Method for input grid kalman filter compensation for single phase Vienna converters

Abstract

The application discloses a low-cost and high-quality input grid Kalman filter compensation method for a single-phase Vienna converter. The application comprises the following steps: S1. initializing the initial state and parameters of the Kalman filter algorithm; S2. obtaining real-time measurement values z from a voltage sensor in the Vienna converter in a single-phase mode k ; S3. predicting the state variable at the next moment according to a system model: predicting by using a state equation and a prediction error covariance matrix; S4. measurement update - updating the state estimation by using a Kalman gain: calculating the Kalman gain according to the measurement value and the prediction value, and updating the state estimation; S5. compensation voltage calculation - calculating a compensation voltage value according to the updated state estimation; S6. adjusting a modulation wave V m according to the compensation voltage value: adjusting the modulation wave V m in real time, and reducing the mistuning phenomenon; and S7. repeating the steps - cyclically executing the above steps: until the system is stable or the expected effect is achieved. The application is applied to the technical field of electronic power.

Description

TECHNICAL FIELD

[0001] The present application relates to a method for input grid Kalman filter compensation of single-phase Vienna converter, in particular to a method for compensating the sinusoidal wave detuning phenomenon caused by the positive and negative asymmetry of sensors and sampling circuits in single-phase mode Vienna converter using Kalman filter technology. BACKGROUND

[0002] In power electronic systems, Vienna converter is a kind of efficient single-phase / three-phase rectifier device, which has been widely used in industrial applications.

[0003] In power electronic devices, sensors are used to detect grid voltage and current signals. However, due to various reasons such as sensor design, manufacturing, use environment, etc., the sensor may exhibit positive and negative asymmetry. Specifically, the sensor may have different response characteristics when measuring in the forward and reverse directions, and there may also be deviations in the positive and negative voltage characteristics of the sampling circuit. These factors will cause the measured values to have positive and negative deviations, thereby affecting the stability and efficiency of the system.

[0004] The existing single-phase Vienna converter input sensor positive and negative asymmetry problem is mainly solved by hardware filter and software algorithm, but these methods have certain limitations. For example, increasing hardware filter or hardware positive and negative compensation circuit to compensate the positive and negative detuning phenomenon of sinusoidal wave, but this method increases the complexity of the system and additional hardware cost, and also increases the volume and reduces the power density. Software algorithm is a more cost-effective solution, but traditional PID control, sliding mode control and other software algorithms have poor effect under complex working conditions. SUMMARY

[0005] The technical problem to be solved by the present application is to overcome the shortcomings of the prior art and provide a low-cost and high-quality method for input grid Kalman filter compensation of single-phase Vienna converter.

[0006] The technical solution adopted by the present application is as follows:

[0007] S1. Initialization - In the single-phase mode Vienna converter, initialize the initial state and parameters of the Kalman filter algorithm: select a suitable initial state estimate and initial error covariance matrix P0;

[0008] S2. Measurement input - Obtain sensor measurement value: obtain real-time measurement value z k from the voltage sensor in the single-phase mode Vienna converter;

[0009] S3. State prediction - predict the state variable at the next time according to the system model: use the state equation and the prediction error covariance matrix to make the prediction;

[0010] S4. Measurement update - update the state estimation using the Kalman gain: calculate the Kalman gain according to the measurement value and the predicted value, and update the state estimation;

[0011] S5. Compensation voltage calculation - calculate the compensation voltage value according to the updated state estimation: use the compensation voltage formula to calculate the compensation voltage value;

[0012] S6. Real-time adjustment - adjust the modulation wave V m in real time according to the compensation voltage value, reduce the mistuning phenomenon; m

[0013] S7. Repeat the steps - loop the above steps: until the system is stable or the desired effect is achieved.

