José Alfredo Covolan Ulson

An Experimental Study on the Effect of Temperature on Piezoelectric Sensors for Impedance-Based Structural Health Monitoring

The electromechanical impedance (EMI) technique is considered to be one of the most promising methods for developing structural health monitoring (SHM) systems. This technique is simple to implement and uses small and inexpensive piezoelectric sensors. However, practical problems have hindered its application to real-world structures, and temperature effects have been cited in the literature as critical problems. In this paper, we present an experimental study of the effect of temperature on the electrical impedance of the piezoelectric sensors used in the EMI technique. We used 5H PZT (lead zirconate titanate) ceramic sensors, which are commonly used in the EMI technique. The experimental results showed that the temperature effects were strongly frequency-dependent, which may motivate future research in the SHM field.

Partial Discharge Monitoring in Power Transformers Using Low-Cost Piezoelectric Sensors.

Power transformers are crucial in an electric power system. Failures in transformers can affect the quality and cause interruptions in the power supply. Partial discharges are a phenomenon that can cause failures in the transformers if not properly monitored. Typically, the monitoring requires high-cost corrective maintenance or even interruptions of the power system. Therefore, the development of online non-invasive monitoring systems to detect partial discharges in power transformers has great relevance since it can reduce significant maintenance costs. Although commercial acoustic emission sensors have been used to monitor partial discharges in power transformers, they still represent a significant cost. In order to overcome this drawback, this paper presents a study of the feasibility of low-cost piezoelectric sensors to identify partial discharges in mineral insulating oil of power transformers. The analysis of the feasibility of the proposed low-cost sensor is performed by its comparison with a commercial acoustic emission sensor commonly used to detect partial discharges. The comparison between the responses in the time and frequency domain of both sensors was carried out and the experimental results indicate that the proposed piezoelectric sensors have great potential in the detection of acoustic waves generated by partial discharges in insulation oil, contributing for the popularization of this noninvasive technique.

Modeling and identification of fertility maps using artificial neural networks

The application of agricultural fertilizers using variable rates along the field can be made through fertility maps previously elaborated or through real-time sensors. In most of the cases previously elaborated maps are applied. These maps are identified from analyses done in soil samples collected regularly (a sample for each field cell) or irregularly along the field. Mathematical interpolation methods such as nearest neighbor, local average, weighted inverse distance, contouring and kriging are currently used for predicting the variables involved with elaboration of fertility maps. However, some of these methods present deficiencies that can generate different fertility maps for the same data set. Moreover, such methods can generate imprecise maps. Artificial neural networks have been applied for elaboration and identification of precise fertility maps which can reduce the production costs and environmental impact.

Assessment of Macro Fiber Composite Sensors for Measurement of Acoustic Partial Discharge Signals in Power Transformers

This paper presents a performance assessment of macro fiber composite (MFC) sensors for measuring acoustic emission (AE) signals from partial discharges (PD) in power transformers filled with mineral oil. MFC sensors are low-profile and flexible, allowing them to be attached to uneven surfaces, such as a transformer wall. Two types of MFC sensors were assessed: P1 (d33 effect) and P2 (d31 effect), which are optimized for different deformations in the structure, such as elongation and contraction, respectively. In addition, a conventional AE sensor, R15I-AST model from Physical Acoustics South America, was also used as a reference for comparative analysis. Four metrics were applied to the signals: root mean square, energy criterion, Akaike criterion, power spectral density, and correlation. The experimental results indicate a high similarity between the MFC sensors and the conventional AE sensor, which expands the research field in acoustic PD measurement in power transformers by using low-cost and flexible sensors.

A low cost system for acoustic monitoring of partial discharge in power transformer by piezeletic sensor

Partial discharge damages in power transformers require high cost monitoring procedures based on corrective maintenance or even interruptions of the power system. The development of online non-invasive monitoring systems to detect partial discharges in power transformers has great relevance since it can reduce significant maintenance costs. Some critical factors in the operation of transformers such as overload, nonlinear loads, transient voltage surges by atmospheric origin and switching, can make the insulation system of transformers to lose their physical and chemical properties. Therefore, these operating conditions can cause early deterioration of the insulation, causing internal partial discharges that may develop into major defects and thus shorten the useful life of electrical equipment. This research aimed to apply a low cost piezoelectric sensors (PZTs) for partial discharge identification in power transformers and a study was conducted on the obtained a good results for identification with PZTs.

