Inverter control method based on feature recognition model

Abstract

The application discloses an inverter control method based on a feature recognition model, real-time collection of input voltage V PV , input current I PV , ambient temperature T temp , bus voltage V bus , real-time power P ac , inverter time-sharing control strategy, output capacity strong period power prediction model construction for soft start, main component analysis for extraction of main factors affecting power fluctuation, inverter efficiency equivalent model, photovoltaic cell equivalent model and boost circuit mathematical model for output power corresponding power, output capacity weak period active shutdown, main component analysis for extraction of main factors affecting power fluctuation, output power reference, adjustment of rear-end control strategy, significant improvement of power generation efficiency, soft start in output strong period, shutdown protection control demand in output weak period, basically no frequent start-stop, reduced damage risk of switch components, more reliable and stable inverter operation control.

Description

Technical Field

[0001] This invention relates to an inverter control method based on a feature recognition model, and belongs to the technical field of inverter control. Background Technology

[0002] High-power string inverters are the mainstream equipment in industrial and commercial power plants. To ensure uninterrupted operation, string inverters integrate ACSPS and DCSPS, which facilitates equipment and power plant monitoring. However, this can lead to conflicts with traditional control logic. For example, for a T-type three-level topology string inverter, when the PV energy is low, the sampling accuracy limitation and device losses can affect the MPPT tracking accuracy. This can cause bus instability and overmodulation, and also cause the neutral point balance controller to diverge, resulting in neutral point imbalance. This can have a significant impact on the lifespan of the bus capacitor and the stability of the switching devices. Summary of the Invention

[0003] The purpose of this invention is to address the shortcomings of the prior art. In response to the problem that the output characteristics of photovoltaic cells in string inverters are very soft under extremely low input power conditions, which leads to poor MPPT accuracy, resulting in bus instability and neutral point control divergence, an inverter control method based on feature recognition model is proposed.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] The inverter control method based on feature recognition model includes the following steps:

[0006] S1 data acquisition, real-time acquisition of input voltage V PV Input current I PV Ambient temperature T temp Bus voltage V bus Real-time power P ac ;

[0007] S2 is based on the input voltage V PV Trend of change, slope CIK, input voltage V PV The fluctuation difference CIvar is used for time-sharing control strategy of inverter;

[0008] During the period when the output capability increases, the input voltage V PV During an upward trend and when CIK > 0;

[0009] During the output plateau period, the input voltage V PV Stable and CIvar tends to 0;

[0010] During the period of output capability decay, the input voltage V PV During a downward trend and when CIK < 0;

[0011] During the period when the output capability of S3 becomes stronger, a power prediction model is constructed for soft start;

[0012] S4 uses principal component analysis to extract the main factors affecting power fluctuations;

[0013] When the main influencing factor is the input voltage V PV Input current I PV At that time, the output power P is calculated using the inverter efficiency equivalent model. ref1 When the main influencing factor is the input voltage V PV Ambient temperature T temp At that time, the output power P using the photovoltaic cell equivalent model is... ref2 When the main influencing factor is the input voltage V PV Bus voltage V bus At that time, the output power P is calculated using the mathematical model of the boost circuit. ref3 ;

[0014] The S5 will automatically shut down during periods of reduced output capability.

[0015] Preferably, in step S4, a dataset X is generated, X = {V}. PV I PV V bus T temp} Calculate the mean CIave and standard deviation CIvar for each data vector, and the sampled average.

[0016] Construct the covariance matrix V: n is the number of sampling points;

[0017] Solve for the eigenvalues ​​λ of V i and eigenvector ω i ;

[0018] Based on variance contribution rate and cumulative variance contribution rate As an evaluation criterion, the eigenvectors corresponding to a cumulative variance contribution rate of over 80% are selected as principal components;

[0019] The eigenvalues ​​are Λ=[λ1, λ2, λ3,...,λ m The corresponding eigenvector W m ,

[0020] W m =[ω1,ω2,ω3,…,ω m ] T m<4, principal components

[0021] Principal Component Reconstruction Dataset

[0022] Preferably, in step S4, when the inverter efficiency equivalent model is output;

[0023] P ref1 =E AC =E DC ·η=V DC ·I DC ·η;

[0024] Among them, E AC It is the inverter output power; E DC η is the DC input power; η is the overall conversion efficiency.