[0014] Further, the single-phase mode Vienna converter is to disconnect one branch of the original three-phase Vienna converter from the circuit through a contactor or directly disconnect the switch tube of the branch, and the input ends of the remaining two branches are connected to the positive and negative ends of the single-phase input respectively, and the single-phase mode Vienna converter further comprises the voltage sensor, the input voltage sampling circuit and the controller connected in sequence, the voltage sensor is connected to the branch of the branch inductor, the input voltage sampling circuit is used for processing the analog quantity obtained by the voltage sensor, and the controller is a microprocessor or a DSP controller, which is used for converting the conditioned analog quantity output by the input voltage sampling circuit into a digital quantity for later data processing and control.

[0015] Further, the positive and negative difference value of the input voltage in step S1 is set as xk, and the formula is:

[0016] x k = U peak+ -U peak-

[0017] Wherein U peak+ is the positive voltage value of the input voltage, and U peak- is the negative voltage value of the input voltage.

[0018] Further, z k in step S2 is an observation variable, which represents the positive and negative difference value of the input voltage measured by the voltage sensor.

[0019] Further, the state equation in step S3 is to describe the change rule of the system state with time, and the formula is:

[0020] ​

[0021] where, is the predicted state at time k, A is the state transition matrix, is the state estimate at time k-1, B is the control input matrix, u k is the control input vector;

[0022] The prediction error covariance matrix is used to describe the uncertainty of the predicted state, and its formula is:

[0023] P k|k-1 = AP k-1|k-1 A T + Q

[0024] where, P k|k-1 is the prediction error covariance matrix at time k, Q is the process noise covariance matrix,

[0025] Further, the step S4 of measuring the update includes the following steps:

[0026] S41. Measurement equation: describes the relationship between the measurement value and the system state, and its formula is:

[0027]

[0028] where, z k is the positive and negative difference value of the input voltage measured at time k, H is the observation matrix, v k is the measurement noise;

[0029] S42. Kalman gain: used to calculate the optimal estimate value, and its formula is:

[0030] K k = P k|k-1 H T (HP k|k-1 H T + R) -1

[0031] where, K k is the Kalman gain at time k, R is the measurement noise covariance matrix;

[0032] S43. State update: updates the state estimate according to the measurement value and the Kalman gain, and its formula is:

[0033]

[0034] S44. Error covariance matrix update: describes the uncertainty of the updated state estimate, and its formula is:

[0035] P k = (I-K k H)Pk|k-1

[0036] Further, in the step S5 compensation voltage calculation, it is assumed that half of the absolute value of the positive and negative difference of the input terminal voltage needs to be compensated to the modulation wave V m , the formula is:

[0037]

[0038] Where, |x k | represents the absolute value of state estimation, V comp is the compensation voltage.

[0039] Further, the modulation wave V m in the step S5 is compensated by adding the compensation voltage V comp to the modulation wave V m , the formula is:

[0040] V m ′=V m +V comp

[0041] Where, V m ′ is the compensated modulation wave.

[0042] The beneficial effects of the present application are: the present application solves the positive and negative mistuning problem of the traditional sine wave caused by the positive and negative deviation characteristics of the sensor and the sampling circuit through the Kalman filtering algorithm, so that the present application has the following advantages: 1. Real-time compensation: can detect and compensate the positive and negative mistuning phenomenon in real time during system operation; 2. High robustness: can adapt to changes in different working environments, improve the robustness of the system; 3. Low-cost implementation: reduce costs through software algorithms rather than adding additional hardware; 4. High efficiency: use the high-efficiency computing power of Kalman filtering to ensure the response speed of the system in real-time applications; 5. Remarkably improve the performance of single-phase Vienna converter, improve the stability and power factor of the system. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 is a schematic diagram of the Vienna converter in single-phase mode;

[0044] Figure 2 is a schematic diagram of the sampling deviation and Kalman compensation process;

[0045] Figure 3 is a step flowchart of the present application;

[0046] Figure 4 is a comparison effect diagram of the prediction and tracking of the positive and negative peak value difference by Kalman filtering and the actual positive and negative peak value difference;

