Yassine Amirat

Condition Monitoring and ault Diagnosis in Wind Energy Conversion Systems: A Review

There is a constant need for the reduction of operational and maintenance costs of wind energy conversion systems (WECS). The most efficient way of reducing these costs would be to continuously monitor the condition of these systems. This allows for early detection of the degeneration of the generator health, facilitating a proactive response, minimizing downtime, and maximizing productivity. Wind generators are also inaccessible since they are situated on extremely high towers, which are normally 20 m or greater in height. There are also plans to increase the number of offshore sites increasing the need for a remote means of WECS monitoring that eliminates some of the difficulties faced due to accessibility problems. Therefore and due to the importance of condition monitoring and fault diagnosis in WECS (blades, drive trains, and generators); and keeping in mind the need for future research, this paper is intended as a tutorial overview based on a review of the state of the art, describing different type of faults, their generated signatures, and their diagnostic schemes.

Diagnosis of Three-Phase Electrical Machines Using Multidimensional Demodulation Techniques

This paper deals with the diagnosis of three-phase electrical machines and focuses on failures that lead to stator-current modulation. To detect a failure, we propose a new method based on stator-current demodulation. By exploiting the configuration of three-phase machines, we demonstrate that the demodulation can be efficiently performed with low-complexity multidimensional transforms such as the Concordia transform (CT) or the principal component analysis (PCA). From a practical point of view, we also prove that PCA-based demodulation is more attractive than CT. After demodulation, we propose two statistical criteria aiming at measuring the failure severity from the demodulated signals. Simulations and experimental results highlight the good performance of the proposed approach for condition monitoring.

Second-order sliding mode control for DFIG-based wind turbines fault ride-through capability enhancement.

This paper deals with the fault ride-through capability assessment of a doubly fed induction generator-based wind turbine using a high-order sliding mode control. Indeed, it has been recently suggested that sliding mode control is a solution of choice to the fault ride-through problem. In this context, this paper proposes a second-order sliding mode as an improved solution that handle the classical sliding mode chattering problem. Indeed, the main and attractive features of high-order sliding modes are robustness against external disturbances, the grids faults in particular, and chattering-free behavior (no extra mechanical stress on the wind turbine drive train). Simulations using the NREL FAST code on a 1.5-MW wind turbine are carried out to evaluate ride-through performance of the proposed high-order sliding mode control strategy in case of grid frequency variations and unbalanced voltage sags.

Wind turbines condition monitoring and fault diagnosis using generator current amplitude demodulation

Wind energy conversion systems have become a focal point in the research of renewable energy sources. In order to make wind turbines as competitive as the classical electric power stations, it is important to reduce the operational and maintenance costs. The most efficient way of reducing these costs would be to continuously monitor the condition of these systems. This allows for early detection of the degradation of the generator health, facilitating a proactive response, minimizing downtime, and maximizing productivity. This paper provides then an approach based on the generator stator current data collection and attempts to highlight the use of Hilbert transformation for failure detection in a Doubly-Fed Induction Generator (DFIG) based wind turbine for stationary and nonstationary cases.

Condition monitoring of wind turbines based on amplitude demodulation

Wind energy conversion systems (WECS) have become a focal point in the research of renewable energy sources. In order to make wind turbine reliable and competitive, it is important to reduce the operational and maintenance costs. The most efficient way to reduce it relies on condition monitoring and fault diagnostics. This paper proposes a new fault detector based on the amplitude demodulation of the three-phase stator current. Simulations show that this low-complexity method is well suited for stationary or non-stationary behavior.

Bearing fault detection in DFIG-based wind turbines using the first Intrinsic Mode Function

Wind energy conversion systems have become a focal point in the research of renewable energy sources. In order to make the DFIG-based wind turbines so competitive as the classical electric power stations it is important to reduce the operational and maintenance costs by continuously monitoring the condition of these systems. This paper provides a method for bearing fault detection in DFIG-based wind turbines. The proposed method uses the first Intrinsic Mode Function (IMF) of the stator current signal. After extracting the first IMF, amplitude-demodulation is performed to reveal a generator bearing fault. Experimental results show that the proposed method significantly improves the result of classical amplitude-demodulation techniques for failure detection.

Wind turbine bearing failure detection using generator stator current homopolar component ensemble empirical mode decomposition

Failure detection has always been a demanding task in the electrical machines community; it has become more challenging in wind energy conversion systems because sustainability and viability of wind farms are highly dependent on the reduction of the operational and maintenance costs. Indeed the most efficient way of reducing these costs would be to continuously monitor the condition of these systems. This allows for early detection of the generator health degeneration, facilitating a proactive response, minimizing downtime, and maximizing productivity. This paper provides then an assessment of a failure detection techniques based on the homopolar component of the generator stator current and attempts to highlight the use of the Ensemble Empirical Mode Decomposition (EEMD) as a tool for failure detection in wind turbine generators for stationary and non stationary cases.

Optimal design of a PV/fuel cell hybrid power system for the city of Brest in France

This paper deals with the optimal design of a stand-alone hybrid photovoltaic and fuel cell power system without battery storage to supply the electric load demand of the city of Brest, Western Brittany in France. The proposed optimal design study is focused on economical performances and is mainly based on the loss of the power supply probability concept. The hybrid power system optimal design is based on a simulation model developed using HOMER. In this context, a practical load demand profile of Brest city is used with real weather data.

Hybrid generation systems planning expansion forecast: A critical state of the art review

In recent years the electric power generation has entered into a new development era, which can be described mainly by increasing concerns about climate change, through the energy transition from hydrocarbon to clean energy resources. In order to power system enhance reliability, efficiency and safety, renewable and nonrenewable resources are integrated together to configure so-called hybrid systems. Despite the experience accumulated in the power networks, designing hybrid system is a complex task. It has become more challenging as far as most renewable energy resources are random and weather/climatic conditions-dependant. In this challenging context, this paper proposes a critical state-of-the-art review of hybrid generation systems planning expansion and indexes multi-objective methods as strategies for hybrid energy systems optimal design to satisfy technical and economical constraints.

Integrated energy management of a plug-in electric vehicle in residential distribution systems with renewables

According to innovation in grid connected transportation industry and with ever increasing concerns on environmental issues and clean energy, electric vehicles (EVs) and hybrid electric vehicles (HEVs) with low noise, zero emission, and high efficiency have attracted more and more attention of researchers, governments and industries, they are becoming the most likely fleets to replace gasoline vehicles in future power systems. In addition to the approved advantages for transportation, EVs have the potential to provide other benefits within the connected residential distribution to micro-grids and smart grids as part of a vehicle-to-grid (V2G) system, knowing that in future systems residential distribution can be seen as an energy resource with decentralized and autonomous decisions in the energy management called smart house or prosumer. They can participate effectively in helping to balance supply and demand by valley filling and peak shaving. The EV battery can be charged during low demand and the stored power can be fed power back into the micro-grid during high-demand periods, providing a spinning reserve to dump short power demand changes. V2G may also be used to buffer renewable energy sources, such as photovoltaic generators, by storing excess energy produced during illumination periods, and feeding it back into the grid during high-load periods, thus effectively stabilizing the intermittency of solar power. In this context, this paper describes an energy management system for a smart house based on hybrid PV-battery and V2G.