Abstract:Microgrid instability poses critical issues to the power delivery following a load change or a tripping event. In island operating mode lack of grid intensifies this challenge. This study aims at controlling several converter-based distributed generations (DG) sharing the power in an island microgrid (MG). At first, the microgrid model including virtual impedances and phase-locked loop (PLL) is introduced. Afterwards a novel small-signal stability analysis for island microgrids is proposed. Finally, an optimiz… Show more
“…The virtual impedance optimization in [19] has minimized the reactive power mismatches among converters and enhanced the microgrid small-signal stability by an off-line PSO algorithm. Following the analysis of microgrid stability domain in [20], it has been suggested that the proportionalderivative reactive power controller enhances the microgrid stability margin.…”
Section: Nomenclature Abbreviationsmentioning
confidence: 99%
“…• The permissible range and optimal values for virtual impedance are affected by the microgrid load levels, this concept has not been considered in the recent researches such as [18] and [19].…”
Section: Nomenclature Abbreviationsmentioning
confidence: 99%
“…i loadD1 , i lineD1 ) are presented in global reference frame. The reference frame of converter 1 is chosen as global reference frame and the transformation between local and global frames are explained in [7] and [19].…”
Section: A State-space Representation Of the Microgridmentioning
confidence: 99%
“…The idea is that the ANFIS network is trained to perform optimally in all operating conditions. The training data is produced by PSO optimization in different load changing scenarios [19]. When the training data is collected, ANFIS networks are trained for all converters in microgrid.…”
Section: The Proposed Anfis-based Optimization Algorithmmentioning
Microgrid as the main building block for future smart grids is prone to instability originated from converter-based distributed generations (DG). Herein, we first analyze the small-signal stability of an inverter-interfaced microgrid. Then, a novel adaptive network fuzzy inference system (ANFIS)-based optimization method is introduced which aims at online tuning of virtual inductances (VI) in the islanded microgrids. The data for ANFIS training is drawn by particle swarm optimization (PSO) algorithm and the proposed objective function. A total of 140 load scenarios are considered to provide optimal VI in each load condition and generate optimal data for ANFIS training. This process yields minimizing reactive power mismatches and improves microgrid stability in different load levels. The simultaneous application of PSO algorithm and ANFIS training facilitates the objectives pursued by current study. Finally, the trained ANFIS networks are installed in the converter control. The adaptive performance of ANFIS controllers makes the converters responses independent from load change location and value. The effectiveness of the proposed control methodology is verified using simulations studies.
“…The virtual impedance optimization in [19] has minimized the reactive power mismatches among converters and enhanced the microgrid small-signal stability by an off-line PSO algorithm. Following the analysis of microgrid stability domain in [20], it has been suggested that the proportionalderivative reactive power controller enhances the microgrid stability margin.…”
Section: Nomenclature Abbreviationsmentioning
confidence: 99%
“…• The permissible range and optimal values for virtual impedance are affected by the microgrid load levels, this concept has not been considered in the recent researches such as [18] and [19].…”
Section: Nomenclature Abbreviationsmentioning
confidence: 99%
“…i loadD1 , i lineD1 ) are presented in global reference frame. The reference frame of converter 1 is chosen as global reference frame and the transformation between local and global frames are explained in [7] and [19].…”
Section: A State-space Representation Of the Microgridmentioning
confidence: 99%
“…The idea is that the ANFIS network is trained to perform optimally in all operating conditions. The training data is produced by PSO optimization in different load changing scenarios [19]. When the training data is collected, ANFIS networks are trained for all converters in microgrid.…”
Section: The Proposed Anfis-based Optimization Algorithmmentioning
Microgrid as the main building block for future smart grids is prone to instability originated from converter-based distributed generations (DG). Herein, we first analyze the small-signal stability of an inverter-interfaced microgrid. Then, a novel adaptive network fuzzy inference system (ANFIS)-based optimization method is introduced which aims at online tuning of virtual inductances (VI) in the islanded microgrids. The data for ANFIS training is drawn by particle swarm optimization (PSO) algorithm and the proposed objective function. A total of 140 load scenarios are considered to provide optimal VI in each load condition and generate optimal data for ANFIS training. This process yields minimizing reactive power mismatches and improves microgrid stability in different load levels. The simultaneous application of PSO algorithm and ANFIS training facilitates the objectives pursued by current study. Finally, the trained ANFIS networks are installed in the converter control. The adaptive performance of ANFIS controllers makes the converters responses independent from load change location and value. The effectiveness of the proposed control methodology is verified using simulations studies.
“…The microgrid small-signal stability analyzed in [2] was a milestone for microgrid stability analysis. The advanced researches such as [3] optimize the virtual impedances based on small-signal stability analysis. However, the phase locked loop (PLL) as a necessary unit to control and coordinate several DGs in the MG must be taken into account, either comparing the units phases to the phase of one local DG or have a central phase reference [4].…”
A converter-based microgrid including several distributed generations (DG) and energy storage systems (ESS) embodies a small power system consisting of several synchronous machines and loads. The stability issue and the coordination between generating units is an essential challenge, either in island or in grid-connected microgrids. The instability of individual DGs cause the microgrid instability as a whole. So, the individual unit stability and the microgrid stability both must be ensured simultaneously. The state feedback concept which is considered in control of converters has a substantial effect on the converter stability and microgrid stability, subsequently. This study will introduce a small-signal model for the microgrid including converter-based DGs and loads considering the previous studies in this field. A phase locked loop (PLL) is required for the control strategy. The effect of current state feedback is scrutinized and analyzed in this study as the main contribution. The effectiveness of the proposed control strategy to enhance the microgrid stability using chosen state feedback is examined through simulation studies. The simulation results ensure that the microgrid stability using the proposed control method and state-feedback is strengthened.
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