A new approach for teaching power electronics in electrical engineering courses

This work presents a new approach for teaching activities at the Laboratory of Power Electronics, at the Undergraduate Electrical Engineering course of the Faculty of Engineering Bauru (UNESP). The proposal is to develop a practical project of a bridge rectifier with Power Factor correction. Students of the course are encouraged to develop at first, the theoretical study of the rectifier, including and not-including Power Factor correction. After this study, the project characteristics such as voltage, current and power in the load, supply voltage, switching frequency, efficiency and Power Factor are defined, at the same time the choice of dedicated integrated circuit to control and correct the Power Factor to be used is done. Components of the power stage as well as the control loop parts are defined based on design features. The next step is the generation of the circuit schematic and the PCB layout for manufacturing the printed circuit board. Once the circuit is all done, students are ready to follow a procedure set to study characteristics and performance parameters of the rectifier. Measurements are first performed on the load side and then on the mains side, with and without correction of Power Factor and after that, the performance parameters in both situations are calculated. Tables of performance parameters due the variation of the supply voltage are generated, with and without correction of the Power Factor and the same curves are plotted for comparison, analysis, and discussion for the proposed project.

Intelligent Systems for the Detection of Internal Faults in Power Transmission Transformers

This chapter presents an approach based on expert systems, which is intended to identify and to locate internal faults in power transformers, as well as to provide an accurate diag‐ nosis (predictive, preventive and corrective), so that proper maintenance can be per‐ formed. In fact, the main difficulty in using conventional methods, based on analysis of acoustic emissions or dissolved gases, lies in how to relate the measured variables when there is an internal fault in a transformer. This kind of situation makes it difficult to de‐ sign optimized systems, because it prevents the efficient location and identification of pos‐ sible defects with sufficient rapidity. In addition, there are many cases where the equipment must be turned off for such tests to be carried out. Thus, this chapter proposes an architec‐ ture for an intelligent expert system for efficient fault detection in power transformers us‐ ing different diagnosis tools, based on techniques of artificial neural networks and fuzzy inference systems. Based on acoustic emission signals and the concentration of gases present in insulating mineral oil and electrical measurements, intelligent expert systems are able to provide, as a final result, the identification, characterization and location of any electrical fault occurring in transformers.

A Comparison of Inductive Sensors in the Characterization of Partial Discharges and Electrical Noise Using the Chromatic Technique

Partial discharges (PDs) are one of the most important classes of ageing processes that occur within electrical insulation. PD detection is a standardized technique to qualify the state of the insulation in electric assets such as machines and power cables. Generally, the classical phase-resolved partial discharge (PRPD) patterns are used to perform the identification of the type of PD source when they are related to a specific degradation process and when the electrical noise level is low compared to the magnitudes of the PD signals. However, in practical applications such as measurements carried out in the field or in industrial environments, several PD sources and large noise signals are usually present simultaneously. In this study, three different inductive sensors have been used to evaluate and compare their performance in the detection and separation of multiple PD sources by applying the chromatic technique to each of the measured signals.

Intelligent Expert System for Protection Optimization Purposes in Electric Power Distribution Systems

The decision process taken into account by the expert system is based on information provided by the software “SimSurto”, which was especially developed to simulate the voltage transients caused by atmospheric discharges in distribution lines, and its objective is the computation of several parameters related to the respective transients, considering the equipments already installed, the geographical location of the distribution line and the respective incidence of atmospheric discharges in the distribution system.

A barrier method for constrained nonlinear optimization using a modified Hopfield network

The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. The paper describes a barrier method using artificial neural networks to solve robust parameter estimation problems for a nonlinear model with unknown-but-bounded errors and uncertainties. This problem can be represented by a typical constrained optimization problem. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach.