Active noise control systems currently in use and/or described in the research literature are typically based on adaptive signal processing theory or, equivalently, adaptive feedforward control theory. This paper presents a modern control approach to the problem of active noise cancellation in a three-dimensional space. The controller is designed based on a direct self-tuning regulator. Two forms of adaptive control, namely, pole placement and minimum variance controls are considered and compared in simulation. An implementation of the adaptive minimum variance controller is used to successfully attenuate a harmonic disturbance in a laboratory setting.
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Commercial buildings have a significant impact on energy and the environment, being responsible for more than 18% of the annual primary energy consumption in the United States. Analyzing their electrical demand profiles is necessary for the assessment of supply-demand interactions and potential; of particular importance are supply- or demand-side energy storage assets and the value they bring to various stakeholders in the smart grid context. This research developed and applied unsupervised classification of commercial buildings according to their electrical demand profile. A Department of Energy (DOE) database was employed, containing electrical demand profiles representing the United States commercial building stock as detailed in the 2003 Commercial Buildings Consumption Survey (CBECS) and as modeled in the EnergyPlus building energy simulation tool. The essence of the approach was: (1) discrete wavelet transformation of the electrical demand profiles, (2) energy and entropy feature extraction (absolute and relative) from the wavelet levels at definitive time frames, and (3) Bayesian probabilistic hierarchical clustering of the features to classify the buildings in terms of similar patterns of electrical demand. The process yielded a categorized and more manageable set of representative electrical demand profiles, inference of the characteristics influencing supply-demand interactions, and a test bed for quantifying the impact of applying energy storage technologies.
SUMMARYThis work presents an iterative method for modelling the effect of ambient air temperature on the air-cooled organic Rankine cycle. The ambient temperature affects the condenser performance, and hence the performance of the whole cycle, in two ways. First, changing the equilibrium pressure inside the condenser, the turbine outlet pressure and the turbine pressure ratio vary. Since the turbine pressure ratio is a major parameter in determining the power generated by a turbine, the plant output is directly affected. Second, changing the condenser outlet temperature with ambient temperature, the pump inlet and outlet conditions are changed. Thus, the vapourizer equilibrium temperature and pressure are influenced. The developed method iteratively seeks the equilibrium conditions for both the condenser and vapourizer. Two case studies based on a real plant performance have been carried out to demonstrate the validity of the method. The developed method demonstrates robustness and converges regardless of the initial conditions allowed by the physical properties of the working fluid. This method is effective for cycles that use saturated vapour as well as superheated vapour under static or dynamic conditions with appropriate initial conditions and constraints. The developed method may be applied to any Rankine cycle with closed cycle operation.
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