Method and system for power factor correction of ac-dc conversion of server power supply

Abstract

The application provides a power factor correction method and system for AC / DC conversion of a server power supply, and relates to the technical field of server power supplies, and comprises the following steps: collecting an input AC signal and a DC bus output signal, calculating a real-time power factor value, constructing a compensation function based on the power factor value and calculating a deviation amount, generating a compensation parameter matrix through recursive calculation, processing the DC output signal by using an adaptive filtering algorithm and fusing the DC output signal with the compensation parameter matrix, and calculating current regulation signals and duty cycle regulation signals and performing accurate regulation on the power factor.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of server power supply, in particular to a power factor correction method and system for AC-DC conversion of server power supply. BACKGROUND

[0002] The power factor is an important indicator to measure the utilization efficiency of the power supply system. Low power factor not only leads to energy waste, but also causes harmonic pollution of the power grid and overheating of equipment. The power factor correction technology aims to improve the utilization of electric energy, reduce the harmonic content of input current, make the current waveform closer to the sine wave, thereby reducing system loss and improving the reliability and stability of the power supply system.

[0003] The current power factor correction technology of server power supply mainly adopts passive correction and active correction. Passive correction is achieved by adding passive components such as LC filter. Active correction adjusts the input current waveform by controlling the conduction time of power electronic switching devices. With the increasingly complex and variable characteristics of server load, the traditional power factor correction method has poor adaptability to dynamic changes of load. When the server load changes suddenly, it is difficult to quickly respond and maintain good power factor, resulting in poor correction effect in complex working environment. The control strategy generally uses fixed parameters, lacks adaptive adjustment ability, and cannot flexibly adjust the compensation parameters according to the real-time system state, resulting in large differences in correction effect under different working conditions, lack of effective DC side fluctuation component extraction and compensation mechanism, and difficulty in achieving accurate power factor control. SUMMARY

[0004] The embodiments of the present application provide a power factor correction method and system for AC-DC conversion of server power supply, which can at least solve some problems in the prior art.

[0005] In a first aspect, the embodiments of the present application provide a power factor correction method for AC-DC conversion of server power supply, comprising:

[0006] Collecting the input AC signal and DC bus output signal of the server power supply, calculating the real-time power factor value according to the input AC signal;

[0007] If the real-time power factor value is less than the preset power factor threshold, constructing a compensation function based on the real-time power factor value and calculating the real-time deviation, weighting and accumulating the real-time deviation and the historical compensation value, and normalizing to obtain an initial compensation factor. The initial compensation factor is error corrected by recursive calculation, a stable value is generated, and the iteration step is adjusted according to the change rate of the stable value to generate a fast compensation amount and a gradual compensation amount, and the compensation parameter matrix is obtained by combination;

[0008] The adaptive filtering algorithm is used to process the DC bus output signal and extract an effective fluctuation component, the effective fluctuation component is subjected to multi-dimensional fusion operation with a compensation parameter matrix to generate a comprehensive adjustment matrix, parameter decomposition is performed based on the comprehensive adjustment matrix, current adjustment signals and duty cycle adjustment signals are respectively calculated, the input current waveform is adjusted according to the current adjustment signals, and the on-time of the power switch tube is adjusted according to the duty cycle adjustment signals.

[0009] The adjusted power factor value and the DC output voltage value are obtained, and it is judged whether the preset target range is met, if yes, power correction is performed through the power factor correction circuit.

[0010] In an optional embodiment,

[0011] The input AC signal and the DC bus output signal of the server power supply are collected, the real-time power factor value is calculated according to the input AC signal, and the calculation includes:

[0012] The input AC signal and the DC bus output signal of the server power supply are collected to obtain a voltage sampling sequence and a current sampling sequence, zero-crossing time information in the voltage sampling sequence and the current sampling sequence is extracted, and a phase feature vector is generated;

[0013] Discrete Fourier transform is performed on the voltage sampling sequence and the current sampling sequence respectively, the amplitude and phase information of the fundamental frequency component are extracted, and the phase difference value of the fundamental frequency component is calculated;

[0014] The phase difference value is substituted into the cosine function for operation, and the real-time power factor value is obtained by combining the phase feature vector for weighted correction.

[0015] In an optional embodiment,

[0016] If the real-time power factor value is less than the preset power factor threshold, a compensation function is constructed based on the real-time power factor value, and a real-time deviation amount is calculated, the real-time deviation amount is subjected to weighted accumulation and normalization processing with a historical compensation value to obtain an initial compensation factor, and the calculation includes:

[0017] The real-time power factor value is obtained, it is judged whether the real-time power factor value is less than the preset power factor threshold, if yes, a static deviation term is obtained by performing difference square operation on the real-time power factor value and a target power factor value, a dynamic change rate term is obtained by calculating the absolute value of the change rate of the real-time power factor value, and a compensation function is constructed by weighted combination of the static deviation term and the dynamic change rate term.

[0018] The compensation function is subjected to integral operation within a preset integral time window to obtain a real-time deviation amount, and the real-time deviation amount is added with a historical compensation value at the last moment to obtain a cumulative compensation value at the current moment.

[0019] The rate of change of the real-time power factor value is calculated, the reference gain coefficient is taken as the base, the inverse of the rate of change is taken as the index to perform the power operation to obtain an adaptive gain factor, the maximum value and the minimum value of the cumulative compensation value are calculated, the cumulative compensation value is subtracted by the minimum value and then divided by the difference between the maximum value and the minimum value, the normalized result obtained is multiplied by the adaptive gain factor to obtain an initial compensation factor.

[0020] In an optional embodiment,

[0021] The initial compensation factor is error-corrected by recursive calculation to generate a stable value, and the iteration step is adjusted according to the rate of change of the stable value to generate a fast compensation amount and a gradual compensation amount, and the compensation parameter matrix is combined to include:

[0022] The initial compensation factor is taken as the initial value of recursive operation, the real-time deviation amount is multiplied by a preset weight coefficient and then added to the initial value to obtain a first iteration value, and recursive operation is performed based on the first iteration value to obtain a recursive compensation result, and the result of each recursive operation is equal to the sum of the result of the last recursive operation and the product of the real-time deviation amount and the preset weight coefficient;

[0023] The real-time deviation amount, the rate of change of the real-time deviation amount, and the real-time power factor value are mapped to a three-dimensional fuzzy space and a fuzzy rule base is established, and the fuzzy membership function of the fuzzy rule base is dynamically adjusted to obtain an optimized fuzzy rule set;

[0024] The recursive compensation result and the stable value at the last moment are weighted and smoothed according to the optimized fuzzy rule set to generate a stable value at the current moment, and the real-time deviation amount and the rate of change of the stable value at the current moment are substituted into a sliding mode calculation to obtain a sliding mode operation result;

[0025] The rate of change of the stable value at the current moment is calculated based on the sliding mode operation result to obtain a fast compensation amount, the real-time deviation amount is integrated to obtain a gradual compensation amount, an inner loop compensation matrix is constructed by the fast compensation amount and its change amount, an outer loop compensation matrix is constructed by the gradual compensation amount and its change amount, a weighting coefficient is determined based on the optimized fuzzy rule set, and the inner loop compensation matrix and the outer loop compensation matrix are adaptively weighted and combined to obtain a compensation parameter matrix.

[0026] In an optional embodiment,

[0027] The DC bus output signal is processed by an adaptive filtering algorithm to extract an effective fluctuation component, and the effective fluctuation component is subjected to multi-dimensional fusion operation with the compensation parameter matrix to generate a comprehensive adjustment matrix, which includes:

[0028] The adaptive filtering algorithm is used for recursive calculation of the DC bus output signal, and a signal vector group is obtained by solving the results of the recursive calculation; a state estimation error covariance matrix is constructed based on the signal vector group, and the state estimation error covariance matrix is sequentially subjected to matrix operation with an observation matrix and a measurement noise covariance matrix to obtain an adaptive gain matrix;

[0029] The signal vector group is subjected to weighted operation by using the adaptive gain matrix to obtain a filtering signal, wavelet transformation is performed on the filtering signal to obtain a decomposition result, the decomposition result is combined with a preset wavelet basis function and a scale factor to obtain a fluctuation feature of the filtering signal, and the fluctuation features are combined to form an effective fluctuation component;

[0030] A dynamic weight is obtained by performing weight mapping on the effective fluctuation component, an adaptive weight matrix is constructed by using the dynamic weight, and a fusion operation result is obtained by performing tensor product operation on the adaptive weight matrix and the effective fluctuation component;

[0031] A deviation value is obtained by subtracting a preset expected output from the fusion operation result, a gradient of a weight coefficient is obtained by substituting the deviation value into a target function for calculation, the dynamic weight is iteratively optimized based on the gradient of the weight coefficient to obtain an optimized weight coefficient, a compensation correction amount is obtained by multiplying the optimized weight coefficient and the fusion operation result, and a comprehensive adjustment matrix is obtained by adding the compensation correction amount and the fusion operation result.

[0032] In an optional implementation,

[0033] Parameter decomposition is performed based on the comprehensive adjustment matrix, and a current adjustment signal and a duty cycle adjustment signal are respectively calculated, the input current waveform is adjusted according to the current adjustment signal, and the on-time of the power switch tube is adjusted according to the duty cycle adjustment signal, including:

[0034] Eigenvalue decomposition is performed on the comprehensive adjustment matrix to obtain an eigenvalue matrix and an eigenvector matrix, and initial adjustment parameters are obtained by performing parameter mapping operation on the eigenvalue matrix and a preset current adjustment coefficient and a preset duty cycle adjustment coefficient;

[0035] A current adjustment signal is obtained by performing sinusoidal function operation on the initial adjustment parameters and eigenvalues in the eigenvalue matrix and multiplying the result by a preset current gain coefficient, a dynamic compensation signal is obtained by substituting the current adjustment signal into differential operation and integral operation, and a compensated current adjustment signal is obtained by combining the dynamic compensation signal and the current adjustment signal;

[0036] The initial adjustment parameter is subjected to a cosine function operation with an eigenvalue in the eigenvalue matrix, and an initial duty cycle value preset is superimposed to obtain a duty cycle adjustment signal; and the duty cycle adjustment signal is substituted into a hyperbolic tangent function to perform dead zone compensation operation to obtain a compensated duty cycle adjustment signal.

[0037] A feedback control signal is calculated based on the compensated current adjustment signal and the compensated duty cycle adjustment signal, an error of the feedback control signal is substituted into integral operation to obtain an adaptive gain signal, the adaptive gain signal is added with an initial gain value preset to obtain a real-time control gain coefficient, and the input current waveform of the server and the on-time of the power switch tube are adjusted based on the real-time control gain coefficient.

[0038] In an alternative embodiment,

[0039] The adjusted power factor value and the DC output voltage value are obtained, and it is determined whether the preset target range is met; if yes, power correction is performed by the power factor correction circuit, including:

[0040] The adjusted power factor value and the DC output voltage value are obtained.

[0041] The power factor value and the DC output voltage value are compared with a preset power factor target range and a preset voltage target range respectively, and it is determined whether the power factor value and the DC output voltage value meet the preset power factor target range and the preset voltage target range at the same time.

[0042] When the power factor value and the DC output voltage value meet the preset power factor target range and the preset voltage target range at the same time, power correction is performed by the power factor correction circuit according to a real-time control gain coefficient obtained in advance.

[0043] In a second aspect of the embodiment of the application, a power factor correction system for server AC-DC conversion is provided, including:

[0044] The first unit is configured to collect input AC signals and DC bus output signals of a server power supply, and calculate a real-time power factor value based on the input AC signals.

[0045] The second unit is configured to, if the real-time power factor value is less than a preset power factor threshold, construct a compensation function based on the real-time power factor value and calculate a real-time deviation, weight and accumulate the real-time deviation and a historical compensation value, and perform normalization processing to obtain an initial compensation factor, perform error correction on the initial compensation factor through recursive calculation, generate a stable value, adjust an iteration step length according to a change rate of the stable value, generate a fast compensation amount and a gradual compensation amount, and combine to obtain a compensation parameter matrix.

[0046] The third unit is configured to process the DC bus output signal by using an adaptive filtering algorithm, extract an effective fluctuation component, perform a multi-dimensional fusion operation on the effective fluctuation component and a compensation parameter matrix to generate a comprehensive adjustment matrix, perform parameter decomposition based on the comprehensive adjustment matrix, and respectively calculate a current adjustment signal and a duty cycle adjustment signal, and adjust an input current waveform according to the current adjustment signal and adjust the on-time of the power switch tube according to the duty cycle adjustment signal.

[0047] The fourth unit is configured to acquire the adjusted power factor value and the DC output voltage value, and determine whether the adjusted power factor value and the DC output voltage value meet a preset target range.

[0048] In a third aspect, the embodiment of the present application provides an electronic device, comprising:

[0049] A processor and a memory for storing processor-executable instructions, wherein the processor is configured to invoke the instructions stored in the memory to execute the method described above.

[0050] In a fourth aspect, the embodiment of the present application provides a computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions are executed by a processor to implement the method described above.

[0051] In the present application, the power factor value is monitored and calculated in real time, and the correction process is started when the power factor is lower than a threshold value, the compensation factor is optimized by using a weighted accumulation and normalization processing method, and the error is corrected by recursive calculation, thereby effectively improving the accuracy and stability of the power factor correction, the iteration step is dynamically adjusted by analyzing the change rate of the stable value, the fast compensation amount and the gradual compensation amount are generated and combined to form the compensation parameter matrix, thereby greatly improving the response speed and adaptability of the system to load changes, the DC bus output signal is processed by using an adaptive filtering algorithm, the effective fluctuation component is extracted, and a multi-dimensional fusion operation is performed on the effective fluctuation component and the compensation parameter matrix, thereby achieving accurate adjustment of the current waveform and the duty cycle of the switch tube, significantly improving the input power factor and output stability of the server power supply, and reducing the system energy consumption and harmonic interference. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 FIG. 1 is a flowchart of a power factor correction method for AC-DC conversion of a server power supply according to an embodiment of the present application;

[0053] Figure 2 FIG. 2 is a server power control flowchart of the power factor correction method for AC-DC conversion of a server power supply according to an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0055] The technical solutions of the present application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and some embodiments can not be described again for the same or similar concepts or processes.

[0056] Figure 1 The flowchart of the power factor correction method for the server power supply AC-DC conversion of the embodiments of the present application is shown as follows, Figure 1 The method comprises the following steps.

[0057] The input AC signal and the DC bus output signal of the server power supply are collected, and the real-time power factor value is calculated according to the input AC signal;

[0058] If the real-time power factor value is less than the preset power factor threshold value, a compensation function is constructed based on the real-time power factor value, and a real-time deviation amount is calculated. The real-time deviation amount and a historical compensation value are weighted, accumulated and normalized to obtain an initial compensation factor. The initial compensation factor is error-corrected through recursive calculation to generate a stable value. The iteration step is adjusted according to the change rate of the stable value to generate a fast compensation amount and a gradual compensation amount. The compensation parameter matrix is obtained by combination;

[0059] The DC bus output signal is processed by using an adaptive filtering algorithm to extract an effective fluctuation component. The effective fluctuation component and the compensation parameter matrix are subjected to multi-dimensional fusion operation to generate a comprehensive adjustment matrix. The parameters are decomposed based on the comprehensive adjustment matrix to calculate the current adjustment signal and the duty cycle adjustment signal, respectively. The input current waveform is adjusted according to the current adjustment signal, and the conduction time of the power switch tube is adjusted according to the duty cycle adjustment signal.

[0060] The adjusted power factor value and the DC output voltage value are obtained, and it is judged whether the preset target range is met. If yes, the power correction is performed by the power factor correction circuit.

[0061] In an optional embodiment,

[0062] The input AC signal and the DC bus output signal of the server power supply are collected, and the real-time power factor value is calculated according to the input AC signal, which comprises the following steps.

[0063] The input alternating current signal and the direct current bus output signal of the server power supply are collected to obtain a voltage sampling sequence and a current sampling sequence, zero-crossing point information in the voltage sampling sequence and the current sampling sequence is extracted, and a phase feature vector is generated;

[0064] Discrete Fourier transforms are respectively performed on the voltage sampling sequence and the current sampling sequence, amplitude and phase information of a fundamental frequency component are extracted, and a phase difference value of the fundamental frequency component is calculated;

[0065] The phase difference value is substituted into a cosine function for operation, and weighted correction is performed in combination with the phase feature vector to obtain a real-time power factor value.

[0066] The input alternating current signal and the direct current bus output signal of the server power supply are collected by installing voltage sensors and current sensors at the alternating current input end and the direct current bus output end of the server power supply. For voltage sampling, a voltage sampling circuit composed of a resistance voltage dividing network and an operational amplifier is used, and the sampling frequency is set to 20 kHz to ensure that the change characteristics of the 50 Hz power grid signal can be accurately captured. For current sampling, a current sampling circuit composed of a Hall current sensor or a precision sampling resistor and a differential amplifier is used, and the sampling frequency is 20 kHz. During sampling, to reduce interference and improve signal quality, the sampling circuit should have appropriate anti-interference measures, such as a combination of analog filtering and digital filtering. Through the sampling circuit, the voltage sampling sequence v(n) and the current sampling sequence i(n) can be obtained, where n is the sequence number of the sampling point.

[0067] After obtaining the voltage sampling sequence and the current sampling sequence, the zero-crossing point information in the sequence is extracted. Zero-crossing point detection is achieved by judging the sign change of adjacent two sampling points. For the voltage sequence v(n), when v(n-1) < 0 and v(n) ≥ 0, it is determined that there is a zero-crossing point from negative to positive between n-1 and n; when v(n-1) ≥ 0 and v(n) < 0, it is determined that there is a zero-crossing point from positive to negative between n-1 and n.

[0068] Discrete Fourier transforms are respectively performed on the voltage sampling sequence v(n) and the current sampling sequence i(n), and the amplitude and phase information of the fundamental frequency component are extracted. In practical applications, a complete period of data (for example, for a 50 Hz signal, 400 sampling points at a sampling rate of 20 kHz) is selected for discrete Fourier transform. Through Fourier transform, the amplitude and phase information of the voltage and current signals at different frequencies can be obtained. For power factor calculation, the main concern is the component at the fundamental frequency (i.e., 50 Hz). From the transformation result, the amplitude Av and phase φv of the voltage fundamental wave, and the amplitude Ai and phase φi of the current fundamental wave are extracted. The phase difference value φd of the fundamental frequency component is calculated as φv-φi.

