Mining dual active bridge dc converter predictive control system input impedance modeling method

Abstract

The application discloses a kind of input impedance modeling methods of mining dual active bridge DC converter prediction control system, belong to power electronic converter and its modeling technical field.The output voltage of dual active bridge DC converter system is sampled, the state space average model and its discrete state prediction model of system are established;Then a kind of double-target performance function is designed using this prediction model, and the expression of optimal phase-shift reference value of dual active bridge DC converter prediction control system is obtained using the way of partial derivative minimum value;Finally, the small signal model based on the above prediction control system is established, and the input impedance analytical expression model of mining dual active bridge DC converter is established by the small signal model.The modeling method can provide theoretical model and basis for the stability analysis of multiple dual active bridge DC converter interconnection systems, effectively improve the output voltage quality and stability of the system, and fill the gap of input impedance analytical modeling difficulty of mining dual active bridge DC converter under prediction control system.

Description

Technical Field

[0001] This invention relates to a method for modeling the input impedance of a predictive control system for a dual active bridge DC-DC converter used in mining, belonging to the field of power electronic converters and their modeling technology. Background Technology

[0002] Traditional mine development has long been constrained by low mining efficiency, high labor costs, and significant safety risks. Therefore, the demand for smart mines, which utilize modern technologies to address these issues, is increasing. Smart mines are based on the digitalization and informatization of the entire mining system and continuously update their technologies. By proactively collecting, analyzing, and processing information from all parts of the mine, they gain a comprehensive understanding of the mine's safety and production status, ultimately achieving the goal of building safe, efficient, clean, and unmanned modern mines. However, due to the complex structure of actual mine sites, the dispersed distribution of resources, and the vast area of ​​the mine, powering smart mine equipment often presents challenges such as excessive distances between multiple power supply and distribution facilities and poor power quality. To ensure safe, stable, and reliable power supply and distribution, and to improve the power quality of mining equipment, distributed generation devices, primarily solar and wind power, are typically used to construct on-site DC microgrid power distribution systems for the mine.

[0003] The DC microgrid power distribution system in mines can effectively interconnect different power supply and distribution facilities, offering advantages such as fast and flexible control, low line loss, high efficiency, and high power quality. However, due to the different power supply and consumption voltage levels of different facilities, voltage level conversion is required when connecting different facilities to a DC bus with a unified voltage level. Currently, the most advantageous power electronic converter topology is the isolated dual active bridge DC converter (DAB). The DAB has advantages such as good dynamic performance, high power transmission efficiency, bidirectional power flow, and simple control methods, making it very suitable as a voltage conversion device between DC power consumption facilities and the DC bus in smart mines. Currently, DABs mostly use traditional proportional-integral (PI) control or single-objective predictive control to regulate their output voltage. Because PI control involves many design parameters, parameter adjustment is difficult in practical engineering and has poor adaptability to sudden changes in operating conditions. In contrast, predictive control, with its intuitive concept, fewer parameters, and multi-objective optimization, has become an effective alternative to traditional PI control. However, most existing dual active bridge DC-DC converters employ single-objective predictive control, resulting in significant steady-state voltage error, which may affect the operational reliability of mining equipment. Furthermore, in actual mine power distribution systems, the diverse voltage levels often necessitate the interconnection of multiple dual active bridge DC-DC converters, leading to system instability. Currently, the industry often uses impedance analysis, specifically impedance modeling of mining dual active bridge DC-DC converters, to assess the stability of interconnected systems. However, current impedance modeling methods for dual active bridge DC-DC converters are based on traditional PI control systems. Predictive control systems differ significantly from traditional PI control in both principle and structure, rendering impedance modeling based on PI control inapplicable. Moreover, analytical modeling methods for the input impedance of dual active bridge DC-DC converters under emerging predictive control systems are currently unreported, compromising the operational stability of dual active bridge DC-DC converter systems under predictive control. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an input impedance modeling method for a predictive control system of a mining dual active bridge DC converter. This modeling method can provide a theoretical model and analytical basis for improving the interconnection stability of multiple dual active bridge DC converter systems in coal mines, improve the output voltage quality and stability of the power supply device of the mining dual active bridge DC converter, and fill the gap in analytical modeling of the input impedance of dual active bridge DC converters under predictive control closed-loop systems.

