Method for debugging PCS of energy storage converter

Abstract

The invention provides an energy storage converter PCS debugging method comprising the following steps: dynamically adjusting the turn-on time and turn-off time of an IGBT or an MOSFET according to the switching characteristic and temperature mapping relation of a power semiconductor device in combination with an obtained real-time temperature detection value, and optimizing the switching time sequence of the power semiconductor device so as to adapt to working states at different temperatures; the output voltage and current waveform of the energy storage converter PCS are sampled and analyzed in real time, the total harmonic distortion rate and the power factor index of the output waveform are calculated, the quality of the output waveform is evaluated, and if the waveform quality index does not meet the preset requirement, the switching time sequence parameters of the power semiconductor device are further optimized.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a debugging method for a PCS of an energy storage converter. Background Art

[0002] During the debugging process of the energy storage converter PCS, the influence of the switching characteristics of power semiconductor devices on the dynamic response of PCS is a complex and critical technical issue. As the core switching device of PCS, IGBT or MOSFET, the length of its turn-on and turn-off time and the parameter drift caused by temperature change will directly affect the quality of PCS output waveform and conversion efficiency. However, the typical parameters given in the data sheet of different power semiconductor device models often have a large deviation from the dynamic characteristics under actual application conditions, which makes it difficult for the PCS control algorithm to accurately estimate the switching delay and dead zone compensation, thereby causing a series of problems such as output current distortion and DC bus voltage fluctuation. At the same time, the ambient temperature of the PCS varies widely, and the dynamic characteristics of power semiconductor devices are very sensitive to junction temperature. Even if margin has been considered during selection, in actual operation, the fluctuation of the junction temperature of the power semiconductor device may still cause unpredictable changes in the switching characteristics, especially the uneven distribution of temperature gradients inside high-power devices, which exacerbates this uncertainty. Therefore, how to monitor the dynamic characteristics of power semiconductor devices in real time on the PCS machine and adaptively optimize the switching timing and dead zone compensation accordingly to ensure stable output of high-quality electrical energy under various working conditions is a technical problem that needs to be overcome urgently. Summary of the invention

[0003] The present invention provides a debugging method for a PCS of an energy storage converter, which mainly includes:

[0004] Obtain the type parameters of the power semiconductor devices in the energy storage converter PCS, including the model, rated voltage, and rated current of the IGBT or MOSFET. According to the parameters of the power semiconductor devices, determine the turn-on time and turn-off time characteristic curves of the power semiconductor devices at different temperatures, and obtain the mapping relationship between the switching characteristics and temperature of the power semiconductor devices.

[0005] A temperature sensor is used to detect the operating temperature of the power semiconductor device in the energy storage converter PCS in real time, and the detected temperature value is compared with the preset temperature threshold. If the detected temperature exceeds the threshold, it is judged that the power semiconductor device is working in a high temperature state, and the switching timing needs to be adjusted to adapt to the temperature change;

[0006] According to the mapping relationship between the switching characteristics and temperature of the power semiconductor device, combined with the real-time temperature detection value, the on-time and off-time of the IGBT or MOSFET are dynamically adjusted to optimize the switching timing of the power semiconductor device to adapt to the working state at different temperatures;

[0007] By sampling and analyzing the output voltage and current waveforms of the energy storage converter PCS in real time, the total harmonic distortion rate and power factor index of the output waveform are calculated, and the output waveform quality is evaluated. If the waveform quality index does not meet the preset requirements, the switching timing parameters of the power semiconductor device are further optimized;

[0008] A power analyzer is used to measure the input power and output power of the energy storage converter PCS, and the working efficiency of the energy storage converter PCS is calculated. The measured efficiency value is compared with a preset efficiency threshold. If the efficiency is lower than the threshold, the switching timing parameters of the power semiconductor device are adjusted to reduce the switching loss.

[0009] According to the different working conditions of the energy storage converter PCS, including light load, heavy load and overload, the corresponding optimal switch timing parameter table of power semiconductor devices is established. The switch timing parameter table clearly lists the optimal turn-on time and turn-off time under various load conditions, including different turn-on time and turn-off time corresponding to light load, heavy load and overload conditions. During the operation of the energy storage converter PCS, the corresponding switch timing parameters are switched in real time according to the load conditions;

[0010] According to the switching characteristics, operating temperature, output waveform quality and working efficiency of the power device of the energy storage converter PCS, a multi-objective optimization model is constructed. The particle swarm optimization algorithm is used to globally optimize the switching timing parameters to obtain the switching timing control strategy with the best comprehensive performance, which is used to guide the debugging and operation of the PCS.

[0011] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0012] The present invention discloses a debugging method for a PCS of an energy storage converter. The method obtains power device parameters and establishes a mapping relationship between switch characteristics and temperature, monitors the device temperature in real time and compares it with a threshold value, and dynamically adjusts the switch timing according to the temperature. At the same time, the switch parameters are further optimized by analyzing the output waveform quality and efficiency. An optimal switch timing parameter table is established for different load conditions to achieve load adaptive control. Finally, a multi-objective optimization model is constructed, and a particle swarm algorithm is used for global optimization to obtain a switch timing strategy with the best comprehensive performance. The present invention can effectively improve the working performance of the PCS under different temperature and load conditions, reduce switching losses, and improve system efficiency and output waveform quality, and has important engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The present invention is a flow chart of a debugging method for a PCS energy storage converter.

[0014] Figure 2 It is a schematic diagram of a debugging method of an energy storage converter PCS according to the present invention.

[0015] Figure 3 It is another schematic diagram of a debugging method of an energy storage converter PCS according to the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] like Figure 1-3 In this embodiment, a debugging method of an energy storage converter PCS may specifically include:

[0018] S101. Obtain the type parameters of the power semiconductor device in the energy storage converter PCS, including the model, rated voltage, and rated current of the IGBT or MOSFET. According to the parameters of the power semiconductor device, determine the turn-on time and turn-off time characteristic curves of the power semiconductor device at different temperatures, and obtain the switching characteristic and temperature mapping relationship of the power semiconductor device.