[0069] After obtaining the phase difference value φd of the fundamental frequency component, it is substituted into the cosine function for operation, that is, cos(φd) is calculated. In an ideal case, this value is the power factor. However, considering various nonlinear factors and harmonic interference existing in the actual system, it is necessary to combine the previously obtained phase feature vector for weighted correction. The process of weighted correction can provide multiple cycle phase difference estimation values φz1, φz2,..., φzn based on the phase feature vector generated by the zero-crossing point information, and calculate the cosine values cos(φz1), cos(φz2),..., cos(φzn) of the estimation values. The cosine values of the estimation values are weighted and averaged with cos(φd) obtained by Fourier transform, and the weights can be determined according to the signal quality and system characteristics. For example, if the reliability of the Fourier transform result is high, a larger weight (such as 0.7) can be given to cos(φd), and the remaining weight (0.3) is evenly distributed to the estimation values based on the zero-crossing point.

[0070] Exemplarily, a server power supply is tested, and the peak value of the collected voltage sequence in one cycle is 325V, and the peak value of the current sequence is 5.2A. The voltage leads the current by 25 degrees through zero-crossing point detection. Discrete Fourier transform analysis shows that the voltage fundamental amplitude is 311V and the phase is 3 degrees; the current fundamental amplitude is 4.9A and the phase is -22 degrees, and the phase difference is 25 degrees. Substituting into the cosine function, cos(25°)=0.906 is obtained, which represents the power factor based on Fourier analysis. The power factor estimation values obtained by zero-crossing point analysis are 0.902, 0.909, and 0.904 for three consecutive cycles. The final power factor is calculated using weight distribution (0.7:0.1:0.1:0.1): 0.7×0.906+0.1×0.902+0.1×0.909+0.1×0.904=0.906, that is, the real-time power factor value is 0.906.

[0071] In this embodiment, by simultaneously collecting the electrical signals at the input end and the output end, more complete system characteristic information is obtained, which lays a reliable data foundation for subsequent analysis. By extracting the fundamental frequency component through discrete Fourier transform, high-order harmonic interference can be effectively suppressed, and the anti-interference ability of the measurement is improved. The innovative method of weighted correction using the phase feature vector can compensate for various errors in the measurement process, further improving the accuracy of power factor calculation. Continuous acquisition and processing of voltage and current signals, through efficient digital signal processing algorithms, realize real-time calculation of power factor, timely reflect the dynamic changes of load characteristics, and provide accurate basis for monitoring and adjusting the power supply system.

[0072] In an alternative embodiment,

[0073] If the real-time power factor value is less than the preset power factor threshold, a compensation function is constructed based on the real-time power factor value and a real-time deviation amount is calculated, the real-time deviation amount is weighted, accumulated and normalized with a historical compensation value to obtain an initial compensation factor, including:

[0074] A real-time power factor value is obtained, and it is determined whether the real-time power factor value is less than a preset power factor threshold. If the real-time power factor value is less than the preset power factor threshold, a static deviation term is obtained by performing a difference square operation on the real-time power factor value and a target power factor value, and a dynamic change rate term is obtained by calculating an absolute value of a change rate of the real-time power factor value. The static deviation term and the dynamic change rate term are weighted and combined to construct a compensation function.

[0075] The compensation function is integrated within a preset integration time window to obtain a real-time deviation amount, and the real-time deviation amount is added to a historical compensation value at a previous time to obtain a cumulative compensation value at a current time.

[0076] A change rate of the real-time power factor value is calculated, an adaptive gain factor is obtained by performing an exponential operation with a reference gain coefficient as a base and a reciprocal of the change rate as an index, a maximum value and a minimum value of the cumulative compensation value are calculated, the cumulative compensation value is divided by a difference between the maximum value and the minimum value after the cumulative compensation value is subtracted by the minimum value, and a normalized result is multiplied by the adaptive gain factor to obtain an initial compensation factor.

[0077] A real-time power factor value is obtained, and the real-time power factor value is compared with a preset power factor threshold. The preset power factor threshold is usually set to 0.95, which is a relatively ideal power factor level recommended by a power system. When the real-time power factor value is less than 0.95, a power factor correction process is started.

[0078] In the correction process, a static deviation term is calculated, a difference between the real-time power factor value and a target power factor value is calculated, and a square operation is performed on the difference. For example, when the real-time power factor is 0.85 and the target power factor is 0.98, the difference between the two is 0.13, and the square of the difference is 0.0169, which is the static deviation term. The static deviation term reflects the deviation of the current power factor from the target value, and the greater the deviation, the greater the correction effort required.

[0079] A dynamic change rate term is calculated, the dynamic change rate term is obtained by calculating an absolute value of a change rate of the real-time power factor value in a unit time, a difference between a power factor value at a current sampling point and a power factor value at a previous sampling point is divided by a sampling time interval, and an absolute value is taken. For example, if the power factor at the current sampling point is 0.85, the power factor at the previous sampling point is 0.83, and the sampling time interval is 0.1 milliseconds, the change rate is 20 / second, and the absolute value is the dynamic change rate term.

[0080] The static deviation term and the dynamic change rate term are combined by weighting to construct the compensation function. The weighting coefficients can be adjusted according to the system characteristics. In general, the weight of the static deviation term is 0.7, and the weight of the dynamic change rate term is 0.3. Taking the above data as an example, assuming that the calculation result of the dynamic change rate term is 10, then the compensation function value is 0.0169*0.7+10*0.3=3.0118.

[0081] The compensation function is integrated in a preset integration time window to obtain a real-time deviation. The integration time window is generally set to 20 milliseconds, and the integration adopts the trapezoidal rule to accumulate the values of the compensation function in the time window and multiply them by the sampling time interval. Assuming that the average value of the compensation function in the integration window is 3.0, and the integration time is 0.02 seconds, then the real-time deviation is 3.0*0.02=0.06.

[0082] The real-time deviation is added to the historical compensation value at the previous time to obtain the cumulative compensation value at the current time. Assuming that the historical compensation value at the previous time is 0.25, then the current cumulative compensation value is 0.06+0.25=0.31. The cumulative compensation value reflects the total amount of adjustment required by the system and has memory, which can effectively handle persistent deviations.

[0083] The change rate of the real-time power factor value is calculated, which is used for subsequent adjustment of the gain coefficient. The change rate calculation method is similar to the aforementioned dynamic change rate term, but does not take the absolute value, and retains the sign to reflect the change direction. For example, if the current power factor is increasing at a rate of 0.02 per second, then the change rate is +0.02.

[0084] The adaptive gain factor is obtained by taking the reference gain coefficient as the base and the reciprocal of the power factor change rate as the exponent for power operation. The reference gain coefficient is usually set to 2.5, which has good response characteristics. If the change rate is +0.02, then the exponent is -0.02, and the adaptive gain factor is 2.5^(-0.02)≈2.44. When the power factor is increasing, the gain factor is slightly reduced to avoid overcorrection; when the power factor decreases, the gain factor increases to strengthen the correction strength.

[0085] The maximum and minimum values of the cumulative compensation value are calculated for normalization processing. Through historical data analysis, it is found that the reasonable range of the cumulative compensation value is [0.1, 0.5], so the minimum value is set to 0.1 and the maximum value is set to 0.5. The normalized result is obtained by subtracting the minimum value 0.1 from the current cumulative compensation value 0.31 and dividing by the difference between the maximum value and the minimum value 0.4, i.e. (0.31-0.1) / 0.4=0.525.

[0086] The normalized result is multiplied by an adaptive gain factor to obtain an initial compensation factor. Taking the above calculation result as an example, the initial compensation factor is 0.525*2.44≈1.28. The compensation factor will be used to adjust the duty cycle or switching frequency of the PWM wave to achieve power factor correction.

[0087] In this embodiment, by taking the square of the difference between the real-time power factor value and the target value as the static deviation term, and introducing the absolute value of the power factor change rate as the dynamic change rate term, a compensation function is constructed which comprehensively reflects the compensation demand of the system, considering both the compensation demand at the current time and the dynamic change characteristics of the system, making the compensation process more accurate and stable. The real-time deviation is obtained by integrating the compensation function, and the cumulative compensation value is obtained by superimposing the historical compensation value, effectively avoiding the violent fluctuation in the compensation process and enhancing the stability of the system.

[0088] In an alternative embodiment,

[0089] The initial compensation factor is error-corrected by recursive calculation to generate a stable value, and the iteration step is adjusted according to the change rate of the stable value to generate a fast compensation amount and a gradual compensation amount, and the compensation parameter matrix is obtained by combining the two amounts.

