Remote parameter control method and system for micro inverter

Abstract

The invention relates to the technical field of inverter remote parameter control, in particular to a micro inverter remote parameter control method and system, and the method comprises the following steps: receiving a control instruction from a management platform; sensing a non-fundamental frequency component of an actual output electrical parameter of the micro inverter; judging whether a matching risk exists or not; when it is judged that the matching risk exists, a power conversion strategy of the micro inverter is adjusted, and at least one of the following two operations is executed: energy of a non-fundamental frequency component of an actual output electrical parameter of the micro inverter is dispersed; enabling the peak frequency of the non-fundamental frequency component of the actual output electrical parameter of the micro inverter to deviate from the sensitive response point of the electric energy storage equipment; and maintaining the actual output electrical parameter of the micro inverter within the target output electrical parameter range of the micro inverter set in the control instruction. The stability and reliability of the system can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of remote parameter control of inverters, and in particular to a remote parameter control method and system for a micro inverter. Background Art

[0002] In distributed power supply systems, particularly in modern residential communities, microinverters are often closely connected to household energy storage devices and centrally controlled via a remote management platform. This remote control aims to optimize power scheduling, stabilize grid voltage, and precisely maintain frequency.

[0003] After executing remote commands, microinverters may enter specific switching modes, inevitably generating non-fundamental frequency components (such as harmonics) in their output. Furthermore, the energy storage devices operating in conjunction with microinverters have dynamically changing electrical response characteristics. For example, their equivalent impedance fluctuates with factors such as state of charge and temperature. When the specific frequency components of the inverter output match the electrical response characteristics of the energy storage device, this can trigger local harmonic amplification, causing signal interference, increased electrical losses, and even impacting system life. Summary of the Invention

[0004] The purpose of the present invention is to address the above-mentioned deficiencies and provide a remote parameter control method and system for a micro-inverter.

[0005] The present invention adopts the following technical solutions:

[0006] A micro-inverter remote parameter control method, the method comprising the following steps:

[0007] Receive control instructions from the management platform, where the control instructions include a target output electrical parameter range of the micro-inverter;

[0008] Sense the non-fundamental frequency components of the actual output electrical parameters of the microinverter and obtain the electrical response characteristics of the electric energy storage device linked to the microinverter;

[0009] Determine whether there is a mismatch risk between the non-fundamental frequency components of the microinverter's actual output electrical parameters and the electrical response characteristics of the energy storage device;

[0010] When it is determined that there is a matching risk, the power conversion strategy of the micro-inverter is adjusted to perform at least one of the following two operations: dispersing the energy of the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter; making the peak frequency of the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter deviate from the sensitive response point of the electric energy storage device;

[0011] And maintain the actual output electrical parameters of the micro-inverter within the target output electrical parameter range of the micro-inverter set in the control instruction.

[0012] The above solution can effectively avoid the risk of matching the non-fundamental frequency components of the micro-inverter output with the electrical response characteristics of the energy storage device, thereby reducing problems such as harmonic amplification, data transmission errors, sensor deviation, additional losses and shortened device life, and improving the stability and reliability of the system.

[0013] Optionally, the present application further proposes that when it is determined that there is a matching risk, the steps of adjusting the power conversion strategy of the micro-inverter include:

[0014] The micro-inverter receives sensitive frequency information of the community power network sent from the management platform;

[0015] The micro-inverter determines adjustment parameters of the power conversion strategy based on the sensitive frequency information of the community power network to perform at least one of the following two operations: dispersing the energy of the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter; making the peak frequency of the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter deviate from the sensitive response point of the electric energy storage device;

[0016] Furthermore, the non-fundamental frequency components of the actual output electrical parameters of the micro-inverter adjusted by the power conversion strategy are made to avoid the sensitive frequencies contained in the sensitive frequency information of the community power network.

[0017] Optionally, the present application further proposes that the step of determining whether there is a mismatch risk between the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter and the electrical response characteristics of the electric energy storage device includes:

[0018] Continuously obtain the amplitude of the non-fundamental frequency component of the actual output electrical parameters of the micro-inverter;

[0019] Calculate the short-term variation trend of the actual output electrical parameters of the micro-inverter based on the amplitude of the non-fundamental frequency component;

[0020] Continuously obtain the equivalent electrical response characteristics of the energy storage device at the corresponding non-fundamental frequency;

[0021] According to the equivalent electrical response characteristics, calculate the short-term change trend of the equivalent electrical response characteristics;

[0022] Determining a coordinated change relationship between the non-fundamental frequency component and the electrical response characteristics of the electric energy storage device based on a short-term change trend of the amplitude of the non-fundamental frequency component and a short-term change trend of the equivalent electrical response characteristics;

[0023] Adjust the sensitivity of the judgment of the coordinated change relationship according to the instantaneous load of the microinverter, the grid voltage fluctuation and the current internal control state;

[0024] Based on the adjusted judgment sensitivity and coordinated change relationship, it is determined whether there is a matching risk between the non-fundamental frequency components of the actual output electrical parameters of the micro-inverter and the electrical response characteristics of the electric energy storage device.

[0025] Optionally, the present application further proposes that the step of adjusting the sensitivity of the judgment of the coordinated change relationship according to the instantaneous load of the micro-inverter, the grid voltage fluctuation and the current internal control state includes:

[0026] The microinverter reports instantaneous load, grid voltage fluctuations, and current internal control status to the management platform;

[0027] The management platform determines the overall operating status of the community power network based on the instantaneous load, grid voltage fluctuations, and current internal control status reported by multiple microinverters;

[0028] The management platform calculates the sensitivity adjustment factor based on the overall operating status of the community power network and sends the sensitivity adjustment factor to multiple micro-inverters;

[0029] The micro-inverter adjusts the sensitivity of the judgment of the coordinated change relationship according to the sensitivity adjustment factor.

[0030] Optionally, the present application further proposes that the management platform calculates the sensitivity adjustment factor according to the overall operating status of the community power network, including:

[0031] The management platform continuously receives connection status information and operation mode information of each device in the community power network;

[0032] The management platform evaluates the equivalent electrical characteristics of the community power network based on the connection status information and operation mode information;

[0033] The management platform adjusts the calculation method of the sensitivity adjustment factor according to the equivalent electrical characteristics of the community power network;

[0034] The management platform calculates the sensitivity adjustment factor using the adjusted calculation method based on the overall operating status of the community power network.

[0035] Optionally, the present application further proposes that the step of evaluating the equivalent electrical characteristics of the community power network includes:

[0036] Construct an equivalent circuit model of the community power network based on the connection status information and operation mode information;

[0037] Equivalent electrical characteristics of the community power network are calculated based on an equivalent circuit model of the community power network. The equivalent electrical characteristics of the community power network include equivalent impedance and / or resonant frequency at different frequencies.

[0038] Optionally, the present application further proposes that the steps of constructing an equivalent circuit model of a community power network include:

[0039] Identify the topology of the community power network based on the connection status information;

[0040] According to the operation mode information, the equivalent electrical parameters of each device are obtained from the preset device electrical characteristics library;

[0041] The topological structure and equivalent electrical parameters of the community power network are combined to form an equivalent circuit model of the community power network.

[0042] Optionally, the present application further proposes that the step of calculating the equivalent impedance and / or resonant frequency of the community power network at different frequencies includes:

[0043] Perform frequency sweep analysis on the equivalent circuit model of the community power network;

[0044] Obtain a frequency response curve of the community power network within a preset frequency range based on the frequency scan analysis results;

[0045] According to the frequency response curve, the peak point and / or the resonant frequency point of the equivalent impedance of the community power network are identified, thereby calculating the equivalent impedance and / or the resonant frequency of the community power network at different frequencies.

[0046] Optionally, the present application further proposes that the step of identifying the peak point and / or resonant frequency point of the equivalent impedance of the community power network includes:

[0047] Analyze the slope change or amplitude extreme value of the frequency response curve;

[0048] According to the analysis results, the peak point and / or the resonant frequency point of the equivalent impedance of the community power network are determined.

[0049] Optionally, the present application further proposes a micro-inverter remote parameter control system, which is applied to the above-mentioned micro-inverter remote parameter control method, and the system includes:

[0050] An instruction receiving module is used to receive a control instruction from the management platform, where the control instruction includes a target output electrical parameter range of the micro-inverter;

[0051] A parameter sensing module is used to sense the non-fundamental frequency components of the actual output electrical parameters of the microinverter and obtain the electrical response characteristics of the electric energy storage device linked to the microinverter;

[0052] a risk judgment module, used to judge whether there is a mismatch risk between the non-fundamental frequency components of the actual output electrical parameters of the micro-inverter and the electrical response characteristics of the electric energy storage device;

[0053] The strategy adjustment module adjusts the power conversion strategy of the micro-inverter when it is determined that there is a matching risk, and performs at least one of the following two operations: dispersing the energy of the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter; making the peak frequency of the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter deviate from the sensitive response point of the electric energy storage device; and maintaining the actual output electrical parameter of the micro-inverter within the target output electrical parameter range of the micro-inverter set in the control instruction.

