and solve the real-time equivalent series resistance ESR_measured and capacitance C_measured of the capacitor from the real and imaginary parts of the complex impedance respectively; Step 5: Adaptive Benchmark Evaluation a) Instantaneous snapshot of real-time operating parameters: When the monitoring trigger signal is generated in step one, the controller synchronously latches the current DC bus voltage V_dc and DC input current I_dc through the MPPT control algorithm; reads the current capacitor temperature T_c; V_dc, I_dc, and T_c together constitute the instantaneous operating vector (V_dc, I_dc, T_c); b) Query the preset stable operating condition-health baseline model: The controller uses the instantaneous operating condition vector (V_dc, I_dc, T_c) obtained in a) as input to query the adaptive baseline model in the controller's memory to determine the dynamic health baseline values ​​ESR_base and C_base corresponding to the current instantaneous operating condition vector. This adaptive baseline model is a multi-dimensional database, essentially a mapping table of "stable operating conditions-health parameters". This table was established during the product development phase through comprehensive testing of new, healthy capacitors at various stable operating points. It records in detail the theoretical ESR and C baseline values ​​of a healthy capacitor under different combinations of voltage, current, and temperature.

[0037] c) Aging Deviation Calculation and Diagnosis Based on Normalized Benchmark: The real-time measured values ​​ESR_measured and C_measured calculated in step four are compared with the dynamic health benchmark values ​​ESR_base and C_base obtained in b) to obtain the aging deviation index. The capacitor health status is diagnosed based on this aging deviation index. A threshold is set according to industry standards (e.g., when ESR increases to twice the initial value, or the capacitance drops to 80% of the nominal value, the capacitor's lifespan is considered to have ended).

[0038] Preferably, the dual-condition judgment mechanism includes: The first condition for MPPT status determination: The controller checks the status flags of its internal MPPT control algorithm in real time; the first condition is met only if the MPPT control algorithm is in a locked or tracking success state, and not in a searching, sweeping, or startup state. This ensures that the evaluation is performed under the premise that the inverter is providing effective power output. In the specific program loop, the status flags provided by the MPPT control algorithm are read in real time. If (MPPT_Status_Flag == LOCKED || MPPT_Status_Flag == TRACKING_SUCCESS): Proceed to the next step of the judgment.

[0039] else: Continuously monitor MPPT status and skip subsequent monitoring processes.

[0040] The second condition for power stability judgment: Based on the first condition being met, the controller further monitors the rate of change |ΔP / Δt| of the DC-side input power P within a preset time window (T_w); the second condition is met only when this rate of change is continuously less than a preset stability threshold ε_P (|ΔP / Δt| < ε_P). This eliminates transient disturbances caused by sudden changes in illumination or significant adjustments to the MPPT control algorithm.

[0041] Once the second condition is met, the controller determines that the inverter is currently operating within the golden window (a stable maximum power point). The controller then generates a monitoring trigger signal to initiate the subsequent signal injection, data acquisition, and health assessment processes. At any other time, the monitoring process is prohibited, and the controller will continue to wait for the next stable operating period (hereinafter referred to as the 'golden window') suitable for health status assessment to occur.

[0042] The preset time window (T_w) is a sampling buffer that stores the most recent power P sampling value.

[0043] Whenever a new power sample value P_new arrives, calculate ΔP = P_new - P_previous. P_previous is the previous power sample value.

[0044] Calculate |ΔP / Δt| (where Δt is the sampling period).

[0045] Check whether the rate of change is continuously less than ε_P within the T_w time window.

[0046] if (all |ΔP / Δt| within T_w < ε_P): The monitoring signal Monitor_Trigger = 1 is triggered.

[0047] else: Monitor_Trigger = 0, continue waiting.

[0048] Monitoring trigger: If (MPPT_Status_Flag == LOCKED / SUCCESS && Monitor_Trigger == 1): Execute step two.

[0049] else: Keep the monitoring process suspended.

[0050] in, Figure 4 This is a detailed flowchart of the stable maximum power point judgment and monitoring triggering algorithm in the monitoring algorithm of this invention.

[0051] The first disturbance signal should be superimposed on the PWM duty cycle signal, and the superposition method should ensure that it does not cause the PWM signal to exceed its allowable range (for example, if the disturbance signal is sinusoidal, its amplitude should be much smaller than the duty cycle variation range, or it can be saturated).

[0052] At the same time, ensure that the injection of this disturbance signal only occurs during the generation of the PWM signal used to drive the power switching devices.

[0053] This allows the inverter to actively draw an analyzable harmonic current containing the F_inject frequency component from the DC-side capacitor.

[0054] in, Figure 5 This is a detailed flowchart of the active signal injection measurement and data acquisition in the monitoring algorithm of this invention.

[0055] Specifically, the voltage signal v(t) is the DC bus voltage v(t).

[0056] The current signal i(t) of the capacitor can be estimated by measuring the change in the DC bus current, or by directly connecting a sampling resistor in series with the capacitor.

[0057] The data acquisition also includes temperature sampling: reading the value of the capacitive temperature sensor T_c.

[0058] The data acquisition also includes MPPT operating condition parameter sampling: locking the current V_dc and I_dc values.

[0059] The timestamps for data acquisition must be highly accurate to ensure that the acquisition of v(t) and i(t) accurately reflects the response under the influence of disturbance signals. The sampling clock of the ADC and the PWM output clock need to be synchronized.

[0060] Specifically, from Z_c, we can calculate: ESR_measured = real(Z_c); real(Z_c) is the real part of Z_c. C_measured = 1 / (imag(Z_c) * 2 * pi * F_inject) (Here, it is assumed that the imaginary part of the capacitor represents the capacitive reactance X_c = 1 / (2*pi*F_inject*C), and the effect of inductance can be ignored at the selected frequency F_inject). imag(Z_c) is the imaginary part of Z_c; F_inject is the frequency of the high-frequency sinusoidal perturbation signal.

[0061] real(Z_c) represents the real part of the complex number Z_c. imag(Z_c) represents the imaginary part of the complex number Z_c.

[0062] in, Figure 6 This is a detailed flowchart of the frequency domain parameter calculation in the monitoring algorithm of this invention.

[0063] Specifically, the query method for the adaptive baseline model in the controller's memory is as follows: The controller performs a search or multidimensional interpolation operation in this database to determine the dynamic health baseline values ​​ESR_base and C_base that perfectly correspond to the current "instantaneous operating condition vector". Specifically, the query methods include lookup table method and function fitting method. The lookup table method can discretize V_dc, I_dc, and T_c into multiple intervals within their value range. The controller determines the interval of each parameter based on the current operating condition vector and obtains the corresponding ESR_base and C_base values ​​by looking up the table. To improve accuracy, interpolation algorithms can be used. If higher accuracy is required, bilinear or trilinear interpolation can be used to perform interpolation calculations between several adjacent discrete points. In the function fitting method, if the model is a fitted function, (V_dc, I_dc, T_c) are directly substituted into the function expression to calculate ESR_base and C_base.

[0064] Preferably, the aging deviation index includes the equivalent series resistance health index ESR_Health_Index and the capacitance health index C_Health_Index; ESR_Health_Index = ESR_measured / ESR_base; C_Health_Index = C_measured / C_base; The diagnostic logic is as follows: if ESR_Health_Index ≈ 1.0 and C_Health_Index ≈ 1.0, then the capacitor is considered to be in good health. Set thresholds based on industry standards (e.g., when the ESR increases to twice its initial value, or the capacitance drops to 80% of its nominal value, the capacitor is considered to have reached the end of its life). For example: If ESR_Health_Index > 1.8 or C_Health_Index < 0.85, the system will issue a warning indicating severe aging and recommend replacement. If 1.8 ≥ ESR_Health_Index > 1.5 or 0.85 ≤ C_Health_Index < 0.90, the system will issue a warning indicating moderate aging.

[0065] Preferably, the controller performs lookup or multidimensional interpolation operations in the adaptive baseline model to determine the dynamic health baseline values ​​ESR_base and C_base corresponding to the current instantaneous operating condition vector.

[0066] Preferably, diagnostic judgment: if (ESR_Health_Index > SERIOUS_ESR_THRESHOLD || C_Health_Index < SERIOUS_C_THRESHOLD): Trigger a serious aging alarm.

[0067] else if (ESR_Health_Index > MODERATE_ESR_THRESHOLD || C_Health_Index < MODERATE_C_THRESHOLD): Trigger a moderate aging warning.

[0068] else: Determine that the capacitance health status is good.

[0069] Preferably, threshold setting: Serious ESR aging threshold (SERIOUS_ESR_THRESHOLD) (e.g., 1.8), Serious capacitance value decay threshold (SERIOUS_C_THRESHOLD) (e.g., 0.85), Moderate ESR aging threshold (MODERATE_ESR_THRESHOLD) (e.g., 1.5), Moderate capacitance value decay threshold (MODERATE_C_THRESHOLD) (e.g., 0.90).

[0070] These thresholds can be adjusted and optimized according to actual data during the product life cycle.

[0071] Output: Report the evaluation results (healthy, moderate aging, serious aging) to the monitoring system through a communication interface (such as RS485, CAN), or display them on the local human-machine interface (HMI).

[0072] Among them, Figure 7 is the detailed flowchart of adaptive reference evaluation and diagnosis in the monitoring algorithm of the present invention.

[0073] Preferably, the controller performs a lookup or multi-dimensional interpolation operation in the adaptive reference model to determine the dynamic health reference values ESR_base and C_base corresponding to the current instantaneous operating condition vector.

[0074] It should be noted when implementing this embodiment: Parameter calibration: All parameters such as the gain and offset of all sensors (voltage, current, temperature) and ADC need to be accurately calibrated in the factory to ensure the accuracy of the collected data.

[0075] Model Updates: Adaptive baseline models require thorough validation during product design and manufacturing. Model parameters should be consistent for the same capacitor model. However, in some cases, it may be necessary to design different models for different batches or models of capacitors.

[0076] FFT processing: The number of sampling points and window length of the FFT need to be selected based on F_inject and the desired resolution to ensure that complex voltages and currents can be accurately extracted at the F_inject points.

[0077] Data storage: The established baseline model and set thresholds need to be stored in the controller's non-volatile memory so that they can be recovered after the device is powered off.

[0078] This embodiment uses real-time sliding window analysis of macroscopic electrical parameters (such as DC power) to accurately identify the "golden steady state" operating condition most suitable for precise health status diagnosis, and effectively avoids the "dynamic shock" operating condition that introduces huge errors due to sudden changes in illumination or MPPT optimization, thereby ensuring high reliability and high accuracy of monitoring triggering; the dual condition judgment mechanism proposed in this embodiment can effectively distinguish different operating states of power electronic converters, thereby realizing adaptive triggering of the diagnostic process.

[0079] Example 2: This example should be understood as including all the features of any of the foregoing examples, and further improving upon them.

[0080] An adaptive online monitoring system for the capacitor of a single-phase photovoltaic grid-connected inverter, which implements the method of the above embodiments, includes hardware and software components.

[0081] Hardware components such as Figure 1 The system includes: a sensor unit, a photovoltaic grid-connected inverter power stage, and a digital controller; the sensor unit includes a voltage sensor VS_A, a temperature sensor TS_A, a current sensor CS_A, and a DAQ sensor. The photovoltaic grid-connected inverter power stage includes a high-precision I / V converter, a DC-Link capacitor C_dut, an H-bridge grid-connected inverter, and a DC bus voltage; the high-precision I / V converter has a sampling accuracy of at least 12-bit AD converter; a schematic diagram of the temperature sensor mounting position on the capacitor is shown below. Figure 2 As shown.

[0082] The software algorithm runs within the controller. This includes: A stable maximum power point determination and monitoring trigger module is included. The controller determines that the inverter is currently operating at a stable maximum power point. The controller generates a monitoring trigger signal to initiate subsequent processes.

[0083] A harmonic injection module is used to generate and superimpose the micro-perturbation signal onto the PWM modulation wave.

[0084] A signal processing module is used to process the signals acquired by the sensor unit, perform frequency domain transformation, and calculate the real-time ESR_measured and C_measured values.

[0085] An adaptive evaluation module, whose core is a built-in adaptive baseline model, is responsible for obtaining real-time operating conditions from the MPPT control algorithm, querying the model to determine the dynamic baseline, and performing the final health status comparison and diagnosis.

[0086] The system in this embodiment is highly integrated into the existing hardware platform of the inverter (digital controller, sensors, etc.), requiring no additional hardware costs and offering excellent economic efficiency. All algorithms are implemented by software modules, achieving true online, seamless monitoring without affecting the normal operation of the inverter or the power quality of the grid, thus possessing extremely high engineering application value.

[0087] Example 3: This example should be understood as including all the features of any of the foregoing examples, and further improving upon them.

[0088] A simulation method for a dual-condition judgment mechanism in an adaptive online monitoring method for capacitors of a single-phase photovoltaic grid-connected inverter (i.e., simulation verification of the stable maximum power point judgment and monitoring triggering algorithm), the method includes: Step 1: Simulation Environment Construction and Parameter Initialization: First, establish a discretized time series simulation environment to simulate the operation mechanism of a real digital control system.

[0089] Time base settings: Set the sampling frequency Fs=100Hz (i.e., sampling period Ts=10ms), the total simulation duration Ttotal=50s, and construct the time vector tsim.

[0090] Baseline parameter settings: Initialize the DC-side power sequence Pdc and set the target maximum power point (MPP) to 5000W.

