A magnetic network energy router capacitor online monitoring method

Abstract

The application discloses a kind of magnetic network energy router capacitance online monitoring methods, according to the secondary side mathematical model of magnetic network energy router, establish its voltage observation model in combination with piecewise hyperbolic tangent function, and utilize generalized second-order integrator to filter piecewise hyperbolic tangent function noise, improve observation accuracy;Then according to the error of voltage observation model and original mathematical model when secondary side capacitor parameter error exists, the error of observation model and original mathematical model is deduced;Finally, the error characteristics are analyzed in combination with the error of observation model and original mathematical model, and the proportional integral control is combined to realize capacitor online monitoring.The control method realizes the real-time monitoring of the capacitor of magnetic network energy router, and improves the stability and reliability of system operation.

Description

Technical Field

[0001] This invention relates to isolated power electronic converter technology, specifically to an online monitoring method for the capacitor of a magnetic network energy router, belonging to the technical field of calculation, estimation, or counting. Background Technology

[0002] The magnetic network energy router adopts an integrated structure of a multi-terminal power electronic converter and a high-voltage, high-power, multi-winding intermediate frequency transformer. This structure offers numerous advantages, such as flexible port voltage level switching, electrical isolation, and controllable power and power quality. Therefore, the magnetic network energy router can meet the access requirements of various future power sources, energy storage, and load devices, and can significantly improve the performance, efficiency, and reliability of multiple types of integrated energy systems. This technology has attracted significant attention in fields such as AC / DC hybrid distribution networks / microgrids, new energy DC grid connection, electric vehicle charging stations, and data center power supplies.

[0003] Capacitors are crucial components in magnetic network power routers, responsible for storing and releasing energy and stabilizing voltage and current. Capacitor failure or aging in magnetic network power routers can impact system performance and stability. Here are some potential effects: Reduced energy storage capacity: Capacitors store and release electrical energy. When they fail or age, their storage capacity may decrease, potentially leading to insufficient energy supply when handling energy-intensive tasks. Increased voltage fluctuations: Capacitors smooth voltage fluctuations in circuits. If they fail or age, they may not effectively smooth these fluctuations, resulting in increased peak and trough voltage volatility, which can adversely affect the normal operation of other components in the system. Difficult system startup: Capacitors are often used to provide startup current and help the system stabilize. If they fail or age, they may not provide sufficient startup current, leading to difficulty or even failure to start the system. Capacitor leakage: Capacitor aging can damage the internal insulation, causing leakage. Capacitor leakage leads to energy loss and may cause malfunctions in other components in the circuit. In summary, capacitor failure or aging can affect the performance and stability of magnetic network power routers. Therefore, realizing online monitoring of the capacitance of magnetic network power routers is crucial to improving the operational reliability of network power routers.

[0004] Existing converter parameter monitoring technologies utilize least squares methods, Kalman filters, or state-space equation systems. Online monitoring of the capacitors in the H-bridges on both sides of a magnetic network energy router can employ these existing converter parameter monitoring technologies. However, the least squares method requires collecting system parameters over multiple control cycles to ensure accurate capacitor monitoring. Furthermore, the accuracy of least squares capacitor monitoring depends on the forgetting factor parameter, necessitating its design. Kalman filters are complex to design. State-space equation system-based parameter monitoring methods require designing multiple error systems, incorporating Lyapunov stability principles to achieve error coefficient design, which is complex. Applying these converter parameter monitoring methods to a magnetic network energy router increases system complexity and makes it difficult to meet the requirements for real-time monitoring of the capacitor parameters in the H-bridges on both sides.

[0005] In summary, this application aims to propose a capacitance monitoring method for magnetic network energy routers to overcome the aforementioned deficiencies. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of the aforementioned background technology by providing a method for monitoring the capacitor of a magnetic network energy router. This method involves modeling the H-bridge of the magnetic network energy router to achieve real-time observation of current and voltage. It also involves modeling the observed voltage and current values ​​with the capacitor error, analyzing the impact of the observed voltage and current values, and combining this with proportional-integral control. This achieves the goal of real-time monitoring of the magnetic network energy router's capacitor and improving the system's operational reliability, thus solving the technical problem of the lack of a simple and effective solution for online monitoring of the capacitor in a magnetic network energy router.

[0007] To achieve the above-mentioned objectives, the present invention employs the following technical solution:

[0008] The two-port magnetic network energy router consists of two power modules: a primary side and a secondary side. Each power module comprises an H-bridge and a capacitor. Energy transfer between the primary and secondary sides is achieved through a bidirectional transformer. The capacitor is a crucial component in the magnetic network energy router, responsible for storing and releasing energy and stabilizing voltage and current. However, capacitors may age or fail during operation. In such cases, the energy storage capacity of the magnetic network energy router decreases, voltage fluctuations increase, and system startup becomes difficult.

