A power quality optimization control method and device of a charging pile module and a storage medium

Abstract

The application discloses a power quality optimization control method, device and storage medium of a charging pile module, which can better charge a vehicle-mounted battery, thereby improving charging efficiency and shortening charging time. The application comprises the following steps: collecting original data signals in real time; extracting fundamental wave parameters of the original data signals; obtaining a target voltage signal according to the fundamental wave parameters and obtaining a voltage fluctuation coefficient through the target voltage signal; obtaining output characteristic quantities according to the voltage fluctuation coefficient; executing a double-layer control decision module based on the output characteristic quantities and generating a control instruction, wherein the double-layer control decision module comprises an upper-layer control decision module and a lower-layer control decision module, the upper-layer control decision module solves optimal compensation parameters through a PSO algorithm, and the lower-layer control decision module corrects the optimal compensation parameters through an MPC algorithm; and realizing power optimization control on the charging pile based on the control instruction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of charging piles, in particular to an electric energy quality optimization control method and device for a charging pile module and a storage medium. BACKGROUND

[0002] A charging pile converts alternating current from a power grid into direct current suitable for a vehicle-mounted battery through rectification and voltage conversion, so as to charge the vehicle-mounted battery. The electric energy quality of the charging pile is directly related to charging efficiency, battery cycle life, and safe and stable operation of a power distribution network. The current popularity of electric vehicles promotes the dense grid connection of charging piles. As a nonlinear load, the charging pile is prone to causing harmonic pollution, voltage fluctuation, power factor imbalance, and other problems, which has become a focus of power distribution network electric energy quality management.

[0003] Among them, in the existing electric energy quality control method of the charging pile, most of the control decision mechanisms only use a single algorithm to solve the compensation parameters, and lack the synergistic effect of parameter optimization and correction. Since the charging pile load changes dynamically with the charging stage, and the input voltage of the power grid fluctuates randomly, a single algorithm is difficult to quickly track the working condition changes and output compensation parameters, and there is a problem of slow response caused by lag in solving speed and insufficient compensation accuracy caused by lack of dynamic correction mechanism, thereby reducing the charging efficiency and threatening the safety of the power grid operation. SUMMARY

[0004] To solve the above technical problems, the present application provides an electric energy quality optimization control method and device for a charging pile module and a storage medium.

[0005] The technical solutions provided in the present application will be described below:

[0006] The first aspect of the present application provides an electric energy quality optimization control method for a charging pile module, the method comprising:

[0007] Real-time acquisition of original data signals, the original data signals including charging pile input side voltage signals, output side current signals, and charging pile electric energy quality control module operating state signals;

[0008] Extracting fundamental wave parameters of the original data signals;

[0009] Obtaining a target voltage signal according to the fundamental wave parameters, and obtaining a voltage fluctuation coefficient through the target voltage signal;

[0010] Obtaining an output characteristic quantity according to the voltage fluctuation coefficient;

[0011] A double-layer control decision module is executed based on the output characteristic quantity, and a control instruction is generated, the double-layer control decision module including an upper-layer control decision module and a lower-layer control decision module, the upper-layer control decision module solving an optimal compensation parameter through a PSO algorithm, and the lower-layer control decision module correcting the optimal compensation parameter through an MPC algorithm;

[0012] A passive filter unit, an active compensation unit and a power factor correction unit are driven based on the control instruction, so as to realize optimal control of electric energy of the charging pile.

[0013] Optionally, fundamental wave parameters of the original data signal are extracted, including:

[0014] Direct current components and high-frequency noises in the original data signal are filtered, and signal synchronization calibration is completed;

[0015] The signal after synchronization calibration is input into a filter, a standard fundamental wave of a power grid is taken as an expected signal, and filter coefficients are updated through error feedback iteration until the error converges;

[0016] An output signal of the filter after convergence is acquired;

[0017] A fundamental wave signal in the output signal of the filter is extracted;

[0018] Fundamental wave parameters are acquired according to an amplitude, a phase and a frequency of the fundamental wave signal.

[0019] Optionally, a target voltage signal is acquired according to the fundamental wave parameters, and a voltage fluctuation coefficient is acquired through the target voltage signal, including:

[0020] A target voltage signal synchronized with a power grid is generated based on frequency and phase information in the fundamental wave parameters and in combination with a rated voltage standard of the power grid;

[0021] An actual fundamental wave voltage amplitude is extracted;

[0022] The actual fundamental wave voltage amplitude is compared with an amplitude of the target voltage signal to acquire a comparison difference value;

[0023] An absolute value of the comparison difference value is calculated, and a voltage fluctuation coefficient is acquired based on a ratio of the absolute value to the target voltage amplitude, the voltage fluctuation coefficient being a voltage fluctuation coefficient quantifying a degree of actual voltage deviation from a reference.

[0024] Optionally, an output characteristic quantity is acquired according to the voltage fluctuation coefficient, including:

[0025] Fundamental wave parameters of the original data signal are acquired, Fourier transform is performed on each harmonic component to extract the harmonic component, and voltage and current total harmonic distortion rates and main harmonic amplitudes are calculated to acquire harmonic characteristic quantities;

[0026] Calculate the reactive power and power factor to obtain the reactive power and power factor characteristic quantities by using the phase difference between the fundamental voltage and the fundamental current;

[0027] Based on the voltage fluctuation coefficient, fill in the index of the charging voltage sag / rise amplitude to form the voltage stability characteristic quantity;

[0028] According to the charging pile power quality control module operating state signal calculation module loss rate;

[0029] Fuse the module loss rate with the harmonic characteristic quantity, the reactive power and power factor characteristic quantity and the voltage stability characteristic quantity to obtain the output characteristic quantity.

[0030] Optionally, execute a double-layer control decision module based on the output characteristic quantity and generate a control instruction, including:

[0031] Determine the optimization target and constraint condition of the upper-layer control decision module according to the output characteristic quantity;

[0032] Obtain the optimal compensation parameter satisfying the constraint condition by using the PSO algorithm;

[0033] Based on the output characteristic quantity, the lower-layer control decision module compares the expected effect of the optimal compensation parameter with the actual deviation by using the MPC algorithm to obtain a comparison result;

[0034] According to the comparison result, correct the optimal compensation parameter to obtain a target compensation parameter;

[0035] Generate a control instruction for driving passive filtering, active compensation and power factor correction according to the target compensation parameter.

[0036] Optionally, drive the passive filtering unit, the active compensation unit and the power factor correction unit based on the control instruction to realize the power optimization control of the charging pile, including:

[0037] According to the control instruction, drive the passive filtering unit to input the corresponding tuning branch to filter out fixed harmonics;

[0038] According to the control instruction, control the active compensation unit to output a reverse compensation current to offset dynamic harmonics and reactive power disturbance;

[0039] According to the control instruction, adjust the circuit switching state by the power factor correction unit to make the input current track the voltage fundamental phase to improve the power factor;

[0040] Realize the power optimization control of the charging pile according to the passive filtering unit, the active compensation unit and the power factor correction unit.

[0041] Optionally, after driving passive filtering, active compensation and power factor correction based on the control instruction to achieve the optimal control of the electric energy of the charging pile, the method further comprises:

[0042] Collecting the charging pile input side voltage, charging pile output side current and charging pile power quality control module running state signal after the optimal control, and extracting the power quality key indicators;

[0043] Comparing the power quality key indicators with the preset standard indicators to obtain deviation information;

[0044] According to the deviation information, judging whether the power quality of the charging pile and the module running state meet the standards;

[0045] If not, the deviation information is sent to the double-layer control decision module to regenerate the control instruction for iterative optimization.

