Grid-connected converter impedance self-adaptive adjusting method and device based on power grid voltage feedforward

Abstract

The invention discloses a grid-connected converter impedance self-adaptive adjustment method based on power grid voltage feedforward. The method comprises the following steps: acquiring operation fluctuation data of a target grid-connected converter system; generating a working condition sample set based on the operation fluctuation data, and performing offline optimization to obtain a control parameter scheme set; performing polynomial function fitting based on the control parameter scheme set to construct a mapping relation between each control parameter of the voltage feed-forward path and an operation function; and monitoring the target grid-connected converter system by using the monitoring model, generating a corresponding control parameter scheme based on the monitoring result and the function mapping relationship, and performing impedance remodeling on the voltage feed-forward path. The invention further provides a grid-connected converter impedance self-adaptive adjusting device. According to the method provided by the invention, the output impedance of the grid-connected converter can be adaptively adjusted and remodeled, so that stable operation of a grid-connected converter system where the grid-connected converter is located is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of grid-connected converter control, and in particular relates to a grid-connected converter impedance adaptive adjustment method and device based on grid voltage feedforward. Background Art

[0002] The impedance-based stability analysis method is widely used to analyze the grid-connected stability problem between the grid-connected converter and the power grid. This method models the converter and the power grid as an independent subsystem respectively, and represents their port characteristics in the form of impedance. Then, the impedance-based stability criterion is used to analyze the stability of the grid connection. This method requires obtaining the output impedance of the grid-connected converter and the input impedance of the grid side. However, when the converter is integrated into the power grid, the grid impedance at different common coupling points (PCC) is different, and with the switching and operating conditions of the grid-connected converter in the line, the grid impedance changes slightly over time, and the changes in the operating conditions of the grid-connected converter itself also cause its output impedance to change within a certain range. These changing impedances may cause the original stable grid-connected converter system to experience small signal oscillation instability.

[0003] One strategy for this type of instability problem is controller parameter tuning. Related studies generally reduce the bandwidth of the phase-locked loop to enhance the stability of the grid-connected converter; however, excessive restrictions may weaken its control performance, so it is necessary to find a delicate balance between stability and control performance. Another strategy is virtual impedance reshaping. The use of virtual impedance can also improve the stability of the grid-connected converter system, but in order to achieve accurate virtual impedance reshaping, a large amount of complex theoretical derivation and calculation is required, and the complexity of accurate embedding is high. Since the weak grid cascade stability of the system and the dynamic performance of the converter itself are often contradictory, how to adjust the instability problem of the grid-connected converter and to what extent it should be adjusted have become widely studied issues.

[0004] Patent document CN111884252A discloses a control method for a grid-connected inverter of a renewable energy power generation system based on impedance reshaping, comprising the following steps: (1) collecting the PCC voltage of the renewable energy power generation system and feeding it forward into the current loop; (2) improving the phase-locked loop structure and adding a dual second-order generalized integrator to the phase-locked loop control loop; (3) performing impedance modeling on the renewable energy power generation system; (4) drawing the proportional feedforward coefficient K in the same Bode diagram; f =i, i = 0.1, 0.2, ..., 1, the output impedance of the grid-connected inverter and the frequency characteristic curve of the grid impedance are used to calculate the phase margin of the positive sequence system at different i; and the phase margin of the negative sequence system PM n , select PM p With PM nThe i corresponding to the maximum value of the sum is used as the final proportional feedforward coefficient; (5) If the control parameters of the renewable energy power generation system or the grid impedance conditions change, return to step (4); If the renewable energy power generation system or the large grid fails or oscillates, disable the control method and start the relay protection device. Summary of the invention

[0005] The object of the present invention is to provide a method and device for adaptively adjusting the impedance of a grid-connected converter based on grid voltage feedforward, which can adaptively adjust and reshape the output impedance of the grid-connected converter to ensure the stable operation of the grid-connected converter system in which the grid-connected converter is located.

