Design method for inhibiting conducted electromagnetic interference of flyback circuit based on chaotic signal

Abstract

The present application relates to the field of conducted interference of switching power supply, and disclose a design method for inhibiting conducted electromagnetic interference of flyback circuit based on chaotic signal, comprising the following steps: first step: numerical simulation of fractional order Chen chaotic system based on Adomian algorithm; second step: output Lyapunov exponent spectrum by initializing order, and then simulate; third step: complexity analysis is carried out on the fractional order Chen chaotic system, and the chaotic attractor of the Chen chaotic system under the target order is obtained; fourth step: generate fractional order chaotic PWM based on the chaotic attractor of the Chen chaotic system under the target order; the present application realizes the inhibition of conducted electromagnetic interference of flyback circuit at the source based on fractional order Chen chaotic system and chaotic spread spectrum theory, which not only provides a new idea for switching power supply researchers, but also widens the application research of fractional order chaotic signal in inhibiting conducted interference.

Description

Technical Field

[0001] This invention relates to the field of conducted interference in switching power supplies, specifically a design method for suppressing conducted electromagnetic interference in flyback circuits based on chaotic signals. Background Technology

[0002] The high-frequency nature of power devices dictates that switching power supplies inevitably generate extremely large transient voltages and currents during operation. This results in a less than ideal EMC environment for both the switching power supply itself and its surroundings, severely impacting the reliability of electrical equipment. Furthermore, the interference energy is primarily conducted interference. Therefore, research on the mechanisms and suppression techniques of conducted interference in switching power supplies has always been a hot topic in the field.

[0003] In recent years, chaos theory has been widely applied in power electronics, communications, and synchronous control. Research shows that if the energy of electromagnetic interference can be evenly distributed across the entire frequency domain, the peak value of EMI can be reduced, enabling the product under test to meet electromagnetic compatibility standards. Chaotic spread spectrum technology can achieve this strategy without reducing the efficiency of switching power supplies, and has attracted widespread attention.

[0004] In conventional pulse width modulation (PWM) switching converters, the switching frequency is fixed. Studies have shown that harmonic components exist in the output voltage during operation, distributed at the switching frequency and its harmonics. It is clear that the power switching transistor is one of the main sources of electromagnetic interference (EMI). To achieve active EMI suppression at its source, chaotic spread spectrum technology has emerged. Utilizing the continuous spectrum characteristics of chaotic systems, chaotic PWM can disperse the peak energy of EMI across surrounding frequency bands, thereby making the discrete EMI energy continuous and effectively reducing the EMI peak value, thus achieving the goal of EMI suppression. Therefore, we propose a design method based on chaotic signals to suppress conducted electromagnetic interference in flyback circuits. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a design method for suppressing conducted electromagnetic interference in flyback circuits based on chaotic signals, thus solving the aforementioned problems.

[0007] (II) Technical Solution

[0008] To achieve the above-mentioned objectives, the present invention provides the following technical solution: a design method for suppressing conducted electromagnetic interference in flyback circuits based on chaotic signals, comprising the following steps:

[0009] Step 1: Numerical simulation of the fractional-order Chen chaotic system based on the Adomian algorithm;

[0010] Step 2: Initialize the Lyapunov exponent spectrum by initializing the order, and then perform the simulation;

[0011] Step 3: Perform complexity analysis on the fractional-order Chen chaotic system to obtain the chaotic attractor of the Chen chaotic system at the target order;

[0012] Step 4: Generate fractional-order chaotic PWM based on the chaotic attractor of the Chen chaotic system at the target order;

[0013] Step 5: Conduct prototype testing of the 5V / 1A flyback circuit;

[0014] Step 6: Obtain and analyze the test results of the 5V / 1A flyback circuit prototype;

[0015] Step 7: Conducted interference test at the power supply end;

[0016] Step 8: Obtain the chaotic PWM of the optimal order for conduction suppression of the flyback circuit.

[0017] Preferably, the Lyapunov exponent spectrum output process in the second step is as follows:

[0018] S1: Start, initialize the order q = 0.72;

[0019] S2: Adomian decomposition method for solving chaotic sequences;

[0020] S3: Solve for LEs using the QR decomposition algorithm, with an order of q+0.002;

[0021] S4: Compare the order q. If q is less than 1.12, return to step 2 to solve. If q is greater than 1.12, output the Lyapunov exponent spectrum.

[0022] Preferably, the complexity analysis of the fractional-order Chen chaotic system in the third step is as follows: the complexity of the fractional-order chaotic system decreases as the order q increases.