[0019] The model, rated voltage and rated current parameters of the power semiconductor devices in the energy storage converter PCS are obtained, and a static parameter database of the power semiconductor devices is established according to the parameters; a temperature scanning test is performed on the power semiconductor devices to obtain a mapping relationship model between the switching characteristics and the temperature; according to the static parameter database and the mapping relationship model, the switching loss and conduction loss of the PCS under different working conditions are calculated; the switching loss and conduction loss data are imported into the thermal simulation software to analyze the junction temperature distribution of the power semiconductor devices; if the junction temperature distribution exceeds the maximum junction temperature specified by the device, the particle swarm optimization algorithm is used to optimize the topological structure of the PCS; according to the optimized topological structure, a PCS control model is built; and the PCS control model is iteratively calculated to obtain the optimal operating parameters of the PCS.

[0020] Specifically, the model, rated voltage, rated current and other parameters of the power semiconductor devices in the energy storage converter PCS are obtained, and a database is established according to the static parameters of the power semiconductor devices. The basic characteristic parameters of different types of power semiconductor devices are obtained by querying the database. The Keysight B1506A power device analyzer is used for temperature scanning test. The turn-on time and turn-off time of the power semiconductor devices are measured in the temperature range of -40℃ to 150℃ with an interval of 10℃. The test data is recorded and the cftool toolbox of MATLAB software is used for curve fitting. The polynomial fitting method is selected to obtain the mapping relationship model between the switching characteristics and the temperature. According to the obtained power semiconductor device parameters and the switching characteristic model, the switching loss and conduction loss of the PCS under different working conditions are calculated using the PLECS simulation software. The calculated loss data is imported into the ANSYS Icepak thermal simulation software, the heat dissipation conditions and boundary parameters are set, and the junction temperature distribution of the power semiconductor device is analyzed. It is judged whether the junction temperature exceeds the maximum junction temperature specified in the device data manual, so as to determine whether the device meets the heat dissipation requirements of the PCS. Based on the switching characteristic model and loss calculation results of power semiconductor devices, the particle swarm optimization algorithm is used to optimize the topology of PCS, adjust the switching frequency and current stress. Use MATLAB / Simulink to build the PCS control model to optimize the closed-loop control strategy. Through iterative calculation, the optimal operating parameters of PCS are obtained to improve the conversion efficiency of the energy storage converter. The parameters of power semiconductor devices in the energy storage converter PCS are obtained through the database, such as the IGBT model FF300R12KT4, the rated voltage is 1200V, and the rated current is 300A. The KeysightB1506A power device analyzer performs temperature scanning tests, measuring the turn-on time and turn-off time every 10℃ in the range of -40℃ to 150℃. For example, the turn-on time is measured to be 200ns and the turn-off time is 300ns at 25℃. The cftool toolbox of MATLAB uses the polynomial fitting method to obtain the mapping relationship model between the switching characteristics and temperature: Ton(T) = 0.01T^2 + 0.5T + 180, Toff(T) = 0.015T^2 + 0.8T + 270, where T is the temperature (℃), and the units of the on-time Ton and the off-time Toff are ns. The PLECS simulation software calculates the loss of the PCS under different working conditions based on the above model. For example, under the conditions of 50kHz switching frequency, 400V DC bus voltage, and 100A load current, the switching loss is 150W and the conduction loss is 200W. The ANSYSIcepak thermal simulation software sets the heat dissipation conditions, such as the heat sink thermal resistance of 0.1℃ / W and the boundary parameters, such as the ambient temperature of 40℃. The analysis shows that the IGBT junction temperature is 125℃, which is lower than the maximum junction temperature of 150℃ specified in the datasheet, meeting the PCS heat dissipation requirements.The particle swarm optimization algorithm optimizes the PCS topology structure, the number of particles is set to 50, the maximum number of iterations is 100, and by adjusting the switching frequency (range 30-100kHz) and current stress, the range is 50-150A, the optimal switching frequency is 60kHz, and the current stress is 120A. MATLAB / Simulink builds the PCS control model to realize the automatic adjustment of PI controller parameters, such as scanning the proportional coefficient Kp from 0.1 to 10 and the integral coefficient Ki from 1 to 100, and the optimal parameters Kp=5, Ki=50 are obtained. After 10 iterative calculations, the optimal operating parameters of PCS are obtained: switching frequency 60kHz, DC bus voltage 420V, current stress 120A, and the conversion efficiency of the energy storage converter reaches 98.5%.

[0021] S102, using a temperature sensor to detect the operating temperature of the power semiconductor device in the energy storage converter PCS in real time, and comparing the detected temperature value with a preset temperature threshold. If the detected temperature exceeds the threshold, it is determined that the power semiconductor device is operating in a high temperature state, and the switching timing needs to be adjusted to adapt to the temperature change.

[0022] A thermocouple temperature sensor is installed on the surface of the power semiconductor device, and temperature data is collected in real time at a preset sampling frequency through a data acquisition card, and the collected temperature values ​​are converted into digital signals and input into the PCS controller; according to the multi-level temperature threshold preset in the PCS controller, the detected temperature is compared with the preset temperature threshold in real time through a comparator circuit, and if the detected temperature exceeds the first-level warning threshold, the temperature compensation in the PCS controller is triggered; the temperature compensation searches for the corresponding switching frequency in a preset temperature and frequency correspondence table according to the current temperature, and adjusts the duty cycle of the PWM wave according to the temperature and duty cycle correspondence table; if the detected temperature continues to rise and exceeds the second-level warning threshold, the PCS controller further reduces the switching frequency and current stress of the power semiconductor device; if the temperature continues to rise, the preset multi-level heat dissipation system, including air cooling and water cooling systems, is started step by step until the temperature drops or reaches the over-temperature protection threshold.

[0023] Specifically, a thermocouple temperature sensor is installed on the surface of the power semiconductor device, and the temperature data is collected in real time at a sampling frequency of 1kHz through a data acquisition card. The collected temperature values ​​are converted into digital signals and input into the PCS controller. The temperature threshold is preset in the PCS controller. According to the working characteristics of the power semiconductor device and the design of the heat dissipation system, multi-level temperature thresholds are set, such as 85℃ as the first-level warning threshold, 100℃ as the second-level warning threshold, and 120℃ as the over-temperature protection threshold. At the same time, a multi-level heat dissipation system including air cooling and water cooling is designed, and the start-up temperature thresholds of each level of the heat dissipation system are preset. The detected temperature is compared with the preset temperature threshold in real time through a comparator circuit. If the detected temperature exceeds the first-level warning threshold, the temperature compensation algorithm in the PCS controller is triggered. The algorithm is based on a lookup table method. According to the current temperature, the appropriate switching frequency is found in the preset temperature-frequency correspondence table, and the duty cycle of the PWM wave is adjusted according to the temperature and duty cycle correspondence table. For example, when the temperature rises from 85℃ to 90℃, the switching frequency is reduced from 50kHz to 45kHz, and the duty cycle is reduced from 0.8 to 0.75. If the detected temperature continues to rise and exceeds the secondary warning threshold, the PCS controller further reduces the switching frequency and current stress of the power semiconductor device. The specific adjustment method is to reduce the switching frequency linearly, such as from 45kHz to 40kHz; at the same time, reduce the current stress by 20%, such as from 100A to 80A. In addition, start the forced air cooling heat dissipation device to increase the heat dissipation efficiency. If the temperature continues to rise, start the water cooling system step by step until the temperature drops or reaches the over-temperature protection threshold. In the energy storage converter PCS, a thermocouple temperature sensor, such as K-type, is installed on the surface of the IGBT module. The temperature data is collected in real time at a sampling frequency of 1kHz through a 16-bit ADC data acquisition card, and the collected analog signal is converted into a digital signal and input into the PCS controller. The PCS controller is based on a DSP chip, such as TI's TMS320F28335, and the preset temperature thresholds are 85℃ (first-level warning), 100℃ (second-level warning) and 120℃ (over-temperature protection). The heat dissipation system includes air cooling, whose starting temperature is 85℃ and water cooling, whose starting temperature is 100℃. The comparator circuit uses the LM339 chip to compare the detected temperature with the preset threshold in real time. When the temperature exceeds 85°C, the temperature compensation algorithm is triggered. The algorithm is implemented in the DSP and uses a 256×2 lookup table to store the corresponding relationship between temperature and frequency and temperature and duty cycle. For example, when the temperature rises from 85°C to 90°C, the table shows that the switching frequency is reduced from 50kHz to 45kHz, and the duty cycle is reduced from 0.8 to 0.75. The IGBT drive circuit adjusts the PWM waveform accordingly. If the temperature continues to rise to 100°C, the controller linearly reduces the switching frequency to 40kHz, and at the same time reduces the current stress from 100A to 80A by adjusting the PWM waveform. At this time, the controller outputs a signal through the GPIO port to start a DC fan rated at 200W to increase heat dissipation.If the temperature still rises, the water cooling system is started at 110℃, and the water pump flow rate is 10L / min. Among them, the DSP continuously monitors the temperature change, records the temperature data every 100ms, and stores it in the onboard EEPROM for subsequent analysis. If the temperature reaches 120℃, the over-temperature protection is triggered, and the controller shuts down the IGBT gate drive and cuts off the main circuit current.

[0024] S103. According to the mapping relationship between the switching characteristics of the power semiconductor device and the temperature, combined with the acquired real-time temperature detection value, dynamically adjust the turn-on time and turn-off time of the IGBT or MOSFET, and optimize the switching timing of the power semiconductor device to adapt to the working conditions at different temperatures.

[0025] A K-type thermocouple temperature sensor is used to collect temperature data of power semiconductor devices in real time; based on a pre-established database of power semiconductor device switch characteristics and temperature mapping, a cubic spline interpolation algorithm is used to calculate the theoretical on-time and off-time corresponding to the temperature data. The actual on-time and off-time of the IGBT or MOSFET are obtained by combining the temperature data, the theoretical on-time and off-time. The actual on-time and off-time are received, and a PWM waveform is generated according to the actual on-time and off-time. The PWM waveform is applied to the gate drive circuit of the power semiconductor device, and the rising and falling edge times of the PWM waveform are adjusted to achieve dynamic adjustment of the switching process of the power semiconductor device. If the power semiconductor device is a bridge arm structure, the dead time is determined according to the actual on-time and off-time to prevent the bridge arm from short-circuiting.

[0026] Specifically, a K-type thermocouple temperature sensor is used to collect temperature data of power semiconductor devices in real time, with a sampling frequency of 1kHz. The analog signal is converted into a digital signal through the ADS1115 16-bit ADC and input into the PCS controller based on the TMS320F28335 DSP through the SPI interface for processing. According to the pre-established database of the mapping relationship between the switching characteristics and temperature of power semiconductor devices, the cubic spline interpolation algorithm is used to calculate the theoretical turn-on time and turn-off time at the current temperature. The database stores the switching characteristic data of a sampling point every 10℃ in the range of -40℃ to 150℃. The preliminary switching timing parameters, including the turn-on time ton and the turn-off time toff, are obtained by interpolation calculation. The adaptive fuzzy control algorithm is used to dynamically adjust the actual turn-on time and turn-off time of the IGBT or MOSFET in combination with the real-time temperature detection value and the preliminary switching timing parameters. The algorithm uses temperature deviation and temperature change rate as input, and outputs the adjustment amount of the turn-on time and turn-off time. The fuzzy rule base contains 25 rules, and the Mamdani reasoning method and the centroid method are used to defuzzify. Finally, an optimized PWM drive signal is generated, including the adjusted turn-on time ton_adj and turn-off time toff_adj. The Xilinx Artix-7 series FPGA is used to achieve precise control of the PWM waveform, with a clock frequency of 100 MHz and a timing accuracy of 1 ns. The optimized switching timing is applied to the gate drive circuit of the power semiconductor device, and the dynamic adjustment of the switching process is achieved by adjusting the rising and falling edge times of the PWM waveform. At the same time, the FPGA also implements adaptive adjustment of the dead time to prevent the bridge arm from short-circuiting. The K-type thermocouple temperature sensor is installed on the surface of the IGBT module with a measurement range of -40°C to 800°C and a sensitivity of approximately 41 μV / °C. The sampling frequency is set to 1 kHz to capture rapid temperature changes. The ADS1115 16-bit ADC converts the temperature signal into a digital quantity with a resolution of