[0025] Preferably, in step S4, when the equivalent model of the photovoltaic cell is output;

[0026]

[0027] Where I is the output current; V is the output voltage; I pv Photocurrent; I o Saturation current; R s Series equivalent resistance; R p Shunt resistance; n ideality factor; k Boltzmann constant; T ambient temperature; T n Rated temperature; q-element charge; Eg0 semiconductor band gap energy; N s Number of modules connected in series;

[0028] P ref2 =V·I.

[0029] Preferably, in step S4, when the mathematical model of the boost circuit is output;

[0030]

[0031] V busref -10V≤V bus ≤V busref +10V;

[0032] Where D is the duty cycle of the boost circuit, and Va, Vb, and Vc are the peak values ​​of the three-phase voltages of the three-phase power grid.

[0033] P ref3 =max(V PV ·I PV ).

[0034] Preferably, in step S2, when the power P ref1 Power P ref2、 Power P ref3 When outputting separately, it is compared with the real-time power P. acAfter performing the difference processing, power control correction is performed to output control pulses.

[0035] Preferably, in the step S2, a data set X is generated, X = {VPV, IPV, Vbus, Ttemp};

[0036] Slope

[0037] The maximum value data set DImax = max{X1, X2,......, X n};

[0038] The minimum value data set CImim = min{X1, X2,......, X n};

[0039] Average value

[0040] Standard deviation

[0041] First derivative

[0042] Second derivative

[0043] is the sampling average value, and n is the number of sampling points.

[0044] Preferably, in the step S3, the energy that the PV battery can provide is predicted through the capacity of the bus capacitor:

[0045]

[0046] E C is the capacity that the bus capacitor can store, C is the capacitance value, and U is the bus voltage;

[0047] Set the grid connection threshold Pb. When E C > Pb, the inverter is allowed to self-check and connect to the grid.

[0048] Preferably, the power Ppv of the photovoltaic cell is calculated according to Vpv and Ipv. When Ppv < P1, enter the active shutdown procedure;

[0049] P1 = PR + Paux + Pfan + Prly + Pboost, where PR is the module loss, Paux is the sps power, Pfan is the fan power, Prly is the relay power, and Pboost is the boost circuit switching loss;

[0050] When Vpv < a1, increase the dummy load boost_duty, simulate the increase of the dummy load, and continuously monitor Vpv and Ipv;

[0051] When Vpv < a2, increasing the dummy load boost_duty can no longer maintain the stability of the BUS voltage, then it is determined that the photovoltaic output is weak, and the system actively disconnects from the grid and opens the relay; the dummy load consumes the photovoltaic power.

[0052] When Vpv < a3, gradually unload the dummy load until all loads are completely cut off.

[0053] Among them, a1 is the minimum operating voltage of the inverter boost, a2 is the grid-connected voltage under the minimum power consumption of the inverter, and a3 is the starting voltage of the switching power supply, and a1 > a2 > a3.

[0054] The beneficial effects of the present invention are mainly reflected in:

[0055] 1. It can implement principal component analysis to extract the main influencing factors of power fluctuations, thereby outputting power references to meet the adjustment requirements of the backend control strategy, and significantly improving the power generation efficiency.

[0056] 2. It meets the soft start control requirements during the output strengthening period and the shutdown protection control requirements during the output weakening period, basically eliminating frequent start and stop, reducing the damage risk of switching components, and making the inverter operation control more reliable and stable. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] By reading the detailed description of the non-restrictive embodiments with reference to the following drawings, other features, objects, and advantages of the present application will become more obvious:

[0058] Figure 1 It is a schematic flow diagram of the inverter control method based on the feature recognition model of the present invention.

[0059] Figure 2 It is a schematic structural diagram of the control module in the inverter control method based on the feature recognition model of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0061] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described in the present application can be combined with each other.

[0062] This invention provides an inverter control method based on a feature recognition model, such as... Figure 1 As shown, it includes the following steps:

[0063] Data acquisition, real-time acquisition of input voltage V PV Input current I PV Ambient temperature T temp Bus voltage V bus Real-time power P ac .