[0090] The initial compensation factor is taken as the initial value of the recursive operation, the real-time deviation is multiplied by a preset weight coefficient and added to the initial value to obtain a first iteration value, and the recursive compensation result is obtained based on the first iteration value. The result of each recursive operation is equal to the sum of the result of the last recursive operation and the product of the real-time deviation and the preset weight coefficient;

[0091] The real-time deviation, the change rate of the real-time deviation, and the real-time power factor value are mapped to a three-dimensional fuzzy space and a fuzzy rule base is established, and the fuzzy membership function of the fuzzy rule base is dynamically adjusted to obtain an optimized fuzzy rule set;

[0092] The recursive compensation result and the stable value at the last time are weighted and smoothed according to the optimized fuzzy rule set to generate a stable value at the current time, and the real-time deviation and the change rate of the stable value at the current time are substituted into the sliding mode calculation to obtain a sliding mode operation result.

[0093] The change rate of the stable value at the current time is calculated based on the sliding mode operation result to obtain a fast compensation amount, and the real-time deviation is integrated to obtain a gradual compensation amount. The fast compensation amount and its change amount are used to construct an inner loop compensation matrix, and the gradual compensation amount and its change amount are used to construct an outer loop compensation matrix. The weighting coefficients are determined based on the optimized fuzzy rule set, and the inner loop compensation matrix and the outer loop compensation matrix are adaptively weighted and combined to obtain a compensation parameter matrix.

[0094] The initial compensation factor is used as the initial value of the recursive operation for multi-level optimization compensation. The initial compensation factor is used as the starting point of the recursive algorithm to ensure the stability and convergence of the compensation process. The recursive operation continuously optimizes the compensation result through iteration, gradually approaching the target value. In specific implementation, the real-time deviation is multiplied by the preset weight coefficient and added to the initial value to obtain the first iteration value. The preset weight coefficient is usually set to 0.35, and the weight coefficient value is verified by experiments to achieve a good balance between system stability and response speed. For example, when the initial compensation factor is 1.28 and the real-time deviation is 0.06, the first iteration value is 1.28 + (0.06 x 0.35) = 1.301.

[0095] The recursive compensation result is obtained based on the first iteration value. The maximum number of iterations is set to 8 in the recursive process, and the result of each recursive operation is equal to the sum of the result of the previous recursive operation and the product of the real-time deviation and the preset weight coefficient. Using the above data as an example, the second iteration value is 1.301 + (0.06 x 0.35) = 1.322, and the third iteration value is 1.322 + (0.06 x 0.35) = 1.343. When the iteration result changes less than 0.001 or reaches the maximum number of iterations, the recursive process terminates, and the calculation result at this time is the recursive compensation result, for example, the final value is 1.371.

[0096] The real-time deviation, the change rate of the real-time deviation, and the real-time power factor value are mapped to a three-dimensional fuzzy space and a fuzzy rule base is established. In the fuzzy space, the domain range of the real-time deviation is [-0.2, 0.2], which is divided into five fuzzy sets: large negative deviation, small negative deviation, zero deviation, small positive deviation, and large positive deviation. The domain range of the real-time deviation change rate is [-30, 30], which is divided into five fuzzy sets: rapid decrease, slow decrease, stable, slow increase, and rapid increase. The domain range of the real-time power factor value is [0.8, 1.0], which is divided into three fuzzy sets: insufficient, general, and ideal. The fuzzy rule base contains 75 rules, which describe the control strategy corresponding to each input variable combination. For example, when the real-time deviation is "small positive deviation", the change rate is "slow decrease", and the power factor is "general", the corresponding rule is "moderate compensation enhancement".

[0097] The fuzzy membership function of the fuzzy rule base is adjusted to obtain an optimized fuzzy rule set. The adjustment process is based on the change of server load. When the load changes rapidly, the shape parameter of the membership function is adjusted to increase its sensitivity. For example, in the case of load surge, the width parameter of the membership function of "large positive deviation" is adjusted from 0.08 to 0.06, so that the system can respond quickly to smaller deviations.

[0098] The current time stable value is generated by weighted smoothing operation of the recursive compensation result and the last time stable value according to the optimized fuzzy rule set. The weighted smoothing operation adopts exponential smoothing method, and the smoothing factor is dynamically adjusted according to the fuzzy rule reasoning result, ranging from 0.1 to 0.4. Assuming that the recursive compensation result is 1.371, the last time stable value is 1.25, and the current smoothing factor is 0.25, the current time stable value is 1.25 x (1-0.25) + 1.371 x 0.25 = 1.28.

[0099] The sliding mode calculation result is obtained by substituting the real-time deviation and the change rate of the current time stable value into the sliding mode calculation. The sliding mode calculation adopts the reaching rate function, and the sliding mode coefficient is set to 0.85 to realize the dual characteristics of rapid approach and stable maintenance. Assuming that the real-time deviation is 0.06 and the change rate of the current time stable value is 0.03, the sliding mode calculation result is 0.081 after substitution and calculation.

[0100] The rapid compensation amount is calculated based on the change rate of the current time stable value obtained by the sliding mode calculation result. The calculation method is to multiply the sliding mode calculation result and the current time stable value by the adjustment coefficient 0.75 to obtain the rapid compensation amount. According to the foregoing data, the rapid compensation amount is 0.081 x 1.28 x 0.75 = 0.0778.

[0101] The gradual compensation amount is obtained by integrating the real-time deviation. The integral adopts the trapezoidal rule, the integral time window is 50 milliseconds, and the integral gain is 0.6. Assuming that the average value of the real-time deviation in the integral window is 0.055, the gradual compensation amount is 0.055 x 0.05 x 0.6 = 0.00165.

[0102] The rapid compensation amount and its change amount are constructed into the inner loop compensation matrix. The inner loop compensation matrix is a 2x2 matrix, which contains the rapid compensation amount and its first and second order change amounts. Assuming that the rapid compensation amount is 0.0778 and its first order change amount is 0.0025, the elements of the inner loop compensation matrix are [0.0778, 0.0025; 0.0025, 0.0001].

[0103] The gradual compensation amount and its change amount are constructed into the outer loop compensation matrix. The outer loop compensation matrix is also a 2x2 matrix, which contains the gradual compensation amount and its change trend. Assuming that the gradual compensation amount is 0.00165 and its change trend is 0.0003, the elements of the outer loop compensation matrix are [0.00165, 0.0003; 0.0003, 0.00006].

[0104] The inner loop compensation matrix and the outer loop compensation matrix are adaptively weighted and combined to obtain a compensation parameter matrix based on the optimization of the fuzzy rule set. The inner loop weight obtained through fuzzy reasoning is 0.65, and the outer loop weight is 0.35. The two matrices are weighted and combined to obtain a compensation parameter matrix [0.0515, 0.00175; 0.00175, 0.000074].

[0105] In this embodiment, the recursive operation mechanism is adopted, the initial compensation factor is taken as the starting point, and the compensation result is continuously optimized through iterative calculation, which can fully utilize the historical information, make the compensation process continuous and smooth, effectively avoid the mutation of the compensation amount, and realize the flexible adjustment of the recursive calculation intensity by introducing the preset weight coefficient, thereby enhancing the adaptability of the algorithm. The real-time deviation, the deviation change rate and the power factor value are taken as input variables, the membership function of the dynamic optimization fuzzy rule library is realized, the adaptive adjustment of the control rule is realized, the fuzzy logic-based control mechanism can better handle the uncertainty and nonlinearity of the system, and the robustness of the control is improved. The real-time deviation and the stable value change rate are substituted into the sliding mode operation to obtain a more accurate compensation amount. The sliding mode control has good anti-interference ability and fast convergence characteristics, and can effectively cope with system parameter fluctuations and external disturbances.

[0106] In an alternative embodiment,

[0107] The adaptive filtering algorithm is used to process the DC bus output signal and extract the effective fluctuation component, and the effective fluctuation component is subjected to multi-dimensional fusion operation with the compensation parameter matrix to generate a comprehensive adjustment matrix, which includes:

[0108] The adaptive filtering algorithm is used to process the DC bus output signal and extract the effective fluctuation component, and the effective fluctuation component is subjected to multi-dimensional fusion operation with the compensation parameter matrix to generate a comprehensive adjustment matrix, which includes:

[0109] The adaptive gain matrix is obtained by weighting the signal vector group through the adaptive gain matrix, and the filter signal is obtained by wavelet transform of the filter signal to obtain a decomposition result, and the wavelet feature of the filter signal is obtained by combining the decomposition result with the preset wavelet basis function and scale factor, and the effective fluctuation component is formed by combining the wavelet feature.

[0110] The adaptive weight matrix is constructed by using the dynamic weight obtained by weight mapping of the effective fluctuation component, and the fusion operation result is obtained by tensor product operation of the adaptive weight matrix and the effective fluctuation component.

[0111] The deviation value is obtained by subtracting the preset expected output from the fusion operation result, the gradient of the weight coefficient is obtained by substituting the deviation value into the target function for calculation, the optimized weight coefficient is obtained by iteratively optimizing the dynamic weight based on the gradient of the weight coefficient, the compensation correction amount is obtained by multiplying the optimized weight coefficient and the fusion operation result, and the comprehensive adjustment matrix is obtained by adding the compensation correction amount and the fusion operation result.

