Micro-inverter performance data processing method and system

Abstract

The application discloses a kind of micro inverter performance data processing method and system, it is related to photovoltaic power generation technical field, method includes: obtaining the instantaneous phase angle corresponding to multiple micro inverters respectively;Current phase dispersion index is calculated as standard deviation;Current phase dispersion index and preset coordination control trigger threshold are compared, and comparison result is obtained;If comparison result is current phase dispersion index is greater than preset coordination control trigger threshold, then power climb rate limit instruction is sent to each micro inverter, to make micro inverter adjust power climb rate;Current phase dispersion index is updated until current phase dispersion index is less than preset safety threshold, then rate limit release instruction is sent to each micro inverter, to make micro inverter restore power climb rate to initial value.The application can realize performance data processing by controlling power climb rate, so as to be able to smoothly adjust power, improve system stability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of photovoltaic power generation, and particularly relates to a micro-inverter performance data processing method and system. BACKGROUND

[0002] In an off-grid photovoltaic power generation system, a plurality of micro-inverters are usually connected in parallel to convert the direct current generated by a plurality of photovoltaic modules into an independent alternating current micro-grid. In this architecture, the consistency of the operation of all micro-inverters is crucial to maintaining the stability of the voltage and frequency of the micro-grid. However, due to differences in installation location, environmental stress, and other factors, the internal electronic components of each micro-inverter will experience different degrees of performance degradation and parameter drift over time. This inconsistency in performance may not be apparent under normal conditions, but it can induce systemic stability problems when the light intensity changes rapidly due to sudden weather changes. When power synchronization adjustment is required, the inconsistent performance of each micro-inverter causes some micro-inverters to respond quickly and others to respond slowly, resulting in chaotic voltage on the alternating current bus and the possibility of misjudging a single micro-inverter with fast response speed as a fault, triggering off-grid protection and further exacerbating power imbalance and oscillation on the bus, resulting in low system stability.

[0003] In summary, the technical problems in the related art need to be improved. SUMMARY

[0004] The main purpose of the embodiments of the present application is to provide a micro-inverter performance data processing method and system that can process performance data by controlling the power climb rate, thereby smoothly adjusting the power and improving system stability.

[0005] In one aspect, the present application provides a micro-inverter performance data processing method, comprising the following steps:

[0006] Obtaining the instantaneous phase angles corresponding to a plurality of micro-inverters, respectively;

[0007] Calculating the standard deviation as the current phase dispersion index according to the instantaneous phase angles corresponding to the plurality of micro-inverters, respectively;

[0008] Comparing the current phase dispersion index with a preset coordination control trigger threshold to obtain a comparison result;

[0009] If the comparison result is that the current phase dispersion index is greater than the preset coordination control trigger threshold, sending a power climb rate limiting instruction to each micro-inverter to make the micro-inverter adjust the power climb rate according to the power climb rate limiting instruction;

[0010] updating the current phase dispersion index until the current phase dispersion index is less than a preset safety threshold, and then sending a rate limit release instruction to each micro-inverter to cause the micro-inverter to restore the power ramp rate to an initial value according to the rate limit release instruction.

[0011] In some embodiments, the calculation of the standard deviation as the current phase dispersion index according to the instantaneous phase angles of the micro-inverters respectively includes:

[0012] obtaining current working state information and preset physical topology information corresponding to each micro-inverter;

[0013] determining an initial micro-inverter set, and the current working state information of each micro-inverter in the initial micro-inverter set being a power output state;

[0014] selecting, according to the preset physical topology information, a plurality of micro-inverters physically adjacent to each other from the initial micro-inverter set as a target micro-inverter set, and the target micro-inverter set being used to represent an operation sub-area formed by the plurality of micro-inverters physically adjacent to each other;

[0015] calculating the standard deviation as the current phase dispersion index according to the instantaneous phase angles of the micro-inverters in the target micro-inverter set respectively.

[0016] In some embodiments, the comparison of the current phase dispersion index and a preset coordination control triggering threshold to obtain a comparison result includes:

[0017] obtaining a historical phase dispersion index under a normal system operation state;

[0018] performing statistical feature calculation according to the historical phase dispersion index to obtain a reference range reflecting a current performance state of the system;

[0019] updating the preset coordination control triggering threshold according to the reference range;

[0020] judging whether the current phase dispersion index is greater than the updated preset coordination control triggering threshold to obtain the comparison result.

[0021] In some embodiments, the sending of the power ramp rate limit instruction to each micro-inverter to cause the micro-inverter to adjust the power ramp rate according to the power ramp rate limit instruction includes:

[0022] determining a target system risk level according to the current phase dispersion index;

[0023] sending a power ramp rate limit instruction to each of the micro-inverters, the power ramp rate limit instruction including the target system risk level;

[0024] The micro-inverter is configured to perform a verification process on the target system risk level to obtain a verification result, and if the verification result is verified and the current system risk level is less than the target system risk level, the current system risk level is updated, and the power ramp rate is adjusted according to the corresponding relationship between the risk level and the power ramp rate and the updated current system risk level.

[0025] In some embodiments, the verification process on the target system risk level to obtain a verification result includes:

[0026] If the target system risk level meets the preset numerical range requirement, the verification result is determined to be verified.

[0027] In some embodiments, the target system risk level is determined according to the current phase dispersion index, including:

[0028] The historical phase dispersion index in the normal operation state of the system is obtained.

[0029] The historical phase dispersion index and the current phase dispersion index are processed in time domain to obtain a time domain smoothing processing result.

[0030] The time domain smoothing processing result is compared with a plurality of preset risk level thresholds to determine an initial system risk level.

[0031] According to the preset risk level duration requirement and the initial system risk level, a state confirmation process is performed to obtain the target system risk level.

[0032] In some embodiments, the time domain smoothing processing result is obtained according to the historical phase dispersion index and the current phase dispersion index, including:

[0033] The historical phase dispersion index and the current phase dispersion index are processed in time domain to obtain a time domain smoothing processing result.

[0034] The real-time fluctuation characteristics are determined according to the real-time fluctuation characteristics.

[0035] The historical phase dispersion index and the current phase dispersion index are processed in time domain according to the time domain smoothing processing parameters to obtain the time domain smoothing processing result.

[0036] In some embodiments, the state confirmation processing is performed according to the preset risk level duration requirement and the initial system risk level to obtain the target system risk level, including:

[0037] The current operation state feature of the off-grid photovoltaic system is acquired, and the current operation state feature includes a fluctuation amplitude or a change rate of a current phase dispersion index;

[0038] The preset risk level duration requirement is updated according to the current operation state feature;

[0039] The initial system risk level is confirmed according to the updated preset risk level duration requirement to obtain the target system risk level.

[0040] In some embodiments, the state confirmation processing is performed according to the preset risk level duration requirement and the initial system risk level to obtain the target system risk level, including:

[0041] An ascending threshold and a descending threshold are defined, the ascending threshold is a threshold for converting from a low risk level to a high risk level, the descending threshold is a threshold for converting from a high risk level to a low risk level, and the descending threshold is less than the ascending threshold;

[0042] If the time domain smoothing processing result is greater than the ascending threshold and the duration meets the updated preset risk level duration requirement, the target system risk level is determined as a high risk level;

[0043] If the time domain smoothing processing result is less than the descending threshold and the duration meets the updated preset risk level duration requirement, the target system risk level is determined as a low risk level.