[0047] Figure 5is the compensation effect demonstration comparison chart of the sine wave by Kalman filtering. DETAILED DESCRIPTION

[0048] As Figure 1 shown in the present embodiment, here is a brief introduction to the modification technical scheme of the compatible single / three-phase input rectifier circuit scheme of the present application. Originally, the traditional Vienna converter has three branches, which is usually used in three-phase three-wire system. Then, each branch is connected to the A / B / C three-phase line respectively. The modification technical scheme of the compatible single / three-phase input rectifier circuit scheme of the present application is to disconnect one of the branches from the circuit through a contactor (or directly operate the switch tube of the branch to disconnect it). Then, the input ends of the remaining two branches are connected to the positive and negative ends of the single-phase input respectively. This is the single-phase mode of the compatible single / three-phase input Vienna converter to which the present application is applicable.

[0049] Figure 1 The basic circuit topology, voltage sensor, sampling circuit and simple connection relationship of the controller are shown in FIG. Figure 1 In FIG. in is a single-phase input AC voltage source; L1 is a branch inductance; D1-D4 are diodes; S s1 -S s4 is a MOS tube; C1 is a voltage dividing capacitor at the positive end; C2 is a voltage dividing capacitor at the negative end; R O represents an output load; U si is an output DC voltage.

[0050] The voltage sensor is connected to the branch of the branch inductance, and the specific form is not limited (it can be measured by a Hall sensor to measure the current at the terminal of a certain converter to obtain the input voltage value, or a transformer can be externally connected to directly measure the voltage value).

[0051] The input voltage sampling circuit will process the analog quantity obtained by the voltage sensor. This is a kind of common circuit in the power electronics industry, which regulates the analog quantity obtained by the sensor to optimize the signal quality. Usually, it is a proportional operational amplifier circuit, and sometimes other filters are added due to certain EMC requirements. Sometimes, some vehicle-grade power electronic products will have a peak value holder circuit, and some flip-flops are added to build a fault warning circuit. Here, because these things have little effect on the present application, only a brief introduction is given, and no further explanation is given.

[0052] The controller is usually a microprocessor or a specialized DSP controller. They usually have an ADC integrated as a peripheral. The regulated analog quantity output by the input voltage sampling circuit can be converted to digital quantity for later data processing and control.

[0053] For the sake of logicality, this paper briefly introduces the common PFC power factor correction method such as Vienna converter. Most of the PFC switching signal modulation process needs a modulation wave Vm. The modulation wave is usually used to generate the pulse width modulation (PWM) signal to control the switching elements (such as MOSFET or IGBT). These PWM signals determine when the switching elements are turned on or off, thereby controlling the energy flow in the circuit. And the power factor correction process needs to control the input current to the same frequency and phase as the input voltage. This modulation wave Vm is usually a regular sine wave, and is the same frequency and phase as the input voltage, which is used as a reference for the control of the input current in PFC technology. This modulation wave helps to improve the quality of the AC input current, making it closer to a sine wave, thereby improving the power factor.

[0054] There are two common ways to use Vm. In traditional PFC, the digital quantity converted from the analog quantity of the measured input voltage is directly used as the modulation wave Vm. In addition, some PFCs use the fundamental component of the same frequency and phase obtained by some method (such as adaptive notch filter ANF) as the modulation wave Vm.

[0055] No matter which way, if the analog quantity of the input voltage is distorted during measurement and conditioning, it will affect the quality of the final power factor correction. In practical applications, due to various reasons, the sensor or sampling conditioning circuit may have positive and negative characteristics that are not symmetrical, which will cause the input signal to be out of tune. This positive and negative characteristic asymmetry is usually caused by the following factors:

[0056] 1. Sensor nonlinearity: The sensor may have nonlinear response when measuring positive and negative signals, resulting in inconsistent gain or offset of positive and negative signals.

[0057] 2. Circuit defects: Design or device defects of the sampling conditioning circuit, such as poor resistance matching, power supply ripple interference, temperature drift, etc., may cause the processing of positive and negative signals to be asymmetrical.