[0046] The second aspect of the application provides a power quality optimization control device of a charging pile module, the device comprising:

[0047] A collection unit for collecting real-time original data signals, the original data signals including charging pile input side voltage signals, output side current signals and charging pile power quality control module running state signals;

[0048] An extraction unit for extracting fundamental wave parameters of the original data signals;

[0049] A first acquisition unit for acquiring target voltage signals according to the fundamental wave parameters, and acquiring voltage fluctuation coefficients through the target voltage signals;

[0050] A second acquisition unit for acquiring output characteristic quantities according to the voltage fluctuation coefficients;

[0051] A generation unit for executing a double-layer control decision module based on the output characteristic quantities, and generating a control instruction, the double-layer control decision module including an upper-layer control decision module and a lower-layer control decision module, the upper-layer control decision module solving optimal compensation parameters through a PSO algorithm, and the lower-layer control decision module correcting the optimal compensation parameters through an MPC algorithm;

[0052] A driving unit for driving a passive filtering unit, an active compensation unit and a power factor correction unit based on the control instruction to achieve the optimal control of the electric energy of the charging pile.

[0053] The third aspect of the application provides a power quality optimization control device of a charging pile module, the device comprising:

[0054] A processor, a memory, an input / output unit and a bus;

[0055] The processor is connected with the memory, the input and output unit and the bus;

[0056] The memory stores a program, and the processor invokes the program to execute the method of any one of the first aspect and the first aspect.

[0057] The fourth aspect of the application provides a computer-readable storage medium, which stores a program, and the program executes the method of any one of the first aspect and the first aspect when executed on a computer.

[0058] From the above technical solutions, the application has the following beneficial effects:

[0059] 1. The application collects real-time charging pile input side voltage signal, output side current signal and charging pile power quality control module running state signal, covering the whole information from power grid input to vehicle battery output and the state of the control module itself during the operation of the charging pile, providing basic data for accurate monitoring of power quality and avoiding monitoring deviation caused by data loss.

[0060] 2. The application extracts fundamental wave parameters from complex raw data signals, eliminating the influence of harmonic interference factors on signal analysis, and the fundamental wave parameters can more accurately reflect the basic power condition of the charging pile, making the subsequent analysis of voltage fluctuation and other power quality indicators more reliable.

[0061] 3. In the application, the double-layer control decision mechanism can quickly track the working condition changes and output appropriate compensation parameters, solving the problem of slow response caused by single algorithm solving speed lag in the prior art. At the same time, the dynamic correction mechanism of the MPC algorithm improves the compensation accuracy, avoids insufficient or excessive compensation caused by lack of dynamic correction, and ensures that the charging pile can obtain good power quality control under different working conditions.

[0062] 4. The application filters out harmonics through the passive filter unit, compensates for reactive power in real time through the active compensation unit, and optimizes the power factor through the power factor correction unit, thereby improving the power quality of the charging pile, reducing the influence of harmonic pollution, voltage fluctuation and power factor imbalance on the charging process, and better charging the vehicle battery, thereby improving the charging efficiency and shortening the charging time.

[0063] 5. The application monitors and optimizes the power quality of the charging pile in real time, reduces the probability of equipment damage and failure caused by power quality problems, prolongs the service life of the charging pile and related power grid equipment, and reduces the maintenance and replacement cost of the equipment. BRIEF DESCRIPTION OF DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the present application, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative effort based on these drawings.

[0065] Figure 1 An embodiment schematic diagram of the power quality optimization control method of the charging pile module of the present application;

[0066] Figure 2 Another embodiment schematic diagram of the power quality optimization control method of the charging pile module of the present application;

[0067] Figure 3 Another embodiment schematic diagram of the power quality optimization control method of the charging pile module of the present application;

[0068] Figure 4 Another embodiment schematic diagram of the power quality optimization control method of the charging pile module of the present application;

[0069] Figure 5 Another embodiment schematic diagram of the power quality optimization control method of the charging pile module of the present application;

[0070] Figure 6 Another embodiment schematic diagram of the power quality optimization control method of the charging pile module of the present application;

[0071] Figure 7 Another embodiment schematic diagram of the power quality optimization control method of the charging pile module of the present application;

[0072] Figure 8 An embodiment schematic diagram of the power quality optimization control device of the charging pile module of the present application;

[0073] Figure 9 Another embodiment schematic diagram of the power quality optimization control device of the charging pile module of the present application. DETAILED DESCRIPTION

[0074] In the existing power quality control method of the charging pile, most of the control decision mechanisms only use a single algorithm to solve the compensation parameters, and lack the synergistic effect of parameter optimization and correction. Since the charging pile load changes dynamically with the charging stage, and the input voltage of the power grid exists random fluctuation, a single algorithm is difficult to quickly track the working condition change and output the compensation parameters, and there is a problem of slow response caused by lagging in solving speed and insufficient compensation accuracy caused by lack of dynamic correction mechanism, thereby reducing the charging efficiency and threatening the safety of the operation of the power grid.

[0075] Based on this, the application provides a power quality optimization control method and device for a charging pile module and a storage medium, which effectively improves the power quality of the charging pile, reduces the influence of harmonic pollution, voltage fluctuation, power factor imbalance and other problems on the charging process, and better charges the vehicle-mounted battery, thereby improving the charging efficiency and shortening the charging time.

[0076] Please refer to Figure 1 The first aspect of the application provides a power quality optimization control method for a charging pile module, which comprises:

[0077] 101, real-time acquisition of original data signals, the original data signals including charging pile input side voltage signals, output side current signals and charging pile power quality control module running state signals;

[0078] 102, extracting the fundamental wave parameters of the original data signals;

[0079] 103, obtaining a target voltage signal according to the fundamental wave parameters, and obtaining a voltage fluctuation coefficient through the target voltage signal;

[0080] 104, obtaining an output characteristic quantity according to the voltage fluctuation coefficient;

[0081] 105, executing a double-layer control decision module based on the output characteristic quantity and generating a control instruction, the double-layer control decision module including an upper-layer control decision module and a lower-layer control decision module, the upper-layer control decision module solving optimal compensation parameters through a PSO algorithm, and the lower-layer control decision module correcting the optimal compensation parameters through an MPC algorithm;

[0082] 106, driving a passive filter unit, an active compensation unit and a power factor correction unit based on the control instruction to realize power optimization control of the charging pile.

[0083] In the embodiments of the application, first, original data signals are real-time acquired, the original data signals including charging pile input side voltage signals, output side current signals and charging pile power quality control module running state signals; then, fundamental wave parameters of the original data signals are extracted; then, a target voltage signal is obtained according to the fundamental wave parameters, and a voltage fluctuation coefficient is obtained through the target voltage signal; then, an output characteristic quantity is obtained according to the voltage fluctuation coefficient; a double-layer control decision module is executed based on the output characteristic quantity, and a control instruction is generated, the double-layer control decision module including an upper-layer control decision module and a lower-layer control decision module, the upper-layer control decision module solving optimal compensation parameters through a PSO algorithm, and the lower-layer control decision module correcting the optimal compensation parameters through an MPC algorithm; finally, a passive filter unit, an active compensation unit and a power factor correction unit are driven based on the control instruction to realize power optimization control of the charging pile.

