A solar inverter system

Abstract

The application discloses a solar inverter system, and particularly relates to the technical field of solar power generation and power electronic control, which comprises the following modules: a photovoltaic module string dynamic reconstruction module, which dynamically adjusts the parallel connection topology of the module string through a matrix switch network, and isolates mismatched modules to output maximum power point direct current; a multi-modal MPPT and predictive control module, which fuses a neural network to output an optimal duty cycle; a high-frequency isolated DC-AC power conversion module, which adopts an LLC resonance topology and dynamically adjusts resonance inductance and capacitance, and combines temperature self-adaptive frequency and dead zone control to perform efficient conversion; an intelligent grid-connected and energy storage interface module, which supports virtual synchronous generator charging and discharging management, and bypasses the converter to reduce no-load loss when off-grid and light load; and a thermoelectricity cooperative self-adaptive cooling module, which adjusts cooling intensity based on loss feedforward prediction, and recycles waste heat to feed back to a direct current bus. The application improves solar inverter power generation efficiency, power conversion energy efficiency and system energy utilization rate.

Description

Technical Field

[0001] This invention relates to the field of solar power generation and power electronic control technology, and more specifically, to a solar inverter system. Background Technology

[0002] Solar inverter systems are the core component of photovoltaic (PV) power generation. Their main function is to convert the direct current (DC) generated by PV modules into alternating current (AC) that meets grid connection or load requirements. Currently, typical solar inverter systems mainly employ the following technical solutions: A common solution is a centralized or string inverter structure. In the centralized solution, a large number of PV modules are connected in a fixed series-parallel configuration to form a PV array, which is then connected to a central inverter. The inverter has a built-in maximum power point tracking (MPPT) module, typically using the perturbation-observation method or the incremental conductance method, to track the global MPPT of the array by uniformly adjusting the DC bus voltage. Simultaneously, the inverter converts DC to AC through a power frequency transformer or a high-frequency isolated DC-AC converter and is equipped with basic grid-connection protection functions. For heat dissipation, fixed-speed fans or simple temperature-controlled on / off air cooling are typically used.

[0003] However, it still has some drawbacks in actual use, such as low power generation efficiency under local shade: Since the series and parallel topology of photovoltaic strings is fixed after installation, when some components are shaded by clouds, trees or buildings, the mismatched components will cause the current of the whole string to drop and may generate multiple local power peaks. The traditional MPPT algorithm is prone to getting stuck in the local maximum power point rather than the global maximum power point, resulting in a large amount of energy loss.

[0004] MPPT response lags when irradiance changes rapidly: Traditional MPPT algorithms make step adjustments based on the current sampled voltage and current. When irradiance changes drastically due to rapid cloud movement, the tracking speed cannot keep up with the migration rate of the power point, causing the actual operating point to deviate from the maximum power point for a long time. Existing systems do not use short-term irradiance prediction information for feedforward correction, but only rely on feedback adjustment, which has an inherent response delay.

[0005] Power conversion module efficiency and temperature coupling imbalance: The switching frequency and resonant parameters of the LLC resonant circuit or phase-shifted full-bridge circuit in the high-frequency isolated DC-AC converter are usually fixed or adjusted only according to a preset curve. When the input voltage, load power or temperature changes, the converter may deviate from the optimal efficiency point, which can easily lead to local overheating, resulting in decreased efficiency or shortened device life.

[0006] Direct discharge of waste heat results in serious energy waste: The existing cooling method only discharges heat to the environment through air cooling or liquid cooling, without recovering and utilizing the waste heat generated by the power module. This part of the heat energy accounts for half of the input energy, and direct loss causes a reduction in the overall energy efficiency of the system. Summary of the Invention

[0007] In order to overcome the above-mentioned defects of the prior art, the present invention provides a solar inverter system, which solves the problems mentioned in the background art through the following solution.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a solar inverter system, comprising:

[0009] Photovoltaic string dynamic reconfiguration module: Its input end is connected to the output end of the photovoltaic module array. It is used to detect the voltage, current and temperature of each photovoltaic module, and change the series and parallel connection topology between photovoltaic modules according to the local shadow discrimination algorithm. The output voltage is DC at the maximum power point.

[0010] Multimodal MPPT and predictive control module: Its first input terminal is connected to the output terminal of the photovoltaic string dynamic reconstruction module, and its second input terminal is used to receive irradiance sensor signals and meteorological data. Based on the reconstructed DC power and short-term irradiance prediction results, it runs a hybrid MPPT algorithm and outputs the optimal duty cycle control signal.

[0011] High-frequency isolated DC-AC power conversion module: The input terminal is connected to the DC output terminal of the photovoltaic string dynamic reconfiguration module, and the control terminal is connected to the output terminal of the multi-mode MPPT and predictive control module. It converts DC power to AC power according to the optimal duty cycle control signal.

[0012] Intelligent grid connection and energy storage interface module: The input end is connected to the high-frequency isolated DC-AC power conversion module, and the output end is used to connect to the power grid, energy storage battery and AC load. The intelligent grid connection and energy storage interface has a built-in islanding detection unit and a charge and discharge management unit to support virtual synchronous generators.

