Device and method for hybrid control for mitigation of ibr sso considering optimized control parameters

Abstract

An inverter-based resource vibration mitigation device and method through hybrid control applying control parameter optimization are disclosed. According to one embodiment, an inverter-based resource vibration mitigation device for controlling a grid-following (GFL) inverter connected to a grid includes a PLL control unit having a synchronous reference frame phase-locked loop (SRF-PLL) control structure, and the PLL control unit includes an auxiliary signal feedback loop that receives the output of the PLL control unit, generates an auxiliary signal, and then provides negative feedback to the PLL.

Description

Technology Field

[0001] The present invention relates to in-grid inverter-based resource output control, and more specifically, to a control device and method for mitigating output oscillations of inverter-based resources by improving an inverter control structure that includes internal / external control and Phase-Locked Loop (PLL) control. Background Technology

[0002] The use of inverter-based resources (IBRs) is increasing worldwide. As the use of IBRs grows, power systems become weakened and vulnerable, requiring improvements in stability control and operational technologies. However, in current IBR-based power systems, control and operation are not fully adaptive or optimized.

[0003] In 2021 and 2022, large-scale IBR tripping events occurred in the Odessa, Texas region, which were caused by the premature operation of the IBR in the event of a fault. Additionally, various instabilities, including overvoltage, overcurrent, and subsynchronous oscillation (SSO), are actually occurring. To address this, various studies are being conducted to analyze the mechanisms of these instabilities. Among these instabilities, SSO events are frequently reported and are known as IBR SSO. In particular, this is observed in typical IBRs such as wind and photovoltaic (PV) power generation.

[0004] Most IBR SSOs are frequently observed under unexpected grid conditions where AC grid strength is weakened. A power grid is considered weak in terms of stability when the Short-Circuit Ratio (SCR) is less than 4. Typically, because PV or wind power plants are installed in remote areas with long transmission lines, grid strength can be vulnerable to disturbances. Under these grid conditions, vibrations occur due to the instability of PLL control.

[0005] Due to the increasing impact of IBR and the resulting increase in instability, IBR has been included in the definition and classification of power system stability. Consequently, new types of inverter-related stability, known as resonance stability and converter-driven stability, have been added. These types of stability are classified into fast interaction and slow interaction, which are primarily influenced by IBR control. For example, external or internal control affects fast interaction, while PLL control affects slow interaction.

[0006] In this invention, a hybrid control structure is proposed that combines improved external loop feedback with auxiliary input signals of PLL control. In addition to the hybrid control structure, Bayesian optimization is applied to select optimal control parameters, and the objective function is tuned through Pearson Correlation Analysis (PCA) to prevent local minimum problems and expand the optimization search area for parameters with non-linear characteristics. Prior art literature

[0007] Republic of Korea Published Patent No. 2023-0069321 (Published May 19, 2023) Republic of Korea Published Patent No. 2022-0143339 (Published October 25, 2022) The problem to be solved

[0008] The technical problem that the present invention aims to solve is to provide an inverter-based resource vibration mitigation device and method through hybrid control applying control parameter optimization. means of solving the problem

[0009] An inverter-based resource vibration mitigation device for controlling a grid-following (GFL) inverter connected to a grid, according to one embodiment of the present invention, comprises a PLL control unit having a synchronous reference frame phase-locked loop (SRF-PLL) control structure, and the PLL control unit comprises an auxiliary signal feedback loop that receives the output of the PLL control unit, generates an auxiliary signal, and then provides negative feedback to the PLL. Effects of the invention

[0010] Through a new effective output feedback structure, control can be configured to converge the effective output error more quickly. In addition, by adding a PLL vibration mitigation auxiliary signal, robust control can be performed even in weak systems and unstable PLL control situations.

