Self-adaptive control method and system for energy storage power converter based on digital twinning

Abstract

The invention discloses a control method, and particularly relates to an energy storage power converter self-adaptive control method based on digital twinning, which comprises the following steps: step 1, digital twinning model synchronization and state sensing; step 2, multi-target collaborative optimization based on prediction; and step 3, optimizing instruction issuing and physical system execution. Through real-time prediction and optimization of the digital twinborn model, it is ensured that the power switch tube always works in a zero-voltage switching state, switching loss is effectively reduced, and overall efficiency is improved. Dynamic thermal balance management is realized by combining thermal model prediction, local overheating is avoided, the service life of devices is prolonged, and the reliability of long-term operation of the system is enhanced.

Description

Technical Field

[0001] This invention relates to the field of engineering equipment technology, and in particular to an adaptive control method and system for energy storage power converters based on digital twins. Background Technology

[0002] With the development of renewable energy, energy storage systems are playing an increasingly important role in the power grid. As the core component of energy storage systems, the power converter's control performance directly affects system efficiency, reliability, and lifespan. Current technologies for energy storage power converters face the following problems: Insufficient efficiency optimization: Traditional converters use hard-switching technology, resulting in high switching losses, significant electromagnetic interference (EMI), and voltage equalization issues affecting the performance of series-connected energy storage devices. Although soft-switching technologies (such as ZVS) can reduce losses, their parameters are fixed and cannot adapt to dynamic load changes. Inaccurate power control: Active and reactive power balance modulation methods respond slowly to grid fluctuations, easily leading to substandard power quality, such as harmonic injection or DC component issues. Mode switching delay: Switching between islanded and grid-connected modes typically relies on voltage or current zero-crossing points, causing time delays that affect system dynamic response and stability, especially performance degradation in emergency power supply scenarios. Digital twin technology, as an emerging method of virtual mapping of physical entities, has been used in industrial fields for real-time monitoring and predictive maintenance, but its application in energy storage power converter control is limited. Existing control methods lack adaptability and cannot adjust parameters according to real-time operating status, resulting in limited overall performance. Summary of the Invention

[0003] In order to solve the problems existing in the prior art, the present invention provides an adaptive control method and system for energy storage power converter based on digital twin, so as to solve the current technical problems.

[0004] The technical solution adopted by this invention to solve its technical problem is: This invention provides an adaptive control method for energy storage power converters based on digital twins, comprising the following steps: Step 1: Synchronization and State Awareness of Digital Twin Models; Step 2: Multi-objective collaborative optimization based on prediction; Step 3: Optimize command issuance and physical system execution.

[0005] Preferably, step 1: digital twin model synchronization and state awareness includes: data reception and preprocessing, model initialization and state injection, and parameter identification.

[0006] Preferably, step 2: multi-objective collaborative optimization based on prediction includes: Step 2.1: Adaptive efficiency optimization; Step 2.2: Adaptive power modulation and power quality optimization; Step 2.3: Rehearsal and compensation for seamless mode switching.

[0007] Preferably, step 3: optimizing instruction issuance and physical system execution includes the steps of instruction packaging and real-time instruction issuance.

[0008] The present invention also provides an adaptive control system for an energy storage power converter based on digital twins, including the above-mentioned adaptive control method, comprising: a main power topology; a sensing system; and an execution unit.

[0009] The beneficial effects of this invention are: Significantly improves system efficiency and reliability: Real-time prediction and optimization through digital twin models ensure that power switches always operate in zero-voltage switching (ZVS) mode, effectively reducing switching losses and improving overall system efficiency. Combined with thermal model prediction, dynamic thermal balance management is achieved, avoiding localized overheating, extending device lifespan, and enhancing the long-term reliability of the system.

[0010] Achieving multi-objective collaborative optimization and power quality improvement: Multiple tasks, including efficiency optimization, thermal management, harmonic suppression, and mode switching simulation, are executed simultaneously within the digital twin platform to achieve optimal overall system performance. Through feedforward harmonic compensation and reactive power support strategies, grid harmonics are effectively suppressed, adaptability to grid faults is enhanced, and grid-connected power quality is improved.

[0011] Enhanced system adaptability and fault tolerance: Key parameters (such as resonant cavity parameters) are identified online using the recursive least squares method, and the model is corrected in real time to adapt to component aging and temperature drift, maintaining model accuracy. In abnormal situations such as communication interruptions, the DSP can automatically switch to a conservative control strategy to ensure basic system safety and possess good fault degradation operation capabilities.

[0012] Supports seamless mode switching and surge suppression: The mode switching process is simulated using a digital twin, generating a feedforward compensation signal to effectively suppress voltage and frequency surges during the switching process, achieving a smooth transition and avoiding impact on the load and power grid.