[0023] Preferably, the generation of fractional-order chaotic PWM in the fourth step includes the following: obtaining three sets of chaotic sequences x, y, and z through numerical solution of fractional-order Chen using MATLAB, selecting one set for normalization to obtain a chaotic sequence, performing 100,000 solutions in the numerical solution, and extracting 10,000 data points by taking one every nine data points and transmitting them to the flash memory of the microcontroller. The microcontroller's internal program calculates and modulates the data into a chaotic triangular carrier wave. The feedback signal of the flyback circuit is compared with the chaotic triangular carrier wave, and the chaotic PWM is output through the port of the STM32. The frequency of the triangular wave is the frequency of the output PWM.

[0024] Preferably, the power supply conducted interference test in step seven includes the following:

[0025] S1: Set up the conducted EMI test environment;

[0026] S2: Analyze the experimental results.

[0027] The preferred and optimal order of chaotic PWM is 2.28-order chaotic PWM.

[0028] Preferably, the complexity analysis of the fractional-order Chen chaotic system in the third step includes three calculation methods: the permutation entropy algorithm to calculate the complexity of the chaotic sequence, the spectral entropy algorithm to calculate the complexity of the chaotic sequence, and the C0 complexity algorithm to calculate the complexity of the chaotic sequence.

[0029] (III) Beneficial Effects

[0030] Compared with the prior art, the present invention provides a design method for suppressing conducted electromagnetic interference in flyback circuits based on chaotic signals, which has the following beneficial effects:

[0031] 1. This invention presents a design method for suppressing conducted electromagnetic interference (EMI) in flyback circuits based on chaotic signals. While there is considerable research on EMI suppression using chaotic spread spectrum at integer order, research on chaotic PWM control strategies at fractional order is relatively rare. Moreover, most studies on fractional-order chaotic systems focus on non-isolated converters. Therefore, this invention, based on fractional-order Chen chaotic systems and chaotic spread spectrum theory, achieves source suppression of conducted EMI in flyback circuits. This not only provides new insights for switching power supply researchers but also broadens the application of fractional-order chaotic signals in suppressing conducted interference. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the time-domain signal curves of the 2.70th order Chen chaotic system x, y, z.

[0033] Figure 2 This is a schematic diagram of the chaotic phase diagram of the 2.70th order Chen chaotic system x, y, z.

[0034] Figure 3 A schematic diagram of the Lyapunov exponential spectrum algorithm for fractional-order chaotic systems;

[0035] Figure 4 A schematic diagram of the Lyapunov exponent spectrum of the x sequence as the order q changes;

[0036] Figure 5 A schematic diagram of the Lyapunov exponent spectrum of the y sequence as the order q changes;

[0037] Figure 6 A schematic diagram of the Lyapunov exponent spectrum of the x, y, and z sequences as the order q changes;

[0038] Figure 7 This is a schematic diagram illustrating the principle of chaotic PWM generation.

[0039] Figure 8 The diagram shows the actual measured PWM output of the microcontroller and the FFT schematic.

[0040] Figure 9 This is a schematic diagram of the flyback circuit prototype.

[0041] Figure 10 This is a schematic diagram of the flyback circuit PCB board;

[0042] Figure 11 This is a schematic diagram of a flyback circuit test prototype;

[0043] Figure 12 A schematic diagram of the fixed-frequency 150kHz output voltage;

[0044] Figure 13 A schematic diagram of the output current at a fixed frequency of 150kHz;

[0045] Figure 14 Diagram showing a 220V AC power supply;

[0046] Figure 15 Schematic diagram of the drain-source voltage of a 220V AC input MOSFET;

[0047] Figure 16 A schematic diagram of drain-source voltage FFT under fixed-frequency, different-order Chen chaotic PWM control;

[0048] Figure 17 A schematic diagram of the setup for a conducted EMI test environment;

[0049] Figure 18 This is a schematic diagram of the conducted interference test results for a fixed-frequency 150kHz L-line.

[0050] Figure 19 This is a schematic diagram of the test results for conducted interference on the L-line of a 3rd-order Chen chaotic PWM.

[0051] Figure 20 A schematic diagram of the test results for conducted interference on the L-line of a 2.7th order Chen chaotic PWM;

[0052] Figure 21 This is a schematic diagram of the test junction for conducted interference in a 2.28th order Chen chaotic PWM L-line.

[0053] Figure 22 This is a schematic diagram showing the comparison (peak value) of conducted interference tests on the L-line.

[0054] Figure 23 This is a schematic diagram showing the comparison (average value) of L-line conducted interference tests. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] Please see Figure 1-23 The design method for suppressing conducted electromagnetic interference in flyback circuits based on chaotic signals includes the following steps:

[0057] Step 1: Numerical simulation of fractional-order Chen chaotic system;

[0058] Its dynamic equations are as follows:

[0059]

[0060] (1-1)

[0061] In the formula: x, y, z are system variables; a, b, c are system parameters;

[0062] When a = 35, b = 3, c = 28, and α = β = γ = 0.9 is taken, the simulation system (1-1) is in a chaotic state. The system (1-1) is decomposed into nonlinear terms, given an initial state.

[0063]

[0064] The iterative formula for the numerical value is derived using the Adomian decomposition algorithm, and the first seven terms are taken:

[0065]

[0066]

[0067]

[0068] Where (t-t0) is the iteration step size. Based on the Adomian decomposition method and the properties of fractional calculus, the coefficients corresponding to the first seven terms are obtained, and the expressions for each coefficient are shown in Equations 1-2 to 1-8:

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075]

[0076] The relevant code was written, and numerical simulation was performed in Matlab. The initial state of the system was taken as (0.1, 0.2, 0.3), the iteration step size was h = (t - t0) = 0.01, and the simulation was performed 10,000 times. The time-domain signal curves and phase diagrams of the x, y, and z components of the 2.70th order Chen chaotic system were obtained, as shown below. Figure 1-2 As shown.

[0077] Step 2: Lyapunov exponent spectrum

[0078] The Lyapunov exponent refers to the average rate of change of two orbits in phase space as they separate or converge exponentially over time. In chaotic systems, the maximum Lyapunov exponent is an important indicator for judging whether the system is chaotic and how severe the chaos is.

[0079] For a continuous chaotic system, the Lyapunov exponent spectra are LE1, LE2, ... LE n The necessary condition for a system to have a chaotic attractor is: (1) there exists at least one Lyapunov exponent LE. i >0; (2) There exists at least one Lyapunov exponent LE i =0; (3) The sum of the Lyapunov exponent spectra of the system: LE1+LE2+…+LE n <0. A positive Lyapunov exponent indicates that the system is unstable, while a negative Lyapunov exponent indicates that the system is in a stable state. The steps for solving the Lyapunov exponent spectrum of a fractional-order Chen system are as follows: Figure 3 As shown.

[0080] The relevant code was written and simulation was performed in Matlab. The results are as follows. Figure 4-6 As shown;

[0081] Depend on Figure 4-6The simulation results show that, under the condition that the system parameters remain unchanged, the dynamic characteristics of q change are as follows: When the system parameters a = 35, b = 3, c = 28 remain unchanged, and the fractional order Chen system is solved by the Adomian decomposition algorithm, (1) the smallest differential order of chaos corresponding to the iteration step size h = 0.01 is q = 0.758, which corresponds to the 2.274th order of Chen chaos; (2) as the order q changes, the Lyapunov exponent of the fractional order Chen chaotic system x sequence changes continuously; (3) the Lyapunov exponent value corresponding to most orders is higher than that of q = 1 (integer order Chen chaos), and the Lyapunov exponent value of the fractional order Chen chaotic system x sequence is the largest when q = 0.76, indicating that the system under this order is in the most chaotic state.

[0082] Step 3: Complexity Analysis of Fractional-Order Chen Chaotic Systems

[0083] Complexity is a quantitative indicator that measures how closely a sequence generated by a chaotic system approximates a random sequence. It can be divided into two categories: behavioral complexity and structural complexity. Behavioral complexity characterizes the similarity between a sequence and a random sequence; the closer a sequence is to a random sequence, the greater its complexity. Structural complexity refers to the complexity of the frequency components of a sequence. This method mainly analyzes the energy distribution of a signal at various frequencies; the more uniform the energy distribution, the greater the entropy, and the closer the signal is to noise, thus increasing its complexity.

[0084] The complexity of calculating chaotic sequences based on the permutation entropy algorithm (PE algorithm)

[0085] Permutation entropy characterizes the space complexity of a single sequence. Based on the permutation entropy (PE) algorithm, MATLAB code was written. See Appendix (1) for details. The PE algorithm was simulated for X sequences with q = 1, 0.9, and 0.76. The results are shown in Table 1-1.

[0086] Table 1-1 PE values ​​of X sequences for q = 1, 0.9, and 0.76

[0087]

[0088]

[0089] The Matlab simulation results show that the pe values ​​of the chaotic sequences, whether integer or fractional, are quite large and similar, indicating that under the metric of behavioral complexity, all sequences are close to random sequences, with the 2.28th order having the largest pe value and the highest complexity.

[0090] The complexity of calculating chaotic sequences based on the spectral entropy algorithm (SE algorithm)

[0091] Spectral entropy characterizes the structural complexity of a sequence. The smaller the value, the smaller the complexity; otherwise, the larger the complexity. MATLAB code for the spectral entropy algorithm is written, see Appendix (2). The SE algorithm is simulated on x sequences with q = 1, 0.9, and 0.76, and the results are shown in Table 1-2.

[0092] Table 1-2 SE values ​​of x sequences with q = 1, 0.9, and 0.76

[0093]

[0094] According to the calculation results from Matlab, the spectral entropy measure of the x sequence increases as the order decreases, but the difference is not significant, and all have high complexity. The spectral entropy value is the largest when q = 0.76.

[0095] The complexity of calculating chaotic sequences based on C0 complexity

[0096] The principle of C0 complexity is to divide the sequence into regular and irregular parts, and obtain the proportion of irregular components in the whole sequence. The larger the proportion of irregular components, the greater the complexity. The MATLAB code for the algorithm is shown in Appendix (3). The algorithm simulation was performed on x sequences with q = 1, 0.9, and 0.76. The results are shown in Table 1-3.

[0097] Table 1-3 x sequence values ​​for q = 1, 0.9, and 0.76

[0098]

[0099] The calculation results from Matlab show that the complexity of the integer order is the lowest, while the complexity of the 2.28 order (q = 0.76) is the highest. This indicates that the chaotic sequence of order 2.28 has the highest complexity compared to other sequences, and the complexity decreases as the order increases.

[0100] In summary, based on the three algorithms used to calculate the complexity of chaotic sequences, we can arrive at a generally consistent conclusion: the complexity of fractional-order chaotic systems tends to decrease with increasing order q. The frequency domain structure complexity of the sequences indicates that fractional-order chaotic systems have higher complexity than integer-order systems. In practical applications, both fractional-order and integer-order Chen have a certain effect on spectral expansion.

[0101] Step 4: Generation of Fractional Chaotic PWM

[0102] The output voltage of the flyback circuit is divided and compared with the modulated triangular carrier wave. The duty cycle of the output PWM changes with the output voltage, and the frequency of the PWM, i.e., the chaotic sequence, is generated by the microcontroller. The chaotic sequence is obtained by numerically solving the fractional-order Chen in MATLAB, resulting in three sets of chaotic sequences x, y, and z. One set is selected and normalized. Since the adjacent data are not significantly different, the numerical solution is performed 100,000 times. 10,000 data points are extracted and transmitted to the microcontroller's flash memory using a method of taking one every nine data points. The microcontroller's internal program calculates and modulates the data into a chaotic PWM, which is then output through the STM32's port.

[0103] The duty cycle of the PWM is determined by comparing the feedback signal from the flyback circuit with the modulated triangular carrier wave inside the microcontroller. The frequency of the modulated triangular wave is the frequency of the output PWM, thus ensuring that the duty cycle of each output PWM meets the operating requirements of the switching converter. The microcontroller used in this design is an STM32F103C8T6.

[0104] Based on the aforementioned PWM generation strategy, code for the STM32F103 was written to obtain a chaotic sequence through the numerical simulation mentioned in section 1.1, and then stored in the microcontroller's internal flash memory. After debugging and compilation, the PWM waveform was output as follows. Figure 8 As shown, it can be seen that the peak frequency of fixed-frequency PWM is concentrated at 150kHz and its harmonics, while chaotic PWM significantly extends the peak energy to nearby frequency bands. Among them, the fractional-order Chen chaotic spread spectrum with q=0.76 has a denser and smoother energy, and the effect is the best.

[0105] Step 5: Testing the 5V / 1A flyback circuit prototype. A brief description of the flyback circuit prototype is provided below. Figure 9 The module in the upper left corner is a filter and rectifier circuit. The L line is connected to a 2A / 250V fuse protection circuit. RT1 is a thermistor used to suppress surge current. RVI is a varistor protection circuit. Safety capacitors and I-shaped inductors work together to suppress conducted interference in the low-frequency band.