[0011] Therefore, through a hybrid control technique combining effective output feedback control and PLL auxiliary control, the oscillation output of the inverter can be rapidly mitigated, ultimately contributing to the improvement of grid stability. Brief explanation of the drawing

[0012] Detailed descriptions of each drawing are provided to help to more fully understand the drawings cited in the detailed description of the present invention. Figure 1 illustrates a grid-connected IBR system with transmission lines. Figure 2 shows a block diagram of SRF-PLL control in a dq frame. Figure 3 shows a Bode plot and step response according to PLL bandwidth. Figure 4 illustrates an analysis model for hybrid control in an IBR dq frame. FIG. 4a is a functional block diagram illustrating an effective output control feedback structure. FIG. 4b is a functional block diagram illustrating a PLL auxiliary control structure. Figure 5 is a circuit diagram of the analysis model. Figure 6 shows root loci according to various control methods. Figure 7 illustrates the results of comparing the time-domain responses for four control methods. Figure 8 shows the Pearson correlation coefficients of the feedback external control and the PLL auxiliary control. Fig. 9 is G out and G aux t for all constraint ranges of damp City. Figure 10 shows t for all constraint ranges. damp City. FIG. 10a illustrates a flowchart for hybrid control parameter optimization operation. Figure 11 illustrates the configuration of the EMT simulation model. Figure 12 shows the simulation results in terms of PCC bus active power output, PCC bus voltage magnitude, d-axis grid current, q-axis grid current, and PCC bus frequency. Figure 13 illustrates the simulation results in the time domain for a hybrid kinetic using non-optimized parameters and optimized parameters. Specific details for implementing the invention

[0013] Specific structural or functional descriptions of embodiments according to the concept of the present invention disclosed herein are provided merely for the purpose of explaining embodiments according to the concept of the present invention, and embodiments according to the concept of the present invention may be implemented in various forms and are not limited to the embodiments described herein.

[0014] Since embodiments according to the concept of the present invention may be subject to various modifications and may take various forms, embodiments are illustrated in the drawings and described in detail in this specification. However, this is not intended to limit the embodiments according to the concept of the present invention to specific disclosed forms, and includes all modifications, equivalents, or substitutions that fall within the spirit and scope of the present invention.

[0015] Terms such as "first" or "second" may be used to describe various components, but said components should not be limited by said terms. For the sole purpose of distinguishing one component from another, for example, without departing from the scope of rights according to the concept of the present invention, the first component may be named the second component and similarly the second component may be named the first component.

[0016] When it is stated that one component is "connected" or "connected" to another component, it should be understood that while it may be directly connected or connected to that other component, there may also be other components in between. Conversely, when it is stated that one component is "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between. Other expressions describing the relationships between components, such as "between" and "exactly between," or "adjacent to" and "directly adjacent to," should be interpreted in the same way.

[0017] The terms used herein are used merely to describe specific embodiments and are not intended to limit the invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this specification, terms such as “comprising” or “having” are intended to indicate the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described herein, and should not be understood as precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0018] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the present invention pertains. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this specification.

[0019] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings attached to this specification. However, the scope of the patent application is not limited or restricted by these embodiments. Identical reference numerals in each drawing indicate identical components.

[0020] First, I will explain the instability of IBR.

[0021] Weak Grid (Weak System)

[0022] FIG. 1 illustrates a grid-connected IBR system with a transmission line. In the present invention, since a grid-following (GFL) inverter is investigated, the IBR operates as a current source, and the IBR controller regulates the output through current control that adjusts the dq current in the IBR control dq-reference frame. d-axis voltage at the Point of Common Coupling (PCC). It is aligned with the IBR control dq-frame in steady-state operation through PLL control. Then, the active power and reactive power are calculated as follows.

[0023] [Mathematical Formula 1]

[0024]

[0025] [Mathematical Formula 2]

[0026]

[0027] Here, V PCC is the voltage magnitude in the PCC, and and are the d-axis current and q-axis current in the IBR-controlled dq frame, respectively. PCC voltage It can be expressed as follows.

[0028] [Mathematical Formula 3]

[0029]

[0030] Here, is the q-axis voltage in the PCC. R g and X g are the resistance and reactance of the transmission line, respectively, and is the grid voltage. Since the d-axis of the IBR-controlled dq-frame is aligned with the reference frame of the PCC voltage, the PCC voltage in the IBR-controlled dq-frame satisfies the following relationship.