[0058] 3. Environmental influence: External environmental conditions such as temperature changes, electromagnetic interference, etc. may also cause changes in sensor or circuit performance, thereby causing asymmetry.

[0059] Therefore, a method is needed to compensate for this positive and negative characteristic asymmetry. The present invention is based on this and proposes a method of predicting the positive and negative difference of the input voltage through Kalman filtering, and adjusting the modulation wave Vm according to the compensation voltage value. As Figure 2 The schematic diagram of the sampling deviation and Kalman compensation process is shown. Figure 2In a healthy power grid, the voltage waveform in the grid voltage is close to an ideal sinusoidal wave. After passing through the voltage sensor and sampling circuit, due to various reasons mentioned above, it becomes a positive-negative detuned sinusoidal wave, and then the compensation voltage value is recalculated through the Kalman compensation process of the present application to adjust the modulation wave V m , so that the final controller can obtain a modulation wave V m that is close to an ideal sinusoidal wave.

[0060] As shown in Figure 3 , in this embodiment, the present application includes the following steps:

[0061] S1. Initialization - In the Vienna converter in single-phase mode, initialize the initial state and parameters of the Kalman filter algorithm: select a suitable initial state estimate and an initial error covariance matrix P0;

[0062] S2. Measurement input - Obtain sensor measurements: obtain real-time measurements z k from the voltage sensor in the Vienna converter in single-phase mode;

[0063] S3. State prediction - Predict the state variable at the next time step according to the system model: use the state equation and the prediction error covariance matrix for prediction;

[0064] S4. Measurement update - Update the state estimate using the Kalman gain: calculate the Kalman gain according to the measurement and the predicted value, and update the state estimate;

[0065] S5. Compensation voltage calculation - Calculate the compensation voltage value according to the updated state estimate: use the compensation voltage formula to calculate the compensation voltage value;

[0066] S6. Real-time adjustment - Adjust the modulation wave V m according to the compensation voltage value: real-time adjust the modulation wave V m , reduce the detuning phenomenon;

[0067] S7. Repeat the steps - Loop the above steps: until the system is stable or the desired effect is achieved.

[0068] In this embodiment, the positive-negative difference of the input voltage is set to xk in step S1, and the formula is:

[0069] x k = U peak+ - U peak-

[0070] where U peak+ is the positive voltage value of the input voltage, and U peak- is the negative voltage value of the input voltage.

[0071] In this embodiment, z k is the observed variable, representing the positive or negative difference of the input voltage measured by the voltage sensor.

[0072] In this embodiment, the state equation in step S3 is used to describe the change of the system state over time, and its formula is:

[0073]

[0074] where, is the predicted state at time k, A is the state transition matrix, is the state estimate at time k-1, B is the control input matrix, u k is the control input vector.

[0075] The prediction error covariance matrix is used to describe the uncertainty of the predicted state, and its formula is:

[0076] P k|k-1 = AP k-1|k-1 A T + Q

[0077] where, P k|k-1 is the prediction error covariance matrix at time k, Q is the process noise covariance matrix, P k-1|k-1 is the prediction error covariance matrix at time k-1, A is the state transition matrix. A T is the transpose of the state transition matrix, T is the transpose symbol of the matrix, when a matrix is transposed, its rows and columns will be interchanged, and the new matrix obtained is denoted as

[0078] In this embodiment, the step S4 measurement update includes the following steps:

[0079] S41. Measurement equation: describes the relationship between the measurement and the system state, and its formula is:

[0080]

[0081] where, z k is the input voltage measured at time k, H is the observation matrix, v k is the measurement noise.

[0082] S42. Kalman gain: used to calculate the optimal estimate, and its formula is:

[0083] K k = P k|k-1 H T (HP k|k-1 H T + R) -1

[0084] where K k is the Kalman gain at time k, and R is the measurement noise covariance matrix.

