Analog method and system for controlling time difference of phase voltage drop

Abstract

The application provides a simulation method and system of controllable phase voltage drop time difference, relates to the technical field of electric power testing, and comprises the following steps: adjusting voltage through a three-phase voltage regulator, realizing voltage drop by using a hybrid switch structure of bidirectional thyristor and IGBT, collecting and processing three-phase voltage signals and extracting zero-crossing point information by using a digital signal processor, generating a control pulse signal with phase compensation, controlling the conduction timing of switch elements to realize soft switching, and receiving and processing signals and displaying voltage drop waveforms by using an industrial computer. The application can accurately control the phase voltage drop time difference, and improves the simulation accuracy and system reliability.

Description

TECHNICAL FIELD

[0001] The present application relates to power testing technology, and in particular to a simulation method and system for controllable phase voltage sag time difference. BACKGROUND

[0002] Voltage sag is a common power quality problem in power systems, which can seriously affect the normal operation of electrical equipment. In particular, in industrial production, voltage sag may cause sensitive equipment to shut down, control system to malfunction, and motor to start difficult, resulting in production interruption and economic loss. Therefore, simulating voltage sag events is of great significance for studying the anti-interference of electrical equipment, evaluating the stability of power systems, and designing and testing related protection devices.

[0003] In voltage sag simulation technology, accurately controlling the depth, duration and time difference between different phases of voltage sag is a key requirement. Traditional voltage sag simulation methods mainly include electromagnetic voltage regulator, power amplifier driven type and power electronic switch type technology routes. Among them, the power electronic switch type has become a research hotspot due to its fast response speed, high control accuracy and other advantages.

[0004] However, the existing voltage sag simulation technology has the following defects and deficiencies:

[0005] The existing simulation method often has large voltage transients and shocks during switching. Such sudden changes can introduce high-order harmonic interference, making it impossible to accurately simulate the voltage sag process in the actual power system and affecting the reliability and accuracy of the test results.

[0006] The existing technology is difficult to accurately control the time difference of voltage sag between different phases, especially when simulating single-phase or two-phase voltage sag. It cannot accurately reflect the time difference of voltage sag in each phase caused by different fault properties in the power system, limiting the comprehensiveness and representativeness of the test work.

[0007] The control system of the existing voltage sag simulation device lacks precision, especially in phase voltage synchronization control and phase compensation, resulting in a large difference between the simulated waveform and the actual power grid fault waveform, reducing the practical value and reference value of the test results. SUMMARY

[0008] The embodiments of the present application provide a simulation method and system for controllable phase voltage sag time difference, which can solve the problems in the prior art.

[0009] In a first aspect of the embodiments of the present application, a simulation method for controllable phase voltage sag time difference is provided, comprising:

[0010] The three-phase AC power input is regulated by a three-phase voltage regulator, which is a sliding contact type structure driven and controlled by a DC motor, and generates a three-phase voltage signal with a set voltage value.

[0011] The three-phase voltage signal is input to corresponding full-voltage power electronic switches and half-voltage power electronic switches, wherein the full-voltage power electronic switches and the half-voltage power electronic switches of each phase adopt a mixed switching structure of thyristors and IGBTs, the thyristors are connected in series at the collector terminal of the IGBT, and the emitter terminal of the IGBT is connected in parallel with an RC buffer circuit.

[0012] The three-phase voltage signals on the power supply side and the load side are collected and processed by a digital signal processor, zero-crossing information is extracted to calculate a phase voltage synchronization control signal, the phase voltage synchronization control signal is phase-locked loop synchronized with a reference clock signal to generate a control pulse signal with phase compensation.

[0013] According to the control pulse signal, the thyristors in the full-voltage power electronic switches are turned off, the IGBTs in the full-voltage power electronic switches are turned off after a preset delay time, the IGBTs in the half-voltage power electronic switches are turned on, and the thyristors in the half-voltage power electronic switches are turned on after the IGBTs in the half-voltage power electronic switches are stably turned on, thereby realizing soft switching during voltage drop.

[0014] An industrial computer receives the signals transmitted by the digital signal processor through a high-speed communication interface, calculates and displays the voltage drop waveform.

[0015] In an alternative embodiment,

[0016] The step of collecting and processing the three-phase voltage signals on the power supply side and the load side by a digital signal processor, extracting zero-crossing information to calculate a phase voltage synchronization control signal, and phase-locked loop synchronizing the phase voltage synchronization control signal with a reference clock signal to generate a control pulse signal with phase compensation includes:

[0017] The three-phase voltage signals on the power supply side and the load side are signal-conditioned by a differential amplification circuit, the differential amplification circuit outputs a digital voltage signal obtained by sampling after being filtered by a second-order Butterworth low-pass filter and converted by an analog-to-digital conversion circuit.

[0018] Three-point linear interpolation operation is performed on the digital voltage signal, the zero-crossing time is calculated according to the voltage values of the adjacent three sampling points and the corresponding sampling time, and positive and negative hysteresis threshold values are set, when the digital voltage signal crosses the positive hysteresis threshold value from negative to positive or crosses the negative hysteresis threshold value from positive to negative, it is determined as a zero-crossing point.

[0019] The phase voltage synchronization control signal is calculated based on a zero-crossing time, a reference clock signal is generated by a phase accumulator, the phase voltage synchronization control signal and the reference clock signal are input into a digital phase-locked loop circuit, the digital phase-locked loop circuit includes a phase detector, a second-order loop filter and a digital controlled oscillator, the proportional coefficient and the integral coefficient of the second-order loop filter are determined according to a system bandwidth and a damping ratio, and the digital phase-locked loop circuit outputs a synchronization phase signal.

[0020] The synchronization phase signal is compensated according to the transmission delay characteristics of the switching device, including fixed delay compensation and dynamic characteristic compensation, and a control pulse signal is generated by using a space vector modulation algorithm according to the compensated phase angle.

[0021] In an alternative embodiment,

[0022] The step of compensating according to the transmission delay characteristics of the switching device includes:

[0023] The synchronization phase signal is input into a dead-time compensation unit, a dead-time is calculated according to the turn-off delay time of the switching device, and a transition level is introduced in the dead-time to suppress voltage spikes in the switching process to obtain a phase angle based on fixed delay compensation;

[0024] The phase angle based on fixed delay compensation is input into a dynamic characteristic compensation unit, and compensation amounts of the turn-on process and the turn-off process are calculated based on the asymmetric characteristics of the rise time and the fall time of the switching device to obtain a compensated phase angle;

[0025] The output voltage distortion rate and the switching loss corresponding to the control pulse signal generated based on the compensated phase angle are monitored in real time, and a comprehensive optimization objective function including the output voltage distortion rate and the switching loss is constructed; when the output voltage distortion rate exceeds a preset distortion threshold, the upper bridge arm dead-time, the lower bridge arm dead-time, the transition level threshold, the turn-on process compensation coefficient and the turn-off process compensation coefficient are encoded into an optimization parameter vector, a particle swarm algorithm with adaptive inertia weight is used to iteratively optimize the optimization parameter vector, the adaptive inertia weight decreases with the square of the iteration number, the asymmetry constraint is applied to the dead-time compensation parameters during the optimization process, the change range constraint is applied to the dynamic characteristic compensation amount, and the smoothing factor is used to weight and combine the new and old compensation parameters to update the compensated phase angle.

[0026] In an alternative embodiment,

[0027] According to the control pulse signal, the triac in the full-voltage power electronic switch is controlled to be turned off, the IGBT in the full-voltage power electronic switch is controlled to be turned off after a preset time delay, and the IGBT in the half-voltage power electronic switch is controlled to be turned on at the same time, and then the triac in the half-voltage power electronic switch is controlled to be turned on after the IGBT in the half-voltage power electronic switch is stably turned on, so as to realize the soft switching in the voltage drop process, and the steps include:

[0028] The conduction voltage drop, the conduction resistance and the passing current of the triac are obtained, and the conduction characteristic parameters of the triac are calculated; the collector-emitter voltage and the collector current of the IGBT are obtained, and the switching loss parameters of the IGBT are calculated; and the preset delay time of the hybrid switch is determined based on the conduction characteristic parameters and the switching loss parameters;

[0029] According to the control pulse signal, the triac in the full-voltage power electronic switch is controlled to be turned off, the IGBT in the full-voltage power electronic switch is controlled to be turned off after a preset time delay, and the IGBT in the half-voltage power electronic switch is controlled to be turned on at the same time, and then the triac in the half-voltage power electronic switch is controlled to be turned on after the IGBT in the half-voltage power electronic switch is stably turned on, so as to realize the soft switching in the voltage drop process, and the steps include:

[0030] In the soft switching process, the voltage stress index and the current stress index of the hybrid switch are calculated in real time, and the switching stress compensation angle is calculated, the voltage stress index is obtained by the root mean square value of the collector-emitter voltage, and the current stress index is obtained by the root mean square value of the collector current;

[0031] According to the switching stress compensation angle, the control pulse signal is dynamically phase-compensated in real time to generate a stress-optimized control pulse signal; and the conduction timing of the hybrid switch is adjusted based on the stress-optimized control pulse signal, so as to realize the stress-controlled soft switching control in the voltage drop process.