[0013] Improve control real-time performance and decision intelligence: Utilize high-performance digital signal processors (DSPs) and edge computing platforms to achieve rapid response of the control loop and parallel processing of complex algorithms; digital twins make predictions faster than physical systems, allowing sufficient time for control decisions and improving the dynamic response quality of the system.

[0014] Reduce maintenance costs and development cycle: With the help of high-precision digital twins, control strategies and parameters can be verified in a virtual environment, reducing the risks and time of on-site debugging; through continuous iteration of models and data, support is provided for system status assessment and predictive maintenance, reducing the total life cycle operation and maintenance costs. Attached Figure Description

[0015] The above-described aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 This is a schematic diagram of an adaptive control method and system for energy storage power converter based on digital twins, according to an embodiment of the present invention. Detailed Implementation

[0016] 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.

[0017] This invention provides an adaptive control system for an energy storage power converter based on digital twins, comprising: Main power topology: Bidirectional DC / DC stage (battery side): Employs a two-phase interleaved parallel LLC resonant half-bridge topology. This topology inherently possesses ZVS capability, boasts high efficiency, and the interleaved parallel connection reduces current ripple. It includes switching transistors S1-S4, resonant inductors Lr1 / Lr2, resonant capacitors Cr1 / Cr2, transformers T1 / T2, and the output rectifier section.

[0018] Bidirectional DC / AC stage (grid side): Employs a three-level T-type neutral point clamped (T-NPC) full-bridge topology. This topology offers high output waveform quality and low switching losses. Includes switching transistors Q1-Q12.

[0019] Sensing system: Electrical quantities: High-precision Hall voltage / current sensor to measure DC bus voltage (V_dc), battery side voltage / current (V_bat, I_bat), and AC side voltage / current (V_ac, I_ac).

[0020] Thermal parameters: Surface mount thermocouples or infrared sensors are used to measure the case temperature of all power switching transistors (S1-S4, Q1-Q12).

[0021] Status signals: Relay / contactor status signals.

[0022] Execution unit: Main controller: Employs TI's TMS320F28379D or a similar high-performance dual-core DSP. One core is used for real-time control, and the other for communication and advanced algorithms.

[0023] Drive circuit: Isolated IGBT / MOSFET driver chip with desaturation protection and active clamping function.

[0024] Operating platform: Runs on an industrial PC or edge computing server, communicating with the DSP via Ethernet.

[0025] Model type: Real-time electromagnetic transient model: A detailed model based on switching devices, used for accurate simulation of switching transients and ZVS processes.

[0026] Loss and thermal model: In the form of a look-up table, the inputs are voltage, current, and switching frequency, and the outputs are conduction loss and switching loss. The junction temperature is then calculated using the Foster thermal network model.

[0027] Control model: an exact mirror image of all algorithms in the physical controller (PIIR controller, PLL, SVPWM generator, etc.).

[0028] This invention also provides an adaptive control method for an energy storage power converter based on digital twins, the adaptive control method comprising: Process Initiation: Sensor data from the physical system is sampled in real time by the ADC module of the DSP and packaged and sent to the digital twin platform via Ethernet UDP protocol.

[0029] Step 1: Synchronization and State Awareness of Digital Twin Models Data reception and preprocessing: The digital twin platform receives data packets and performs CRC check and filtering.

[0030] Model initialization and state injection: The real-time state of the physical system (such as V_dc, I_bat, V_ac) is used as initial conditions and injected into each sub-model of the digital twin. At this point, the digital twin model and the physical entity are in state synchronization.

[0031] Parameter identification: Method: Recursive least squares (RLS) method is used.

[0032] Implementation: For DC / DC resonant cavities, a small signal disturbance is injected, and the actual values ​​of the resonant inductor L_r and resonant capacitor C_r are identified online by comparing the model output current with the actual inductor current, in order to compensate for component aging and temperature drift.

[0033] Output: Update the L_r and C_r parameters in the digital twin model to ensure model accuracy.

[0034] Step 2: Multi-objective collaborative optimization based on prediction The digital twin model simulates (predicts) forward based on the current synchronization state at a speed that is one control cycle faster than the physical system (e.g., 50 μs physical cycle, 10 μs twin prediction cycle).

[0035] Step 2.1: Adaptive Efficiency Optimization (Solving ZVS and Thermal Management) Prediction: In the LLC model, the simulation checks whether the Vds voltage of the switching transistor (such as S1) can drop to zero before the dead time ends in the next switching cycle. Simultaneously, the thermal model predicts the future junction temperature T_j of each switching transistor.

[0036] decision making: ZVS Guarantee: If a potential ZVS failure is predicted, the optimization algorithm immediately calculates the new switching frequency F_sw_new and phase shift angle φ_new, and verifies them in the model. After successful verification, (F_sw_new, φ_new) is added to the instruction set to be issued.

