Single- and three-phase synchronisation methods based on optimum filtering theory are proposed. These methods are based mainly on the Kalman filter and are therefore termed Kalman filter-phase locked loop. They explicitly include in the problem formulation the presence of harmonics, voltage unbalance, measurement noise, transients and frequency deviation. Such perturbations degrade the performance of many synchronisation structures presented in literature. The formulation presented here makes the synchronisation signals less sensitive to these perturbations. It is also shown that the proposed methods can be helpful by also providing the amplitude, instantaneous phase and frequency of grid voltages that can be useful for the analysis of power quality. Furthermore, the Kalman filter provides a way of obtaining the best compromise between transient response and measurement noise rejection for the synchronisation signals. The paper sets out the development of the proposed methods together with the choice of tuning parameters and their physical meaning. Simulations and experimental results using a DSP TMS320F2812 are presented to show the effectiveness of the proposed schemes.

A new control scheme based on the Kalman filter and the linear quadratic regulator (LQR) is proposed to improve the performance of power quality conditioning devices. Grid perturbations such as load variations, frequency deviation, voltage distortion, line impedance, unbalance, and measurement noise are taken into account. A new model of the plant is developed that allows the use of the LQR controller with a better performance. This new controller structure is feasible thanks to an algorithm based on the Kalman filter (KF), which estimates the state space variables at the point of common coupling, used in the proposed control system. This algorithm is also responsible for generating the references for the controller. The proposed control system was implemented using a digital signal controller. Extensive tests and experimental results are presented in order to verify the performance of the proposal.

Energy Storage Systems (ESS) and Distributed Generation (DG) are topics in a large number of recent research works. Moreover, given the increasing adoption of EVs, high capacity EV batteries can be used as ESS, as most vehicles remain idle for long periods during work or home parking. However, the high EV penetration introduces some issues related to the charging power requirements, thereby increasing the peak demand for microgrids where EV chargers are installed. In addition, photovoltaic distributed generation is becoming another issue to deal with in EV charging microgrids. Therefore, this new scenario requires an Energy Management System (EMS) able to deal with charging demand, as well as with generation intermittency. This paper presents an EMS strategy for Microgrids that contain an EV parking lot (EVM), Photovoltaic (PV) arrays, and dynamic loads connected to the grid considering a Point of Common Coupling (PCC). The EVM-EMS utilizes the projections of future PV generation and future demand to accomplish a dynamic programming technique that optimizes the EVs’ charging (G2V) or discharging (V2G) profiles. This algorithm attends to user preferences while reducing the demand grid dependences and improves the microgrid efficiency.

The dual-active bridge (DAB) topology is commonly preferred in bidirectional applications due to several attractive features, including auto-adjust of power flow, galvanic insulation, wide voltage gain, and zero voltage switching (ZVS) capability over some power ranges. However, the efficiency of the converter drops at light loads because the ZVS range is directly dependent on the circulating current. Assuming that the processed power is variable, the DAB converter's design must find a compromise between extending ZVS ranges and reducing the reactive power processing to ensure higher efficiency. Aiming this compromise, many papers propose hybrid approaches in which the modulation strategy is selected according to the processed power. However, this is not a simple solution because it demands multivariable control and offline optimizations. This paper proposes a modulation strategy to ensure ZVS and reduce the circulating current for the DAB converters. While the usual phase-shift modulation provides ZVS operation in a range of 40% to 100% of rated power, the proposed asymmetrical pulse-width modulation can obtain ZVS operation in a range of 2% to 100% of rated power. Experimental results demonstrated that the proposed strategy improves the converter efficiency for all power ranges, especially at light loads.

Pattern classification of ingestive behavior in grazing animals has extreme importance in studies related to animal nutrition, growth and health. In this paper, a system to classify chewing patterns of ruminants in in vivo experiments is developed. The proposal is based on data collected by optical fiber Bragg grating sensors (FBG) that are processed by machine learning techniques. The FBG sensors measure the biomechanical strain during jaw movements, and a decision tree is responsible for the classification of the associated chewing pattern. In this study, patterns associated with food intake of dietary supplement, hay and ryegrass were considered. Additionally, two other important events for ingestive behavior were monitored: rumination and idleness. Experimental results show that the proposed approach for pattern classification is capable of differentiating the five patterns involved in the chewing process with an overall accuracy of 94%.

Nanogrids for single consumers have gained importance based on the development of standards for prosumers’ electrical installations (PEIs) such as the IEC 60364 series. All distributed energy resources (DERs) are usually integrated into a single place using one or more dc buses to reduce power conversion losses in those systems. Also, an electrical energy management system (EEMS) is required for the nanogrids smart operation, where droop controls and state machines have been considered for power management. The drawback of these strategies is that the power management is poor when droop controls are considered at the second layer because all DERs must contribute to the dc bus voltage regulation. In addition, the state machines are limited due to their reduced flexibility. The main contribution of this article is a distributed price-based power management (d-PBPM) proposed to overcome these challenges. The d-PBPM is based on dc bus signaling and the price theory, and no droop control is required in the second layer, improving the management efficiency. Experimental results show that the d-PBPM makes the nanogrids’ EEMS scalable to new DERs and new dc buses, considering the addition of distributed sources, dc buses, and energy price to an existing nanogrid. The experimental setup includes a dual dc bus nanogrid with photovoltaic (PV) systems, energy storage, loads management, and ac grid interface.