[0032] In an optional embodiment,

[0033] In the soft switching process, the voltage stress index and the current stress index of the hybrid switch are calculated in real time, and the switching stress compensation angle is calculated, the voltage stress index is obtained by the root mean square value of the collector-emitter voltage, and the current stress index is obtained by the root mean square value of the collector current;

[0034] The collector-emitter voltage and the collector current are sampled by using a sliding time window, and the real-time voltage stress index and the real-time current stress index are calculated based on the sampling data, and the calculation period of the real-time voltage stress index and the real-time current stress index is an integer multiple of the switching period;

[0035] The real-time voltage stress index and the real-time current stress index are input into an adaptive fuzzy neural network, the adaptive fuzzy neural network includes a stress evaluation layer and a compensation decision layer, the stress evaluation layer is constructed based on fuzzy rule library according to expert experience, and the compensation decision layer updates network weights by using an online learning algorithm.

[0036] determining a reference value of the switching stress compensation angle according to an output of the adaptive fuzzy neural network, and introducing a dynamic limiting mechanism;

[0037] monitoring a compensation effect of the switching stress compensation angle in real time, constructing a comprehensive evaluation index including compensation accuracy and compensation stability, and updating a weight coefficient of a fuzzy rule based on a recursive least square method when the comprehensive evaluation index is lower than a preset evaluation threshold.

[0038] In an alternative embodiment,

[0039] the step of inputting the real-time voltage stress index and the real-time current stress index into the adaptive fuzzy neural network comprises:

[0040] inputting the real-time voltage stress index and the real-time current stress index into a stress evaluation layer of the adaptive fuzzy neural network, mapping the real-time voltage stress index and the real-time current stress index into a plurality of language variables respectively by the stress evaluation layer, constructing a fuzzy rule base including a basic rule group and a dynamic rule group based on expert experience, the basic rule group including a plurality of expert experience rules, and the dynamic rule group generating rules adaptively according to an operating state;

[0041] calculating a rule confidence based on historical operation data, the rule confidence being determined by a ratio of a number of successful rule applications to a total number of rule applications, and calculating a rule priority according to the rule confidence and a rule execution effect evaluation value, the rule priority being updated in an exponential smoothing manner;

[0042] inputting a rule activation intensity of the stress evaluation layer into the compensation decision layer, the compensation decision layer being designed with a variable structure neuron and updating network weights through an online learning algorithm, the learning rate of the online learning algorithm being exponentially decayed with iteration number and being set with a minimum learning rate;

[0043] dynamically optimizing the fuzzy rule base, including: calculating a rule intensity, the rule intensity being determined based on a control error change rate and being updated through a forgetting factor mechanism; merging rules when a rule similarity exceeds a preset similarity threshold; increasing rule accuracy when a control error exceeds a preset error threshold; and deleting rules when the rules are not activated within a preset period and the rule intensity is lower than a preset intensity threshold.

[0044] In an alternative embodiment,

[0045] the dynamic limiting mechanism comprises:

[0046] The switching voltage and the switching current are collected, the root mean square values of the switching voltage change rate and the switching current change rate are calculated in a sliding sampling window to obtain voltage stress characteristics and current stress characteristics, a thermal stress characteristic is calculated based on a switching junction temperature and a switching loss power, and the voltage stress characteristics, the current stress characteristics and the thermal stress characteristic are constructed as a stress characteristic vector;

[0047] A state transition relationship and a control action relationship are calculated according to a historical data sequence of the stress characteristic vector, a stress characteristic vector of a next period is predicted based on the state transition relationship and the control action relationship, and a stress overrun probability is calculated according to the predicted stress characteristic vector;

[0048] A limiting boundary curve is constructed based on a hyperbolic tangent function, an amplitude coefficient of the limiting boundary curve is adaptively adjusted according to the stress overrun probability, a slope coefficient of the limiting boundary curve is proportional to a second derivative of a compensation angle, and an initial limiting result is obtained by comparing a current compensation angle with the limiting boundary curve;

[0049] A stress balance evaluation function is constructed, the stress balance evaluation function is based on a balance degree of three-dimensional stress characteristics to calculate an adjustment coefficient, and a limiting compensation angle is obtained by modifying the initial limiting result according to the adjustment coefficient;

[0050] The limiting compensation angle is input into a dynamic buffer area, a smoothing coefficient of the dynamic buffer area is in an exponential decay relationship with a compensation angle change rate, and a final compensation angle is obtained by weighted combination of an original compensation angle and a buffered compensation angle according to the smoothing coefficient.

[0051] The second aspect of the embodiment of the application provides a simulation system with controllable phase-to-phase voltage drop time difference, comprising:

[0052] A first unit is configured to adjust the voltage of an input three-phase alternating current power supply through a three-phase voltage regulator, wherein the three-phase voltage regulator adopts a sliding contact structure and is driven and controlled by a direct current motor to generate a three-phase voltage signal with a set voltage value;

[0053] A second unit is configured to input the three-phase voltage signal into corresponding full-voltage power electronic switches and half-voltage power electronic switches, wherein the full-voltage power electronic switches and the half-voltage power electronic switches of each phase adopt a bidirectional thyristor and IGBT hybrid switch structure, the bidirectional thyristor is connected in series at the collector electrode end of the IGBT, and the emitter electrode end of the IGBT is connected in parallel with an RC buffer circuit;

[0054] A third unit is configured to collect and process three-phase voltage signals on the power supply side and the load side through a digital signal processor, extract zero-crossing point information to calculate phase voltage synchronous control signals, perform phase-locked loop synchronization processing on the phase voltage synchronous control signals and a reference clock signal, and generate control pulse signals with phase compensation.

[0055] The fourth unit is configured to control the turn-on timing of the triac and the IGBT according to the control pulse signal, control the triac in the full-voltage power electronic switch to be turned off, control the IGBT in the full-voltage power electronic switch to be turned off after a preset time delay, control the IGBT in the half-voltage power electronic switch to be turned on, control the triac in the half-voltage power electronic switch to be turned on after the IGBT in the half-voltage power electronic switch is turned on stably, and realize soft switching in the voltage drop process.

[0056] The fifth unit is configured to receive the signal transmitted by the digital signal processor through the high-speed communication interface by using the industrial computer, and calculate and display the voltage drop waveform.

[0057] In a third aspect, the embodiment of the present application provides an electronic device, comprising:

[0058] a processor;

[0059] a memory for storing processor-executable instructions;

[0060] The processor is configured to invoke the instructions stored in the memory to execute the method described above.

[0061] In a fourth aspect, the embodiment of the present application provides a computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions are executed by a processor to implement the method described above.

[0062] The present application realizes accurate controllable simulation of phase voltage drop time difference, solves the technical problem that the traditional method is difficult to accurately control the phase voltage drop time difference, and through the combination of digital signal processor synchronous control technology and hybrid switch structure, soft switching can be realized in the process of grid voltage drop, transient impact generated in the process of voltage drop is effectively inhibited, and the safety performance of the simulation system is improved.

[0063] The triac and IGBT hybrid switch structure adopted in the present application, in combination with the RC buffer circuit design, realizes soft start and soft turn-off in the process of voltage drop switching, effectively reduces voltage waveform distortion, significantly reduces electromagnetic interference in the switching process, and ensures the quality of the simulated voltage drop waveform and system stability.

[0064] The present application realizes flexible adjustment of voltage amplitude and drop time by combining the sliding contact type three-phase voltage regulator with the digital control system, can accurately simulate various complex voltage drop scenarios, meets the testing requirements of power equipment under different working conditions, and provides a reliable testing method for power system safety and stability operation and equipment voltage disturbance resistance evaluation. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 A flowchart of the simulation method for the phase voltage drop time difference controllable embodiment of the present application is shown in

[0066] Figure 2 A comparison chart of output voltage distortion rate and switching loss for different compensation methods is shown in

[0067] Figure 3 A comparison chart of stress reduction rate for three compensation methods in hybrid switching soft switching process is shown in DETAILED DESCRIPTION

[0068] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in connection with the drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0069] The technical solutions of the present application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and some embodiments may not be described again for the same or similar concepts or processes.

[0070] Figure 1 A flowchart of the simulation method for the phase voltage drop time difference controllable embodiment of the present application is shown in Figure 1 as shown, the method comprises:

[0071] The three-phase AC power input is subjected to voltage regulation by a three-phase voltage regulator, which adopts a sliding contact structure and is driven and controlled by a DC motor to generate a three-phase voltage signal of a set voltage value;

[0072] The three-phase voltage signal is input to corresponding full-voltage power electronic switches and half-voltage power electronic switches, respectively, wherein the full-voltage power electronic switches and the half-voltage power electronic switches of each phase adopt a hybrid switching structure of thyristors and IGBTs, the thyristors are connected in series at the collector terminal of the IGBT, and the emitter terminal of the IGBT is connected in parallel with an RC buffer circuit;

[0073] The three-phase voltage signals at the power supply side and the load side are collected and processed by a digital signal processor to extract zero-crossing point information and calculate phase voltage synchronization control signals, the phase voltage synchronization control signals are subjected to phase-locked loop synchronization processing with a reference clock signal to generate control pulse signals with phase compensation;

[0074] The digital signal processor controls the turn-on timing of the bidirectional thyristor and the IGBT according to the control pulse signal, controls the turn-off of the bidirectional thyristor in the full-voltage power electronic switch, controls the turn-off of the IGBT in the full-voltage power electronic switch after a preset time delay, controls the turn-on of the IGBT in the half-voltage power electronic switch at the same time, controls the turn-on of the bidirectional thyristor in the half-voltage power electronic switch after the IGBT in the half-voltage power electronic switch is stably turned on, and realizes soft switching in the voltage drop process.

[0075] An industrial computer is adopted to receive the signal transmitted by the digital signal processor through a high-speed communication interface, calculate and display the voltage drop waveform.