[0031] [Mathematical Formula 4]

[0032]

[0033] [Mathematical Formula 5]

[0034]

[0035] Here, V G,d and V G,q These are the d-axis and q-axis grid voltages in the IBR-controlled dq frame, respectively.

[0036] Since IBR oscillation is a type of small-signal instability, its root cause can be analyzed through linearization. To this end, the power deviation △P in the bus is expressed as follows.

[0037] [Mathematical Formula 6]

[0038]

[0039] Here, Igo is. In the relationship between PCC voltage and grid voltage, is the angle difference σ Since it leads, It can be expressed as follows.

[0040] [Mathematical Formula 7]

[0041]

[0042] Here, V G,d = V G cosσ and V G,q = -V G It is sinσ. Then, V G,d It can be rewritten as follows.

[0043] [Mathematical Formula 8]

[0044]

[0045] Generally, X on transmission lines g is R g Assume it is much larger than. If the X / R ratio is close to 10, then R in Equation 8 g can be ignored. Therefore, V PCC wa △V PCC is X g and has the following relationship with α.

[0046] [Mathematical Formula 9]

[0047]

[0048] [Mathematical Formula 10]

[0049]

[0050] Here, am.

[0051] Ultimately, grid reactance X g is △P v It is related to. The magnitude of the voltage deviation in the PCC bus is α 2 It increases as α approaches 1, and △P v The increase in size leads to grid instability. IBR current Since the IBR current cannot be negative when flowing through the grid, a short-circuit current (i.e., V G / X g Instability occurs when approaching ). Therefore, high X g The grid becomes sensitive to disturbances, which can cause instability that generates significant fluctuations. This grid state is called a weak grid.

[0052] PLL instability

[0053] In a conventional PLL, the PCC voltage undergoes an abc / dq transformation using the PLL angle θ. In the SRF-PLL (synchronous reference frame phase-locked loop) control structure shown in Fig. 2, the q-axis voltage of the PCC bus (i.e., ) is the PCC voltage magnitude (i.e., V PCC ) and the phase angle difference between the PCC voltage and the PLL (i.e., It can be obtained from ). Then, through PI control and integration blocks It is used to generate.

[0054] PLL control is IBR control phase angle PCC voltage phase angle It synchronizes with, which indicates that it regulates the IBR output and consequently affects the grid. In practice, the bandwidth of PLL control has a significant impact on synchronization performance. From Fig. 2, the open-loop transfer function G(s) can be expressed as follows.

[0055] [Mathematical Formula 11]

[0056]

[0057] Here, K p,pll and K i,pll These are PI control parameters.

[0058] Figure 3 shows the PI control parameter K p,pll and K i,pll This demonstrates how the bandwidth and response of the PLL are determined. PLL control with low bandwidth can lead to PCC synchronization failure due to a slow response. On the other hand, if the bandwidth is too high, it can allow high-frequency noise to pass through, causing system instability. Ultimately, an appropriate PLL bandwidth that is neither too high nor too low must be selected.

[0059] Hybrid control structure

[0060] To mitigate the instability described above, the present invention proposes a hybrid control structure comprising two improved control methods. The improved control methods are based on existing control structures. Hybrid control, which combines external control and PLL control, can more effectively mitigate vibrations by utilizing the advantages of both control methods.

[0061] A. External Feedback Control

[0062] External control uses a PI controller and power and voltage feedback from the PCC bus, dq-current references and It adjusts. According to mathematical equations 6 and 10, the power deviation △P in the PCC bus is wa △V PCC Affected by, △V PCC is X g It has a high correlation with and It has a negative relationship with . Therefore, the feedback external control, as shown in Fig. 4, It must be designed to mitigate vibrations in.

[0063] In external feedback control, △P can be expressed as follows.