[0009] For the aforementioned magnetic network power router, this invention proposes an online capacitance monitoring method for magnetic network power routers, comprising the following steps:

[0010] S1. Establish an equivalent model of the magnetic network energy router and model its secondary voltage. Based on the mathematical model of the secondary side of the magnetic network energy router, and combined with the piecewise hyperbolic tangent function, establish its voltage observation model, and use a generalized second-order integrator to filter the noise of the piecewise hyperbolic tangent function to improve the observation accuracy.

[0011] S2. Based on the voltage observation model and the original mathematical model, derive the error between the observation model and the original mathematical model when the secondary capacitor parameter error exists.

[0012] S3. By combining the error between the observation model and the original mathematical model, the error characteristics are analyzed, and proportional-integral control is combined to realize online monitoring of the capacitor.

[0013] As a further optimization of the online capacitance monitoring method for magnetic network energy routers, the mathematical model for the secondary side of the magnetic network energy router established by S1 is as follows: Where C2 is the secondary capacitor of the magnetic network energy router, V dc2 I represents the sampled value of the secondary voltage of the magnetic network energy router. s2 I0 is the output current of the H-bridge on the secondary side of the magnetic network energy router, and I2 is the sampled value of the current on the secondary side of the magnetic network energy router.

[0014] As a further optimization of the online capacitor monitoring method for magnetic network energy routers, S1 establishes the secondary voltage observation model of the magnetic network energy router based on the secondary side mathematical model and combined with the piecewise hyperbolic tangent function as follows: , in, Let k1 be the observed voltage on the secondary side of the magnetic network energy router, and k2 be the voltage estimation coefficient on the secondary side of the magnetic network energy router. It is the piecewise hyperbola tangent function.

[0015] As a further optimization of the online capacitor monitoring method for magnetic network energy routers, S1 establishes a secondary current observation model for the magnetic network energy router based on the secondary side mathematical model and combined with the piecewise hyperbolic tangent function. The method involves subtracting the secondary side mathematical model and the secondary side voltage observation model of the magnetic network energy router, and then... The secondary current is obtained by subtracting the results when the current is less than 0, and then filtered using a second-order generalized integrator to obtain the secondary current observation model of the magnetic network energy router. ,in, G represents the observed value of the secondary current of the magnetic network energy router. SOGI Let be the transfer function of the second-order generalized integrator. , k is the system gain of SOGI, and ω0 is the resonant center frequency of SOGI.

[0016] As a further optimization of the online capacitor monitoring method for magnetic network energy routers, the error between the secondary voltage observation model and the secondary mathematical model when errors in the secondary capacitor parameters exist, derived in S2, is: , This is due to the error in the secondary capacitor parameters. , C2 is the estimated parameter of the secondary side capacitance.

[0017] As a further optimization scheme for the online capacitor monitoring method of magnetic network energy routers, the error characteristics of the secondary-side voltage observation model and the secondary-side mathematical model in S3 are obtained by discretizing S2 to obtain the error between the secondary-side voltage observation model and the secondary-side mathematical model when the secondary-side capacitor parameter error exists. Where I2(k-1) is the sampled value of the secondary current at time k-1. The observed current of the secondary side current at time k-1. , The observed voltages on the secondary side of the magnetic network energy router at time k and time k-1 are respectively.

[0018] As a further optimization of the online capacitor monitoring method for magnetic network energy routers, the expression for proportional-integral control of the error between the secondary side observed value and the actual value in S3 is as follows: k p2 and k i2 These are the quadratic proportional coefficient and the quadratic integral coefficient, respectively, C 20 This is the initial value of the secondary capacitor.

[0019] The present invention, by adopting the above technical solution, has the following beneficial effects:

[0020] (1) The magnetic network energy router capacitor monitoring method proposed in this invention can monitor capacitor parameters in real time during each control cycle, quickly identify capacitors that have aged or failed, and effectively improve the reliability of system operation.

[0021] (2) The magnetic network energy router capacitor monitoring method proposed in this invention introduces a piecewise hyperbolic tangent function to establish a secondary side observation model. By designing the observation model of secondary side voltage and current and combining it with PI control, the capacitor is monitored online. The observer and PI controller are simple to design, and only two control cycles of data are needed to accurately identify the capacitor parameters. In the process of establishing the secondary side current observation model, the secondary side observation value is filtered by a generalized second-order integrator to improve the observation accuracy, thereby improving the accuracy of capacitor parameter estimation. Attached Figure Description

[0022] Figure 1 This is a block diagram of the capacitance monitoring and control proposed in this invention.