[0054] Through the above solution, a system entity for implementing the above control method can be provided, which is convenient for actual deployment and application and improves the integration and automation level of the system.

[0055] From the above, it can be seen that the present application provides a micro-inverter remote parameter control method and system, which can effectively avoid problems such as harmonic amplification, data transmission errors and device loss by sensing the non-fundamental frequency components output by the micro-inverter and the electrical response characteristics of the energy storage device in real time, and intelligently judge the matching risk. It can effectively avoid the matching risk between the non-fundamental frequency components output by the micro-inverter and the electrical response characteristics of the energy storage device, thereby reducing problems such as harmonic amplification, data transmission errors, sensor deviation, additional loss and shortened device life, and improving the stability and reliability of the system.

[0056] To further understand the features and technical contents of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are only for reference and illustration and are not intended to limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a flow chart of a method for remote parameter control of a micro-inverter according to the present invention;

[0058] Figure 2 This is a structural diagram of a micro-inverter remote parameter control system of the present invention. DETAILED DESCRIPTION

[0059] The following is an explanation of the embodiments of the present invention through specific embodiments. Those skilled in the art can understand the advantages and effects of the present invention from the contents disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and the 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. In addition, the drawings of the present invention are only for simple schematic illustrations and are not depicted according to actual dimensions. It is stated in advance. The following embodiments will further explain the relevant technical contents of the present invention in detail, but the disclosed contents are not intended to limit the scope of protection of the present invention.

[0060] This embodiment provides a micro-inverter remote parameter control method and system, combined with Figure 1 and Figure 2 shown.

[0061] refer to Figure 1 A method for remote parameter control of a micro-inverter, the method comprising the following steps: receiving a control instruction from a management platform, the control instruction including a target output electrical parameter range of the micro-inverter; sensing the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter, and obtaining the electrical response characteristics of an energy storage device linked to the micro-inverter; judging whether there is a matching risk between the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter and the electrical response characteristics of the energy storage device; when judging that there is a matching risk, adjusting the power conversion strategy of the micro-inverter, and performing at least one of the following two operations: dispersing the energy of the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter; making the peak frequency of the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter deviate from the sensitive response point of the energy storage device; and maintaining the actual output electrical parameter of the micro-inverter within the target output electrical parameter range of the micro-inverter set in the control instruction.

[0062] The non-fundamental frequency components of the microinverter's actual output electrical parameters refer to the various harmonics, interharmonics, and other high-frequency noise components contained in the microinverter's output voltage or current, in addition to the fundamental frequency, during the process of converting DC power into AC power. These components can be detected and analyzed using signal processing techniques such as Fourier transform, wavelet analysis, or fast Fourier transform (FFT). The primary purpose is to obtain the harmonic spectrum characteristics of the microinverter under specific operating modes in order to assess its potential interference with the energy storage device. The electrical response characteristics of the energy storage device linked to the microinverter refer to the energy storage device's ability to respond to external electrical signals at different frequencies. Specifically, these characteristics include electrical parameters such as equivalent impedance, admittance, or resonant frequency. These characteristics vary dynamically with factors such as its state of charge, internal temperature, and charge and discharge current. These characteristics can be obtained using frequency response analysis, impedance spectroscopy measurement, or model-based prediction. The primary purpose is to identify the energy storage device's sensitivity to non-fundamental components or its resonance point at specific frequencies. Mismatch risk refers to the potential coupling between the spectral characteristics of the non-fundamental frequency components of the microinverter's output and the electrical response characteristics of the energy storage device. This coupling can amplify the non-fundamental components in local circuits, potentially interfering with normal operation within the device. This is primarily intended to provide early warning of potential electrical incompatibilities that could degrade device performance or shorten device life. A power conversion strategy refers to the algorithm and parameter set used to control the switching behavior of power semiconductor devices within the microinverter. These strategies, such as pulse-width modulation (PWM), space vector modulation (SVM), or harmonic injection control, can include adjustments to parameters such as switching frequency, dead-time, modulation depth, or carrier waveform. These strategies are primarily designed to alter the spectral characteristics of the microinverter's output electrical parameters to proactively mitigate mismatch risk with the energy storage device. Dispersing the non-fundamental frequency components of the microinverter's actual output electrical parameters involves adjusting the power conversion strategy to distribute the non-fundamental energy, originally concentrated at a specific frequency, over a wider frequency range, thereby reducing the energy density at a single frequency. This can be achieved using random PWM, spread spectrum modulation, or multi-level inversion technologies. The primary goal is to reduce the impact of specific harmonics on sensitive points in the energy storage device. Making the peak frequency of the non-fundamental frequency component of the actual output electrical parameters of the micro-inverter deviate from the sensitive response point of the electric energy storage device means changing the power conversion strategy to move the frequency point with the most concentrated energy in the non-fundamental frequency component of the micro-inverter output to a frequency region in which the electric energy storage device is insensitive to the response. This can be achieved by using methods such as dynamic frequency offset, adaptive harmonic suppression or harmonic injection control, mainly to avoid resonance or amplification effect between the micro-inverter and the electric energy storage device.

[0063] The solution of this application effectively addresses potential electrical mismatch risks between microinverters and energy storage devices through an intelligent remote control and adaptive adjustment mechanism. First, the system receives control instructions from a management platform. These instructions specify the target output electrical parameter range that the microinverter must achieve. This serves as the starting point and fundamental constraint for the entire control process. During operation, the microinverter continuously senses the presence of non-fundamental frequency components in its actual output electrical parameters. These components are an inevitable byproduct of the inverter's power conversion process. Simultaneously, the system acquires the electrical response characteristics of the energy storage device linked to the microinverter. These characteristics change dynamically, reflecting the energy storage device's sensitivity to different frequency signals in its current state. Based on the real-time sensing and acquisition of the microinverter's non-fundamental frequency components and the energy storage device's electrical response characteristics, the system makes a critical determination: whether mismatch risks exist. This determination aims to identify whether the non-fundamental components of the microinverter's output could adversely couple with sensitive response points or resonant frequencies of the energy storage device, thereby amplifying local power fluctuations. If mismatch risks are determined, the system immediately activates the adaptive adjustment mechanism to adjust the microinverter's power conversion strategy. This adjustment is not made blindly, but rather specifically performs at least one of the following two operations: first, it disperses the energy of the non-fundamental frequency components of the actual output electrical parameters of the microinverter, reducing the energy concentration at a single frequency by changing the distribution of harmonic energy; second, it deviates the peak frequency of the non-fundamental frequency components of the actual output electrical parameters of the microinverter from the sensitive response point of the energy storage device, thereby avoiding resonance or amplification effects at the frequencies that the energy storage device is most susceptible to. Throughout the risk avoidance and strategy adjustment process, the system always maintains the actual output electrical parameters of the microinverter within the target output electrical parameter range set by the control instructions. This collaborative work of dynamic perception, intelligent judgment, and adaptive adjustment enables the system to proactively respond to complex electrical environment changes, thereby improving the overall operational stability and reliability of the distributed power system.

[0064] In a specific implementation, the management platform sends control instructions to the microinverter, such as requiring it to feed power to the grid at a specific power level within a specified time period and maintain the output voltage within a preset range. The microinverter integrates a signal acquisition and processing unit that continuously collects voltage and current signals from the microinverter's output. By performing real-time Fourier transform (FFT) analysis on these signals, it can accurately sense and extract non-fundamental frequency components from the actual output electrical parameters, such as the amplitude and frequency of the third and fifth harmonics. Simultaneously, the microinverter exchanges data with the connected energy storage device to obtain operating parameters such as its current state of charge, internal temperature, and charge and discharge current. Based on these parameters, the control module within the microinverter can query or calculate in real time the equivalent electrical response characteristics of the energy storage device at different frequencies, such as its equivalent impedance curve or resonant frequency point at specific harmonic frequencies. The risk assessment logic unit within the microinverter then compares and analyzes the spectral characteristics of the sensed non-fundamental frequency components with the acquired electrical response characteristics of the energy storage device. For example, if the peak frequency of the fifth harmonic output by the microinverter is found to be close to a resonant frequency point of the energy storage device at its current state of charge, and the amplitude of the harmonic exceeds a preset threshold, a matching risk is determined to exist. When a matching risk is determined to exist, the microinverter will immediately adjust its internal power conversion strategy. For example, if the risk is caused by the concentration of specific harmonic energy, the microinverter can switch to a random pulse width modulation (RPWM) mode, which randomizes the switching frequency or pulse width to disperse the originally concentrated harmonic energy into a wider frequency range, thereby reducing the amplitude of a single harmonic. Alternatively, if the risk is caused by the peak frequency coinciding with a sensitive response point, the microinverter can fine-tune its carrier frequency or modulation method so that the peak frequency of the output non-fundamental frequency component is slightly offset, thereby avoiding the sensitive response point of the energy storage device. While making these strategic adjustments, the microinverter's control system continuously monitors its output fundamental voltage, current, and power to ensure that these parameters always remain within the target output electrical parameter range set by the management platform's control instructions. For example, the output power is kept stable at the target value and the voltage fluctuation is within the allowable range.