[0013] Thermoelectric adaptive cooling module: It is connected to the high-frequency isolated DC-AC power conversion module and is also connected to the detection unit of the multi-mode MPPT and predictive control module and the high-frequency isolated DC-AC power conversion module. The thermoelectric adaptive cooling module is used to adjust the cooling intensity based on real-time loss calculation and to recover waste heat through a thermoelectric generator and convert it into electrical energy to feed back to the DC bus.

[0014] The technical effects and advantages of this invention are as follows:

[0015] 1. This invention uses a photovoltaic string dynamic reconfiguration module to collect the voltage, current and temperature of individual modules in real time, and uses both power mean-standard deviation and voltage conversion value to determine mismatched modules. Combined with a matrix switching network, it realizes dynamic optimization of series and parallel topology, and ensures shock-free reconfiguration by soft switching with first-on and then-off. It fundamentally solves the problems of multi-peak power curves and power generation drop caused by shading and module aging under fixed topology, so that the photovoltaic array can continuously output maximum power point DC under complex operating conditions.

[0016] 2. This invention integrates multimodal MPPT with a predictive control module to predict short-term irradiance using a neural network, substring independent MPPT, global MPPT scanning, and predictive feedforward correction. In steady state, it employs a low-oscillation conductivity increment method and an automatic switching variable step-size perturbation observation method for sudden changes, which can predict irradiance changes in advance and correct the duty cycle. Compared with the traditional single MPPT algorithm, it improves the tracking response speed and effectively avoids the power drop caused by rapid cloud movement.

[0017] 3. This invention uses an LLC resonant topology in a high-frequency isolated DC-AC power conversion module, which can dynamically adjust the equivalent value of the resonant inductor and the parameters of the resonant capacitor matrix according to the output power, bus voltage and temperature, so that the converter always works at the highest efficiency point; at the same time, it combines temperature-adaptive switching frequency and dead time optimization to reduce switching loss and conduction loss.

[0018] 4. This invention uses a thermoelectric adaptive cooling module to predict heat generation based on real-time loss feedforward, and adopts PID + fuzzy rules to jointly adjust the intensity of liquid cooling and air cooling, enhancing cooling 0.2~0.5 seconds in advance and avoiding temperature overshoot; at the same time, the waste heat is converted into electrical energy and fed back to the DC bus through a thermoelectric generator, and the recovered power can cover the power consumption of the cooling system itself and have a surplus. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall system structure of the present invention.

[0020] Figure 2 This is a schematic diagram of the photovoltaic string dynamic reconfiguration module structure of the present invention.

[0021] Figure 3 This is a schematic diagram of the multimodal MPPT and predictive control module structure of the present invention.

[0022] Figure 4 This is a schematic diagram of the intelligent grid connection and energy storage interface module of the present invention.

[0023] Figure 5 This is a schematic diagram of the thermoelectric adaptive cooling module structure of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Please see Figure 1 - Figure 5 As shown, an embodiment of the present invention provides a solar inverter system, comprising:

[0026] Photovoltaic string dynamic reconfiguration module: Its input end is connected to the output end of the photovoltaic module array. It is used to detect the voltage, current and temperature of each photovoltaic module, and change the series and parallel connection topology between photovoltaic modules according to the local shadow discrimination algorithm. The output voltage is DC at the maximum power point.

[0027] In this embodiment, the photovoltaic string dynamic reconfiguration module includes a matrix switching network, a multi-channel voltage and current sampling circuit, a temperature sensor array, and a microcontroller. The dynamic reconfiguration process includes:

[0028] A101: Real-time acquisition of operating parameters for each photovoltaic module

[0029] The microcontroller reads the output voltage of each photovoltaic module at a preset sampling frequency through the multi-channel voltage and current sampling circuit. and output current Simultaneously, the backsheet temperature of each photovoltaic module is read through the temperature sensor array. ,in , This indicates the number of photovoltaic modules.

[0030] A102: Determine local shadows and mismatch status

[0031] The microcontroller calculates the real-time output power of each photovoltaic module. And based on the average power of all components and standard deviation Determine the degree of mismatch:

[0032] If a component ,in The preset threshold coefficient has a value of [value]. If so, the photovoltaic module is marked as a mismatched module;

[0033] Calculate the voltage conversion value of each photovoltaic module at standard temperature. ,in Voltage temperature coefficient, If the calculated voltage deviates from the average voltage by more than a set threshold, it is also marked as a mismatched component, using the reference temperature as the reference.

[0034] A103: Determine the optimal series-parallel topology

[0035] The microcontroller runs a topology optimization algorithm based on the labeling results, and the specific steps include:

[0036] A1031: Constructing the candidate topology matrix

[0037] Treating all photovoltaic modules as nodes, the switching matrix allows for connection or disconnection between the positive and negative terminals of any two modules. The algorithm enumerates feasible series-parallel structures and the number of modules in each series branch. Desirable Block, number of parallel branches Based on the number of photovoltaic modules Confirmed, satisfied ;

[0038] A1032: Calculate the expected output power for each topology

[0039] For each candidate topology, the microcontroller simulates its voltage and current characteristic curves and calculates the global maximum power point using an equivalent single diode model. Photovoltaic modules marked as mismatched are preferentially assigned to independent low-voltage branches or isolated via bypass diodes;

[0040] A1033: Selecting the optimal topology

[0041] Select the point that maximizes global power The highest-order topology is taken as the target topology; if multiple topologies are available... If the difference is within 1%, the topology with the fewest switching operations is selected, that is, the photovoltaic module connection relationship changes the least, in order to reduce switching losses.

