A multiplying-by-two CMOS amplifier for high-speed ADCs based on parametric amplification

15th International Conference on Mixed Design of Integrated Circuits and Systems, pp. 177 – 180, Poznan, Polónia

New low-power 1.5-bit time-interleaved MDAC based on MOS capacitor amplification

15th IEEE International Conference on Electronics, Circuits and Systems, Malta

Measurements of environmental background radiation in the surrounding area of a coal-fired power plant

Certain materials used and produced in a wide range of non-nuclear industries contain enhanced activity concentrations of natural radionuclides. In particular, electricity production from coal is one of the major sources of increased exposure to man from enhanced naturally occurring materials. Over the past decades there has been some discussion about the elevated natural background radiation in the area near coal-fired power plants due to high uranium and thorium content present in coal. This work describes the methodology developed to assess the radiological impact due to natural radiation background increasing levels, potentially originated by a coal-fired power plant’s operation. Gamma radiation measurements have been done with two different instruments: a scintillometer (SPP2 NF, Saphymo) and a gamma ray spectrometer with energy discrimination (Falcon 5000, Canberra). A total of 40 relevant sampling points were established at locations within 20 km from the power plant: 15 urban and 25 suburban measured stations. The highest values were measured at the sampling points near to the power plant and those located in the area within the 6 and 20 km from the stacks. This may be explained by the presence of a huge coal pile (1.3 million tons) located near the stacks contributing to the dispersion of unburned coal and, on the other hand, the height of the stacks (225 m) which may influence ash’s dispersion up to a distance of 20 km. In situ gamma radiation measurements with energy discrimination identified natural emitting nuclides as well as their decay products (212Pb, 214Pb, 226Ra 232Th, 228Ac, 234Th 234Pa, 235U, etc.). This work has been primarily done to in order to assess the impact of a coal-fired power plant operation on the background radiation level in the surrounding area. According to the results, an increase or at least an influence has been identified both qualitatively and quantitatively.

Evaluation of the Electric Vehicle Impact in the Power Demand Curve in a Smart Grid Environment

Smart grids with an intensive penetration of distributed energy resources will play an important role in future power system scenarios. The intermittent nature of renewable energy sources brings new challenges, requiring an efficient management of those sources. Additional storage resources can be beneficially used to address this problem; the massive use of electric vehicles, particularly of vehicle-to-grid (usually referred as gridable vehicles or V2G), becomes a very relevant issue. This paper addresses the impact of Electric Vehicles (EVs) in system operation costs and in power demand curve for a distribution network with large penetration of Distributed Generation (DG) units. An efficient management methodology for EVs charging and discharging is proposed, considering a multi-objective optimization problem. The main goals of the proposed methodology are: to minimize the system operation costs and to minimize the difference between the minimum and maximum system demand (leveling the power demand curve). The proposed methodology perform the day-ahead scheduling of distributed energy resources in a distribution network with high penetration of DG and a large number of electric vehicles. It is used a 32-bus distribution network in the case study section considering different scenarios of EVs penetration to analyze their impact in the network and in the other energy resources management.

Electrical ageing studies of polymeric insulation for power cables (estudo do envelhecimento eléctrico do isolante polimérico de cabos eléctricos

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Física - Física Aplicada pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Matrix converter as unified power flow controller: design and implementation of decoupled direct power controllers

This paper presents the design and implementation of direct power controllers for three-phase matrix converters (MC) operating as Unified Power Flow Controllers (UPFC). Theoretical principles of the decoupled linear power controllers of the MC-UPFC to minimize the cross-coupling between active and reactive power control are established. From the matrix converter based UPFC model with a modified Venturini high frequency PWM modulator, decoupled controllers for the transmission line active (P) and reactive (Q) power direct control are synthesized. Simulation results, obtained from Matlab/Simulink, are presented in order to confirm the proposed approach. Results obtained show decoupled power control, zero error tracking, and fast responses with no overshoot and no steady-state error.

Short-term Wind Forecasting to Support Virtual Power Player Operation

This paper proposes a wind speed forecasting model that contributes to the development and implementation of adequate methodologies for Energy Resource Man-agement in a distribution power network, with intensive use of wind based power generation. The proposed fore-casting methodology aims to support the operation in the scope of the intraday resources scheduling model, name-ly with a time horizon of 10 minutes. A case study using a real database from the meteoro-logical station installed in the GECAD renewable energy lab was used. A new wind speed forecasting model has been implemented and it estimated accuracy was evalu-ated and compared with a previous developed forecast-ing model. Using as input attributes the information of the wind speed concerning the previous 3 hours enables to obtain results with high accuracy for the wind short-term forecasting.