[0064] According to the input voltage V PV Trend of change, slope CIK, input voltage V PV The fluctuation difference CIvar is used for the inverter's time-sharing control strategy.

[0065] During the period when the output capability increases, the input voltage V PV During an upward trend and when CIK > 0;

[0066] During the output plateau period, the input voltage V PV Stable and CIvar tends to 0;

[0067] During the period of output capability decay, the input voltage V PV During a downward trend and when CIK < 0;

[0068] During the period of increased output capability, a power prediction model is constructed for soft start.

[0069] Principal component analysis was used to extract the main factors affecting power fluctuations; when the main influencing factor was the input voltage V... PV Input current I PV At that time, the output power P is calculated using the inverter efficiency equivalent model. ref1 When the main influencing factor is the input voltage V PV Ambient temperature T temp At that time, the output power P using the photovoltaic cell equivalent model is... ref2 When the main influencing factor is the input voltage V PV Bus voltage V bus At that time, the output power P is calculated using the mathematical model of the boost circuit. ref3 .

[0070] Actively shut down during periods of reduced output capacity.

[0071] Detailed implementation process and principle explanation:

[0072] Real-time data acquisition of the inverter and the adoption of a time-sharing control strategy for the inverter based on the PV voltage change trend are all within the scope of protection of this case. Time-sharing control strategies are generally existing technologies, and any scheme that meets the requirements of the time-sharing control strategy is within the scope of protection of this case.

[0073] Under normal circumstances, the inverter's time-sharing strategy can distinguish between the morning when the photovoltaic capacity increases from weak to strong, the daytime when the photovoltaic output is stable, and the evening when the photovoltaic capacity decreases from strong to weak. As long as the time-sharing control of photovoltaic output can be achieved, it is sufficient.

[0074] In this case, the slope CIK and the input voltage V are used. PV The volatility CIvar is used to differentiate the values. CIvar tending to 0 is an existing technology in this field. Generally, a standard deviation threshold is set, and values ​​within the standard deviation threshold range tend to be 0. This will not be elaborated on here.

[0075] During daytime photovoltaic power generation, photovoltaic systems are affected by various factors such as cloud cover, ambient temperature, and load, leading to unstable power generation efficiency. To address this, this project can perform principal component analysis to identify the principal components that significantly impact power fluctuations. Based on these key influencing factors, the project can then output power according to the corresponding model and implement backend control pulse references, thereby maximizing power generation efficiency.

[0076] More specifically, when Vpv and Vbus are the main influencing factors, the response rate can be dynamically adjusted by adjusting the PI parameters of the boost loop. When Vpv and IPV are the main influencing factors, it indicates that the maximum power point fluctuation is caused by cloudy weather. The MPPT step size and disturbance direction can be dynamically adjusted to quickly track the maximum power point. When Vpv and T are the main influencing factors, it indicates that the ambient temperature is too high, the power is limited, and the internal fan is running. At this time, the power is limited by the inverter's rated power, supporting a certain degree of over-matching. The MPPT side must balance the current of each circuit to ensure uniform module temperature.

[0077] In one specific embodiment, when performing principal component analysis, a dataset X is generated, X = {V}. PV I PV V bus T temp} Calculate the mean CIave and standard deviation CIvar for each data vector, and the sampled average.

[0078] Construct the covariance matrix V: n is the number of sampling points.

[0079] Solve for the eigenvalues ​​λ of Vi and eigenvector ω i .

[0080] Based on variance contribution rate and cumulative variance contribution rate As an evaluation criterion, the eigenvectors corresponding to the cumulative variance contribution rate of more than 80% are selected as principal components.

[0081] The eigenvalues ​​are Λ=[λ1, λ2, λ3,...,λ m The corresponding eigenvector W m .