[0006] In order to achieve the first object of the present invention, the following technical solution is provided: a method for adaptively adjusting impedance of a grid-connected converter based on grid voltage feedforward, comprising the following steps:

[0007] Acquiring operation fluctuation data of the target grid-connected converter system, which includes the fluctuation range of the grid impedance and the fluctuation range of the output power;

[0008] Generate a gridded operating condition sample set at equal intervals based on the operating fluctuation data, and use the simulated annealing-particle swarm algorithm to perform offline optimization on the voltage feedforward path in the grid-connected converter system to obtain a control parameter solution set of the stability margin under the operating condition sample set;

[0009] Performing polynomial function fitting based on the control parameter scheme set to construct a function mapping relationship between each control parameter of the voltage feedforward path and the operation fluctuation data;

[0010] The target grid-connected converter system is monitored using a pre-built monitoring model, a corresponding control parameter scheme is generated using the monitoring result and the function mapping relationship, and the impedance of the voltage feedforward path is reshaped based on the generated control parameter scheme.

[0011] The present invention constructs offline data and fits it in combination with the relationship function to obtain the corresponding function mapping relationship, and then obtains the current control parameter scheme based on the pre-constructed monitoring model, thereby further realizing the impedance reshaping of the voltage feedforward path, thereby ensuring that the grid-connected converter system has a reasonable cascade stability margin under different grid impedances, and greatly improving the adaptability of the grid-connected converter to different working conditions and grid strengths.

[0012] Specifically, the impedance reshaping of the voltage feedforward path is implemented based on a Q-axis grid-connected point voltage feedforward control strategy of an adaptive proportional link and a bandpass filter.

[0013] Specifically, the transfer function expression of the bandpass filter is as follows:

[0014]

[0015] Among them, ω c =2πf c represents the center frequency of the bandpass filter, K represents the passband width of the bandpass filter, and f c Represents the center frequency of the bandpass filter.

[0016] Specifically, the control parameter scheme includes the center frequency and action width of the bandpass filter in the voltage feedforward path, and the voltage feedforward coefficient.

[0017] Specifically, the center frequency of the bandpass filter is obtained by selecting the amplitude cutoff frequency of the grid-connected converter output impedance and the grid-side input impedance in the grid-connected converter system.

[0018] Specifically, the voltage feed-forward path is optimized offline with the optimal amplitude margin and phase margin being 2 dB and 30° respectively as the target, so as to obtain a set of control parameter solutions.

[0019] Specifically, the objective function expression of the offline optimization is as follows:

[0020] fitness=αG m_c -2|+βP m_c -30|+γK|+χK v |

[0021] Among them, G m_c Represents the grid-connected cascade amplitude margin of the grid-connected converter system, P m_c represents the phase margin of the grid-connected converter system, K represents the filter action width, K v is the voltage feed-forward coefficient, and α, β, γ and χ are weight coefficients.

[0022] In order to achieve the second object of the present invention, the following technical solution is provided: a grid-connected converter impedance adaptive adjustment device, which is implemented by the above-mentioned grid-connected converter impedance adaptive adjustment method based on grid voltage feedforward.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] The grid-connected converter impedance adaptive adjustment method based on grid voltage feedforward can dynamically and adaptively adjust the grid-connected converter port impedance according to the current grid impedance and converter operating conditions, which can ensure that the grid-connected converter system has a certain stability margin within a certain range without excessively deteriorating the system's own characteristics. It is suitable for occasions where the converter output conditions and the grid-side impedance vary within a certain range and are prone to oscillation and instability. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1A schematic diagram of a grid-connected converter impedance adaptive adjustment scheme based on grid voltage feedforward provided in this embodiment;

[0026] Figure 2 The impedance reshaping principle diagram of the voltage feed-forward path provided in this embodiment;

[0027] Figure 3 Bode diagram of the qq-axis impedance of the grid-connected converter and the power grid under different conditions provided in this embodiment;

[0028] Figure 4 The SA-PSO algorithm flow chart provided for this embodiment;

[0029] Figure 5 A schematic diagram of a fitting surface for the control parameter solution provided in this embodiment. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0031] like Figure 1 As shown, the adaptive impedance reshaping method provided in this embodiment includes the following steps:

[0032] Acquiring operation fluctuation data of the target grid-connected converter system, which includes the fluctuation range of the grid impedance and the fluctuation range of the output power;

[0033] Generate a gridded operating condition sample set at equal intervals based on the operating fluctuation data, and use the simulated annealing-particle swarm algorithm to perform offline optimization on the voltage feedforward path in the grid-connected converter system to obtain a control parameter solution set of the stability margin under the operating condition sample set;

[0034] Performing polynomial function fitting based on the control parameter scheme set to construct a function mapping relationship between each control parameter of the voltage feedforward path and the operation fluctuation data;

[0035] The target grid-connected converter system is monitored using a pre-built monitoring model, a corresponding control parameter scheme is generated using the monitoring result and the function mapping relationship, and the impedance of the voltage feedforward path is reshaped based on the generated control parameter scheme.