[0085] S43. State update: update the state estimate according to the measurement and the Kalman gain, which is given by:

[0086]

[0087] S44. Error covariance matrix update: describe the uncertainty of the updated state estimate, which is given by:

[0088] P k = (I - K k H) P k|k-1

[0089] I is an identity matrix of the same order, which is a special square matrix whose main diagonal elements are all 1 and all other elements are 0.

[0090] In this embodiment, the step S5 compensation voltage calculation, assuming that the input voltage of the positive and negative difference value of the absolute value of half of the need to compensate to the modulation wave V m , which is given by:

[0091]

[0092] where |x k | represents the absolute value of the state estimate, and V comp is the compensation voltage.

[0093] In this embodiment, the step S5 in the modulation wave V m compensation is to add the compensation voltage V comp to the modulation wave V m , which is given by:

[0094] V m ' = V m + V comp

[0095] where V m ' is the compensated modulation wave.

[0096] Here is a simple matlab to demonstrate the process of predicting the positive and negative peak value difference through Kalman filtering and calculating the compensation signal, the code can be implemented in other high-level language or other forms:

[0097]

[0098]

[0099] To demonstrate this process of Kalman filter prediction and compensation of input voltage sampling deviation in single-phase Vienna converter, we reproduce this process through matlab simulation.

[0100] As Figure 4 shown. The first row of the figure is the actual positive and negative peak value difference over time. The second row of the figure is the estimated positive and negative peak value difference over time. The third row of the figure is the value of the compensation signal over time.

[0101] It can be seen that the Kalman filter has good prediction and tracking effect on the positive and negative peak value difference, and can perfectly estimate the change value of the positive and negative peak value, and at the same time calculate the compensation signal.

[0102] Figure 5 is the compensation effect demonstration diagram of sine wave through Kalman filter in matlab simulation, Figure 5 The first row of the figure is the original sine wave of the actual input grid voltage, which can be seen as a relatively ideal sine wave. The second row of the figure is the sine wave after the sensor or sampling conditioning circuit, which has occurred detuning phenomenon. The third row of the figure is the sine wave after Kalman filter compensation. Finally, this sine wave signal after Kalman filter compensation can be used as the modulation wave Vm.

[0103] In summary, the present application has the following advantages:

[0104] 1. Real-time and adaptability: Kalman filter algorithm can process input voltage signal in real time, and quickly respond in dynamic environment, ensuring that the system can compensate in time under various working conditions. Moreover, the method can dynamically adjust compensation parameters according to the actual state of the system, improving the adaptive ability and robustness of the system, so that the system can better cope with different working environments.

[0105] 2. High precision compensation: the present application accurately predicts and compensates the input voltage signal through Kalman filter algorithm, eliminates the detuning phenomenon of sine wave caused by the asymmetry of positive and negative characteristics of sensor and sampling circuit, and significantly improves the measurement accuracy and control accuracy of the system.

[0106] 3. Improve system stability and power factor: through the effective compensation of Kalman filter algorithm, the negative effects caused by the asymmetry of positive and negative characteristics of input voltage signal are significantly reduced, the stability and power factor of the system are improved, and the overall performance of power electronic converter is optimized.

[0107] 4. Low-cost implementation: Compared with traditional hardware filters and compensation circuits, the present application implements compensation functions through software algorithms, avoiding the cost of adding additional hardware, reducing the complexity and volume of the system, and improving the power density.

[0108] 5. Dynamic adjustment capability: By monitoring sensor measurement values in real time and dynamically adjusting the compensation voltage, it is ensured that the modulation wave is accurately compensated at any time, thereby improving the dynamic response capability and anti-interference capability of the system.

[0109] The present application is applied to the technical field of power electronics.

[0110] Although the embodiments of the present application are described in actual schemes, they do not constitute a limitation on the meaning of the present application, and modifications of the embodiments thereof according to the present specification and combinations with other schemes are obvious to those skilled in the art.

