Grid-connected multi-objective optimization control method for inverter

Abstract

The invention discloses an inverter grid-connected multi-objective optimization control method, relates to the technical field of new energy, and aims to acquire state information of a power grid in real time through a multi-objective scheduling algorithm and quickly adjust the working state of an inverter so as to cope with the dynamic change of the power grid. Output can be adjusted according to the weight coefficients of different targets, and it is ensured that all the targets can be reasonably optimized under different conditions. Flexible and efficient grid-connected control is realized, the power factor is optimized, the stability of the power grid is improved, the voltage fluctuation is reduced, the efficiency of the inverter is improved, and the optimal interaction between the power grid and the inverter is finally realized.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a multi-objective optimization control method for a grid-connected inverter. Background Art

[0002] With the rapid development of renewable energy, especially the application of photovoltaic power generation and wind power generation, the inverter, as a key device connecting the power grid and the renewable energy system, undertakes the important task of converting DC power into AC power and stably connecting it to the power grid. In practical applications, the inverter faces many challenges during the grid connection process, such as grid voltage fluctuations, current harmonic pollution, grid frequency changes, and the improvement of inverter efficiency. Therefore, how to optimize multiple goals in the grid connection process while ensuring the stable operation of the inverter has become a problem that needs to be solved urgently in the current power electronics field.

[0003] Most existing inverter control methods focus on the optimization of a single objective and are unable to take into account multiple objectives at the same time, such as power factor, system stability, load adaptability, etc. Summary of the invention

[0004] In order to overcome the deficiencies of the above technologies, the present invention provides a method for improving the efficiency and adaptability of the inverter, reducing energy loss, and improving the stability of grid operation while meeting grid requirements.

[0005] The technical solution adopted by the present invention to overcome the technical problems is:

[0006] A multi-objective optimization control method for inverter grid connection, comprising:

[0007] S1. Calculate the power factor PF representing the phase relationship between the output current of the inverter and the grid voltage;

[0008] S2. Calculate the grid frequency fluctuation Δf representing the grid frequency deviation;

[0009] S3. Calculate the grid voltage fluctuation ΔV representing the grid voltage change;

[0010] S4. Calculate the inverter efficiency η for improving the energy conversion efficiency of the inverter;

[0011] S5. Construct objective function J using power factor PF, grid frequency fluctuation Δf, grid voltage fluctuation ΔV, and inverter efficiency η;

[0012] S6. Iterate the objective function J through the multi-objective scheduling algorithm to obtain the optimal solution.

[0013] Furthermore, in step S1, the formula The power factor PF is calculated, where p is the active power and s is the apparent power. is the phase difference between current and voltage.

[0014] Furthermore, in step S2, the formula Δf=|f grid (t)-f ref |Calculate the grid frequency fluctuation Δf, where f grid (t) is the current grid frequency, f ref The reference frequency set for the grid.

[0015] The reference frequency f set by the power grid ref The value can be 50Hz or 60Hz.

[0016] Furthermore, in step S3, the formula ΔV=|V grid (t)-V ref |Calculate the grid voltage fluctuation ΔV, where V grid (t) is the current grid voltage, V ref is the reference voltage.

[0017] Reference voltage V ref The value can be 220V or 380V.

[0018] Further, in step S4, the formula Calculate the inverter efficiency η, where P out is the inverter output power, P in Input power to the inverter.

[0019] Furthermore, in step S5, the objective function J is obtained by the formula J=ω1PF+ω2Δf+ω3ΔV+ω4η, where ω1, ω2, ω3, and ω4 are all weight coefficients.

[0020] Preferably, the multi-objective scheduling algorithm is a genetic algorithm or a particle swarm optimization algorithm.