[0027] 0.125℃ / LSB. Data is transmitted to TMS320F28335DSP via SPI interface at 10Mbps. The switch characteristic database pre-stored in DSP covers -40℃ to 150℃, with 10℃ intervals, for a total of 20 data points. Using the cubic spline interpolation algorithm, for the measured temperature of 25℃, the turn-on time ton is calculated to be 150ns and the turn-off time toff is 200ns. The adaptive fuzzy control algorithm receives the temperature deviation, which is the difference between the target temperature and the measured temperature, and the temperature change rate, which can be the temperature change within 10ms as input. The fuzzy rule base contains 25 IF-THEN rules, such as "IF the temperature deviation is positive AND the temperature change rate is positive THEN the turn-on time increases AND the turn-off time increases". After Mamdani reasoning and centroid method defuzzification, ton_adj is 155ns and toff_adj is 210ns. Xilinx Artix-7 FPGA (model XC7A100T) receives the adjusted timing parameters output by DSP, and its 100MHz clock provides a basic time resolution of 10ns. FPGA achieves 1ns fine-tuning through a digital delay link to generate an accurate PWM waveform. At the same time, FPGA dynamically adjusts the dead time from the initial value of 200ns to 180ns according to the switching speed to ensure switching safety. Finally, the optimized PWM signal drives the IGBT gate to achieve temperature-adaptive switching control.

[0028] S104. By real-time sampling and analysis of the output voltage and current waveforms of the energy storage converter PCS, the total harmonic distortion rate and power factor index of the output waveform are calculated, and the output waveform quality is evaluated. If the waveform quality index does not meet the preset requirements, the switching timing parameters of the power semiconductor device are further optimized.

[0029] The output voltage and current of the energy storage converter PCS are sampled in real time to obtain sampling data of the voltage and current; based on the sampling data, a fast Fourier transform algorithm is used to perform harmonic analysis to obtain the amplitude and phase of the harmonic component; the total harmonic distortion rate and the power factor are calculated through the amplitude and phase of the harmonic component; it is determined whether the total harmonic distortion rate is greater than a preset total harmonic distortion rate threshold, or whether the power factor is less than a preset power factor threshold; if the total harmonic distortion rate is greater than a preset total harmonic distortion rate threshold, or the power factor is less than a preset power factor threshold, a particle swarm optimization algorithm is used to optimize the switching timing parameters of the power semiconductor device; based on the optimized switching timing parameters, a new switching control signal is generated, and the optimized parameters are transmitted to the PWM generator.

[0030] Specifically, the output voltage and current of the energy storage converter PCS are sampled in real time using the AD7606 high-speed data acquisition card of ADI, with the sampling frequency set to 100kHz and the sampling accuracy set to 16 bits. The sampled data are transmitted to the TMS320F28379D digital signal processor of TI at a rate of 400Mbps through the LVDS high-speed digital interface for subsequent processing. The voltage and current data obtained by the sampling are harmonic analyzed using the base-2Cooley-Tukey fast Fourier transform algorithm. Each analysis uses 8192 points of data to calculate the amplitude and phase of the first 40 harmonic components. The total harmonic distortion rate THD is calculated by the formula, where Vn is the nth harmonic voltage and V1 is the fundamental voltage. The power factor PF is calculated by the ratio of active power P to apparent power S, that is, PF = P / S. The calculated total harmonic distortion rate and power factor are compared with the preset quality requirements. The preset total harmonic distortion rate threshold is 3%, and the power factor threshold is 0.95. If the total harmonic distortion rate is greater than 3% or the power factor is less than 0.95, the output waveform quality is judged to be substandard and the optimization process is triggered. At the same time, the specific parameter values ​​that do not meet the standards are recorded in the system log for subsequent analysis. According to the waveform quality evaluation results, the particle swarm optimization algorithm is used to optimize the switching timing parameters of the power semiconductor device. The particle swarm size is set to 50 and the maximum number of iterations is 100. The optimization objective function includes the weighted sum of the total harmonic distortion rate and the power factor. The optimization variables include the frequency of the PWM waveform, which ranges from 10kHz to 50kHz, the duty cycle, which ranges from 40% to 60%, and the dead time, which ranges from 0.5μs to 2μs. Through iterative optimization, a new switch control signal is generated, and the optimized parameters are transmitted to the PWM generator through the SPI interface. The AD7606 high-speed data acquisition card samples the PCS output at a frequency of 100kHz, with a voltage range of ±10V and a current range of ±5A. The resolution reaches 0.3mV and 0.15mA respectively at 16-bit accuracy. The sampled data is transmitted to the TMS320F28379D processor at a rate of 400Mbps through the LVDS interface, forming an 8192-point data frame every 10ms. The processor executes the radix-2Cooley-TukeyFFT fast Fourier transform algorithm to calculate 40 harmonic components in the range of 0-2kHz. For example, in a standard 50Hz system, the fundamental voltage is 220V, the third harmonic is 6.6V (3%), the fifth harmonic is 4.4V (2%), and the seventh harmonic is 2.2V (1%). The total harmonic distortion rate THD is calculated to be 3.74%. At the same time, the power factor is 0.952 calculated by the active power of 10kW and the apparent power of 10.5kVA. After comparing these indicators with the preset thresholds (THD3%, power factor 0.95), the optimization process is triggered. The particle swarm algorithm initializes 50 particles, each of which contains three parameters: PWM frequency, duty cycle, and dead time.The optimization objective function f=0.6THD+0.4(1-PF) was set, and after 78 iterations, the optimal PWM parameters were obtained: frequency 20kHz, duty cycle 52%, and dead time 1.2μs. The optimized parameters were transmitted to the PWM generator via the SPI interface at a rate of 10Mbps to generate a new switch control signal, which reduced the output waveform THD to 2.8% and increased the power factor to 0.978, meeting the quality requirements.