[0044] In another aspect, an embodiment of the present application provides a micro-inverter performance data processing system, including:

[0045] An acquisition module is configured to acquire instantaneous phase angles respectively corresponding to a plurality of micro-inverters;

[0046] A calculation module is configured to calculate a standard deviation as a current phase dispersion index according to the instantaneous phase angles respectively corresponding to the plurality of micro-inverters;

[0047] A judgment module is configured to compare the current phase dispersion index with a preset coordinated control trigger threshold to obtain a comparison result;

[0048] The limiting instruction issuing module is configured to send a power ramp rate limiting instruction to each micro-inverter if the comparison result is that the current phase dispersion index is greater than the preset coordinated control triggering threshold, so that the micro-inverter adjusts the power ramp rate according to the power ramp rate limiting instruction.

[0049] The releasing instruction issuing module is configured to update the current phase dispersion index until the current phase dispersion index is less than the preset safety threshold, and then send a rate limit releasing instruction to each micro-inverter, so that the micro-inverter restores the power ramp rate to the initial value according to the rate limit releasing instruction.

[0050] The embodiments of the present application have at least the following beneficial effects: The embodiments of the present application first acquire the instantaneous phase angles corresponding to a plurality of micro-inverters respectively, calculate the standard deviation as the current phase dispersion index, then compare the current phase dispersion index with the preset coordinated control triggering threshold to obtain a comparison result, if the comparison result is that the current phase dispersion index is greater than the preset coordinated control triggering threshold, then send a power ramp rate limiting instruction to each micro-inverter, so that the micro-inverter adjusts the power ramp rate according to the power ramp rate limiting instruction, and then update the current phase dispersion index until the current phase dispersion index is less than the preset safety threshold, and then send a rate limit releasing instruction to each micro-inverter, so that the micro-inverter restores the power ramp rate to the initial value according to the rate limit releasing instruction, so that the performance data processing can be realized by controlling the power ramp rate, and the power can be smoothly adjusted, thereby improving the system stability.

[0051] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application can be achieved and obtained by the structure particularly pointed out in the description and the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0053] Figure 1 A flow chart of a micro-inverter performance data processing method according to an embodiment of the present application;

[0054] Figure 2 A structural schematic diagram of a micro-inverter performance data processing system according to an embodiment of the present application. DETAILED DESCRIPTION

[0055] In order to make the purposes, technical solutions and advantages of the present application clearer, further detailed description will be made to the present application in combination with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not intended to limit the present application. When the following description refers to the accompanying drawings, the same numerals in different drawings represent the same or similar elements unless otherwise specified.

[0056] It can be understood that the terms "first", "second" and the like used in the present application can be used herein to describe various concepts, but unless specifically stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiments of the present application, the first information can also be referred to as the second information, and similarly, the second information can also be referred to as the first information. Depending on the context, the word "if" as used herein can be interpreted as "when" or "upon determining" or "in response to determining".

[0057] The terms "at least one", "multiple", "each", "any" and the like used in the present application include one, two or more than two, multiple includes two or more than two, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0059] Before the embodiments of the present application are described in detail, first, some nouns and terms involved in the embodiments of the present application are described, and the nouns and terms involved in the embodiments of the present application are applicable to the following explanations.

[0060] Micro-inverter: refers to the power of the photovoltaic power generation system less than or equal to 1000 watts, with component-level MPPT inverter, the full name is micro photovoltaic grid-connected inverter. "Micro" is relative to the traditional centralized inverter. Micro-inverter inverts each component, which has the advantages of independent MPPT control for each component, can greatly improve the overall efficiency, and can also avoid the direct current high voltage, poor weak light effect, wood barrel effect and other problems of centralized inverter.

[0061] In the related art, when a plurality of micro-inverters are connected in parallel and operate, the power response speed of each micro-inverter is different due to the inconsistency in performance caused by long-term operation of each micro-inverter when a global instantaneous change from extremely weak to extremely strong light intensity is encountered, which causes serious out-of-step of power injection of each micro-inverter at the moment of grid connection. This out-of-step causes temporary chaos on the AC bus, causing a large amount of reactive power to flow back and forth between the micro-inverters, and further causing power oscillation at the system level. The existing technology can only determine the fault code reported by a single micro-inverter after the fault occurs, and cannot identify this systemic problem caused by group performance differentiation, often leading to incorrect isolation and replacement of healthy equipment, and cannot fundamentally solve the problem, thereby failing to effectively prevent cascading off-grid faults and affecting the power supply continuity of the entire independent power grid.

[0062] For example, assume that an off-grid photovoltaic system consisting of forty micro-inverters has been operating without fault for four years. Due to the unevenness of long-term environmental stress, the internal components of each micro-inverter have aged to varying degrees, causing changes in the time constant and gain of their digital control loops, so that their actual response behavior is no longer uniform when facing instructions. In a weather mutation scenario where the light intensity recovers from extremely weak to extremely strong in a very short time, all micro-inverters simultaneously issue instructions to the maximum power point tracking program, requiring the power stage to quickly increase the output current. At this time, some micro-inverters, such as micro-inverters numbered 8, 15, and 29, have more sensitive control loop responses and increase the output current to the target value within tens of milliseconds. Other micro-inverters, such as micro-inverters 9 and 16, respond slowly by more than a hundred milliseconds. This time difference causes serious out-of-step of power injection, causing the current waveform phase of the micro-inverter with a fast response to lead the bus voltage phase, resulting in a large amount of reactive power flowing between the micro-inverters, causing a sharp oscillation of the total reactive power of the system. This oscillation can cause the internal temperature of the micro-inverter to rise sharply, triggering its protection logic and executing a protective off-grid instruction, causing the micro-inverter to suddenly exit, further exacerbating the imbalance and oscillation of the system.

[0063] If the above problems are not solved, off-grid photovoltaic systems will face serious operation risks when encountering dramatic changes in light. The loss of synchronization between the power injections of micro-inverters will directly lead to dramatic fluctuations in the AC bus voltage and frequency, threatening the stability of the system. Continuous reactive power oscillation will increase the electrical and thermal stress of the internal components of micro-inverters, accelerating the aging of the equipment and even causing component damage. More seriously, the protective disconnection of a single micro-inverter can trigger a chain reaction, causing more micro-inverters to be forced to exit due to system instability, ultimately leading to the collapse of the entire independent power grid and interrupting the power supply to critical loads. Such systemic failures are difficult to identify and resolve through traditional single-point fault diagnosis methods, and they also result in unnecessary equipment replacement and maintenance costs, severely affecting the reliability and power supply continuity of the system, and the system stability is low.

[0064] Therefore, the present application first considers how to solve the performance inconsistency by periodically calibrating each micro-inverter or replacing aged components. However, this method is costly and difficult to implement frequently in actual operation, especially for off-grid systems in remote areas, which are difficult to maintain. In addition, even if calibration, it cannot completely eliminate the subtle differences produced in long-term operation. In this regard, the present application further considers how to avoid loss of synchronization by uniformly limiting the power ramping rate of all micro-inverters. However, this static and global limitation sacrifices the fast response capability of the system under good light conditions, reduces the overall power generation efficiency, and cannot dynamically adapt to different degrees of performance difference and system risk. When the system is in good actual operating condition, such limitation is unnecessary and will affect the system performance. The present application determines that the core of the problem lies in the deviation of the consistency of the instantaneous phase angle of the micro-inverter group under certain working conditions. Therefore, a strategy is needed that can sense this group deviation in real time and dynamically coordinate control according to the deviation degree. Based on this, the present application monitors the instantaneous phase angle of the micro-inverter in real time and quantifies its dispersion degree, which is used as an indicator of system stability. When the indicator exceeds the pre-set safety range, the system can actively send instructions to the micro-inverter to dynamically adjust its power ramping rate, thereby effectively suppressing power oscillation caused by performance differences without affecting the overall efficiency of the system, and removing the limitation after the system recovers to stability, ensuring the continuity of power supply and improving the stability of the system.