[0005] To achieve the above technical objectives, the present invention provides a method for modeling the input impedance of a predictive control system for a mining dual active bridge DC-DC converter, comprising the following steps:

[0006] Step 1: Acquire the state variables of the dual active bridge DC-DC converter system during each control cycle: system output voltage u at the sampling time. ok ;

[0007] Step 2: Based on the state variable information of the dual active bridge DC-DC converter system collected in Step 1, establish a state-space average model of the dual active bridge DC-DC converter system.

[0008] Step 3: Based on the state-space average model of the dual active bridge DC-DC converter system established in Step 2, construct the discrete state prediction model of the dual active bridge DC-DC converter system.

[0009] Step 4: Based on the discrete state prediction model of the dual active bridge DC-DC converter system established in Step 3, construct a dual objective performance function g based on the prediction output voltage reference tracking error and its integral minimization;

[0010] Step 5: Based on the bi-objective performance function g constructed in Step 4, calculate the optimal phase-shifting reference value d for the predictive control system that minimizes the performance function g. * The expression;

[0011] Step 6: Compare the state-space average model of the dual active bridge DC-DC converter system in Step 2 with the optimal phase-shifting reference value d for predictive control in Step 5. * The state variables in the expression are converted to small-signal form to establish a small-signal model of the dual active bridge DC converter under a predictive closed-loop control system.

[0012] Step 7: Based on the small-signal model of the closed-loop system obtained in Step 6, establish the analytical model of the input impedance of the mining dual active bridge DC converter under the predictive closed-loop control system.

[0013] Furthermore, in step 2, the dual active bridge DC-DC converter operates in single-phase-shift mode, and the system state-space average model within one sampling period can be expressed as:

[0014]

[0015] In the formula, The average system input current over one sampling period. The average value of the system output current over one sampling period. The average value of the system input voltage over one sampling period. The average system output voltage over one sampling period is given by R, the load resistance is given by C, the parallel capacitance at the output is given by n, the transformer turns ratio is given by L, and the auxiliary inductance is given by f. s d is the switching frequency, and d is the phase shift value of the controller.

[0016] Furthermore, the discrete state prediction equation of the dual active bridge DC-DC converter in step 3 can be expressed as:

[0017]

[0018] In the formula, u ok+1 The predicted value of the system output voltage at the next sampling time can be obtained by simplifying the state-space average model in step 2 using the following forward Euler discretization method:

[0019]

[0020] In the formula, t k and t k+1 T represents the current sampling time and the next sampling time, respectively. c The sampling period.

[0021] Furthermore, the specific design of the bi-objective performance function g in step 4, which is based on minimizing the predicted output voltage reference tracking error and its integral, is as follows:

[0022]

[0023] In the formula, U oref K is the reference value for the system output voltage. i E is the performance function adjustment factor. acc This is the cumulative error between the predicted system output voltage and the reference system output voltage obtained from each iteration before the k-th iteration.

[0024] Furthermore, in step 5, the partial derivative of the performance function g with respect to the controller phase shift value d is calculated to determine the optimal phase shift reference value d of the predictive control system that minimizes the performance function g. * The expression is as follows:

[0025]

[0026] Among them, variable M re The specific expression is as follows:

[0027]

[0028] In the formula, U inref This is the reference value for the system input voltage.

[0029] Furthermore, in step 6, the system state-space average model in step 2 and the phase-shifting reference value d in step 5 are compared... * The state variables in the expression can be signaled as follows:

[0030]

[0031] In the formula, I in The steady-state value of the system input current. I is the small disturbance value of the system input current. out This represents the steady-state value of the system output current. This represents the small disturbance value of the system output current. This represents the small disturbance value of the system input voltage. D represents the small disturbance value of the system output voltage, and D represents the steady-state phase-shift value of the controller. This represents the small disturbance value for phase shift in the controller.