[0065] This application further proposes that when it is determined that there is a matching risk, the steps of adjusting the power conversion strategy of the micro-inverter include:

[0066] The micro-inverter receives sensitive frequency information of the community power network sent from the management platform;

[0067] The micro-inverter determines adjustment parameters of the power conversion strategy based on the sensitive frequency information of the community power network to perform at least one of the following two operations: dispersing the energy of the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter; making the peak frequency of the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter deviate from the sensitive response point of the electric energy storage device;

[0068] Furthermore, the non-fundamental frequency components of the actual output electrical parameters of the micro-inverter adjusted by the power conversion strategy are made to avoid the sensitive frequencies contained in the sensitive frequency information of the community power network.

[0069] Among them, the sensitive frequency information of the community power network refers to the frequency point or frequency range that responds to or is interfered with by the power fluctuation of a specific frequency, which is identified by the management platform based on the operating status of the entire community power network, the connection status of the equipment and the analysis of historical data. It can be obtained by frequency scanning, harmonic analysis or impedance characteristic evaluation. Its purpose is to provide a global grid environment risk warning for the micro-inverter; the adjustment parameters of the power conversion strategy refer to the control variables used to change the power conversion process inside the micro-inverter, such as the switching frequency, modulation depth, dead time or filter circuit parameters of the pulse width modulation (PWM). It can be determined by a preset lookup table, an adaptive algorithm or an optimized calculation. Its purpose is to control the spectral distribution of the non-fundamental frequency components of the output electrical parameters of the micro-inverter; the energy of the non-fundamental frequency components of the actual output electrical parameters of the dispersed micro-inverter is It refers to adjusting the power conversion strategy to spread the non-fundamental energy originally concentrated at a specific frequency point into a wide frequency range, thereby reducing the energy density of a single frequency point. It can be achieved by using random pulse width modulation (RPWM), spread spectrum technology or multi-level modulation. Its purpose is to reduce the risk of resonance or strong coupling between specific harmonic frequencies and energy storage devices or other grid components; making the peak frequency of the non-fundamental frequency component of the actual output electrical parameters of the microinverter deviate from the sensitive response point of the energy storage device means actively moving the energy concentration point of the non-fundamental frequency component output by the microinverter away from the affected frequency point of the energy storage device by changing the power conversion strategy. It can be achieved by adjusting the switching frequency, changing the modulation waveform or introducing a frequency offset. Its purpose is to avoid the non-fundamental component from causing a local amplification effect inside the energy storage device, thereby protecting the stable operation of the equipment.

[0070] The solution of this application further improves the mechanism by which microinverters adjust their power conversion strategies when mismatching risks exist by considering the overall environment of the community power network. Specifically, when a microinverter detects a mismatching risk between its own output non-fundamental frequency components and the electrical response characteristics of the energy storage device, it no longer makes adjustments based solely on local information. Instead, it first receives sensitive frequency information about the community power network from the management platform. This information enables the microinverter to identify "sensitive areas" of the entire community power network—frequency points or frequency ranges susceptible to interference from specific frequencies. Based on this sensitive frequency information, the microinverter determines the adjustment parameters for the power conversion strategy. These parameters are determined not only to disperse the energy of the non-fundamental frequency components or to shift their peak frequencies away from the sensitive response points of the energy storage device, but more importantly, to proactively plan the spectrum of the adjusted non-fundamental frequency components to avoid the sensitive frequencies included in the sensitive frequency information. This means that while the microinverter addresses mismatching risks between itself and the energy storage device, it also avoids transferring the problem to other parts of the community power network. This mechanism enables microinverters to achieve a balance between local optimization and global stability when adjusting their power conversion strategies. By receiving sensitive frequency information from the community power network, microinverters gain comprehensive decision-making insights, enabling them to select a strategy that both addresses the risk of mismatching with energy storage devices and ensures that their adjusted output does not cause new interference to the community power network.

[0071] In a specific embodiment, when a micro-inverter determines that there is a matching risk, the specific process of adjusting its power conversion strategy can be implemented as follows. The micro-inverter first receives sensitive frequency information of the community power network from the management platform through its communication module, such as based on the Modbus TCP or MQTT protocol. This information can be a frequency list, such as [150Hz, 250Hz, 350Hz], or a set of frequency ranges, such as [[140Hz, 160Hz], [240Hz, 260Hz]], which represent frequency points in the community power network that may have resonance or be sensitive to harmonics. Next, the processor inside the micro-inverter determines the adjustment parameters of the power conversion strategy based on the received sensitive frequency information of the community power network, combined with the current operating status of the micro-inverter and the electrical response characteristics of the energy storage device. For example, if the goal is to disperse the energy of non-fundamental frequency components, the microinverter can employ random pulse width modulation (RPWM) technology to disperse harmonic energy by adjusting the variation range and distribution characteristics of the PWM carrier frequency. Adjustment parameters may include the carrier frequency center value, the random perturbation amplitude, and the seed of the random number generation algorithm. If the goal is to shift the peak frequency of the non-fundamental frequency components away from the sensitive response point of the energy storage device, the microinverter can adjust its PWM switching frequency. For example, by adjusting the original fixed switching frequency from 20kHz to 22kHz or 18kHz, the switching harmonics and their multiples avoid the sensitive response point of the energy storage device. The adjustment parameters may include the new switching frequency value. When determining these adjustment parameters, the microinverter performs internal verification to ensure that the non-fundamental frequency components of the actual output electrical parameters adjusted by the power conversion strategy do not fall within the sensitive frequency ranges included in the sensitive frequency information of the community power network. For example, by analyzing the spectrum through Fourier transform, the peak value or main energy distribution of the non-fundamental frequency components does not fall within the sensitive frequency ranges included in the sensitive frequency information of the community power network. For example, if the management platform indicates that 250Hz is a sensitive frequency, the microinverter will avoid any major harmonic components of its output falling near 250Hz when adjusting the switching frequency. If the initially determined adjustment parameters lead to a new sensitive frequency conflict, the microinverter can recalculate or select alternative parameters until a solution is found that can both resolve the matching risk with the energy storage device and avoid the sensitive frequency of the community power network. In this way, the microinverter can manage its own output harmonics and ensure its stable operation in the distributed power network.

[0072] The present application further proposes the steps of determining whether there is a matching risk between the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter and the electrical response characteristics of the electric energy storage device, including: continuously obtaining the amplitude of the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter; calculating its short-term change trend based on the amplitude of the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter; continuously obtaining the equivalent electrical response characteristics of the electric energy storage device at the corresponding non-fundamental frequency; calculating the short-term change trend of the equivalent electrical response characteristics based on the equivalent electrical response characteristics; determining the coordinated change relationship between the non-fundamental frequency component and the electrical response characteristics of the electric energy storage device based on the short-term change trend of the amplitude of the non-fundamental frequency component and the short-term change trend of the equivalent electrical response characteristics; adjusting the judgment sensitivity of the coordinated change relationship based on the instantaneous load of the micro-inverter, the grid voltage fluctuation and the current internal control state; and determining whether there is a matching risk between the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter and the electrical response characteristics of the electric energy storage device based on the adjusted judgment sensitivity and the coordinated change relationship.

[0073] Among them, the short-term change trend refers to the rate and direction of change of a certain electrical parameter or electrical characteristic value over time within a continuous time window. It can be calculated by performing sliding average, exponential smoothing or linear regression on historical data. Its purpose is to predict the evolution of the parameter or characteristic in the near future; the synergistic change relationship refers to the correlation between the amplitude change trend of the non-fundamental frequency component output by the microinverter and the change trend of the equivalent electrical response characteristics of the energy storage device. It can be determined by analyzing the synchronization, directionality or relative rate of the trends of the two. Its purpose is to identify potential harmonic amplification risks; adjusting the judgment sensitivity of the synergistic change relationship refers to dynamically adjusting the threshold or judgment standard used to judge whether the synergistic change relationship constitutes a matching risk according to external operating conditions or internal states. Specifically, it can be modified by introducing adjustment factors or dynamic weights to improve the accuracy and adaptability of risk judgment.

