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Wireless Power Transfer (WPT) is a technique employed to transmit energy from a source to one or multiple loads, usually located over short distances by near-field inductive coupling. An inductive link is connected to each load through a matching capacitive network for tuning the system and optimizing the whole power transfer process. However, when the frequency of the source is constant and the capacitive compensation network is fixed, the optimized operating points can only be guaranteed for fixed loads and for fixed positions between the transmitting and receiving coils. When the coils are nonstationary or the load is variable, the tuning of the inductive link must be dynamically adjusted to maintain a constant output power. This work presents the development of an optimized WPT control method for the application of an endoscopy procedure, guaranteeing sufficient energy to power a camera and a communication system allocated inside a small capsule of $\approx 26.1\times 9$ mm. A 3D receiver coil is located inside the capsule moving freely inside a transmitter coil with 400 mm diameter through translations and revolutions. The proposed system compensates disturbances such as the mechanical misalignments between the transmitter and three receiver coils in quadrature and load variation, by the dynamic adjustment of both the frequency and the matching compensation network. Preliminary results have shown reasonable power levels on the load even with low magnetic coupling coefficients ( $k\ \approx\ 0.002$ ), featured by the experimental endoscopic arrangement, providing energy to power the electronics to collect and transmit data to an external receiver.

This paper introduces the setup of a Distributed Generation Microgrid (DGM) focused onto research on monitoring (electrical and environmental), controling and operating microgrids. This network is located at the Polytech Center Campus of the Federal University of Parana (UFPR), in Curitiba, Brazil. It consists of nine 13.8 kV feeders, three Distributed Generation (DG) sources and is connected to the main utility through a 69 kV distribution substation of the state electricity company named as Copel DIS. In addition to the electrical configuration, all components of the microgrid are presented, such as power generation, electrical and environmental meters, $\mu \mathrm{PMU}$ and the system monitoring computing environment.

This paper presents the results of an ongoing project involving the monitoring of micro phasor measurement units (micro-PMU) at Universidade Federal do Parana (UFPR). The infrastructure for receiving, processing and storing data from micro-PMUs, energy meters and power quality analysers was deployed. Free software were tested for data storage and visualization. We modified an existing open-source Energy Monitoring System (EMS) for proper use with PMUs. The software native communication protocol was adapted from hypertext protocol (HTTP) to direct reception of user datagram protocol (UDP) in IEEE Std. C37.118.2-2011 format to receive micro-PMUs transmission at 120 phasor per second. We modified data storage format to efficiently deal with time information allowing reduction of 60% of storage space in comparison to commonly used phasor data concentrator (openPDC).

Procedures for modeling potential/ power transformers and reactors in order to support electromagnetic transients analysis have been addressed by many recent researchers. Among all methods for obtaining black-box models, the Frequency Domain Vector Fitting (FD-VF) or, simply VF, has several successful application reports in electrical power systems. This article is two purpose: first to describe a procedure that combines instrumental variables to the VF method, called the Instrumental Variable VF (IV-VF). The main advantage of the IV-VF is the fact that if the iterative procedure converged, an optimal local solution is obtained. Also to present a case study based on actual single-phase transformer measurements (Benchmark JWG A2/C4.52 for Power Transformers) and a GIS installation, where IV-VF is used for evaluating internal resonances in power transformers due to high frequency transients. The knowledge of external and internal winding response to these transients contribute to insulation design and reliability in field operation.

The wireless power transfer can be useful in applications where the use of cables for powering remote devices are not suitable or even feasible. However, the transferred power is highly dependent on the relative position between the transmitter and receiver coils as well as other circuit parameters. The use of adaptive matching network in wireless power transfer system allows to track the maximum power transferred to the load while changing the magnetic coupling between the coils. By controlling both, the excitation frequency and matching capacitor at the primary side of the inductive link, the output power can be hold approximately constant over a large range of magnetic coupling coefficient. This paper presents an automatic compensation method for an inductive power transfer system. The proposed method monitors the magnitude and phase of the input current of the inductive link. Thus, the data is processed by a fuzzy logic controller actuating on the working frequency and on the matching capacitor in order to maximize the power transferred to the load. The proposed method can tolerate the movement of remote devices while charging processes.