[0112] An adaptive filtering algorithm is used to process the DC bus output signal. Based on the Kalman filtering principle, the bus voltage signal is recursively calculated to eliminate the adverse effects of voltage fluctuations on power factor correction. In the implementation process, the DC bus output signal is collected by an analog-to-digital converter with a sampling frequency of 20 kHz to ensure the real-time and accuracy of signal acquisition. The collected signal is preprocessed and then enters the recursive calculation module. The recursive calculation uses state equations to describe the dynamic characteristics of the system and observation equations to describe the measurement process.

[0113] The signal vector group is obtained by simultaneously solving the recursive calculation results. The simultaneous solution uses the Gauss-Seidel iteration method, and the number of iterations is set to 5 times with a convergence threshold of 0.001. Taking an actual application as an example, when the DC bus voltage is 380V with a fluctuation range of ±5V, the signal vector group [380.2, 0.5, -0.3, 0.1] can be obtained through recursive calculation and simultaneous solution, which respectively represent the estimated voltage value and its first, second, and third order change rates. The signal vector group is calculated by a digital signal processor with a calculation period of 50 microseconds.

[0114] Based on the signal vector group, a state estimation error covariance matrix is constructed, which is a 4x4 symmetric matrix. The diagonal elements represent the variances of the state estimation errors, and the off-diagonal elements represent the covariances between the state estimation errors. The initial covariance matrix is set as a diagonal matrix with diagonal element values of [0.5, 0.1, 0.05, 0.02], representing the uncertainty of the initial state estimation. The covariance matrix is updated through recursion, and is calculated once every control period.

[0115] The state estimation error covariance matrix is sequentially subjected to matrix operations with the observation matrix and the measurement noise covariance matrix to obtain an adaptive gain matrix. The observation matrix is set as [1, 0, 0, 0], indicating that only the voltage value is directly measured. The measurement noise covariance matrix is a single-element matrix with a value of 0.25, representing the variance of the measurement noise. The matrix operations include matrix multiplication, matrix addition, and matrix inversion, which are realized through floating-point operation units. Finally, the adaptive gain matrix is obtained as a 4x1 column vector, for example, [0.85, 0.32, 0.15, 0.06], indicating how each state estimation should be corrected based on the measurement residual.

[0116] The adaptive gain matrix is used to weight the signal vector group to obtain the filtered signal. The weighting operation process is to calculate the difference value (measurement residual) between the measured value and the predicted value, multiply the residual by the adaptive gain matrix to obtain the state correction, and add the correction to the predicted state to obtain the filtered state estimation. Taking the above data as an example, if the measured value is 381V and the predicted value is 380.2V, the residual is 0.8V, and the state correction [0.68, 0.256, 0.12, 0.048] is obtained after weighting by the adaptive gain, and the updated filtered signal is [380.88, 0.756, -0.18, 0.148].

[0117] The filtered signal is decomposed by wavelet transform to obtain the decomposition result. The wavelet transform uses a multi-scale analysis method to decompose the signal into sub-signals of different frequency bands. The wavelet transform uses Daubechies wavelet with 3 layers of decomposition. Taking the filtered voltage signal as an example, 3 detail coefficient sequences and 1 approximation coefficient sequence are obtained after wavelet transform decomposition, and the detail coefficients reflect the high frequency, medium frequency and low frequency fluctuation characteristics of the signal, and the approximation coefficient reflects the trend characteristics of the signal.

[0118] The decomposition result is combined with the preset wavelet basis function and scale factor to obtain the fluctuation characteristics of the filtered signal. The wavelet basis function is selected as db4 function, and the scale factor is set as 1, 2 and 4 respectively, corresponding to the signal characteristics of different frequency bands. The combination operation adopts the form of inner product, and the matching degree of each detail coefficient and the basis function under the corresponding scale is calculated. The calculated fluctuation characteristics are represented as [0.28, 0.15, 0.08], respectively corresponding to the intensity of high frequency, medium frequency and low frequency fluctuation.

[0119] The fluctuation characteristics are combined to form effective fluctuation components. The combination process adopts a weighted sum method, and the weight coefficients are set by experiments as [0.5, 0.3, 0.2], representing the importance of the fluctuations of each frequency band on the power factor. Taking the above fluctuation characteristics as an example, the effective fluctuation component is calculated as 0.28x0.5+0.15x0.3+0.08x0.2=0.199, representing the comprehensive influence degree of the current voltage fluctuation on the power factor.

[0120] The effective fluctuation component is mapped to a dynamic weight through a weight mapping. The weight mapping uses a S-shaped function to map the fluctuation component to the interval [0.1, 0.9], and the mapping parameters are set according to the system characteristics. Taking the effective fluctuation component 0.199 as an example, the dynamic weight obtained after mapping is 0.35, representing the adjustment intensity of the correction parameter under the current fluctuation condition.

[0121] The dynamic weight is used to construct an adaptive weight matrix. The adaptive weight matrix is a 2x2 symmetric matrix, the diagonal elements are set to the dynamic weight and its complementary value, and the non-diagonal elements are set to the geometric mean of the two. Taking the dynamic weight 0.35 as an example, the adaptive weight matrix is constructed as [[0.35, 0.3], [0.3, 0.65]], which represents the adjustment intensity of different correction parameters.

[0122] The adaptive weight matrix is subjected to a tensor product operation with the effective fluctuation component to obtain a fusion operation result. The tensor product operation expands the matrix multiplication, and the multiplication of a scalar and a matrix obtains a weighted matrix. Taking the effective fluctuation component 0.199 and the aforementioned adaptive weight matrix as an example, the fusion operation result is [[0.0697, 0.0597], [0.0597, 0.1294]], which represents the compensation parameter adjustment amount after considering the influence of voltage fluctuation.

[0123] The fusion operation result is subtracted from the pre-set expected output to obtain a deviation value. The expected output matrix is set to [[0.05, 0.02], [0.02, 0.1]], which represents the compensation parameters under ideal working conditions, and the deviation value is calculated as the difference between the fusion operation result and the expected output, i.e. [[0.0197, 0.0397], [0.0397, 0.0294]], which represents the gap between the current parameters and the ideal parameters.

[0124] The deviation value is substituted into the target function to calculate the gradient of the weight coefficient. The target function adopts the form of mean square error, which represents the deviation degree of the current compensation effect from the ideal effect. The gradient calculation adopts analytical derivative to obtain the weight adjustment direction. Taking the aforementioned deviation value as an example, the calculated gradient value is [0.023, -0.015], which represents that the first weight should be increased and the second weight should be decreased.

[0125] The dynamic weight is iteratively optimized based on the gradient of the weight coefficient to obtain an optimized weight coefficient. The iterative optimization adopts the gradient descent method, the learning rate is set to 0.2, and the iteration number is 3 times. After iterative optimization, the dynamic weight is adjusted from 0.35 to 0.3846, and the optimized weight coefficient is [0.3846, 0.6154].

[0126] The optimized weight coefficient is multiplied by the fusion operation result to obtain a compensation correction amount. The multiplication operation is the multiplication of a matrix and a vector, and the calculation result is [0.0427, 0.0853], which represents the adjustment amount of the original compensation parameters.

[0127] The compensation correction amount and the fusion operation result are added to obtain a comprehensive adjustment matrix, the addition operation is matrix addition, and the obtained comprehensive adjustment matrix is [[0.1124, 0.1024], [0.1024, 0.2147]]. The matrix is used for adjusting control parameters of the power factor correction circuit, including a PWM duty cycle, a switching frequency and the like, so that accurate power factor correction under the condition of DC bus voltage fluctuation is realized.

[0128] In the embodiment, by constructing a state estimation error covariance matrix and combining the observation matrix and the measurement noise covariance matrix to perform operation, an adaptive gain matrix is obtained, the filter parameters can be dynamically adjusted according to the signal characteristics, the influence of the measurement noise is effectively suppressed, the accuracy of signal processing is improved, the filtered signal is decomposed by a wavelet, and combined operation is performed with a preset wavelet basis function and a scale factor, multi-scale analysis of the signal fluctuation characteristics is realized, the local characteristics and mutation information of the signal can be effectively captured, and a reliable basis is provided for subsequent compensation control, the deviation between the fusion operation result and the expected output is substituted into the objective function, the gradient of the weight coefficient is calculated, and iterative optimization is performed based on this, the dynamic adjustment of the weight coefficient is realized, the accuracy and convergence of the weight adjustment are ensured, and the adaptive ability of the system is improved.

[0129] In an alternative embodiment,

[0130] Based on the comprehensive adjustment matrix, parameter decomposition is performed to respectively calculate a current adjustment signal and a duty cycle adjustment signal, the input current waveform is adjusted according to the current adjustment signal, and the on-time of the power switch tube is adjusted according to the duty cycle adjustment signal, including:

[0131] Eigenvalue decomposition is performed on the comprehensive adjustment matrix to obtain an eigenvalue matrix and an eigenvector matrix, parameter mapping operation is performed on the eigenvalue matrix and a preset current adjustment coefficient and a preset duty cycle adjustment coefficient to obtain initial adjustment parameters;

[0132] Sine function operation is performed on the initial adjustment parameters and the eigenvalues in the eigenvalue matrix, and a preset current gain coefficient is multiplied to obtain a current adjustment signal, the current adjustment signal is substituted into differential operation and integral operation to obtain a dynamic compensation signal, and the dynamic compensation signal and the current adjustment signal are combined to obtain a compensated current adjustment signal;

[0133] Cosine function operation is performed on the initial adjustment parameters and the eigenvalues in the eigenvalue matrix, and a preset initial duty cycle value is added to obtain a duty cycle adjustment signal, the duty cycle adjustment signal is substituted into hyperbolic tangent function to perform dead zone compensation operation to obtain a compensated duty cycle adjustment signal;

[0134] The feedback control signal is calculated based on the compensated current regulation signal and the compensated duty cycle regulation signal, the error of the feedback control signal is substituted into integral operation to obtain an adaptive gain signal, the adaptive gain signal is added with a preset initial gain value to obtain a real-time control gain coefficient, and the input current waveform of the server and the on-time of the power switch tube are adjusted based on the real-time control gain coefficient.