[0074] The solution of this application achieves refined assessment of the electrical matching risk between microinverters and energy storage devices through a series of dynamic analysis steps. First, the amplitude of the non-fundamental frequency components in the microinverter's output electrical parameters, as well as the equivalent electrical response characteristics of the energy storage device at the corresponding frequency, are continuously acquired. This ensures real-time monitoring of potential risk sources and receptor states. Furthermore, the short-term trends of these amplitudes and characteristics are calculated, capturing their respective dynamic evolution directions and rates, rather than just instantaneous values. Furthermore, based on these two short-term trends, the synergistic relationship between the non-fundamental frequency components and the electrical response characteristics of the energy storage device is determined. This enables the system to identify whether harmonic intensity and energy storage device sensitivity are developing synchronously towards a direction that may trigger amplification, thereby providing a more forward-looking risk indicator. Furthermore, considering the impact of external and internal factors on matching risk assessment, such as the microinverter's instantaneous load, grid voltage fluctuations, and the current internal control state, this solution dynamically adjusts the sensitivity of the synergistic relationship. This means that under different operating conditions, the system can adaptively adjust the strictness of risk assessment to avoid misjudgments or missed judgments. Ultimately, based on the adjusted judgment sensitivity and the determined synergistic change relationship, the system can accurately determine whether there is an electrical matching risk. Through the above-mentioned dynamic and adaptive judgment mechanism, this solution provides a solid foundation for the risk judgment link in the remote parameter control method of the micro-inverter. It enables the system to upgrade from static, preset threshold-based judgment to dynamic, trend-based and environmental factor-based intelligent judgment. As a result, when there is a matching risk, the micro-inverter can adjust its power conversion strategy more promptly and accurately, such as dispersing the energy of non-fundamental frequency components or deviating its peak frequency from the sensitive response point of the energy storage device, thereby effectively avoiding the internal data transmission and measurement interference of the device due to harmonic amplification, as well as the accelerated loss of key electronic components.

[0075] In some preferred embodiments, the present application is implemented as follows: a digital signal processor (DSP) within the microinverter can continuously sample the output current and voltage waveforms at high speed and, using a real-time Fourier transform (FFT) algorithm, accurately extract the instantaneous amplitude of each non-fundamental frequency component. Simultaneously, the energy storage device can integrate an impedance measurement module that periodically injects a weak sweeping frequency signal into its internal circuit or analyzes its response to system disturbances to obtain, in real time, its equivalent electrical response characteristics at different non-fundamental frequencies, such as equivalent impedance or resonant frequency. This data is then transmitted to the microinverter via an internal communication bus. Specifically, after receiving this real-time data, the microinverter control unit can employ a sliding average filter or exponential smoothing algorithm to calculate the short-term trend of the amplitude sequence and equivalent electrical response characteristic sequence of the non-fundamental frequency components. For example, an upward or downward trend can be determined by comparing the current value with the average of the previous N sampling points. Furthermore, the control unit can analyze the correlation or phase relationship between the two short-term trends to determine whether they exhibit a synergistic relationship. For example, if both trends show a synchronous upward trend, this indicates a potential mismatch risk. In a specific embodiment, to dynamically adjust the judgment sensitivity, the microinverter can monitor its instantaneous load, grid voltage fluctuations, and the current internal control mode (e.g., maximum power point tracking mode, constant voltage mode, or reactive power compensation mode) in real time. This operating status information can be used as input to dynamically generate a sensitivity adjustment factor using a preset lookup table or fuzzy logic controller. For example, when the microinverter is heavily loaded or the grid voltage fluctuates significantly, the sensitivity adjustment factor can be set to a higher value, allowing even a slight synergistic relationship to be identified as a mismatch risk. Finally, the microinverter combines the determined synergistic relationship with the adjusted judgment sensitivity, for example, by quantifying the synergistic relationship into a risk index and comparing it with a dynamic threshold value modified by the sensitivity adjustment factor to determine whether an electrical mismatch risk exists. When the risk index exceeds the dynamic threshold, the system determines that a mismatch risk exists and triggers subsequent adjustments to the power conversion strategy.

[0076] The present application further proposes that the steps of adjusting the sensitivity of judging the coordinated change relationship according to the instantaneous load, grid voltage fluctuation and current internal control state of the micro-inverter include: the micro-inverter reports the instantaneous load, grid voltage fluctuation and current internal control state to the management platform; the management platform determines the overall operating state of the community power network according to the instantaneous load, grid voltage fluctuation and current internal control state reported by multiple micro-inverters; the management platform calculates the sensitivity adjustment factor according to the overall operating state of the community power network, and sends the sensitivity adjustment factor to multiple micro-inverters; the micro-inverter adjusts the sensitivity of judging the coordinated change relationship according to the sensitivity adjustment factor.

[0077] The management platform refers to a central system responsible for centralized monitoring, data collection, analysis, and remote control of multiple micro-inverters and related equipment in a distributed power network. It can be a cloud computing-based software platform or a control center deployed on a local server. Its purpose is to achieve coordinated management and optimized operation of the entire power network. The overall operating status of a community power network refers to the comprehensive operating conditions of all or most of the micro-inverters and their associated equipment in the community at a specific point in time, including the total load level, voltage stability, frequency deviation, power flow distribution, and the operating mode of each device. It can be determined by aggregating, analyzing, and modeling data such as instantaneous load, grid voltage fluctuations, and internal control status reported by multiple micro-inverters. Its purpose is to provide a macro, global perspective to assess the health status and potential risks of the power network. The sensitivity adjustment factor refers to a value or parameter used to dynamically adjust the sensitivity of the microinverter to match risk judgment. It can be a multiplier, an offset, or a threshold adjustment. Its purpose is to enable the microinverter to flexibly adjust its judgment criteria for the coordinated change relationship between non-fundamental components and the electrical response characteristics of the energy storage device based on the overall operating status of the community power network, thereby avoiding misjudgments or missed judgments. The current internal control state refers to the operating mode or parameter configuration of the microinverter's internal control system at a specific moment. For example, it can be its current power output mode, reactive power compensation mode, fault protection state, or internal algorithm parameter settings. It can be directly obtained through the microinverter's internal sensors or control modules. Its purpose is to reflect the microinverter's own operating strategy and behavior mode, and provide local information for the management platform to evaluate the overall status of the network.

[0078] The solution of the present application realizes the dynamic adjustment of the sensitivity of micro-inverter matching risk judgment by introducing a management platform to perceive and coordinate the overall operating status of the community power network. Specifically, the micro-inverter no longer adjusts the judgment sensitivity based solely on its own local state information, but reports key operating data such as its instantaneous load, grid voltage fluctuations, and current internal control status to the management platform. As a central coordinator, the management platform can aggregate and comprehensively analyze these real-time data from multiple micro-inverters to determine the overall operating status of the entire community power network. For example, when most micro-inverters in the community are under high load at the same time or the grid voltage fluctuates frequently, the management platform can identify the unstable state of the overall operation of the network.

[0079] Based on an assessment of the overall operating status of the community power network, the management platform can calculate a sensitivity adjustment factor. This adjustment factor reflects the sensitivity level that the microinverter should adopt when judging the matching risk between the non-fundamental component and the electrical response characteristics of the energy storage device under the current network environment. For example, when the overall operating status of the network tends to be unstable, the management platform can calculate an adjustment factor that increases the sensitivity of the microinverter to prompt the microinverter to identify potential matching risks earlier; conversely, when the network operates smoothly, it can calculate an adjustment factor that reduces the sensitivity to avoid misjudgments caused by excessive judgment. The management platform then sends the calculated sensitivity adjustment factor to the corresponding microinverter.

[0080] After receiving the sensitivity adjustment factor, the microinverter adjusts its internal sensitivity for determining coordinated change relationships based on this factor. This adjustment allows each microinverter to consider not only its own local operating conditions but also the overall operational context of the entire community power network when determining whether there is a mismatch risk between its own output non-fundamental wave components and the electrical response characteristics of the energy storage device. This global information feedback and dynamic adjustment mechanism allows microinverters to more accurately identify mismatch risks that may be exacerbated by changes in the overall network operating status, allowing them to adjust their power conversion strategies at an earlier and more precise stage, effectively avoiding the amplification of local power fluctuations and improving the overall effectiveness of remote parameter control.

[0081] In some preferred embodiments, the system is implemented as follows: In a community-based distributed photovoltaic energy storage system, each household is equipped with a microinverter connected to a household energy storage device. These microinverters communicate with a cloud-based management platform via wireless communication modules.

[0082] When the microinverter is operating, its internal sensors collect real-time information about instantaneous load, grid voltage fluctuations, and the current internal control status. This data is packaged into data frames and periodically reported to the management platform via the wireless communication module.

[0083] After receiving data from all microinverters within the community, the management platform initiates a data fusion and analysis module. This module aggregates this data, performing tasks such as calculating the community's total instantaneous load, assessing the overall grid voltage stability, and compiling statistics on the current control mode distribution of each microinverter. Based on this comprehensive data, the management platform can determine the overall operational status of the community's power network, such as whether the community is experiencing peak electricity demand or whether the grid is experiencing regional voltage drops or frequency deviations.

[0084] The sensitivity calculation module in the management platform then calculates a sensitivity adjustment factor based on the overall operational status of the community power network. For example, if the management platform determines that the overall load on the community power network is high and the grid voltage fluctuates frequently, indicating a complex network environment and potential risks, the sensitivity calculation module can calculate a sensitivity adjustment factor greater than 1. Conversely, if the network operates smoothly and the load is balanced, a sensitivity adjustment factor close to 1 or less than 1 can be calculated. This adjustment factor is distributed to all microinverters in the community through the management platform.

