Solid-State Bipolar Marx Generator with Voltage Droop Compensation

This paper addresses the voltage droop compensation associated with long pulses generated by solid-stated based high-voltage Marx topologies. In particular a novel design scheme for voltage droop compensation in solid-state based bipolar Marx generators, using low-cost circuitry design and control, is described. The compensation consists of adding one auxiliary PWM stage to the existing Marx stages, without changing the modularity and topology of the circuit, which controls the output voltage and a LC filter that smoothes the voltage droop in both the positive and negative output pulses. Simulation results are presented for 5 stages Marx circuit using 1 kV per stage, with 1 kHz repetition rate and 10% duty cycle.

A new control strategy with saturation effect compensation for an autonomous induction generator drivenby wide speed range turbines

This paper presents a variable speed autonomous squirrel cage generator excited by a current-controlled voltage source inverter to be used in stand-alone micro-hydro power plants. The paper proposes a system control strategy aiming to properly excite the machine as well as to achieve the load voltage control. A feed-forward control sets the appropriate generator flux by taking into account the actual speed and the desired load voltage. A load voltage control loop is used to adjust the generated active power in order to sustain the load voltage at a reference value. The control system is based on a rotor flux oriented vector control technique which takes into account the machine saturation effect. The proposed control strategy and the adopted system models were validated both by numerical simulation and by experimental results obtained from a laboratory prototype. Results covering the prototype start-up, as well as its steady-state and dynamical behavior are presented. (C) 2011 Elsevier Ltd. All rights reserved.

Fast optimum-predictive control and capacitor voltage balancing strategy for bipolar back-to-back NPC converters in high-voltage direct current transmission systems

Multilevel power converters have been introduced as the solution for high-power high-voltage switching applications where they have well-known advantages. Recently, full back-to-back connected multilevel neutral point diode clamped converters (NPC converter) have been used inhigh-voltage direct current (HVDC) transmission systems. Bipolar-connected back-to-back NPC converters have advantages in long-distance HVDCtransmission systems over the full back-to-back connection, but greater difficulty to balance the dc capacitor voltage divider on both sending and receiving end NPC converters. This study shows that power flow control and dc capacitor voltage balancing are feasible using fast optimum-predictive-based controllers in HVDC systems using bipolar back-to-back-connected five-level NPC multilevel converters. For both converter sides, the control strategytakes in account active and reactive power, which establishes ac grid currents in both ends, and guarantees the balancing of dc bus capacitor voltages inboth NPC converters. Additionally, the semiconductor switching frequency is minimised to reduce switching losses. The performance and robustness of the new fast predictive control strategy, and its capability to solve the DC capacitor voltage balancing problem of bipolar-connected back-to-back NPCconverters are evaluated.

HVDC transmission systems: Bipolar back-to-back diode clamped multilevel converter with fastoptimum-predictive control and capacitor balancing strategy

Voltage source multilevel power converter structures are being considered for high power high voltage applications where they have well known advantages. Recently, full back-to-back connected multilevel neutral diode clamped converters (NPC) have been used in high voltage direct current (HVDC) transmission systems. Bipolar back-to-back connection of NPCs have advantages in long distance HVDC transmission systems, but highly increased difficulties to balance the dc capacitor voltage dividers on both sending and receiving end NPCs. This paper proposes a fast optimum-predictive controller to balance the dc capacitor voltages and to control the power flow in a long distance HVDCsystem using bipolar back-to-back connected NPCs. For both converter sides, the control strategy considers active and reactive power to establish ac grid currents on sending and receiving ends, while guaranteeing the balancing of both NPC dc bus capacitor voltages. Furthermore, the fast predictivecontroller minimizes the semiconductor switching frequency to reduce global switching losses. The performance and robustness of the new fast predictive control strategy and the associated dc capacitors voltage balancing are evaluated. (C) 2011 Elsevier B.V. All rights reserved.