[0082] W m =[ω1,ω2,ω3,…,ω m ] T m<4, principal components

[0083] Principal Component Reconstruction Dataset

[0084] Detailed explanations are provided for the equivalent model of inverter efficiency, the equivalent model of photovoltaic cells, and the mathematical model of boost circuit:

[0085] When the inverter efficiency equivalent model outputs;

[0086] P ref1 =E AC =E DC ·η=V DC ·I DC ·η;

[0087] Among them, E AC It is the inverter output power; E DC η is the DC input power; η is the overall conversion efficiency.

[0088] When the equivalent model of a photovoltaic cell is output;

[0089]

[0090] Where I is the output current; V is the output voltage; I pv Photocurrent; I o Saturation current; R s Series equivalent resistance; R p Shunt resistance; n ideality factor; k Boltzmann constant; T ambient temperature; T n Rated temperature; q-element charge; Eg0 semiconductor band gap energy; N s Number of modules connected in series;

[0091] P ref2 =V·I.

[0092] Preferably, in step S4, when the mathematical model of the boost circuit is output;

[0093]

[0094] V busref -10V≤V bus ≤V busref +10V;

[0095] Where D is the duty cycle of the boost circuit, and Va, Vb, and Vc are the peak values ​​of the three-phase voltages of the three-phase power grid.

[0096] P ref3 =max(V PV ·I PV ).

[0097] This means adjusting the MPPT algorithm model according to the main influencing factors to improve power generation efficiency.

[0098] In one specific embodiment, at power P ref1 Power P ref2、 Power P ref3 When outputting separately, it is compared with the real-time power P. ac After differential processing, power control correction outputs control pulses.

[0099] Specifically, in general, the control strategy is based on the power P. ref1 Power P ref2、 Power P ref3 The outputs are processed separately, and corresponding control pulses are adjusted accordingly. Correction can be achieved through methods such as difference calculation and waveform correction.

[0100] In this embodiment, principal component analysis is used to reduce the dimensionality of the original data vector, and the variance contribution rate (η) is reduced. i >80% of the variables are transmitted as principal component factors to the power control correction module. In the controller's calculation module, the weights of the principal component factors are adjusted in real time, and the final output control pulse is calculated through methods such as difference: boost module EPWM output (Boost_duty), inverter module EPWM output (Inv_duty), and relay control module EPWM (Relay_control).

[0101] This embodiment is merely one way to implement power control correction; other power P samples using the three models described in this case are also included. ref1 Power P ref2、 Power P ref3 The solutions for corresponding correction and adjustment control are all within the protection scope of this case.

[0102] In one specific embodiment, the time-sharing control strategy is described in detail:

[0103] Generally, a feature recognition model is used to propose a series of data feature values, which typically include: slope CIK, maximum value CImax; minimum value CImin, average value CIave; standard deviation CIvar; first derivative CI′; and second derivative CI″.

[0104] Generate a dataset X, where X = {VPV, IPV, Vbus, Ttemp};

[0105] slope

[0106] The maximum value dataset CImax = max{X1,X2,......,X n );

[0107] Minimum value dataset CImin=min{X1,X2,......,X n};

[0108] average value

[0109] Standard deviation

[0110] First derivative

[0111] Second derivative

[0112] This is the average value of the samples, where n is the number of sampling points. To achieve real-time performance and accuracy, the sampling interval is set to 20ms and the number of sampling points to 300. Of course, the sampling interval and the number of sampling points can be adjusted flexibly.

[0113] PV voltage rising trend: Perform a moving average processing on the sampled data: k is the sampling window. The slope of the moving average series is calculated to reflect the overall trend of voltage change. When the voltage is on an upward trend, CIk(MA)>0.

[0114] PV voltage stability trend: When the voltage is stable, the standard deviation CIvar(MA) approaches 0;

[0115] PV voltage decreasing trend: When the voltage decreases, CIk(MA) < 0;

[0116] This forms the basis for time-sharing regulation.

[0117] In one specific embodiment, the energy output of the PV cell is predicted by the capacity of the bus capacitor:

[0118]

[0119] E C is the capacity that the bus capacitor can store, C is the capacitance value, and U is the bus voltage;

[0120] Set the grid connection threshold Pb. When E C > Pb, the inverter is allowed to self-check and connect to the grid. That is, for starting up in the morning, the power prediction method is adopted to make the inverter soft-start, avoiding damage to the switching devices caused by the frequent start and stop of the boost circuit.

