This paper develops a system identification approach and procedure that is employed for distributed control system design for large wind turbine load reduction applications. The primary goal of the study is to identify the process that can be used with multiple sensor inputs of varying types (such as aerodynamic or structural) that can be used to construct state-space models compatible with MIMO modern control techniques (such as LQR, LQG, H∞, robust control, etc.). As an initial step, this study employs LQR applied to multiple flap actuators on each blade as control inputs and local deflection rates at the flap spanwise locations as measured outputs. Future studies will include a variety of other sensor and actuator locations for both design and analysis with respect to varying wind conditions (such as high turbulence and gust) to help reduce structural loads and fatigue damage. The DU SWAMP aeroservoelastic simulation environment is employed to capture the complexity of the control design scenario. The NREL 5MW UpWind reference wind turbine provides the large wind turbine dynamic characteristics used for the study. Numerical simulations are used to demonstrate the feasibility of the overall approach. This study shows that the distributed controller design can provide load reductions for turbulent wind profiles that represent operation in above-rated power conditions.

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Empirically based modeling is an essential aspect of design for a wave energy converter. Empirically based models are used in structural, mechanical and control design processes, as well as for performance prediction. Both the design of experiments and methods used in system identification have a strong impact on the quality of the resulting model. This study considers the system identification and model validation process based on data collected from a wave tank test of a model-scale wave energy converter. Experimental design and data processing techniques based on general system identification procedures are discussed and compared with the practices often followed for wave tank testing. The general system identification processes are shown to have a number of advantages, including an increased signal-to-noise ratio, reduced experimental time and higher frequency resolution. The experimental wave tank data is used to produce multiple models using different formulations to represent the dynamics of the wave energy converter. These models are validated and their performance is compared against one another. While most models of wave energy converters use a formulation with surface elevation as an input, this study shows that a model using a hull pressure measurement to incorporate the wave excitation phenomenon has better accuracy.

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a b s t r a c tMicrogrids are a key technology to help improve the reliability of electric power systems and increase the integration of renewable energy sources. Interconnection and networking of smaller microgrids into larger systems have potential for even further improvements. This paper presents a novel approach to a distributed droop control and energy storage in networked dc microgrids. Distributed control is necessary to prevent single points of failure along with flexibility and adaptability to changing energy resources. The results show that systems with random sources and fast update rates, a networked microgrid structure can minimize required energy storage requirements.