[0037] Thermal Equalization: If the predicted junction temperature T_j of a certain switch is > 110°C, a frequency reduction strategy or a dynamic current sharing strategy will be prioritized. By adjusting the phase shift angle of the two interleaved parallel phases, power will be transferred from the overheated phase to the other phase. The instructions (Thermal_derate_flag, φ_balance) are added to the instruction set.

[0038] Step 2.2: Adaptive Power Modulation and Power Quality Optimization Prediction: Based on the current grid voltage and phase-locked loop (PLL) state, the DC / AC model predicts the grid voltage waveform for the next few switching cycles. It also predicts the harmonic current components generated during load abrupt changes.

[0039] decision making: Modulation mode switching: When the grid voltage drop is predicted to be <0.9pu, the system automatically switches from unity power factor mode to reactive power support mode, calculates and issues a new reactive current command I_q_ref.

[0040] Harmonic suppression: A virtual harmonic extractor (such as a frequency-locked loop based on a second-order generalized integrator) is added to the model to predict the main harmonic components, such as the 5th and 7th harmonics. Then, compensation voltages V_comp_5th and V_comp_7th are generated by a quasi-PR controller and superimposed on the modulated wave to achieve active feedforward harmonic compensation.

[0041] Step 2.3: Rehearsal and Compensation for Seamless Mode Switching Scenario: The system is currently in island mode, and the power grid has been restored and the grid connection conditions are met.

[0042] Preview: The digital twin immediately (without waiting for the zero-crossing point) performs a simulation of the switch from V / f control to PQ control in virtual space. The simulation will accurately calculate the voltage change ΔV and frequency change Δf caused by the abrupt changes in the control loop and switching actions at the moment of switching.

[0043] Decision: Generate a feedforward compensation signal (V_ff = -ΔV, f_ff = -Δf). The purpose of this signal is to "cancel out" the predicted shock at the moment of switching.

[0044] Step 3: Optimize command issuance and physical system execution Instruction Packaging: The digital twin platform packages all the instructions (F_sw_new, φ_new, I_q_ref, V_comp_5th, V_ff, f_ff, ...) calculated in this round of optimization into a single data frame.

[0045] Commands are issued in real time: sent to the DSP via Ethernet.

[0046] DSP execution: After the DSP receives the instruction, in its interrupt service routine: Update the period register (corresponding to F_sw_new) and phase register (corresponding to φ_new) of the PWM generation module.

[0047] Update the q-axis reference of the current loop (corresponding to I_q_ref).

[0048] At the start of the next PWM cycle, the feedforward compensation (V_ff, f_ff) is added to the control output all at once, and the control mode is switched at the same time.

[0049] Closed-loop verification: After the switchover is complete, the new sensor data is uploaded to the digital twin again to verify the control effect. If the deviation between the actual response and the prediction exceeds a threshold (e.g., voltage overshoot > 5%), the model recalibration process is triggered.

[0050] Parameter example: Communication protocol: UDP protocol is used to ensure real-time performance, but an acknowledgment and retransmission mechanism needs to be designed at the application layer to ensure that critical instructions are not lost.

[0051] Control timing: Physical control loop: 50μs (20kHz) interrupt. Digital twin optimization cycle: 10ms (100Hz). Real-time calculations are triggered for critical events such as mode switching.

[0052] Key protection logic: Any frequency commands issued by the digital twin must be limited to the safe range allowed by the hardware (e.g., 85kHz ~ 200kHz). If the communication interruption with the digital twin exceeds 100ms, the DSP automatically switches to the built-in, conservative default control parameter set to ensure the basic safe operation of the system.

[0053] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. 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 digital-twin-based adaptive control method for energy storage power converters, characterized in that: Comprising the following steps: Step 1: Digital twin model synchronization and state awareness; Step 2: Forecast-based multi-objective collaborative optimization; Step 3: Optimization instruction issuing and physical system execution.

2. The digital-twin-based adaptive control method for energy storage power converters according to claim 1, characterized in that: The step 1: Digital twin model synchronization and state awareness comprises: data receiving and preprocessing, model initialization and state injection, and parameter identification.

3. The digital-twin-based adaptive control method for energy storage power converters according to claim 1, wherein: The step 2: Forecast-based multi-objective collaborative optimization comprises: Step 2.1: Adaptive efficiency optimization; Step 2.2: Adaptive power modulation and power quality optimization; Step 2.3: Seamless mode switching pre-rehearsal and compensation.

4. The digital-twin-based adaptive control method for energy storage power converters according to claim 1, wherein: The step 3: Optimization instruction issuing and physical system execution comprises: instruction packaging and instruction real-time issuing steps.

5. A digital-twin-based adaptive control system for energy storage power converters, comprising the adaptive control method of claim 1, characterized in that: Comprising: Main power topology; Sensing system; Execution unit.

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