[0076] In an optional embodiment, the digital signal processor is adopted to collect and process the three-phase voltage signals on the power supply side and the load side, extract zero-crossing point information to calculate a phase voltage synchronization control signal, and the step of performing phase-locked loop synchronization processing on the phase voltage synchronization control signal and a reference clock signal to generate a control pulse signal with phase compensation includes:

[0077] A differential amplification circuit is adopted to perform signal conditioning on the three-phase voltage signals on the power supply side and the load side, the differential amplification circuit outputs a digital voltage signal obtained by sampling after being filtered by a second-order Butterworth low-pass filter and converted by an analog-to-digital conversion circuit;

[0078] Three-point linear interpolation operation is performed on the digital voltage signal, zero-crossing point time is calculated according to the voltage values of adjacent three sampling points and the corresponding sampling time, and a positive hysteresis threshold and a negative hysteresis threshold are set, and the zero-crossing point is determined when the digital voltage signal crosses the positive hysteresis threshold from negative to positive or crosses the negative hysteresis threshold from positive to negative, wherein the absolute values of the positive hysteresis threshold and the negative hysteresis threshold are both 0.005% of the rated voltage.

[0079] The phase voltage synchronization control signal is calculated based on the zero-crossing point time, a reference clock signal is generated by a phase accumulator, the phase voltage synchronization control signal and the reference clock signal are input into a digital phase-locked loop circuit, the digital phase-locked loop circuit includes a phase detector, a second-order loop filter and a digital controlled oscillator, the proportional coefficient and the integral coefficient of the second-order loop filter are determined according to the system bandwidth and the damping ratio, and the digital phase-locked loop circuit outputs a synchronization phase signal.

[0080] The synchronization phase signal is compensated according to the transmission delay characteristics of the switching device, including fixed delay compensation and dynamic characteristic compensation, and a control pulse signal is generated by using a space vector modulation algorithm according to the phase angle after compensation.

[0081] Exemplarily, in the signal acquisition link, a high-precision operational amplifier LT1167 is used in the differential amplification circuit, the differential gain is set to 0.01, and the common-mode rejection ratio is greater than 90 dB, so as to effectively suppress common-mode interference signals. The output signal of the differential amplification circuit is processed through a second-order Butterworth low-pass filter, the cutoff frequency of the filter is set to 1 kHz, so as to sufficiently filter out high-frequency noise while retaining the basic waveform characteristics of the signal. The filtered signal is sampled through an analog-to-digital conversion circuit, a 16-bit AD converter AD7656 is used, and the sampling frequency is set to 10 kHz, so as to ensure that at least 200 sampling points are obtained in each power frequency cycle, thereby meeting the high-precision phase detection requirement. The analog-to-digital conversion adopts bipolar conversion with a reference voltage of ±10 V, the corresponding digital quantity range is -32768 to 32767, and the conversion result is transmitted to a digital signal processor through an SPI bus.

[0082] In the zero-crossing detection link, a three-point linear interpolation operation is performed on the digital voltage signal to calculate the accurate zero-crossing time. When the signs of the voltage values of adjacent sampling points change, it is determined that there is a zero-crossing point between the two sampling points. Assuming that the three continuous sampling points are (t1, V1), (t2, V2) and (t3, V3), V1<0 and V2>0, which indicates that the voltage value crosses the zero point from negative to positive. The three-point linear interpolation can obtain a more accurate zero-crossing time t0. In actual operation, when the sampling frequency is 10 kHz and the power grid environment is 50 Hz, test results show that the zero-crossing detection accuracy of this method can be controlled within ±5 μs. In order to enhance the anti-interference ability, the positive and negative hysteresis threshold values are set, and the absolute values are both 0.5% of the rated voltage, that is, ±0.5 V (for a 220 V rated voltage system). When the voltage crosses the +0.5 V threshold value from negative to positive or crosses the -0.5 V threshold value from positive to negative, it is determined as an effective zero-crossing point.

[0083] In the phase synchronization control link, the detected zero-crossing time is used to calculate the phase voltage synchronization control signal. At the same time, a reference clock signal is generated through a phase accumulator, and the phase increment value of the phase accumulator is set to 0.00314 rad, which corresponds to a 50 Hz power frequency reference signal. The phase voltage synchronization control signal and the reference clock signal are input into a digital phase-locked loop circuit for synchronization processing. The digital phase-locked loop circuit includes a phase detector, a second-order loop filter and a digital controlled oscillator. The phase detector is implemented by phase difference multiplication, and the output range is ±π; the second-order loop filter adopts a proportional-integral structure, wherein the proportional coefficient kp is set to 0.2 and the integral coefficient ki is set to 0.02. These parameters are determined according to the system bandwidth of 10 Hz and the damping ratio of 0.707, so as to ensure the balance between the response speed and the stability of the system; and the digital controlled oscillator is implemented by a 32-bit phase accumulator, and the frequency resolution reaches 0.001 Hz.

[0084] In the phase compensation and pulse generation link, the synchronous phase signal is compensated according to the transmission delay characteristics of the switching device. The compensation is divided into fixed delay compensation and dynamic characteristic compensation. The fixed delay compensation value is 4us, which is used to compensate the turn-on delay of the IGBT device; the dynamic characteristic compensation is dynamically adjusted according to the switching current size, and the compensation value ranges from 0 to 2us. The compensation curve is realized by table lookup method, and the compensation value increases by 0.2us for every 10A increase in current. The compensated phase angle is used for the space vector modulation algorithm to generate control pulse signals. The space vector modulation adopts a seven-segment modulation method, the carrier frequency is set to 10kHz, and the dead time is set to 2us. The minimum harmonic output is realized by evenly dividing the effective vector action time.

[0085] The application processes the voltage signal through the differential amplification circuit and the Butterworth filter, accurately identifies the zero-crossing point by combining the three-point linear interpolation operation and the hysteresis threshold mechanism, and significantly improves the accuracy of the phase voltage synchronous control signal. The introduction of the digital phase-locked loop circuit guarantees the phase synchronization stability under the condition of power grid disturbance and waveform distortion, and avoids the phase tracking error of the traditional synchronization method under the non-ideal power grid condition. The combination of the phase compensation based on the switching characteristics and the space vector modulation algorithm effectively reduces the timing error of the control pulse.

[0086] In an alternative embodiment, the step of compensating according to the transmission delay characteristics of the switching device comprises:

[0087] The synchronous phase signal is input into a dead zone compensation unit, the dead zone time is calculated according to the turn-off delay time of the switching device, and a transition level is introduced in the dead zone time to suppress the voltage spike in the switching process, so as to obtain a phase angle based on fixed delay compensation;

[0088] The phase angle based on fixed delay compensation is input into a dynamic characteristic compensation unit, and the compensation amount of the turn-on process and the turn-off process is calculated based on the asymmetric characteristics of the rise time and the fall time of the switching device, so as to obtain the compensated phase angle;

[0089] The real-time monitoring is performed on an output voltage distortion rate and a switching loss corresponding to a control pulse signal generated based on the compensated phase angle, a comprehensive optimization objective function including the output voltage distortion rate and the switching loss is constructed, the output voltage distortion rate is calculated based on an effective value of a fundamental component and effective values of harmonic components, and the switching loss is calculated based on turn-on energy loss and turn-off energy loss of the switching device; when the output voltage distortion rate exceeds a preset distortion threshold, an upper bridge arm dead time, a lower bridge arm dead time, a transition level threshold, a turn-on process compensation coefficient and a turn-off process compensation coefficient are encoded into an optimization parameter vector, a particle swarm algorithm with an adaptive inertia weight is used to iteratively optimize the optimization parameter vector, the adaptive inertia weight decreases with the square of the iteration number, an asymmetry constraint is applied to the dead time compensation parameter during the optimization process, a change range constraint is applied to the dynamic characteristic compensation amount, a smoothing factor is used to combine the new and old compensation parameters to suppress parameter mutation, and the compensated phase angle is updated.

[0090] For example, in the dead time compensation unit, a synchronization phase signal is received as input. The dead time is calculated based on the turn-off delay time of the switching device, which can be obtained by measurement. For example, for a specific type of MOSFET switching device, the turn-off delay time is 120 ns. The dead time is usually set to 1.2 to 1.5 times the turn-off delay time, and in this embodiment, it is set to 150 ns. During the dead time, a transition level between the high level and the low level is introduced, and the initial transition level threshold can be set to 50% of the power supply voltage. For example, when the power supply voltage is 400 V, the transition level threshold is set to 200 V. The introduction of such a transition level can effectively suppress the voltage spike caused by the sharp change in voltage during switching. Measurement shows that after introducing the transition level, the voltage spike can be reduced from 650 V to 480 V, a reduction of about 26%. Through this step, the phase angle based on fixed delay compensation can be obtained, and the initial compensation amount is usually set to between 2° and 5°, and in this embodiment, it is set to 3.5°.

[0091] Subsequently, the phase angle based on fixed delay compensation is input into the dynamic characteristic compensation unit. Since the rise time and the fall time of the switching device have asymmetric characteristics, the compensation amounts for the turn-on process and the turn-off process need to be calculated respectively. For example, the rise time of a certain type of MOSFET is 80 ns, and the fall time is 60 ns. This asymmetry can cause distortion of the actual output waveform. The initial value of the turn-on process compensation amount can be set to the phase angle corresponding to 30% of the rise time, which is about 0.86°; and the initial value of the turn-off process compensation amount can be set to the phase angle corresponding to 25% of the fall time, which is about 0.54°. Through these compensations, the compensated phase angle can be obtained, which more accurately reflects the actual switching behavior of the switching device.

[0092] The performance indicators of the control pulse signal generated based on the compensated phase angle are monitored in real time. The output voltage distortion rate is a key indicator, which is calculated by the effective value of the fundamental component and the effective value of each harmonic component. Specifically, assuming that the effective value of the fundamental component is 380V, the effective value of the 3rd harmonic is 15V, the effective value of the 5th harmonic is 8V, and the effective value of the 7th harmonic is 4V, the total harmonic distortion rate is about 4.5%. Another important indicator is the switching loss, including the turn-on energy loss and the turn-off energy loss. For example, at a switching frequency of 1kHz, the turn-on energy loss is about 0.25mJ each time, the turn-off energy loss is about 0.18mJ, and the total switching loss is 0.43W.

[0093] When the output voltage distortion rate exceeds the preset distortion threshold (e.g., 4%), the compensation parameter optimization program will be started. The optimization parameter vector includes five key parameters: upper bridge arm dead time, lower bridge arm dead time, transition level threshold, turn-on process compensation coefficient, and turn-off process compensation coefficient. The initial parameters can be set as: upper bridge arm dead time 150ns, lower bridge arm dead time 150ns, transition level threshold 50%, turn-on process compensation coefficient 0.3, and turn-off process compensation coefficient 0.25.

[0094] The optimization process uses a particle swarm algorithm with adaptive inertia weight. The initial value of the adaptive inertia weight is set to 0.9, which decreases with the square of the iteration number, and can be reduced to 0.4 at the lowest. For example, at the 10th iteration, the inertia weight is 0.9-0.5x(10 / 100) 2 =0.85; at the 50th iteration, the inertia weight is reduced to 0.65. In the algorithm, the number of particles is set to 20, and the maximum number of iterations is 100. Each particle represents a possible combination of compensation parameters.

[0095] During the optimization process, constraints are imposed on the parameters to ensure system stability. The dead-time compensation parameter asymmetry constraint ensures that the ratio of the upper bridge arm dead time to the lower bridge arm dead time is between 0.8 and 1.2. The dynamic characteristic compensation range constraint limits the turn-on process compensation coefficient to between 0.2 and 0.4, and the turn-off process compensation coefficient to between 0.15 and 0.35. To prevent system instability caused by parameter mutation, a smoothing factor α=0.8 is used to combine the new and old compensation parameters: final parameter = α x old parameter + (1-α) x new parameter.

[0096] After optimization, the parameter adjustment is: upper bridge arm dead time 168ns, lower bridge arm dead time 142ns, transition level threshold 45%, turn-on process compensation coefficient 0.32, and turn-off process compensation coefficient 0.28.

[0097] Figure 2The output voltage distortion rate and the switching loss of different compensation methods are compared in the graph, the horizontal axis of the graph represents the switching frequency (kHz), the left vertical axis represents the voltage distortion rate (%), and the right vertical axis represents the switching loss (W). In terms of voltage distortion rate, the method of the present application has a significant advantage, and this advantage is more obvious as the switching frequency increases. In terms of switching loss, the present application also exhibits excellent performance. From 1 kHz to 11 kHz, the method of the present application maintains a significant advantage in both voltage distortion rate and switching loss. As the switching frequency increases, the advantage of the method of the present application over the other two methods is more significant, indicating that it has better adaptability in high-frequency application scenarios.

[0098] The double compensation strategy adopted by the present application successfully solves the problem caused by the non-ideal characteristics of the switching device in the actual switching process. By adaptively optimizing the compensation parameters, the method of the present application can reduce the voltage distortion while reducing the switching loss, achieving an excellent balance between the output waveform quality and system efficiency, and having important practical value for improving the performance of power electronic switching systems. The dynamic characteristic compensation unit compensates for the asymmetric characteristics of the turn-on and turn-off processes, reducing waveform distortion at the switching instant. The comprehensive optimization objective function considers voltage distortion rate and switching loss, and the adaptive particle swarm algorithm can dynamically optimize the compensation parameters during operation, achieving a balance between output waveform quality and system efficiency.

[0099] In an alternative embodiment, according to the control pulse signal, the triac in the full-voltage power electronic switch is controlled to turn off, the IGBT in the full-voltage power electronic switch is controlled to turn off after a preset time delay, and the IGBT in the half-voltage power electronic switch is controlled to turn on. After the IGBT in the half-voltage power electronic switch is stably turned on, the triac in the half-voltage power electronic switch is controlled to turn on, realizing the step of soft switching in the voltage drop process, which comprises:

[0100] The turn-on voltage drop and turn-on resistance of the triac are obtained, and the turn-on characteristic parameters of the triac are calculated. The collector-emitter voltage and collector current of the IGBT are obtained, and the switching loss parameters of the IGBT are calculated. The preset delay time of the hybrid switch is determined based on the turn-on characteristic parameters and the switching loss parameters.

[0101] According to the control pulse signal, the hybrid switch performs a soft switching operation, which comprises: controlling the triac on the full-voltage side to turn off, controlling the IGBT on the full-voltage side to turn off after a preset delay time, and controlling the IGBT on the half-voltage side to turn on. After the IGBT on the half-voltage side is stably turned on, the triac on the half-voltage side is controlled to turn on.

[0102] In the soft switching process, the voltage stress index and the current stress index of the hybrid switch are calculated in real time, the voltage stress index is obtained by the root mean square value of the collector-emitter voltage, and the current stress index is obtained by the root mean square value of the collector current; and a switching stress compensation angle is calculated.

[0103] The control pulse signal is dynamically phase compensated in real time according to the switching stress compensation angle, to generate a stress-optimized control pulse signal; and the on-off timing of the hybrid switch is adjusted based on the stress-optimized control pulse signal, to realize stress-controlled soft switching control in the voltage drop process.

[0104] For example, the voltage drop value of the triac in the on state is measured, which is usually between 0.8V and 1.2V in actual application scenarios. At the same time, the on-resistance of the triac is measured, which is typically between 0.01 ohm and 0.05 ohm. The current flowing through the triac is recorded, which may vary between 10A and 100A depending on the application scenario. Based on these collected data, the on-state characteristic parameters of the triac are calculated, including the power loss coefficient and the temperature coefficient. In actual applications, the power loss coefficient is usually between 0.01 and 0.03 W / A, and the temperature coefficient is between 0.005 and 0.01 Ω / ℃.

[0105] The working parameters of the IGBT are obtained by collecting the collector-emitter voltage of the IGBT, which usually varies between 0 and 600V during switching. At the same time, the collector current is recorded, which is usually between 5A and 50A in applications. Based on these measurement data, the switching loss parameters of the IGBT are calculated, including the turn-on loss and the turn-off loss. In actual applications, the typical value of the turn-on loss is between 0.5mJ and 2.0mJ, and the typical value of the turn-off loss is between 1.0mJ and 3.0mJ.

[0106] Based on the above-mentioned on-state characteristic parameters and switching loss parameters, the preset delay time of the hybrid switch is determined. The delay time must be long enough to ensure that the triac on the full voltage side is completely turned off, but not too long to cause response delay. Through analysis of experimental data, the optimal preset delay time is between 50μs and 100μs under the working conditions of 380V voltage and 30A current. In practice, the delay time can be dynamically adjusted between 40μs and 150μs for different load conditions.

[0107] When the control pulse signal is received, the soft switching operation is performed. First, the full voltage side triac is controlled to be turned off, which is achieved by applying a reverse control signal to the control terminal of the triac. After waiting for a preset delay time (for example, 80 μs), the full voltage side IGBT is controlled to be turned off, which is achieved by reducing the IGBT gate voltage from 15 V to -5 V. At the same time, the half voltage side IGBT is controlled to be turned on, which is achieved by raising the gate voltage from -5 V to 15 V. The collector current and collector-emitter voltage of the half voltage side IGBT are monitored, and when the collector current is stable at more than 95% of the set value and the collector-emitter voltage is reduced to less than 2 V, it is determined that the IGBT has been turned on stably. At this time, the half voltage side triac is controlled to be turned on, and the entire soft switching process is completed. This process is usually completed within 200 μs to 400 μs.

[0108] During the soft switching process, the voltage stress index and the current stress index of the hybrid switch are calculated in real time. The collector-emitter voltage of the hybrid switch is sampled using a sliding time window technique, with a sampling frequency of 50 kHz. The sliding window length is set to 10 switching cycles, and 100 data points are collected for each switching cycle. Based on these sampling data, the root mean square value of the collector-emitter voltage is calculated as the voltage stress index. For example, under a 380 V operating voltage, the typical value of the voltage stress index is 420 V to 460 V. The current stress index is obtained by sampling the IGBT collector current and calculating its root mean square value. Similarly, current data is collected every 10 μs, and the root mean square value is calculated based on 100 sampling points. Under a 30 A load condition, the typical value of the current stress index is 33 A to 37 A.

[0109] Based on the calculated voltage stress index and current stress index, the switching stress compensation angle is calculated. The compensation angle is in a positive proportional relationship with the amount of stress exceeding its threshold value. For example, when the voltage stress index exceeds the preset threshold value of 420 V to 450 V (exceeding 30 V), the compensation angle is calculated to be about 1.5 degrees according to a proportional coefficient of 0.05 degrees per volt; when the current stress index exceeds the preset threshold value of 32 A to 35 A (exceeding 3 A), the compensation angle is calculated to be about 0.3 degrees according to a proportional coefficient of 0.1 degrees per ampere; when both stresses exceed the threshold value, the larger compensation angle value calculated is taken.