[0032] Ignoring the DC and nonlinear terms in the above equation, the small-signal model of the dual active bridge DC converter established in step 6 under the predictive closed-loop control system can be specifically expressed as follows:

[0033]

[0034]

[0035] Furthermore, the analytical model of the input impedance of the mining dual active bridge DC converter established in step 7 under the predictive closed-loop control system can be established as follows:

[0036]

[0037] In the formula, Z inDAB (s) represents the input impedance of the mining dual active bridge DC-DC converter under the predictive closed-loop control system, Z outDAB (s) represents the load impedance, expressed as:

[0038]

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0040] 1. The method designed in this invention provides a theoretical model and basis for analyzing the interconnection stability of multiple dual active bridge DC-DC converter systems in coal mines;

[0041] 2. The method provided by this invention helps to improve the steady-state accuracy and stability of the output voltage quality of the mining dual active bridge DC converter power supply device;

[0042] 3. The method provided by this invention fills the gap in analytical modeling of the input impedance of dual active bridge DC converters under predictive closed-loop control systems. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the topology of the predictive control system for a mining dual active bridge DC-DC converter according to the method of the present invention.

[0044] Figure 2 Here is a detailed schematic diagram of the small signal model;

[0045] Figure 3 A schematic diagram showing the analytical modeling and simulation frequency sweep comparison verification of the input impedance of a dual active bridge DC-DC converter under predictive control;

[0046] Figure 4 This is a schematic diagram of the steady-state output waveform of a predictive control system for a dual active bridge DC-DC converter used in mining. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0048] Figure 1 This is a schematic diagram of the predictive control system structure of the mining dual active bridge DC-DC converter of the present invention. The DC power supply terminal U in the diagram... in A capacitor C is connected in parallel to the input side of the DAB converter, and the output side of the DAB converter is connected to the load side. The load side consists of capacitor C and load R connected in parallel. The left side of the DAB converter consists of an H-bridge composed of four Insulated Gate Bipolar Transistors (IGBTs) S1-S4 and diodes connected in anti-parallel to these IGBTs. An auxiliary inductor L and the primary side of a transformer T are connected in series between the midpoint of the left and right arms of the H-bridge. The transformer has a turns ratio of n:1. The secondary side of the transformer is connected between the midpoints of the two arms of the H-bridge on the right side of the DAB converter. The H-bridge on the right side of the DAB converter consists of four IGBTs S5-S8 and diodes connected in anti-parallel to these four IGBTs. The system control section acquires the system output voltage U. o Subsequently, after calculations using the discrete state prediction model obtained from the system state-space average model, the results are substituted into the bi-objective performance function to finally calculate the optimal phase-shifting reference value d. * This result is applied to the control signals of the two H-bridges of the DAB converter, ultimately enabling the system output voltage to track its reference value; based on the optimal phase-shifting reference value d... * The small-signal model of the dual active bridge DC-DC converter under the predictive closed-loop control system is obtained by comparing the system state-space average model with the small-signal model. Based on this small-signal model, the analytical model of the input impedance of the mining dual active bridge DC-DC converter under the predictive closed-loop control system can be established; in the figure, I in with I outThese are the input current and output current of the DAB converter system, respectively.

[0049] Figure 2 This is a detailed block diagram of the small-signal model. From this small-signal model, the analytical model of the input impedance of the mining dual active bridge DC-DC converter under the predictive closed-loop control system can be derived; the transfer functions in the block diagram are respectively... , , , , .

[0050] A method for modeling the input impedance of a predictive control system for a mining dual active bridge DC-DC converter includes the following steps.

[0051] Step 1: Acquire the state variables of the dual active bridge DC-DC converter system during each control cycle: system output voltage u at the sampling time. ok .

[0052] Step 2: Based on the state variable information of the dual active bridge DC-DC converter system collected in Step 1, establish the state-space average model of the dual active bridge DC-DC converter system as follows:

[0053]

[0054] In the formula, The average system input current over one sampling period. The average value of the system output current over one sampling period. The average value of the system input voltage over one sampling period. The average system output voltage over one sampling period is given by R, the load resistance is given by C, the parallel capacitance at the output is given by n, the transformer turns ratio is given by L, and the auxiliary inductance is given by f. s d is the switching frequency, and d is the phase shift value of the controller.