[0084] In step 101, the original data signal is collected in real time. Specifically, the high-precision data acquisition device is used to synchronously collect the key electrical signals and module operating state signals of the charging pile. The charging pile input side voltage signal is obtained through a voltage sensor connected in parallel at both ends of the input bus. The sensor can cover the full range of the charging pile 380V three-phase input voltage. The output side current signal is collected through a current sensor connected in series in the output loop. The current sensor is a direct current sensor, which is suitable for the output current variation interval of 0-250A of the charging pile. The operating state signal of the charging pile power quality control module is obtained through the communication interface of the module, including the key operating parameters such as the internal power device temperature, capacitor voltage, and trigger signal state of the module. It should be noted that the collection period of the charging pile power quality control module is synchronized with the collection of the input side voltage signal and the output side current signal of the charging pile, so as to ensure the consistency of the data timestamp.

[0085] In step 102, the fundamental wave parameters of the original data signal are extracted. Since the original data signal inevitably contains harmonic, noise and other interference components, a synchronous reference frame phase-locked loop (SRF-PLL) combined with a fast Fourier transform (FFT) algorithm is used for fundamental wave extraction. Specifically, the input side voltage signal and the output side current signal are first synchronized and phase-tracked by SRF-PLL to obtain the fundamental frequency and initial phase of the signal. Then the signal is synchronized to the rotating coordinate system, and after filtering out high-frequency interference, it is transformed back to the stationary coordinate system to obtain the preliminary fundamental wave signal. Then the FFT algorithm is used to analyze the frequency spectrum of the preliminary fundamental wave signal, and the fundamental wave voltage amplitude, fundamental wave current amplitude, fundamental wave power factor and other fundamental wave parameters are calculated.

[0086] In step 103, after the fundamental parameter extraction is completed, the target voltage signal is obtained according to the fundamental parameter, and the voltage fluctuation coefficient is obtained through the target voltage signal. Specifically, in the present application, the target voltage signal is defined as the signal obtained by smoothing the fundamental voltage signal at the input side of the charging pile. Specifically, the sliding average filtering algorithm is used to process the fundamental voltage amplitude obtained in step 102, and the filter window width is set to 5 sampling periods to filter out the instantaneous disturbance in the fundamental voltage to obtain a stable target voltage signal. The voltage fluctuation coefficient is obtained by calculating the difference between the maximum and minimum values of the target voltage signal in a set time window, and then dividing by the average value in the time window. The time window is 0.2s, and the calculation formula is: voltage fluctuation coefficient = (U_max - U_min) / U_avg, wherein U_max is the maximum value of the target voltage signal in the time window, U_min is the minimum value, and U_avg is the average value in the time window. The voltage fluctuation coefficient can be calculated in real time through the above calculation formula, and the voltage fluctuation coefficient can directly reflect the stability of the voltage at the input side of the charging pile. The size of the voltage fluctuation coefficient is related to the value of the subsequent output characteristic quantity.

[0087] In step 104, the output characteristic quantity is obtained according to the voltage fluctuation coefficient. The output characteristic quantity is an index that comprehensively reflects the state of the power quality of the charging pile, and is calculated by the voltage fluctuation coefficient and the fundamental parameter. Specifically, the voltage fluctuation coefficient is normalized to map its value range to the interval [0, 1], and then combined with the fundamental power factor, fundamental current distortion rate and other parameters extracted in step 102, a weighted sum method is used to construct the output characteristic quantity, and the weight coefficients are determined based on genetic algorithm optimization. The weight proportion of the voltage fluctuation coefficient is 0.4, the weight proportion of the fundamental power factor is 0.3, and the weight proportion of the fundamental current distortion rate is 0.3. The calculation formula of the output characteristic quantity is: output characteristic quantity = 0.4 x λ + 0.3 x cosφ + 0.3 x (1 - THDi), wherein λ is the normalized voltage fluctuation coefficient, cosφ is the fundamental power factor, and THDi is the fundamental current distortion rate. The output characteristic quantity can quantitatively represent the current power quality level of the charging pile, and the larger the value is, the better the power quality is.

[0088] In step 105, a double-layer control decision module is executed based on the output characteristic quantity, and a control instruction is generated. Specifically, the double-layer control decision module adopts an upper and lower layer cooperative working architecture, wherein the upper layer control decision module takes the maximization of the output characteristic quantity as an optimization target, and solves the optimal compensation parameter through a particle swarm optimization (PSO) algorithm. The particle dimension of the PSO algorithm is set to 3, which respectively corresponds to the filtering parameter of the passive filter unit, the compensation current parameter of the active compensation unit and the correction coefficient of the power factor correction unit. In the iteration process, the maximization of the upper layer control target function output characteristic quantity is taken as the fitness function, and the particle position and velocity are constantly updated, and finally the optimal compensation parameter is obtained.

[0089] The lower layer control decision module takes the optimal compensation parameter output by the upper layer as a reference value, combines the power quality control module operating state signals such as power device temperature and capacitor voltage collected in real time in step 101, and adopts a model predictive control (MPC) algorithm to correct the parameter. The MPC algorithm is based on the equivalent circuit model of the charging pile, takes the minimization of the deviation between the actual operating parameter and the optimal compensation parameter as the target, solves the corrected compensation parameter through rolling optimization, and then generates the corresponding PWM trigger signal control instruction, so as to ensure the dynamic adaptability of the control parameter.

[0090] In step 106, the passive filter unit, the active compensation unit and the power factor correction unit are driven based on the control instruction, and the power optimization control of the charging pile is realized. Specifically, after the control instruction is transmitted to each execution unit, the passive filter unit adjusts the switching state of the filter reactor and the filter capacitor according to the control instruction, mainly for filtering the main harmonics such as 3rd, 5th and 7th harmonics, to reduce the current distortion rate. The active compensation unit outputs a compensation current equal in size and opposite in phase to the harmonic current through the IGBT power module under the driving of the control instruction, further suppresses the harmonic interference, and compensates the reactive power. The power factor correction unit adjusts the duty cycle of the Boost converter according to the instruction, so that the input current tracks the input voltage waveform, and the power factor is improved. Therefore, through the cooperative work of the above three units, the voltage stability of the charging pile can be effectively improved, the harmonic pollution can be reduced, the power factor can be improved, and finally the power optimization control can be realized.

[0091] Please refer to Figure 2 According to some embodiments of the present application, the fundamental wave parameter of the original data signal extracted in step 104 can specifically include, but is not limited to, the following:

[0092] 201, filtering the direct current component and high frequency noise in the original data signal, and completing signal synchronization calibration;

[0093] 202, inputting the signal after synchronization calibration into a filter, taking the grid standard fundamental wave as an expected signal, and updating the filter coefficient through error feedback iteration until the error converges;

[0094] 203、acquiring the converged filter output signal;

[0095] 204、extracting a fundamental signal in the filter output signal;

[0096] 205、acquiring a fundamental parameter according to an amplitude, a phase and a frequency of the fundamental signal.

[0097] In the embodiments of the present application, after the original data signal is collected, the original data signal is filtered to remove the DC component and high-frequency noise, and signal synchronization calibration is completed. Due to the influence of factors such as sensor zero drift and electromagnetic interference, the input side voltage signal and the output side current signal of the charging pile inevitably mix in DC component and high-frequency noise during the collection process. Therefore, the collected signal needs to be filtered first. The signal synchronization calibration is based on the power quality control module clock signal obtained in step 101, and is realized by using the time stamp alignment method. Specifically, in actual application, a unique time stamp is assigned to each collected voltage and current signal, and is compared with the module clock signal. If there is a time deviation, the signal timing is adjusted through a delay compensation algorithm to ensure that the input side voltage signal, the output side current signal and the module running state signal are completely synchronized in the time dimension, so as to avoid deviation in subsequent analysis caused by different signals.