[0065] The embodiments of the present application will be specifically explained below in conjunction with the drawings:

[0066] Figure 1 is an optional flowchart of a micro-inverter performance data processing method provided by an embodiment of the present application, Figure 1 The method in can include but is not limited to steps S101 to S105.

[0067] Step S101, obtain the instantaneous phase angles corresponding to the plurality of micro inverters respectively;

[0068] Step S102, calculate the standard deviation as the current phase dispersion index according to the instantaneous phase angles corresponding to the plurality of micro inverters respectively;

[0069] Step S103, compare the current phase dispersion index with the preset coordination control trigger threshold to obtain a comparison result;

[0070] Step S104, if the comparison result is that the current phase dispersion index is greater than the preset coordination control trigger threshold, send a power ramp rate limiting instruction to each micro inverter, so that the micro inverter adjusts the power ramp rate according to the power ramp rate limiting instruction;

[0071] Step S105, update the current phase dispersion index until the current phase dispersion index is less than the preset safety threshold, then send a rate limit removal instruction to each micro inverter, so that the micro inverter restores the power ramp rate to the initial value according to the rate limit removal instruction.

[0072] The steps S101 to S105 shown in the embodiments of the application can realize performance data processing by controlling the power ramp rate, thereby smoothly adjusting the power and improving the system stability.

[0073] In some embodiments, steps S101-S105, by obtaining the instantaneous phase angles of the plurality of micro inverters in real time, and taking the standard deviation thereof as the current phase dispersion index, and then comparing it with the dynamically adjusted preset coordination control trigger threshold, so that when the system shows potential instability signs, the power ramp rate limiting instruction is actively sent to the micro inverter, and the limitation is removed after the system recovers to stability, achieving the effect of prevention, rather than relying on post-fault diagnosis.

[0074] The instantaneous phase angles corresponding to the plurality of micro inverters can be obtained first, which are key parameters reflecting the degree of synchronization of the output waveforms of each micro inverter with the system reference. Then, the standard deviation is calculated as the current phase dispersion index according to the instantaneous phase angles corresponding to the plurality of micro inverters respectively, which intuitively quantifies the consistency of the phase synchronization among all online micro inverters. The greater the index value, the greater the difference between each inverter, and the higher the potential risk of the system. Then, the current phase dispersion index is compared with the preset coordination control trigger threshold to obtain a comparison result.

[0075] If the comparison result is that the current phase dispersion index is greater than the preset coordinated control triggering threshold, it indicates that the system may be in or about to enter an unstable state, and immediate intervention measures need to be taken. A power ramp rate limiting instruction can be sent to each micro-inverter to make the micro-inverter adjust the power ramp rate according to the power ramp rate limiting instruction, that is, limit the increase speed of its output power. The purpose of this limitation is to slow down the impact of micro-inverters with too fast response speed on the power grid, and to give more time to micro-inverters with slower response to synchronize, thereby effectively suppressing the power injection out-of-step caused by the inconsistent response speed of micro-inverters, and further avoiding or mitigating systemic power oscillation and potential off-grid failure.

[0076] Finally, update the current phase dispersion index until the current phase dispersion index is less than the preset safety threshold, which indicates that the synchronization of the micro-inverter group has returned to a safe level. A rate limit removal instruction is sent to each micro-inverter to make the micro-inverter restore the power ramp rate to the initial value according to the rate limit removal instruction, that is, allow it to increase power output at a normal or faster speed, in order to fully utilize the light resources, ensure that the system can operate efficiently and stably, and maximize energy capture. The preset safety threshold is less than the preset coordinated control triggering threshold, which represents the judgment standard for the system to return to a safe and stable operating state.

[0077] It can be understood that the instantaneous phase angle refers to the real-time angular position of the micro-inverter output alternating voltage or current waveform at a certain moment relative to the system reference phase (such as the bus voltage phase provided by the grid-forming energy storage inverter). It can be realized by phase-locked loop (PLL) technology, digital signal processing (DSP) algorithm or phase detection method based on fast Fourier transform (FFT), for example, by sampling the inverter output voltage or current waveform and comparing it with the reference sine wave to calculate it. The main purpose is to obtain the synchronization state information of each micro-inverter during grid-connected operation, to provide basic data for subsequent performance evaluation.

[0078] To make the technical solution clearer, specific examples are used for explanation. In an off-grid photovoltaic system composed of multiple micro-inverters, a central controller can be deployed as a data processing unit. The central controller periodically acquires the instantaneous phase angle data output by the internal phase-locked loop (PLL) module of each micro-inverter through a high-speed communication network, such as an Ethernet network based on Modbus TCP or MQTT protocol. These data can be digital quantized angle values, such as degrees or radians. After receiving the instantaneous phase angle data of all online micro-inverters, the central controller immediately performs a standard deviation calculation algorithm. For example, in a system containing N micro-inverters, the controller calculates the average of the N instantaneous phase angles, then calculates the square of the difference between each phase angle and the average, sums these squared differences, divides by N-1 (or N), and takes the square root to obtain the current phase dispersion index. Subsequently, the central controller compares this calculated current phase dispersion index with a pre-stored coordinated control trigger threshold. This threshold can be a dynamically adjusted value, such as being optimized in real-time based on system historical operation data and current environmental conditions (such as light change rate). If the current phase dispersion index exceeds the threshold, the central controller immediately generates a power ramp rate limit instruction. The instruction can contain a specific power ramp rate upper limit value, such as watts per second (W / s), or a risk level code indicating that the micro-inverter should adopt a pre-set corresponding rate. The central controller broadcasts this instruction to all micro-inverters through the communication network. After receiving the instruction, the internal control unit of each micro-inverter adjusts its internal maximum power point tracking (MPPT) algorithm or current control loop according to the rate limit parameter contained in the instruction, so that the increase speed of its output power does not exceed the upper limit set by the instruction. For example, if the instruction requires that the power ramp rate be limited to 100 W / s, even if the light intensity allows a higher ramp speed, the inverter will limit the actual ramp speed to this value. During the power ramp rate limit period, the central controller continuously monitors and updates the current phase dispersion index. When the index continues to decrease and falls below a pre-set safety threshold, for example, when the index stabilizes at a lower level for a period of time, the central controller determines that the system has recovered to stability. At this time, the central controller generates a rate limit removal instruction and sends it to all micro-inverters through the communication network. After receiving the removal instruction, the internal control unit of the micro-inverter cancels the power ramp rate limit and restores to its initial maximum power ramp rate determined by the MPPT algorithm, to ensure that the system can quickly respond to light changes and maximize energy output.

[0079] By the technical solution, the embodiment can monitor the operation synchronization between the multiple micro-inverters in the off-grid photovoltaic system in real time, and perform risk assessment based on the phase dispersion index. When a potential instability risk is detected, the power climbing rate of the micro-inverter can be actively and timely limited, so as to effectively inhibit the power injection out-of-step and systematic power oscillation caused by the difference in response speed of each inverter. The preventive control mechanism avoids the limitations of traditional post-fault diagnosis, significantly reduces the probability of cascading off-grid failure, and ensures the power supply continuity and system operation stability of the independent power grid under the condition of dramatic change in illumination.

