Abstract

Microgrid comprises of several distributed generations (DGs), which are typically integrated through power electronic inverters. The existence of low inertial devices combined with the dynamic nature of the load challenges the stability of a microgrid and the effectiveness of the controller, mainly when operated in islanded mode. It is essential to optimize the parameters of the controller to enhance its efficacy under various operating conditions. In this paper, parameter optimization of universal droop and internal model control (IMC) is proposed based on an accurate small-signal model for an inverter dominated microgrid. In order to achieve robust control performance under different load conditions, a four-step approach is proposed: 1) an accurate small-signal model of a parallel multi-inverter system is prepared, which operates with the universal droop and internal model controller. The developed small-signal model is more accurate because it considers the dynamics of filter and phase-locked loop; 2) an investigation of critical control parameters of universal droop and internal model controller influencing the system stability is carried out, and their corresponding stability domain is identified through eigenvalue analysis; 3) particle swarm optimization (PSO) is used to optimize the critical parameters; and 4) the obtained result is validated under different load disturbances. Following the above approach, the time domain simulation is performed, which establishes that the proposed scheme improves the dynamic response of the DGs, counteracts the disturbances effectively and simultaneously improves the power-sharing. The proposed model is also compared with the well-established conventional PI-based droop controller, which demonstrates the efficacy of the proposed scheme.

Highlights

  • The concept of a microgrid is rising to be a promising alternative approach with the increasing penetration of renewable energy resources for clustering DGs, storage and loads

  • The major contributions of this work can be summarized as follows: (i) The mathematical analysis of internal model control is developed for the voltage controller considering filter dynamics. Based on these mathematical equations, the expression for control parameters are obtained in terms of system parameters. (ii) Following the developed mathematical model, the parameters influencing the system stability are identified by using small-signal analysis. (iii) The PSO technique is used to obtain the optimal values of the identified parameters, which are influencing the system stability. (iv) A simulation study is performed based on mathematical modeling using optimally tuned parameters, and it is found that the proposed scheme produces effective coordination to handle disturbance in the system. (v) the proposed scheme is compared with the conventional PI-based droop controller, and the results are found to be improved with the proposed scheme

  • In this paper, a design strategy for a robust controller is presented based on the accurate small-signal model of multi inverter-fed DGs

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Summary

INTRODUCTION

The concept of a microgrid is rising to be a promising alternative approach with the increasing penetration of renewable energy resources for clustering DGs, storage and loads. The choice of droop coefficients plays a significant role among droop based sources for improving the dynamic response of power-sharing during transient and steady states This can be accomplished through sensitivity analysis [4], [7]–[12]. Small-signal analysis for autonomous microgrid was carried out with conventional droop [4]–[19] technique for parallel operated inverters. This technique is widely used to enhance reliability. (iv) A simulation study is performed based on mathematical modeling using optimally tuned parameters, and it is found that the proposed scheme produces effective coordination to handle disturbance in the system.

MICROGRID SYSTEM MODELLING
OVERVIEW OF INTERNAL MODEL CONTROL
PLL FOR DG
LCL FILTER
TRANSMISSION LINE MODELLING
Lline2
LINEARISED MODEL OF THE STUDY SYSTEM
VALIDATION OF PROPOSED APPROACH
Findings
CONCLUSION
Full Text
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