This paper is concerned with the computation of uncertainty bounds for the expansion of uncertain Volterra models into an orthonormal basis of functions, such as the Laguerre or Kautz bases. This problem has already been addressed in the context of linear systems by means of an approach in which the uncertainty bounds of the expansion coefficients have been estimated from a structured set of impulse responses describing a linear uncertain process. This approach is extended here towards nonlinear Volterra models through the computation of the uncertainty bounds of the expansion coefficients from a structured set of uncertain Volterra kernels. The proposed formulation assures that the resulting model is able to represent all the original uncertainties with minimum intervals for the expansion coefficients. An example is presented to illustrate the effectiveness of the proposed formulation.

This paper is focused on the problem of uncertain process control by using RMPC (Robust Model Predictive Control). A relevant class of RMPC algorithms is the one characterized by the use of the LMI framework. This field started in the middle of nineties and since then several works applying LMIs in the context of RMPC have been proposed. Most of them assume a polytopic representation of the process uncertainties and require full-state feedback. On the other hand, several works describing the theory and applicability of OBF (Orthonormal Basis Functions) in identification and control fields can be found in the literature. So, the present paper proposes the use of OBF for uncertain process modelling and for RMPC algorithm synthesis. It is shown that, due to the proposed approach, one obtains an output feedback control law, with set-point tracking. This represents an advantage in actual applications. Moreover, well established state feedback RMPC strategies can be reviewed under the OBF modelling perspective. Simulation results illustrate the proposed methodology.

This paper is focused on the problem of obtaining wide band transformer models for electrical power system transient simulation purposes. It is analyzed the use of orthonormal basis functions models in such context. A model structure selection strategy, based on the transformer properties, is discussed and the frequency-domain parameter estimation procedure is presented. Identification results, based on actual transformer frequency response data, are presented and discussed.

The present paper is focused on thermal comfort control for building occupants. Thermal comfort is addressed here by the use of PMV index for such measurement. Based on PMV, two predictive strategies characterized by having terminal constraints are proposed and compared. The first is based on generating a temperature set-point signal that optimizes the building (single zone) internal PMV value. The second includes the PMV model in the controller prediction computations, generating a non-linear PMV model having Wiener structure. In both cases, the linear part of the model is built by using Laguerre basis. Simulation results, conducted with actual climate data, illustrate the performance of the thermal comfort control algorithms.

This paper deals with the problem of finding the pole(s) in Orthonormal Basis Functions (OBF) models. In fact, any stable system can be exactly represented by an infinite orthonormal functions series, assuming that the basis used in such representation is complete. However, in practice a truncated basis functions expansion is used. For certain basis choice and fixed number of functions, there is a pole or set of poles that minimizes the truncation error. The problem is then to find such set. Therefore, the present paper is focused on the problem of finding the basis functions poles when more than one dynamic is involved, for instance, in SISO Generalized Orthonormal Basis Functions (GOBF) models or MIMO Laguerre, Kautz and/or GOBF models. It is proposed and analyzed here the use of Particle Swarm Optimization (PSO) for such problem. Simulation results presented shows the validity of the proposed method.

One of the phenomena that affect the water loss in newborn infants is the humidity. The evaporation is determined by the vapour partial pressure. As well as temperature control, the reduction of the vapour partial pressure oscillations has an important role in the establishment of thermal comfort for newborn infants in incubators. This work presents an algorithm based on multiple models to water vapour partial pressure control. The tests have been made in an acrylic prototype simulating a commercial incubator. The results shows that the implemented control algorithm presents oscillations minor to /spl plusmn/0.08 kPa.

This work focuses on predictive control of nonlinear systems modelled by Volterra series with orthonormal basis functions expansion (Volterra-OBF). By using Volterra series, any nonlinear analytical operator with finite memory can be approximated with an arbitrary precision. A Laguerre basis expansion is then used in the model parameterization to reduce the number of coefficients of the Volterra model. In this context, a predictive control with terminal constraints window based on cited model is proposed. Finally, simulated results using a CSTR unit illustrates the control scheme performance.

Presents a framework for fuzzy modeling of dynamic systems using orthonormal basis functions in the representation of the model input signals. The main objective of using orthonormal bases is to overcome the task of estimating the order and time delay of the process. The result is a nonlinear moving average fuzzy model which, consequently, has no feedback of prediction errors. Although any technique of fuzzy modeling can be used in the proposed framework, a relational approach is considered. The performance of fuzzy models with orthonormal basis functions is illustrated by examples and the results are compared with those provided by conventional fuzzy models and Volterra models.