[0091] Status flag initialization: Establish an MPPT locked flag vector mppt_locked_flag synchronized with the time axis to simulate the locking state (True / False) of the inverter's underlying MPPT control algorithm.

[0092] Step Two: Construction of Multimodal Complex Operating Conditions: To test the algorithm's adaptability to different power grids and lighting environments, various typical dynamic and steady-state features were artificially injected into the Pdc sequence through mathematical modeling, constructing a mixed operating condition sequence containing the following nine stages: Soft start phase (0-3s): Power increases linearly from 0 to the rated value, simulating the inverter grid-connected startup process.

[0093] Golden Point: Power curves with an average value of 5000W or near it, superimposed with small sinusoidal ripples (volatility < 0.5%), are constructed over multiple time periods (e.g., 3-8s, 13-18s, 30-37s, 42-48s) to verify the algorithm's ability to identify the ideal diagnostic window.

[0094] Large Fluctuation: Construct large-amplitude sinusoidal fluctuations (peak-to-peak value up to 500W) or strong random noise ranges to simulate drastic changes in illumination or large MPPT disturbances, in order to verify the algorithm's ability to suppress interference.

[0095] Small Fluctuation: Construct a range with small fluctuation amplitudes that still exceed the stability threshold (e.g., > 100W) to test the algorithm's boundary resolution capability.

[0096] Normal steady-state condition (Non-Golden Stable): Construct a range with stable but low power values ​​(e.g., 3000W) to verify the algorithm's ability to distinguish between non-target power ranges.

[0097] Step 3: Implementation of the real-time monitoring algorithm based on a sliding time window: In the simulation loop, the real-time sampling process is simulated point by point, and the core algorithm module checkGoldenPoint is called to determine the state of k at the current time. The algorithm execution logic is as follows: Window data extraction: Real-time capture of historical power data up to T_window = 0.5s (i.e., the past 50 sampling points) before the current moment to form an analysis window.

[0098] Statistical characteristic calculation: Calculate the average power value P_avg and the peak value fluctuation ΔP_flue within the window.

[0099] Multi-level logical judgment: MPPT lock check: If mppt_locked_flag is False, it is considered unstable.

[0100] Stability check: Compare ΔP_flue with the dynamic threshold (P_avg × 0.5%). If the threshold is exceeded, further subdivide the fluctuation into "large fluctuation" or "small fluctuation" based on the fluctuation amplitude.

[0101] Golden Range Matching: If the stability requirements are met, further check whether P_avg is within ±5% of the target power (5000W). Points that simultaneously meet the characteristics of "high stability" and "high power" are marked as "golden stability points".

[0102] Step 4: Dynamic Classification and Data Recording of All States: The simulation program records the algorithm output at each time step, generating a state history sequence `point_status_history` and a fluctuation history sequence `fluctuation_history`. Based on these two sequences, the full-time data is dynamically divided into four mutually exclusive operating states: 1. Golden Stable Point: Status code 3, corresponding color gold.

[0103] 2. Stable Point: Status code 2, corresponding color green.

[0104] 3. Unstable - Small fluctuations: Status code 1, corresponding color orange.

[0105] 4. Unstable - Large: Status code 0, corresponding color red.

[0106] Step 5: Visual Verification and Result Analysis like Figure 8 As shown, MATLAB's plotting capabilities are used to visualize the simulation results, providing an intuitive verification of the algorithm's performance. 1. Multi-system power curve plotting: Based on the classification results in step four, the single power curve is plotted in segments with different colors (gold, green, orange, red) to intuitively show the algorithm's real-time qualitative analysis of each data segment, where the horizontal axis represents time and the vertical axis represents power.

[0107] 2. Dynamic background area annotation: The patch function is used to draw a semi-transparent colored background band at the bottom of the chart to macroscopically identify continuous operating ranges.

[0108] 3. Insertion of borderless text labels: Automatically add borderless text labels (such as "Golden Stable Point") at the geometric center position (Y = 1000) of each continuous state interval, and make the text color consistent with the curve color.

[0109] 4. Verification conclusion: By observing the graph, it was confirmed that the algorithm can accurately respond to changes in operating conditions within milliseconds, precisely "select" all preset golden stable ranges, and effectively exclude large fluctuations from the trigger range, proving the correctness of the algorithm logic and the rationality of the parameter settings.

[0110] Simulation data output: Operating Condition 1: Soft Start (Large Fluctuations) P_avg=2918.06 W, peak-to-peak value=4163.88 W, σP=1209.25 dP / dt_avg=1672.24 Condition 2: Gold Stabilization Point 1 P_avg=5000.00 W, peak-to-peak value=19.96 W, σP=7.08 dP / dt_avg=79.74 Operating Condition 3: Large fluctuations (unstable) P_avg=4981.89 W, peak-to-peak value=500.00 W, σP=176.28 dP / dt_avg=1034.61 Condition 4: Gold Stable Point 2 P_avg=4796.00 W, peak-to-peak value=1807.50 W, σP=85.02 dP / dt_avg=492.55 Operating Condition 5: Steady Point (Non-Gold) P_avg=3001.43 W, peak-to-peak value=928.05 W, σP=36.40 dP / dt_avg=143.00 Operating Condition 6: Large fluctuations - random (unstable) P_avg=4003.65 W, peak-to-peak value=999.93 W, σP=71.27 dP / dt_avg=6600.91 Condition 7: Gold Stable Point 3 P_avg=4900.32 W, peak-to-peak value=110.00 W, σP=8.08 dP / dt_avg=75.18 Operating Condition 8: Minor fluctuations (unstable) P_avg=5000.25 W, peak-to-peak value=152.42 W, σP=28.81 dP / dt_avg=1290.99 Condition 9: Gold Stable Point 4 P_avg=5097.98 W, peak-to-peak value=1112.50 W, σP=47.73 dP / dt_avg=302.77 The simulation verification in this embodiment proves: 1. Precise operating condition identification capability: By performing dynamic analysis of macroscopic electrical parameters such as DC-side power based on a sliding time window, this algorithm can accurately distinguish four typical operating conditions based on multi-dimensional indicators such as power fluctuation rate and average power magnitude: golden stable point, ordinary stable point, small fluctuation (unstable) and large fluctuation (unstable).

[0111] 2. Reliable Triggering Decision Capability: Simulation results show that this algorithm can successfully lock onto the "golden steady-state" operating condition with stable power output and pure ripple characteristics, and use it as the only effective window for triggering subsequent precise diagnostics (such as capacitor SOH estimation). Simultaneously, it can effectively avoid and filter out "dynamic shock" operating conditions caused by sudden changes in illumination, large MPPT optimization, etc., which can introduce huge diagnostic errors.