[0064] [Mathematical Formula 12]

[0065]

[0066] [Mathematical Formula 13]

[0067]

[0068] Here, G out is the gain parameter, and V ref is the IBR voltage reference. External feedback control is It has a negative feedback loop from to △P, and adjusts the power order accordingly. At this time, V ref It is assumed to be 1 pu. G out By appropriately adjusting, △P out △P v It can compensate for fluctuations. As a result, the feedback outer loop can quickly dampen vibrations and prevent excessive current fluctuations.

[0069] B. PLL Auxiliary Control

[0070] Even if the PLL bandwidth is correctly set, oscillations may occur under weak grid conditions. Therefore, as shown in FIG. 4, PLL auxiliary control is added to the PLL control to maintain synchronization and mitigate oscillations. For robust synchronization, an auxiliary input is adopted and designed as follows.

[0071] [Mathematical Formula 14]

[0072]

[0073] Here, △u aux is an auxiliary input. PLL phase-angle deviation To compensate for, a washout filter, lead-lag filters, and a gain parameter G aux is used. The washout filter is T w It is a high-pass filter that blocks low-frequency inputs with a time constant. Then, a Reed-Lag filter with parameters T1, T2, T3, and T4 compensates for the phase-angle deviation caused by disturbance. Consequently, It can be compensated as follows.

[0074] [Mathematical Formula 15]

[0075]

[0076] In the end, △u aux By adding this, the PLL phase angle can actively follow the phase angle of the PCC voltage. Accordingly, PLL control can quickly detect and compensate for oscillations.

[0077] C. Hybrid Control Structure

[0078] Figure 4 illustrates a proposed hybrid control model including feedback external control and PLL auxiliary control. This model includes a cascade control structure and SRF-PLL control. Cascade control consists of two control loops: an outer loop and an inner loop. If the inner control is performed quickly, the external control can significantly contribute to the stability of the IBR control. Therefore, the proposed method improves the external control through feedback external control, thereby reducing the system reactance X g It compensates for △P associated with it. Ultimately, it can contribute to vibration relief in the weak system.

[0079] Considering that system dynamics are influenced by line impedance, filter components, and the topology between the IBR and the system, the PLL auxiliary control of the proposed method can overcome the limitations of SRF-PLL control (following the same unstable phase angle when the PCC-connected system becomes unstable). Ultimately, auxiliary PLL control can help mitigate oscillations caused by system instability and incorrect PLL bandwidth. However, unlike the above, either external feedback control or PLL auxiliary control alone may be adopted and utilized.

[0080] Referring again to FIG. 4, the hybrid control unit for grid-linked inverter control applied in the present invention may be composed of an internal / external control unit (100), a PLL control unit (200), an axis conversion unit (110), and a grid connection (20). The inverter internal / external control unit (100) is largely composed of an internal control unit and an external control unit. The d-axis external control unit generates a d-axis current command through effective output control, and the q-axis external control unit generates a q-axis current command through connection point voltage control. The d and q-axis internal control units generate voltage commands that follow the current commands generated by external control. The corresponding command is converted into a three-phase command through axis conversion of the axis conversion calculation unit (110) using the phase angle synchronized through the PLL control unit (200). Power is supplied to the interconnected system (20) by the operation of the switching element (11) by the PWM control signal (which may mean the three-phase command itself, 12) according to the three-phase command. Unlike conventional control techniques, an effective output feedback control unit (120) is included in the inverter internal / external control unit (100). A PLL auxiliary control unit (220) is added to the PLL control unit (200) to improve control performance.

[0081] FIG. 4a is a functional block diagram illustrating an effective output control feedback structure.

[0082] Referring to FIG. 4a, the active power feedback control unit applied to the hybrid control of the present invention is composed of a negative feedback loop (120) that compensates for current error, a control coefficient (130, which may also be referred to as a control unit), and a connection point voltage command value (140). Through the negative feedback loop (120), the error between the current command value and the measured value is added to the active power command compensation to improve control performance. The compensation can be adjusted through the control coefficient (130), and the connection point voltage command value (140) can be adjusted according to the grid conditions.

[0083] FIG. 4b is a functional block diagram illustrating a PLL auxiliary control structure.