[0110] According to the calculated switching stress compensation angle, real-time dynamic phase compensation is performed on the control pulse signal to generate a stress-optimized control pulse signal. For a 50 Hz power frequency, a phase angle of 1 degree corresponds to a time adjustment of about 55.6 μs. If the compensation angle is 2 degrees, the control pulse signal needs to be sent out about 111.2 μs in advance. Precise phase adjustment is achieved through a phase shifting circuit, with an accuracy of 0.1 degrees, i.e., about 5.6 μs.

[0111] Finally, the conduction timing of the hybrid switch is adjusted based on the stress-optimized control pulse signal. In actual operation, the turn-off time of the full-voltage side thyristor is advanced by 2 to 5 degrees, and the IGBT switch timing is adjusted accordingly. The conduction timing of the half-voltage side component is also optimized, ensuring smooth transition. Through this stress-controlled soft switching strategy, the voltage drop process is more stable, the switching stress is effectively controlled, and the reliability is significantly improved.

[0112] The soft switching method of the present application fully utilizes the low loss characteristics of the thyristor and the fast switching capability of the IGBT, significantly reducing the switching loss in the voltage drop process. By accurately calculating the conduction characteristic parameters of the thyristor and the switching loss parameters of the IGBT, the optimization setting of the preset delay time of the hybrid switch is realized, ensuring the reliability and stability of the soft switching process.

[0113] In an alternative embodiment, the voltage stress index and current stress index of the hybrid switch are calculated in real time during the soft switching process, and the step of calculating the switching stress compensation angle includes:

[0114] The collector-emitter voltage and collector current are sampled using a sliding time window, and the real-time voltage stress index and real-time current stress index are calculated based on the sampling data. The voltage stress index is obtained by the root mean square value of the collector-emitter voltage, and the current stress index is obtained by the root mean square value of the collector current. The calculation period of the real-time voltage stress index and the real-time current stress index is an integer multiple of the switching period.

[0115] The real-time voltage stress index and real-time current stress index are compared with their respective preset thresholds. When the voltage stress index or the current stress index exceeds the respective preset threshold, the compensation angle calculation is performed.

[0116] The real-time voltage stress index and real-time current stress index are input into the adaptive fuzzy neural network. The adaptive fuzzy neural network includes a stress evaluation layer and a compensation decision layer. The stress evaluation layer is based on expert experience to construct a fuzzy rule base, and the compensation decision layer uses an online learning algorithm to update the network weights.

[0117] The reference value of the switching stress compensation angle is determined according to the output of the adaptive fuzzy neural network. The reference value calculation follows the principle that the compensation angle is proportional to the stress excess, and a dynamic amplitude limiting mechanism is introduced.

[0118] The compensation effect of the switching stress compensation angle is monitored in real time, and a comprehensive evaluation index including compensation accuracy and compensation stability is constructed. When the comprehensive evaluation index is lower than the preset evaluation threshold, the weight coefficients of the fuzzy rules are updated based on the recursive least squares method, the exponential forgetting factor is used to reduce the influence of historical data, and the upper limit of the weight update step is set to ensure the stability of the system.

[0119] For example, the collector-emitter voltage and the collector current of the hybrid switch are sampled using the sliding time window technique. The length of the sliding window is set to 10 switching periods, and the sampling frequency is 50 kHz, collecting 100 data points per switching period. The sliding window moves forward by 1 switching period each time, maintaining a 9-period data overlap to ensure continuity and smoothness of the calculation. Based on the collected data, real-time voltage stress indicators and real-time current stress indicators are calculated. The voltage stress indicator is obtained by the root mean square value of the collector-emitter voltage. Specifically, for n voltage sampling points vi(i = 1, 2,..., n) in the sliding window, the voltage root mean square value Vrms is calculated. Similarly, for n current sampling points ii(i = 1, 2,..., n) in the sliding window, the current root mean square value Irms is calculated as the current stress indicator. The real-time calculation period is set to 10 times the switching period, i.e., the stress indicators are updated every 10 switching periods.

[0120] The preset threshold of the voltage stress indicator is set to 1.2 times the rated voltage, for example, for a switch device with a rated voltage of 600V, the voltage stress threshold is set to 720V; the preset threshold of the current stress indicator is set to 1.1 times the rated current, for example, for a switch device with a rated current of 50A, the current stress threshold is set to 55A. When the real-time voltage stress indicator exceeds 720V or the real-time current stress indicator exceeds 55A, the compensation angle calculation process is triggered immediately.

[0121] The adaptive fuzzy neural network consists of five layers: input layer, fuzzification layer, rule layer, defuzzification layer, and output layer. The input layer receives the normalized voltage stress indicator and current stress indicator, normalizing the input range to the [0, 1] interval. The fuzzification layer uses a Gaussian membership function to convert the input into a fuzzy set, with each input variable divided into "low", "medium", "high" three linguistic variables, and a total of 9 fuzzy rules are set. The rule layer is based on the rule base constructed by expert experience, for example: "If the voltage stress is high and the current stress is low, then the compensation angle is medium". The initial rule weight is set to 1.0, which is dynamically adjusted during system operation. The defuzzification layer uses the weighted average method to synthesize the output of multiple rules. The output layer generates the final compensation angle reference value.

[0122] The compensation angle calculation follows the principle of being proportional to the stress excess. When the voltage stress index exceeds 120% of the rated value or the current stress index exceeds 110% of the rated value, phase compensation is performed. When the voltage stress exceeds the threshold, the compensation angle is proportional to the excess, and the proportional coefficient is initially set to 0.05 degrees per volt; when the current stress exceeds the threshold, the compensation angle is proportional to the excess, and the proportional coefficient is initially set to 0.1 degrees per ampere. A dynamic limiting mechanism is introduced, and the upper limit of the compensation angle is set to 5 degrees and the lower limit is 0 degrees to avoid excessive compensation leading to system instability. In actual application, when the voltage stress index is detected to be 750V, which exceeds the threshold by 30V, the initial compensation angle is 30x0.05=1.5 degrees.

[0123] The compensation effect of the real-time monitoring switch stress compensation angle is monitored, and a comprehensive evaluation index J including compensation accuracy and compensation stability is constructed. The compensation accuracy is quantified by the closeness of the stress index to the threshold, and the compensation stability is quantified by the change rate of the continuous compensation angle. The compensation accuracy weight coefficient is set to 0.7, and the compensation stability weight coefficient is set to 0.3. When the comprehensive evaluation index J is lower than the preset evaluation threshold 0.8, the fuzzy rule weight adjustment mechanism is started, and the weight coefficients of the fuzzy rules are updated by the recursive least squares method. The adjustment mechanism uses an exponential forgetting factor λ=0.95 to reduce the influence of historical data, and sets the upper limit of the weight update step to 0.1 to ensure system stability. For example, when the rule "if the voltage stress is high and the current stress is medium, then the compensation angle is high" is detected to be ineffective, the system adjusts the weight of this rule from the initial value 1.0 to 0.95, so that the influence of this rule in decision-making is weakened.

[0124] For example, running under the condition of rated voltage 600V and rated current 50A. Initially, the voltage stress index is detected to be 735V, and the current stress index is 52A, both of which exceed the preset threshold. The adaptive fuzzy neural network outputs a compensation angle reference value of 1.8 degrees. After applying the compensation angle, the voltage stress index is reduced to 705V, and the current stress index is reduced to 51A. After 5 cycles of adaptive adjustment, the compensation angle is optimized to 2.3 degrees, the voltage stress index is further reduced to 695V, and the current stress index is reduced to 49A, which is lower than the respective preset threshold.

[0125] Figure 3A comparison chart of stress reduction rates of the three compensation methods in the hybrid switch soft switching process. In the voltage stress exceeding scenario, the stress reduction rate of the method reaches 31.0%, which is significantly higher than that of the fixed compensation (11.0%) and the fuzzy compensation (21.0%); in the current stress exceeding scenario, the method realizes a reduction rate of 36.0%, which is superior to the fixed compensation (15.0%) and the fuzzy compensation (26.0%); in the complex working condition of simultaneous current and voltage exceeding, the effect of the method is more prominent, with a reduction rate of up to 51.0%, far exceeding the fixed compensation (19.0%) and the fuzzy compensation (33.0%). These results show that the compensation method based on the adaptive fuzzy neural network can monitor the switch stress in real time and dynamically adjust the compensation strategy, and it performs significantly better in all test scenarios. Through the sliding time window technology and the online learning algorithm, the method effectively reduces the voltage and current stress of the hybrid switch, improves the system reliability and efficiency, and provides reliable technical support for high-frequency power conversion systems. The method realizes real-time monitoring and adaptive compensation of voltage and current stress in the hybrid switch soft switching process, effectively reduces switch loss, and improves system reliability and efficiency. The method is suitable for various high-frequency power conversion systems and has wide application prospects.

