Abstract:A significant challenge for designing a coordinated and effective protection architecture of a microgrid (MG) is the aim of an efficient, reliable, and fast protection scheme for both the grid-connected and islanded modes of operation. To this end, bidirectional power flow, varying short-circuit power, low voltage ride-through (LVRT) capability, and the plug-and-play characteristics of distributed generation units (DGUs), which are key issues in a MG system must be considered; otherwise, a mal-operation of pro… Show more
“…Protection devices in existing DN (Distribution network) consist of non-directional overcurrent (OC) protective relays, reclosers, fuses and sectionalisers [1] and are mostly radial. The protection schemes were initially designed for the unidirectional flow of power, but with the increase in bidirectional power flow, coordination between fault protection devices can be compromised [2], especially when the microgrid is in an autonomous (AUTO) mode of operation. Additionally, achieving correct selectivity and sensitivity poses a great challenge in the practical application of microgrids [3].…”
The microgrid (MG) is a popular concept to handle the high penetration of distributed energy resources, such as renewable and energy storage systems, into electric grids. However, the integration of inverter-interfaced distributed generation units (IIDGs) imposes control and protection challenges. Fault identification, classification and isolation are major concerns with IIDGs-based active MGs where IIDGs reveal arbitrary impedance and thus different fault characteristics. Moreover, bidirectional complex power flow creates extra difficulties for fault analysis. This makes the conventional methods inefficient, and a new paradigm in protection schemes is needed for IIDGs-dominated MGs. In this paper, a machine-learning (ML)-based protection technique is developed for IIDG-based AC MGs by extracting unique and novel features for detecting and classifying symmetrical and unsymmetrical faults. Different signals, namely, 400 samples, for wide variations in operating conditions of an MG are obtained through electromagnetic transient simulations in DIgSILENT PowerFactory. After retrieving and pre-processing the signals, 10 different feature extraction techniques, including new peaks metric and max factor, are applied to obtain 100 features. They are ranked using the Kruskal–Wallis H-Test to identify the best performing features, apart from estimating predictor importance for ensemble ML classification. The top 18 features are used as input to train 35 classification learners. Random Forest (RF) outperformed all other ML classifiers for fault detection and fault type classification with faulted phase identification. Compared to previous methods, the results show better performance of the proposed method.
“…Protection devices in existing DN (Distribution network) consist of non-directional overcurrent (OC) protective relays, reclosers, fuses and sectionalisers [1] and are mostly radial. The protection schemes were initially designed for the unidirectional flow of power, but with the increase in bidirectional power flow, coordination between fault protection devices can be compromised [2], especially when the microgrid is in an autonomous (AUTO) mode of operation. Additionally, achieving correct selectivity and sensitivity poses a great challenge in the practical application of microgrids [3].…”
The microgrid (MG) is a popular concept to handle the high penetration of distributed energy resources, such as renewable and energy storage systems, into electric grids. However, the integration of inverter-interfaced distributed generation units (IIDGs) imposes control and protection challenges. Fault identification, classification and isolation are major concerns with IIDGs-based active MGs where IIDGs reveal arbitrary impedance and thus different fault characteristics. Moreover, bidirectional complex power flow creates extra difficulties for fault analysis. This makes the conventional methods inefficient, and a new paradigm in protection schemes is needed for IIDGs-dominated MGs. In this paper, a machine-learning (ML)-based protection technique is developed for IIDG-based AC MGs by extracting unique and novel features for detecting and classifying symmetrical and unsymmetrical faults. Different signals, namely, 400 samples, for wide variations in operating conditions of an MG are obtained through electromagnetic transient simulations in DIgSILENT PowerFactory. After retrieving and pre-processing the signals, 10 different feature extraction techniques, including new peaks metric and max factor, are applied to obtain 100 features. They are ranked using the Kruskal–Wallis H-Test to identify the best performing features, apart from estimating predictor importance for ensemble ML classification. The top 18 features are used as input to train 35 classification learners. Random Forest (RF) outperformed all other ML classifiers for fault detection and fault type classification with faulted phase identification. Compared to previous methods, the results show better performance of the proposed method.
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