Virtual Power Players Internal Negotiation and Management in MASCEM

Electricity Markets are not only a new reality but an evolving one as the involved players and rules change at a relatively high rate. Multi-agent simulation combined with Artificial Intelligence techniques may result in very helpful sophisticated tools. This paper presents a new methodology for the management of coalitions in electricity markets. This approach is tested using the multi-agent market simulator MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), taking advantage of its ability to provide the means to model and simulate Virtual Power Players (VPP). VPPs are represented as coalitions of agents, with the capability of negotiating both in the market and internally, with their members in order to combine and manage their individual specific characteristics and goals, with the strategy and objectives of the VPP itself. A case study using real data from the Iberian Electricity Market is performed to validate and illustrate the proposed approach.

Reactive power management strategies in future smart grids

The reactive power management in distribution network with large penetration of distributed energy resources is an important task in future power systems. The control of reactive power allows the inclusion of more distributed recourses and a more efficient operation of distributed network. Currently, the reactive power is only controlled in large power plants and in high and very high voltage substations. In this paper, several reactive power control strategies considering a smart grids paradigm are proposed. In this context, the management of distributed energy resources and of the distribution network by an aggregator, namely Virtual Power Player (VPP), is proposed and implemented in a MAS simulation tool. The proposed methods have been computationally implemented and tested using a 32-bus distribution network with intensive use of distributed resources, mainly the distributed generation based on renewable resources. Results concerning the evaluation of the reactive power management algorithms are also presented and compared.

Cost Allocation Model for Distribution Networks Considering High Penetration of Distributed Energy Resources

The high penetration of distributed energy resources (DER) in distribution networks and the competitiveenvironment of electricity markets impose the use of new approaches in several domains. The networkcost allocation, traditionally used in transmission networks, should be adapted and used in the distribu-tion networks considering the specifications of the connected resources. The main goal is to develop afairer methodology trying to distribute the distribution network use costs to all players which are usingthe network in each period. In this paper, a model considering different type of costs (fixed, losses, andcongestion costs) is proposed comprising the use of a large set of DER, namely distributed generation(DG), demand response (DR) of direct load control type, energy storage systems (ESS), and electric vehi-cles with capability of discharging energy to the network, which is known as vehicle-to-grid (V2G). Theproposed model includes three distinct phases of operation. The first phase of the model consists in aneconomic dispatch based on an AC optimal power flow (AC-OPF); in the second phase Kirschen’s andBialek’s tracing algorithms are used and compared to evaluate the impact of each resource in the net-work. Finally, the MW-mile method is used in the third phase of the proposed model. A distributionnetwork of 33 buses with large penetration of DER is used to illustrate the application of the proposedmodel.

Real-Time Tariffs for Electric Vehicles in Wind Power based Power Systems

The use of Electric Vehicles (EVs) will change significantly the planning and management of power systems in a near future. This paper proposes a real-time tariff strategy for the charge process of the EVs. The main objective is to evaluate the influence of real-time tariffs in the EVs owners’ behaviour and also the impact in load diagram. The paper proposes the energy price variation according to the relation between wind generation and power consumption. The proposed strategy was tested in two different days in the Danish power system. January 31st and August 13th 2013 were selected because of the high quantities of wind generation. The main goal is to evaluate the changes in the EVs charging diagram with the energy price preventing wind curtailment.

Electrospun Polyaniline-Reduced Graphene Oxide Composite Nanofibers Based High Sensitive Ammonia Gas Sensor