[0098] After the signal filtering and synchronization calibration are completed, the synchronized and calibrated signal is input into the filter, the grid standard fundamental wave is set as the expected signal, and the filter coefficient is updated through error feedback iteration until the error converges. The adaptive linear neuron (ADALINE) filter selected in the present application has the characteristics of real-time adjustment of the coefficient, and can adapt to the slight fluctuation of the grid frequency. The grid standard fundamental wave is set as a sinusoidal signal with a frequency of 50 Hz and an amplitude of 380 V, which is used as the expected output signal of the filter, and the synchronized and calibrated signal is used as the input signal of the filter.

[0099] The error value between the expected signal and the actual output signal of the filter is calculated, wherein the error calculation formula is e(n)=d(n)-y(n), wherein d(n) is the expected signal, y(n) is the filter output signal, and e(n) is the error value of the actual output signal. The filter coefficient is updated iteratively by using the least mean square (LMS) algorithm. The initial coefficient of the filter is obtained by offline training, and the coefficient update formula in the iteration process is w(n+1)=w(n)+2μe(n)x(n), wherein μ is a convergence factor, and x(n) is the filter input signal. The iteration stop condition is set as that the absolute values of the errors of 100 consecutive sampling points are less than 0.01 V of the voltage signal or 0.01 A of the current signal, at which time it is determined that the error converges, and the filter coefficient reaches the optimal state.

[0100] When the filter error converges, the converged filter output signal is obtained. Compared with the signal after synchronous calibration, the high-frequency noise of the output signal is further suppressed, and the signal waveform is closer to the sinusoidal shape of the grid standard fundamental wave. During the acquisition process, the output signal needs to be monitored in real time, and the filtering effect is verified by comparing it with the signal after synchronous calibration. If the harmonic distortion rate of the output signal is reduced by the threshold set for the input signal, the output signal is determined to be valid; if it does not meet the requirements, the coefficient iteration process of step 202 is triggered again until the output signal meets the filtering effect index, ensuring the accuracy of subsequent fundamental wave extraction.

[0101] Based on the converged filter output signal, the fundamental wave signal in the filter output signal is extracted. Here, the improved Hilbert transform combined with synchronous coordinate system transformation method is adopted: first, the Hilbert transform is performed on the filter output signal to obtain its quadrature component, and then the analytical signal is constructed to extract the amplitude variation law of the signal through the envelope line of the analytical signal; subsequently, combined with the calibrated synchronous clock signal in step 201, the SRF-PLL algorithm is used to lock the phase of the analytical signal, and the fundamental wave phase and frequency of the signal are obtained. After converting the signal to the d-q rotating coordinate system, the alternating current component on the q-axis of the orthogonal axis is filtered out by a low-pass filter, only the fundamental wave component on the d-axis of the straight axis is retained, and then it is converted back to the abc stationary coordinate system, and finally the pure fundamental wave signal is obtained.

[0102] Finally, the fundamental wave parameters are obtained according to the amplitude, phase and frequency of the fundamental wave signal. Through the fundamental wave signal extracted in step 204, the peak value detection method is used to obtain the fundamental wave voltage amplitude and fundamental wave current amplitude. Specifically, the sliding window is set to 1 fundamental wave period, and the signal peak value in the window is detected in real time. After mean value processing, the stable amplitude parameter is obtained; the fundamental wave phase is directly obtained through the output result of the SRF-PLL algorithm; and the fundamental wave frequency is obtained by calculating the time difference between adjacent zero-crossing points.

[0103] Combined with the above parameters, the fundamental wave power factor and fundamental wave active power and other key fundamental wave parameters are further calculated. These parameters are consistent with the fundamental wave parameters extracted in step 102, and can be directly used as the input basis for the calculation of the target voltage signal in step 103.

[0104] Please refer to Figure 3 According to some embodiments of the present application, the target voltage signal is obtained based on the fundamental wave parameters in step 105, and the voltage fluctuation coefficient is obtained through the target voltage signal. Specifically, it can include, but is not limited to, the following:

[0105] 301, based on the frequency and phase information in the fundamental wave parameters, and combined with the grid rated voltage standard, a target voltage signal synchronized with the grid is generated;

[0106] 302, the actual fundamental wave voltage amplitude is extracted;

[0107] 303. Compare the actual fundamental voltage amplitude with the amplitude of the target voltage signal to obtain the comparison difference;

[0108] 304. Calculate the absolute value of the comparison difference, and obtain the voltage fluctuation coefficient based on the ratio of the absolute value to the target voltage amplitude. The voltage fluctuation coefficient is a voltage fluctuation coefficient that quantifies the degree of deviation of the actual voltage from the reference.

[0109] In this embodiment, a target voltage signal synchronized with the power grid is generated based on the frequency and phase information in the fundamental parameters and in conjunction with the grid rated voltage standard. The fundamental frequency and phase reflect the dynamic phase and frequency state of the power grid operation in real time. In this embodiment, the grid rated voltage standard adopts the State Grid's three-phase AC rated line voltage of 380V, corresponding to a peak value of 537V. The target voltage signal is generated using digital signal synthesis technology. A standard sinusoidal voltage signal model is constructed using the real-time frequency in the fundamental parameters as the signal frequency, the real-time phase as the initial phase, and the peak value of the grid rated voltage as the amplitude. To ensure synchronization between the target voltage signal and the power grid, the signal model parameters are corrected according to the updated fundamental frequency and phase in each sampling period. Simultaneously, the generated target voltage signal is time-aligned with the calibrated synchronization clock signal from step 201 to achieve precise synchronization between the target voltage signal and the power grid.

[0110] After generating the target voltage signal, the actual fundamental voltage amplitude is extracted. This actual fundamental voltage amplitude originates from the fundamental parameters obtained in step 205. To further improve data stability, the original fundamental voltage amplitude needs secondary processing. Specifically, a weighted recursive average filtering algorithm is used. The fundamental voltage amplitude at the current sampling point is assigned a weight of 0.6, and the fundamental voltage amplitudes at the previous four sampling points are assigned weights of 0.2, 0.1, 0.06, and 0.04, respectively. The smoothed actual fundamental voltage amplitude is obtained through weighted summation. This weighted recursive average filtering algorithm effectively suppresses instantaneous pulse interference in the fundamental voltage amplitude while preserving the dynamic characteristics of the signal. The fluctuation range of the processed actual fundamental voltage amplitude is controlled within ±0.5V, providing stable measured data for subsequent comparisons.

[0111] Next, the amplitude of the target voltage signal is compared with the amplitude of the actual fundamental voltage to obtain the comparison difference. Specifically, the amplitude of the target voltage signal is first extracted in real time, and then the difference between the actual fundamental voltage amplitude obtained in step 302 and the target voltage amplitude is calculated. It should be noted that during the calculation process, the consistency of the sampling timestamps of the two must be ensured to achieve real-time updates of the comparison difference. This difference can directly reflect the direction and initial degree of deviation of the actual fundamental voltage from the reference.

[0112] Finally, the absolute value of the comparison difference is calculated, and the voltage fluctuation coefficient is obtained based on the ratio of the absolute value to the target voltage amplitude. Specifically, first, the absolute value of the comparison difference obtained in step 303 is taken to eliminate the influence of the deviation direction on the subsequent calculation, and then the voltage fluctuation coefficient λ is calculated according to the formula λ = |ΔU| / U_ref, where |ΔU| is the absolute value of the comparison difference, and U_ref is the target voltage amplitude. To ensure that the voltage fluctuation coefficient can reflect the voltage stability in a short time, the calculation period is set to 1s, that is, the λ value of 2000 sampling points is calculated continuously every 1s, and the average value is taken as the final voltage fluctuation coefficient in the period. The value range of the coefficient is [0, +∞), that is, when λ = 0, it indicates that the actual fundamental voltage is completely consistent with the target voltage, and the voltage does not fluctuate; the larger the λ value, the more serious the deviation of the actual voltage from the reference, which can quantify the fluctuation state of the voltage at the input side of the charging pile. The coefficient is the core parameter for calculating the output feature quantity in step 104.