[0084] Referring to FIG. 4b, the PLL auxiliary signal control unit (200) applied to the hybrid control of the present invention may be composed of an auxiliary signal feedback loop (220), a washout filter (230), a reed-lag filter (240), and a control coefficient (250, which may also be referred to as a control unit). The PLL control output vibration component is extracted through a washout filter (230) that blocks low-frequency components. The component can contribute to the mitigation of PLL control output vibration by operating as an auxiliary signal through negative feedback after phase adjustment through the reed-lag filter (240). The degree of mitigation can be adjusted through the control coefficient (250) included in the auxiliary signal feedback loop (220).

[0085] Control parameter optimization

[0086] The proposed hybrid control is G out , G aux , T w It has seven additional parameters including T1, T2, T3, and T4. The present invention performs PCA (Pearson correlation analysis) between the objective function and control parameters, and then selects appropriate parameters to enhance system stability through Bayesian optimization. PCA is applied to feedback external control and PLL auxiliary control.

[0087] A. Objective function

[0088] The objective function F(x) is the damping time t with two weights λ1 and λ2. damp and the maximum magnitude of the d-axis system current It is set to minimize. After performing PCA on the feedback external control and PLL auxiliary control, λ1 and λ2 are determined. The objective function and constraints of optimization are formulated as follows.

[0089] [Mathematical Formula 16]

[0090]

[0091] PCA is performed before optimization, and accordingly, the following parameters can be determined.

[0092] [Mathematical Formula 17]

[0093]

[0094] [Mathematical Formula 18]

[0095]

[0096] [Mathematical Formula 19]

[0097]

[0098] [Mathematical Formula 20]

[0099]

[0100] Here, a out and a aux are respectively t damp and G out , G aux It is the Pearson correlation coefficient between, and b out and b aux are each and G out , G aux These are the Pearson correlation coefficients. m and n are fixed proportional gains. Also, α and β are the average values ​​of the absolute Pearson correlation coefficients.

[0101] The Pearson correlation coefficient indicates a linear dependency between the control parameter and the objective function. A high correlation coefficient indicates a linear relationship between the control parameter and the objective function. In this case, the optimal control parameter can be easily found. On the other hand, a low correlation coefficient indicates that there is no linear relationship between the two variables, making it difficult to find the optimal parameter. Therefore, for an objective function with a low correlation coefficient, the search area can be expanded and the local minimal problem prevented during the optimization process by adjusting the ratio of the objective function by assigning a higher λ.

[0102] B. Bayesian Optimization

[0103] The key elements of Bayesian optimization are the surrogate model and the acquisition function. Furthermore, to estimate the true objective function, a Gaussian process model is often used within the surrogate model. This function is defined as follows.

[0104] [Mathematical Formula 21]

[0105]

[0106] Here, X = [x1, x2, … , x n ] T is the input, Y = [y1, y2, … , y n ] T is the output, n is the number of data, D n = (X, Y) are data pairs, and Since the Gaussian process model follows a Gaussian distribution, Y and the estimated output y can be expressed as follows.

[0107] [Mathematical Formula 22]

[0108]

[0109] Here, I is the identity matrix, = is an n×n correlation matrix, and = is the correlation vector calculated for all pairs of X and x. Equation 22 is for given data pairs D n and predict the conditional distribution of the new input x.

[0110] Then, to evaluate the objective function, optimize the gain function to the next point x *Find . The expected improvement (EI) is used as the gain function, and it is expressed as follows.

[0111] [Mathematical Formula 23]

[0112]

[0113] Here, p(y|x) is the predictive marginal density of the objective function at x, and y * is the minimum value among the previous results. Next point x * Using, the output y of the true objective function n This is obtained, and data pairs D n This is being updated. D n A Gaussian process model reflecting this is updated, and the optimization process is repeated until the maximum number of iterations is reached.

[0114] The optimization process described above may be performed by a computing device comprising at least a processor and / or memory. In other words, at least some of the steps constituting the optimization process (or optimization method) may be understood as the operation of a processor included in the computing device, in which case the computing device may be referred to as an optimization device. The computing device may include a PC (Personal Computer), a tablet PC, a server, a laptop computer, etc. According to an embodiment, the computing device may be implemented as a single device or may be implemented as a plurality of devices to form a distributed environment.