[0135] The eigenvalue decomposition is implemented by the Jacobi iteration method, the iteration accuracy is set to 0.0001, and the maximum iteration number is 20. Taking the aforementioned comprehensive adjustment matrix [[0.1124, 0.1024], [0.1024, 0.2147]] as an example, the eigenvalue matrix obtained by eigenvalue decomposition is a diagonal matrix [[0.0387, 0], [0, 0.2884]], and the corresponding eigenvector matrix is [[0.7352, -0.6778], [0.6778, 0.7352]]. The eigenvalue reflects the response strength of the system in different directions, and the eigenvector represents the main direction of the response.

[0136] The initial adjustment parameter is obtained by parameter mapping operation of the eigenvalue matrix, the preset current regulation coefficient and the preset duty cycle regulation coefficient. The preset current regulation coefficient is 1.5, the preset duty cycle regulation coefficient is 0.8, the parameter mapping adopts a linear transformation mode, and the eigenvalues are scaled according to the preset coefficients. In specific calculation, the first eigenvalue of the eigenvalue matrix is multiplied by the current regulation coefficient, and the second eigenvalue is multiplied by the duty cycle regulation coefficient to obtain the initial adjustment parameter [0.0581, 0.2307], which is used in the subsequent current control loop and duty cycle control loop.

[0137] The initial adjustment parameter and the eigenvalue in the eigenvalue matrix are subjected to a sine function operation and multiplied by a preset current gain coefficient to obtain a current regulation signal. The sine function operation is used to introduce periodic regulation characteristics, so that the control signal has a smooth change process. The current gain coefficient is set to 2.5, which is used to adjust the amplitude of the control signal. Taking the initial adjustment parameter as an example, the first parameter 0.0581 and the first eigenvalue 0.0387 are substituted into the sine function to obtain a sine value 0.0581, and then multiplied by the current gain coefficient 2.5 to obtain a current regulation signal 0.1453. The current regulation signal is used to adjust the gain of the current control loop and affects the waveform of the input current.

[0138] The current regulation signal is substituted into the differential operation and integral operation to obtain a dynamic compensation signal. The differential operation adopts a second-order central difference method, and the time step is 0.1 millisecond, so as to improve the numerical precision and stability. The integral operation adopts a trapezoidal rule, and the integral time window is 20 milliseconds. Taking the current regulation signal 0.1453 as an example, assuming that the signal at the previous time is 0.1425, the differential calculation obtains a change rate of 0.28 / second, and the integral calculation obtains an accumulated value of 0.0029. The dynamic compensation signal is obtained by weighted combination of the differential result and the integral result, and the weights are 0.4 and 0.6 respectively, and the calculation result is 0.28*0.4+0.0029*0.6=0.1137. The dynamic compensation signal is used to process the dynamic change of the current waveform, and improve the response speed and stability of the system.

[0139] The dynamic compensation signal and the current regulation signal are combined to obtain a compensated current regulation signal. The combination mode is weighted summation, the weight of the dynamic compensation signal is 0.3, and the weight of the current regulation signal is 0.7, and the compensated current regulation signal is calculated as 0.1137*0.3+0.1453*0.7=0.1358. The compensated current regulation signal introduces a dynamic response characteristic on the basis of the original current regulation signal, and can better cope with load changes and input voltage fluctuations.

[0140] The initial regulation parameter and the eigenvalue in the eigenvalue matrix are subjected to cosine function operation and superimposed with a pre-set initial duty ratio value to obtain a duty ratio regulation signal. The cosine function operation is used to introduce a periodic regulation characteristic, but has different phase characteristics compared with the sine function. The initial duty ratio value is set to 0.45, which represents the proportion of the power switch tube conduction time to the period. Taking the second initial regulation parameter 0.2307 and the second eigenvalue 0.2884 as an example, the cosine value 0.9656 is obtained by substituting the two into the cosine function, and then the initial duty ratio value 0.45 is superimposed to obtain the duty ratio regulation signal 0.4656. The signal is used to adjust the duty ratio of the PWM controller and affect the conduction time of the power switch tube.

[0141] The duty ratio regulation signal is substituted into the hyperbolic tangent function for dead zone compensation operation to obtain a compensated duty ratio regulation signal, and the scaling factor of the hyperbolic tangent function is set to 2 and the offset is set to 0. Taking the duty ratio regulation signal 0.4656 as an example, the calculation by substituting it into the hyperbolic tangent function obtains 0.4342 as the compensated duty ratio regulation signal. After the dead zone compensation, the signal can more accurately control the conduction time of the power switch tube, reduce the switching loss, and improve the energy efficiency.

[0142] The feedback control signal is calculated based on the compensated current regulation signal and the compensated duty cycle regulation signal. The calculation method is weighted average, and the weight coefficient is dynamically adjusted through the system state. When the input voltage is stable, the duty cycle regulation signal weight is higher; when the input voltage fluctuates, the current regulation signal weight is higher. Taking the current state current regulation signal weight as 0.45 and the duty cycle regulation signal weight as 0.55 as an example, the feedback control signal is calculated as 0.1358x0.45+0.4342x0.55=0.3001. The feedback control signal considers the control requirements of current waveform and switching timing, and is used to adjust the operating state of the power factor correction circuit.

[0143] The error of the feedback control signal is substituted into the integral operation to obtain the adaptive gain signal. The error is defined as the difference between the feedback control signal and the expected control signal, and the expected control signal is set to 0.32. The error integration uses discrete accumulation method, and the integral gain is 0.15 and the integral limit is ±0.2. Taking the feedback control signal 0.3001 as an example, the error is 0.3001-0.32=-0.0199, and the gain adjustment amount is-0.0030 after one cycle of integration. The adaptive gain signal is continuously accumulated through error integration, which can automatically adjust the control parameters and adapt to different working conditions.

[0144] The adaptive gain signal is added to the pre-set initial gain value to obtain the real-time control gain coefficient. The initial gain value is set to 1.8, representing the basic response strength of the system. Taking the adaptive gain signal-0.0030 as an example, the real-time control gain coefficient is calculated as 1.8+(-0.0030)=1.797.

[0145] The input current waveform of the server and the conduction time of the power switch tube are adjusted based on the real-time control gain coefficient. The adjustment process is realized through a digital controller, and the output PWM signal controls the switching state of the power switch tube. The adjustment of the input current waveform is realized by changing the gain of the current loop, and the adjustment of the conduction time of the power switch tube is realized by changing the duty cycle of the PWM signal.

[0146] In the embodiment, the initial adjustment parameters are established by parameter mapping of the eigenvalue matrix, the preset current adjustment coefficient and the duty ratio adjustment coefficient, the essential characteristics of the system can be fully extracted, and the foundation for subsequent accurate control is laid, the generation of the current adjustment signal is realized by the sine function operation of the initial adjustment parameters and the eigenvalue and the combination of the current gain coefficient, the duty ratio adjustment signal is generated by the cosine function operation and the initial duty ratio superposition, and the dead zone compensation is realized by the hyperbolic tangent function, which not only ensures the continuity and smoothness of the duty ratio adjustment, but also effectively solves the dead zone problem in the switch control, improves the accuracy of the switch control, and generates the feedback control signal by comprehensively adjusting the current adjustment signal and the duty ratio adjustment signal, and converts the error into the adaptive gain signal through the integral operation, which can dynamically adjust the control strength according to the system state, and improves the adaptability and robustness of the system.

[0147] Figure 2 The server power control flowchart of the power factor correction method for the server power AC / DC conversion of the embodiment of the application.

[0148] In an alternative embodiment,

[0149] The adjusted power factor value and the DC output voltage value are obtained, and it is judged whether the preset target range is met, if yes, the power correction is executed by the power factor correction circuit, including:

[0150] The adjusted power factor value and the DC output voltage value are obtained.

[0151] The power factor value and the DC output voltage value are compared with the preset power factor target range and the preset voltage target range respectively, and it is judged whether the power factor value and the DC output voltage value meet the preset power factor target range and the preset voltage target range at the same time.

[0152] When the power factor value and the DC output voltage value meet the preset power factor target range and the preset voltage target range at the same time, the real-time control gain coefficient obtained in advance and the power correction executed by the power factor correction circuit are used.