[0113] Please refer to Figure 4 According to some embodiments of the application, the output feature quantity obtained in step 106 based on the voltage fluctuation coefficient can specifically include, but is not limited to, the following:

[0114] 401. Based on the fundamental parameters of the original data signal, the harmonic components are extracted by Fourier transform, and the voltage, current total harmonic distortion and main harmonic amplitude are calculated to obtain the harmonic feature quantity;

[0115] 402. The phase difference between the fundamental voltage and the fundamental current is used to calculate the reactive power and the power factor to obtain the reactive power and the power factor feature quantity;

[0116] 403. Based on the voltage fluctuation coefficient, and filling the index of the charging voltage sag / rise amplitude to form the voltage stability feature quantity;

[0117] 404. The module loss rate is calculated according to the charging pile power quality control module operating state signal;

[0118] 405. The module loss rate is fused with the harmonic feature quantity, the reactive power and the power factor feature quantity, and the voltage stability feature quantity to obtain the output feature quantity.

[0119] In the embodiments of the present application, first, based on the fundamental parameters of the original data signal, the harmonic components are extracted by Fourier transform, and the voltage, current total harmonic distortion and main harmonic amplitude are calculated to obtain the harmonic feature quantity. In step 205, the fundamental frequency, phase and amplitude parameters have been obtained, and in this step, the windowed-FFT (Windowed-FFT) is used to extract the harmonics of the original voltage and current signals collected in step 201 based on the fundamental parameters.

[0120] After that, the phase difference between the fundamental voltage and the fundamental current is used to calculate the reactive power and the power factor to obtain the reactive power and power factor characteristic quantities. Specifically, based on the phase of the fundamental voltage φ_U and the phase of the fundamental current φ_I, the phase difference Δφ = φ_U - φ_I, the reactive power Q and the power factor PF can be directly calculated through the phase difference. Wherein, the calculation formula of the reactive power Q is Q = U1 x I1 x sinΔφ, wherein U1 is the effective value of the fundamental voltage, I1 is the effective value of the fundamental current, the power factor PF is divided into displacement power factor (DPF) and total power factor (TPF), DPF = cosΔφ, TPF = P / S. Wherein P is the active power, P = U1 x I1 x cosΔφ; S is the apparent power, S = U1 x I1. In order to comprehensively reflect the state of reactive power and power factor, the reactive power and power factor characteristic quantity is defined as [Q, DPF, TPF], wherein the unit of reactive power Q is kvar, and the value range of power factor is mapped to the interval [0, 1], so as to ensure that the characteristic quantity can accurately reflect the reactive power demand and power utilization efficiency of the charging pile.

[0121] After that, based on the voltage fluctuation coefficient, the voltage sag / swell amplitude index is filled to form the voltage stability characteristic quantity. Specifically, the voltage fluctuation coefficient λ is calculated through the target voltage signal, which mainly reflects the short-term stationarity of the voltage, and the voltage sag / swell amplitude index is newly added to improve the evaluation dimension of voltage stability. The voltage effective value detection algorithm is used to detect the voltage sag / swell amplitude, taking the fundamental voltage effective value U1 as the detection object, setting the sag threshold to 0.9U_N and the swell threshold to 1.1U_N, when U1 is lower than 0.9U_N and the duration is ≥0.1s, it is judged as voltage sag, the sag amplitude ΔU_sag=(U_N - U1) / U_N x 100%; When U1 is higher than 1.1U_N and the duration is ≥0.1s, it is judged as voltage swell, the swell amplitude ΔU_swell=(U1- U_N) / U_N x 100%; If no sag / swell occurs, the amplitude value is 0. The voltage stability characteristic quantity is composed of the voltage fluctuation coefficient λ and the sag / swell amplitude, and adopts the vector form of [λ, ΔU_sag, ΔU_swell].

[0122] Then the module loss rate is calculated according to the charging pile power quality control module operating state signal. The module operating state signal includes power device temperature T, DC bus voltage U_dc, module input power P_in and output power P_out, and the module loss rate η_loss is calculated by the ratio of the difference between the input and output power to the input power, and the calculation formula is η_loss=(P_in - P_out) / P_in×100%. In order to eliminate the influence of instantaneous power fluctuation on the calculation result, first, the first-order low-pass filter is used to process P_in and P_out, and then the difference is calculated; at the same time, the loss rate is corrected in combination with the power device temperature T.

[0123] Finally, the module loss rate is fused with the harmonic characteristic quantity, the reactive power and power factor characteristic quantity and the voltage stability characteristic quantity to obtain the output characteristic quantity. The output characteristic quantity F has a value range of [0, 1], and the value closer to 1 indicates that the charging pile power quality is better and the module operating efficiency is higher.

[0124] Please refer to Figure 5 According to some embodiments of the application, the step 107 of executing a double-layer control decision module based on the output characteristic quantity and generating a control instruction can specifically include, but is not limited to, the following:

[0125] 501. Determine the optimization target and constraint condition of the upper-layer control decision module according to the output characteristic quantity;

[0126] 502. Obtain the optimal compensation parameter satisfying the constraint condition by using the PSO algorithm;

[0127] 503. Based on the output characteristic quantity, the lower-layer control decision module compares the expected effect of the optimal compensation parameter with the actual deviation by using the MPC algorithm to obtain a comparison result;

[0128] 504. Correct the optimal compensation parameter according to the comparison result to obtain a target compensation parameter;

[0129] 505. Generate a control instruction for driving passive filtering, active compensation and power factor correction according to the target compensation parameter.

[0130] In the embodiments of the application, the optimization target and constraint condition of the upper-layer control decision module are determined according to the output characteristic quantity. The output characteristic quantity F comprehensively reflects the charging pile power quality and the module operating efficiency, and its value range [0, 1] directly determines the optimization direction, so the upper-layer control takes the maximization of the output characteristic quantity F as the core optimization target, that is, by adjusting the compensation parameter, F tends to 1, corresponding to the ideal state of optimal power quality and lowest module loss.

[0131] The constraint condition is set based on the charging pile hardware operating limit and the power quality national standard requirement, and mainly includes three types:

[0132] First, the parameter boundary constraint, the filter reactor inductance value of the passive filter unit is 0.1 mH-5 mH, the filter capacitor capacitance value is 10 pF-100 pF, the compensation current amplitude of the active compensation unit is not more than 30% of the rated output current, that is, 75 A, and the correction coefficient of the power factor correction unit is 0.8-1.2.

[0133] Second, the state variable constraint, combined with the collected operating state signals, the temperature of the power device is set to not more than 100℃, and the DC bus voltage fluctuation range is not more than ±5% of the rated value, that is, 540V±27V.

[0134] Third, the performance index constraint, the total harmonic distortion rate THDu of the compensated voltage is required to be less than or equal to 5%, the total harmonic distortion rate THDi of the current is required to be less than or equal to 8%, and the power factor PF is required to be greater than or equal to 0.95, to ensure that the optimization process meets the actual operation requirements and standard specifications.

