Smart Loads for Voltage Control in Distribution Networks

This paper shows that the smart loads (SLs) could be effective in mitigating voltage problems caused by photovoltaic (PV) generation and electric vehicle (EV) charging in low-voltage (LV) distribution networks. Limitations of the previously reported SL configuration with only series reactive compensator (SLQ) (one converter) is highlighted in this paper. To overcome these limitations, an additional shunt converter is used in back-to-back (B2B) configuration to support the active power exchanged by the series converter, which increases the flexibility of the SL without requiring any energy storage. Simulation results on a typical U.K. LV distribution network are presented to compare the effectiveness of an SL with B2B converters (SLBCs) against an SLQ in tackling under- and over-voltage problems caused by EV or PV. It is shown that SLBCs can regulate the main voltage more effectively than SLQs especially under overvoltage condition. Although two converters are required for each SLBC, it is shown that the apparent power capacity of each converter is required to be significantly less than that of an equivalent SLQ

An Analysis of Rhythmic Staccato-Vocalization Based on Frequency Demodulation for Laughter Detection in Conversational Meetings

Human laugh is able to convey various kinds of meanings in human communications. There exists various kinds of human laugh signal, for example: vocalized laugh and non vocalized laugh. Following the theories of psychology, among all the vocalized laugh type, rhythmic staccato-vocalization significantly evokes the positive responses in the interactions. In this paper we attempt to exploit this observation to detect human laugh occurrences, i.e., the laughter, in multiparty conversations from the AMI meeting corpus. First, we separate the high energy frames from speech, leaving out the low energy frames through power spectral density estimation. We borrow the algorithm of rhythm detection from the area of music analysis to use that on the high energy frames. Finally, we detect rhythmic laugh frames, analyzing the candidate rhythmic frames using statistics. This novel approach for detection of `positive' rhythmic human laughter performs better than the standard laughter classification baseline.Comment: 5 pages, 1 figure, conference pape

Energy Deposition Profiles and Entropy in Galaxy Clusters

We report the results of our study of fractional entropy enhancement in the intra-cluster medium (ICM) of the clusters from the representative XMM-Newton cluster structure survey (REXCESS). We compare the observed entropy profile of these clusters with that expected for the ICM without any feedback, as well as with the introduction of preheating and entropy change due to gas cooling. We make the first estimate of the total, as well as radial, non-gravitational energy deposition up to r500 for a large, nearly flux-limited, sample of clusters. We find that the total energy deposition corresponding to the entropy enhancement is proportional to the cluster temperature (and hence mass), and that the energy deposition per particle as a function of gas mass shows a similar profile in all clusters, with its being more pronounced in the central region than in the outer region. Our results support models of entropy enhancement through AGN feedback.Comment: version submitted to journal. Typos corrected. Main results and conclusions unchanged. 4 figures, 1 Tabl

A unified smith predictor approach for power system damping control design using remote signals

Published versio

A study on LQG/LTR control for damping inter-area oscillations in power systems

Published versio

Damping Control in Power Systems Under Constrained Communication Bandwidth: A Predictor Corrector Strategy

Damping electromechanical oscillations in power systems using feedback signals from remote sensors is likely to be affected by occasional low bandwidth availability due to increasing use of shared communication in future. In this paper, a predictor corrector (PC) strategy is applied to deal with situations of low-feedback data rate (bandwidth), where conventional feedback (CF) would suffer. Knowledge of nominal system dynamics is used to approximate (predict) the actual system behavior during intervals when data from remote sensors are not available. Recent samples of the states from a reduced observer at the remote location are used to periodically reset (correct) the nominal dynamics. The closed-loop performance deteriorates as the actual operating condition drifts away from the nominal dynamics. Nonetheless, significantly better performance compared to CF is obtained under low-bandwidth situations. The analytical criterion for closed-loop stability of the overall system is validated through a simulation study. It is demonstrated that even for reasonably low data rates the closed-loop stability is usually ensured for a typical power system application confirming the effectiveness of this approach. The deterioration in performance is also quantified in terms of the difference between the nominal and off-nominal dynamics