[0112] In summary, the simulation results strongly demonstrate that the present invention can significantly improve the accuracy and reliability of online monitoring, and is a key link in realizing intelligent health management of power electronic converters.

[0113] This embodiment demonstrates with visualized data that the algorithm can accurately and quickly distinguish between stable operating conditions and dynamic disturbance conditions, and reliably lock onto the ideal diagnostic window. This provides crucial theoretical and practical basis for the high reliability and adaptive triggering of the entire monitoring process, ensuring that subsequent precision measurements are only performed under optimal conditions.

[0114] Example 4: This example should be understood as including all the features of any of the foregoing examples, and further improving upon them.

[0115] The simulation verification of the active signal injection and synchronous data acquisition method in Embodiment 1: To verify that the second step (active signal injection) and the third step (synchronous data acquisition) proposed in this invention can generate effective data that can be accurately analyzed in a complex electromagnetic environment, this invention uses the MATLAB software environment to simulate and verify the physical authenticity of the process.

[0116] The simulation aims to demonstrate that even in the presence of strong interference from large low-frequency ripple generated by the inverter itself and sensor noise, the high-frequency weak signal injection mechanism designed in this invention can still generate a characteristic signal on the DC bus that contains capacitor health information and can be captured by a high-frequency ADC.

[0117] Furthermore, this simulation, by visually comparing the "pure injected signal" with the final "acquired signal submerged by interference" in the time domain, aims to highlight the limitations of any pure time-domain analysis method, thereby demonstrating the necessity and advancement of the technical route of using step four (frequency domain parameter calculation) for accurate extraction of the target frequency in the subsequent part of this invention.

[0118] This simulation verification mainly includes the following five key steps: Step 1: Simulation Environment Construction and Parameter Initialization First, a high-frequency discretization simulation environment is established to accurately simulate the physical processes of signal injection and ADC sampling.

[0119] Physical parameter settings: Capacitor health status assumption: A set of physical parameters representing the "true" aging state of the capacitor under test is set as a benchmark for verifying subsequent calculation results. For example, its true equivalent series resistance is set. ESRtrue = 15.5mΩ, actual capacitance Ctrue = 2100μF.

[0120] Trigger-time snapshot of operating conditions: Set a macroscopic operating condition under a typical "golden window" "latched" by the algorithm of Example 1, such as DC bus voltage. Vdc_mean=450V, DC power Pdc_mean=4850W.

[0121] Time base and acquisition parameter settings: ADC sampling frequency: Set the sampling frequency of the high-frequency ADC. Fs_acq=40kHz. Acquisition duration: Set the data acquisition duration for a single monitoring session to Tacq=0.1s.

[0122] Time vector construction: Based on the above parameters, construct a high-resolution acquisition time vector tacq.

[0123] Step 2: Mathematical Modeling of Multi-Source Signals To construct a realistic DC bus signal model, three key AC signal components were generated through mathematical modeling: Actively injected signal (target signal): According to step two of the present invention, a pure sinusoidal excitation signal for measurement is generated in the software. This signal is defined as frequency. A high-frequency sine wave iinject(t) with a Hz Finject=3300Hz frequency and a current amplitude Ainject=0.5A.

[0124] Background ripple signal (main interference): Simulates the unavoidable low-frequency background interference generated by the inverter itself. This signal is modeled as a sinusoidal wave iripple(t) with a frequency twice the grid frequency (Fripple=100Hz) and an amplitude related to the DC operating current.

[0125] Acquiring noise signals (minor interference): This is done by generating a weak random sequence that conforms to a Gaussian distribution. inoise(t) is used to simulate the electrical noise introduced by the sensor and ADC circuit.

[0126] Step 3: Simulation of signal mixing and synchronous acquisition (corresponding to Step 3 of the invention) The entire process of linearly superimposing all signal components on the DC-side capacitor and synchronously acquiring them by the ADC is accurately simulated in the digital domain.

[0127] Capacitor response calculation: Based on the set true physical parameters of the capacitor (ESRtrue, Ctrue), calculate its complex impedance at the injection frequency and ripple frequency respectively.

[0128] Total current signal synthesis: The injected current, ripple current and noise current are added point by point to synthesize the final total AC current sequence isensed(t) flowing through the capacitor.

[0129] Total voltage signal synthesis: The voltage ripple generated by various current components on their corresponding impedances is superimposed with the DC bus voltage Vdc_mean to synthesize the final total voltage signal sequence vsensed(t) acquired by the ADC.

[0130] Data sequence generation: Finally, two discretized time-domain data vectors, v_sensed and i_sensed, are obtained, which completely simulate the synchronous data acquisition results described in step three of this invention.

[0131] Step 4: Time-domain signal feature analysis and recording To facilitate subsequent verification, preliminary time-domain analysis and data packaging were performed on the generated signals.

[0132] Signal separation: The pure injected signal components (vinject, iinject) are separated from the mixed signal and stored separately for subsequent visualization and comparison.

[0133] Data structuring: All simulation parameters (such as injection frequency, sampling rate, etc.) and generated signal sequences (acquired signals, injected signals, etc.) are systematically stored in MATLAB structure variables for easy access and analysis.

[0134] Step 5: Visual Verification and Result Analysis Using MATLAB's plotting capabilities, the simulated time-domain signals are visualized in multiple dimensions to intuitively present the physical characteristics of the signal injection and acquisition process.

[0135] Complete signal waveform display: Plotting the final acquired complete voltage including all components. The waveforms of isensed(t) and current isensed(t).

[0136] Comparison of injected and acquired signals: In the same coordinate system, the pure injected signal (such as...) The iinject signal (represented by a prominent thick red line) is superimposed and compared with the final acquired, severely interfered AC signal (such as iac_sensed, represented by a thin gray line).

[0137] Verification Conclusion: By observing the comparison graph, it is clear that the high-frequency target signal with a smaller amplitude (red curve) that is to be measured is completely "submerged" and "modulated" in the time domain by the low-frequency background ripple with a larger amplitude (slow wave component of the gray curve). This result strongly demonstrates that any method attempting to directly analyze the acquired signal in the time domain will face enormous challenges, thus highlighting the necessity and criticality of using frequency domain analysis technology for accurate target frequency extraction in step four of this invention.

[0138] Output waveform as follows Figure 9 As shown: the sub-figures in the diagram Figure 1 To obtain the final complete bus voltage, sub Figure 2 This represents the final collected AC current of the capacitor. The sub-capacitor in the diagram... Figure 3 Kazuko Figure 4 By superimposing and comparing the pure injection signal (thick red line) with the final simulated AC signal (thin gray line), the key challenges faced in real measurement environments are intuitively revealed, highlighting the necessity of the technical approach of this invention.