[0115] Analysis model-based simulation

[0116] Below, as illustrated in Fig. 5, simulation results and analysis utilizing an analysis model are presented. MATLAB / Simulink is used for the simulation and analysis. Small-signal analysis based on the model and linearization is performed to calculate eigenvalues. Finally, control methods are compared in terms of small-signal stability, and the results are verified through time-domain simulation.

[0117] PCA uses time-domain simulation results to find mathematical relationships between control parameters and oscillation mitigation time, and between parameters and the maximum magnitude of the current. This relationship is expressed by Pearson correlation coefficients and is used to tune the objective function in Bayesian optimization.

[0118] A. Evaluation of the stability of the control method

[0119] Figure 6 shows root loci according to various control methods. Looking at Figure 6, Figure 6 (a) is similar to Figure 6 (c), and Figure 6 (d) appears to be a combination of the characteristics of Figures 6 (b) and (c). Table I lists the parameters of the analysis model. However, the values ​​in Table I are not optimized.

[0120] [Table I]

[0121]

[0122] Figure 6 shows the grid reactance X for various control methods. g This shows the trajectories of the root locus as α increases from 0.2 pu. As can be seen in the figure, X at the boundary root locus between the stable and unstable conditions g varies depending on the control method. That is, X gare 0.83 pu, 0.8 pu, 0.872 pu, and 0.878 for conventional control, external feedback control, PLL auxiliary control, and hybrid control, respectively. Therefore, the hybrid control method is the most robust against grid weakening. At this time, X g The larger the value, the weaker the grid strength.

[0123] Figure 7 illustrates the results of comparing the time-domain responses for four control methods. In the simulation, a small perturbation occurs at 1 second, and the IBR power reference increases slightly from 1 pu to 1.01 pu. The system and control parameters are set according to Table I. As can be seen in Figure 7, oscillations of approximately 5 Hz appear in all control methods, and the response diverges in the case of the conventional control method. Furthermore, the PLL-assisted control method and the hybrid control method show better performance than the other two methods because the response converges quickly within 2 seconds after the perturbation. The performance of the hybrid control is slightly superior to that of the PLL-assisted control because the hybrid control exhibits smaller current fluctuations. In conclusion, these time-domain responses are consistent with the eigenvalue analysis in Figure 6, and through eigenvalue and time-domain analysis, it was verified that the proposed hybrid control method shows the best performance among the four control methods.

[0124] B. Correlation Analysis

[0125] PCA is based on time-domain simulation X g It is performed by varying the parameter from 0.2 to 0.84 pu. In this simulation, divergence results are excluded. Figure 8 plots the Pearson correlation coefficients for feedback external control and PLL auxiliary control. For feedback external control, G out class The Pearson correlation coefficient between them is -0.9923, and G out and t dampThe Pearson correlation coefficient between them is -0.8436. This indicates a high linear correlation. For PLL auxiliary control, G aux class The Pearson correlation coefficient between them is -0.5275, and G aux and t damp The Pearson correlation coefficient between them is -0.466. This indicates a lower linear correlation than the external feedback control.

[0126] C. Bayesian Method-Based Parameter Optimization

[0127] Based on the Pearson correlation coefficients, the objective function parameters α, β, λ1, and λ2 can be calculated as 0.6548, 0.7599, 1.3456, and 0.7396, respectively. Here, m and n are fixed at 1. To verify the proposed Bayesian optimization performance, G in Equation 16 for the hybrid control model out and G aux For all constraint ranges, time domain simulations are performed, and the results are shown in Figures 9 and 10.