[0121] In a specific embodiment, calculate the photovoltaic cell power Ppv according to Vpv and Ipv. When Ppv < P1, enter the active shutdown program;

[0122] P1 = PR + Paux + Pfan + Prly + Pboost, where PR is the module loss, Paux is the sps power, Pfan is the fan power, Prly is the relay power, and Pboost is the switching loss of the boost circuit;

[0123] When Vpv < a1, increase the fake load boost_duty to simulate the increase of the fake load, and continuously monitor Vpv and Ipv;

[0124] When Vpv < a2, increasing the fake load boost_duty can no longer maintain the stability of the BUS voltage, then it is determined that the photovoltaic output is weak, actively disconnect from the grid, and disconnect the relay; the fake load consumes the photovoltaic power;

[0125] When Vpv < a3, gradually unload the fake load until all loads are cut off.

[0126] Among them, a1 is the minimum operating voltage of the inverter boost, a2 is the grid connection voltage under the minimum power consumption of the inverter, and a3 is the starting voltage of the switching power supply, and a1 > a2 > a3.

[0127] That is, for the condition of insufficient photovoltaic energy in the evening, respond to the active shutdown program of the inverter, use the fake load to determine the shutdown moment, and at the same time consume the battery power under low irradiance, avoiding the repeated start and stop of DCSPS without intervention and damaging the switching devices.

[0128] In addition, there is an optimization of the MPPT algorithm in the case of large PV voltage changes in the morning and evening:

[0129] Taking morning and evening data as an example, the most significant change is in PV voltage. The slope reflects the overall trend and is used to determine the characteristics of morning and evening. CIK>0 indicates morning, and CIK<0 indicates evening. The absolute value of the derivative is used to determine the rate of voltage change. Variance CIvar reflects the fluctuation of PV voltage and reflects the control accuracy of the MPPT algorithm. When the variance is large, it is necessary to dynamically adjust the step size and perturbation direction of the MPPT algorithm.

[0130] Secondly, the characteristics of MPPT control reflected by the BUS voltage must be considered. Generally, when energy is sufficient, the BUS voltage remains above the minimum rectification point. The pressure difference between the actual bus and the reference bus is used as the characteristic criterion: ΔV bus =V busref -V bus Similarly, the slope is used to determine the current time. Since the analyzed data is BUS error, CIK < 0 indicates morning, and CIK > 0 indicates evening. The characteristic response of the bus is more pronounced than that of the PV because when the PV output is insufficient, the IV characteristic is softer, and the boost output voltage is prone to failing to reach the target value.

[0131] Temperature changes have a lag effect, but they reflect the overall trend of photovoltaic cell output to a certain extent, excluding the randomness introduced by other variables due to differences in control methods or sampling errors.

[0132] As described above, principal component analysis can be used to extract the main factors affecting power fluctuations, thereby outputting a power reference that meets the adjustment requirements of the back-end control strategy, resulting in a significant improvement in power generation efficiency. It also meets the soft-start requirements during periods of output strength and the shutdown protection control requirements during periods of output weakness, essentially eliminating frequent start-stop cycles, reducing the risk of damage to switching components, and making the inverter's operation and control more reliable and stable.

[0133] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent in such process, method, article, or apparatus / device.

[0134] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. An inverter control method based on a feature recognition model, characterized in that... It includes the following steps: S1 data acquisition, real-time acquisition of input voltage V PV Input current I PV Ambient temperature T temp Bus voltage V bus Real-time power P ac ; S2 is based on the input voltage V PV Trend of change, slope CIK, input voltage V PV The fluctuation difference CIvar is used for time-sharing control strategy of inverter; During the period when the output capability increases, the input voltage V PV During an upward trend and when CIK > 0; During the output plateau period, the input voltage V PV Stable and CIvar tends to 0; During the period of output capability decay, the input voltage V PV During a downward trend and when CIK < 0; During the period when the output ability of S3 becomes stronger, a power prediction model is constructed for soft start; S4 uses principal component analysis to extract the main influencing factors on power fluctuation; When the main influencing factor is the input voltage V PV Input current I PV At that time, the output power P is calculated using the inverter efficiency equivalent model. ref1 When the main influencing factor is the input voltage V PV Ambient temperature T temp At that time, the output power P using the photovoltaic cell equivalent model is... ref2 When the main influencing factor is the input voltage V PV Bus voltage V bus At that time, the output power P is calculated using the mathematical model of the boost circuit. ref3 ; During the period when the output ability of S5 declines, active shutdown is performed.