[0126] In an alternative embodiment, the step of inputting the real-time voltage stress indicator and the real-time current stress indicator into the adaptive fuzzy neural network comprises:

[0127] The real-time voltage stress indicator and the real-time current stress indicator are input into the stress evaluation layer of the adaptive fuzzy neural network, the stress evaluation layer maps the real-time voltage stress indicator and the real-time current stress indicator into a plurality of linguistic variables respectively, a fuzzy rule base containing a basic rule set and a dynamic rule set is constructed based on expert experience, the basic rule set contains a plurality of expert experience rules, and the dynamic rule set generates rules adaptively according to the operating state;

[0128] The rule confidence is calculated based on historical operation data, the rule confidence is determined by the ratio of the number of successful rule applications to the total number of rule applications, the rule priority is calculated according to the rule confidence and the rule execution effect evaluation value, and the rule priority is updated in an exponential smoothing manner;

[0129] The rule activation strength of the stress evaluation layer is input into the compensation decision layer, the compensation decision layer is designed with variable structure neurons, the network weights are updated through an online learning algorithm, the learning rate of the online learning algorithm exponentially decays with the number of iterations and is set to a minimum learning rate;

[0130] The dynamic optimization of the fuzzy rule base includes: calculating the rule strength, which is determined based on the control error change rate and updated by a forgetting factor mechanism; merging rules when the rule similarity exceeds a preset similarity threshold; increasing the rule accuracy when the control error exceeds a preset error threshold; and deleting rules when the rule is not activated within a preset period and the rule strength is lower than a preset strength threshold.

[0131] For example, after the collected voltage and current data are preprocessed, real-time voltage stress indicators and real-time current stress indicators are calculated. These two indicators are input into the stress evaluation layer of the adaptive fuzzy neural network as input variables. The stress evaluation layer performs fuzzy mapping on the input real-time voltage stress indicators, mapping a voltage stress indicator value of 0.85, for example, to two linguistic variables of "low-medium" and "medium", and assigning them membership degrees of 0.3 and 0.7, respectively. Similarly, for a real-time current stress indicator of 0.92, the mapping is performed to two linguistic variables of "medium" and "high-medium", with membership degrees of 0.4 and 0.6, respectively. This fuzzy mapping process is implemented using a triangular membership function, and each indicator is divided into seven levels of "very low", "low", "low-medium", "medium", "high-medium", "high", and "very high".

[0132] The fuzzy rule base is composed of a basic rule set and a dynamic rule set. The basic rule set contains experience rules summarized by power system experts, such as the rule "if the voltage stress is high and the current stress is high, then the system stress state is severe". In practical applications, the basic rule set contains 25 similar rules covering common operating states. The dynamic rule set is generated adaptively according to the actual operating state of the system. For example, when a special state of voltage stress of 0.82 and current stress of 0.95 is monitored, if the existing rules cannot effectively evaluate it, the system will generate a new rule "if the voltage stress is low-medium and the current stress is high, then the system stress state is relatively severe" and add it to the dynamic rule set.

[0133] For the calculation of rule confidence, the application history of each rule is recorded. Assuming that a rule has been applied 85 times in the past 100 applications, and the evaluation results of 85 times are consistent with the actual system state, then the confidence of this rule is 0.85. The rule priority is calculated by combining the rule confidence and the rule execution effect evaluation value. The rule execution effect evaluation value is determined based on the control bias, with a value range of 0 to 1, and the smaller the bias, the higher the evaluation value. If the current execution effect evaluation value of the rule is 0.9, then the rule priority is updated by exponential smoothing: new priority = 0.7 × original priority + 0.3 × (0.85 × 0.9) = 0.7 × 0.75 + 0.3 × 0.765 = 0.755, where 0.7 is the smoothing coefficient. This updating mechanism allows the rule priority to change smoothly, avoiding drastic changes caused by temporary fluctuations.

[0134] The rule activation strength of stress assessment layer is transferred to the compensation decision layer, which uses a variable structure neuron design. The variable structure neuron has the characteristic of dynamically adjusting the network structure according to the characteristics of the input data, for example, when the system detects a new type of stress feature, it can automatically increase the neuron nodes. The initial network contains an input layer, a hidden layer and an output layer, the input layer corresponds to the rule activation strength, and the output layer corresponds to the compensation control signal. In order to adapt to the complex and variable operating environment of the power system, the compensation decision layer uses an online learning algorithm to update the network weights. The learning rate is designed in the form of exponential decay with the number of iterations, and the initial learning rate is 0.05. Every 100 iterations, the learning rate is reduced to 80% of the original, but not less than the minimum learning rate of 0.001. This learning rate adjustment strategy allows larger weight adjustments in the initial stage of the system to adapt quickly, and makes small adjustments to fine-tune the performance after the system stabilizes.

[0135] The dynamic optimization mechanism of the fuzzy rule base includes rule strength calculation, rule merging, rule precision increase and rule deletion. The rule strength is determined based on the control error change rate, for example, after the application of a certain rule, the system stress decreases from 0.85 to 0.65, and the control error change rate is 23.5%, indicating that the rule has good effect, and the rule strength increases. The rule strength is updated through the forgetting factor mechanism, and the new rule strength = 0.8 × original rule strength + 0.2 × current effect, where 0.8 is the forgetting factor, which makes the system gradually forget the historical performance and pay more attention to the recent effect.

[0136] When the rule similarity exceeds the preset similarity threshold of 0.9, rule merging is performed, for example, the similarity of the two rules "If the voltage stress is medium-high and the current stress is high, the system stress state is serious" and "If the voltage stress is high and the current stress is high, the system stress state is serious" is calculated as 0.92, which exceeds the threshold, and the system will be merged into "If the voltage stress is medium-high or high and the current stress is high, the system stress state is serious". When the control error exceeds the preset error threshold of 0.15, the system increases the rule precision, which is achieved by subdividing the fuzzy set. For example, the "medium" voltage stress is subdivided into "medium-low" and "medium-high" two more accurate language variables. When the rule is not activated within 30 days and the rule strength is lower than the preset strength threshold of 0.3, the system will delete the rule from the dynamic rule group to keep the rule base simple and efficient.

[0137] Taking the operation data of a certain distribution network as an example, when the voltage stress index of region A is monitored to be 0.78 and the current stress index is 0.89, the stress evaluation layer maps the voltage stress to "low" (0.6) and "medium" (0.4), and maps the current stress to "medium" (0.2) and "high" (0.8). The activated fuzzy rule includes "if the voltage is low and the current is high, then the system stress is high", and the rule activation strength is calculated as 0.6*0.8=0.48. According to this activation strength, the compensation decision layer outputs a compensation control signal of 0.42, indicating that moderate control measures need to be taken. After 20 similar scene applications, the credibility of the rule increases from the initial 0.75 to 0.82, and the rule priority is also correspondingly increased to 0.79, indicating that the system gradually adapts to the operation mode and optimizes its decision performance.

[0138] The present application combines the basic rule set constructed by expert experience with the dynamic rule set adaptively generated according to the operation state, significantly improving the adaptability of the system to complex power environment. The rule credibility and priority calculation mechanism makes the decision process more consistent with actual operation experience, improving the reliability of the system. The compensation decision layer designed by the variable structure neuron realizes the dynamic optimization of network weights through online learning algorithm. The dynamic optimization strategy of fuzzy rule base keeps the rule base in a state of simplicity and efficiency at all times, improving the decision speed and reducing the consumption of computing resources.

[0139] In an optional embodiment, the dynamic limiting mechanism comprises:

[0140] The switch voltage and the switch current are collected, the root mean square values of the switch voltage change rate and the switch current change rate are calculated in the sliding sampling window to obtain the voltage stress feature and the current stress feature, the thermal stress feature is calculated based on the switch junction temperature and the switch loss power, and the voltage stress feature, the current stress feature and the thermal stress feature are constructed into a stress feature vector;

[0141] The state transition relationship and the control action relationship are calculated according to the historical data sequence of the stress feature vector, the stress feature vector of the next period is predicted based on the state transition relationship and the control action relationship, and the stress overrun probability is calculated according to the predicted stress feature vector;

[0142] A limiting boundary curve is constructed based on the hyperbolic tangent function, the amplitude coefficient of the limiting boundary curve is adaptively adjusted according to the stress overrun probability, the slope coefficient of the limiting boundary curve is proportional to the second derivative of the compensation angle, and the initial limiting result is obtained by comparing the current compensation angle with the limiting boundary curve;

[0143] A stress balance evaluation function is constructed, which calculates an adjustment coefficient based on the balance degree of three-dimensional stress characteristics, and the initial clipping result is modified according to the adjustment coefficient to obtain a clipping compensation angle;

[0144] The clipping compensation angle is input into a dynamic buffer zone, the smoothing coefficient of the dynamic buffer zone is in an exponential decay relationship with the compensation angle change rate, and the final compensation angle is obtained by weighted combination of the original compensation angle and the buffer compensation angle according to the smoothing coefficient.

[0145] For example, the first step of the dynamic clipping mechanism is to construct a stress characteristic vector. The switching voltage and switching current of the power switching device are collected, the sampling frequency is set to 50 kHz, and the sampling accuracy is 12 bits. Within a 200 μs sliding sampling window, the collected voltage and current data are processed. Specifically, the switching voltage change rate is calculated as the difference between the voltage values of two adjacent sampling points divided by the sampling time interval, and the root mean square value of all voltage change rates in the sliding window is calculated to obtain the voltage stress characteristic. For example, if the voltage change rate data in the window is 5 V / μs, 6 V / μs, 4 V / μs, and 7 V / μs, the voltage stress characteristic calculation result is about 5.59 V / μs. Similarly, the root mean square value of the switching current change rate is calculated to obtain the current stress characteristic. The thermal stress characteristic is calculated using a direct measurement method, which monitors the junction temperature of the power switching device in real time through a temperature sensor, and calculates the instantaneous loss power of the switching device. The thermal stress characteristic value can be simply represented by the product of the junction temperature and the loss power, with the unit of W·℃. For example, when the junction temperature is measured to be 85℃ and the instantaneous loss power is 20W, the thermal stress characteristic value is 85℃×20W=1700W·℃. This characteristic value reflects the degree of thermal burden of the switching device during operation, and the larger the value, the higher the thermal stress. By comparing with a preset safety threshold (such as 2000W·℃), it can be determined whether the current thermal stress is within the safety range. The above three characteristics are combined into a three-dimensional vector [5.59V / μs, 3.25A / μs, 1700W·℃] as the current stress characteristic vector.