[0128] Figure 9 shows t derived by the proposed Bayesian optimization and generalized Bayesian optimization after 80 iterations. damp The comparison results are plotted, and the top 30 results are indicated by red and green dots, respectively. The red dots, representing the results of the proposed Bayesian optimization, show a wider distribution than general Bayesian optimization, indicating that the proposed method covers a broader search area. Figure 10 shows t for all constraint ranges. damp Plotting this, the top 30 results after 200 iterations in the proposed Bayesian optimization are indicated by red dots. As can be seen in Fig. 10, the optimization results are identical G aux It converges to. Unoptimized G aux and G out At the average t damp is 2.04 s, and the optimized G aux and G out At the average tdamp is 1.27 s. This result shows that significant improvement was achieved through optimization.

[0129] EMT simulation-based verification

[0130] In the following, the effectiveness of the proposed hybrid control method is verified through EMT simulation using MATLAB SimPowerSystems. As shown in FIG. 11, the test system (1000, which may refer to a system according to one embodiment of the present invention) includes a 100 kW IBR, and the system and control parameters are shown in Table II. The AC grid is a voltage source with a fixed voltage magnitude and frequency of 1 pu and 60 Hz, respectively. It is assumed that the inverter is connected to a 500 V DC power source and generates 3-phase 260 V power from the AC grid.

[0131] [Table II]

[0132]

[0133] Referring to FIG. 11, the system (20) connected to the inverter may be composed of an inverter filter (21), a transmission line representing the system impedance (22), and an external system (23) which is the upper system. The system impedance (22) is directly related to the actual line length and can simulate a weak system condition through adjustment. The grid-connected inverter is composed of a power source (10) that supplies power and a switching element (11), and supplies power to the system according to a PWM control signal (12). The PWM control signal (12) is generated by an output command determined by an internal / external controller (100) for inverter control. Phase synchronization with the connection point voltage is possible through the PLL control unit (200), and this is used for axis conversion calculations by the inverter internal / external control unit (100) and the axis conversion unit (110). An axis conversion unit (110) is used for conversion between the three-phase and dq axes for control, and the axis conversion unit (110) may include an axis conversion unit for conversion between the three-phase and dq axes and an axis conversion unit for conversion between the dq axes and three-phase.

[0134] A. Verification of EMT using hybrid control method

[0135] Below, the hybrid control method and the conventional control method are compared to demonstrate vibration mitigation. Time-domain simulations are performed using the non-optimized parameters in Table I. In the simulation, the perturbation event represents a situation where the IBR active power output rapidly increases to 0.02 pu. The total simulation time is 3 seconds, and the perturbation event occurs at 1 second and persists until the end of the simulation. To reflect weak system conditions, the grid reactance X g is assumed to be 0.87 pu.

[0136] FIG. 12 shows the PCC bus active power output P and PCC bus voltage magnitude V. PCC , d-axis grid current , q-axis grid current Simulation results in terms of , and PCC bus frequency are plotted. In the case of the conventional control method, divergence occurs with 5 Hz oscillation, which is consistent with the eigenvalue analysis in Fig. 6 (a). In contrast, the hybrid control method quickly mitigates oscillation despite the unoptimized parameters. P quickly converges to 1.02 pu, and other variables also stabilize, finding a new operating point.

[0137] B. Verification of EMT in Hybrid Control Method Using Bayesian Optimization

[0138] To enhance vibration mitigation capability, optimized control parameters are applied to the hybrid control method. Bayesian optimization is performed to appropriately select the control parameters. Based on PCA, the optimization parameters m, n, α, β, λ1, and λ2 are selected as previously calculated. After 100 iterations of Bayesian optimization, the control parameters are optimized and listed in Table III. For weak system conditions, the grid reactance X gis assumed to be 0.89 pu.

[0139] [Table III]

[0140]

[0141] The simulation results are shown in Fig. 13. The hybrid control method with optimized control parameters successfully mitigates vibration and rapidly stabilizes all variables. As shown in Fig. 13, the optimized control parameters are effectively applied to IBR control by adjusting the control gains, washout constant, and lead-lag filter constants. Consequently, the proposed hybrid control design and optimization approach contribute to vibration mitigation and the formation of a robust control structure.