[0023] Figure 2 This is a flowchart of the capacitance monitoring process proposed in this invention. Detailed Implementation

[0024] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0025] The present invention proposes an online capacitor monitoring method for a magnetic network energy router. The method models the secondary voltage and current of the magnetic network energy router separately, and feeds back the difference between the observed voltage and the sampled voltage into the voltage and current models on both sides in each control cycle. This establishes a model of the observed value and the capacitance value. Combined with a proportional-integral controller, the method realizes real-time capacitor monitoring, thereby ensuring the reliability of the magnetic network energy router system.

[0026] A method for online monitoring of the capacitance of a magnetic network energy router, wherein the magnetic network energy router has the following structure: Figure 1 As shown, the magnetic network energy router contains two power modules: a primary side and a secondary side. Each power module consists of an H-bridge and a capacitor. Energy transfer between the primary and secondary sides is achieved through a bidirectional transformer.

[0027] like Figure 2 The diagram shown is a flowchart of the capacity monitoring process proposed in this invention. It includes the following steps:

[0028] S1. Establish an equivalent model of the magnetic network energy router and model its secondary side voltage. Based on the secondary side mathematical model of the magnetic network energy router and the piecewise hyperbolic tangent function, establish a secondary side voltage observation model. Based on the secondary side mathematical model and the secondary side voltage observation model, establish a secondary side current observation model. Use a generalized second-order integrator to filter the piecewise hyperbolic tangent function noise in the secondary side current observation model to improve the observation accuracy.

[0029] S2. Based on the voltage observation model and the original mathematical model, derive the error between the observation model and the original mathematical model when the secondary capacitor parameter error exists.

[0030] S3. By combining the error between the observation model and the original mathematical model, the error characteristics are analyzed, and proportional-integral control is combined to realize online monitoring of the capacitor.

[0031] The equivalent model of the magnetic network energy router in S1 is as follows: Figure 2 As shown, according to the equivalent model, the secondary voltage and current of the magnetic network energy router can be expressed as:

[0032] (1)

[0033] In the formula: C2 is the secondary capacitor of the magnetic network energy router, V dc2 I represents the sampled value of the secondary voltage of the magnetic network energy router. s2I0 is the output current of the H-bridge on the secondary side of the magnetic network energy router, and I2 is the sampled value of the current on the secondary side of the magnetic network energy router.

[0034] Based on the mathematical model of the magnetic network energy router shown in formula (1), the following secondary voltage observer can be established, satisfying:

[0035] (2)

[0036] In the formula: denoted as , where k2 is the observed voltage on the secondary side of the magnetic network energy router, k2 is the voltage estimation coefficient on the secondary side of the magnetic network energy router, and Y is the piecewise hyperbolic tangent function. Where:

[0037] (3)

[0038] In the formula: x is the observed value, δ is the actual value, and th is the hyperbolic tangent function.

[0039] Subtracting formula (1) from formula (2) yields:

[0040] (4)

[0041] To ensure the convergence of the piecewise hyperbolic tangent function, it is necessary to satisfy the following condition: < 0. At this time, in formula (4) It is also less than zero. According to formula (4), at this time, the secondary current I2 of the magnetic network energy router can be expressed as:

[0042] (5)

[0043] Since the piecewise hyperbolic tangent function Y contains noise, a second-order generalized integrator (SOGI) can be used for filtering. The filtered magnetic network energy router secondary side observation current... It can be represented as:

[0044] (6)

[0045] Where: G SOGI Let be the transfer function of the second-order generalized integrator, which can be expressed as:

[0046] (7)

[0047] In the formula, k is the system gain of SOGI, and ω0 is the cutoff frequency of SOGI.

[0048] When errors exist in the secondary capacitor parameters of the S2 magnetic network energy router, the errors between the observation model and the original mathematical model are as follows:

[0049] In the voltage and current observations shown in formula (2), when the secondary capacitance parameter C2 becomes the estimated secondary capacitance parameter... When formula (6) is substituted into formula (2), it can be expressed as:

[0050] (8)

[0051] Subtracting formula (1) from formula (8) yields:

[0052] (9)

[0053] Considering:

[0054] (10)

[0055] In the formula: This is due to the error in the secondary capacitor parameters. ; The voltage estimation error is due to observations on the secondary side of the magnetic network energy router. .