[0146] Stress prediction and over-limit probability calculation is the second step. A stress feature vector history data queue containing nearly 100 cycles is maintained. Based on the queue data, the state transition relationship matrix and the control action relationship matrix are calculated using the recursive least squares algorithm. In specific implementation, a third-order state space model is established, and the stress feature vectors and the corresponding control inputs (compensation angles) of the last 10 cycles are used as training data. After the model training is completed, the current stress state and control input are substituted into the model to predict the stress feature vector of the next cycle. For example, the current cycle stress feature vector is [5.59 V / μs, 3.25 A / μs, 1700 W·℃], and the predicted next cycle is [6.02 V / μs, 3.40 A / μs, 1820 W·℃]. The stress safety threshold is set to [7 V / μs, 4 A / μs, 2000 W·℃], and the probability that the predicted stress vector exceeds the safety threshold is calculated based on the statistical distribution characteristics of the historical data. In this example, the stress over-limit probability is calculated to be 0.15 by the kernel density estimation method.

[0147] The third step is to construct the amplitude limiting boundary curve. The hyperbolic tangent function is used to construct the amplitude limiting boundary curve, which defines the feasible range of the compensation angle. The amplitude coefficient K is adaptively adjusted according to the stress over-limit probability p: when p is 0.15, K takes the value of 0.85; when p increases to 0.3, K decreases to 0.7, and the limit is more stringent. The slope coefficient of the amplitude limiting boundary curve is proportional to the second derivative of the compensation angle, which reflects the dynamic characteristics. For example, when the second derivative of the compensation angle is 0.2, the slope coefficient is set to 0.6; when the second derivative increases to 0.5, the slope coefficient increases to 1.5. Compare the current compensation angle 15° with the amplitude limiting boundary curve. If it exceeds the boundary (assuming the boundary is ±12°), the initial amplitude limiting result is 12°.

[0148] The fourth step is stress balance evaluation. A balance degree evaluation function of three-dimensional stress features is constructed, which represents the balance degree by calculating the variance of the normalized three-dimensional stress features. The higher the balance degree, the smaller the variance, indicating that the distribution of the three stresses is more uniform. For example, the normalized stress features are [0.8, 0.81, 0.85], the variance is 0.0007, and the balance degree is high, so the adjustment coefficient is set to 0.95; if it is [0.6, 0.8, 0.9], the variance is 0.023, and the balance degree is low, so the adjustment coefficient is set to 0.8. Multiply the initial amplitude limiting result 12° by the adjustment coefficient 0.95 to get the amplitude compensation angle 11.4°.

[0149] Dynamic buffer processing is the last link. A dynamic buffer is designed, and the smoothing coefficient alpha of the buffer has an exponential decay relationship with the compensation angle change rate. When the compensation angle change rate is 5° / ms, alpha is 0.7; when the change rate increases to 10° / ms, alpha decreases to 0.5. The buffer processes the compensation angle as follows: assuming that the buffered compensation angle of the previous period is 10°, the current original compensation angle is 11.4°, and the smoothing coefficient alpha is 0.7, the current buffered compensation angle calculation result is 0.7*10°+0.3*11.4°=10.42°. This processing method ensures the smooth change of the compensation angle and avoids the shock caused by sudden change.

[0150] The application realizes comprehensive evaluation of switch stress, considers not only voltage stress and current stress but also thermal stress characteristics, and ensures balanced protection of the switch device in various stress dimensions. The limiting amplitude boundary curve based on the hyperbolic tangent function provides smooth limiting amplitude characteristics, avoiding the control discontinuity problem caused by traditional hard limiting. The stress balance evaluation function ensures balanced processing between multi-dimensional stresses, preventing other stress deterioration caused by single stress optimization. The smoothing coefficient design of the dynamic buffer makes the compensation angle adjustment more stable, improving the stability and controllability in the voltage drop simulation process.

[0151] In a second aspect of the embodiment of the application, a simulation system with controllable phase voltage drop time difference is provided, comprising:

[0152] A first unit is configured to adjust the voltage of the input three-phase alternating current power supply through a three-phase voltage regulator, wherein the three-phase voltage regulator adopts a sliding contact structure and is driven and controlled by a direct current motor to generate a three-phase voltage signal with a set voltage value;

[0153] A second unit is configured to input the three-phase voltage signal into corresponding full-pressure power electronic switches and half-pressure power electronic switches, wherein the full-pressure power electronic switches and the half-pressure power electronic switches of each phase adopt a hybrid switch structure of thyristors and IGBTs, the thyristors are connected in series at the collector terminal of the IGBTs, and the emitter terminal of the IGBTs is connected in parallel with an RC buffer circuit;

[0154] A third unit is configured to collect and process the three-phase voltage signals of the power supply side and the load side through a digital signal processor, extract zero-crossing point information to calculate phase voltage synchronous control signals, perform phase-locked loop synchronization processing on the phase voltage synchronous control signals and a reference clock signal, and generate control pulse signals with phase compensation;

[0155] The fourth unit is configured to control the turn-on timing of the bidirectional thyristor and the IGBT according to the control pulse signal, control the bidirectional thyristor in the full-voltage power electronic switch to be turned off, control the IGBT in the full-voltage power electronic switch to be turned off after a preset time delay, control the IGBT in the half-voltage power electronic switch to be turned on, control the bidirectional thyristor in the half-voltage power electronic switch to be turned on after the IGBT in the half-voltage power electronic switch is turned on stably, and realize soft switching in the voltage drop process.

[0156] The fifth unit is configured to receive the signal transmitted by the digital signal processor through the high-speed communication interface by using the industrial computer, and calculate and display the voltage drop waveform.

[0157] In a third aspect, an electronic device is provided, including:

[0158] a processor;

[0159] a memory for storing processor-executable instructions;

[0160] The processor is configured to invoke the instructions stored in the memory to execute the method described above.

[0161] In a fourth aspect, a computer-readable storage medium is provided, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the method described above.

[0162] The present application can be a method, device, system and / or computer program product. The computer program product can include a computer-readable storage medium having stored thereon computer-readable program instructions that, when executed by a computer, cause the computer to carry out various aspects of the present application.

[0163] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part or all of the technical features; and 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. An analog method for controlling the time difference of voltage sag in phase, characterized in that, The application relates to a three-phase voltage regulator. The three-phase voltage regulator adopts a sliding contact structure and is driven and controlled by a direct-current motor to generate a three-phase voltage signal with a set voltage value; The three-phase voltage signal is input into corresponding full-voltage power electronic switches and half-voltage power electronic switches, wherein the full-voltage power electronic switches and the half-voltage power electronic switches of each phase adopt a hybrid switch structure of thyristors and IGBTs, the thyristors are connected in series at the collector end of the IGBT, and the emitter end of the IGBT is connected in parallel with an RC buffer circuit; A digital signal processor is used to collect and process three-phase voltage signals on the power supply side and the load side, to extract zero-crossing point information and calculate phase voltage synchronous control signals, to perform phase-locked loop synchronization processing on the phase voltage synchronous control signals and a reference clock signal, and to generate control pulse signals with phase compensation; According to the control pulse signals, the thyristors in the full-voltage power electronic switches are controlled to be turned off, the IGBTs in the full-voltage power electronic switches are controlled to be turned off after a preset time delay, the IGBTs in the half-voltage power electronic switches are controlled to be turned on, and the thyristors in the half-voltage power electronic switches are controlled to be turned on after the IGBTs in the half-voltage power electronic switches are stably turned on, so that soft switching in the voltage drop process is realized; An industrial computer is used to receive signals transmitted by the digital signal processor through a high-speed communication interface, to calculate and display voltage drop waveforms.

2. The method of claim 1, wherein, The step of collecting and processing three-phase voltage signals on the power supply side and the load side by a digital signal processor, extracting zero-crossing point information, calculating phase voltage synchronous control signals, performing phase-locked loop synchronization processing on the phase voltage synchronous control signals and a reference clock signal, and generating control pulse signals with phase compensation comprises the following steps: A differential amplification circuit is used to process signal conditioning of three-phase voltage signals on the power supply side and the load side, the differential amplification circuit outputs digital voltage signals obtained by sampling after second-order Butterworth low-pass filtering and through an analog-to-digital conversion circuit; Three-point linear interpolation operation is performed on the digital voltage signals, zero-crossing point time is calculated according to voltage values of adjacent three sampling points and corresponding sampling time, positive and negative hysteresis threshold values are set, and zero-crossing point is determined when the digital voltage signal crosses the positive hysteresis threshold value from negative to positive or crosses the negative hysteresis threshold value from positive to negative; Phase voltage synchronous control signals are calculated based on zero-crossing point time, a reference clock signal is generated through a phase accumulator, the phase voltage synchronous control signals and the reference clock signal are input into a digital phase-locked loop circuit, the digital phase-locked loop circuit comprises a phase detector, a second-order loop filter and a digital controlled oscillator, the proportional coefficient and the integral coefficient of the second-order loop filter are determined according to system bandwidth and damping ratio, and the digital phase-locked loop circuit outputs a synchronous phase signal; The synchronous phase signal is compensated according to transmission delay characteristics of switching devices, including fixed delay compensation and dynamic characteristic compensation, and control pulse signals are generated according to the phase angle after compensation through a space vector modulation algorithm.

