Global Optimization Method for Multi-controller Parameters of DC Microgrids Based on Small-Signal Stability

doi:10.12204/j.issn.1000-7229.2023.06.012

Abstract

Abstract:

DC microgrid systems have multiple controllers, and the combination of different controller parameters has different effects on the stability of the entire system. To improve system stability, a multi-controller parameter global optimization method based on small-signal stability is proposed. First, the small-signal model of the system is deduced, and eigenvalue and participation factor analyses of the system are performed to determine the stability domain of key control parameters such as PI parameters, droop coefficients, and virtual inertia coefficients. The objective function, including the maximum real part of the eigenvalue, damping ratio, and maximum output power of the energy storage, is established. The sample data are obtained by using an orthogonal experiment, and the multi-objective is weighted by using a comprehensive weighting method. The key parameters of the system are then optimized using improved particle swarm optimization based on the grey wolf algorithm, thereby yielding the optimization results. The results show that the eigenvalues of the system after parameter optimization are farther away from the imaginary axis, the damping ratio increases, and the maximum output power of the energy storage increases, which improves system stability. Finally, the effectiveness and superiority of the proposed method are verified by building a model on the MATLAB Simulink simulation platform.

Key words: DC microgrid, optimization of controller parameters, comprehensive weighting method, improved particle swarm algorithm, stability analysis

CLC Number:

TM73

ZHU Xiaorong, FENG Tianjiao. Global Optimization Method for Multi-controller Parameters of DC Microgrids Based on Small-Signal Stability[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(6): 112-125.

Figures/Tables 32

Fig.1 Structure of DC microgrid

Fig.2 Structure of simplified DC microgrid model

Fig.3 Structure diagram of virtual inertia control block

Fig.4 Control strategy of constant power load

Fig.5 Structure of DC transmission line

Fig.6 Characteristic root of system

Fig.7 Stability region of proportional integral coefficients of energy storage unit

Fig.8 Stability region of virtual inertial coefficients of energy storage unit

Fig.9 Stability region for droop coefficients of energy storage unit

Fig.10 Stability region of droop coefficients at maximum output power

Fig.11 Stability region of proportional integral coefficients of constant power load

Table 1 The value range of the controller parameters

Table 2 Indicator comparison degree meaning

Table 3 Judgment matrix

Table 4 Weight of each index obtained by AHP

Table 5 Weight of each index obtained by CRITIC

Table 6 Weights of each index obtained by AHP-CRITIC

Fig.12 Optimization results of different particle swarm algorithm

Table 7 Optimization results of controller parameters

Table 8 Comparison of system performance indicators

Table 9 Optimization results of controller parameters for traditional objective functions

Table 10 System performance indicators for traditional objective functions

Fig.13 Parameter optimization simulation results for different objective functions

Table 11 Optimization results of controller parameters for different weighting methods

Table 12 System performance indicators for different weighting methods

Fig.14 Simulation results of parameter optimization with different weighting methods

Table A1 DC microgrid system parameters

Table A2 Eigenvalue analysis results

Fig.A1 System participation factor analysis

Table A3 The value of the factor level of the orthogonal test

Fig.A2 Flow chart of controller parameter optimization

Table A4 Orthogonal experiment results

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