[0056] Therefore, formula (9) can be further expressed as:

[0057] (11)

[0058] When the piecewise hyperbolic tangent function converges =0. Therefore, formula (11) can be further expressed as:

[0059] (12)

[0060] Discretizing formula (12) yields:

[0061] (13)

[0062] The online capacitor monitoring based on proportional-integral control in S3 is shown below:

[0063] To further illustrate the impact of capacitance error on voltage and current observations, we can see from formula (13):

[0064] (14)

[0065] According to formula (14), a method for online capacitance monitoring can be designed using a PI controller, as shown in the following formula:

[0066] (15)

[0067] In the formula: k p2 and ki2 Let C represent the proportional coefficient and integral coefficient of the second side, respectively. 20 This represents the initial value of the secondary capacitor.

[0068] The above embodiments are merely illustrative of the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solutions based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

Claims

1. A method for online monitoring of the capacitance of a magnetic network energy router, characterized in that, Includes the following steps: S1. Establish an equivalent model of the magnetic network energy router, establish a mathematical model of the secondary side of the magnetic network energy router, and then establish a secondary side voltage observation model and a secondary side current observation model of the magnetic network energy router based on the secondary side mathematical model of the magnetic network energy router and in combination with the piecewise hyperbolic tangent function. S2. Based on the secondary voltage observation model and the secondary mathematical model of the magnetic network energy router, derive the error between the secondary voltage observation model and the secondary mathematical model when the secondary capacitor parameter error exists. S3. Obtain the error characteristics of the secondary voltage observation model and the secondary mathematical model when the error of the secondary capacitor parameters derived by discretization S2 exists. Based on the error characteristics, perform proportional-integral control on the variables proposed for the error between the observed value and the actual value of the secondary current and the difference between the observed values ​​of the secondary voltage at adjacent times to obtain the estimated parameters of the secondary capacitor.

2. The method for online monitoring of capacitance in a magnetic network energy router according to claim 1, characterized in that, The secondary mathematical model of the magnetic network energy router established by S1 is as follows: Where C2 is the secondary capacitor of the magnetic network energy router, V dc2 I represents the sampled value of the secondary voltage of the magnetic network energy router. s2 I0 is the output current of the H-bridge on the secondary side of the magnetic network energy router, and I2 is the sampled value of the current on the secondary side of the magnetic network energy router.

3. The method for online monitoring of capacitance in a magnetic network energy router according to claim 2, characterized in that, The voltage observation model for the secondary side of the magnetic network energy router, established by S1 based on the mathematical model of the secondary side of the magnetic network energy router and combined with the piecewise hyperbolic tangent function, is as follows: , in, Let k1 be the observed voltage on the secondary side of the magnetic network energy router, and k2 be the voltage estimation coefficient on the secondary side of the magnetic network energy router. It is the piecewise hyperbola tangent function.

4. The method for online monitoring of capacitance in a magnetic network energy router according to claim 3, characterized in that, The method for establishing the secondary-side current observation model of the magnetic network energy router based on the secondary-side mathematical model and the piecewise hyperbolic tangent function in S1 is as follows: The difference between the secondary-side mathematical model and the secondary-side voltage observation model of the magnetic network energy router is calculated, and then... The secondary current is obtained by subtracting the results when the current is less than 0, and then filtered using a second-order generalized integrator to obtain the secondary current observation model of the magnetic network energy router. ,in, G represents the observed value of the secondary current of the magnetic network energy router. SOGI Let be the transfer function of the second-order generalized integrator. , k is the system gain of SOGI, and ω0 is the resonant center frequency of SOGI.

5. The method for online monitoring of capacitance in a magnetic network energy router according to claim 4, characterized in that, When the secondary capacitor parameter error derived in S2 exists, the error between the secondary voltage observation model and the secondary mathematical model is: , This is due to the error in the secondary capacitor parameters. , C1 represents the estimated parameters of the secondary side capacitance, and C2 represents the secondary side capacitance parameters.

6. The method for online monitoring of capacitance in a magnetic network energy router according to claim 5, characterized in that, In step S3, when the error of the secondary-side capacitor parameter derived through discretization step S2 exists, the error characteristics of the secondary-side voltage observation model and the secondary-side mathematical model are obtained. Specifically: Where I2(k-1) is the sampled value of the secondary current at time k-1. The observed current of the secondary side current at time k-1. , The observed voltages on the secondary side of the magnetic network energy router at time k and time k-1 are respectively.

7. The method for online monitoring of capacitance in a magnetic network energy router according to claim 6, characterized in that, The expression for proportional-integral control of the variables in S3, which are the errors between the observed and actual secondary current values ​​and the differences between the observed secondary voltage values ​​at adjacent times, is as follows: k p2 and k i2 These are the quadratic proportional coefficient and the quadratic integral coefficient, respectively, C 20 This is the initial value of the secondary capacitor.

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