[0031] S105. Use a power analyzer to measure the input power and output power of the energy storage converter PCS, calculate the working efficiency of the energy storage converter PCS, and compare the measured efficiency value with a preset efficiency threshold. If the efficiency is lower than the threshold, reduce the switching loss by adjusting the switching timing parameters of the power semiconductor device.

[0032] The voltage and current data of the input and output ends of the energy storage converter PCS are processed by using the fast Fourier transform method to determine the input power and the output power; the real-time working efficiency of the energy storage converter PCS is calculated according to the input power and the output power; if the real-time working efficiency is lower than the preset efficiency threshold, the switch timing optimization process is triggered; the switch timing parameters of the power semiconductor device are optimized by using a genetic algorithm, and the switch timing parameters include PWM frequency, duty cycle and dead time; a new switch control signal is generated according to the switch timing parameters to reduce the switch loss; and the working state parameters of the energy storage converter PCS are obtained, and the working state parameters include input voltage, input current, output power, switching frequency and duty cycle.

[0033] Specifically, the Yokogawa WT5000 high-precision power analyzer is used to sample the voltage and current at the input and output ends of the energy storage converter PCS in real time. The sampling frequency is set to 500kHz, and the measurement accuracy reaches 0.1%. The voltage range is 0-1000V, and the current range is 0-100A. The collected data is transmitted to the data processing unit based on the Intel Core i7 processor at a rate of 1MB / s through the GP-IB high-speed digital interface. The data processing unit processes the sampled data using a fast Fourier transform based on the Cooley-Tukey algorithm, processing 16384 points of data each time. Calculate the effective value of input power and output power, input power Pin = Vin_rms*Iin_rms*PF_in, output power Pout = Vout_rms*Iout_rms*PF_out; Vin_rms is the effective value of input voltage, indicating the root mean square value of input voltage, Iin_rms is the effective value of input current, indicating the root mean square value of input current, PFin is the power factor of input; Vout_rms is the effective value of output voltage, indicating the root mean square value of output voltage, Iout_rms is the effective value of output current, indicating the root mean square value of output current, PFout is the power factor of output. According to the efficiency calculation formula η = Pout / Pin*100%, the real-time working efficiency of the energy storage converter PCS is obtained. Compare the calculated efficiency value with the preset efficiency threshold, where the preset efficiency threshold is 97%. If the efficiency is lower than 97%, the switch timing optimization process is triggered. At the same time, the current working state parameters are recorded in the system log, including input voltage (accurate to 0.1V), input current (accurate to 0.01A), output power (accurate to 1W), switching frequency, duty cycle, etc. According to the recorded working state parameters, the switching timing parameters of power semiconductor devices are optimized by using an improved genetic algorithm. The population size is set to 100, and the maximum number of iterations is 500. The chromosome encoding includes PWM frequency, which ranges from 10kHz to 50kHz, stepping 1kHz, duty cycle, which ranges from 40% to 60%, stepping 0.1%, and dead time, which ranges from 0.5μs to 2μs, stepping 0.1μs. The fitness function is set to efficiency η, and a new generation of population is generated through roulette selection, single-point crossover and uniform mutation operations. After iterative optimization, the parameters of the optimal individual are transmitted to the PWM generator through the SPI interface to generate a new switch control signal to reduce switching loss. The Yokogawa WT5000 power analyzer samples the PCS, measuring 750V voltage and 80A current at the input, 380V voltage and 150A current at the output, and acquiring 16,384 data points at a sampling frequency of 500kHz. The data is transmitted to the Intel Core i7 processor at a rate of 1MB / s via the GP-IB interface.The processor executes the Cooley-Tukey FFT algorithm and calculates the input power of 59.4 kW, its power factor of 0.99, and the output power of 57.0 kW, its power factor of 1.0. According to η=57.0 / 59.4*100%, the efficiency is 96.0%. This efficiency is lower than the preset threshold of 97%, triggering the optimization process. Record the current state, input 750.2V / 79.98A, output 57023W, switching frequency 20kHz, duty cycle 50%. The genetic algorithm initializes 100 chromosomes, each containing three genes of PWM frequency, duty cycle and dead time, such as {25kHz, 52.5%, 1.2μs}. After 350 generations of iterations, the optimal individual is {23kHz, 51.8%, 0.9μs}, and the predicted efficiency is increased to 97.2%. The optimized parameters are transmitted to the PWM generator TIUCC27714A at a rate of 10Mbps through the SPI interface to update the IGBT drive signal. After completing a round of optimization, the power analyzer was re-measured and verified that the efficiency was increased to 97.1%, meeting the expected target.

[0034] S106. For different working conditions of the energy storage converter PCS, including light load, heavy load and overload, a corresponding optimal switching timing parameter table of power semiconductor devices is established. The switching timing parameter table clearly lists the optimal turn-on time and turn-off time under various load conditions, including different turn-on times and turn-off times corresponding to light load, heavy load and overload conditions. During the operation of the energy storage converter PCS, the corresponding switching timing parameters are switched in real time according to the load conditions.

[0035] The input and output power of the energy storage converter PCS under different load conditions is measured to obtain power measurement data. Based on the power measurement data, the particle swarm optimization algorithm is used to optimize the switch timing parameters of the power semiconductor device in each load interval to obtain the optimal switch timing parameters. The optimal switch timing parameters are organized into a two-dimensional parameter table and stored in a binary format in the memory of the PCS controller to form a parameter lookup table. By sampling the output voltage and current, the current output power is calculated in real time based on the sampled data. If the calculated current output power changes, the optimal switch timing parameters corresponding to the current output power are read from the parameter lookup table. A drive signal is generated based on the read optimal switch timing parameters, and the drive signal is applied to the IGBT drive circuit to achieve real-time adjustment of the switch timing.