[0036] Furthermore, based on the operation fluctuation data of the grid-connected converter system in a certain working scenario, the grid impedance L is selected. g The fluctuation range is 10~16mH, and the output d-axis active current I d The fluctuation range is 20~40A.

[0037] Within the selected working range, grid sample operating points are generated at equal intervals. For each sample operating condition, a simulated annealing-particle swarm optimization (SA-PSO) hybrid algorithm is used to perform offline optimization design on the relevant parameters of the voltage feedforward path, so as to obtain a set of control parameter solutions for the stability margin under the sample set of operating conditions.

[0038] Among them, the process of searching for the optimal solution of parameters using the SA-PSO algorithm is as follows:

[0039] Step 2-1, initialize the inertia weight ω, learning factors c1, c2 and annealing rate δ required by the algorithm.

[0040] Step 2-2: Randomly initialize the position and velocity of the particles.

[0041] Step 2-3: Calculate the fitness of each particle and record the position of each particle and the global optimal position.

[0042] Step 2-4: Calculate the initial temperature T of the annealing algorithm based on the global optimal fitness.

[0043] Step 2-5: Calculate the fitness f of each particle annealing algorithm at the current temperature SA .

[0044] Step 2-6: Use the roulette strategy to select the individual optimal solution instead of the global optimal solution.

[0045] Step 2-7: Update the speed and position of each particle, as well as the optimal position of individuals and groups.

[0046] Step 2-8: Annealing operation, gradually lowering the current temperature.

[0047] Step 2-9: Determine whether the termination condition is met. If not, jump to step 2-5.

[0048] Specifically, the fitness objective function expression in the SA-PSO optimization algorithm is as follows:

[0049] fitness=αG m_c -2|+βPm_c -30|+γK|+χK v

[0050] Among them, G m_c and P m_c They represent the grid-connected cascade amplitude margin and phase margin of the grid-connected converter system, K represents the filter action width, and K v is the voltage feed-forward coefficient, and α, β, γ and χ are weight coefficients.

[0051] Specifically, the fitness function f of the annealing algorithm SA The expression is as follows:

[0052]

[0053] Where P pd represents the global optimal position, P id represents the position of the ith particle, fitness(P pd )、fitness(P id ) represent the corresponding fitness function values, while the inertia weight ω, learning factors c1, c2, initial temperature T and annealing rate δ can be determined according to actual conditions and experience.

[0054] According to the control parameter scheme set obtained above, a polynomial function is fitted, and the fitting order is selected as n(L g )=5、n(I d )=3, a functional mapping relationship between each parameter of the feedforward path and the grid impedance and output power is established to achieve coverage from discrete sample points to continuous full operating conditions.

[0055] The obtained function mapping relationship is deployed in the target grid-connected converter system, combined with the monitoring model for detecting the output power and grid-side impedance, to achieve the parameter adaptive adjustment of the voltage feedforward path. The specific process is as follows:

[0056] Step 1-1: Establish the dq domain port impedance model Z of the grid-connected converter and the grid respectively o and Z g , extract the qq-axis impedance Z that dominates the grid stability oqq and Z gqq Used for stability analysis.

[0057] Step 1-2: According to the possible working scenarios, different power levels and different grid impedances are changed to construct data sample sets, and the required bandpass filter center frequency ω under different samples is determined by the amplitude intersection frequency of the theoretical impedance Bode diagram of the two. c .

[0058] Step 1-3: Define the optimal grid-connected amplitude margin of the grid-connected converter system as 2 dB, the optimal grid-connected phase margin as 30°, and ensure that K and K are as small as possible. v The SA-PSO algorithm is used for different samples to realize the filter action width K and voltage feedforward coefficient K. v The optimal solution search is formed to form a complete sample point set containing the design results {(L g ,I d ,f c1 ,K,K v )}.

[0059] Step 1-4: Based on the polynomial function fitting tool, L g and I d As independent variables, determine ω c , K, K v About L g and I d The functional relationship ω c =f(L g ,I d ), K = g(L g ,I d ), K v =h(L g ,I d ).