[0021] The beneficial effects of the present invention are: through the multi-objective scheduling algorithm, the state information of the power grid can be obtained in real time, and the working state of the inverter can be quickly adjusted to cope with the dynamic changes of the power grid. The output can be adjusted according to the weight coefficients of different objectives to ensure that each objective can be reasonably optimized under different circumstances. Flexible and efficient grid-connected control is achieved, power factor is optimized, grid stability is improved, voltage fluctuations are reduced, and inverter efficiency is improved, ultimately achieving optimal interaction between the power grid and the inverter. DETAILED DESCRIPTION

[0022] The present invention is further described below.

[0023] A multi-objective optimization control method for inverter grid connection, comprising:

[0024] S1. Calculate and obtain the power factor PF representing the phase relationship between the output current of the inverter and the grid voltage.

[0025] S2. Calculate and obtain the grid frequency fluctuation Δf representing the grid frequency deviation.

[0026] S3. Calculate and obtain the grid voltage fluctuation ΔV representing the grid voltage change.

[0027] S4. Calculate and obtain the inverter efficiency η for improving the energy conversion efficiency of the inverter.

[0028] S5. Construct the objective function J using the power factor PF, grid frequency fluctuation Δf, grid voltage fluctuation ΔV, and inverter efficiency η.

[0029] S6. Iterate the objective function J through the multi-objective scheduling algorithm to obtain the optimal solution.

[0030] By optimizing the inverter's grid-connected control strategy, its performance in the grid-connected process is improved, including comprehensive optimization of multiple objectives such as power factor control, grid stability, load response capability, and inverter efficiency. It is able to obtain grid status information in real time and quickly adjust the inverter's working state to cope with dynamic changes in the grid. It is able to adjust the output according to the weight coefficients of different objectives to ensure that each objective can be reasonably optimized under different circumstances. It achieves flexible and efficient grid-connected control, optimizes power factor, improves grid stability, reduces voltage fluctuations, and improves inverter efficiency, ultimately achieving optimal interaction between the grid and the inverter. In one embodiment of the present invention, in step S1, the formula The power factor PF is calculated, and the goal is to maximize the power factor. Where p is the active power, s is the apparent power, The power factor PF is the phase difference between the current and voltage. Ideally, the power factor PF should be close to 1, indicating that the current output by the inverter is in phase with the grid voltage to avoid reactive power waste. The power factor PF reflects the phase relationship between the output current of the inverter and the grid voltage. The goal is to optimize the phase difference between the output current of the inverter and the grid voltage to reduce reactive power.

[0031] In one embodiment of the present invention, the frequency fluctuation of the power grid may affect the stability of the power system. The inverter needs to adapt to the change of the power grid frequency and quickly adjust its output power to reduce the power grid frequency deviation. The power grid frequency deviation can be obtained by calculating the difference between the current frequency and the reference frequency. Specifically, in step S2, the formula Δf=|f grid (t)-f ref |Calculate the grid frequency fluctuation Δf. The goal is to minimize the grid frequency fluctuation, that is, to stabilize the grid frequency near the reference value as much as possible. In the formula, f grid(t) is the current grid frequency, f ref The reference frequency set for the grid. The grid frequency fluctuation Δf indicates the deviation of the grid frequency. The goal is to ensure that the inverter can adjust the output power and stabilize the grid frequency.

[0032] In this embodiment, preferably, the reference frequency f set by the power grid ref The value can be 50Hz or 60Hz.

[0033] In one embodiment of the present invention, grid voltage fluctuations may cause the inverter efficiency to decrease or generate unnecessary current harmonics. The inverter needs to automatically adjust the output power according to the grid voltage changes to reduce the voltage fluctuations. Therefore, in step S3, the formula ΔV=|V grid (t)-V ref |Calculate the grid voltage fluctuation ΔV. The goal is to minimize the grid voltage fluctuation, that is, to ensure that the voltage remains stable. In the formula, V grid (t) is the current grid voltage, V ref The grid voltage fluctuation ΔV represents the change of grid voltage, and the goal is to alleviate the voltage fluctuation by adjusting the output power.

[0034] In this embodiment, preferably, the reference voltage V ref The value can be 220V or 380V.