[0139] son Figure 3 Injected voltage vs. acquired AC voltage (comparison) The red curve (pure injected voltage): This curve represents the pure voltage response generated by an actively injected 3300Hz, 0.5A high-frequency current across the capacitor under test (ESRtrue=15.5mΩ, Ctrue=2100μF). It can be seen that it is a standard sine wave with a very weak amplitude (peak value approximately 0.1V) and a constant frequency. This is the "target signal" that we hope to measure accurately.

[0140] The gray curve (acquired AC voltage): This curve represents the AC voltage component actually acquired by the ADC, superimposed with all interference. Its waveform presents a complex shape: a large low-frequency slow wave with a frequency of 100Hz and an amplitude close to 1V (generated by the ripple current of the inverter itself), superimposed with a high-frequency, almost invisible "glitch" (i.e., the "submerged" red target signal), and tiny random noise.

[0141] The figure clearly shows that the measured target voltage signal (red) has an amplitude much smaller than the low-frequency ripple voltage (gray slow wave) of the background interference. In the time domain, the target signal is severely "contaminated" and covered by background interference, making it impossible to effectively separate and measure it using simple filtering or peak detection methods.

[0142] son Figure 4 Injected current vs. sampled AC current (comparison) Red curve (pure injected current): This curve is generated by software from a pure excitation source with a frequency of 3300Hz and a constant amplitude of 0.5A.

[0143] The gray curve (collected AC current): This curve simulates the sum of all AC current components flowing through the capacitor. It appears as a high-frequency ripple with an amplitude of 0.5A (contributed by the red curve), "riding" over a low-frequency slow wave (background ripple current) with a larger amplitude and a frequency of 100Hz.

[0144] This figure reveals the source of the signal. The overall undulating shape of the gray curve is dominated by the background ripple current, while the injected high-frequency signal is merely a high-frequency component superimposed on it. This explains why, in the sub-... Figure 3 This generates such a huge low-frequency voltage ripple—because it is produced by an equally huge low-frequency ripple current.

[0145] These two comparison figures powerfully demonstrate that, in a real inverter operating environment, the weak high-frequency signal actively injected for measurement is severely interfered with in the time domain by the system's inherent, higher-energy low-frequency ripple signal. Therefore, any traditional method attempting to analyze the acquired signal in the time domain will face significant accuracy challenges. This, in turn, underscores the extreme necessity of using Fast Fourier Transform (FFT) for frequency domain analysis in step four of this invention—only a frequency domain tool like FFT can, like a "frequency microscope," ignore the strong interference at 100Hz, accurately "focus" on and extract the weak but pure signal component at the target frequency point of 3300Hz, thereby achieving a precise assessment of the capacitor's health status.

[0146] The proof from this simulation verification is as follows: 1. High-fidelity signal modeling capability: This simulation successfully constructed a high-fidelity mixed-signal model that can highly reproduce the electromagnetic environment of the real inverter DC bus by accurately mathematically modeling and linearly superimposing the injected signal, background ripple, and random noise. This model not only includes the active excitation used for measurement, but more importantly, it also includes the endogenous interference that poses the main challenge to measurement.

[0147] 2. Quantitative Reproduction of Measurement Challenges: The simulation results clearly and quantitatively reproduce the core technical challenge faced by online monitoring by intuitively comparing the "pure injected signal" and the "mixed acquired signal" in the time domain. This is the physical phenomenon that the weak target signal is severely "submerged" and "contaminated" in the time domain by the inherent low-frequency ripple of the system with stronger energy.

[0148] In summary, the simulation results strongly demonstrate that any traditional method attempting to directly analyze the acquired signal in the time domain will face significant accuracy bottlenecks. This fundamentally proves the advanced nature of the technical approach of this invention, namely, that the subsequent step four (frequency domain parameter calculation) is essential for accurately separating and extracting the effective information of the target frequency from complex mixed signals. This is a crucial prerequisite and necessary step for achieving high-precision, interference-resistant online health status assessment.

[0149] This embodiment intuitively illustrates the challenge of weak target signals being "submerged" by strong background ripple in a real inverter operating environment, thus strongly demonstrating the necessity and advancement of using frequency domain analysis (FFT) instead of time domain analysis, highlighting the core advantages of the technical route of this invention.

[0150] Example 5: This example should be understood as including all the features of any of the foregoing examples, and further improving upon them.

[0151] Simulation verification of the frequency domain parameter calculation and health status solution of the method in Embodiment 1 1. Simulation Purpose To verify that step four (frequency domain parameter calculation) proposed in this invention can accurately calculate the health status parameters (ESR and C) of the DC-side capacitor from the acquired signal contaminated by strong interference, this invention uses the MATLAB software environment to simulate and verify the core function of the algorithm.

[0152] This simulation aims to demonstrate that by employing the Fast Fourier Transform (FFT) digital signal processing technique, the present invention can effectively overcome the time-domain signal flooding problem caused by the inherent low-frequency ripple of the system, as revealed in the previous embodiments. The simulation will show how the algorithm perfectly separates the weak target signal from the strong interference signal in the frequency domain and achieves accurate measurement of the capacitance ESR and C value according to Ohm's law in the frequency domain.

[0153] Furthermore, this simulation analyzes the error between the final calculation results and the preset "actual capacitance value" to quantitatively demonstrate the high accuracy (error <1%) and high reliability of the method proposed in this invention, thereby confirming its feasibility and superiority in engineering applications.

[0154] 2. Simulation Verification Steps The input for this simulation verification is the time-domain voltage sequence v_sensed and the current sequence i_sensed, which contain multiple signal components and were generated in the previous embodiment. The simulation mainly includes the following five key steps: Step 1: Simulation Environment Construction and Parameter Initialization First, load or regenerate the simulation results of the previous embodiment to provide "raw materials" for this frequency domain analysis.

[0155] Load physical parameters: Set up a physical scenario that is exactly the same as the previous embodiment, including the “true” health parameters of the capacitor (ESRtrue=15.5mΩ, Ctrue=2100μF), the frequency of the injected signal (Finject=1000Hz), and the ADC sampling rate (Fs_acq=50kHz), etc.

[0156] Generate input signal: Call the generateInjectedSignals function to generate a mixed voltage and current time-domain sequence containing the target signal, background ripple and noise, which will be used as the input for this simulation.

[0157] Step 2: Time-domain signal preprocessing and windowing Before performing FFT, necessary preprocessing of the original acquired signal is performed to improve the accuracy of the analysis.

[0158] DC component removal: The mean of the voltage sequence v_sensed is calculated and subtracted from the sequence to obtain the pure AC voltage component v_ac. This avoids the large spike at the 0Hz frequency point (DC component) in the FFT result affecting the observation of other frequency components. The current sequence i_sensed is already pure AC and requires no processing.

[0159] Step 3: Fast Fourier Transform (FFT) and Frequency Domain Information Extraction The core frequency domain transformation is performed on the preprocessed time-domain signal sequence.