[0042] A104: Perform switch matrix switching

[0043] The microcontroller generates on / off drive signals for each controllable switch in the switch matrix based on the target topology. To ensure that no voltage spikes or current surges are generated during the switching process, a soft switching strategy of turning on first and then turning off is adopted.

[0044] First, close the switches required for the new topology to form a temporary parallel path, then disconnect the switches that are no longer needed in the original topology. The entire switching process is completed within 20 milliseconds.

[0045] A105: Verify the reconstruction results and perform closed-loop adjustments.

[0046] After the switch is completed, the microcontroller re-collects the voltage and current of each photovoltaic module to confirm the DC bus voltage under the new topology. Is it within the preset maximum power point window? This window is dynamically provided by the subsequent MPPT module;

[0047] like Within the window, the current topology is locked, and a stable DC power is output to the multimodal MPPT and predictive control module;

[0048] like If the voltage exceeds the window, repeat steps A103 to A104, selecting a suboptimal topology that brings the voltage closer to the center of the window, until the requirement is met.

[0049] A106: Periodic Re-judgment

[0050] The microcontroller repeats A101 to A105 at regular intervals to cope with changes in light and temperature. When it detects that the power change rate of any photovoltaic module exceeds a preset threshold, it immediately triggers a rapid re-judgment to achieve dynamic real-time reconstruction.

[0051] Multimodal MPPT and predictive control module: Its first input terminal is connected to the output terminal of the photovoltaic string dynamic reconstruction module, and its second input terminal is used to receive irradiance sensor signals and meteorological data. Based on the reconstructed DC power and short-term irradiance prediction results, it runs a hybrid MPPT algorithm and outputs the optimal duty cycle control signal.

[0052] In this embodiment, the multimodal MPPT and predictive control module includes a voltage and current sampling interface, an irradiance sensor interface, a meteorological data receiving unit, a digital signal processor (DSP), and a pulse width modulation (PWM) signal generator, specifically including:

[0053] B201: Obtain the reconstructed DC electrical parameters

[0054] The multimodal MPPT and predictive control module reads the DC bus voltage from the output of the photovoltaic string dynamic reconfiguration module in real time through the first input terminal. and DC bus current And calculate the current actual power. Simultaneously, it receives topology information from the photovoltaic string dynamic reconfiguration module, including the number of series-connected modules. Number of parallel branches and the voltage range of each substring.

[0055] B202: Collecting Irradiance and Meteorological Data

[0056] The current instantaneous irradiance is obtained through an irradiance sensor connected to the second input terminal, such as a silicon photovoltaic cell or a solar intensity meter. The sampling frequency is not lower than ;

[0057] Simultaneously, short-term weather forecast data, including predicted irradiance change curves, are acquired through the meteorological data receiving unit. Ambient temperature Wind speed and cloud cover .

[0058] B203: Perform short-term irradiation prediction and pretreatment

[0059] The digital signal processor runs a lightweight neural network prediction model, such as a three-layer feedforward network, with the number of input layer nodes including the current irradiance historical sequence. The hidden layer has 10 nodes, and the number of nodes in the output layer includes the predicted irradiance value for the next 5 seconds. ;

[0060] The model was trained in advance using local historical data and fine-tuned online every 24 hours.

[0061] The prediction results are used to determine whether there will be rapid changes in irradiance in the future:

[0062] If the rate of change in irradiance is predicted within the next 5 seconds ,in This indicates a threshold preset based on the historical rate of change in irradiance (e.g., When the rate of change of irradiance is greater than the preset threshold, the fast tracking mode is triggered; otherwise, the steady-state optimization mode is maintained.

[0063] B204: Running the hybrid MPPT algorithm

[0064] Upon receiving the substring information provided by the mode and reconstruction module of B203, the digital signal processor selects and executes the following steps:

[0065] B2041: Multi-channel substring independent MPPT

[0066] Since the reconfiguration module divides the photovoltaic module into multiple parallel branches, each branch has similar voltages but potentially different currents. A virtual MPPT channel is assigned to each branch; each channel independently runs a hybrid algorithm combining the perturbation-observation method and the incremental conductance method.

[0067] In steady-state optimization mode, the incremental conductance method is mainly used, with a step size of... The derivative of power with respect to voltage Proportional to each other, used to achieve fast and low-oscillation tracking;

[0068] When large power fluctuations are detected When the time is right, it automatically switches to the perturbation observation method, which uses variable step size perturbation, with the step size decreasing as the absolute value of the error decreases.

[0069] B2042: Global MPPT Optimization

[0070] When the photovoltaic string dynamic reconfiguration module reports the presence of multi-peak characteristics, i.e., multiple local maximum power points, a global scan is initiated:

[0071] Within the allowable range of the DC bus voltage, i.e., the window provided by the reconfiguration module. A full-range scan was performed with a voltage step size of 5%, and the power at each photovoltaic module voltage point was recorded.

