No abstract

In this paper, we present a very general design approach for optimal linear-phase phasor filter bank algorithms for PMUs based on convex semi-infinite optimization. A detailed presentation of the formulation of both the cost functions and constraints is included for the positive-sequence estimation problem. The design method is extremely powerful and flexible as it allows to control precisely the behavior of the system in terms of the total vector error (TVE), frequency error (FE), and rate of change of frequency error (RFE) metrics for several different scenarios. This feature is extremely useful for tailored designs of these filters for different applications. We also determine numerically the uniform feasibility limits of the system as a function of the filter lengths and, in particular, study the required filter lengths for compliance with the IEEE Standards C37.118. 1-2011 and C37.118.1a-2014. It is found that all requirements can be achieved with a significant margin with the exception of the M class FE constraint for interharmonic components, an issue which is also reported in other works and briefly discussed here. Finally, an interesting comparison with the Taylor-Fourier filters is made to illustrate the advantages of our approach.

Smart meters (SMs) play a pivotal rule in the smart grid by being able to report the electricity usage of consumers to the utility provider (UP) almost in real-time. However, this could leak sensitive information about the consumers to the UP or a third-party. Recent works have leveraged the availability of energy storage devices, e.g., a rechargeable battery (RB), in order to provide privacy to the consumers with minimal additional energy cost. In this paper, a privacy-cost management unit (PCMU) is proposed based on a model-free deep reinforcement learning algorithm, called deep double Q-learning (DDQL). Empirical results evaluated on actual SMs data are presented to compare DDQL with the state-of-the-art, i.e., classical Q-learning (CQL). Additionally, the performance of the method is investigated for two concrete cases where attackers aim to infer the actual demand load and the occupancy status of dwellings. Finally, an abstract information-theoretic characterization is provided.