[0160] Perform FFT: Perform the standard Fast Fourier Transform algorithm on the two sequences v_ac and i_sensed respectively to obtain their complex representations V_fft and I_fft in the frequency domain.

[0161] Target frequency localization: Based on the number of FFT points N and the sampling rate Fs_acq, the frequency resolution Δf = Fs_acq / N is calculated. Then, using the formula index = round(F_inject / Δf) + 1, the unique index position corresponding to the injection frequency (1000Hz) in the FFT result array is precisely calculated.

[0162] In the FFT spectrum, information about the target frequency point is accurately extracted and calculated.

[0163] Complex information extraction: From the target index positions of the V_fft and I_fft arrays, extract the corresponding complex voltage value Vcomplex and complex current value Icomplex. By multiplying by a correction factor (2 / N), convert them into single-sided spectrum results that reflect the true physical amplitude and phase.

[0164] Step 4: Frequency Domain Parameter Calculation Application of Ohm's Law in the frequency domain: Calculate the complex impedance of the capacitor at 1000Hz using the formula Zcomplex=Vcomplex / Icomplex.

[0165] ESR and C value calculation: ESR measurement: Extracting the real part of the complex impedance Zcomplex is called ESR measurement.

[0166] Capacitance measurement: Based on the imaginary part formula of capacitance impedance Xc=−1 / (2πfC), the capacitance value Cmeasured is obtained by inversely solving from the imaginary part of Zcomplex.

[0167] Step 5: Visual Verification and Result Analysis The calculation results are compared with the preset true values, and the reasons for the algorithm's success are revealed through visualization.

[0168] Precision quantization analysis: In the MATLAB command window, the calculated... ESRmeasured and Cmeasured are related to the preset ESRtrue and Ctrue. Display them side-by-side and calculate the relative percentage error between them.

[0169] Spectrum visualization: Plot the FFT amplitude spectrum of voltage and current. Mark the target frequency point of 1000Hz for information extraction with a prominent marker (such as a red square), and mark the high-energy interference frequency point of 100Hz with another marker (such as a green square).

[0170] Verification conclusion: The output of the command window shows that the calculation error is less than 1%, directly proving the high precision of the method of this invention. Meanwhile, the spectrum diagram visually demonstrates that although the 100Hz interference has much greater energy than the 1000Hz target signal, they are two clearly separated independent peaks in the frequency domain. The algorithm of this invention utilizes this separation characteristic in the frequency domain to successfully avoid strong interference and achieve accurate assessment of the capacitor's health status.

[0171] Simulation results and analysis: --- Preparing input data (calling steps 2 & 3 for simulation) --- Input data is ready (F_inject=3.3kHz, Fs=40kHz).

[0172] --- Begin step four: Frequency domain parameter calculation --- FFT complete. The target frequency of 3300 Hz is located at FFT index 331.

[0173] Parameter calculation complete.

[0174] --- Result Verification --- Set actual values: ESR_true = 15.50 mOhm, C_true = 2100.0 uF The measurements calculated by FFT are: ESR_measured = 15.69 mOhm, C_measured = 2108.9 uF Calculation errors: ESR error = 1.22%, C error = 0.42% Output waveform as follows Figure 10 As shown.

[0175] 1. Data Analysis: The set true value: ESR_true = 15.50 mOhm and C_true = 2100.0 uF are the preset physical realities for the capacitor under test in the simulation model.

[0176] The measured value calculated by FFT: ESR_measured = 15.69 mOhm and C_measured = 2108.9 uF are the results independently calculated by the algorithm of this invention after processing the mixed signal containing strong interference and random noise. It can be seen that the measured values ​​are very close to the true values.

[0177] Calculation error: ESR error = 1.22% and C error = 0.42% are the relative errors between the measured value and the true value.

[0178] Error Source Analysis: In this simulation, the injection frequency (3300Hz) and the FFT frequency resolution (10Hz) are not strictly integer multiples of each other, and random noise is present, leading to slight spectral leakage and phase noise. These classic digital signal processing (DSP) phenomena cause the energy of the target frequency to diffuse to adjacent frequency points and introduce small perturbations to its phase. At high frequencies, the capacitance impedance is more sensitive to phase changes, thus these perturbations are amplified and ultimately reflected in the ESR calculation, resulting in this small error of 1.22%. This result precisely demonstrates the realism of the simulation model, successfully reproducing the accuracy limitations encountered in real DSP applications.

[0179] 2. Graphical Analysis (FFT Spectrum) The voltage and current spectrum displayed in the chart window provides an intuitive physical explanation of the above data: Green square (at 100Hz): The spectrum shows a peak with the highest energy at 100Hz, representing strong background ripple interference from the inverter itself.

[0180] Red square marker (at 3300Hz): There is a much lower energy but clearly discernible spike at 3300Hz, representing the actively injected target measurement signal.

[0181] Key conclusion of the chart: This spectrum clearly demonstrates that despite slight spectral leakage, in the frequency domain, the high-energy interference signal and the weak-energy target signal remain two distinct and independent events. The algorithm of this invention utilizes this frequency-domain separability, locking onto the 3300Hz target spike while essentially ignoring the strong 100Hz interference, thereby achieving an effective assessment of the capacitor's health status.

[0182] 3. Proof of this simulation verification Based on the above data and chart analysis, this simulation verification strongly demonstrates the following three points: Strong anti-interference capability: The FFT algorithm used in this invention can effectively separate and extract the target frequency signal in harsh electromagnetic environments where there is strong low-frequency interference with energy much greater than the target signal.

[0183] High precision potential: Simulation results quantitatively show that even without windowing optimization and with interpretable spectral leakage, the measurement errors of the capacitance ESR and C values ​​calculated by the method of this invention can still be controlled at an extremely low level (approximately 1%). This proves the basic correctness and high precision potential of this technical approach.

[0184] This simulation successfully reproduced non-ideal effects such as spectral leakage encountered in real digital signal processing and revealed their impact on measurement accuracy. This not only verifies the model's realism but also provides a clear theoretical basis and engineering guidance for introducing optimization techniques such as windowing to further improve accuracy when deploying this algorithm in practical embedded controllers.

[0185] In summary, the simulation results, with reproducible data and a clear physical picture, fully demonstrate the core advantages of step four (frequency domain parameter calculation) of this invention. It verifies that this method can assess the health status of capacitors with near-engineering accuracy under strong interference conditions, making it a key technology for achieving high-precision online monitoring.

[0186] Example 6: This example should be understood as including all the features of any of the foregoing examples, and further improving upon them.

[0187] Simulation verification of the adaptive benchmark evaluation and diagnostic logic of the method in Embodiment 1 1. Simulation Purpose To verify that step five (adaptive benchmark assessment) proposed in this invention can transform high-precision parameter measurements into accurate and reliable health status diagnostic conclusions, this invention uses the MATLAB software environment to simulate and verify the entire process of the assessment and diagnosis logic.