[0055] Step 3: Based on the system state-space average model of the dual active bridge DC-DC converter in Step 2, construct the discrete state prediction model of the dual active bridge DC-DC converter system, which can be expressed as:

[0056]

[0057] In the formula, u ok+1 The predicted value of the system output voltage at the next sampling time can be obtained by simplifying the state-space average model in step 2 using the following forward Euler discretization method:

[0058]

[0059] In the formula, t k and t k+1T represents the current sampling time and the next sampling time, respectively. c The sampling period.

[0060] Step 4: Based on the discrete state prediction model of the dual active bridge DC-DC converter system established in Step 3, construct a dual-objective performance function g based on the prediction output voltage reference tracking error and its integral minimization. The specific design is as follows:

[0061]

[0062] In the formula, U oref K is the reference value for the system output voltage. i E is the performance function adjustment factor. acc This is the cumulative error between the predicted system output voltage and the reference system output voltage obtained from each iteration before the k-th iteration.

[0063] Step 5: Based on the bi-objective performance function g constructed in Step 4, calculate the partial derivative of the performance function g with respect to the controller phase shift value d, thereby calculating the optimal phase shift reference value d of the predictive control system that minimizes the performance function g. * The expression is as follows:

[0064]

[0065] in, Let it be a variable.

[0066] Step 6: Compare the state-space average model of the dual active bridge DC-DC converter system in Step 2 with the optimal phase-shifting reference value d for predictive control in Step 5. * The state variables in the expression are signal-mode, as follows:

[0067]

[0068] In the formula, I in The steady-state value of the system input current. I is the small disturbance value of the system input current. out This represents the steady-state value of the system output current. This represents the small disturbance value of the system output current. This represents the small disturbance value of the system input voltage. D represents the small disturbance value of the system output voltage, and D represents the steady-state phase-shift value of the controller. This represents the small disturbance value for phase shift in the controller.

[0069] Ignoring the DC and nonlinear terms in the above equation, the small-signal model of the dual active bridge DC converter under a predictive closed-loop control system can be specifically expressed as follows:

[0070]

[0071] In the formula , , , These are their coefficients.

[0072] Step 7: Based on the small-signal model of the closed-loop system obtained in Step 6, the analytical model of the input impedance of the mining dual active bridge DC-DC converter under this predictive closed-loop control system can be established as follows:

[0073]

[0074] In the formula, Z inDAB (s) represents the input impedance of the mining dual active bridge DC-DC converter under a predictive closed-loop control system. This is the load impedance.

[0075] To verify the input impedance modeling method of the predictive control system for a dual active bridge DC-DC converter in mining provided by this invention, the method provided by this invention was applied to the predictive control system for a dual active bridge DC-DC converter, and its system parameters are given in Table 1.

[0076] Table 1

[0077]

[0078] Figure 3 This diagram illustrates the analytical modeling and simulation frequency sweep comparison of the input impedance of a dual active bridge DC-DC converter under predictive control. The solid line represents the Bode plot curve drawn based on the analytical expression of the DAB input impedance model under predictive control, while the circles represent the frequency sweep using MATLAB / Simulink software. Specifically, a set of small sinusoidal perturbation signals with a fixed amplitude of 5V and a frequency varying from 1Hz to 1kHz is injected into the DAB input terminal, and the DAB input impedance at each frequency point is plotted by measuring the corresponding response on the output side. Figure 3 The comparison reveals that the input impedance curve obtained using the DAB input impedance analytical model established in this invention completely coincides with the impedance value obtained by frequency sweeping using MATLAB / Simulink. This proves the correctness of the input impedance modeling method for the predictive control system of the mining dual active bridge DC converter provided in this invention.

[0079] Figure 4 The three parts are the steady-state output voltage waveform, the steady-state output voltage error waveform, and the auxiliary inductor current waveform of the predictive control system of the mining dual active bridge DC converter; Figure 4 It can be seen that under the control of the dual objective performance function, the output voltage of the system can be stabilized at around 200 V, which basically eliminates static error and can meet the requirements for reliable operation of equipment under mining conditions.