3. The method of claim 2, wherein, The step of compensating according to transmission delay characteristics of switching devices comprises the following steps: The synchronous phase signal is input into a dead-time compensation unit, a dead-time is calculated according to the turn-off delay time of the switching device, and a transition level is introduced in the dead-time to suppress voltage spikes in the switching process, so as to obtain a phase angle based on fixed delay compensation; The phase angle based on fixed delay compensation is input into a dynamic characteristic compensation unit, compensation amounts of the switching-on process and the switching-off process are calculated based on the asymmetric characteristics of the rise time and the fall time of the switching device, and a compensated phase angle is obtained; Real-time monitoring of the output voltage distortion rate and the switching loss corresponding to the control pulse signal generated based on the compensated phase angle is performed, and a comprehensive optimization objective function including the output voltage distortion rate and the switching loss is constructed; when the output voltage distortion rate exceeds a preset distortion threshold, the upper bridge arm dead-time, the lower bridge arm dead-time, the transition level threshold, the switching-on process compensation coefficient and the switching-off process compensation coefficient are encoded into an optimization parameter vector, and a particle swarm algorithm with an adaptive inertia weight is used to iteratively optimize the optimization parameter vector, the adaptive inertia weight decreases with the square of the iteration number, the asymmetry constraint is applied to the dead-time compensation parameters during the optimization process, the change range constraint is applied to the dynamic characteristic compensation amount, and a smoothing factor is used to weight and combine the new and old compensation parameters to update the compensated phase angle.

4. The method of claim 1, wherein, According to the control pulse signal, the triac in the full-voltage power electronic switch is turned off, the IGBT in the full-voltage power electronic switch is turned off after a delay of a preset time, and the IGBT in the half-voltage power electronic switch is turned on at the same time. After the IGBT in the half-voltage power electronic switch is turned on and stabilized, the triac in the half-voltage power electronic switch is turned on, thereby realizing soft switching in the voltage drop process. The conduction voltage drop, conduction resistance and through current of the triac are obtained, and the conduction characteristic parameters of the triac are calculated. The collector-emitter voltage and collector current of the IGBT are obtained, and the switching loss parameters of the IGBT are calculated. The preset delay time of the hybrid switch is determined based on the conduction characteristic parameters and the switching loss parameters. According to the control pulse signal, the hybrid switch performs a soft switching operation, which includes turning off the triac on the full-voltage side, turning off the IGBT on the full-voltage side after a delay of a preset delay time, and turning on the IGBT on the half-voltage side at the same time. After the IGBT on the half-voltage side is turned on and stabilized, the triac on the half-voltage side is turned on. The voltage stress index and the current stress index of the hybrid switch are calculated in real time during the soft switching process, and a switching stress compensation angle is calculated. The voltage stress index is obtained by the root mean square value of the collector-emitter voltage, and the current stress index is obtained by the root mean square value of the collector current. According to the switching stress compensation angle, the control pulse signal is dynamically phase-compensated in real time to generate a stress-optimized control pulse signal. Based on the stress-optimized control pulse signal, the conduction timing of the hybrid switch is adjusted to realize stress-controlled soft switching control in the voltage drop process.

5. The method of claim 4, wherein, The step of calculating the voltage stress index and the current stress index of the hybrid switch in real time during the soft switching process comprises: Sliding time windows are used to sample the collector-emitter voltage and the collector current, and real-time voltage stress indexes and real-time current stress indexes are calculated based on the sampling data, and the calculation period of the real-time voltage stress indexes and the real-time current stress indexes is an integer multiple of the switching period; The real-time voltage stress indexes and the real-time current stress indexes are input into an adaptive fuzzy neural network, the adaptive fuzzy neural network comprises a stress evaluation layer and a compensation decision layer, wherein the stress evaluation layer is constructed based on expert experience, and the compensation decision layer updates network weights using an online learning algorithm; A reference value of the switching stress compensation angle is determined according to the output of the adaptive fuzzy neural network, and a dynamic limiting mechanism is introduced; The compensation effect of the switching stress compensation angle is monitored in real time, a comprehensive evaluation index including compensation accuracy and compensation stability is constructed, and when the comprehensive evaluation index is lower than a preset evaluation threshold, the weight coefficients of the fuzzy rules are updated based on the recursive least squares method.

6. The method of claim 5, wherein, The step of inputting the real-time voltage stress indexes and the real-time current stress indexes into the adaptive fuzzy neural network comprises: The real-time voltage stress indexes and the real-time current stress indexes are input into the stress evaluation layer of the adaptive fuzzy neural network, the stress evaluation layer maps the real-time voltage stress indexes and the real-time current stress indexes into a plurality of linguistic variables respectively, a fuzzy rule base including a basic rule group and a dynamic rule group is constructed based on expert experience, the basic rule group includes a plurality of expert experience rules, and the dynamic rule group generates rules adaptively according to the operating state; A rule confidence is calculated based on historical operating data, the rule confidence is determined by the ratio of the number of successful rule applications to the total number of rule applications, a rule priority is calculated according to the rule confidence and a rule execution effect evaluation value, and the rule priority is updated using an exponential smoothing method; The rule activation intensity of the stress evaluation layer is input into the compensation decision layer, the compensation decision layer is designed using a variable structure neuron, network weights are updated using an online learning algorithm, and the learning rate of the online learning algorithm exponentially decays with the iteration number and is set to a minimum learning rate; The fuzzy rule base is dynamically optimized, including: calculating a rule intensity, the rule intensity is determined based on a control error change rate and is updated through a forgetting factor mechanism; merging rules when the rule similarity exceeds a preset similarity threshold; increasing rule accuracy when the control error exceeds a preset error threshold; and deleting rules when the rules are not activated within a preset period and the rule intensity is lower than a preset intensity threshold.

7. The method of claim 5, wherein, The dynamic limiting mechanism comprises: Switching voltage and switching current are collected, the root mean square values of the switching voltage change rate and the switching current change rate are calculated in a sliding sampling window to obtain voltage stress characteristics and current stress characteristics, thermal stress characteristics are calculated based on the switching junction temperature and the switching loss power, and the voltage stress characteristics, the current stress characteristics and the thermal stress characteristics are constructed into a stress feature vector; Calculate a state transition relationship and a control action relationship according to a historical data sequence of the stress feature vector, predict a stress feature vector of a next period based on the state transition relationship and the control action relationship, and calculate a stress overrun probability according to the predicted stress feature vector; A hyperbolic tangent function is used to construct a limiting boundary curve, a magnitude coefficient of the limiting boundary curve is adaptively adjusted according to the stress overrun probability, a slope coefficient of the limiting boundary curve is proportional to a second derivative of a compensation angle, and an initial limiting result is obtained by comparing the current compensation angle with the limiting boundary curve; A stress balance evaluation function is constructed, the stress balance evaluation function is used to calculate an adjustment coefficient based on a balance degree of three-dimensional stress features, and a limiting compensation angle is obtained by modifying the initial limiting result according to the adjustment coefficient; The limiting compensation angle is input into a dynamic buffer area, a smoothing coefficient of the dynamic buffer area is in an exponential decay relationship with a compensation angle change rate, and a final compensation angle is obtained by weighted combination of an original compensation angle and a buffered compensation angle according to the smoothing coefficient.

8. Analog system for controlling the time difference of voltage dips in phase, for implementing the method according to any one of the preceding claims 1-7, characterized in that, Comprise: The first unit is used for voltage regulation of the input three-phase AC power supply by the three-phase voltage regulator, the three-phase voltage regulator adopts a sliding contact structure and is driven and controlled by a DC motor to generate a three-phase voltage signal of a set voltage value; The second unit is used for inputting the three-phase voltage signal into corresponding full-pressure power electronic switches and half-pressure power electronic switches, wherein the full-pressure power electronic switches and the half-pressure power electronic switches of each phase adopt a hybrid switching structure of thyristors and IGBTs, the thyristors are connected in series at the collector end of the IGBT, and the emitter end of the IGBT is connected in parallel with an RC buffer circuit; The third unit is used for collecting and processing three-phase voltage signals of the power supply side and the load side by the digital signal processor, extracting zero-crossing point information to calculate phase voltage synchronous control signals, performing phase-locked loop synchronization processing on the phase voltage synchronous control signals and a reference clock signal, and generating control pulse signals with phase compensation; The fourth unit is used for controlling the conduction timing of the thyristors and the IGBTs according to the control pulse signals by the digital signal processor, controlling the thyristors in the full-pressure power electronic switches to be turned off, controlling the IGBTs in the full-pressure power electronic switches to be turned off after a delay of a preset time, controlling the IGBTs in the half-pressure power electronic switches to be turned on at the same time, controlling the thyristors in the half-pressure power electronic switches to be turned on after the IGBTs in the half-pressure power electronic switches are stably turned on, and realizing soft switching in the voltage drop process; The fifth unit is used for receiving signals transmitted by the digital signal processor by the industrial computer through a high-speed communication interface, calculating and displaying a voltage drop waveform.

9. An electronic device, comprising: Comprise: A processor; A memory for storing processor-executable instructions; The processor is configured to invoke the instructions stored in the memory to execute the method of any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon computer program instructions, wherein, The computer program instructions are executed by the processor to implement the method of any one of claims 1 to 7.

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