Claims

1. A method for input-side grid Kalman filter compensation in a single-phase Vienna converter, characterized in that, It includes the following steps: S1. Initialization - In the Vienna converter in single-phase mode, initialize the initial state and parameters of the Kalman filter algorithm: select an appropriate initial state estimate. and the initial error covariance matrix P0; S2. Measurement Input - Acquire Sensor Measurements: Acquire real-time measurement values ​​z from the voltage sensor in the Vienna converter in single-phase mode. k ; S3. State Prediction - Predicting the state variables at the next moment based on the system model: using the state equation and the prediction error covariance matrix for prediction; S4. Measurement Update - Update State Estimate with Kalman Gain: Calculate the Kalman gain based on the measurements and predictions, and update the state estimate; S5. Compensation Voltage Calculation - Calculate the compensation voltage value based on the updated state estimate: Calculate the compensation voltage value using the compensation voltage formula; S6. Real-time adjustment - Adjust the modulation wave V according to the compensation voltage value. m Real-time adjustment of modulation wave V m Reduce detuning; S7. Repeat the steps - execute the above steps in a loop until the system is stable or achieves the expected results; In step S1, the positive and negative difference value of the input terminal voltage is set to x. k Its formula is: ， U peak+ U is the peak positive voltage at the input terminal. peak- The negative peak voltage at the input terminal; In step S5, the compensation voltage calculation requires half the absolute value of the positive and negative difference between the input voltages to compensate for the modulation wave V. m In this context, the formula is: ， in, V represents the absolute value of the state estimate. comp To compensate for voltage; The modulation wave V in step S5 m Compensation is to compensate the voltage V comp Added to the modulated wave V m In this context, the formula is: ， in, It is the compensated modulated wave.

2. The method for input-side grid Kalman filter compensation of a single-phase Vienna converter according to claim 1, characterized in that: The single-phase Vienna converter is constructed by disconnecting one branch of the original three-phase Vienna converter from the circuit via a contactor or by directly operating the switch of that branch. The input terminals of the remaining two branches are connected to the positive and negative terminals of the single-phase input, respectively. The single-phase Vienna converter also includes the voltage sensor, the input voltage sampling circuit, and the controller, which are connected in sequence. The voltage sensor is connected to the branch of the inductor. The input voltage sampling circuit is used to process the analog signal obtained by the voltage sensor. The controller is a microprocessor or DSP controller used to convert the conditioned analog signal output by the input voltage sampling circuit into a digital signal for subsequent data processing and control.

3. The method for input-side grid Kalman filter compensation of a single-phase Vienna converter according to claim 1, characterized in that: z in step S2 k The observed variable represents the positive and negative difference in the input voltage measured by the voltage sensor.

4. The method for input-side grid Kalman filter compensation for a single-phase Vienna converter according to claim 3, characterized in that: The state equation in step S3 describes the change of the system state over time, and its formula is: ， in, Let A be the predicted state at time k, and let A be the state transition matrix. This is the state estimate at time k-1, and B is the control input matrix. It controls the input vector; The prediction error covariance matrix describes the uncertainty of the predicted state, and its formula is: ， in, Q is the prediction error covariance matrix at time k, and Q is the process noise covariance matrix.

5. The method for input-side grid Kalman filter compensation for a single-phase Vienna converter according to claim 4, characterized in that: The measurement update in step S4 includes the following steps: S41. Measurement Equation: Describes the relationship between measured values ​​and system state; its formula is: ， in, H is the positive and negative difference of the input voltage measured at time k, and H is the observation matrix. It measures noise; S42. Kalman Gain: Used to calculate the optimal estimate; its formula is as follows: ， in, is the Kalman gain at time k, and R is the measurement noise covariance matrix; S43. State Update: The state estimate is updated based on the measured values ​​and the Kalman gain. The formula is as follows: ， S44. Error Covariance Matrix Update: Describes the uncertainty of the updated state estimate, and its formula is: 。

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