Claims

1. A method for modeling the input impedance of a predictive control system for a dual active bridge DC-DC converter used in mining, characterized in that, Includes the following steps: Step 1: Acquire the state variables of the dual active bridge DC-DC converter system during each control cycle: system output voltage u at the sampling time. ok ; Step 2: Based on the state variable information of the dual active bridge DC-DC converter system collected in Step 1, establish a state-space average model of the dual active bridge DC-DC converter system. Step 3: Based on the state-space average model of the dual active bridge DC-DC converter system established in Step 2, construct the discrete state prediction model of the dual active bridge DC-DC converter system. Step 4: Based on the discrete state prediction model of the dual active bridge DC-DC converter system established in Step 3, construct a dual objective performance function g based on the prediction output voltage reference tracking error and its integral minimization; Step 5: Based on the bi-objective performance function g constructed in Step 4, calculate the optimal phase-shifting reference value d for the predictive control system that minimizes the performance function g. * The expression; Step 6: Compare the state-space average model of the dual active bridge DC-DC converter system in Step 2 with the optimal phase-shifting reference value d for predictive control in Step 5. * The state variables in the expression are converted to small-signal form to establish a small-signal model of the dual active bridge DC converter under a predictive closed-loop control system. Step 7: Based on the small-signal model of the closed-loop system obtained in Step 6, establish the analytical model of the input impedance of the mining dual active bridge DC converter under the predictive closed-loop control system. The discrete state prediction model is shown below: In the formula, u ok+1 u represents the predicted value of the system output voltage at the next sampling time. ok This represents the system's output voltage at the current sampling moment. The input voltage is the average value of the system over one sampling period, R is the load resistance, C is the parallel capacitance at the output, L is the auxiliary inductance, and f is the capacitance. s Where n is the switching frequency, n is the transformer turns ratio, d is the controller phase shift value, and T is the switching frequency. c The sampling period is obtained by simplifying the state-space averaged model in step 2 using the following forward Euler discretization method: In the formula, The average value of the system output voltage over one sampling period is t. k and t k+1 These represent the current sampling time and the next sampling time, respectively. The dual-objective performance function g is shown below: In the formula, U oref K is the reference value for the system output voltage. i E is the performance function adjustment factor. acc This is the cumulative error between the predicted system output voltage value and the reference system output voltage value obtained in each iteration before the k-th iteration. The optimal phase-shifting reference value d of the predictive control system * The expression is shown below; Among them, variable M re The specific expression is as follows: In the formula, U inref This is the reference value for the system input voltage.

2. The input impedance modeling method for the predictive control system of a mining dual active bridge DC-DC converter according to claim 1, characterized in that, In step 2, the dual active bridge DC-DC converter operates in single-phase-shift mode, and the system state-space average model within one sampling period is: In the formula, The average input current of the system over one sampling period. This represents the average system output current over one sampling period.

3. The method for modeling the input impedance of a predictive control system for a mining dual active bridge DC-DC converter according to claim 2, characterized in that, In step 6, the system state-space average model from step 2 and the phase-shifting reference value d from step 5 are compared. * The state variables in the expression are signal-mode, as follows: In the formula, I in The steady-state value of the system input current. I is the small disturbance value of the system input current. out This represents the steady-state value of the system output current. This represents the small disturbance value of the system output current. This represents the small disturbance value of the system input voltage. D represents the small disturbance value of the system output voltage, and D represents the steady-state phase-shift value of the controller. This represents the small disturbance value for phase shift in the controller. Ignoring the DC and nonlinear terms in the above equation, the small-signal model of the dual active bridge DC converter established in step 6 under the predictive closed-loop control system is specifically expressed as follows: 。 4. The method for modeling the input impedance of a predictive control system for a mining dual active bridge DC-DC converter according to claim 3, characterized in that, The analytical model of the input impedance of the mining dual active bridge DC converter established in step 7 under the predictive closed-loop control system is as follows: In the formula, Z inDAB (s) represents the input impedance of the mining dual active bridge DC-DC converter under the predictive closed-loop control system, Z outDAB (s) represents the load impedance, expressed as: 。

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