[0085] After each micro-inverter receives the sensitivity adjustment factor sent by the management platform, it will apply it to its own internal matching risk judgment algorithm. For example, if the original threshold for judging the coordinated change relationship within the micro-inverter is T, then after receiving the sensitivity adjustment factor F, the new judgment threshold can be adjusted to T / F. In this way, when F is greater than 1, the judgment threshold will be lowered, allowing the micro-inverter to more sensitively identify small coordinated change trends as matching risks; when F is less than 1, the judgment threshold will be increased, thereby avoiding unnecessary power conversion strategy adjustments in a stable environment. In this way, the micro-inverter can dynamically adjust its matching risk identification strategy according to the overall operating status of the community power network, thereby achieving more accurate risk judgment and control.

[0086] This application further proposes that the management platform calculates the sensitivity adjustment factor based on the overall operating status of the community power network, including the following steps: the management platform continuously receives connection status information and operating mode information of each device in the community power network; the management platform evaluates the equivalent electrical characteristics of the community power network based on the connection status information and operating mode information; the management platform adjusts the calculation method of the sensitivity adjustment factor based on the equivalent electrical characteristics of the community power network; the management platform calculates the sensitivity adjustment factor using the adjusted calculation method based on the overall operating status of the community power network.

[0087] Among them, connection status information refers to the physical or logical connection relationship between each device in the community power network, which can be the topological structure data, communication link status or power connection status between devices, and its purpose is to reflect the real-time structure of the network; operation mode information refers to the current working status of each device in the community power network, which can be the power output mode of the inverter, the charging and discharging status of the energy storage device or the operating power level of the load device, and its purpose is to reflect the electrical behavior characteristics of the device; equivalent electrical characteristics refer to the overall electrical response characteristics of the community power network under specific frequencies or working conditions, which can be the equivalent impedance, equivalent susceptance or resonant frequency of the network, and its purpose is to characterize the dynamic electrical behavior of the network; the calculation method of the sensitivity adjustment factor refers to the specific algorithm or model used to determine the sensitivity adjustment factor, which can be based on a mathematical formula, a lookup table or a machine learning model, and its purpose is to dynamically adjust the calculation logic of the sensitivity adjustment factor according to the network status.

[0088] The solution of this application achieves dynamic optimization of the sensitivity of microinverter matching risk assessments by using a refined management platform to assess the overall operational status of the community power network and calculate sensitivity adjustment factors. Specifically, the management platform continuously receives connection status and operating mode information for each device in the community power network. This information is essential for a comprehensive understanding of the network's real-time status. Connection status information reveals the network topology, such as which devices are connected and what electrical paths they form. Operating mode information provides the current specific operating status of each device, such as whether the inverter is generating or in standby mode, whether the energy storage device is charging or discharging, and their power levels. Based on this detailed connection status and operating mode information, the management platform can assess the equivalent electrical characteristics of the community power network. This assessment is more than a simple parameter aggregation; rather, it comprehensively analyzes this information to construct an equivalent model that reflects the overall electrical response of the network, such as the network's equivalent impedance or resonant frequency. Precisely assessing the network's equivalent electrical characteristics enables the management platform to dynamically adjust the calculation of the sensitivity adjustment factor based on these characteristics. This means that the algorithm or model used to calculate the sensitivity adjustment factor is no longer fixed but can be adaptively adjusted based on the actual electrical behavior of the network, ensuring that the calculated factor more accurately reflects the current dynamic characteristics of the network. Ultimately, the management platform uses this adjusted calculation method to calculate the sensitivity adjustment factor based on the overall operating status of the community power network. This process ensures that the sensitivity adjustment factor can be dynamically adjusted according to changes in network status, allowing microinverters to effectively adapt to different operating conditions.

[0089] This refined sensitivity adjustment factor calculation method, combined with the microinverter's mechanism for determining sensitivity based on the coordinated change relationship adjusted by this factor, forms a closed-loop system. When the management platform provides a more accurate sensitivity adjustment factor, the microinverter's sensitivity can accurately adapt to the current network environment when determining whether there is a mismatch risk between the non-fundamental frequency components of its output electrical parameters and the electrical response characteristics of the energy storage device. For example, when the network's electrical characteristics indicate a high risk of resonance, the sensitivity can be increased, allowing the microinverter to identify potential mismatch risks earlier and more accurately. When the network environment is relatively stable, the sensitivity can be appropriately reduced to avoid excessive intervention. This dynamic adaptability enhances the microinverter's ability to identify and respond to potential mismatch risks, thereby effectively avoiding non-fundamental wave amplification and ensuring the accuracy of data transmission within the device and the long-term stable operation of key electronic components.

[0090] In some preferred embodiments, the management platform can specifically implement the calculation of the sensitivity adjustment factor in the following manner. The management platform can continuously receive connection status information and operating mode information from various micro-inverters, energy storage devices, smart meters and other devices in the community power network. For example, the connection status information may include the device ID, the bus node ID to which it is connected, and topological data indicating which other devices are directly connected; the operating mode information may include the real-time power output of the micro-inverter, such as photovoltaic power generation power, power fed to the grid, the state of charge of the energy storage device, the charge and discharge current, and its internal temperature.

[0091] Based on the received connection status information and operating mode information, the management platform can evaluate the equivalent electrical characteristics of the community power network. Specifically, the management platform can use this information to build a simplified network model, such as an equivalent circuit model based on the node admittance matrix, where the operating mode information of each device can be used to determine its equivalent impedance or admittance parameters in the model. By analyzing this equivalent circuit model, the management platform can calculate the equivalent impedance curve of the community power network at different frequencies or identify potential resonant frequency points, which are the equivalent electrical characteristics of the network.

[0092] Furthermore, the management platform can dynamically adjust the calculation method of the sensitivity adjustment factor based on the assessed equivalent electrical characteristics of the community power network. For example, if the assessment results show that the network has a high equivalent impedance peak or resonance point near a specific frequency, the management platform can switch to a more conservative calculation method, which will generate a higher sensitivity adjustment factor to make the microinverter more sensitive to non-fundamental components near that frequency; conversely, if the network electrical characteristics show good stability, the management platform can use a more relaxed calculation method to generate a lower sensitivity adjustment factor. This adjustment can be achieved by selecting one of several preset calculation algorithms or by dynamically adjusting the weight parameters or thresholds in a general calculation formula.

[0093] Ultimately, the management platform calculates the final sensitivity adjustment factor based on the overall operational status of the community power network, such as a comprehensive assessment based on macro-load, voltage fluctuations, and internal control status, combined with this adjusted calculation method. For example, if the overall operational status indicates that the network is heavily loaded and voltage fluctuates frequently, and the equivalent electrical characteristics indicate potential resonance risk, the management platform will use an adjusted, more conservative calculation method to generate a higher sensitivity adjustment factor and send it to each microinverter, guiding them to more cautiously judge matching risks.

[0094] The present application further proposes that the steps of evaluating the equivalent electrical characteristics of the community power network include: constructing an equivalent circuit model of the community power network based on connection status information and operation mode information; and calculating the equivalent electrical characteristics of the community power network based on the equivalent circuit model of the community power network, where the equivalent electrical characteristics of the community power network include equivalent impedance and / or resonant frequency at different frequencies.

[0095] Among them, connection status information refers to the physical connection relationship and topological structure information between various devices (such as micro inverters, energy storage devices, loads, etc.) in the community power network. It can be obtained through network topology discovery protocols, device registration information or manual configuration, etc. Its purpose is to clarify the flow path of current and power; operation mode information refers to the current working status or operating parameters of each device in the community power network, which may include the power output / input status, charging and discharging mode, voltage level, current size, internal control parameters, etc. of the device. Its purpose is to reflect the electrical behavior of the device under specific working conditions; the equivalent circuit model refers to the abstraction of a complex community power network into a simplified circuit representation composed of basic circuit elements such as resistors, inductors, and capacitors. It can be constructed using a lumped parameter model or a distributed parameter model. Its purpose is to predict and evaluate through standard circuit analysis methods. The electrical characteristics of the network; equivalent electrical characteristics refer to the overall electrical response characteristics of the community power network under specific working conditions, which may include the network's response capability to signals of different frequencies, energy storage and dissipation capabilities, etc. Its purpose is to comprehensively describe the electrical behavior of the network and provide a basis for subsequent system analysis and control; equivalent impedance refers to the obstruction of the community power network to alternating current at a specific frequency. It can be expressed in complex form, including resistance components and reactance components. Its purpose is to reflect the energy loss and energy storage capacity of the network at different frequencies; resonant frequency refers to the frequency point where the network's response to a specific frequency signal reaches its maximum or minimum due to the interaction between the inductor and capacitor elements in the community power network. It can be divided into series resonant frequency and parallel resonant frequency. Its purpose is to identify sensitive frequency points in the network where overvoltage or overcurrent amplification may occur.