[0036] Specifically, the input and output power of the energy storage converter PCS under different load conditions were measured using a Yokogawa WT5000 high-precision power analyzer. The sampling frequency was set to 500kHz, and the measurement accuracy reached 0.1%. According to the measurement results, the load conditions were divided into three intervals: light load 0-30% rated power, heavy load 30%-80% rated power, and overload 80%-120% rated power. In each interval, multiple sampling points were selected for detailed measurement with a step size of 10% rated power. The particle swarm optimization algorithm was used to optimize the switching timing parameters of the power semiconductor devices in each load interval. The particle swarm size was set to 50 and the maximum number of iterations was 200. The optimization objective function is f=w1*(1-η)+w2*THD, where η is efficiency, THD is total harmonic distortion, and w1 and w2 are weight coefficients. The optimization variables include the turn-on time, which ranges from 100ns to 500ns, and the turn-off time, which ranges from 200ns to 800ns. Through iterative optimization, the optimal switch timing parameters for each load interval are obtained. The optimized switch timing parameters are organized into a two-dimensional parameter table, with the horizontal axis being the load power (0%-120%, step size 10%) and the vertical axis being the parameter type, which includes the turn-on time and turn-off time. The table clearly lists the optimal turn-on time and turn-off time corresponding to each load point. The parameter table is stored in the Flash memory of the PCS controller in binary format, with each parameter occupying 16 bits and a total storage space of approximately 1KB. During the operation of the energy storage converter PCS, the output voltage and current are sampled by the ADS1256 24-bit ADC with a sampling frequency of 10kHz. The FPGA calculates the current output power in real time and maps the power value to the corresponding load interval in the parameter table. According to the mapping results, the corresponding optimal switch timing parameters are read from the Flash. Through the PWM module implemented by the FPGA, the new switch timing parameters are applied to the IGBT drive circuit to achieve real-time adjustment of the switch timing, and the adjustment process is completed within 100μs. Yokogawa WT5000 power analyzer measures 1MW energy storage PCS, taking a sampling point every 10% in the rated power range of 0-120%, for a total of 13 points. The efficiency measured in the light load interval of 0-30% is 92-95%, the efficiency in the heavy load interval of 30-80% is 96-98%, and the efficiency in the overload interval of 80-120% is 94-97%. The particle swarm algorithm sets 50 particles, iterates 200 times, and the optimization function weights w1=0.7 and w2=0.3. The optimization results at the 30% load point are turn-on time 250ns and turn-off time 450ns; the results at the 80% load point are turn-on time 180ns and turn-off time 320ns; the results at the 120% load point are turn-on time 150ns and turn-off time 280ns. The optimized parameter table is stored in the 128KB capacity AT25SF641 flash memory, occupying 1KB space.During operation, ADS1256 samples the output voltage range of 0-1000V and the current range of 0-1000A at a frequency of 10kHz, and XilinxArtix-7FPGA (XC7A100T) calculates the output power every 100μs. If the output power is detected to be 750kW, the FPGA maps it to the 75% load point in the parameter table and reads the corresponding turn-on time of 190ns and turn-off time of 340ns from the flash memory. The built-in PWM module of the FPGA runs at a clock frequency of 100MHz, achieving a turn-on time of 19 clock cycles and a turn-off time of 34 clock cycles, and transmits the updated PWM signal to the IGBT gate drive circuit CONCEPT2SP0320 through optical fiber to complete the switch timing adjustment.

[0037] S107. According to the switching characteristics, operating temperature, output waveform quality and working efficiency of the power device of the energy storage converter PCS, a multi-objective optimization model is constructed, and the particle swarm optimization algorithm is used to globally optimize the switching timing parameters to obtain the switching timing control strategy with the best comprehensive performance, which is used to guide the debugging and operation of the PCS.

[0038] A power analyzer is used to measure the input and output power of the PCS to obtain the PCS input and output power measurement data; an oscilloscope is used to measure the switching waveform of the IGBT to obtain the IGBT switching waveform information; an infrared thermal imager is used to monitor the temperature of the power device to obtain the power device temperature data; the power measurement data, switching waveform information and temperature data are synchronously collected through a data acquisition card; a multi-objective optimization model is constructed based on the synchronously collected data, and the objective function of the multi-objective optimization model includes switching loss, temperature, total harmonic distortion rate and efficiency; a multi-objective particle swarm algorithm is used to perform global optimization on the multi-objective optimization model, and the optimization variables of the global optimization include switching frequency, dead time and driving voltage; and the switch timing control strategy obtained by the global optimization is applied to the PCS controller.

[0039] Specifically, the input and output power of PCS is measured by Yokogawa WT5000 power analyzer with a sampling frequency of 1MHz. The switching waveform of IGBT is measured by LeCroy HDO6104A oscilloscope with a bandwidth of 500MHz. The temperature of power devices is monitored by Fluke Ti 480 PRO infrared thermal imager with an accuracy of ±1℃. All data are synchronously collected by National Instruments PXIe-6363 data acquisition card with a sampling rate of 1MS / s. A multi-objective optimization model is constructed, and the objective functions are f1=w1*(Psw / Prated)+w2*(Tmax-Tmin) / Trated and f2=w3*THD+w4*(1-η), where Psw is the switching loss, Prated is the rated power, Tmax and Tmin are the maximum and minimum operating temperatures, Trad is the rated temperature, THD is the total harmonic distortion rate, and η is the efficiency. The corresponding weight coefficients w1 to w4 are determined by the hierarchical analysis method. The improved MOPSO (multi-objective particle swarm optimization) algorithm is used for global optimization. The improvements include the use of adaptive inertia weights, the introduction of crowding distance mechanism and the concept of ε-dominance. The particle swarm size is set to 100 and the maximum number of iterations is set to 500. The optimization variables include switching frequency, dead time and driving voltage, which range from 5kHz to 20kHz, 0.5μs to 2μs and 15V to 20V respectively. The optimized switch timing control strategy is applied to the PCS controller based on TITMS320F28379D. The PWM waveform is accurately generated by FPGA, and the timing accuracy reaches 10ns. During the operation of PCS, performance data is collected every 100ms, and the short-term performance indicators are calculated using the sliding window method. If the performance deviation exceeds the preset threshold, the online fine-tuning process is triggered, and the gradient descent method is used to locally optimize the switch parameters. Yokogawa WT5000 samples the input and output power of 500kW PCS at a frequency of 1MHz, and the efficiency peak is measured to be 98.2%. LeCroy HDO6104A records the IGBT switching waveform, with a rise time of 120ns and a fall time of 180ns. Fluke Ti 480 PRO monitors the IGBT temperature, with the highest point at 134°C. NIP XIe-6363 collects all data synchronously, acquiring 1 million sampling points within 1 second. In the multi-objective optimization model, the weight coefficients are obtained through the hierarchical analysis method as w1 = 0.3, w2 = 0.2, w3 = 0.3, and w4 = 0.2. The improved MOPSO algorithm sets 100 particles and converges after 355 iterations. The optimal solution is a switching frequency of 12.5kHz, a dead time of 0.8μs, and a drive voltage of 18.2V. Under this parameter combination, the switching loss is reduced by 15%, the temperature fluctuation is reduced by 5°C, the THD is reduced to 1.8%, and the efficiency is increased by 0.3%.The TITMS320F28379D controller receives the optimized parameters and generates a PWM waveform through the XilinxArtix-7FPGA with a timing accuracy of 9.8ns. During operation, data is collected every 100ms and a sliding window with a width of 200ms is used to calculate performance indicators. When a sudden increase in THD to 2.2% is detected, online fine-tuning is triggered. The gradient descent method adjusts the switching frequency in steps of 0.1kHz, and the THD is restored to 1.9% after 3 iterations.

