Single sensor boost converter-based maximum power point tracking algorithms

Two new maximum power point tracking algorithms are presented: the input voltage sensor, and duty ratio maximum power point tracking algorithm (ViSD algorithm); and the output voltage sensor, and duty ratio maximum power point tracking algorithm (VoSD algorithm). The ViSD and VoSD algorithms have the features, characteristics and advantages of the incremental conductance algorithm (INC); but, unlike the incremental conductance algorithm which requires two sensors (the voltage sensor and current sensor), the two algorithms are more desirable because they require only one sensor: the voltage sensor. Moreover, the VoSD technique is less complex; hence, it requires less computational processing. Both the ViSD and the VoSD techniques operate by maximising power at the converter output, instead of the input. The ViSD algorithm uses a voltage sensor placed at the input of a boost converter, while the VoSD algorithm uses a voltage sensor placed at the output of a boost converter. © 2011 IEEE.

Voltage sensing buck converter-based technique for maximum photovoltaic solar energy delivery

A voltage sensing buck converter-based technique for maximum solar power delivery to a load is presented. While retaining the features and advantages of the incremental conductance algorithm, this technique is more desirable because of single sensor use. The technique operates by maximising power at the buck converter output instead of the input.

Distributed maximum likelihood for simultaneous self-localization and tracking in sensor networks

We show that the sensor self-localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we implement fully decentralized versions of the Recursive Maximum Likelihood and on-line Expectation-Maximization algorithms to localize the sensor network simultaneously with target tracking. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a novel message passing algorithm. The latter allows each node to compute the local derivatives of the likelihood or the sufficient statistics needed for Expectation-Maximization. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we demonstrate that the developed algorithms are able to learn the localization parameters. © 2012 IEEE.

Spectroscopy of neutron-rich Dy168,170: Yrast band evolution close to the NpNn valence maximum

The yrast sequence of the neutron-rich dysprosium isotope Dy168 has been studied using multinucleon transfer reactions following collisions between a 460-MeV Se82 beam and an Er170 target. The reaction products were identified using the PRISMA magnetic spectrometer and the γ rays detected using the CLARA HPGe-detector array. The 2+ and 4+ members of the previously measured ground-state rotational band of Dy168 have been confirmed and the yrast band extended up to 10+. A tentative candidate for the 4+→2+ transition in Dy170 was also identified. The data on these nuclei and on the lighter even-even dysprosium isotopes are interpreted in terms of total Routhian surface calculations and the evolution of collectivity in the vicinity of the proton-neutron valence product maximum is discussed. © 2010 The American Physical Society.

A Maximum-Likelihood Interpretation for Slow Feature Analysis

The brain extracts useful features from a maelstrom of sensory information, and a fundamental goal of theoretical neuroscience is to work out how it does so. One proposed feature extraction strategy is motivated by the observation that the meaning of sensory data, such as the identity of a moving visual object, is often more persistent than the activation of any single sensory receptor. This notion is embodied in the slow feature analysis (SFA) algorithm, which uses “slowness” as an heuristic by which to extract semantic information from multi-dimensional time-series. Here, we develop a probabilistic interpretation of this algorithm showing that inference and learning in the limiting case of a suitable probabilistic model yield exactly the results of SFA. Similar equivalences have proved useful in interpreting and extending comparable algorithms such as independent component analysis. For SFA, we use the equivalent probabilistic model as a conceptual spring-board, with which to motivate several novel extensions to the algorithm.

Single-sensor maximum power point tracking algorithms

Single-sensor maximum power point tracking algorithms for photovoltaic systems are presented. The algorithms have the features, characteristics and advantages of the widely used incremental conductance (INC) algorithm. However; unlike the INC algorithm which requires two sensors (the voltage sensor and the current sensor), the single-sensor algorithms are more desirable because they require only one sensor: the voltage sensor. The algorithms operate by maximising power at the DC-DC converter output, instead of the input. © 2013 The Institution of Engineering and Technology.