[0096] The solution of the present application first receives the connection status information and operation mode information of each device in the community power network, which is the basis for constructing the network electrical model. It is precisely because the connection status information can clearly depict the physical topology of the network, and the operation mode information can reflect the electrical parameters of each device under actual working conditions, that it is possible to accurately construct the equivalent circuit model of the community power network. This equivalent circuit model is a high-fidelity abstraction of the actual complex power network at the electrical level, which takes into account the dynamic electrical behavior of all key devices in the network. On this basis, by performing rigorous circuit analysis on the constructed equivalent circuit model, the equivalent impedance and / or resonant frequency of the community power network at different frequencies can be calculated. These electrical characteristic parameters are key indicators for measuring the network's response to disturbances of different frequencies, and can fully reveal the network's electrical vulnerabilities and sensitive frequencies.

[0097] In some preferred embodiments, evaluating the equivalent electrical characteristics of a community power network can be implemented as follows. The management platform can continuously receive connection status and operating mode information from each device in the community power network. This connection status information can be obtained by polling or subscribing to the communication interfaces of each device (e.g., microinverters, energy storage units, smart loads, distribution transformers, etc.) through the management platform to obtain their IP addresses, MAC addresses, device IDs, and physical connection port information within the network, thereby generating a network topology diagram. Operating mode information can be collected from real-time operating data from each device, such as the current output power, voltage, current, switching frequency, and modulation mode of microinverters; the state of charge, battery temperature, charge and discharge currents, and internal impedance parameters of energy storage devices; and the power factor and harmonic content of smart loads. The management platform can use this connection status and operating mode information to dynamically generate an equivalent circuit model consisting of nodes and branches in memory. For example, each micro-inverter, energy storage device, load, and grid connection point is abstracted as a node, and the connecting lines and internal equivalent components between them are abstracted as branches. Each branch is assigned corresponding equivalent resistance, inductance, and capacitance values ​​based on the operating mode information. Once the equivalent circuit model is constructed, the management platform can call the built-in circuit simulation or analysis module to perform a frequency sweep analysis on the equivalent circuit model. This module can increase the frequency in small steps within a preset frequency range and calculate the equivalent impedance of the network at each frequency point. By analyzing the frequency response curve, the peak or valley points of the impedance can be identified, and the frequencies corresponding to these points are the resonant frequencies of the network. At the same time, the impedance value calculated at each frequency point is the equivalent impedance at that frequency. In this way, the equivalent impedance and / or resonant frequency of the community power network at different frequencies can be accurately obtained.

[0098] This application further proposes that the steps of constructing an equivalent circuit model of a community power network include: identifying the topology of the community power network based on connection status information; obtaining the equivalent electrical parameters of each device from a preset device electrical characteristics library based on operation mode information; and combining the topology and equivalent electrical parameters of the community power network to form an equivalent circuit model of the community power network.

[0099] Connection status information refers to real-time or near-real-time status data on the electrical connections between devices in a community energy network. This information can be obtained using network topology discovery protocols, inter-device communication handshake signals, or physical connection sensor data. The topology of a community energy network refers to the physical or logical layout of all electrical devices and their interconnections in the network. It can be represented as a graph of nodes and edges or an adjacency matrix. Operating mode information refers to the current operating status or configuration parameters of each device in the community energy network. This information may include the device's power output level, charge / discharge status, control strategy (e.g., maximum power point tracking mode, constant voltage mode), or internal temperature. A pre-set device electrical characteristics library is a pre-established database containing equivalent electrical parameters for various types of electrical devices under different operating modes. This library can be stored as a lookup table, parameterized model, or empirical curve. The equivalent electrical parameters of each device refer to a set of parameters that simplify the electrical behavior of a single electrical device in a specific operating mode in the community energy network. These parameters may include equivalent resistance, equivalent inductance, equivalent capacitance, or equivalent impedance.

[0100] In some preferred embodiments, constructing an equivalent circuit model of a community power network can be implemented as follows. First, to identify the topology of the community power network, the management platform can deploy a network topology discovery module. This module can periodically send query instructions to all devices in the community power network (e.g., microinverters, energy storage devices, smart loads, etc.) to collect their respective connection status information. For example, each device can report the identifier of its directly connected device or the identifier of the bus node to which it is connected. After receiving this information, the management platform can use graph theory algorithms (e.g., depth-first search or breadth-first search) to construct and update the network topology map, in which each device or bus can be represented as a node, and the connections between them are represented as edges. Second, to obtain the equivalent electrical parameters of each device, the management platform can maintain a preset device electrical characteristics library. This characteristic library can be a structured database that stores equivalent electrical parameter sets for various devices potentially present in a community energy network (e.g., microinverters of different models, battery energy storage systems of varying capacities) under various typical operating modes (e.g., full load, light load, charging mode, discharging mode, standby mode, and different ambient temperatures). Once the topology is identified, the management platform can query and extract the equivalent electrical parameters that best match the current operating mode of each device from the characteristic library based on the device's current operating mode information (e.g., obtained through real-time data reported by the device or control instructions issued by the management platform). For example, for a microinverter operating in maximum power point tracking mode, the system can retrieve its equivalent impedance model in that mode from the library. Finally, to combine the community energy network's topology and equivalent electrical parameters to form an equivalent circuit model of the community energy network, the management platform can utilize professional circuit simulation tools or internally developed modeling algorithms. The identified topology serves as the circuit skeleton, and the acquired equivalent electrical parameters of each device serve as the property values ​​of the corresponding component (for example, a microinverter is represented as a series connection between a controlled current source and an equivalent impedance, and an energy storage device is represented as a series connection between an equivalent voltage source and an equivalent impedance). This constructs a complete, dynamically updated equivalent circuit model of the community power network. This model can be a mathematical model based on a node admittance matrix or state-space equations, which is used for subsequent electrical characteristic calculations and analysis.

[0101] The present application further proposes a method for calculating the equivalent impedance and / or resonant frequency of a community power network at different frequencies. The method includes the following steps: performing a frequency sweep analysis on an equivalent circuit model of the community power network; obtaining a frequency response curve of the community power network within a preset frequency range based on the frequency sweep analysis results; and identifying the peak point and / or resonant frequency point of the equivalent impedance of the community power network based on the frequency response curve, thereby calculating the equivalent impedance and / or resonant frequency of the community power network at different frequencies.

[0102] The meaning and implementation of some key technical features of the above method can be further clarified. Frequency sweep analysis involves gradually varying the frequency of the excitation signal within a certain frequency range and measuring the response of the system under test to these varying frequencies. This can be simulated using simulation software (such as SPICE, MATLAB / Simulink) or measured using actual circuit testing equipment (such as a network analyzer). The goal is to obtain data on the system's electrical behavior at different frequencies. A frequency response curve is a graphical representation of the amplitude ratio and / or phase difference between the system's output and input at different frequencies. Specifically, it can be presented as a Bode plot or Nyquist plot. Its purpose is to visually demonstrate the system's dynamic characteristics across the entire frequency range. A preset frequency range refers to a frequency interval predetermined based on actual application requirements or specific electrical phenomena of interest. It can be set based on the harmonic frequency range that may occur in a community power grid or the operating frequency range of the device. Its purpose is to focus the analysis on key frequency regions relevant to system operation and stability, improving the relevance and efficiency of the analysis. The peak point of the equivalent impedance refers to the frequency point on the frequency response curve where the equivalent impedance amplitude reaches a local maximum, while the resonant frequency point refers to the frequency point at which the energy exchange between the inductor and capacitor elements of the system reaches a balance at a specific frequency, resulting in an extreme impedance value. It can be identified based on the amplitude change trend or phase mutation of the frequency response curve. Its purpose is to reveal the resonance phenomenon or impedance mutation that may occur in the community power network at a specific frequency. These phenomena may lead to abnormal amplification of voltage or current, affecting the stable operation of the system.

[0103] The solution of this application achieves accurate assessment of the frequency characteristics of a community power network through the synergistic effect of the aforementioned technical features. Its operating principle is as follows. The solution of this application achieves accurate assessment of the frequency characteristics of a community power network through a series of sequential steps. First, a frequency sweep analysis is performed on the equivalent circuit model of the community power network. This fundamental step simulates the network's behavior when exposed to various frequency signals, thereby obtaining comprehensive frequency response data. Next, based on the results of the frequency sweep analysis, a frequency response curve of the community power network within a preset frequency range is obtained. This curve intuitively depicts the electrical characteristics of the network at different frequencies, providing data support for subsequent in-depth analysis. Based on this frequency response curve, the peak points and / or resonant frequencies of the community power network's equivalent impedance are identified, and the equivalent impedance and / or resonant frequencies of the community power network at different frequencies are calculated. These peak points and resonant frequencies are key indicators of the network's electrical characteristics, revealing potential resonance or impedance mutations that may occur in the network at specific frequencies. By identifying these key points and calculating the equivalent impedance at different frequencies, the network's frequency characteristics can be more precisely understood, providing a more reliable basis for subsequent control strategies. The introduction of this solution enables the evaluation of the equivalent electrical characteristics of community power networks to move beyond simple model construction and delve deeper into the frequency dimension, accurately capturing the network's dynamic response at different frequencies. This precise frequency characteristic assessment further improves the accuracy of the management platform's sensitivity adjustment factor calculation, as the sensitivity adjustment factor calculation must consider the overall operating status and equivalent electrical characteristics of the community power network. By more accurately evaluating the equivalent electrical characteristics, the management platform can calculate sensitivity adjustment factors that better reflect actual network conditions. This allows microinverters to make more precise sensitivity adjustments when determining whether there is a mismatch risk between non-fundamental frequency components and the electrical response characteristics of energy storage devices. Ultimately, this enables microinverters to more effectively adjust their power conversion strategies when mismatch risks exist, such as dispersing the energy of non-fundamental frequency components or shifting peak frequencies away from sensitive response points. This prevents the amplification of local power fluctuations, ensures the accuracy of data transmission and measurement within the device, and extends the life of key electronic components.