[0072] Determine the global maximum power point voltage Then switch back to the incremental conductance method for fine tracking;

[0073] The global scan is initiated when: the power is still below 80% of the average power of the previous 24 hours after three consecutive local MPPTs, or in the first cycle after the reconfiguration module has just completed the topology switch.

[0074] B2043: Predictive Feedforward Correction

[0075] Short-term irradiance predictions obtained using B203 Feedforward correction is performed on the MPPT output duty cycle:

[0076] Establish an irradiance and optimal voltage model ;

[0077] When the predicted irradiance changes from 1 second later Become At that time, adjust the duty cycle in advance to direct the working voltage to Move, movement speed is ,in This represents the feedforward gain coefficient, used to suppress power drops caused by rapid cloud movement.

[0078] B205: Output optimal duty cycle control signal

[0079] The digital signal processor calculates the optimal duty cycle signal for each channel. It is converted into a PWM waveform of the corresponding frequency and output to the control terminal of the high-frequency isolated DC-AC power conversion module through the pulse width modulation PWM signal generator;

[0080] Simultaneously, the current MPPT status information (including operating voltage, power, and tracking mode) is sent to the thermoelectric adaptive cooling module and the intelligent grid connection and energy storage interface module for coordinated control.

[0081] B206: Dynamically adjust the MPPT benchmark based on external feedback

[0082] The multimodal MPPT and predictive control module receives temperature feedback signals from the thermoelectric adaptive cooling module. And efficiency feedback from the high-frequency isolated DC-AC power conversion module. ;

[0083] like (Temperature safety threshold) will then actively reduce the MPPT voltage reference. This is because the operating voltage of photovoltaic modules decreases at high temperatures, which can also reduce inverter switching losses.

[0084] like If the value is below the set threshold, the duty cycle is finely adjusted to make the operating point slightly deviate from the pure maximum power point in order to achieve higher inverter efficiency.

[0085] The adjusted voltage reference recalculates the duty cycle using the hybrid MPPT algorithm of B204 to achieve closed-loop coordination.

[0086] High-frequency isolated DC-AC power conversion module: The input terminal is connected to the DC output terminal of the photovoltaic string dynamic reconfiguration module, and the control terminal is connected to the output terminal of the multi-mode MPPT and predictive control module. It converts DC power to AC power according to the optimal duty cycle control signal.

[0087] C301: Receives DC input and performs pre-charging and soft-start

[0088] The high-frequency isolated DC-AC power conversion module receives the DC bus voltage from the photovoltaic string dynamic reconfiguration module through its input terminal. ;

[0089] During startup, the pre-charge relay is first closed, and the input filter capacitor is slowly charged through the current-limiting resistor until the capacitor voltage reaches the specified value. Then close the main contactor and disconnect the current-limiting resistor to prevent the inrush current from damaging the switching transistor.

[0090] C302: Receives and analyzes the optimal duty cycle control signal.

[0091] The control terminal receives the duty cycle signal of the multimodal MPPT and the PWM output from the predictive control module. , usually The values ​​between; the internal microcontroller will Switching frequency of the primary-side full-bridge inverter circuit and phase shift angle ,in:

[0092] For LLC resonant converters, the duty cycle signal Primarily mapped to switching frequency :

[0093]

[0094] in The lowest switching frequency, To achieve the highest switching frequency, if a phase-shifted full-bridge topology is used, then... Convert to phase angle .

[0095] C303: Dynamically Adaptive Adjustment of Resonance Parameters

[0096] The microcontroller adjusts the output power based on the current output power. DC bus voltage Temperature feedback signal Dynamically change the resonant inductance and resonant capacitor Equivalent value:

[0097] C3031: Calculate the target resonant frequency

[0098] To ensure the converter always operates at Near the efficiency point of the resonant converter, the target resonant frequency Set as: ;

[0099] in The efficiency optimization factor is determined by the microcontroller based on real-time efficiency. Perform online search: fine-tuning every 10 seconds ,make maximum.

[0100] C3032: Adjusting the resonant inductor

[0101] The resonant inductor consists of the main inductor With auxiliary magnetic switch inductor It can be connected in parallel or in series, and its equivalent inductance value can be changed by a controllable switch;

[0102] When it is necessary to increase the resonant frequency, the microcontroller turns on the auxiliary branch switch to reduce the equivalent inductance. ;

[0103] When it is necessary to reduce the resonant frequency, disconnect the auxiliary branch switch; the equivalent inductance... The step size of the equivalent inductance value change is It can switch between multiple discrete gears.

[0104] C3033: Adjusting the resonant capacitor

[0105] The resonant capacitor uses a capacitor matrix, which consists of multiple capacitors connected in parallel, with each capacitor connected in series with a MOSFET switch. The microcontroller changes the equivalent capacitance by varying the capacitance values. :

[0106]

[0107] The microcontroller selects the capacitor combination closest to the calculated value, and the switching process occurs during... It must be completed internally and synchronized with the change in switching frequency to avoid voltage overshoot.