[0080] In some embodiments, in step S102, calculating the standard deviation as the current phase dispersion index according to the instantaneous phase angles corresponding to the multiple micro-inverters can include but is not limited to the following steps:

[0081] Obtaining current working state information and preset physical topology information corresponding to each micro-inverter;

[0082] Determining an initial micro-inverter set, and the current working state information of each micro-inverter in the initial micro-inverter set is a power output state;

[0083] According to the preset physical topology information, selecting multiple micro-inverters that are physically adjacent from the initial micro-inverter set as a target micro-inverter set, and the target micro-inverter set is used to represent an operation sub-region formed by the multiple micro-inverters that are physically adjacent;

[0084] According to the instantaneous phase angles corresponding to each micro-inverter in the target micro-inverter set, calculating the standard deviation as the current phase dispersion index.

[0085] In some embodiments, since the phase dispersion of all micro-inverters is directly calculated, the difference in the operation state of the micro-inverters in the local area can be ignored, and the potential risk of the system cannot be accurately reflected, thereby causing lag or misjudgment of the coordinated control.

[0086] Therefore, it is necessary to more accurately evaluate the operation state of the micro-inverter system in order to timely and effectively perform coordinated control. The current working state information and preset physical topology information corresponding to each micro-inverter can be obtained first, and an initial micro-inverter set is determined, wherein the current working state information of each micro-inverter in the initial micro-inverter set is a power output state. The current working state information is used to identify and exclude micro-inverters that do not participate in actual power output, so as to determine the initial micro-inverter set, and ensure that the phase dispersion calculated subsequently can truly reflect the system operation state and avoid invalid data interference.

[0087] Then, according to the preset physical topology information, a plurality of micro-inverters physically adjacent to each other are selected from the initial micro-inverter set as a target micro-inverter set, wherein the target micro-inverter set is used to represent an operation sub-region formed by the plurality of micro-inverters physically adjacent to each other. The entire micro-inverter system is divided into a plurality of operation sub-regions, and the micro-inverters in each sub-region are more likely to interact with each other due to physical proximity. In this way, potential risks inside the system can be more sensitively captured, for example, an increase in phase inconsistency of micro-inverters in a local region due to aging or environmental factors, and such local problems can be hidden by global average calculation.

[0088] According to the instantaneous phase angle corresponding to each micro-inverter in the target micro-inverter set, a standard deviation is calculated as a current phase dispersion index. Since the standard deviation is calculated based on the local adjacent micro-inverter set, it can more accurately reflect the phase consistency level in the region, thereby providing a more accurate basis for subsequent coordinated control.

[0089] It can be understood that the current working state information refers to the operation mode or output state of the micro-inverter at a specific time, which can be a power output mode, a standby mode, a fault mode, etc., and its purpose is to filter out micro-inverters that actually participate in grid-connected power output. The preset physical topology information refers to pre-stored data about the arrangement, connection relationship or relative position of micro-inverters in the physical space, which can be represented by coordinate data, network connection diagram or region division identifier, and its purpose is to identify micro-inverters that are physically adjacent to each other. The power output state refers to the operation mode in which the micro-inverter is outputting active power to the grid, and its purpose is to clearly identify which inverters are active power contributors. The operation sub-region refers to a system partition formed by a plurality of micro-inverters physically adjacent to each other, which has local operation characteristics, and its purpose is to achieve fine evaluation of the operation state of the micro-inverter system.

[0090] In order to more clearly illustrate the technical solution, specific examples are used for explanation. First, a central monitoring unit can periodically send query instructions to each micro-inverter through Modbus or CAN bus protocol to obtain the operation mode (for example, 0x01 represents power output, 0x02 represents standby, and 0x03 represents fault) indicated in the internal register as the current working state information. At the same time, the preset physical topology information can be a mapping table established in advance in the system configuration database, which records the unique identifier (such as serial number) of each micro-inverter and its physical position coordinates (such as row number and column number) in the photovoltaic array, or the port information connected to the specific combiner box.

[0091] Then, all the registered micro-inverters can be traversed and all the micro-inverters in the power output state can be screened out according to the current working state information obtained by them. For example, if there are 50 micro-inverters in the system, 45 of which report the power output state, then the 45 micro-inverters will be included in the initial micro-inverter set.

[0092] Subsequently, according to the preset physical topology information, a plurality of micro-inverters physically adjacent to each other are selected from the initial micro-inverter set as a target micro-inverter set. For example, if the micro-inverters are arranged in a matrix form, a "adjacent" relationship can be defined as the micro-inverters adjacent to each other in the up, down, left and right directions in the matrix. For each micro-inverter in the initial set, the micro-inverters directly adjacent to it in the physical topology information can be found, and these adjacent micro-inverters also in the power output state can be combined with the current micro-inverter to form a local target micro-inverter set. This process can be performed in a sliding window manner for the entire array to generate a plurality of target micro-inverter sets representing different operating sub-regions. For example, a local grid composed of 3x3 micro-inverters can be selected as a target micro-inverter set.

[0093] Finally, for each determined target micro-inverter set, the instantaneous phase angle data of each micro-inverter in the target micro-inverter set can be obtained in real time. For example, if a target micro-inverter set contains 5 micro-inverters, the instantaneous phase angle values of the 5 micro-inverters can be collected, and then a standard deviation statistical calculation method can be applied to obtain the phase dispersion index of the set. This index will serve as an evaluation indicator of the phase consistency of the specific operating sub-region.

[0094] Through the above technical solutions, the embodiment can overcome the problem that the running state difference of micro-inverters in a local region can be ignored when directly calculating the phase dispersion of all micro-inverters. By obtaining the current working state information of the micro-inverters and the preset physical topology information, and screening out the micro-inverters in the power output state and physically adjacent to each other to form a target micro-inverter set, the embodiment realizes a fine evaluation of the running state of the micro-inverter system. This phase dispersion calculation based on a local operating sub-region can more accurately reflect the phase consistency level in a specific region, so as to timely discover and identify potential local risks within the system, avoid the risk covering caused by global average calculation and the lag or misjudgment of coordinated control, and thus provide a more accurate basis for the coordinated control of the micro-inverter system, improving the stability and reliability of the system operation.

[0095] In some embodiments, the comparison between the current phase dispersion index and the preset coordinated control triggering threshold in step S103 can include, but is not limited to, the following steps:

[0096] obtaining a historical phase dispersion index under a normal operation state of the system;

[0097] performing statistical feature calculation according to the historical phase dispersion index to obtain a reference range reflecting a current performance state of the system;

[0098] updating the preset coordinated control triggering threshold according to the reference range;

[0099] judging whether the current phase dispersion index is greater than the updated preset coordinated control triggering threshold to obtain a comparison result.

[0100] In some embodiments, since the preset coordinated control triggering threshold is a fixed value, it cannot be adaptively adjusted according to the change of the system operation state, which can cause the system to frequently trigger the coordinated control during normal operation or fail to timely trigger the coordinated control when the system is abnormal, thereby affecting the stability and control effect of the system.

[0101] To improve the accuracy of the comparison result, the historical phase dispersion index under the normal operation state of the system can be obtained first, which truly reflects the inherent fluctuation characteristics of the system in the healthy state. Then, statistical feature calculation is performed according to the historical phase dispersion index to obtain a reference range reflecting the current performance state of the system, which represents the reasonable fluctuation interval of the phase dispersion index under normal working conditions. Then, the preset coordinated control triggering threshold is updated according to the reference range, and finally, it is judged whether the current phase dispersion index is greater than the updated preset coordinated control triggering threshold to obtain a comparison result. Based on this, the preset coordinated control triggering threshold is no longer a fixed value, but is updated in real time according to the dynamically changing reference range. For example, the updated preset coordinated control triggering threshold can be set as the upper limit of the reference range, or a proper margin can be added to the upper limit. This adaptive threshold adjustment mechanism makes the comparison result between the current phase dispersion index and the updated threshold more accurately reflect whether the system is truly deviated from the normal operation state. If the current phase dispersion index exceeds the updated threshold, it indicates that the system can be abnormal and needs to timely trigger the coordinated control.