[0060] Step 1-5: By deploying the relationship obtained in step 1-4 in the converter system, ω under different grid impedance and active current conditions can be determined in real time. c , K, K v The value of is used to realize dynamic adaptive voltage feedforward impedance adjustment.

[0061] like Figure 2 As shown in FIG. 1 , it is a schematic diagram of the impedance reshaping provided in this embodiment, that is, a Q-axis voltage feedforward strategy via an adaptive proportional link and a bandpass filter is adopted, wherein K v is the feedforward proportional coefficient, G BPF is the bandpass filter transfer function, expressed as follows:

[0062]

[0063] Among them, ω c =2πf c represents the center frequency of the bandpass filter, and K is the passband width of the bandpass filter.

[0064] This method can realize the impedance reshaping strategy at the precise instability frequency. The instability frequency can be uniquely determined by the grid impedance and active current. By using a bandpass filter, the range of virtual impedance can be accurately controlled to achieve precise impedance reshaping without affecting other frequency bands. Furthermore, the virtual impedance parameter ω c Determine as follows:

[0065] Output impedance Z of grid-connected converter in dq domain o and grid input impedance Z g They can be expressed as:

[0066]

[0067] Since the cause of weak grid instability of the grid-connected converter is the negative resistance characteristic introduced by the phase-locked loop in the qq axis, which makes the phase difference between the output impedance of the grid-connected converter and the input impedance of the grid in the cutoff frequency band too large, resulting in oscillation instability, the qq axis port impedance that dominates the stability can be extracted, and the grid-connected converter system can be simplified into a single-input single-output (SISO) system for analysis.

[0068] like Figure 3 As shown in FIG. 1 , the qq-axis impedance bode diagram of the grid-connected converter and the grid under different grid impedance and converter working conditions provided by this embodiment is shown. As the power or grid impedance increases, the intersection frequency of the converter output impedance and the grid input impedance gradually decreases. Therefore, the impedance intersection frequency (i.e., the center frequency ω c ) can be uniquely determined by the system power and grid impedance, and the center frequency ω obtained based on the impedance Bode diagram c The value can be fitted by polynomial as follows:

[0069]

[0070] In addition, K and K under different grid impedance and active current conditions mentioned in the above embodiments are v The value of is determined using the SA-PSO optimization algorithm.

[0071] Since the simulated annealing (SA) algorithm is a global optimization method based on the metal annealing mechanism, it can accept inferior solutions and jump out of local optimization. However, the convergence time is often too long. On the contrary, the particle swarm optimization (PSO) algorithm is simple and converges quickly, but it is easy to fall into the local optimal value. Therefore, by introducing SA into PSO, the PSO algorithm can accept inferior solutions with a certain probability, which is conducive to breaking through local optimization.

[0072] like Figure 4 As shown in the figure, it is the SA-PSO algorithm flow chart, and the key process is explained as follows:

[0073] (1) Determination of ω, c1, and c2. The specific expressions are as follows:

[0074]

[0075] Among them, ω max and ω min Respectively represent the maximum and minimum values ​​of the inertia weight. k and K max Respectively represent the current number of iterations and the maximum number of iterations. The rest are the same. In this example, the selected parameters are ω max =0.9,ω min =0.4, c 1max =c 2max =1.69, c 1min =c 2min =1.2.

[0076] (2) Determination of initial temperature T: Generally, a higher initial temperature can be set at the beginning. The initial temperature expression is as follows:

[0077]

[0078] Where P pd represents the global optimal position, fitness(P pd ) represents the corresponding fitness function value.

[0079] (3) Fitness f SA Determination: The annealing fitness function of each particle at the current temperature T is:

[0080]

[0081] Where P id represents the position of the ith particle.

[0082] (4) Determination of objective function: In order to optimize K and K through the SA-PSO algorithm v The design requires a fitness function to evaluate each sample. The larger the cascade stability margin, the stronger the stability and robustness of the system, but the modification of the original system sacrifices the control performance of the system itself. In order to comprehensively consider the cascade stability and the performance of the system itself, 30° and 2dB are defined as the optimal phase margin and amplitude margin of the cascade system. In addition, the passband width K and the feedforward coefficient K v It needs to be as small as possible to reduce the impact on the original system impedance. The specific expression is as follows:

[0083] fitness=αG m_c -2|+βP m_c -30|+γK|+χK v

[0084] In this example, the selected weights are α=6, β=4, γ=3, and χ=6.