Reactive power compensation by EPSO technique

This paper presents a methodology to address reactive power compensation using Evolutionary Particle Swarm Optimization (EPSO) technique programmed in the MATLAB environment. The main objective is to find the best operation point minimizing power losses with reactive power compensation, subjected to all operational constraints, namely full AC power flow equations, active and reactive power generation constraints. The methodology has been tested with the IEEE 14 bus test system demonstrating the ability and effectiveness of the proposed approach to handle the reactive power compensation problem.

Optimal dispatch with reactive power compensation by Genetic Algorithm

This paper presents a Unit Commitment model with reactive power compensation that has been solved by Genetic Algorithm (GA) optimization techniques. The GA has been developed a computational tools programmed/coded in MATLAB. The main objective is to find the best generations scheduling whose active power losses are minimal and the reactive power to be compensated, subjected to the power system technical constraints. Those are: full AC power flow equations, active and reactive power generation constraints. All constraints that have been represented in the objective function are weighted with a penalty factors. The IEEE 14-bus system has been used as test case to demonstrate the effectiveness of the proposed algorithm. Results and conclusions are dully drawn.

Distributed Model Predictive Control for Housing with Hourly Auction of Available Energy

This paper presents a distributed model predictive control (DMPC) for indoor thermal comfort that simultaneously optimizes the consumption of a limited shared energy resource. The control objective of each subsystem is to minimize the heating/cooling energy cost while maintaining the indoor temperature and used power inside bounds. In a distributed coordinated environment, the control uses multiple dynamically decoupled agents (one for each subsystem/house) aiming to achieve satisfaction of coupling constraints. According to the hourly power demand profile, each house assigns a priority level that indicates how much is willing to bid in auction for consume the limited clean resource. This procedure allows the bidding value vary hourly and consequently, the agents order to access to the clean energy also varies. Despite of power constraints, all houses have also thermal comfort constraints that must be fulfilled. The system is simulated with several houses in a distributed environment.

Habitat predictive modelling of demersal fish species in the Azores

Dissertação de Mestrado, Estudos Integrados dos Oceanos, 25 de Março de 2013, Universidade dos Açores.

Denoising of LSFCM images with compensation for the photoblinking/photobleaching effects

Fluorescence confocal microscopy images present a low signal to noise ratio and a time intensity decay due to the so called photoblinking and photobleaching effects. These effects, together with the Poisson multiplicative noise that corrupts the images, make long time biological observation processes very difficult.

Inverse dynamics based predictive control for unified power flow controllers without DC bus

This paper presents a new predictive digital control method applied to Matrix Converters (MC) operating as Unified Power Flow Controllers (UPFC). This control method, based on the inverse dynamics model equations of the MC operating as UPFC, just needs to compute the optimal control vector once in each control cycle, in contrast to direct dynamics predictive methods that needs 27 vector calculations. The theoretical principles of the inverse dynamics power flow predictive control of the MC based UPFC with input filter are established. The proposed inverse dynamics predictive power control method is tested using Matlab/Simulink Power Systems toolbox and the obtained results show that the designed power controllers guarantees decoupled active and reactive power control, zero error tracking, fast response times and an overall good dynamic and steady-state response.

On the analysis of compensation correctness

One fundamental idea of service-oriented computing is that applications should be developed by composing already available services. Due to the long running nature of service interactions, a main challenge in service composition is ensuring correctness of transaction recovery. In this paper, we use a process calculus suitable for modelling long running transactions with a recovery mechanism based on compensations. Within this setting, we discuss and formally state correctness criteria for compensable processes compositions, assuming that each process is correct with respect to transaction recovery. Under our theory, we formally interpret self-healing compositions, that can detect and recover from faults, as correct compositions of compensable processes. Moreover, we develop an automated verification approach and we apply it to an illustrative case study.