[0102] It can be understood that the reference range refers to mathematical statistical analysis on the obtained historical phase dispersion index set to quantify its distribution characteristics and fluctuation interval, which can be specifically calculating statistical quantities such as mean, standard deviation, median, percentile of historical data, or constructing a probability distribution model, so as to determine an interval that can represent the reasonable fluctuation range of the phase dispersion index of the system under normal operation state. The purpose is to establish a dynamic reference standard that can reflect the "health" state of the system, so as to facilitate subsequent real-time monitoring and abnormal judgment.

[0103] In order to more clearly illustrate the technical scheme, specific examples are used for explanation below. First, the instantaneous phase angles of the micro-inverter group under normal operation state can be continuously collected and stored, and a series of historical phase dispersion indexes can be calculated accordingly. For example, the current phase dispersion index can be recorded once every minute, and stored in a historical database. When the system operation state is determined to be normal, these data will be marked as "normal operation data". Then, in order to obtain the reference range reflecting the current performance state of the system, the statistical characteristics of the historical phase dispersion indexes under normal operation state obtained in the recent period (for example, the past 24 hours or the past week) can be calculated regularly (for example, every hour or every day). For example, the arithmetic mean (mean) and standard deviation of these historical data can be calculated. The reference range can be defined as the interval of "mean ± 3 times standard deviation", or more simply, only the mean plus a fixed multiple of the standard deviation as the upper limit. Subsequently, according to the reference range calculated, the preset coordination control trigger threshold is dynamically updated. For example, the updated preset coordination control trigger threshold can be set as the upper limit value of the reference range, that is, "mean + 3 times standard deviation". When the system operation state changes, this threshold will also be adjusted accordingly to adapt to the new normal operation mode. Finally, the current phase dispersion index can be calculated in real time, and compared with the updated preset coordination control trigger threshold. If the current phase dispersion index is greater than the dynamically updated threshold, the judgment result is that coordination control needs to be triggered, and the power ramp rate limiting instruction is sent to the micro-inverter. On the contrary, if the current phase dispersion index is less than or equal to the updated threshold, the judgment result is that coordination control does not need to be triggered. In this way, the risk judgment standard can be intelligently adjusted according to its "health" condition, so as to realize more accurate coordination control.

[0104] By the above technical solutions, the embodiment can adaptively adjust the coordinated control trigger threshold according to historical operation data, avoiding the problems of false triggering or missed triggering caused by a fixed threshold. This makes the system reduce unnecessary coordinated control operations during normal operation, improves the operation efficiency and stability. At the same time, when the system appears abnormal, it can more timely and accurately identify the potential phase out-of-step risk, thereby effectively starting the power ramp rate limitation, preventing the occurrence of systemic power oscillation and cascading trip-out failures, and significantly improving the overall stability and power supply reliability of the micro-inverter system.

[0105] In some embodiments, in step S104, the power ramp rate limitation instruction is sent to each micro-inverter, so that the micro-inverter adjusts the power ramp rate according to the power ramp rate limitation instruction, which can include but is not limited to the following steps:

[0106] Step S201, determining a target system risk level according to the current phase dispersion index;

[0107] Step S202, sending a power ramp rate limitation instruction to each micro-inverter, the power ramp rate limitation instruction containing the target system risk level;

[0108] Step S203, the micro-inverter is used to check the target system risk level to obtain a check result, if the check result is a check pass and the current system risk level is less than the target system risk level, updating the current system risk level, and adjusting the power ramp rate according to the corresponding relationship between the risk level and the power ramp rate and the updated current system risk level.

[0109] In some embodiments, since only the power ramp rate limitation instruction is relied on, the micro-inverter may not accurately understand the risk level faced by the system, or the state of the micro-inverter itself may not match the system risk level, resulting in inaccurate power ramp rate adjustment, or even false judgment or safety hazards.

[0110] To improve the accuracy of power ramp rate adjustment, the target system risk level can be determined according to the current phase dispersion index first, which can more accurately reflect the degree of challenge the system is currently facing. Then send a power ramp rate limit instruction to each micro-inverter, which contains the target system risk level; the micro-inverter can perform a verification process on the target system risk level to obtain a verification result, which ensures the effectiveness and safety of the instruction and prevents misoperation caused by data transmission errors or external interference. If the verification result is verified and the current system risk level is less than the target system risk level, update the current system risk level, and adjust the power ramp rate according to the corresponding relationship between the risk level and the power ramp rate and the updated current system risk level. This grading, verification and dynamic adjustment mechanism enables the micro-inverter to adjust its power ramp behavior in a more matched manner according to the overall risk status of the system, avoiding excessive or insufficient restrictions, thereby maximizing the use of photovoltaic power generation capacity while ensuring system stability.

[0111] It can be understood that the corresponding relationship between the risk level and the power ramp rate refers to a pre-established rule or mapping table for guiding the micro-inverter to adjust its power ramp rate according to the system risk level, which can be a lookup table, a function relationship or a set of logical judgment rules. The purpose is to keep the power ramp rate adjustment of the micro-inverter consistent with the overall risk status of the system, thereby achieving dynamic optimization of system stability.

[0112] To make the technical solution clearer, specific examples are used for explanation below. In an off-grid photovoltaic system, the central controller continuously monitors the instantaneous phase angle of each micro-inverter and calculates the current phase dispersion index. When the current phase dispersion index is detected to suddenly rise, for example, from the normal value of 5 to 30, and exceeds the preset coordination control trigger threshold of 20, the central controller will start the power ramp rate limiting process. At this time, the central controller will determine a target system risk level according to the current phase dispersion index of 30, combined with the preset risk assessment model. For example, if the phase dispersion index corresponds to the “medium risk” level between 20-40, the central controller will determine the target system risk level as “medium risk”. Subsequently, the central controller will generate a power ramp rate limiting instruction, and embed the target system risk level information of “medium risk” into the instruction, and then broadcast the instruction to all micro-inverters in the system. After each micro-inverter receives the instruction, the internal control unit of the micro-inverter will immediately perform a check processing on the received target system risk level. For example, the micro-inverter will check whether the received risk level is within the predefined valid level list (such as “low risk”, “medium risk”, “high risk”). If the check is passed, and the current system risk level recorded by the micro-inverter itself (for example, previously “low risk”) is lower than the received “medium risk” target system risk level, the micro-inverter will update its internal current system risk level to “medium risk”. Once the current system risk level is updated to “medium risk”, the micro-inverter will adjust its power ramp rate according to the corresponding relationship table between risk level and power ramp rate stored in its internal storage. For example, the corresponding relationship table may stipulate that “low risk” corresponds to an initial power ramp rate of 100%, “medium risk” corresponds to an initial power ramp rate of 50%, and “high risk” corresponds to an initial power ramp rate of 20%. Therefore, the micro-inverter will adjust its power ramp rate from the initial value to 50% of the initial value. In this way, all micro-inverters in the system can cooperatively and strategically reduce the power ramp rate, thereby effectively suppressing the system oscillation caused by sudden light changes and ensuring the stable operation of the micro-grid.