[0035] In one embodiment of the present invention, the efficiency of the inverter directly affects the energy conversion efficiency and the overall system performance. An efficient inverter can reduce energy loss and improve power conversion efficiency. Specifically, in step S4, the formula The inverter efficiency η is calculated. The goal is to maximize the inverter efficiency, that is, to improve the conversion efficiency of the inverter. out is the inverter output power, P in Input power to the inverter. The inverter efficiency η aims to improve the energy conversion efficiency of the inverter and reduce energy loss.

[0036] In one embodiment of the present invention, in step S5, the objective function J is obtained by the formula J=ω1PF+ω2Δf+ω3ΔV+ω4η, where ω1, ω2, ω3, and ω4 are all weight coefficients. The selection of ω1, ω2, ω3, and ω4 determines the relative importance of each target. In different operating scenarios, these weights can be adjusted according to actual needs to achieve the priority of different targets. For example: in the case where the grid frequency is very unstable, the weight of the ω2 grid frequency fluctuation can be increased to prioritize the grid frequency. In the case of large grid voltage fluctuations, the weight of the ω3 grid voltage fluctuation can be increased to ensure grid voltage stability. In the scenario where the overall energy efficiency of the inverter needs to be improved, the weight of the ω4 inverter efficiency can be increased to improve the conversion efficiency. For example, the weight coefficients can be set to: ω1=0.4, ω2=0.3, ω3=0.2, ω4=0.1, which means that in this case, power factor optimization has the highest priority, followed by grid frequency fluctuations, and voltage fluctuations and inverter efficiency have lower weights. The above multi-objective scheduling algorithm is a genetic algorithm or a particle swarm optimization algorithm.

[0037] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-objective optimization control method for inverter grid connection, characterized in that: include: S1. Calculate the power factor PF representing the phase relationship between the output current of the inverter and the grid voltage; S2. Calculate the grid frequency fluctuation Δf representing the grid frequency deviation; S3. Calculate the grid voltage fluctuation ΔV representing the grid voltage change; S4. Calculate the inverter efficiency η for improving the energy conversion efficiency of the inverter; S5. Construct objective function J using power factor PF, grid frequency fluctuation Δf, grid voltage fluctuation ΔV, and inverter efficiency η; S6. Iterate the objective function J through the multi-objective scheduling algorithm to obtain the optimal solution.

2. The inverter grid-connected multi-objective optimization control method according to claim 1, characterized in that: In step S1, the formula The power factor PF is calculated, where p is the active power and s is the apparent power. is the phase difference between current and voltage.

3. The inverter grid-connected multi-objective optimization control method according to claim 1, characterized in that: In step S2, the grid frequency fluctuation Δf is calculated by the formula Δf=|fgrid(t)-fref|, where f grid (t) is the current grid frequency, f ref The reference frequency set for the grid.

4. The inverter grid-connected multi-objective optimization control method according to claim 3 is characterized in that: The reference frequency f set by the power grid ref The value can be 50Hz or 60Hz.

5. The inverter grid-connected multi-objective optimization control method according to claim 1, characterized in that: In step S3, the grid voltage fluctuation ΔV is calculated by the formula ΔV=|Vgrid(t)-Vref|, where V grid (t) is the current grid voltage, V ref is the reference voltage.

6. The inverter grid-connected multi-objective optimization control method according to claim 5, characterized in that: Reference voltage V ref The value can be 220V or 380V.

7. The inverter grid-connected multi-objective optimization control method according to claim 1, characterized in that: In step S4, the formula Calculate the inverter efficiency η, where P out is the inverter output power, P in Input power to the inverter.

8. The inverter grid-connected multi-objective optimization control method according to claim 1, characterized in that: In step S5, the objective function J is obtained by the formula J=ω1PF+ω2Δf+ω3ΔV+ω4η, where ω1, ω2, ω3, and ω4 are all weight coefficients.

9. The inverter grid-connected multi-objective optimization control method according to claim 1, characterized in that: The multi-objective scheduling algorithm is a genetic algorithm or a particle swarm optimization algorithm.

Images