[0108] C304: Performs LLC resonant conversion and DC-AC inversion

[0109] C3041: Primary-side full-bridge drive

[0110] The gate drive circuit operates according to the current switching frequency. Four complementary PWM signals are generated: the upper and lower bridge arms are alternately turned on with a 50% duty cycle, forming a high-frequency AC square wave voltage applied to the resonant cavity and dead time. Initially set to And adjust dynamically based on temperature feedback;

[0111] C3042: Resonance and Energy Transfer

[0112] High-frequency square wave voltage excitation LLC resonant cavity (equivalent inductance) Equivalent capacitance and transformer magnetizing inductance This makes the resonant cavity current approximate a sine wave;

[0113] When switching frequency equal to the resonant frequency When the converter gain is 1, the efficiency is the highest. When the gain deviates from 1, the gain changes, thereby achieving output voltage regulation.

[0114] C3043: Secondary-side rectification and filtering

[0115] The secondary voltage of the high-frequency transformer is rectified by a full-bridge rectifier to obtain pulsating DC, which is then filtered by the output filter inductor. and capacitor Smoothed DC bus for stability;

[0116] The output is DC power. To generate AC power, a secondary inversion is required, which is a two-stage DC-AC structure: the front stage is an isolated DC-DC converter, and the back stage is a DC-AC inverter. Its switching frequency is the grid frequency, which converts the DC bus into sinusoidal AC power. The modulation ratio of the back stage inverter is obtained from the duty cycle signal of the MPPT module.

[0117] C305: Adaptively adjusts switching frequency and dead time based on temperature feedback.

[0118] The high-frequency isolated DC-AC power conversion module receives temperature feedback signals from the thermoelectric adaptive cooling module in real time. The microcontroller includes:

[0119] Frequency reduction control: when At the same time, gradually reduce the switching frequency. Switching losses decrease at low frequencies, but losses in magnetic components increase. Closed-loop regulation is used to stabilize the temperature at [temperature range]. and between;

[0120] Dead time optimization: Dead time Adaptive adjustment based on temperature changes: Increased temperature increases the turn-off delay time of the blocker, therefore the microcontroller size is increased. To prevent straight-through of the bridge arm; appropriately reduce the size when the temperature drops. To improve efficiency;

[0121] Temperature-compensated voltage gain: The microcontroller automatically adjusts the modulation ratio of the downstream inverter or fine-tunes the switching frequency of the upstream LLC to ensure that the output voltage is stable at the rated value of 220V.

[0122] C306: Outputs AC power and monitors operating status.

[0123] The final output is a pure sine wave AC voltage. ,in and Controlled by the intelligent grid connection and energy storage interface module, this module continuously monitors the following parameters:

[0124] Estimation of input voltage, current, output voltage, current, peak resonant cavity current, temperature of each power transistor, and junction temperature;

[0125] If any parameter exceeds the safety threshold, the PWM output is immediately blocked and a fault signal is sent to the system main controller.

[0126] C307: Closed-loop interaction with the multimodal MPPT module

[0127] Current actual switching frequency ,efficiency The output voltage ripple is sent back to the multi-mode MPPT and predictive control module;

[0128] If efficiency If the value is lower than the preset value, the MPPT module will fine-tune the duty cycle signal to make the operating point slightly deviate from the pure maximum power point, so as to reduce the AC electrical stress of the inverter and thus improve the overall system efficiency.

[0129] Intelligent grid connection and energy storage interface module: The input end is connected to the high-frequency isolated DC-AC power conversion module, and the output end is used to connect to the power grid, energy storage battery and AC load. The intelligent grid connection and energy storage interface has a built-in islanding detection unit and a charge and discharge management unit to support virtual synchronous generators.

[0130] In this embodiment, the intelligent grid connection and energy storage interface module includes: an AC input / output port, a grid connection relay array, an energy storage battery interface circuit, an islanding detection unit, a charge / discharge management unit, and a virtual synchronous generator. The module operates according to the following steps:

[0131] D401: Receives AC power from the inverter and determines the connection mode.

[0132] The input terminal receives a pure sinusoidal AC voltage from the high-frequency isolated DC-AC power conversion module. Simultaneously detect the type of the external object connected to the output terminal:

[0133] If the grid voltage, frequency, and phase are detected to be normal, the system will automatically enter grid-connected mode.

[0134] If no grid voltage is detected but an energy storage battery is connected, the system will enter off-grid mode.

[0135] If both the power grid and the battery are detected simultaneously, the system enters hybrid mode.

[0136] D402: Synchronization and Power Regulation in Grid-Connected Mode

[0137] Pre-synchronization: Before the grid-connected relay closes, the VSG control unit measures the amplitude of the grid voltage. ,frequency and phase And adjust the inverter output:

[0138]

[0139] Once the synchronization condition is met, close the grid-connected relay;

[0140] Active / Reactive Power Control: By adjusting the virtual inertia and damping coefficient of the VSG, the active power P and reactive power Q fed into the grid are controlled; at the same time, the charge and discharge management unit decides whether to draw power from the grid to charge the battery based on the state of charge (SOC) of the energy storage battery.

[0141] D403: Virtual synchronous generator operation in off-grid mode

[0142] When a power grid failure is detected or the grid connection relay is actively disconnected, the VSG control unit simulates the electromechanical transient characteristics of a synchronous generator to provide stable voltage and frequency support for the local AC load.

[0143] The virtual rotor angular velocity is automatically adjusted based on changes in load power, using the following formula:

[0144]

[0145] in For virtual inertia, For damping coefficient, For a given power, To output electromagnetic power, The rated angular frequency is used; the output voltage amplitude is kept constant by a virtual excitation regulator, and the total harmonic distortion of the voltage is less than 3%.