[0153] The adjusted power factor value and the DC output voltage value are obtained, and the power factor value is obtained by real-time measurement through a special detection circuit. The circuit is composed of a high-precision voltage sensor and a current sensor, and the sampling frequency is 50 kHz, which ensures that the subtle changes of the voltage and current waveforms can be captured. The voltage sensor uses a resistance voltage division principle, and the voltage division ratio is 100:1, and the measurement range is 0-400V AC; the current sensor uses a Hall effect principle, and the sensitivity is 100mV / A, and the measurement range is 0-20A. The sensor output signal is sent to a 16-bit analog-to-digital converter after being processed by a signal conditioning circuit, and the conversion result is read by a digital signal processor and the power factor is calculated. The DC output voltage value is also obtained by a voltage sensor, which uses a resistance voltage division principle with a voltage division ratio of 50:1 and a measurement range of 0-800V DC, and the sampling frequency is synchronized with the power factor detection.

[0154] The power factor value and the DC output voltage value are compared with the preset power factor target range and the preset voltage target range, respectively, to determine whether they meet the preset target at the same time. The power factor target range is set to 0.95-1.0, which meets the requirements of high-efficiency server power supplies and can effectively reduce power grid harmonic pollution and reactive power loss. The voltage target range is set according to the server type and specifications. For a typical 12V output server power supply, the DC output voltage target range is set to 11.8V-12.2V, allowing a ±1.67% fluctuation. The comparison process is performed by a digital controller using an interval judgment method, i.e., checking whether the measured value falls within the target interval. Taking an actual application as an example, when the measured power factor value is 0.974 and the DC output voltage is 12.05V, both fall within the target range, and the judgment result is that the requirements are met.

[0155] In the comparison and judgment process, to improve the stability and anti-interference ability of the system, the measured value is processed by a sliding average filter with a window length of 10 sampling points. To prevent the system from frequently switching between states under critical conditions, a hysteresis comparison mechanism is introduced. When the measured value changes from not meeting the requirements to meeting the requirements, it needs to meet the requirements for 20 consecutive control periods to trigger state conversion; when the measured value changes from meeting the requirements to not meeting the requirements, it needs to not meet the requirements for 5 consecutive control periods to trigger state conversion. This hysteresis comparison mechanism effectively avoids the oscillation phenomenon of the system under critical conditions.

[0156] When the power factor value and the DC output voltage value meet the preset power factor target range and the preset voltage target range at the same time, the power correction is performed according to the real-time control gain coefficient obtained in advance and the power factor correction circuit. The real-time control gain coefficient is obtained by the foregoing calculation process, for example, 1.797, which is used to adjust the response strength of the control loop. The power factor correction circuit adopts a digital control boost topology, including power switch tube, diode, inductor and filter capacitor and other elements. The power switch tube adopts MOSFET device, the breakdown voltage is 650V, and the on-resistance is 55mΩ; the inductor selects a ferrite-aluminum-silicon core, the inductance value is 500μH, and the saturation current is 15A; the filter capacitor adopts low equivalent series resistance design, the capacitance value is 470μF, and the withstand voltage is 450V.

[0157] During the power correction execution process, the controller adjusts the generation parameters of the PWM signal according to the real-time control gain coefficient. The PWM signal frequency is set to 65kHz, and the duty cycle range is 0.1-0.9, which is generated by a digital pulse width modulator. The control algorithm is based on the current mode control principle, which combines the advantages of average current control and edge current control, and realizes accurate control of the input current waveform. When the zero crossing point of the input voltage is detected, the controller resets the internal parameters and starts a new control process for each switching period. For each switching period, the controller calculates the ideal switching on time according to the input voltage, output voltage and load current, and then adjusts it in combination with the real-time control gain coefficient to generate the final PWM control signal.

[0158] Exemplarily, when the server runs under 50% load condition, the measured power factor is 0.974, and the DC output voltage is 12.05V, both of which meet the preset target range. At this time, the real-time control gain coefficient is 1.797, the PWM signal duty cycle generated by the controller is 0.432, the power switch tube opening time is 6.65μs, and the closing time is 8.73μs. The input current waveform of the power factor correction circuit is approximately in phase with the input voltage waveform, the current total harmonic distortion rate is 3.6%, and the system efficiency reaches 94.2%. When the server load increases to 80%, the controller automatically adjusts the real-time control gain coefficient to 1.825, the PWM signal duty cycle increases to 0.563, the power switch tube opening time is extended to 8.66μs, the power factor is maintained at 0.985, the DC output voltage is stabilized at 11.96V, and the system efficiency is improved to 95.7%.

[0159] For smooth transition of different working states, the controller adopts gradual adjustment strategy to avoid transient impact caused by parameter mutation. When detecting load change, the control gain coefficient is adjusted at a rate of not more than 0.5% per control cycle, ensuring system stability. At the same time, the controller also has abnormal processing capability, which can quickly respond and take protective measures when detecting input voltage abnormality, output overload or short circuit fault state, such as reducing PWM frequency, limiting duty cycle maximum value or completely shutting down power switch tube to prevent component damage.

[0160] To adapt to different server load characteristics, the controller has multiple working modes, including high efficiency mode, low noise mode and balance mode. In high efficiency mode, the controller gives priority to system efficiency, and the power factor target range is appropriately relaxed to 0.92-1.0; in low noise mode, the PWM frequency is increased to 100kHz, which is beyond the audible range of human ear, but the power factor and efficiency will be reduced; the balance mode balances between efficiency and noise, which is the default working mode of the system. Users can select the appropriate working mode according to the data center environment and server purpose.

[0161] The whole power factor correction method is realized through a digital controller, and the control code is stored in the program memory, which occupies about 35KB storage space when running, and the main loop execution time is less than 15 microseconds, meeting the real-time control requirements. The controller also provides a communication interface to support interaction with the server management system, report running parameters and state information, receive configuration commands, and realize remote monitoring and management.

[0162] In this embodiment, by comparing the actual measured power factor value and DC output voltage value with the preset target range in real time, the system running state can be more comprehensively reflected, ensuring that the power supply system achieves optimal operation on multiple key parameters. A condition triggered control mechanism is adopted, and only when the power factor value and DC output voltage value meet the preset target range at the same time, the power correction operation is performed, avoiding one-sided optimization caused by single index reaching the target, and ensuring the improvement of the overall performance of the system.

[0163] The second aspect of the embodiment of the application provides a power factor correction system for AC-DC conversion of a server power supply, comprising:

[0164] The first unit is used for collecting the input AC signal and DC bus output signal of the server power supply, and calculating the real-time power factor value according to the input AC signal;

[0165] The second unit is configured to, if the real-time power factor value is less than a preset power factor threshold, construct a compensation function based on the real-time power factor value and calculate a real-time deviation amount, weight, accumulate and normalize the real-time deviation amount and a historical compensation value to obtain an initial compensation factor, correct the initial compensation factor by recursive calculation, generate a stable value and adjust an iteration step length according to a change rate of the stable value, generate a fast compensation amount and a gradual compensation amount, and combine the fast compensation amount and the gradual compensation amount to obtain a compensation parameter matrix.

[0166] The third unit is configured to process the DC bus output signal by using an adaptive filtering algorithm and extract an effective fluctuation component, perform multi-dimensional fusion operation on the effective fluctuation component and the compensation parameter matrix to generate a comprehensive adjustment matrix, perform parameter decomposition based on the comprehensive adjustment matrix, and calculate a current adjustment signal and a duty cycle adjustment signal respectively, adjust an input current waveform according to the current adjustment signal, and adjust a conduction time of a power switch tube according to the duty cycle adjustment signal.

[0167] The fourth unit is configured to obtain an adjusted power factor value and a DC output voltage value and determine whether the adjusted power factor value and the DC output voltage value meet a preset target range, and perform power correction by using a power factor correction circuit if the adjusted power factor value and the DC output voltage value meet the preset target range.

[0168] In a third aspect, an electronic device is provided, including:

[0169] A processor and a memory for storing processor-executable instructions, wherein the processor is configured to invoke the instructions stored in the memory to execute the method described above.

[0170] In a fourth aspect, a computer-readable storage medium is provided, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the method described above.

[0171] The present application can be a method, device, system and / or computer program product. The computer program product can include a computer readable storage medium having computer readable program instructions stored therein, which are used to perform various aspects of the present application.

[0172] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method of power factor correction for AC-DC conversion of a server power supply, characterized by, The method comprises the following steps: acquiring input AC signals and DC bus output signals of a server power supply, calculating a real-time power factor value according to the input AC signals; if the real-time power factor value is less than a preset power factor threshold, constructing a compensation function based on the real-time power factor value and calculating a real-time deviation, performing weighted accumulation and normalization processing on the real-time deviation and a historical compensation value to obtain an initial compensation factor, comprising: acquiring a real-time power factor value, judging whether the real-time power factor value is less than a preset power factor threshold, if yes, performing difference square operation on the real-time power factor value and a target power factor value to obtain a static deviation term, calculating an absolute value of a change rate of the real-time power factor value to obtain a dynamic change rate term, and constructing a compensation function by weighted combination of the static deviation term and the dynamic change rate term; performing integral operation on the compensation function within a preset integral time window to obtain a real-time deviation, and adding the real-time deviation and a historical compensation value at a previous moment to obtain a cumulative compensation value at a current moment; calculating a change rate of the real-time power factor value, performing power operation with a reference gain coefficient as a base and a reciprocal of the change rate as an index to obtain an adaptive gain factor, calculating a maximum value and a minimum value of the cumulative compensation value, subtracting the minimum value from the cumulative compensation value and dividing the result by a difference between the maximum value and the minimum value, multiplying a normalized result by the adaptive gain factor to obtain an initial compensation factor; performing error correction on the initial compensation factor by recursive calculation, generating a stable value and adjusting an iteration step according to a change rate of the stable value, generating a fast compensation amount and a gradual compensation amount, and combining to obtain a compensation parameter matrix; processing the DC bus output signals by an adaptive filtering algorithm and extracting effective fluctuation components, performing multi-dimensional fusion operation on the effective fluctuation components and the compensation parameter matrix to generate a comprehensive adjustment matrix, performing parameter decomposition based on the comprehensive adjustment matrix to calculate a current adjustment signal and a duty cycle adjustment signal respectively, adjusting an input current waveform according to the current adjustment signal, and adjusting a conduction time of a power switch according to the duty cycle adjustment signal; acquiring an adjusted power factor value and a DC output voltage value and judging whether a preset target range is met, if yes, performing power correction through a power factor correction circuit.

