This paper provides a systematic method to build wind speed models based on stochastic differential equations (SDEs). The resulting models produce stochastic processes with a given probability distribution and exponential decaying autocorrelation function. The only information needed to build the models is the probability density function of the wind speed and its autocorrelation coefficient. Unlike other methods previously proposed in the literature, the proposed method leads to models able to reproduce an exact exponential autocorrelation even if the probability distribution is not Gaussian. A sufficient condition for the property above is provided. The paper includes the explicit formulation of SDE-based wind speed models obtained from several probability distributions used in the literature to describe different wind speed behaviors.
This paper introduces a new method for performing actuator fault detection and diagnostics (FDD) in heating ventilation and air conditioning (HVAC) systems. The proposed actuator FDD strategy, for testing whether an actuator is stuck in a single position, uses a two-tier approach that includes a dynamic model-based detector and a fast-deciding steady-state detector. The model-based detector is formulated to provide detection performance that asymptotically bounds both the probability of miss and probability of false alarm. To provide a quick confirmation the actuator is working, the steady-state detector utilizes a goodness-of-fit detection strategy to decide if the measurements could be described by an actuator failure. An architecture is introduced that requires multiple steady-state detection experiments to decide that the measurements could be explained by an actuator failure before performing model-based detection. An experimental test bed using a the KTH Royal Institute of Technology campus HVAC system is described and used to evaluate the steady-state and model-based detectors. The experimental test bed is utilized to identify a building dynamics model, that is employed through monte carlo analysis, to characterize the detection performance of both the model-based detector and the steady-state detector.
This work originates from the observation of the frequency distribution of the Irish system as obtained from a Frequency Disturbance Recorder lent to the last author by the University of Tennessee. The probability density function of such a distribution appears to be bimodal. The paper first investigates how stochastic sources, in particular, load and wind power estimation errors, impact on the distribution of the frequency of a high-voltage transmission system. Then, possible routes to obtain a bimodal distribution of the frequency are explored and the most likely cause that leads to the observed behaviour of the Irish system is identified. Finally, the paper presents a comparison of different frequency regulation strategies and their impact on the distribution of the frequency. A sensitivity analysis of wind speed and load parameters is presented and discussed based on the IEEE-14 bus system.
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