[0106] The DC output from the rectifier bridge is filtered and then connected to the main circuit on the right. It has a wide input range of 120-375V DC and a low power output of 5V / 1A. The primary-to-secondary turns ratio of the transformer is 135:9, and the transformer frame is EE20. Both the primary and secondary sides are designed with RCD buffer circuits to suppress voltage spikes caused by fast turn-off of the power switch and leakage inductance of the transformer.

[0107] The working principle of the feedback compensation circuit: The flyback circuit output samples the Vos2 voltage value, which is then divided by two 7.5K resistors R7 and R8 and compared with the 2.5V reference voltage inside the TL431. When the output voltage deviates from 5V, if the voltage across R8 is lower than 2.5V, the error signal will be amplified internally by the TL431. Assuming the TL431 output voltage is V2, V2 increases, Vos1-V2 decreases, and the current flowing through the diode side of the optocoupler also decreases. This, in turn, causes the current through the transistor side of the optocoupler to decrease, resulting in an increase in the voltage across R10. This increases the amplitude of the feedback signal Vsin1 input to the microcontroller, achieving a negative voltage feedback control effect. The PWM output from the STM32 microcontroller has an amplitude of approximately 3.3V, sufficient to drive the TC4428 chip. With the external circuit designed, the TC4428 drives the IRF840B MOSFET, enabling the entire circuit to complete closed-loop control and stabilize the output voltage at 5V.

[0108] PCB design of flyback circuits, such as Figure 10 As shown, in order to achieve better EMC, in terms of wiring, high-speed channels and low-speed channels should be distinguished, high-voltage and high-current sections should have their loops minimized as much as possible, copper pour connections should be used instead of thin and long wires, and they should be laid out on the same layer as much as possible. Ground wires should be as short and thick as possible, traces on the same layer should be as parallel as possible, and traces on different layers should be as perpendicular as possible.

[0109] Step 6: Experimental Results and Analysis

[0110] Input 220V AC power to the flyback circuit prototype and observe the test data. For example... Figure 12 As shown, the output voltage stabilizes at 5.04V, and the output ripple is approximately 880mV. Figure 13 As shown, the output current quality is high, with the current stabilizing at 1.20A and the current ripple around 0.46mA. Figure 2-6 As shown, the flyback prototype is supplied with a 220V AC input from an AC voltage source, with an input power of 7.3W. The output power is 5.04 × 1.20 = 6.048W. Therefore, the efficiency of this flyback circuit is 6.048 ÷ 7.3 = 0.84, or 84%, which meets the design requirements. Figure 2-7 The waveform of the drain-source voltage of the MOSFET is shown.

[0111] like Figure 16 As shown, after FFT analysis, the peak values ​​of the drain-source voltage under fixed-frequency PWM control are concentrated at 150kHz and its harmonics. However, after FFT analysis, the drain-source voltage under chaotic PWM control shows a significant spectrum expansion effect, and the amplitude at the harmonics is well suppressed and improved. As the order decreases, the peak value of the spectrum is improved.

[0112] Step 7: Conducted Interference Test at the Power Supply Terminal

[0113] Set up a conducted EMI testing environment

[0114] Setting up a conducted EMI testing environment, such as Figure 17 As shown, the mains power supply is 234VAC, which is connected to the LISN via an isolation transformer. The isolation transformer is used to prevent conducted interference energy from flowing into the mains, causing errors in the test results and circuit breaker tripping. The LISN has two output ports: one is to power the device under test (EUT), and the other is connected to an R&S brand pre-certified EMI receiver via a coaxial cable and attenuator. The test results can be processed by the ELEKTRA EMI software on the PC, which is simple and efficient to operate.

[0115] Experimental Results Analysis

[0116] like Figure 18 As shown, under PWM control at a fixed frequency of 150kHz, the peak value of conducted interference on the L line is concentrated at the switching frequency and its harmonics, only falling below the test standard value in the 20MHz to 25MHz range; significant interference energy exists in other frequency bands. For example... Figure 19 , 20 Figures 21 show the conducted interference spectra under Chen chaotic PWM control for q = 1.00, q = 0.90, and q = 0.76, respectively. It can be seen that chaotic PWM has a good suppression effect on conducted EMI in the mid-to-high frequency band of 5MHz to 15MHz, with both peak and average values ​​below the standard values. Although chaotic PWM has a suppression effect in the 20MHz to 30MHz frequency band, it is not significant, and conducted EMI is still on the rise. Chaotic PWM has a relatively obvious suppression effect on the average value, but its suppression effect in the mid-to-low frequency band of 150kHz to 5MHz is not good. The reason is that this design only explores the suppression of conducted interference by fractional-order chaotic PWM, and does not design EMI filters or other suppression treatments for the low frequency band.