[0146] D404: Islanding Detection and Protection

[0147] The islanding detection unit monitors the voltage, frequency, and phase transitions on the grid side in real time, employing a combination of active frequency offset detection and passive voltage phase transition detection.

[0148] Active type: A 0.5Hz frequency disturbance is injected into the inverter every 2 seconds. If the grid is disconnected, the frequency will quickly deviate beyond the allowable range and trigger the protection.

[0149] Passive: Detects voltage phase transition angle If the value exceeds a preset threshold, and the value exceeds the threshold for three consecutive cycles, the system is determined to be in islanded mode. Once islanded mode is confirmed, the grid-connected relay is disconnected within 20ms, and the system switches to off-grid mode.

[0150] D405: Charge / Discharge Management and Energy Storage Interface Control

[0151] The charge / discharge management unit monitors the voltage, current, temperature, and SOC of the energy storage battery in real time through the battery interface circuit, and executes the following management strategies:

[0152] Charging management: In grid-connected mode and when the SOC is below the lower limit, charging is started, and the process goes through trickle charging, constant current charging, and constant voltage charging in sequence. Charging stops when the SOC reaches 95%.

[0153] Discharge management: In off-grid mode, power is drawn from the battery according to load demand, and the depth of discharge is limited to within 80%; when the SOC is below 20%, a low battery alarm is issued and the load is gradually reduced.

[0154] Bidirectional energy flow: The built-in bidirectional DC-DC converter can draw power from the grid and store it in the battery when connected to the grid, and discharge the battery and supply the load through the inverter when disconnected from the grid.

[0155] D406: Linkage with the dynamic reconfiguration module of the photovoltaic string

[0156] When the islanding detection confirms off-grid status and the load power is low, the intelligent grid-connected and energy storage interface module sends a command to the photovoltaic string dynamic reconfiguration module, requesting it to configure the photovoltaic string in low-voltage, high-current mode; the internal energy storage interface directly charges the low-voltage DC bus and bypasses the high-frequency isolated DC-AC power conversion module to avoid high-voltage inverter no-load losses and improve off-grid light-load efficiency.

[0157] Status feedback and coordinated control: The current operating mode (grid-connected / off-grid / hybrid), grid status (voltage, frequency), battery SOC, and islanding flag are sent in real time to the multimodal MPPT and predictive control module and the thermoelectric adaptive cooling module to adjust the MPPT strategy and cooling intensity. For example, when the battery SOC is close to full, the MPPT module can actively reduce output power to reduce waste.

[0158] Thermoelectric adaptive cooling module: It is connected to the high-frequency isolated DC-AC power conversion module and is also connected to the detection unit of the multi-mode MPPT and predictive control module and the high-frequency isolated DC-AC power conversion module. The thermoelectric adaptive cooling module is used to adjust the cooling intensity based on real-time loss calculation and to recover waste heat through a thermoelectric generator and convert it into electrical energy to feed back to the DC bus.

[0159] E101: Real-time acquisition of temperature and power loss data

[0160] The microcontroller communicates with the following via an internal communication bus:

[0161] The high-frequency isolated DC-AC power conversion module obtains its current input power, output power, and switching frequency. and estimated junction temperature of power transistors;

[0162] The multimodal MPPT and predictive control module acquires the current operating mode (steady-state / fast tracking) and output power command;

[0163] The intelligent grid connection and energy storage interface module acquires grid connection power, battery charging and discharging status, and load requirements;

[0164] E102: Calculate real-time power loss and predict heat generation

[0165] The microcontroller calculates the current total loss based on the acquired data. ,in Indicates input power, Indicates output power, Indicates switching losses, This represents the conduction loss; furthermore, a feedforward prediction model is used to estimate the heat generation in the short term. :

[0166]

[0167] in For feedforward coefficients, To predict the step size, the predicted heat generation is increased in advance based on the load change signal transmitted by the intelligent grid-connected module.

[0168] E103: Feedforward Adjustment of Cooling Intensity

[0169] Based on the predicted heat generation and the current temperature, the microcontroller uses a combination of PID and fuzzy rules to control the liquid cooling and air cooling of the solar panel, respectively.

[0170] Liquid cooling circuit: Set target coolant temperature ,in The actual coolant temperature is proportional to the predicted heat output; adjust the circulating pump speed to achieve the desired temperature. Follow the target value;

[0171] Air-cooled auxiliary: If the current temperature If the predicted heat generation exceeds 80% of the solar panel's rated heat dissipation capacity, start the cooling fan; the fan speed will adjust according to the current temperature. Linear increase; feedforward regulation can be implemented before power changes occur. Increase cooling capacity within seconds to prevent temperature overshoot.

[0172] E104: Waste Heat Recovery and Thermoelectric Conversion

[0173] The hot end of the thermoelectric generator array absorbs waste heat carried by the coolant, while the cold end is kept at a low temperature by ambient air or auxiliary air cooling, generating Seebeck voltage. ;

[0174] when and At that time, the DC-DC boost converter starts, and... Boost to DC bus voltage And it is fed back to the DC bus through the anti-reverse diode;

[0175] Feedback power ,in For TEG power generation efficiency, The microcontroller monitors the heat flow through the TEG in real time. The amount of waste heat recovered is recorded on the system display interface.