[0040] The description of the above embodiments is only used to help understand the technical solutions and core ideas of the present application. Ordinary technical personnel in this field should understand that they can still modify the technical solutions recorded in the aforementioned embodiments, or replace some of the technical features therein with equivalents. However, these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A debugging method for a PCS energy storage converter, characterized in that: The method comprises: Obtain the type parameters of the power semiconductor devices in the energy storage converter PCS, including the model, rated voltage, and rated current of the IGBT or MOSFET. According to the parameters of the power semiconductor devices, determine the turn-on time and turn-off time characteristic curves of the power semiconductor devices at different temperatures, and obtain the mapping relationship between the switching characteristics and temperature of the power semiconductor devices. A temperature sensor is used to detect the operating temperature of the power semiconductor device in the energy storage converter PCS in real time, and the detected temperature value is compared with the preset temperature threshold. If the detected temperature exceeds the threshold, it is judged that the power semiconductor device is working in a high temperature state, and the switching timing needs to be adjusted to adapt to the temperature change; According to the mapping relationship between the switching characteristics and temperature of the power semiconductor device, combined with the real-time temperature detection value, the on-time and off-time of the IGBT or MOSFET are dynamically adjusted to optimize the switching timing of the power semiconductor device to adapt to the working state at different temperatures; By sampling and analyzing the output voltage and current waveforms of the energy storage converter PCS in real time, the total harmonic distortion rate and power factor index of the output waveform are calculated, and the output waveform quality is evaluated. If the waveform quality index does not meet the preset requirements, the switching timing parameters of the power semiconductor device are further optimized; A power analyzer is used to measure the input power and output power of the energy storage converter PCS, and the working efficiency of the energy storage converter PCS is calculated. The measured efficiency value is compared with a preset efficiency threshold. If the efficiency is lower than the threshold, the switching timing parameters of the power semiconductor device are adjusted to reduce the switching loss. According to the different working conditions of the energy storage converter PCS, including light load, heavy load and overload, the corresponding optimal switch timing parameter table of power semiconductor devices is established. The switch timing parameter table clearly lists the optimal turn-on time and turn-off time under various load conditions, including different turn-on time and turn-off time corresponding to light load, heavy load and overload conditions. During the operation of the energy storage converter PCS, the corresponding switch timing parameters are switched in real time according to the load conditions; According to the switching characteristics, operating temperature, output waveform quality and working efficiency of the power device of the energy storage converter PCS, a multi-objective optimization model is constructed. The particle swarm optimization algorithm is used to globally optimize the switching timing parameters to obtain the switching timing control strategy with the best comprehensive performance, which is used to guide the debugging and operation of the PCS.

2. The method according to claim 1, characterized in that The method of obtaining the type parameters of the power semiconductor device in the energy storage converter PCS, including the model, rated voltage, and rated current of the IGBT or MOSFET, and determining the turn-on time and turn-off time characteristic curves of the power semiconductor device at different temperatures according to the parameters of the power semiconductor device, and obtaining the switching characteristic and temperature mapping relationship of the power semiconductor device, includes: Obtain the model, rated voltage and rated current parameters of the power semiconductor device in the energy storage converter PCS, and establish a static parameter database of the power semiconductor device according to the parameters; Perform temperature scanning tests on power semiconductor devices to obtain a mapping relationship model between switching characteristics and temperature; Calculating the switching loss and conduction loss of the PCS under different working states according to the static parameter database and the mapping relationship model; Importing the switching loss and conduction loss data into thermal simulation software to analyze the junction temperature distribution of the power semiconductor device; If the junction temperature distribution exceeds the maximum junction temperature specified by the device, a particle swarm optimization algorithm is used to optimize the topology of the PCS; According to the optimized topology structure, a PCS control model is built; The PCS control model is used to perform iterative calculations to obtain the optimal operating parameters of the PCS.

3. The method according to claim 1, characterized in that: The temperature sensor is used to detect the operating temperature of the power semiconductor device in the energy storage converter PCS in real time, and the detected temperature value is compared with a preset temperature threshold. If the detected temperature exceeds the threshold, it is determined that the power semiconductor device is working in a high temperature state, and the switching timing needs to be adjusted to adapt to the temperature change, including: Thermocouple temperature sensors are installed on the surface of power semiconductor devices. The temperature data is collected in real time at a preset sampling frequency through a data acquisition card. The collected temperature values ​​are converted into digital signals and input into the PCS controller. According to the multi-level temperature thresholds preset in the PCS controller, the detected temperature is compared with the preset temperature threshold in real time through a comparator circuit, and if the detected temperature exceeds the first-level warning threshold, the temperature compensation in the PCS controller is triggered; The temperature compensation searches for the corresponding switching frequency in a preset temperature and frequency correspondence table according to the current temperature, and adjusts the duty cycle of the PWM wave according to the temperature and duty cycle correspondence table; If the detected temperature continues to rise and exceeds the secondary warning threshold, the PCS controller further reduces the switching frequency and current stress of the power semiconductor device; If the temperature continues to rise, the preset multi-stage cooling system, including air cooling and water cooling systems, will be started step by step until the temperature drops or reaches the over-temperature protection threshold.