[0113] By the above technical solution, the embodiment can dynamically evaluate and determine the risk level of the target system according to the current phase dispersion index, and explicitly include the risk level information in the power ramp rate limit instruction, so that each micro-inverter can accurately obtain the risk state of the whole system. After receiving the instruction, the micro-inverter will perform a verification process to ensure the validity and safety of the instruction, avoiding misoperation caused by false information. At the same time, only when the verification is passed and the received risk level is higher than the current recorded risk level of the micro-inverter, the update and adjustment are performed, which avoids unnecessary frequent adjustment and ensures the stability and logic of the system response. This mechanism enables the micro-inverter to adjust the power ramp rate in a more accurate and more matched manner according to the actual risk level of the system, thereby effectively suppressing system oscillation, preventing cascading off-grid failure, and improving the operation stability and reliability of the off-grid photovoltaic system under complex working conditions.

[0114] In some embodiments, the verification process of the target system risk level in step S203 can include but is not limited to the following steps:

[0115] If the target system risk level meets the preset numerical range requirement, it is determined that the verification result is verified.

[0116] In some embodiments, during the verification process, it can be judged whether the target system risk level meets the preset numerical range requirement. If the target system risk level meets the preset numerical range requirement, it is determined that the verification result is verified. It can be understood that the preset numerical range requirement refers to a reasonable numerical interval or a discrete value set that the target system risk level should meet, which is determined on the basis of system design or operation experience. Specifically, it can be a continuous interval between a minimum value and a maximum value, or a set of allowed discrete risk level values. The purpose is to define the effective boundary of the target system risk level.

[0117] By the above technical solution, the verification process of the target system risk level can effectively identify and exclude abnormal risk level values that exceed the reasonable range. This ensures that the risk level data used for power ramp rate adjustment is accurate and reliable, thereby avoiding improper power ramp rate adjustment caused by false risk level, and significantly improving the operation stability and safety of the micro-inverter system.

[0118] In some embodiments, in step S201, determining the target system risk level according to the current phase dispersion index can include but is not limited to the following steps:

[0119] Step S301, obtaining a historical phase dispersion index under a normal operation state of the system;

[0120] In step S302, time domain smoothing processing is performed according to the historical phase dispersion index and the current phase dispersion index, to obtain a time domain smoothing processing result.

[0121] In step S303, the time domain smoothing processing result is compared with a plurality of preset risk level thresholds, to determine an initial system risk level.

[0122] In step S304, state confirmation processing is performed according to a preset risk level duration requirement and the initial system risk level, to obtain a target system risk level.

[0123] In some embodiments, since the system risk level is determined only according to the instantaneous current phase dispersion index, it is susceptible to noise interference, leading to misjudgment of the risk level, and further affecting the adjustment of the power ramp rate, so that the system control strategy is not stable and reliable enough.

[0124] To improve the accuracy of determining the target system risk level, the historical phase dispersion index under the normal operation state of the system can be obtained first, providing a stable reference benchmark for subsequent risk assessment. Then, according to the historical phase dispersion index and the current phase dispersion index, time domain smoothing processing is performed to obtain a time domain smoothing processing result, which can effectively filter out transient noise and mutations and extract the trend information of the phase dispersion change. This smoothing processing makes the system's perception of the risk state more stable and accurate, avoiding misjudgment due to transient data fluctuations. Then, the time domain smoothing processing result is compared with a plurality of preset risk level thresholds, to determine an initial system risk level. Finally, according to the preset risk level duration requirement and the initial system risk level, state confirmation processing is performed to obtain a target system risk level, so as to effectively identify the frequent jump of the risk level. This means that only when the system risk state lasts for a period of time and meets the preset conditions, it will be confirmed as the final target system risk level.

[0125] It can be understood that the preset risk level duration requirement refers to the requirement that after the system risk level is switched from one state to another, the new risk level state must last for a certain length of time. It can be implemented by using a fixed time length, such as 5 seconds, 10 seconds, or a dynamically adjusted time length according to the system operation state. The purpose is to identify the frequent jump of the risk level for state confirmation.

[0126] By the technical solution, the embodiment can effectively overcome the instability problem caused by determining the system risk level only according to the instantaneous phase dispersion index. By introducing the historical phase dispersion index as a reference and combining the time domain smoothing processing, the instantaneous noise and accidental fluctuations can be effectively filtered out, so that the evaluation of the system risk state is more accurate and stable. Further, through the state confirmation processing, the frequent jump of the risk level is identified, and the robustness of the system control strategy is ensured. This enables the micro-inverter to adjust the power climbing rate according to more reliable risk level information, thereby significantly improving the operation stability and reliability of the entire off-grid photovoltaic system under complex working conditions, and effectively preventing the system problem caused by the difference in power response.

[0127] In some embodiments, in step S302, the time domain smoothing processing is performed according to the historical phase dispersion index and the current phase dispersion index, and the time domain smoothing processing result can include but is not limited to the following steps:

[0128] The fluctuation characteristics of the historical phase dispersion index and the current phase dispersion index are extracted to obtain real-time fluctuation characteristics;

[0129] The time domain smoothing processing parameters are determined according to the real-time fluctuation characteristics;

[0130] The historical phase dispersion index and the current phase dispersion index are time domain smoothed according to the time domain smoothing processing parameters to obtain the time domain smoothing processing result.

[0131] In some embodiments, since only fixed parameters are used for time domain smoothing processing, the consideration of the fluctuation characteristics of the data itself is lacking, which may lead to the fact that the smoothing processing result cannot reflect the actual state of the system, especially when the system operating state changes rapidly, the fixed parameter smoothing processing may have the problems of over-smoothing or insufficient smoothing, thereby affecting the authenticity of the subsequent risk evaluation.

[0132] To improve the accuracy of the time domain smoothing processing, the fluctuation characteristics of the historical phase dispersion index and the current phase dispersion index can be extracted first to obtain real-time fluctuation characteristics, which can quantify the dynamic change degree of the current running state of the system. Then, according to the real-time fluctuation characteristics, the time domain smoothing processing parameters are determined, so that the time domain smoothing processing parameters can be dynamically determined, which means that the smoothing algorithm no longer uses fixed parameters, but adaptively adjusts according to the fluctuation of the data itself. For example, when the fluctuation degree of the phase dispersion index is large, the smoothing strength can be increased accordingly to achieve noise suppression; and when the fluctuation degree is small, the smoothing strength can be reduced to retain more detailed information. Then, according to the time domain smoothing processing parameters, the historical phase dispersion index and the current phase dispersion index are subjected to time domain smoothing to obtain the time domain smoothing processing result. The adaptive smoothing processing mechanism of the embodiment makes the obtained time domain smoothing processing result more truly reflect the running state of the micro-inverter group, effectively avoiding the lag or excessive sensitivity problems that may be caused by the traditional fixed parameter smoothing.

[0133] It can be understood that the real-time fluctuation characteristics refer to the quantitative indicators that can immediately reflect the instability degree and change trend of the system running state, which are obtained from the current and historical phase dispersion indexes by the fluctuation characteristic extraction method, and can be characterized by calculating the root mean square error, peak-valley difference or short-time Fourier transform energy distribution of the phase dispersion index in the recent period of time. The purpose is to provide a basis for subsequent dynamic adjustment of the smoothing processing parameters.

[0134] Through the above technical solutions, the embodiment can dynamically determine the time domain smoothing processing parameters according to the real-time fluctuation characteristics extracted from the historical phase dispersion index and the current phase dispersion index, thereby realizing adaptive time domain smoothing processing of the phase dispersion index. This processing method avoids the problems of excessive smoothing or insufficient smoothing that may be caused by the traditional fixed parameter smoothing, so that the time domain smoothing processing result can more truly reflect the actual running state of the system, effectively filter out noise and transient interference, provide a more reliable and stable data basis for subsequent system risk assessment, and further improve the authenticity and robustness of the entire micro-inverter performance data processing method.

