Luigi Galotto

Non-linear controller applied to boost DC-DC converters using the state space average model

This work aims to present a methodology to use the large signal state space average model of the DC-DC converters for high performance controllers design. The design using classic control theories need the SISO transfer function. Often, these functions are obtained using linearization around the point of operation. It reduces the range of operation to the designed controller with the expected performance. Also, the presented controller showed to have high disturbance rejection dependent on the sensors used. The presented controller is described, simulated and experimentally confirmed.

Multiple signal processing techniques based power quality disturbance detection, classification, and diagnostic software

This work presents the development steps of the software PQMON, which targets power quality analysis applications. The software detects and classifies electric system disturbances. Furthermore, it also makes diagnostics about what is causing such disturbances and suggests line of actions to mitigate them. Among the disturbances that can be detected and analyzed by this software are: harmonics, sag, swell and transients. PQMON is based on multiple signal processing techniques. Wavelet transform is used to detect the occurrence of the disturbances. The techniques used to do such feature extraction are: fast Fourier transform, discrete Fourier transform, periodogram, and statistics. Adaptive artificial neural network is also used due to its robustness in extracting features such as fundamental frequency and harmonic amplitudes. The probable causes of the disturbances are contained in a database, and their association to each disturbance is made through a cause-effect relationship algorithm, which is used to diagnose. The software also allows the users to include information about the equipments installed in the system under analysis, resulting in the direct nomination of any installed equipment during the diagnostic phase. In order to prove the effectiveness of software, simulated and real signals were analyzed by PQMON showing its excellent performance.

Voltage estimation in electrical distribution systems

The increasing demand for high power quality has increased the demand for power quality monitoring tools. The voltage performance monitoring for each feeder is one of the needs found by the utility companies. Since it is not economically viable to measure every single node in the system, it is necessary to use estimation techniques in order to get all needed information with a reduced the number of meters. These are basically interpolation techniques. Each interpolation provides different performance of estimations accordingly the application. In this work, it is proposed a non-linear non-parametric method which was found to get the best voltage estimates of feeders in relation to other more usual techniques. Comparative results for different methodologies in a hypothetic system are presented and discussed.

Auto-Associative Neural Network Based Sensor Drift Compensation in Indirect Vector Controlled Drive System

The paper proposes an auto-associative neural network (AANN) based sensor drift compensation in an indirect vector-controlled induction motor drive. The feedback signals from the phase current sensors are given as the AANN input. The AANN then performs the auto-associative mapping of these signals so that its output is an estimate of the sensed signals. Since the AANN exploits the physical and analytical redundancy, whenever a sensor starts to drift, the drift is compensated at the output, and the performance of the drive system is barely affected. The paper describes the drive system, gives a brief overview of the AANN, presents the technical approach, and then gives some performance of the system demonstrating validity of the approach. Although current sensors are considered only in the paper, the same approach can be applied to voltage, speed, torque, flux, or any other type sensor.

Three-phase Tri-State Current Source Inverter for photovoltaic energy stand-alone applications

This paper proposes the design of a Tri-State Boost Current Source Inverter with a Modified Space Vector Pulse Width Modulation. This converter is promising for many renewable energy applications. The Modified Space Vector PWM was created in order to determinate the converter state times and generate the switching signals, depending on the photovoltaic panel MPPT algorithm output and the modulation index. The Tri-State Boost CSI eliminates the RHP zero that is present in the Traditional Current Source Inverter by adding a freewheeling state. The partial experimental results show that this converter can operate as expected and is suitable for stand-alone situations that require high efficiency and reliability. The target application of the converter is to track the point which occur the maximum power transfer from the panel to the load.

Modeling approach based on experimental results for prediction of measurement errors in energy meters

This paper presents a general modeling approach to investigate and to predict measurement errors in active energy meters both induction and electronic types. The measurement error modeling is based on generalized additive model (GAM), ridge regression method and experimental results of meter provided by a measurement system. The measurement system provides a database of 26 pairs of test waveforms captured in a real electrical distribution system, with different load characteristics (industrial, commercial, agricultural, and residential), covering different harmonic distortions, and balanced and unbalanced voltage conditions. In order to illustrate the proposed approach, the measurement error models are discussed and several results, which are derived from experimental tests, are presented in the form of three-dimensional graphs, and generalized as error equations.

Evaluation of the Auto-Associative Neural Network Based Sensor Compensation in Drive Systems

The paper performs a deep analysis of the sensor drift compensation in motor drives approach presented in past publications [11-12]. In the past, the auto-associative neural networks (AANN) were found to be effective for this application. However, it is still unclear how much improvement may be obtained compared with other modeling techniques and when it is adequate to be applied. Therefore, the modeling techniques, specially the AANN, are detailed and evaluated using performance metrics. Additional experimental results in a motor drive are provided to show the compensation capability of the AANN. The feedback signals are given as the AANN inputs. The AANN then performs the auto-associative mapping of these signals so that its outputs are estimations of the sensed signals. Since the AANN exploits the physical and analytical redundancy, whenever a sensor starts to drift, the drift is compensated, and the performance of the drive system is barely affected.

Data based tools for sensors continuous monitoring in industry applications

This paper presents a 10 years experience of data driven models for sensor validation applied for petroleum and natural gas industry. Auto-associative kernel regression has been used as the main modeling method. The models achieved were embedded in software called Sentinell, which is used for sensors diagnosis. The software is being used in a natural gas compression station, and it has been evaluated in other industries such as: refineries, offshore petroleum platforms, and thermoelectric power plants. In this work the theoretical background is presented, as well as the performance metrics indexes used to evaluate the models. The developed methodology and the results in the real plants are presented and discussed. The experience of these previous works might open future applications in high reliability automated processes.

New topology and predictive control for photovoltaic application with inductive power decoupling capability

An important trend is the use of plug and play microinverters attached to the PV panel, allowing the so called AC module. However, these converters are subjected to high temperature and humidity. Thus, in order to improve reliability and achieve long lifetime, this work proposes a new topology that provides inductive power decoupling and eliminates electrolytic and even film capacitors, using a single ceramic or mica capacitor. The topology also provides inductor miniaturization and efficiency improvements.

Decision support system for the management of electricity consumption contracts for Smart Grids environment using Differential Evolution and Artificial Neural Network

The objective of this paper is to present a support system to manage electricity consumption contracts for Smart Grid environment. The system modeling uses historical data consumption and energy trading rules to find the optimal contract structure. Focused Time Lagged Feed forward Network was used to model the historical data. The global search tool Differential Evolution was used to find the best contract structure. This paper presents the use of the tool with current Brazilian pricing rules. However, to change the rules for a dynamic scenario of Smart Grid can be easily implemented. The results are satisfactory and indicate the feasibility of the system for different cases.