4. The method according to claim 1, characterized in that The method dynamically adjusts the on-time and off-time of the IGBT or MOSFET according to the mapping relationship between the switching characteristics and the temperature of the power semiconductor device and the acquired real-time temperature detection value, and optimizes the switching timing of the power semiconductor device to adapt to the working state at different temperatures, including: Use K-type thermocouple temperature sensor to collect temperature data of power semiconductor devices in real time; According to the pre-established database of mapping relationship between switching characteristics and temperature of power semiconductor devices, the theoretical turn-on time and turn-off time corresponding to the temperature data are calculated using a cubic spline interpolation algorithm; Combine temperature data, theoretical turn-on time and turn-off time to obtain the actual turn-on time and turn-off time of IGBT or MOSFET; Receive the actual on-time and off-time, and generate a PWM waveform according to the actual on-time and off-time; Applying the PWM waveform to a gate drive circuit of a power semiconductor device, and dynamically adjusting the switching process of the power semiconductor device by adjusting the rising edge and falling edge time of the PWM waveform; If the power semiconductor device is a bridge arm structure, the dead time is determined according to the actual turn-on time and turn-off time to prevent the bridge arm from short-circuiting.

5. The method according to claim 1, characterized in that The method comprises: performing real-time sampling and analysis on the output voltage and current waveforms of the energy storage converter PCS, calculating the total harmonic distortion rate and power factor index of the output waveform, evaluating the output waveform quality, and further optimizing the switching timing parameters of the power semiconductor device if the waveform quality index does not meet the preset requirements, including: The output voltage and current of the energy storage converter PCS are sampled in real time to obtain sampling data of voltage and current; According to the sampled data, a fast Fourier transform algorithm is used to perform harmonic analysis to obtain the amplitude and phase of the harmonic component; Calculating the total harmonic distortion and power factor by the amplitude and phase of the harmonic components; Determining whether the total harmonic distortion rate is greater than a preset total harmonic distortion rate threshold, or whether the power factor is less than a preset power factor threshold; If the total harmonic distortion rate is greater than a preset total harmonic distortion rate threshold, or the power factor is less than a preset power factor threshold, a particle swarm optimization algorithm is used to optimize the switching timing parameters of the power semiconductor device; According to the optimized switch timing parameters, a new switch control signal is generated, and the optimized parameters are transmitted to the PWM generator.

6. The method according to claim 1, characterized in that The power analyzer is used to measure the input power and output power of the energy storage converter PCS, calculate the working efficiency of the energy storage converter PCS, compare the measured efficiency value with a preset efficiency threshold, and if the efficiency is lower than the threshold, reduce the switching loss by adjusting the switching timing parameters of the power semiconductor device, including: The fast Fourier transform method is used to process the voltage and current data at the input and output ends of the energy storage converter PCS to determine the input power and output power; Calculating the real-time working efficiency of the energy storage converter PCS according to the input power and the output power; If the real-time working efficiency is lower than a preset efficiency threshold, a switch timing optimization process is triggered; Using a genetic algorithm to optimize the switching timing parameters of a power semiconductor device, the switching timing parameters include PWM frequency, duty cycle and dead time; Generating a new switch control signal according to the switch timing parameter to reduce the switch loss; The operating state parameters of the energy storage converter PCS are obtained, wherein the operating state parameters include input voltage, input current, output power, switching frequency and duty cycle.

7. The method according to claim 1, characterized in that According to the different working conditions of the energy storage converter PCS, including light load, heavy load and overload, a corresponding optimal switch timing parameter table of power semiconductor devices is established. The switch timing parameter table clearly lists the optimal turn-on time and turn-off time under each load condition, including different turn-on times and turn-off times corresponding to light load, heavy load and overload conditions. During the operation of the energy storage converter PCS, the corresponding switch timing parameters are switched in real time according to the load conditions, including: Measure the input and output power of the energy storage converter PCS under different load conditions to obtain power measurement data; According to the power measurement data, the switching timing parameters of the power semiconductor device in each load interval are optimized by using a particle swarm optimization algorithm to obtain the optimal switching timing parameters; Arranging the optimal switch timing parameters into a two-dimensional parameter table and storing the table in a memory of the PCS controller in a binary format to form a parameter lookup table; By sampling the output voltage and current, the current output power is calculated in real time based on the sampled data; If the calculated current output power changes, then the optimal switch timing parameter corresponding to the current output power is read from the parameter lookup table; A drive signal is generated according to the read optimal switch timing parameters, and the drive signal is applied to the IGBT drive circuit to achieve real-time adjustment of the switch timing.

8. The method according to claim 1, characterized in that: According to the power device switching characteristics, operating temperature, output waveform quality and working efficiency of the energy storage converter PCS, a multi-objective optimization model is constructed, and a particle swarm optimization algorithm is used to globally optimize the switch timing parameters to obtain a switch timing control strategy with the best comprehensive performance, which is used to guide the debugging and operation of the PCS, including: The input and output power of the PCS is measured using a power analyzer to obtain the PCS input and output power measurement data; Use an oscilloscope to measure the switching waveform of the IGBT and obtain the IGBT switching waveform information; Use an infrared thermal imager to monitor the temperature of power devices and obtain the temperature data of power devices; The power measurement data, the switching waveform information and the temperature data are synchronously collected through a data acquisition card; Constructing a multi-objective optimization model based on the synchronously collected data, wherein the objective function of the multi-objective optimization model includes switching loss, temperature, total harmonic distortion rate and efficiency; A multi-objective particle swarm algorithm is used to perform global optimization on the multi-objective optimization model, wherein the optimization variables of the global optimization include switching frequency, dead time and driving voltage; The switch timing control strategy obtained by global optimization is applied to the PCS controller.

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