[0104] To further illustrate the implementation details of this application, a specific example is provided below. In some preferred embodiments, calculating the equivalent impedance and / or resonant frequency of a community power network at different frequencies can be implemented as follows. First, an equivalent circuit model of the community power network can be established using professional circuit simulation software, such as PSCAD / EMTDC or MATLAB / Simulink. This model can include equivalent models of the photovoltaic inverter, energy storage system, load, and grid connection point. Then, within this simulation environment, a frequency sweep analysis is performed on the equivalent circuit model. This can be achieved by applying a swept sinusoidal voltage or current source to the input of the model and measuring the voltage and current responses at key points in the network. For example, the frequency sweep range can be set from tens of hertz to several kilohertz, and the step size can be adjusted as needed to capture subtle frequency characteristics. Based on the results of the frequency sweep analysis, the frequency response curve of the community power network within a preset frequency range can be automatically or manually extracted. For example, the measured impedance magnitude and phase data can be plotted as a Bode plot, where the horizontal axis represents frequency and the vertical axis represents impedance magnitude and phase angle. The preset frequency range can be determined based on the common harmonic frequencies in the community power network and the high-frequency components near the inverter switching frequency. Finally, based on the generated frequency response curve, the peak points and / or resonant frequency points of the community power network's equivalent impedance can be identified. This can be achieved by analyzing the shape of the curve, for example, by finding local maxima on the impedance amplitude curve or observing whether the phase curve changes sharply near specific frequencies. Once these points are identified, the equivalent impedance value and / or resonant frequency value of the community power network at these specific frequencies can be accurately calculated. This data can be stored and used for subsequent risk assessment and control strategy adjustments to ensure the stable operation of the community power network.

[0105] The present application further proposes that the step of identifying the peak point and / or resonant frequency point of the equivalent impedance of the community power network includes: analyzing the slope change or amplitude extreme value of the frequency response curve; and determining the peak point and / or resonant frequency point of the equivalent impedance of the community power network based on the analysis results.

[0106] Specifically, the key features of the above method can be further explained as follows. Among them, the slope change of the frequency response curve refers to the trend of the inclination of the tangent at each point on the frequency response curve changing with frequency. It can be obtained by numerical differentiation or differential operation of the frequency response curve. Its purpose is to capture the inflection point or mutation point of the curve. These points often correspond to the peak value or resonant frequency of the equivalent impedance. The amplitude extreme value refers to the maximum or minimum value reached by the frequency response curve at a specific frequency point. It can be identified by using a local maximum or minimum search algorithm. Its purpose is to directly locate the peak value or resonant frequency of the equivalent impedance.

[0107] Based on the above features, the overall operating principle of the scheme of the present application is as follows. The scheme of the present application identifies the peak point and / or resonant frequency point of the equivalent impedance of the community power network by conducting an in-depth analysis of the frequency response curve, specifically analyzing the slope change or amplitude extreme value of the frequency response curve. In practical applications, the frequency response curve is often affected by noise and interference, and directly searching for peak or resonant points may lead to misjudgment. By analyzing the slope change, the inflection point of the curve can be captured more sensitively. These inflection points usually indicate a significant change in the equivalent impedance or the occurrence of a resonance phenomenon. At the same time, combined with the analysis of the amplitude extreme value, the identification results can be verified from different dimensions, thereby effectively suppressing the influence of noise and interference on the identification process. Based on these analysis results, the peak point and / or resonant frequency point of the equivalent impedance of the community power network can be determined more accurately.

[0108] This more accurate identification method makes the data basis for subsequent calculations of the equivalent impedance and / or resonant frequency of the community power network at different frequencies more reliable. Since equivalent impedance and resonant frequency are key parameters for evaluating the electrical characteristics of the community power network, the improvement in their identification accuracy directly enhances the accuracy of the assessment of the overall operating status of the community power network. Furthermore, this accurate assessment can provide a more reliable input for the management platform to calculate the sensitivity adjustment factor, and ultimately improve the accuracy and effectiveness of the remote parameter control of the micro-inverter, thereby better avoiding the risk of matching the non-fundamental frequency components of the micro-inverter output with the electrical response characteristics of the energy storage device, and effectively solving the problems of amplifying local power fluctuations, interfering with data transmission and measurement within the equipment, and accelerating the loss of key electronic components.

[0109] As a specific implementation, the above-mentioned identification method can be implemented as follows. In some preferred embodiments, analyzing the slope change or amplitude extreme value of the frequency response curve can be specifically implemented as follows: First, the acquired frequency response curve can be preprocessed, for example, using a moving average filter or a Gaussian filter to smooth the curve to reduce the impact of noise on subsequent analysis. Then, the first-order derivative or the second-order derivative of the smoothed curve can be calculated to analyze the slope change. For example, when the first-order derivative changes from positive to negative and the second-order derivative is close to zero, it may indicate a peak point; when the first-order derivative changes significantly near a certain frequency point, it may indicate a resonant frequency point. At the same time, a peak detection algorithm can be used to directly identify the amplitude extreme value, for example, by setting a threshold, when the curve amplitude exceeds the threshold and reaches a local maximum value in its neighborhood, it is marked as a peak point.

[0110] Based on these analysis results, the peak points and / or resonant frequencies of the equivalent impedance of the community power network can be comprehensively determined. For example, if a frequency point satisfies both the inflection point characteristics indicated by the slope change and the extreme amplitude characteristics, it can be confirmed as a peak point or resonant frequency point with high confidence. For complex curves with multiple potential peaks or resonant points, further screening and verification can be combined with pre-set physical models or empirical rules to ensure the accuracy of the identification results.

[0111] refer to Figure 2 The present application further proposes a micro-inverter remote parameter control system, which is applied to a micro-inverter remote parameter control method, the system comprising: an instruction receiving module, for receiving a control instruction from a management platform, the control instruction including a target output electrical parameter range of the micro-inverter; a parameter sensing module, for sensing the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter, and obtaining the electrical response characteristics of an energy storage device linked to the micro-inverter; a risk judgment module, for judging whether there is a matching risk between the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter and the electrical response characteristics of the energy storage device; a strategy adjustment module, when judging that there is a matching risk, adjusting the power conversion strategy of the micro-inverter, and performing at least one of the following two operations: dispersing the energy of the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter; making the peak frequency of the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter deviate from the sensitive response point of the energy storage device; and maintaining the actual output electrical parameter of the micro-inverter within the target output electrical parameter range of the micro-inverter set in the control instruction.

[0112] Among them, the command receiving module refers to the unit responsible for receiving external control signals. Specifically, it can be a communication interface circuit, such as a data transceiver based on Ethernet, Wi-Fi or cellular network. Its purpose is to obtain the target operating parameters of the microinverter issued by the management platform. The parameter sensing module refers to the unit used to monitor and obtain electrical parameters in real time. Specifically, it can be a measurement circuit integrating a current sensor, a voltage sensor and a frequency analyzer. Its purpose is to capture the non-fundamental frequency components of the microinverter output and the dynamic electrical response characteristics of the energy storage device. The risk judgment module refers to the unit used to analyze and evaluate potential electrical matching risks. Specifically, it can be a digital signal processor or microcontroller with a specific algorithm embedded. Its purpose is to identify whether there is a matching relationship between the non-fundamental components of the microinverter and the electrical response characteristics of the energy storage device that may cause amplification. The strategy adjustment module refers to the unit used to dynamically modify the microinverter operation strategy. Specifically, it can be a programmable logic controller or high-performance microprocessor. Its purpose is to avoid the non-fundamental amplification effect by adjusting the power conversion strategy when a matching risk is identified, while ensuring that the microinverter remains within the preset output parameter range.