2. The inverter control method based on the feature recognition model according to claim 1, wherein: In step S4, a dataset X is generated, X={V}. PV I PV V bus T temp } Calculate the mean CIave and standard deviation CIvar for each data vector, and the deviation value. ; Construct the covariance matrix V: n is the number of sampling points; Solve for the eigenvalues ​​of V and eigenvectors ; Based on variance contribution rate and cumulative variance contribution rate As an evaluation criterion, the eigenvectors corresponding to a cumulative variance contribution rate of over 80% are selected as principal components; eigenvalues The corresponding eigenvector , m<4, principal components ; Principal Component Reconstruction Dataset .

3. The inverter control method based on the feature recognition model according to claim 1, wherein: In the step S4, when the inverter efficiency equivalent model outputs; ； in, It is the inverter output power; It is the DC input power; It refers to the overall conversion efficiency.

4. The inverter control method based on the feature recognition model according to claim 1, wherein: In the step S4, when the photovoltaic cell equivalent model outputs; ； ； in, For output current; This refers to the output voltage. Photocurrent; Saturation current; Series equivalent resistance; Shunt resistor; Ideal factor; Boltzmann constant; Ambient temperature; Rated temperature; Charge element; Semiconductor bandgap energy; Number of modules connected in series; 。 5. The inverter control method based on the feature recognition model according to claim 1, wherein: In the step S4, when the boost circuit mathematical model outputs; ； Vbusref=2 ; V busref -10V≤V bus ≤V busref +10V; where, D is the duty cycle of the boost circuit, and Va, Vb, and Vc are the three-phase voltage peaks of the three-phase power grid; 。 6. The inverter control method based on the feature recognition model according to any one of claims 1 to 5, wherein: In step S2, at power P ref1 Power P ref2、 Power P ref3 When outputting separately, it is compared with the real-time power P. ac After differential processing, power control correction outputs control pulses.

7. The inverter control method based on the feature recognition model according to claim 1, wherein: In the step S2, a data set X is generated, X = {VPV, IPV, Vbus, Ttemp}; slope ; Maximum Dataset ; Minimum value dataset ; average value ; Standard deviation ; First derivative ; Second derivative ; = , where n is the number of sampling points.

8. The inverter control method based on the feature recognition model according to claim 1, wherein: In the step S3, the energy that the PV cell can provide is predicted by the capacity of the bus capacitor: E C C is the capacitance that the bus capacitor can store, C is the capacitance value, and U is the bus voltage. Set the grid connection threshold Pb, when E C When the value is greater than Pb, the inverter is allowed to perform self-test and connect to the grid.

9. The inverter control method based on a feature recognition model according to claim 1, characterized in that... The step S5 includes: The photovoltaic cell power Ppv is calculated according to Vpv and Ipv. When Ppv < P1, enter the active shutdown program; P1 = PR + Paux + Pfan + Prly + Pboost, where PR is the module loss, Paux is the sps power, Pfan is the fan power, Prly is the relay power, and Pboost is the boost circuit switching loss; When Vpv < a1, the dummy load boost_duty is increased to simulate the increase of the dummy load, and Vpv and Ipv are continuously monitored; When Vpv < a2, increasing the dummy load boost_duty can no longer maintain the stability of the BUS voltage, then it is determined that the photovoltaic output is weak, and the grid is actively disconnected, and the relay is disconnected; the dummy load consumes the photovoltaic power; When Vpv < a3, the dummy load is gradually unloaded until all loads are completely cut off; where, a1 is the minimum operating voltage of the inverter boost, a2 is the grid-connected voltage under the minimum power consumption of the inverter, a3 is the starting voltage of the switching power supply, and a1 > a2 > a3.

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