[0176] E105: Temperature feedback to MPPT and power conversion module

[0177] The thermoelectric adaptive cooling module will adjust the current power module temperature. and coolant temperature Send to: in real time

[0178] The multi-modal MPPT and predictive control module is used to adjust the MPPT voltage reference and reduce the voltage reference at high temperatures; the high-frequency isolated DC-AC power conversion module is used to adjust the switching frequency and dead time.

[0179] If the current temperature is detected If the safety threshold is exceeded, an overheating derating signal is sent to the intelligent grid-connected and energy storage interface module to request a reduction in output power or a temporary switch to low power mode to protect the power inverter devices.

[0180] E106: Coordinated Energy Management with Smart Grid-Connected Modules

[0181] The microcontroller feeds back the waste heat to power. and the power consumption of the cooling system itself The information is reported to the intelligent grid connection and energy storage interface module.

[0182] In grid-connected mode, the intelligent grid-connected module will recover net power. Included in the overall power generation efficiency statistics of the system;

[0183] In off-grid mode, if the battery SOC is low and the net recovery power is positive, the smart grid-connected module can prioritize using this portion of the power to supply critical loads or supplement battery charging, thereby improving off-grid range.

[0184] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0185] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A solar inverter system, characterized in that, include: Photovoltaic string dynamic reconfiguration module: Its input end is connected to the output end of the photovoltaic module array. It is used to detect the voltage, current and temperature of each photovoltaic module, and change the series and parallel connection topology between photovoltaic modules according to the local shadow discrimination algorithm. The output voltage is DC at the maximum power point. Multimodal MPPT and predictive control module: Its first input terminal is connected to the output terminal of the photovoltaic string dynamic reconstruction module, and its second input terminal is used to receive irradiance sensor signals and meteorological data. Based on the reconstructed DC power and short-term irradiance prediction results, it runs a hybrid MPPT algorithm and outputs the optimal duty cycle control signal. High-frequency isolated DC-AC power conversion module: The input terminal is connected to the DC output terminal of the photovoltaic string dynamic reconfiguration module, and the control terminal is connected to the output terminal of the multi-mode MPPT and predictive control module. It converts DC power to AC power according to the optimal duty cycle control signal. Intelligent grid connection and energy storage interface module: The input end is connected to the high-frequency isolated DC-AC power conversion module, and the output end is used to connect to the power grid, energy storage battery and AC load. The intelligent grid connection and energy storage interface has a built-in islanding detection unit and a charge and discharge management unit to support virtual synchronous generators. Thermoelectric adaptive cooling module: It is connected to the high-frequency isolated DC-AC power conversion module and is also connected to the detection unit of the multi-mode MPPT and predictive control module and the high-frequency isolated DC-AC power conversion module. The thermoelectric adaptive cooling module is used to adjust the cooling intensity based on real-time loss calculation and to recover waste heat through a thermoelectric generator and convert it into electrical energy to feed back to the DC bus.

2. The solar inverter system according to claim 1, characterized in that, The photovoltaic string dynamic reconfiguration module includes a matrix switching network, a multi-channel voltage and current sampling circuit, a temperature sensor array, and a microcontroller. Dynamic reconfiguration is performed by: A101: Real-time acquisition of output voltage, output current, and backsheet temperature of each photovoltaic module; A102: Calculate the real-time output power of each photovoltaic module, determine the mismatched module based on the average power value and standard deviation, and calculate the voltage conversion value at the standard temperature according to the voltage temperature coefficient. If the converted voltage deviates from the average voltage by more than the set threshold, it is marked as a mismatched module. A103: Run the topology optimization algorithm based on the labeling results, construct a candidate topology matrix, calculate the expected output power under each topology, and select the topology with the global maximum power point as the target topology; A104: Generate drive signals for the switch matrix based on the target topology, and perform switch matrix switching using a soft switching strategy of first turning on and then turning off; A105: After the switch is completed, re-collect the voltage and current of each photovoltaic module to confirm whether the DC bus voltage is within the preset maximum power point window. If it exceeds the window, select the suboptimal topology. A106: Repeat A101 to A105 at regular time intervals. When the power change rate of the photovoltaic module is detected to exceed the preset threshold, a re-judgment is triggered immediately.

3. A solar inverter system according to claim 1, characterized in that, The multimodal MPPT and predictive control module includes a voltage and current sampling interface, an irradiance sensor interface, a meteorological data receiving unit, a digital signal processor, and a PWM signal generator. Specifically, it performs the following: B201: Real-time reading of DC bus voltage and DC bus current, and receiving topology information from the photovoltaic string dynamic reconfiguration module; B202: Obtain current instantaneous irradiance and short-term weather forecast data; B203: Runs a lightweight neural network prediction model, outputs the irradiance prediction value for the next 5 seconds, and determines whether the future irradiance change rate will trigger the fast tracking mode. B204: Run the hybrid MPPT algorithm according to the fast tracking mode or steady-state optimization mode. The algorithm includes multi-channel substring independent MPPT, global MPPT optimization and prediction feedforward correction. B205: Converts the calculated optimal duty cycle signal into a PWM waveform and outputs it to the high-frequency isolated DC-AC power conversion module; B206: Receives temperature feedback signals from the thermoelectric adaptive cooling module and efficiency feedback from the high-frequency isolated DC-AC power conversion module, and adjusts the MPPT voltage reference.