[0135] In some embodiments, in step S304, according to the preset risk level duration requirement and the initial system risk level, the state confirmation processing is performed to obtain a target system risk level, which can include but is not limited to the following steps:

[0136] In step S401, the current running state characteristics of the off-grid photovoltaic system are obtained, and the current running state characteristics include the fluctuation amplitude or change rate of the current phase dispersion index.

[0137] In step S402, the preset risk level duration requirement is updated according to the current operating state feature.

[0138] In step S403, the initial system risk level is confirmed according to the updated preset risk level duration requirement, and a target system risk level is obtained.

[0139] In some embodiments, since the preset risk level duration requirement is a preset fixed value, in the actual operation of the off-grid photovoltaic system, the operating state is constantly changing, for example, the fluctuation amplitude or change rate of the current phase dispersion index, and these factors will affect the judgment of the system risk level. If only the fixed preset risk level duration requirement is relied on, it may lead to misjudgment of the system risk level, thereby affecting the power climbing rate adjustment of the micro-inverter, and further affecting the stability of the entire off-grid photovoltaic system.

[0140] To improve the accuracy of system risk level judgment, the current operating state feature of the off-grid photovoltaic system can be obtained first, wherein the current operating state feature includes the fluctuation amplitude or change rate of the current phase dispersion index. The current operating state feature can reflect the stability of the system operation. The greater the fluctuation or the faster the change, the more unstable the system and the higher the risk. Then, the preset risk level duration requirement is updated according to the current operating state feature. For example, when the system operating state shows high instability and risk, for example, the phase dispersion index fluctuates sharply or changes rapidly, the system shortens the risk level duration requirement, so as to respond to potential risk changes more quickly. Conversely, when the system operating state is stable, the risk level duration requirement can be appropriately extended to avoid frequent triggering of risk judgment due to transient disturbances. Then, the initial system risk level is confirmed according to the updated preset risk level duration requirement, and a target system risk level is obtained, so that the entire risk assessment process is more refined and adaptive. It can be understood that the current operating state feature refers to the operating characteristics of the off-grid photovoltaic system at a specific moment, which can be obtained by real-time monitoring data, historical data analysis or prediction model, etc.

[0141] Through the above technical solution, the embodiment can dynamically adjust the risk level duration requirement according to the real-time operating state of the off-grid photovoltaic system. This avoids the risk level misjudgment that may be caused by using a fixed time threshold, so that the system's identification of risk is more accurate and timely. When the system operating state is unstable, the risk confirmation time can be shortened, so as to respond to potential risks more quickly. When the system operating state is stable, the confirmation time can be appropriately extended to avoid excessive sensitivity. This adaptive risk level confirmation mechanism ensures the accuracy and effectiveness of the power climbing rate adjustment of the micro-inverter, thereby improving the operating stability and reliability of the entire off-grid photovoltaic system.

[0142] In some embodiments, in step S403, the state of the initial system risk level is confirmed according to the updated preset risk level duration requirement, and a target system risk level is obtained, which can include but is not limited to the following steps:

[0143] An ascending threshold and a descending threshold are defined, the ascending threshold is a threshold for converting from a low risk level to a high risk level, and the descending threshold is a threshold for converting from a high risk level to a low risk level, and the descending threshold is less than the ascending threshold;

[0144] If the time domain smoothing processing result is greater than the ascending threshold and the duration meets the updated preset risk level duration requirement, the target system risk level is determined to be a high risk level;

[0145] If the time domain smoothing processing result is less than the descending threshold and the duration meets the updated preset risk level duration requirement, the target system risk level is determined to be a low risk level.

[0146] In some embodiments, due to the reliance on only the dynamically adjusted duration requirement, the system risk level may be frequently switched between high and low when the risk indicator fluctuates around the threshold, which unnecessary frequent switching will introduce instability of the system, and may cause the power ramp rate adjustment strategy to be too aggressive or conservative, and cannot accurately reflect the real changes of the system risk, especially when the off-grid photovoltaic system is in a fast-changing operating state, which may cause misjudgment of the risk level, thereby affecting the adjustment effect of the power ramp rate.

[0147] To improve the accuracy of risk level state confirmation, an ascending threshold and a descending threshold can be defined, the ascending threshold is a threshold for converting from a low risk level to a high risk level, and the descending threshold is a threshold for converting from a high risk level to a low risk level, which can be determined by historical data statistical analysis, expert experience setting, or self-adaptive algorithm dynamic adjustment, and the descending threshold is less than the ascending threshold. If the time domain smoothing processing result is greater than the ascending threshold and the duration meets the updated preset risk level duration requirement, the target system risk level is determined to be a high risk level, which effectively avoids misjudgment of the risk level caused by instantaneous fluctuations or temporary disturbances, and ensures the reliability of high risk determination. If the time domain smoothing processing result is less than the descending threshold and the duration meets the updated preset risk level duration requirement, the target system risk level is determined to be a low risk level. The design of the double thresholds makes the conversion of the risk level not only consider the instantaneous value and duration of the risk indicator, but also consider its change trend and stability.

[0148] To make the technical solution clearer, specific examples are used for explanation below. Assume that an off-grid photovoltaic system obtains a risk index through time domain smoothing processing, and the numerical range of the risk index is 0 to 100, and the higher the numerical value is, the greater the risk is. In order to determine the target system risk level, an upward threshold can be defined, for example, set to 60, and a downward threshold can be defined, for example, set to 40. Obviously, the downward threshold 40 is less than the upward threshold 60. At the same time, according to the current operating state characteristics of the off-grid photovoltaic system, for example, the fluctuation amplitude of the phase dispersion index, the preset risk level duration requirement is dynamically updated, for example, currently determined to be 5 seconds. When the system risk index (time domain smoothing processing result) starts to rise from the low risk state, for example, gradually increases from 30 to 65. At this time, since 65 is greater than the upward threshold 60, the system will start timing. If the risk index continuously remains above 60 for a time that reaches or exceeds the updated 5-second duration requirement, the system will determine the target system risk level as a high risk level. Conversely, when the system is in a high risk state, the risk index (time domain smoothing processing result) starts to decrease from 80, for example, gradually decreases to 35. At this time, since 35 is less than the downward threshold 40, the system will start timing. If the risk index continuously remains below 40 for a time that reaches or exceeds the updated 5-second duration requirement, the system will determine the target system risk level as a low risk level. In this way, the system avoids frequent switching of the risk level when the risk index fluctuates between 40 and 60, thereby ensuring the stability and accuracy of the risk level determination.

[0149] Through the above technical solution, the embodiment introduces an upward threshold and a downward threshold, and sets the downward threshold to be less than the upward threshold, thereby introducing a hysteresis effect when the risk level is converted. This effectively avoids frequent and unnecessary switching of the risk level when the system risk index fluctuates near the critical value, significantly improves the accuracy and stability of the target system risk level determination, thereby more reliably identifying the real risk state, reducing the improper adjustment of the power climbing rate caused by misjudgment, and thereby improving the running stability and reliability of the off-grid photovoltaic system under complex and variable working conditions.

[0150] The beneficial effects of implementing the embodiment of the present application include that the embodiment of the present application first acquires the instantaneous phase angles corresponding to the plurality of micro inverters respectively, calculates the standard deviation as the current phase dispersion index, then compares the current phase dispersion index and the preset coordination control trigger threshold to obtain a comparison result, if the comparison result is that the current phase dispersion index is greater than the preset coordination control trigger threshold, sends the power ramp rate limiting instruction to each micro inverter to make the micro inverter adjust the power ramp rate according to the power ramp rate limiting instruction, and then updates the current phase dispersion index until the current phase dispersion index is less than the preset safety threshold, and then sends the rate limit release instruction to each micro inverter to make the micro inverter restore the power ramp rate to the initial value according to the rate limit release instruction, so that the performance data processing can be realized by controlling the power ramp rate, and then the power can be smoothly adjusted, and the system stability is improved.