Predictive value of serologic tests in the diagnosis and follow-up of patients with paracoccidioidomycosis

A serologic study was undertaken in a group of 43 patients with active paracoccidioidomycosis who were treated in the same form (ketoconazole), for identical periods of time (6 months), and folio wed-up for various periods posttherapy. The tests employed were agar gel immunodiffusion (AGID) and complement fixation (FC). Also studied were 50 sera from patients with proven histoplasmosis and pulmonary aspergilloma, 30 patients with culturaly proven tuberculosis as well as 92 specimens from healthy individuals, residents in the endemic area for paracoccidioidomycosis. A single lot of yeast filtrate antigen was used throughout the study. The value of each test was measured according to GALEN and GAMBINO6. Both tests were highly sensitive, 89 and 93% respectively. Regarding their specificity, the AGID was totally specific while the CF exhibited 96.6% and 97% specificity in front of tuberculosis patients and healthy individuals respectively and 82% in comparison with patients with other mycoses. The concept of predictive value, that is, the certainty one has in accepting a positive test as diagnostic of paracoccidioidomycosis, favored the AGID procedure (100%) over the CF test. The latter could sort out with 93% certainty a patient with paracoccidioidomycosis among a group of healthy individuals and with 97.5% in the case of TB patients; when the group in question was composed by individuals with other deep mycoses, such certainty was lower (81%). The above results indicate that both the AGID and the CF tests furnish results of high confidence; one should not relay, however, in the CF alone as a means to establish the specific diagnosis of paracoccidioidomycosis.

On performance of distributed model predictive control in power system frequency regulation

This paper describes the implementation of a distributed model predictive approach for automatic generation control. Performance results are discussed by comparing classical techniques (based on integral control) with model predictive control solutions (centralized and distributed) for different operational scenarios with two interconnected networks. These scenarios include variable load levels (ranging from a small to a large unbalance generated power to power consumption ratio) and simultaneously variable distance between the interconnected networks systems. For the two networks the paper also examines the impact of load variation in an island context (a network isolated from each other).

Confronting predictive texture zeros in lepton mass matrices with current data

Several popular Ansatze of lepton mass matrices that contain texture zeros are confronted with current neutrino observational data. We perform a systematic chi(2) analysis in a wide class of schemes, considering arbitrary Hermitian charged-lepton mass matrices and symmetric mass matrices for Majorana neutrinos or Hermitian mass matrices for Dirac neutrinos. Our study reveals that several patterns are still consistent with all the observations at the 68.27% confidence level, while some others are disfavored or excluded by the experimental data. The well-known Frampton-Glashow-Marfatia two-zero textures, hybrid textures, and parallel structures (among others) are considered.

The role of functional polymorphisms in immune response genes as biomarkers of BCG Immunotherapy outcome in bladder cancer: Establishment of a predictive profile in a Southern Europe population

OBJECTIVE: To evaluate the predictive value of genetic polymorphisms in the context of BCG immunotherapy outcome and create a predictive profile that may allow discriminating the risk of recurrence. MATERIAL AND METHODS: In a dataset of 204 patients treated with BCG, we evaluate 42 genetic polymorphisms in 38 genes involved in the BCG mechanism of action, using Sequenom MassARRAY technology. Stepwise multivariate Cox Regression was used for data mining. RESULTS: In agreement with previous studies we observed that gender, age, tumor multiplicity and treatment scheme were associated with BCG failure. Using stepwise multivariate Cox Regression analysis we propose the first predictive profile of BCG immunotherapy outcome and a risk score based on polymorphisms in immune system molecules (SNPs in TNFA-1031T/C (rs1799964), IL2RA rs2104286 T/C, IL17A-197G/A (rs2275913), IL17RA-809A/G (rs4819554), IL18R1 rs3771171 T/C, ICAM1 K469E (rs5498), FASL-844T/C (rs763110) and TRAILR1-397T/G (rs79037040) in association with clinicopathological variables. This risk score allows the categorization of patients into risk groups: patients within the Low Risk group have a 90% chance of successful treatment, whereas patients in the High Risk group present 75% chance of recurrence after BCG treatment. CONCLUSION: We have established the first predictive score of BCG immunotherapy outcome combining clinicopathological characteristics and a panel of genetic polymorphisms. Further studies using an independent cohort are warranted. Moreover, the inclusion of other biomarkers may help to improve the proposed model.