[0085] like Figure 5 As shown, it is the fitting result of the control parameters mentioned in this implementation, where Figure 5 a is f c The fitting curve of Figure 5 b is the K fitting surface, Figure 5 c is K v Fit the surface.

[0086] This embodiment further provides a grid-connected converter impedance adaptive adjustment device, which is implemented by the grid-connected converter impedance adaptive adjustment method provided by the above embodiment.

[0087] In the specific implementation process, the grid impedance and output power of the current grid-connected converter system are obtained through the monitoring model to generate the corresponding control parameter scheme, thereby realizing the impedance reshaping of the voltage feedforward path.

[0088] In addition, the terms "upper", "lower", "inner", "outer", "front", "rear" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. Unless otherwise specifically stated, the relative steps, numerical expressions and values ​​of the components and steps described in these embodiments do not limit the scope of the present invention.

[0089] Of course, the above description is only a specific embodiment of the present invention and is not intended to limit the scope of implementation of the present invention. All equivalent changes or modifications made according to the structure, characteristics and principles described in the patent application scope of the present invention should be included in the patent application scope of the present invention.

[0090] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A grid-connected converter impedance adaptive adjustment method based on grid voltage feedforward, characterized in that: The following steps are involved: Acquiring operation fluctuation data of the target grid-connected converter system, which includes the fluctuation range of the grid impedance and the fluctuation range of the output power; Generate a gridded operating condition sample set at equal intervals based on the operating fluctuation data, and use the simulated annealing-particle swarm algorithm to perform offline optimization on the voltage feedforward path in the grid-connected converter system to obtain a control parameter solution set of the stability margin under the operating condition sample set; Performing polynomial function fitting based on the control parameter scheme set to construct a function mapping relationship between each control parameter of the voltage feedforward path and the operation fluctuation data; The target grid-connected converter system is monitored using a pre-built monitoring model, a corresponding control parameter scheme is generated based on the monitoring result and the function mapping relationship, and the impedance of the voltage feedforward path is reshaped based on the generated control parameter scheme.

2. The grid-connected converter impedance adaptive adjustment method based on grid voltage feedforward according to claim 1 is characterized in that: The impedance reshaping of the voltage feedforward path is achieved by a Q-axis grid-connected point voltage feedforward control strategy based on an adaptive proportional link and a bandpass filter.

3. The grid-connected converter impedance adaptive adjustment method based on grid voltage feedforward according to claim 2 is characterized in that: The transfer function expression of the bandpass filter is as follows: Among them, ω c =2πf c represents the center frequency of the bandpass filter, K represents the passband width of the bandpass filter, and f c Represents the center frequency of the bandpass filter.

4. The grid-connected converter impedance adaptive adjustment method based on grid voltage feedforward according to claim 1, characterized in that: The control parameter scheme includes the center frequency and action width of the bandpass filter in the voltage feedforward path, and the voltage feedforward coefficient.

5. The grid-connected converter impedance adaptive adjustment method based on grid voltage feedforward according to claim 4 is characterized in that: The center frequency of the bandpass filter is obtained by selecting the amplitude intersection frequency of the grid-connected converter output impedance and the grid-side input impedance in the grid-connected converter system.

6. The grid-connected converter impedance adaptive adjustment method based on grid voltage feedforward according to claim 1, characterized in that: The voltage feed-forward path is optimized offline with the optimal amplitude margin and phase margin being 2 dB and 30 degrees respectively as the target, so as to obtain a set of control parameter solutions.

7. The grid-connected converter impedance adaptive adjustment method based on grid voltage feedforward according to claim 1 or 6, characterized in that: The objective function expression of the offline optimization is as follows: fitness=αG m_c -2|+βP m_c -30|+γK|+χK v | Among them, G m_c Represents the grid-connected cascade amplitude margin of the grid-connected converter system, P m_c represents the phase margin of the grid-connected converter system, K represents the filter action width, K v is the voltage feed-forward coefficient, and α, β, γ and χ are weight coefficients.

8. A grid-connected converter impedance adaptive adjustment device, characterized in that: The method is realized by the grid-connected converter impedance adaptive adjustment method based on grid voltage feedforward as claimed in any one of claims 1 to 7.

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