Ammonia is an important gas in many power plants and industrial processes so its detection is of extreme importance in environmental monitoring and process control due to its high toxicity. Ammonia’s threshold limit is 25 ppm and the exposure time limit is 8 h, however exposure to 35 ppm is only secure for 10 min. In this work a brief introduction to ammonia aspects are presented, like its physical and chemical properties, the dangers in its manipulation, its ways of production and its sources. The application areas in which ammonia gas detection is important and needed are also referred: environmental gas analysis (e.g. intense farming), automotive-, chemical- and medical industries. In order to monitor ammonia gas in these different areas there are some requirements that must be attended. These requirements determine the choice of sensor and, therefore, several types of sensors with different characteristics were developed, like metal oxides, surface acoustic wave-, catalytic-, and optical sensors, indirect gas analyzers, and conducting polymers. All the sensors types are described, but more attention will be given to polyaniline (PANI), particularly to its characteristics, syntheses, chemical doping processes, deposition methods, transduction modes, and its adhesion to inorganic materials. Besides this, short descriptions of PANI nanostructures, the use of electrospinning in the formation of nanofibers/microfibers, and graphene and its characteristics are included. The created sensor is an instrument that tries to achieve a goal of the medical community in the control of the breath’s ammonia levels being an easy and non-invasive method for diagnostic of kidney malfunction and/or gastric ulcers. For that the device should be capable to detect different levels of ammonia gas concentrations. So, in the present work an ammonia gas sensor was developed using a conductive polymer composite which was immobilized on a carbon transducer surface. The experiments were targeted to ammonia measurements at ppb level. Ammonia gas measurements were carried out in the concentration range from 1 ppb to 500 ppb. A commercial substrate was used; screen-printed carbon electrodes. After adequate surface pre-treatment of the substrate, its electrodes were covered by a nanofibrous polymeric composite. The conducting polyaniline doped with sulfuric acid (H2SO4) was blended with reduced graphene oxide (RGO) obtained by wet chemical synthesis. This composite formed the basis for the formation of nanofibers by electrospinning. Nanofibers will increase the sensitivity of the sensing material. The electrospun PANI-RGO fibers were placed on the substrate and then dried at ambient temperature. Amperometric measurements were performed at different ammonia gas concentrations (1 to 500 ppb). The I-V characteristics were registered and some interfering gases were studied (NO2, ethanol, and acetone). The gas samples were prepared in a custom setup and were diluted with dry nitrogen gas. Electrospun nanofibers of PANI-RGO composite demonstrated an enhancement in NH3 gas detection when comparing with only electrospun PANI nanofibers. Was visible higher range of resistance at concentrations from 1 to 500 ppb. It was also observed that the sensor had stable, reproducible and recoverable properties. Moreover, it had better response and recovery times. The new sensing material of the developed sensor demonstrated to be a good candidate for ammonia gas determination.

Cost allocation model for distribution networks considering high penetration of distributed energy resources

The high penetration of distributed energy resources (DER) in distribution networks and the competitive environment of electricity markets impose the use of new approaches in several domains. The network cost allocation, traditionally used in transmission networks, should be adapted and used in the distribution networks considering the specifications of the connected resources. The main goal is to develop a fairer methodology trying to distribute the distribution network use costs to all players which are using the network in each period. In this paper, a model considering different type of costs (fixed, losses, and congestion costs) is proposed comprising the use of a large set of DER, namely distributed generation (DG), demand response (DR) of direct load control type, energy storage systems (ESS), and electric vehicles with capability of discharging energy to the network, which is known as vehicle-to-grid (V2G). The proposed model includes three distinct phases of operation. The first phase of the model consists in an economic dispatch based on an AC optimal power flow (AC-OPF); in the second phase Kirschen's and Bialek's tracing algorithms are used and compared to evaluate the impact of each resource in the network. Finally, the MW-mile method is used in the third phase of the proposed model. A distribution network of 33 buses with large penetration of DER is used to illustrate the application of the proposed model.

Adaptive modeling and high quality spectral estimation for speech enhancement

In this work an adaptive modeling and spectral estimation scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for speech enhancement. Both speech and noise signals are modeled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. The model parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The speech enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. This approach is particularly useful as a pre-processing module for parametric based speech recognition systems that rely on spectral time dependent models. The system performance has been evaluated by a set of human listeners and by spectral distances. In both cases the use of this pre-processing module has led to improved results.

Adaptive filtering for high quality HMM based speech synthesis

In this work an adaptive filtering scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for Hidden Markov Model (HMM) based speech synthesis quality enhancement. The objective is to improve signal smoothness across HMMs and their related states and to reduce artifacts due to acoustic model's limitations. Both speech and artifacts are modelled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. Themodel parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The quality enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. The system's performance has been evaluated using mean opinion score tests and the proposed technique has led to improved results.