[0151] As shown in Figure 2 the embodiment of the present application also provides a micro inverter performance data processing system, which comprises:

[0152] The acquisition module 501 is used to acquire the instantaneous phase angles corresponding to the plurality of micro inverters respectively;

[0153] The calculation module 502 is used to calculate the standard deviation as the current phase dispersion index according to the instantaneous phase angles corresponding to the plurality of micro inverters respectively;

[0154] The judgment module 503 is used to compare the current phase dispersion index and the preset coordination control trigger threshold to obtain a comparison result;

[0155] The limiting instruction issuing module 504 is used to send the power ramp rate limiting instruction to each micro inverter if the comparison result is that the current phase dispersion index is greater than the preset coordination control trigger threshold, so that the micro inverter adjusts the power ramp rate according to the power ramp rate limiting instruction;

[0156] The release instruction issuing module 505 is used to update the current phase dispersion index until the current phase dispersion index is less than the preset safety threshold, and then send the rate limit release instruction to each micro inverter, so that the micro inverter restores the power ramp rate to the initial value according to the rate limit release instruction.

[0157] The contents in the above method embodiments are all applicable to the system embodiment, the system embodiment specifically realizes the same functions as the above method embodiments, and achieves the same beneficial effects as the above method embodiments.

[0158] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and can include more or fewer steps than the figures shown, or combine certain steps, or different steps.

[0159] The embodiments described in the embodiments of the present application are for more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

Claims

1. A method for processing performance data of a micro-inverter, characterized in that, Includes the following steps: Obtain the instantaneous phase angles corresponding to multiple micro-inverters; The standard deviation is calculated as the current phase dispersion index based on the instantaneous phase angles corresponding to the multiple micro-inverters. The current phase dispersion index and the preset coordination control trigger threshold are compared to obtain the comparison result; If the comparison result is that the current phase dispersion index is greater than the preset coordination control trigger threshold, then a power ramp-up rate limit command is sent to each of the micro-inverters so that the micro-inverters adjust the power ramp-up rate according to the power ramp-up rate limit command. The current phase dispersion index is updated until it is less than a preset safety threshold. Then, a rate limit release command is sent to each microinverter so that the microinverter can restore its power ramp-up rate to the initial value according to the rate limit release command.

2. The method according to claim 1, characterized in that, The step of calculating the standard deviation as the current phase dispersion index based on the instantaneous phase angles corresponding to the plurality of micro-inverters includes: Obtain the current operating status information and preset physical topology information corresponding to each of the microinverters; An initial set of microinverters is determined, wherein the current operating state information of each microinverter in the initial set of microinverters is the power output state; Based on the preset physical topology information, multiple physically adjacent micro-inverters are selected from the initial micro-inverter set as a target micro-inverter set. The target micro-inverter set is used to characterize the operating sub-region formed by multiple physically adjacent micro-inverters. The standard deviation is calculated as the current phase dispersion index based on the instantaneous phase angle corresponding to each microinverter in the target microinverter set.

3. The method according to claim 1, characterized in that, The comparison between the current phase dispersion index and the preset coordination control trigger threshold yields a comparison result, including: Obtain the historical phase dispersion index under normal system operation; Based on the historical phase dispersion index, statistical characteristics are calculated to obtain a benchmark range reflecting the current performance state of the system. Update the preset coordination control trigger threshold according to the benchmark range; Determine whether the current phase dispersion index is greater than the updated preset coordination control trigger threshold to obtain the comparison result.

4. The method according to claim 1, characterized in that, Sending a power ramp-up rate limit command to each of the microinverters, so that the microinverters adjust their power ramp-up rate according to the power ramp-up rate limit command, includes: The risk level of the target system is determined based on the current phase dispersion index. Send a power ramp rate limit command to each of the microinverters, the power ramp rate limit command including the target system risk level; The micro-inverter is used to verify the risk level of the target system and obtain a verification result. If the verification result is that the verification is passed and the current system risk level is less than the target system risk level, the current system risk level is updated, and the power ramp-up rate is adjusted according to the correspondence between the risk level and the power ramp-up rate and the updated current system risk level.

5. The method according to claim 4, characterized in that, The verification process for the risk level of the target system, to obtain the verification result, includes: If the risk level of the target system meets the preset numerical range requirement, then the verification result is determined to be a successful verification.

6. The method according to claim 4, characterized in that, The step of determining the risk level of the target system based on the current phase dispersion index includes: Obtain the historical phase dispersion index under normal system operation; Based on the historical phase dispersion index and the current phase dispersion index, time-domain smoothing is performed to obtain the time-domain smoothing result. The time-domain smoothing result is compared with multiple preset risk level thresholds to determine the initial system risk level. Based on the preset risk level duration requirement and the initial system risk level, a status confirmation process is performed to obtain the target system risk level.

7. The method according to claim 6, characterized in that, The step of performing time-domain smoothing based on the historical phase dispersion index and the current phase dispersion index to obtain the time-domain smoothing result includes: The fluctuation characteristics are extracted from the historical phase dispersion index and the current phase dispersion index to obtain the real-time fluctuation characteristics; Based on the real-time fluctuation characteristics, determine the time-domain smoothing processing parameters; Based on the time-domain smoothing parameters, the historical phase dispersion index and the current phase dispersion index are smoothed in the time domain to obtain the time-domain smoothing result.

8. The method according to claim 6, characterized in that, The step of performing status confirmation processing based on the preset risk level duration requirement and the initial system risk level to obtain the target system risk level includes: Obtain the current operating status characteristics of the off-grid photovoltaic system, wherein the current operating status characteristics include the fluctuation amplitude or rate of change of the current phase dispersion index; Update the preset risk level duration requirement based on the current operating status characteristics; Based on the updated preset risk level duration requirement, the initial system risk level is confirmed to obtain the target system risk level.

9. The method according to claim 8, characterized in that, The step of confirming the status of the initial system risk level according to the updated preset risk level duration requirement to obtain the target system risk level includes: Define an upward threshold and a downward threshold, wherein the upward threshold is the threshold for transitioning from a low-risk level to a high-risk level, and the downward threshold is the threshold for transitioning from a high-risk level to a low-risk level, and the downward threshold is less than the upward threshold; If the result of the time-domain smoothing process is greater than the rising threshold and the duration meets the updated preset risk level duration requirement, then the risk level of the target system is determined to be high risk level. If the result of the time-domain smoothing process is less than the decrease threshold and the duration meets the updated preset risk level duration requirement, then the risk level of the target system is determined to be low risk.

10. A micro-inverter performance data processing system, characterized in that, include: The acquisition module is used to acquire the instantaneous phase angles corresponding to multiple micro-inverters. The calculation module is used to calculate the standard deviation as the current phase dispersion index based on the instantaneous phase angles corresponding to the multiple micro-inverters. The judgment module is used to compare the current phase dispersion index with the preset coordination control trigger threshold to obtain the comparison result; The limit command issuing module is used to send a power ramp-up rate limit command to each of the micro-inverters if the comparison result is that the current phase dispersion index is greater than the preset coordination control trigger threshold, so that the micro-inverters adjust the power ramp-up rate according to the power ramp-up rate limit command; The release command issuing module is used to update the current phase dispersion index until the current phase dispersion index is less than a preset safety threshold. Then, it sends a rate limit release command to each micro-inverter so that the micro-inverter can restore the power ramp-up rate to the initial value according to the rate limit release command.

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