[0142] The device described above may be implemented as a set of hardware components, software components, and / or a combination of hardware components and software components. For example, the device and components described in the embodiments may be implemented using one or more general-purpose or special-purpose computers, such as, for example, a processor, a controller, an Arithmetic Logic Unit (ALU), a Digital Signal Processor (DSP), a microcomputer, a Field Programmable Array (FPA), a Programmable Logic Unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing unit may execute an Operation System (OS) and one or more software applications executed on said OS. Additionally, the processing unit may access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing unit may be described as being used as a single unit, but those skilled in the art will understand that the processing unit may include multiple processing elements and / or multiple types of processing elements. For example, the processing unit may include multiple processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are also possible.

[0143] Software may include computer programs, code, instructions, or a combination of one or more of these, and may configure a processing unit to operate as desired or command the processing unit independently or collectively. Software and / or data may be permanently or temporarily embodied in any type of machine, component, physical device, virtual equipment, computer storage medium or device, or transmitted signal wave in order to be interpreted by the processing unit or to provide instructions or data to the processing unit. Software may be distributed over networked computer systems and may be stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

[0144] The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., either individually or in combination. The program instructions recorded on the medium may be those specifically designed and configured for the embodiment, or they may be those known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; ROMs; RAMs; flash memory; and hardware devices specifically configured to store and execute program instructions. Examples of program instructions include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operation of the embodiment, and vice versa.

[0145] The present invention has been described with reference to the embodiments illustrated in the drawings, but this is merely illustrative, and those skilled in the art will understand that various modifications and equivalent alternative embodiments are possible therefrom. For example, suitable results may be achieved even if the described techniques are performed in a different order than described, and / or the components of the described system, structure, device, circuit, etc. are combined or assembled in a form different from described, or replaced or substituted by other components or equivalents. Accordingly, the true technical scope of protection of the present invention should be determined by the technical spirit of the appended claims.

Claims

Claim 1 An inverter-based resource vibration mitigation device for controlling a grid-following (GFL) inverter connected to a grid, comprising a PLL control unit having an SRF-PLL (synchronous reference frame phase-locked loop) control structure, wherein the PLL control unit comprises an auxiliary signal feedback loop that receives the output of the PLL control unit, generates an auxiliary signal, and then provides negative feedback to the PLL. Claim 2 An inverter-based resource vibration mitigation device according to claim 1, wherein the auxiliary signal feedback loop comprises a washout filter that receives the output of the PLL control unit, a lead-lag filter that receives the output of the washout filter, and a control unit that applies gain to the output of the lead-lag filter. Claim 3 In paragraph 2, the above auxiliary signal is and, the above G aux An inverter-based resource vibration mitigation device, wherein is the gain of the control unit, T1, T2, T3, and T4 are predefined parameters, and △θ is the output of the PLL control unit. Claim 4 In paragraph 3, the auxiliary signal is the q-axis voltage of the connection point in the PLL ( Inverter-based resource vibration damping device that receives negative feedback to ). Claim 5 An inverter-based resource vibration mitigation device according to claim 4, further comprising an internal / external control unit that outputs a d-axis voltage command and a q-axis voltage command, wherein the internal / external control unit comprises an external control unit that outputs a d-axis current command and an internal control unit that receives the d-axis current at a connection point with the output of the external control unit and outputs the d-axis voltage command. Claim 6 An inverter-based resource vibration mitigation device according to claim 5, wherein the external control unit further includes a feedback structure that receives the difference between the d-axis current command and the d-axis current at the linkage point and provides negative feedback to the input of the external control unit. Claim 7 In claim 6, the external control unit receives a power command, an output at the connection point, and negative feedback, an inverter-based resource vibration mitigation device. Claim 8 In claim 7, the feedback structure provides a negative feedback value obtained by applying a predetermined gain to the value obtained by multiplying the difference between the d-axis current command and the d-axis current at the linkage point by a voltage command. Claim 9 An inverter-based resource vibration mitigation device according to claim 8, further comprising a first axis conversion unit that receives the output of the internal / external control unit, performs dq-3 phase axis conversion, and outputs a Pulse Width Modulation (PWM) control signal of the grid following (GFL) inverter.

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