[0113] In some preferred embodiments, the system can be implemented as follows: The command receiving module can be a microcontroller unit with integrated Wi-Fi communication capabilities, such as an ESP32. It establishes a wireless connection with a remote management platform and receives and interprets control commands sent by the platform. These commands, in the form of data packets, contain the target output voltage, current, or power range of the microinverter. The parameter sensing module can be composed of a high-precision current sensor, a voltage sensor, and a high-speed analog-to-digital converter. These sensors respectively acquire the current and voltage signals at the output of the microinverter. The analog-to-digital converter converts the analog signal into a digital signal and feeds it into a digital signal processor. The digital signal processor executes a fast Fourier transform algorithm to analyze and extract the non-fundamental frequency components and their amplitudes from the actual output electrical parameters in real time. Furthermore, the parameter sensing module can obtain information such as the energy storage device's state of charge and temperature through a communication interface with the energy storage device (e.g., a CAN bus or Modbus). Combining this with a preset electrical model or real-time measurement data, it can infer the equivalent electrical response characteristics of the energy storage device at different frequencies. The risk assessment module, which can be an embedded microprocessor such as an STM32 series chip, receives non-fundamental frequency component data and electrical response characteristic data from the parameter sensing module. This microprocessor runs a matching risk assessment algorithm. Based on preset thresholds, trend analysis, or machine learning models, this algorithm determines whether the current spectral distribution of the non-fundamental frequency component overlaps or approaches the sensitive response frequency points of the energy storage device, thereby identifying potential non-fundamental amplification risks. The strategy adjustment module, which can be a high-performance field-programmable gate array or another independent microcontroller, is directly connected to the microinverter's power conversion circuit (such as the IGBT or MOSFET driver circuit in the inverter bridge). When the risk assessment module signals a matching risk, the strategy adjustment module dynamically adjusts the microinverter's pulse width modulation waveform based on a preset strategy library or real-time calculation results. For example, it can vary the pulse width modulation frequency, duty cycle, or employ a specific modulation algorithm (such as random pulse width modulation) to disperse the energy of the non-fundamental frequency component, making its spectral distribution wider, or actively shift the peak frequency of the non-fundamental frequency component away from the sensitive response points of the energy storage device. While making these adjustments, the strategy adjustment module continuously monitors the output of the microinverter to ensure that its output electrical parameters (such as voltage, frequency, and power) remain within the target range set by the instruction receiving module, thereby avoiding risks while ensuring the normal operation and power quality of the microinverter.

[0114] The contents disclosed above are only preferred feasible embodiments of the present invention and do not limit the scope of protection of the present invention. Therefore, all equivalent technical changes made using the contents of the present invention description and drawings are included in the scope of protection of the present invention. In addition, the elements therein can be updated as technology develops.

Claims

1. A remote parameter control method for a micro-inverter, characterized in that: The method comprises the following steps: Receive control instructions from the management platform, where the control instructions include a target output electrical parameter range of the micro-inverter; Sense the non-fundamental frequency components of the actual output electrical parameters of the microinverter and obtain the electrical response characteristics of the electric energy storage device linked to the microinverter; Determine whether there is a mismatch risk between the non-fundamental frequency components of the microinverter's actual output electrical parameters and the electrical response characteristics of the energy storage device; When it is determined that there is a matching risk, the power conversion strategy of the micro-inverter is adjusted to perform at least one of the following two operations: dispersing the energy of the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter; making the peak frequency of the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter deviate from the sensitive response point of the electric energy storage device; And maintain the actual output electrical parameters of the micro-inverter within the target output electrical parameter range of the micro-inverter set in the control instruction.

2. A micro-inverter remote parameter control method according to claim 1, characterized in that: When it is determined that there is a matching risk, the steps for adjusting the power conversion strategy of the microinverter include: The micro-inverter receives sensitive frequency information of the community power network sent from the management platform; The micro-inverter determines adjustment parameters of the power conversion strategy based on the sensitive frequency information of the community power network to perform at least one of the following two operations: dispersing the energy of the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter; making the peak frequency of the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter deviate from the sensitive response point of the electric energy storage device; Furthermore, the non-fundamental frequency components of the actual output electrical parameters of the micro-inverter adjusted by the power conversion strategy are made to avoid the sensitive frequencies contained in the sensitive frequency information of the community power network.

3. A micro-inverter remote parameter control method according to claim 1, characterized in that: The steps for determining whether there is a mismatch risk between the non-fundamental frequency components of the actual output electrical parameters of the microinverter and the electrical response characteristics of the electric energy storage device include: Continuously acquiring the amplitude of the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter; Calculate the short-term variation trend of the actual output electrical parameters of the micro-inverter based on the amplitude of the non-fundamental frequency component; Continuously obtain the equivalent electrical response characteristics of the energy storage device at the corresponding non-fundamental frequency; According to the equivalent electrical response characteristics, calculate the short-term change trend of the equivalent electrical response characteristics; Determining a coordinated change relationship between the non-fundamental frequency component and the electrical response characteristics of the electric energy storage device based on a short-term change trend of the amplitude of the non-fundamental frequency component and a short-term change trend of the equivalent electrical response characteristics; Adjust the sensitivity of the judgment of the coordinated change relationship according to the instantaneous load of the microinverter, the grid voltage fluctuation and the current internal control state; Based on the adjusted judgment sensitivity and coordinated change relationship, it is determined whether there is a matching risk between the non-fundamental frequency components of the actual output electrical parameters of the micro-inverter and the electrical response characteristics of the electric energy storage device.

4. A micro-inverter remote parameter control method according to claim 3, characterized in that: The steps for adjusting the sensitivity of the coordinated change relationship based on the instantaneous load of the microinverter, the grid voltage fluctuation, and the current internal control state include: The micro-inverter reports instantaneous load, grid voltage fluctuation and current internal control status to the management platform; The management platform determines the overall operating status of the community power network based on the instantaneous load, grid voltage fluctuations, and current internal control status reported by multiple microinverters; The management platform calculates the sensitivity adjustment factor based on the overall operating status of the community power network and sends the sensitivity adjustment factor to multiple micro-inverters; The micro-inverter adjusts the sensitivity of the judgment of the coordinated change relationship according to the sensitivity adjustment factor.

5. A micro-inverter remote parameter control method according to claim 4, characterized in that: The management platform calculates the sensitivity adjustment factor based on the overall operating status of the community power network, including the following steps: The management platform continuously receives connection status information and operation mode information of each device in the community power network; The management platform evaluates the equivalent electrical characteristics of the community power network based on the connection status information and operation mode information; The management platform adjusts the calculation method of the sensitivity adjustment factor according to the equivalent electrical characteristics of the community power network; The management platform calculates the sensitivity adjustment factor using the adjusted calculation method based on the overall operating status of the community power network.

6. A micro-inverter remote parameter control method according to claim 5, characterized in that: The steps for evaluating the equivalent electrical characteristics of a community energy network include: Construct an equivalent circuit model of the community power network based on the connection status information and operation mode information; Equivalent electrical characteristics of the community power network are calculated based on an equivalent circuit model of the community power network. The equivalent electrical characteristics of the community power network include equivalent impedance and / or resonant frequency at different frequencies.

7. A micro-inverter remote parameter control method according to claim 6, characterized in that: The steps to construct an equivalent circuit model of a community energy network include: Identify the topology of the community power network based on the connection status information; According to the operation mode information, the equivalent electrical parameters of each device are obtained from the preset device electrical characteristics library; The topological structure and equivalent electrical parameters of the community power network are combined to form an equivalent circuit model of the community power network.

8. A micro-inverter remote parameter control method according to claim 6, characterized in that: The steps for calculating the equivalent impedance and / or resonant frequency of a community energy network at different frequencies include: performing a frequency sweep analysis on an equivalent circuit model of the community power network; Obtain a frequency response curve of the community power network within a preset frequency range based on the frequency scan analysis results; According to the frequency response curve, the peak point and / or the resonant frequency point of the equivalent impedance of the community power network are identified, thereby calculating the equivalent impedance and / or the resonant frequency of the community power network at different frequencies.

9. A micro-inverter remote parameter control method according to claim 8, characterized in that: The steps of identifying the peak point and / or resonant frequency point of the equivalent impedance of the community power network include: Analyze the slope change or amplitude extreme value of the frequency response curve; According to the analysis results, the peak point and / or the resonant frequency point of the equivalent impedance of the community power network are determined.

10. A micro-inverter remote parameter control system, applied to the micro-inverter remote parameter control method according to claim 1, characterized in that: The system includes: An instruction receiving module is used to receive a control instruction from the management platform, where the control instruction includes a target output electrical parameter range of the micro-inverter; A parameter sensing module is used to sense the non-fundamental frequency components of the actual output electrical parameters of the microinverter and obtain the electrical response characteristics of the electric energy storage device linked to the microinverter; a risk judgment module, used to judge whether there is a mismatch risk between the non-fundamental frequency components of the actual output electrical parameters of the micro-inverter and the electrical response characteristics of the electric energy storage device; The strategy adjustment module adjusts the power conversion strategy of the micro-inverter when it is determined that there is a matching risk, and performs at least one of the following two operations: dispersing the energy of the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter; making the peak frequency of the non-fundamental frequency component of the actual output electrical parameter of the micro-inverter deviate from the sensitive response point of the electric energy storage device; and maintaining the actual output electrical parameter of the micro-inverter within the target output electrical parameter range of the micro-inverter set in the control instruction.

Images