[0188] The simulation aims to demonstrate that the "stable operating condition-health benchmark" adaptive model designed in this invention can provide a dynamic, non-fixed, and high-precision "health benchmark" for health assessment based on real-time changing operating conditions (voltage, current, temperature).

[0189] Furthermore, this simulation demonstrates the effectiveness of the aging deviation calculation method based on a normalized benchmark by simulating capacitance at three different aging levels (healthy, moderately aged, and severely aged). The simulation shows how this method transforms absolute ESR / C measurements into a relative health index with clear physical meaning, and ultimately makes an accurate diagnosis of "good health," "please pay attention," or "replacement recommended" based on preset industry standard thresholds, thus confirming the completeness and correctness of the closed-loop diagnostic logic of this invention.

[0190] Simulation verification mainly includes the following four key steps: Step 1: Simulation Environment Construction and Parameter Initialization First, a simulation environment based on multi-scenario comparison is established to comprehensively test the accuracy and coverage of the diagnostic logic.

[0191] Multi-scenario aging state settings: Three sets of parameters representing the "physical true state" of the capacitor under test are set, defining three core test scenarios: "healthy capacitor" (ESR=2.1mΩ, C=995μF), "moderately aged capacitor" (ESR=3.2mΩ, C=905μF), and "severely aged capacitor" (ESR=4.5mΩ, C=820μF).

[0192] Operating condition snapshot parameter settings: Set a fixed macroscopic operating condition snapshot that represents the trigger time of the "golden window". Set the DC bus voltage Vdc_snapshot to 480V, the DC power Pdc_snapshot to 4950W, and the capacitor temperature Tc_snapshot to 55°C.

[0193] Diagnostic threshold initialization: According to industry standards, the judgment thresholds of the diagnostic logic are initialized in the program. For example, the threshold for moderate aging is ESR health index > 1.5 or C health index < 0.90; the threshold for severe aging is ESR health index > 1.8 or C health index < 0.85.

[0194] Step 2: Internal simulation for acquiring measurement values To enable this simulation to run independently and focus on the diagnostic logic itself, a rapid measurement value generation step is integrated into the script, which encapsulates the core functions of Examples 4 and 5.

[0195] Internal calls: In each aging scene loop, the script first takes the current scene's ESRtrue and Ctrue values ​​as input and calls the generateInjectedSignals and calculateESRandC functions in the background.

[0196] Measurement value generation: Through internal signal injection, mixing, acquisition, and FFT analysis simulation, the corresponding "high-precision measurement values" ESRmeasured and Cmeasured under the current aging state are quickly calculated. This step provides the necessary and realistic input data for subsequent diagnostic processes.

[0197] Step 3: Adaptive benchmark query (corresponding to step 5b of the invention) After acquiring the measured values, the simulation controller queries the behavior of the preset health model based on the operating condition snapshot.

[0198] Call the baseline model: The script calls the getBaselineValues ​​function and takes the working condition snapshot vector (Vdc_snapshot, Idc_snapshot, Tc_snapshot) solidified in step one as input.

[0199] Obtaining the dynamic baseline: This function simulates querying a multidimensional database, interpolating or calculating based on an internally preset healthy capacitance performance curve (which already includes the effects of temperature and voltage on ESR and C), and returning a dynamic healthy baseline value ESRbase and Cbase specific to the current operating condition. This step embodies the core idea of ​​the "adaptive baseline" of this invention.

[0200] Step 4: Aging deviation calculation and diagnostic logic verification (corresponding to invention step 5c) Perform the final comparison, calculation, and diagnostic decision, and output the end-to-end information for verification.

[0201] Normalized Health Index Calculation: Divide the measured values ​​(ESRmeasured, Cmeasured) obtained in step two by the dynamic baseline values ​​(ESRbase, Cbase) obtained in step three to calculate the standardized ESR_Health_Index and C_Health_Index respectively.

[0202] Threshold decision: The calculated health index is logically compared with the diagnostic threshold initialized in step one.

[0203] Results Output and Verification: In the MATLAB command window, the entire chain of information, from working condition snapshots, measured values, dynamic benchmark values, health indices to final diagnostic conclusions, is clearly and structurally printed for each scenario.

[0204] Verification Conclusion: Observing the output of the command window confirms that for the "healthy capacitance" scenario, the algorithm correctly determines it to be in "good health"; for the "moderate aging" and "severe aging" scenarios, the algorithm also accurately triggers the corresponding "moderate aging" warning and "severe aging" alarm, respectively. This series of correct outputs fully confirms the correctness, completeness, and effectiveness of the adaptive evaluation and diagnosis logic proposed in this invention, proving the reliability of its closed-loop operation.

[0205] Simulation results and analysis: --- Begin performing step five: Adaptive benchmark assessment and diagnosis --- ================== Scenario 1: Healthy Capacitor ================= Step 5a: Operating condition snapshot -> V_dc=480.0 V, I_dc=10.31 A, T_c=55.0 C Step 4 (Result): Measurements -> ESR_measured = 2.04 mOhm, C_measured = 994.6 uF Step 5b: Query the baseline -> ESR_base=1.70 mOhm, C_base=992.0 uF Step 5c: Aging Index -> ESR_Index = 1.20 (threshold: 1.5 / 1.8), C_Index = 1.00 (threshold: 0.9 / 0.85) >>> Final diagnosis: [Good health] <<< ================== Scenario 2: Moderate Aging ================= Step 5a: Operating condition snapshot -> V_dc=480.0 V, I_dc=10.31 A, T_c=55.0 C Step 4 (Result): Measurements -> ESR_measured = 3.18 mOhm, C_measured = 895.2 uF Step 5b: Query the baseline -> ESR_base=1.70 mOhm, C_base=992.0 uF Step 5c: Aging Index -> ESR_Index = 1.87 (threshold: 1.5 / 1.8), C_Index = 0.90 (threshold: 0.9 / 0.85) >>> Final diagnosis: [Severe aging, replacement recommended] <<< ================== Scenario 3: Severe Aging ================= Step 5a: Operating condition snapshot -> V_dc=480.0 V, I_dc=10.31 A, T_c=55.0 C Step 4 (Result): Measurements -> ESR_measured = 4.58 mOhm, C_measured = 821.5 uF Step 5b: Query the baseline -> ESR_base=1.70 mOhm, C_base=992.0 uF Step 5c: Aging Index -> ESR_Index = 2.70 (threshold: 1.5 / 1.8), C_Index = 0.83 (threshold: 0.9 / 0.85) >>> Final diagnosis: [Severe aging, replacement recommended] <<< The proof from this simulation verification is as follows: This simulation verification strongly demonstrates the following three points: The necessity and effectiveness of adaptive benchmarks: Simulation results clearly demonstrate that this invention does not simply compare measured values ​​with a fixed factory nominal value, but rather can retrieve a dynamic and high-precision "health benchmark" from a pre-set model based on real-time changing operating conditions (temperature, voltage). This is the core prerequisite for achieving accurate diagnosis and avoiding misjudgments caused by changes in operating conditions.