[0135] After determining the optimization target and the constraint condition, the PSO algorithm is used to obtain the optimal compensation parameters that meet the constraint condition. The particle dimension of the PSO algorithm is set to 6 dimensions, which respectively correspond to the reactor inductance L and the capacitor capacitance C of the passive filter unit, the compensation current amplitude I_c of the active compensation unit, the compensation phase f_c of the active compensation unit, the duty ratio D of the Boost converter of the power factor correction unit, and the correction coefficient K of the power factor correction unit. The particle population size is set to 60, and the maximum iteration number is 120 to balance the solution accuracy and speed.

[0136] Specifically, when initializing the particle position, the initial range is determined based on each characteristic component, for example, the higher the harmonic characteristic component proportion, the more the initial particle value in the filter parameter dimension is biased towards the harmonic suppression optimal interval. In the iteration process, the maximum output characteristic F is used as the fitness function, a nonlinear inertia weight strategy is adopted, and each generation of particles is subjected to constraint verification. If the particle position exceeds the parameter boundary or the corresponding state variable and performance index violates the constraint, the fitness value is reduced by the penalty function method, and the particle is forced to converge to the feasible region. After the iteration is completed, the particle position with the highest fitness value is selected as the optimal compensation parameter.

[0137] After completing the optimal compensation parameter solution, based on the output characteristic, the lower-level control decision module compares the expected effect of the optimal compensation parameter with the actual deviation through the MPC algorithm to obtain the comparison result. The lower-level control takes real-time performance and dynamic correction as the core, and the prediction model of the MPC algorithm is established based on the equivalent circuit of the charging pile. The output characteristic F is decomposed into harmonic components, reactive and power factor components, voltage stability components, and module loss components, and the prediction equations of each component are constructed.

[0138] First, according to the optimal compensation parameters, combined with the current output characteristic quantity F, the expected output characteristic quantity of each period is calculated through the prediction model, and the actual running data before compensation is collected in real time, and the actual output characteristic quantity is compared with the expected output characteristic quantity. The deviation is calculated by weighted Euclidean distance, and the weight is consistent with the feature fusion weight in step 405. The larger the value of ΔF, the more significant the deviation between the expected effect and the actual deviation.

[0139] Then, the optimal compensation parameters are corrected according to the comparison result to obtain the target compensation parameters. The correction process adopts a deviation grading correction strategy. Specifically, when ΔF≤0.05, it is determined that the deviation is small, and only the optimal compensation parameters are fine-tuned. The fine-tuning coefficient is 0.95-1.05, wherein the harmonic component deviation dominates the fine-tuning filter parameters, and the voltage component deviation dominates the fine-tuning compensation current parameters. When 0.05<ΔF≤0.15, moderate correction is performed using MPC rolling optimization, taking the minimization of ΔF as the objective function, and constructing 20 candidate parameter groups in the neighborhood of the optimal compensation parameters. The ΔF value of each candidate group is calculated through the prediction model, and the parameter group corresponding to the minimum ΔF is selected as the intermediate value of the correction. When ΔF>0.15, it is determined that the optimal compensation parameters have poor adaptability to the actual working condition, and the parameter reselection mechanism is triggered. The historical optimal particle library of the upper PSO algorithm is called to select the three groups of historical optimal parameters with the highest similarity to the current output characteristic quantity F, and the cosine similarity is greater than or equal to 0.9. The correction value is obtained by weighted fusion combined with the MPC prediction result. The target compensation parameters after correction need to be verified again by the constraint condition in step 501 to ensure that they meet the hardware and performance constraints, and finally the target compensation parameters are obtained.

[0140] Finally, the control instructions for driving passive filtering, active compensation and power factor correction are generated according to the target compensation parameters. The control instructions adopt the form of digital pulse signals combined with analog adjustment signals to generate adaptive instructions for different unit characteristics: for the passive filtering unit, relay trigger instructions are generated to realize the combination of filter reactors and capacitors through the on-off control of relays. The instruction adopts TTL level signal, high level is switching instruction, low level is off instruction, and trigger delay. For the active compensation unit, space vector pulse width modulation (SVPWM) algorithm is used to generate power device trigger pulses. The pulse frequency is set to 10kHz, and the pulse duty cycle and phase are adjusted to realize accurate output of compensation current. The pulse amplitude is 15V to match the power electric appliance driving circuit. For the power factor correction unit, PWM adjustment signal and analog voltage signal are used. The PWM signal controls the on-off time of the switch tube of the Boost converter to adjust the duty cycle, and the analog voltage signal is input into the correction controller to adjust the correction coefficient, realizing dynamic correction of power factor. All control instructions are transmitted to each execution unit through CAN bus to ensure the real-time and reliability of instruction transmission, and finally the optimization control of charging pile power quality is realized.

[0141] Please refer to Figure 6 According to some embodiments of the present application, the step of driving the passive filter unit, the active compensation unit and the power factor correction unit based on the control instruction to achieve the power optimization control of the charging pile in step 107 can specifically include, but is not limited to, the following:

[0142] 601. According to the control instruction, the passive filter unit is driven to put into the corresponding tuning branch to filter out fixed harmonics;

[0143] 602. According to the control instruction, the active compensation unit outputs a reverse compensation current to offset dynamic harmonics and reactive disturbances;

[0144] 603. According to the control instruction, the power factor correction unit adjusts the circuit switch state to make the input current track the fundamental phase of the voltage to improve the power factor;

[0145] 604. According to the passive filter unit, the active compensation unit and the power factor correction unit, the power optimization control of the charging pile is achieved.

[0146] In the embodiments of the present application, according to the control instruction, the passive filter unit is driven to put into the corresponding tuning branch to filter out fixed harmonics. The passive filter unit adopts a multi-branch tuning structure, and three tuning branches are provided, which correspond to the third, fifth and seventh main fixed harmonics respectively. Each branch is composed of a filter reactor, a filter capacitor and a switching relay in series. When the control instruction contains the relay trigger instruction of the passive filter unit, the instruction analysis module first determines the tuning branch to be switched according to the target compensation parameter. For example, when the third harmonic accounts for more than 4% in the fundamental current distortion rate, the relay trigger instruction of the third harmonic branch is triggered. After the instruction is converted by the opto-isolating circuit, the relay coil is energized, the moving contact is closed, and the third harmonic tuning branch is connected to the input side bus of the charging pile. The branch presents extremely low impedance at the third harmonic frequency, so that the third harmonic current flows through the filter branch preferentially, thereby filtering out the fixed harmonic at the main circuit.

[0147] At the same time that the passive filter unit is put into operation, according to the control instruction, the active compensation unit outputs a reverse compensation current to offset dynamic harmonics and reactive disturbances. The active compensation unit adopts a static var generator (SVG) structure based on a three-phase full-bridge topology, and the DC side supporting capacitor is 4500 μF. The output side is connected to the power grid through a connecting reactor. The control instruction contains a trigger pulse of the active compensation unit, which is generated from the target compensation parameter. The pulse analysis unit first extracts the duty cycle and phase information in the trigger pulse, and converts them into the conduction time and off time control signals of the power device.

[0148] In the specific working process, the current detection module collects the actual current signal of the input side of the charging pile in real time, compares it with the compensation current amplitude and compensation phase set in the instruction, and obtains the current deviation signal; after the deviation signal is processed by the proportional integral regulator, it is input into the SVPWM algorithm module to generate a trigger pulse to drive the power device module to alternate on and off, so that the active compensation unit outputs a compensation current equal in size and opposite in phase to the dynamic harmonic current, and at the same time outputs a capacitive or inductive reactive current matching the reactive power demand. For example, when the charging pile generates a 20A 5th dynamic harmonic current due to load mutation, the active compensation unit instantly outputs a 20A, 180°-phase 5th compensation current to realize the cancellation of the dynamic harmonic. When the power factor is detected to be reduced to 0.88, an inductive reactive current compensation is output to raise the power factor to the target value.

