Adaptive Filtering-Based Pseudo Open-Loop Three-Phase Grid-Synchronization Technique

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Hybrid Estimator-Based Harmonic Robust Grid Synchronization Technique

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Gain Normalized Adaptive Observer For Three-Phase System

International audienceThis paper proposes the estimation of parameters and symmetrical components of unbalanced grid using adaptive observer framework. Recent adaptive observers proposed in the literature doesn’t employ any gain normalization in their frequency estimation loop. This can be problematic in the presence of large voltage dip. This paper proposes a solution to overcome this limitation using a novel gain normalized - frequency-locked loop (GN-FLL). Technical details of GN-FLL, stability analysis and tuning are provided in this paper. Comparative experimental results with adaptive Luenberger observer, second-order generalized integrator - phase-locked loop (SOGI-PLL) and enhanced PLL (EPLL) are provided to demonstrate the effectiveness the proposed technique in the single-phase case. Comparative experimental results with double SOGI-FLL (DSOGI-FLL) and adaptive notch filter (ANF) are provided to demonstrate the effectiveness the proposed technique in the three-phase case

Marine Current Turbine System Post-Fault Behavior under an Open Circuit Fault

This paper describes the modeling and simulation of a Permanent Magnet Synchronous Generator (PMSG) based Marine Current Turbine (MCT) under converter faulty conditions. The modeling of the generator is represented in the d-q reference frame. The Proportional Integral (PI) controllers are used for the direct current, the quadratic current, and the speed Control. The faulty mode describes an open-circuit fault in the generator-side converter. Simulations results show that the dynamic performances and the power generation of the MCT are highly degraded due to the fault

Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring

Nowadays, the main grid is facing several challenges related to the integration of renewable energy resources, deployment of grid-level energy storage devices, deployment of new usages such as the electric vehicle, massive usage of power electronic devices at different electric grid stages and the inter-connection with microgrids and prosumers. To deal with these challenges, the concept of a smart, fault-tolerant, and self-healing power grid has emerged in the last few decades to move towards a more resilient and efficient global electrical network. The smart grid concept implies a bi-directional flow of power and information between all key energy players and requires smart information technologies, smart sensors, and low-latency communication devices. Moreover, with the increasing constraints, the power grid is subjected to several disturbances, which can evolve to a fault and, in some rare circumstances, to catastrophic failure. These disturbances include wiring issues, grounding, switching transients, load variations, and harmonics generation. These aspects justify the need for real-time condition monitoring of the power grid and its subsystems and the implementation of predictive maintenance tools. Hence, researchers in industry and academia are developing and implementing power systems monitoring approaches allowing pervasive and effective communication, fault diagnosis, disturbance classification and root cause identification. Specifically, a focus is placed on power quality monitoring using advanced signal processing and machine learning approaches for disturbances characterization. Even though this review paper is not exhaustive, it can be considered as a valuable guide for researchers and engineers who are interested in signal processing approaches and machine learning techniques for power system monitoring and grid-disturbance classification purposes

Parametric signal processing approach

International audienc

Marine Tidal and Wave Energy Converters

International audienc

An Energy Management System for Hybrid Energy Sources-based Stand-alone Marine Microgrid

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