Sliding Mode Observer-Based Fault-Tolerant Secondary Control of Microgrids

Abstract

This paper proposes two sliding mode observer (SMO)-based fault-tolerant secondary control schemes for microgrids. The first scheme consists of a central SMO-based fault tolerant controller that uses outputs from the microgrid and estimates all states in the microgrid as well as the fault. The estimated fault is then used to reject the effect of faults on the microgrid. The second scheme is decentralised, where each distributed generator (DG) has its own SMO-based fault tolerant controller which would estimate faults affecting that DG alone, and compensate for faults in only that DG. By rejecting the effect of faults in each DG, the effect of faults on the entire microgrid can be negated. Finally, we simulate an example using both schemes, and its results verify the efficacy of the schemes for fault-tolerant secondary control of microgrids.

A Nonlinear Integral Backstepping Controller to Regulate the Voltage and Frequency of an Islanded Microgrid Inverter

“…The results of voltage variation during load change with the application of NLIB controller show that the system has great robustness with this controller. Because of the extra drop of voltage across filter elements and load resistor voltage falls at time instant = 0.5 s NLIB controller works to regain the rated voltage at this time instant by manipulating the voltage to track back reference voltage by using control law presented in Equations ( 33) and (40). The system is simulated with load variations to decrease voltage dips and to achieve the reference voltage.…”

“…of 24 age variation during load change with the application of NLIB controller show that the system has great robustness with this controller. Because of the load decrease the voltage across filter elements and load resistor voltage increase at time instant = 0.5 s NLIB controller works to regain the rated voltage at this time instant by manipulating the voltage to track back reference voltage by using control law presented in Equations ( 33) and (40). across filter elements and load resistor voltage increase at time instant = 0.5 s NLIB controller works to regain the rated voltage at this time instant by manipulating the voltage to track back reference voltage by using control law presented in Equations ( 33) and (40).…”

“…Because of the load decrease the voltage across filter elements and load resistor voltage increase at time instant = 0.5 s NLIB controller works to regain the rated voltage at this time instant by manipulating the voltage to track back reference voltage by using control law presented in Equations ( 33) and (40). across filter elements and load resistor voltage increase at time instant = 0.5 s NLIB controller works to regain the rated voltage at this time instant by manipulating the voltage to track back reference voltage by using control law presented in Equations ( 33) and (40). In Figure 6 it is observed that when the load changes the NLIB controller rapidly converges the voltage to its rated value in settling time of just 0.02 s. The behaviour of The reference voltage tracking in the d-q frame by the proposed controller is presented in Figures 7 and 8.…”

“…Nonlinear controllers, which can realize the operation in a wide operating range can overcome the limitations of linear controllers. Recently, many nonlinear sliding mode controller-based approaches were proposed [37][38][39][40][41] realize the voltage and frequency regulation in both grid-connected and islanded mode operations, and these approaches exhibited a reasonable efficiency. Nevertheless, such controllers often exhibit the chattering phenomenon, which severely reduces the life of power electronic-based devices in the MG system.…”

Design of distributed functional interval observers for large-scale networks impulsive systems

“…Therefore, it is necessary to design state observers for these systems to reconstruct state information. A wide range of techniques for state estimation has been reported in the literature (see, for example, Ahmed-Ali et al, 2012; Chan and Lee, 2020; Darouach, 2001; Huong, 2018; Huong and Trinh, 2015, 2016; Huong and Thuan, 2018; Huong et al, 2014; Kaczorek, 2014, 2016; Mohajerpoor et al, 2017). However, the existing observer design techniques may not be suitable for systems with many types of uncertainties (Cacace et al, 2015; Efimov et al, 2012, 2013a, 2013b; Gouzé et al, 2000; Gu et al, 2018; He and Xie, 2016; Huang et al, 2020a, 2020b; Khan and Xie, 2019; Mazenc and Bernard, 2011).…”