[0149] At the same time, according to the control instruction, the power factor correction unit adjusts the circuit switch state to make the input current track the voltage fundamental phase to improve the power factor. The power factor correction unit adopts a Boost converter topology structure, and the output side is connected to the charging pile rectification filtering circuit. The control instruction contains the PWM adjustment signal and the analog voltage signal of the power factor correction unit, wherein the PWM adjustment signal corresponds to the duty ratio of the Boost converter of the target compensation parameter, and the analog voltage signal corresponds to the correction coefficient. After the control instruction is input, the PWM signal is amplified by the drive circuit to control the switching state of the MOSFET, and the duty ratio of the Boost converter ranges from 0.3 to 0.7. By adjusting the on-time of the switching tube to change the charging and discharging process of the inductor, the input current waveform is changed from a pulsating rectification waveform to a continuous sinusoidal waveform. At the same time, the analog voltage signal is input into the PI correction controller to adjust the gain coefficient of the controller, so that the phase of the input current accurately tracks the fundamental phase of the input voltage. For example, when the fundamental phase of the input voltage deviates by 10°, the correction controller instantly adjusts the response speed of the current loop to make the input current phase deviate by 10° synchronously, ensuring that the power factor is stable above 0.95, thereby effectively reducing the reactive power loss.

[0150] Finally, the power optimization control of the charging pile is realized according to the passive filter unit, the active compensation unit and the power factor correction unit. In the actual operation process, the three units form a three-level regulation mechanism of rough filtering, fine compensation and correction. The passive filter unit first filters out 3rd, 5th and 7th fixed harmonics through tuning branches to reduce the total amount of harmonics. The active compensation unit further suppresses harmonics and compensates for reactive power by accurately canceling 2nd, 4th and other dynamic harmonics generated by load mutation and instantaneous reactive disturbance. The power factor correction unit ensures that the input current is in phase with the fundamental wave of the voltage through current phase tracking to maximize active power transmission efficiency. At the same time, the operating state of each unit is collected in real time through the state feedback module. The passive filter unit feeds back the relay switching state of each branch and the temperature of the reactor. The active compensation unit feeds back the temperature of the power device and the capacitor voltage on the DC side. The power factor correction unit feeds back the MOSFET conduction state and the input current waveform. The feedback data is transmitted to the upper control decision module through the CAN bus and compared with the output characteristic quantity F for analysis.

[0151] If the feedback data shows that the adjustment effect of a unit does not meet the expectation, the control instruction correction mechanism is triggered, the MPC algorithm is called again to adjust the target compensation parameters and generate new control instructions, and closed-loop optimization is realized.

[0152] Please refer to Figure 7 According to some embodiments of the present application, after step 106: driving passive filtering, active compensation and power factor correction based on the control instruction to realize power optimization control of the charging pile, it can also specifically include, but is not limited to, the following:

[0153] 701, collect the charging pile input side voltage, charging pile output side current and charging pile power quality control module operating state signal after optimization control, and extract the power quality key indicators;

[0154] 702, compare the power quality key indicators with the preset standard indicators to obtain deviation information;

[0155] 703, determine whether the power quality of the charging pile and the module operating state meet the standard according to the deviation information;

[0156] 704, if not, send the deviation information to the double-layer control decision module to regenerate the control instruction for iterative optimization.

[0157] In the embodiments of the present application, after the passive filter unit, the active compensation unit and the power factor correction unit complete the first round of power optimization control, continuous optimization is realized through a closed-loop feedback mechanism.

[0158] Specifically, first, the input side voltage of the charging pile after optimization control, the output side current of the charging pile and the operation state signal of the power quality control module of the charging pile are collected, and key indicators of power quality are extracted. Among them, the input side voltage signal is collected twice through the original Hall voltage sensor, and the output side current signal is collected through the original DC current sensor multiplexing; the power quality control module operation state signal is read in real time through the communication interface.

[0159] Based on the collected signals, key indicators of power quality are extracted. In terms of time domain analysis, the input side voltage effective value, the output side current effective value, the voltage fluctuation amplitude and the current pulsation coefficient are calculated through the sliding window algorithm; the improved FFT algorithm is used for frequency spectrum analysis, and the voltage total harmonic distortion rate, the current total harmonic distortion rate, the 3-13 characteristic harmonic content and the fundamental power factor are calculated; the module operation state indicators are extracted, including the temperature threshold compliance rate of each unit, the DC side voltage stability of the active compensation unit and the switch tube conduction rate of the power factor correction unit. The finally formed key indicator set is attached with time stamp and collection serial number for traceability analysis.

[0160] After completing the extraction of key indicators, the power quality key indicators are compared with the preset standard indicators to obtain deviation information. The preset standard indicators are formulated according to GB / T 14549-2011 and GB / T 18487.1-2015 standards, and combined with the rated operating parameters of the charging pile. The difference value calculation method is used in the comparison process. At the same time, in order to quantify the overall deviation degree, the weighted sum method is used to calculate the comprehensive deviation coefficient, and the value range of the comprehensive deviation coefficient is 0-1. The larger the value, the more significant the deviation.

[0161] Then, according to the deviation information, it is judged whether the power quality of the charging pile and the module operation state meet the standard. In this application, there is a judgment standard. The single indicator meets the standard, which requires that the out-of-limit degree of all key indicators is 0; if there is any indicator out-of-limit, it is directly determined that it does not meet the standard. If it is determined that it does not meet the standard, the deviation information is sent to the double-layer control decision module to regenerate the control instruction for iteration optimization, so as to ensure that the power quality of the charging pile is in the optimal state.

[0162] Please refer to Figure 8 The second aspect of the present application provides a power quality optimization control device of a charging pile module, which comprises:

[0163] The acquisition unit 801 is used for collecting original data signals in real time, and the original data signals include charging pile input side voltage signals, output side current signals and charging pile power quality control module operation state signals;

[0164] The extraction unit 802 extracts the fundamental parameters of the original data signals;

[0165] The first acquisition unit 803 is configured to acquire a target voltage signal according to the fundamental wave parameter, and acquire a voltage fluctuation coefficient through the target voltage signal.

[0166] The second acquisition unit 804 is configured to acquire an output characteristic quantity according to the voltage fluctuation coefficient.

[0167] The generation unit 805 executes a double-layer control decision module based on the output characteristic quantity, and generates a control instruction, the double-layer control decision module including an upper-layer control decision module and a lower-layer control decision module, the upper-layer control decision module solving an optimal compensation parameter through a PSO algorithm, and the lower-layer control decision module correcting the optimal compensation parameter through an MPC algorithm.

[0168] The driving unit 806 drives a passive filter unit, an active compensation unit and a power factor correction unit based on the control instruction, so as to realize the optimal control of the electric energy of the charging pile.

[0169] Please refer to Figure 9 The application further provides an electric energy quality optimal control device of a charging pile module, the device including:

[0170] The processor 901, the storage 902, the input and output unit 903 and the bus 904 are connected.

[0171] The processor 901 is connected with the storage 902, the input and output unit 903 and the bus 904.

[0172] The storage 902 stores a program, and the processor 901 calls the program to execute any of the above methods.

[0173] The application further relates to a computer readable storage medium, and the computer readable storage medium stores a program, when the program runs on a computer, the computer executes any of the above methods.

[0174] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiments, and will not be described here.

[0175] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the described device embodiments are merely schematic. For example, the division of the units is only a logical function division. There can be another division manner for the actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0176] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments of the present application.