4. A solar inverter system according to claim 3, characterized in that, Specifically, B204 includes: The multi-channel substring independent MPPT is as follows: a virtual MPPT channel is allocated for each parallel branch, and each channel runs a hybrid algorithm of perturbation observation method and conductance increment method independently; in steady-state optimization mode, conductance increment method is used, and the step size is proportional to the derivative of power with respect to voltage; when the power fluctuation is greater than 5% of the rated power, the perturbation observation method is switched to use variable step size perturbation. The global MPPT optimization is as follows: when there are multi-peak characteristics, a full-range scan is performed with a voltage step size of 5% within the allowable range of DC bus voltage. After determining the global maximum power point voltage, the incremental conductance method is switched back. The prediction feedforward correction is as follows: using short-term irradiance prediction values, an irradiance and optimal voltage model is established, and the duty cycle is adjusted in advance.

5. A solar inverter system according to claim 1, characterized in that, The high-frequency isolated DC-AC power conversion module includes a primary-side full-bridge circuit, an LLC resonant cavity, a high-frequency transformer, a secondary-side rectifier and filter circuit, and a subsequent DC-AC inverter. Specifically, it performs the following: C301: Receives DC bus voltage and performs pre-charge and soft start; C302: Receives the optimal duty cycle control signal and converts it into switching frequency and phase shift angle; C303: Based on the current output power, DC bus voltage and temperature feedback signal, dynamically change the equivalent values ​​of resonant inductance and resonant capacitance to make the converter operate near the efficiency point; C304: Performs LLC resonant conversion and DC-AC inversion; C305: Adaptively adjusts switching frequency and dead time based on temperature feedback signal; C306: Outputs pure sine wave AC voltage and monitors operating status; C307: Sends the actual switching frequency, efficiency, and output voltage ripple back to the multi-mode MPPT and predictive control module.

6. A solar inverter system according to claim 5, characterized in that, In the C303, the specific method for dynamically changing the equivalent values ​​of the resonant inductance and resonant capacitance is as follows: the microcontroller searches for the target resonant frequency online based on the real-time efficiency, changes the connection state of the auxiliary inductor branch through a controllable switch to adjust the equivalent inductance value, and changes the equivalent capacitance value through the parallel combination of multiple capacitors in the capacitor matrix. The switching process is completed within 50 microseconds and is synchronized with the change of the switching frequency.

7. A solar inverter system according to claim 1, characterized in that, The intelligent grid connection and energy storage interface module includes AC input and output ports, a grid connection relay array, an energy storage battery interface circuit, an islanding detection unit, a charge and discharge management unit, and a virtual synchronous generator. Specific operating modes include: D401: Receives AC power from the inverter and determines the connection mode as grid-connected, off-grid, or hybrid. D402: Performs pre-synchronization and active / reactive power control in grid-connected mode; D403: Simulates the electromechanical transient characteristics of a synchronous generator in off-grid mode, providing voltage and frequency support for local AC loads; D404: Islanding detection and protection are achieved by combining active frequency offset detection with passive voltage phase transition detection. D405: Real-time monitoring of energy storage battery status, execution of charge and discharge management and bidirectional energy flow control; D406: When off-grid and with low load power, send instructions to the photovoltaic string dynamic reconfiguration module to configure it in low-voltage, high-current mode, bypassing the high-frequency isolated DC-AC power conversion module.

8. A solar inverter system according to claim 1, characterized in that, The thermoelectric adaptive cooling module includes a microcontroller, a liquid cooling circuit, an air-cooled auxiliary device, a thermoelectric generator array, and a DC-DC boost converter, specifically including: E101: Real-time acquisition of temperature and power loss data of high-frequency isolated DC-AC power conversion module, multi-modal MPPT and predictive control module and intelligent grid connection and energy storage interface module; E102: Calculate the current total loss based on the input power and output power, and use a feedforward prediction model to estimate the heat generation in the short term. E103: Based on the predicted heat generation and current temperature, the cooling intensity of the liquid cooling circuit and air-cooled auxiliary circuit is adjusted forward using a combination of PID and fuzzy rules. E104: The Seebeck voltage is generated by absorbing waste heat through a thermoelectric generator array. When the conditions are met, the DC-DC boost converter is started to feed electrical energy back to the DC bus. E105: Sends the power module temperature and coolant temperature to the multi-mode MPPT and predictive control module and the high-frequency isolated DC-AC power conversion module in real time; E106: Reports waste heat recovery power and cooling system power consumption to the smart grid-connected and energy storage interface module for system total power generation efficiency statistics and off-grid energy management.

9. A solar inverter system according to claim 8, characterized in that, The calculation of real-time power loss in E102 includes: Total loss ,in Indicates input power, This represents the output power; furthermore, a feedforward prediction model is used to estimate the heat generation in the near future. : in For feedforward coefficients, To predict the step size, the predicted heat generation is increased in advance based on the load change signal transmitted by the intelligent grid-connected module.

Images