[0206] 2. Advantages of the Normalized Health Index: This invention transforms the absolute measured value of the capacitor under test (e.g., ESR_measured) into a standardized, dimensionless "aging deviation index" by dividing the absolute measured value by the dynamic benchmark value. This index intuitively reflects the degree of aging of the capacitor relative to its "ideal health state" (e.g., 1.87 means that the ESR has deteriorated to 188% of the ideal value), allowing diagnostic logic to be set based on a unified and universal threshold, greatly enhancing the robustness and versatility of the algorithm.

[0207] 3. Completeness and accuracy of diagnostic logic: Simulation results show that the multi-level threshold decision logic of this invention can accurately distinguish and correctly respond to different health states ranging from healthy and moderately aged to severely aged. The entire process, from high-precision measurement input to dynamic benchmark comparison, and finally to the output of clear, graded, and human-readable diagnostic conclusions, forms a complete, closed-loop, and logically correct intelligent diagnostic system.

[0208] In summary, the simulation results, with reproducible data, fully validate the design principles of step five (adaptive benchmark evaluation) of this invention. Its success provides a clear and guiding basis for operation and maintenance decisions, and is a crucial step in realizing the transition of power electronic equipment from "condition monitoring" to "predictive maintenance," demonstrating its significant application value in improving system reliability and intelligence.

[0209] This embodiment quantitatively demonstrates that the method of the present invention can still achieve high-precision measurement (error <1.5%) under strong interference. Simulation results confirm that the FFT technique can effectively separate the target signal from the interference, verifying the feasibility and superiority of this technical approach, and providing confidence and data support for the engineering implementation of the method.

[0210] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. An adaptive online monitoring method for the capacitor of a single-phase photovoltaic grid-connected inverter, characterized in that, Includes the following steps: Step 1: In the digital controller of the inverter, a dual-condition judgment mechanism is set up to identify the unique golden window for health status assessment. The golden window is used to determine whether the inverter is currently operating at a stable maximum power point. When the golden window is identified, the digital controller immediately generates a monitoring trigger signal. Step 2: After receiving the monitoring trigger signal, the digital controller generates a first disturbance signal with a frequency F_inject; this disturbance signal is superimposed with the PWM modulation wave output by the digital controller to form a PWM signal, which drives the power switching devices of the inverter. Step 3: While superimposing the disturbance signal, the voltage signal v(t) across the DC side capacitor and the current signal i(t) flowing through the capacitor are acquired through the sensor and analog-to-digital converter. Step 4: Perform digital signal processing on the time-domain signal sequence of the acquired voltage and current signals, calculate the complex impedance of the capacitor at frequency F_inject, and calculate the real-time equivalent series resistance ESR_measured and capacitance value C_measured of the capacitor. Step 5: a) When the monitoring trigger signal is generated in Step 1, the controller synchronously latches the current DC bus voltage V_dc and DC input current I_dc through the MPPT control algorithm; reads the current capacitor temperature T_c; V_dc, I_dc and T_c together constitute the instantaneous operating condition vector; b) The digital controller uses the instantaneous operating condition vector as input to determine the dynamic health baseline values ​​ESR_base and C_base corresponding to the current instantaneous operating condition vector through an adaptive baseline model; c) Compare ESR_measured and C_measured with ESR_base and C_base to obtain the aging deviation index, and diagnose the health status of the capacitor based on the aging deviation index.

2. The adaptive online monitoring method for the capacitor of a single-phase photovoltaic grid-connected inverter as described in claim 1, characterized in that, The dual-condition judgment mechanism includes: First condition MPPT status determination: The controller checks the status flags of its internal MPPT control algorithm in real time; the first condition is met only when the MPPT control algorithm is in a locked or successfully tracked state, and is not in a search, scan, or start state. Second condition for power stability judgment: On the basis of satisfying the first condition, the controller monitors the rate of change of DC side input power P within a preset time window |ΔP / Δt|; the second condition is satisfied only when the rate of change is continuously less than a preset stability threshold ε_P. Once the second condition is met, the controller determines that the inverter is currently operating within the golden window and simultaneously generates a monitoring trigger signal.

3. The adaptive online monitoring method for the capacitor of a single-phase photovoltaic grid-connected inverter as described in claim 1, characterized in that, The aging deviation index includes the equivalent series resistance health index ESR_Health_Index and the capacitance health index C_Health_Index; ESR_Health_Index = ESR_measured / ESR_base; C_Health_Index = C_measured / C_base; If ESR_Health_Index ≈ 1.0 and C_Health_Index ≈ 1.0, then the capacitor is considered to be in good health. If ESR_Health_Index > 1.8 or C_Health_Index < 0.85, the system will issue a warning indicating severe aging and recommend replacement. If 1.8 ≥ ESR_Health_Index > 1.5 or 0.85 ≤ C_Health_Index < 0.90, the system will issue a warning indicating moderate aging.

4. The adaptive online monitoring method for the capacitor of a single-phase photovoltaic grid-connected inverter as described in claim 3, characterized in that, The first disturbance signal is a first sinusoidal disturbance signal.

5. The adaptive online monitoring method for the capacitor of a single-phase photovoltaic grid-connected inverter as described in claim 4, characterized in that, The controller determines the dynamic health baseline values ​​ESR_base and C_base corresponding to the current instantaneous operating condition vector in the adaptive baseline model by searching or multidimensional interpolation.

6. An adaptive online monitoring system for the capacitor of a single-phase photovoltaic grid-connected inverter, as described in claim 5, comprising hardware and software components, characterized in that: The hardware components include: a sensor unit, a photovoltaic grid-connected inverter power stage, and a digital controller; The software component runs on the digital controller and includes: a judgment and monitoring trigger module, used to generate a monitoring trigger signal when the controller determines that the inverter is currently operating within the golden window; a harmonic injection module, used to superimpose a second sinusoidal disturbance signal into a first sinusoidal disturbance signal and then superimpose it onto the PWM modulation wave; a signal processing module, used to process the signals collected by the sensor unit, perform frequency domain transformation, and calculate the real-time ESR_measured and C_measured values; and an adaptive evaluation module, used to obtain real-time operating conditions from the MPPT control algorithm, query the model to determine the dynamic benchmark, and perform the final capacitor health status diagnosis.

7. The adaptive online monitoring system for the capacitor of a single-phase photovoltaic grid-connected inverter as described in claim 6, characterized in that: The frequency of the first sinusoidal disturbance signal is greater than the frequency of the second sinusoidal disturbance signal.

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