[0177] In addition, each functional unit in the embodiments of the present application can be integrated in one processing unit, or each unit can exist physically as a separate unit, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware, or in the form of a software functional unit.

[0178] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such an understanding, the technical solutions of the present application essentially or substantially, or all or part of the technical solutions, can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the embodiments of the present application. The foregoing storage medium includes: U disk, mobile hard disk, read-only memory (ROM, read-only memory), random access memory (RAM, random access memory), magnetic disk or optical disk, and various other media that can store program codes.

Claims

1. A power quality optimization control method of a charging pile module, characterized in that, The method comprises: Real-time acquisition of original data signals, including charging pile input side voltage signals, output side current signals and charging pile power quality control module operating state signals; Extracting fundamental wave parameters of the original data signals; According to the fundamental wave parameters, target voltage signals are obtained, and voltage fluctuation coefficients are obtained through the target voltage signals; Based on the fundamental wave parameters of the original data signals, each harmonic component is extracted by Fourier transform, and voltage and current total harmonic distortion rates and main harmonic amplitudes are calculated to obtain harmonic characteristic quantities; Using the phase difference between the fundamental wave voltage and the fundamental wave current, the reactive power and the power factor are calculated to obtain the reactive power and power factor characteristic quantities; Based on the voltage fluctuation coefficient, and filling in the index of voltage sag / boost amplitude to form the voltage stability characteristic quantity; According to the charging pile power quality control module operating state signal, the module loss rate is calculated; The module loss rate is fused with the harmonic characteristic quantity, the reactive power and power factor characteristic quantity and the voltage stability characteristic quantity to obtain the output characteristic quantity; Based on the output characteristic quantity, a double-layer control decision module is executed, and a control instruction is generated, the double-layer control decision module comprises an upper-layer control decision module and a lower-layer control decision module, the upper-layer control decision module solves optimal compensation parameters through a PSO algorithm, and the lower-layer control decision module corrects the optimal compensation parameters through an MPC algorithm; Based on the control instruction, a passive filter unit, an active compensation unit and a power factor correction unit are driven to realize the power optimization control of the charging pile.

2. The method of power quality optimization control of a charging station module according to claim 1, characterized in that, Extracting the fundamental wave parameters of the original data signals comprises: Filtering direct current components and high-frequency noise in the original data signals, and completing signal synchronization calibration; The synchronized calibrated signal is input into a filter, taking the grid standard fundamental wave as the expected signal, and the filter coefficient is updated through error feedback iteration until the error converges; The converged filter output signal is obtained; The fundamental wave signal in the filter output signal is extracted; The fundamental wave parameters are obtained according to the amplitude, phase and frequency of the fundamental wave signal.

3. The method of power quality optimization control of a charging station module according to claim 1, characterized in that, According to the fundamental wave parameters, target voltage signals are obtained, and voltage fluctuation coefficients are obtained through the target voltage signals, which comprises: Based on the frequency and phase information in the fundamental wave parameters, and combining the grid rated voltage standard, a target voltage signal synchronized with the grid is generated; The actual fundamental wave voltage amplitude is extracted; The actual fundamental wave voltage amplitude is compared with the amplitude of the target voltage signal to obtain a comparison difference value; The absolute value of the comparison difference value is calculated, and the voltage fluctuation coefficient is obtained based on the ratio of the absolute value to the amplitude of the target voltage signal, the voltage fluctuation coefficient being a voltage fluctuation coefficient quantifying the degree of actual voltage deviation from the reference.

4. The method of power quality optimization control of a charging station module according to claim 1, characterized in that, Based on the output characteristic quantity, a double-layer control decision module is executed, and a control instruction is generated, which comprises: According to the output characteristic quantity, the optimization target and the constraint condition of the upper-layer control decision module are determined; The PSO algorithm is adopted to obtain optimal compensation parameters satisfying the constraint condition; Based on the output characteristic quantity, the lower-layer control decision module compares the expected effect of the optimal compensation parameters with the actual deviation through the MPC algorithm to obtain a comparison result; According to the comparison result, the optimal compensation parameter is corrected to obtain a target compensation parameter; According to the target compensation parameter, a control instruction for driving passive filtering, active compensation and power factor correction is generated.

5. The method of power quality optimization control of a charging station module according to claim 1, characterized in that, Based on the control instruction, a passive filtering unit, an active compensation unit and a power factor correction unit are driven to realize the optimal control of the electric energy of the charging pile, including: According to the control instruction, the passive filtering unit is driven to input the corresponding tuning branch to filter out fixed harmonics; According to the control instruction, the active compensation unit is controlled to output a reverse compensation current to offset dynamic harmonics and reactive power disturbance; According to the control instruction, the circuit switching state of the power factor correction unit is adjusted to make the input current track the voltage fundamental wave phase to improve the power factor; According to the passive filtering unit, the active compensation unit and the power factor correction unit, the optimal control of the electric energy of the charging pile is realized.

6. The method of power quality optimization control of a charging station module according to claim 1, characterized in that, After driving passive filtering, active compensation and power factor correction based on the control instruction to realize the optimal control of the electric energy of the charging pile, the method further includes: Collecting the input side voltage of the charging pile after the optimal control, the output side current of the charging pile and the running state signal of the electric energy quality control module of the charging pile, and extracting the key indicators of electric energy quality; Comparing the key indicators of electric energy quality with the preset standard indicators to obtain deviation information; According to the deviation information, it is judged whether the electric energy quality of the charging pile and the module running state meet the standard; If not, the deviation information is sent to the double-layer control decision module to regenerate the control instruction for iterative optimization.

7. A power quality optimization control device for a charging station module, characterized by, The device includes: The acquisition unit is used for collecting the original data signal in real time, and the original data signal includes the input side voltage signal of the charging pile, the output side current signal and the running state signal of the electric energy quality control module of the charging pile; The extraction unit extracts the fundamental wave parameter of the original data signal; The first acquisition unit is used for acquiring the target voltage signal according to the fundamental wave parameter, and acquiring the voltage fluctuation coefficient through the target voltage signal; The second acquisition unit extracts each harmonic component through Fourier transform based on the fundamental wave parameter of the original data signal, and calculates the total harmonic distortion rate of voltage and current and the main harmonic amplitude to obtain the harmonic characteristic quantity; the phase difference between the fundamental wave voltage and the fundamental wave current is used to calculate the reactive power and the power factor to obtain the reactive power and the power factor characteristic quantity, the voltage fluctuation coefficient is filled in the index of voltage drop / rise amplitude to form the voltage stability characteristic quantity; the module loss rate is calculated according to the running state signal of the electric energy quality control module of the charging pile; the module loss rate is fused with the harmonic characteristic quantity, the reactive power and the power factor characteristic quantity and the voltage stability characteristic quantity to obtain the output characteristic quantity; The generation unit executes the double-layer control decision module based on the output characteristic quantity and generates the control instruction, the double-layer control decision module includes an upper-layer control decision module and a lower-layer control decision module, the upper-layer control decision module solves the optimal compensation parameter through the PSO algorithm, and the lower-layer control decision module corrects the optimal compensation parameter through the MPC algorithm; The driving unit drives the passive filter unit, the active compensation unit and the power factor correction unit based on the control instruction to realize the optimal control of the electric energy of the charging pile.

8. A power quality optimization control device for a charging station module, characterized by, The device comprises: a processor, a memory, an input / output unit and a bus; the processor is connected with the memory, the input / output unit and the bus; the memory stores a program, and the processor invokes the program to execute the method in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a program, and the program executes the method in any one of claims 1 to 6 when executed on the computer.

Images