Abstract: A management device includes at least one processor communicatively coupled to at least one energy resource controller controlling at least one energy resource and to at least one deferrable load controller controlling power to at least one deferrable load. The is configured to receive an indication, determined based on a frequency value of an electrical network and a nominal frequency value, that a frequency anomaly event has occurred. Responsive to receiving the indication that the frequency anomaly event has occurred, the processor is also configured to determine, for at least one of the energy resource and the deferrable load, based on the frequency value, the nominal frequency value, and a power value of the electrical network, a respective power command, and cause at least one of the at least one energy resource and the at least one deferrable load to modify operation based on the respective power command.

Abstract: A management device includes at least one processor communicatively coupled to at least one energy resource controller controlling at least one energy resource and to at least one deferrable load controller controlling power to at least one deferrable load. The is configured to receive an indication, determined based on a frequency value of an electrical network and a nominal frequency value, that a frequency anomaly event has occurred.

Abstract: A management device includes at least one processor communicatively coupled to at least one energy resource controller controlling at least one energy resource and to at least one deferrable load controller controlling power to at least one deferrable load. The is configured to receive an indication, determined based on a frequency value of an electrical network and a nominal frequency value, that a frequency anomaly event has occurred. Responsive to receiving the indication that the frequency anomaly event has occurred, the processor is also configured to determine, for at least one of the energy resource and the deferrable load, based on the frequency value, the nominal frequency value, and a power value of the electrical network, a respective power command, and cause at least one of the at least one energy resource and the at least one deferrable load to modify operation based on the respective power command.

Abstract: A management device includes at least one processor communicatively coupled to at least one energy resource controller controlling at least one energy resource and to at least one deferrable load controller controlling power to at least one deferrable load. The is configured to receive an indication, determined based on a frequency value of an electrical network and a nominal frequency value, that a frequency anomaly event has occurred.

Abstract: An approach to determine cantilever movement is presented. An observer based state estimation and statistical signal detection and estimation techniques are applied to Atomic Force Microscopes. A first mode approximation model of the cantilever is considered and an observer is designed to estimate the dynamic states. The cantilever-sample interaction is modeled as an impulsive force applied to the cantilever in order to detect the presence of sample. A generalized likelihood ratio test is performed to obtain the decision rule and the maximum likelihood estimation of the unknown arrival time of the sample profile and unknown magnitude of it. The use of the transient data results in sample detection at least ten times faster than using the steady state characteristics.

Abstract: An approach to detect when a cantilever loses interaction with a sample, thereby detecting when a portion of an image obtained using a cantilever is spurious is presented. An observer based estimation of cantilever deflection is compared to the cantilever deflection and the resulting innovation is used to detect when the cantilever loses interaction. The loss of interaction is determined when the innovation is outside of and/or below a threshold level.

Abstract: An approach to determine cantilever movement is presented. An observer based state estimation and statistical signal detection and estimation techniques are applied to Atomic Force Microscopes. A first mode approximation model of the cantilever is considered and a Kalman filter is designed to estimate the dynamic states. The tip-sample interaction is modeled as an impulsive force applied to the cantilever in order to detect the presence of sample. A generalized likelihood ratio test is performed to obtain the decision rule and the maximum likelihood estimation of the unknown arrival time of the sample profile and unknown magnitude of it. The use of the transient data results in sample detection at least ten times faster than using the steady state characteristics.