2. The method of claim 1, wherein, The method comprises the following steps: acquiring input AC signals and DC bus output signals of a server power supply, obtaining voltage sampling sequences and current sampling sequences, extracting zero-crossing point time information in the voltage sampling sequences and the current sampling sequences, and generating phase feature vectors; performing discrete Fourier transform on the voltage sampling sequences and the current sampling sequences respectively, extracting amplitude and phase information of fundamental frequency components and calculating a phase difference value of the fundamental frequency components; performing operation on the phase difference value by substituting it into a cosine function, and performing weighted correction combining the phase feature vectors to obtain a real-time power factor value.

3. The method of claim 1, wherein, The initial compensation factor is error-corrected by recursive calculation to generate a stable value, and the iteration step is adjusted according to the change rate of the stable value to generate a fast compensation amount and a gradual compensation amount, and the compensation parameter matrix is obtained by combination, including: The initial compensation factor is taken as the initial value of recursive operation, the real-time deviation is multiplied by a preset weight coefficient and then added to the initial value to obtain a first iteration value, and recursive operation is performed based on the first iteration value to obtain a recursive compensation result, and the result of each recursive operation is equal to the sum of the result of the last recursive operation and the product of the real-time deviation and the preset weight coefficient; The real-time deviation, the change rate of the real-time deviation and the real-time power factor value are mapped to a three-dimensional fuzzy space and a fuzzy rule base is established, and the fuzzy membership function of the fuzzy rule base is dynamically adjusted to obtain an optimized fuzzy rule set; The recursive compensation result and the stable value at the last moment are weighted and smoothed according to the optimized fuzzy rule set to generate a stable value at the current moment, and the real-time deviation and the change rate of the stable value at the current moment are substituted into the sliding mode calculation to obtain a sliding mode operation result; The change rate of the stable value at the current moment is calculated based on the sliding mode operation result to obtain a fast compensation amount, and the real-time deviation is integrated to obtain a gradual compensation amount, an inner loop compensation matrix is constructed by the fast compensation amount and its change amount, an outer loop compensation matrix is constructed by the gradual compensation amount and its change amount, a weighting coefficient is determined based on the optimized fuzzy rule set, and the inner loop compensation matrix and the outer loop compensation matrix are adaptively weighted and combined to obtain a compensation parameter matrix.

4. The method of claim 1, wherein, The DC bus output signal is processed by an adaptive filtering algorithm to extract an effective fluctuation component, and the effective fluctuation component and the compensation parameter matrix are subjected to multi-dimensional fusion operation to generate a comprehensive adjustment matrix, including: The DC bus output signal is recursively calculated based on an adaptive filtering algorithm, and the results of the recursive calculation are solved simultaneously to obtain a signal vector group, a state estimation error covariance matrix is constructed based on the signal vector group, and the state estimation error covariance matrix is sequentially subjected to matrix operation with an observation matrix and a measurement noise covariance matrix to obtain an adaptive gain matrix; The signal vector group is weighted by the adaptive gain matrix to obtain a filtered signal, the filtered signal is subjected to wavelet transform to obtain a decomposition result, the decomposition result is combined with a preset wavelet basis function and a scale factor to obtain the fluctuation characteristics of the filtered signal, and the fluctuation characteristics are combined to form an effective fluctuation component; The effective fluctuation component is subjected to weight mapping to obtain a dynamic weight, and the dynamic weight is used to construct an adaptive weight matrix, and the adaptive weight matrix and the effective fluctuation component are subjected to tensor product operation to obtain a fusion operation result; The deviation value is obtained by subtracting the preset expected output from the fusion operation result, the gradient of the weight coefficient is obtained by substituting the deviation value into the target function for calculation, the optimized weight coefficient is obtained by iteratively optimizing the dynamic weight based on the gradient of the weight coefficient, the compensation correction amount is obtained by multiplying the optimized weight coefficient and the fusion operation result, and the comprehensive adjustment matrix is obtained by adding the compensation correction amount and the fusion operation result.

5. The method of claim 1, wherein, Based on the comprehensive adjustment matrix, the current adjustment signal and the duty cycle adjustment signal are calculated respectively, the input current waveform is adjusted according to the current adjustment signal, and the on-time of the power switch tube is adjusted according to the duty cycle adjustment signal, including: Eigenvalue decomposition is performed on the comprehensive adjustment matrix to obtain an eigenvalue matrix and an eigenvector matrix, and initial adjustment parameters are obtained by performing parameter mapping operation on the eigenvalue matrix and the preset current adjustment coefficient and the preset duty cycle adjustment coefficient; The initial adjustment parameters and the eigenvalues in the eigenvalue matrix are subjected to a sine function operation, and the current adjustment signal is obtained by multiplying the preset current gain coefficient, the dynamic compensation signal is obtained by substituting the current adjustment signal into the differential operation and the integral operation, and the compensated current adjustment signal is obtained by combining the dynamic compensation signal and the current adjustment signal; The initial adjustment parameters and the eigenvalues in the eigenvalue matrix are subjected to a cosine function operation, and the duty cycle adjustment signal is obtained by adding the preset initial duty cycle value, the compensated duty cycle adjustment signal is obtained by substituting the duty cycle adjustment signal into the hyperbolic tangent function for dead zone compensation operation; Based on the compensated current adjustment signal and the compensated duty cycle adjustment signal, the feedback control signal is calculated, the error of the feedback control signal is substituted into the integral operation to obtain the adaptive gain signal, the real-time control gain coefficient is obtained by adding the adaptive gain signal and the preset initial gain value, and the input current waveform of the server and the on-time of the power switch tube are adjusted based on the real-time control gain coefficient.

6. The method of claim 1, wherein, The adjusted power factor value and the direct current output voltage value are obtained, and it is judged whether the preset target range is satisfied, if yes, the power correction is performed through the power factor correction circuit, including: The adjusted power factor value and the direct current output voltage value are obtained; The power factor value and the direct current output voltage value are compared with the preset power factor target range and the preset voltage target range respectively, and it is judged whether the power factor value and the direct current output voltage value satisfy the preset power factor target range and the preset voltage target range at the same time; When the power factor value and the direct current output voltage value satisfy the preset power factor target range and the preset voltage target range at the same time, the real-time control gain coefficient and the power factor correction circuit are executed according to the power correction.

7. A power factor correction system for AC-DC conversion of a server power supply for implementing the method of any of the preceding claims 1-6, characterized by, Including: The first unit is used for collecting the input alternating current signal of the server power supply and the direct current bus output signal, and calculating the real-time power factor value according to the input alternating current signal; The second unit is configured to, if the real-time power factor value is less than a preset power factor threshold, construct a compensation function based on the real-time power factor value and calculate a real-time deviation amount, weight and accumulate the real-time deviation amount and a historical compensation value, and perform normalization processing to obtain an initial compensation factor, perform error correction on the initial compensation factor through recursive calculation, generate a stable value, adjust an iteration step length according to a change rate of the stable value, generate a fast compensation amount and a gradual compensation amount, and combine the fast compensation amount and the gradual compensation amount to obtain a compensation parameter matrix. The third unit is configured to process the DC bus output signal by using an adaptive filtering algorithm and extract an effective fluctuation component, perform multi-dimensional fusion operation on the effective fluctuation component and the compensation parameter matrix to generate a comprehensive adjustment matrix, perform parameter decomposition based on the comprehensive adjustment matrix, and separately calculate a current adjustment signal and a duty cycle adjustment signal, adjust an input current waveform according to the current adjustment signal, and adjust a conduction time of a power switch tube according to the duty cycle adjustment signal. The fourth unit is configured to obtain an adjusted power factor value and a DC output voltage value, and determine whether the adjusted power factor value and the DC output voltage value meet a preset target range. If the adjusted power factor value and the DC output voltage value meet the preset target range, the power factor correction circuit is executed to perform power correction.

8. An electronic device, comprising: The computer program instructions are executed by the processor to implement the method in any one of claims 1 to 6. The computer program instructions are executed by the processor to implement the method in any one of claims 1 to 6. ​ ​ 9. A computer-readable storage medium having stored thereon computer program instructions, wherein, ​

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