[0117] like Figure 22 , 23 As shown, through the comparison of test results, chaotic PWM can effectively disperse the peak energy of conducted EMI to nearby frequency bands, making the interference curve more compact and the peak value lower than that under fixed frequency PWM control, proving the effectiveness of chaotic spread spectrum; except for the 5M to 9M frequency band, the fractional order suppression effect is better than the integer order in other frequency bands; the 2.28th order chaotic PWM has the best conduction suppression effect on flyback circuits.

[0118] In summary, a flyback circuit prototype was fabricated. Experiments showed an output voltage of 5.04V, an output current of 1.20A, a power of 6.048W, and an efficiency of 84% at 220VAC. A conducted interference testing environment was set up, and the flyback circuit prototype was tested. The test results verified that fractional-order Chen chaotic PWM has a suppressive effect on conducted interference in the flyback circuit, with significant suppression effects in the mid-to-high frequency range. Comparing the suppression effects of chaotic PWM at different orders, it was found that the 2.28th-order Chen chaotic PWM has the best suppression effect on conducted interference in the flyback circuit. This demonstrates a strategy for suppressing conducted interference at its source using a flyback circuit based on fractional-order Chen chaotic spread spectrum.

[0119] The Matlab code for permutation entropy is as follows:

[0120]

[0121]

[0122] (2) The Matlab code for spectral entropy is as follows:

[0123]

[0124]

[0125] (3) The Matlab program code for C0 complexity is as follows:

[0126]

[0127] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A design method for suppressing conducted electromagnetic interference in flyback circuits based on chaotic signals, characterized in that, Includes the following steps: Step 1: Numerical simulation of the fractional-order Chen chaotic system based on the Adomian algorithm; Step 2: Initialize the Lyapunov exponent spectrum by initializing the order, and then perform the simulation; Step 3: Perform complexity analysis on the fractional-order Chen chaotic system to obtain the chaotic attractor of the Chen chaotic system at the target order; Step 4: Generate fractional-order chaotic PWM based on the chaotic attractor of the Chen chaotic system at the target order; Step 5: Conduct prototype testing of the 5V / 1A flyback circuit; Step 6: Obtain and analyze the test results of the 5V / 1A flyback circuit prototype; Step 7: Conducted interference test at the power supply end; Step 8: Obtain the chaotic PWM with the optimal order for conduction suppression of the flyback circuit; The third step involves a complexity analysis of the fractional-order Chen chaotic system, which includes three calculation methods: permutation entropy algorithm, spectral entropy algorithm, and C0 complexity algorithm. The complexity analysis of the fractional-order Chen chaotic system is as follows: the complexity of the fractional-order chaotic system decreases as the order q increases. The generation of fractional-order chaotic PWM in the fourth step includes the following: Three chaotic sequences (x, y, z) are obtained by numerically solving the fractional-order Chen using MATLAB. One of these sequences is selected and normalized to obtain a chaotic sequence. The numerical solution is performed 100,000 times. 10,000 data points are extracted and transmitted to the microcontroller's flash memory using a method of taking one every nine data points. These data are then modulated into a chaotic triangular carrier wave by the microcontroller's internal program. The feedback signal from the flyback circuit is compared with the chaotic triangular carrier wave, and the chaotic PWM is output through the STM32's port. The frequency of the triangular wave is the frequency of the output PWM.

2. The design method for suppressing conducted electromagnetic interference in flyback circuits based on chaotic signals according to claim 1, characterized in that: The Lyapunov exponent spectrum output process in the second step is as follows: S1: Start, initialize the order q = 0.72; S2: Adomian decomposition method for solving chaotic sequences; S3: Solve for LEs using the QR decomposition algorithm, with an order of q+0.002; S4: Compare the order q. If q is less than 1.12, return to step 2 to solve. If q is greater than 1.12, output the Lyapunov exponent spectrum.

3. The design method for suppressing conducted electromagnetic interference in flyback circuits based on chaotic signals according to claim 1, characterized in that: The seventh step, power-side conducted interference testing, includes the following: S1: Set up the conducted EMI test environment; S2: Analyze the experimental results.

4. The design method for suppressing conducted electromagnetic interference in flyback circuits based on chaotic signals according to claim 1, characterized in that: The optimal order of Chen chaotic PWM is 2.28th order Chen chaotic PWM.

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