Otacílio da Mota Almeida

Application of an artificial neural network in the use of physicochemical properties as a low cost proxy of power transformers DGA data

This paper is about the relationship between dissolved gases and the quality of the insulating mineral oil used in power transformers. Artificial Neural Networks are used to approach operational conditions assessment issue of the insulating oil in power transformers, which is characterized by a non-linear dynamic behavior. The operation conditions and integrity of a power transformer can be inferred by analysis of physicochemical and chromatographic (DGA - Dissolved Gas Analysis) profiles of the isolating oil, which allow establishing procedures for operating and maintaining the equipment. However, while the costs of physicochemical tests are less expensive, the chromatographic analysis is more informative and reliable. This work presents a method that can be used to extract chromatographic information using physicochemical analysis through Artificial Neural Networks. Its believed that, the power utilities could improve reliability in the prediction of incipient failures at a lower cost with this method. The results show this strategy might be promising. The purpose of this work is the direct implementation of the diagnosis of incipient faults through the use of physicochemical properties without the need to make an oil chromatography.

Comparison between sliding model control and vector control for a DSP-based position control applied to squirrel-cage induction motor

The principle of vector control of AC machine enables the dynamic control of AC motors, and induction motors in particular to a level comparable to that of a DC machine. The vector control of currents and voltages results in control of spatial orientation of the electromagnetic fields in the machine and has led to term field orientation [1]. Field oriented control schemes provide significant improvement to the dynamic performance of ac motors. The usual method of induction motor position and torque control, which is becoming an industrial standard [2], uses the indirect field orientation principle in which the rotor speed is sensed or estimated by rotor position and slip frequency is added to form the stator impressed frequency. Sliding-mode control has gained wide attention because of its simple design and implementation, fast dynamic response, and robustness to parameters variations and load disturbances. This paper proposes a performance comparison between sliding mode-control and field oriented control using synchronous reference applied to a small squirrel-cage induction motor using space vector pulse width modulation(SVPWM) for position and torque control and a digital signal processor and relay feedback method to evaluate the controller parameters.

Projeto e estudo de caso da implementação de um sistema de controle nebuloso

This paper presents the design of a fuzzy control system applied to a multivariable nonlinear process and the state of art of fuzzy control applications in the industrial environment. The paper is divided in two parts. Initially the fuzzy control design is assessed on a horizontal balance process, a prototype in laboratory scale, with two propellers driven by two DC motors. An overview of design aspects and characteristics of industrial applications of fuzzy controllers is also included.

Design and implementation of a Mamdani Fuzzy Inference System on an FPGA using VHDL

The growth of fuzzy logic applications led to the need of finding efficient ways to implement them. The FPGAs (Field Programmable Gate Arrays) are reconfigurable logic devices that provide mainly practicality and portability, with low consumption of energy, high speedy of operation and large capacity of data storage. These characteristics, combined with the ability of synthesizing circuits, make FPGAs powerful tools for project development and prototyping of digital controllers. In this paper, the implementation of a Mamdani Fuzzy Inference System has been demonstrated using VHDL programming language. The accuracy of the model on FPGA was compared with simulation results obtained using MATLAB & Fuzzy Logic Tool Box.

Filtered model-based predictive control applied to the temperature and humidity control of a neonatal incubator

This paper proposes a robust multivariable predictive control algorithm that improves the robustness of closed loop systems, even when they have multiple time delays between the inputs and outputs. The desired robustness is achieved by including an appropriate filter on the disturbances model. The proposed algorithm is applied to the control of humidity and temperature of a neonatal incubator. Simulation and experimental results show the advantages of the proposed algorithm compared to others proposed in the literature.

Artificial Neural Network Application in Estimation of Dissolved Gases in Insulating Mineral Oil from Physical-Chemical Datas for Incipient Fault Diagnosis

In this paper, Artificial Neural Networks are used to solve a complex problem concerning to power transformers and characterized by non-linearity and hard dynamic modeling. The operation conditions and integrity of a power transformer can be detected by analysis of physical-chemical and chromatographic isolating oil, allowing establish procedures for operating and maintaining the equipment. However, while the costs of physical- chemical tests are smaller, the chromatographic analysis is more informative. This work presents an estimation study of the information that would be obtained in the chromatographic test from the physical-chemical analysis through Artificial Neural Networks. Thus, the power utilities can achieve greater reliability in the prediction of incipient failures at a lower cost. The results show this strategy to be a promising, with accuracy of 100% in best cases. The application in the thermal fault diagnosis presents more than 91% accuracy in best cases.

Assessment and Certification of Neonatal Incubator Sensors through an Inferential Neural Network

Measurement and diagnostic systems based on electronic sensors have been increasingly essential in the standardization of hospital equipment. The technical standard IEC (International Electrotechnical Commission) 60601-2-19 establishes requirements for neonatal incubators and specifies the calibration procedure and validation tests for such devices using sensors systems. This paper proposes a new procedure based on an inferential neural network to evaluate and calibrate a neonatal incubator. The proposal presents significant advantages over the standard calibration process, i.e., the number of sensors is drastically reduced, and it runs with the incubator under operation. Since the sensors used in the new calibration process are already installed in the commercial incubator, no additional hardware is necessary; and the calibration necessity can be diagnosed in real time without the presence of technical professionals in the neonatal intensive care unit (NICU). Experimental tests involving the aforementioned calibration system are carried out in a commercial incubator in order to validate the proposal.Measurement and diagnostic systems based on electronic sensors have been increasingly essential in the standardization of hospital equipment. The technical standard IEC (International Electrotechnical Commission) 60601-2-19 establishes requirements for neonatal incubators and specifies the calibration procedure and validation tests for such devices using sensors systems. This paper proposes a new procedure based on an inferential neural network to evaluate and calibrate a neonatal incubator. The proposal presents significant advantages over the standard calibration process, i.e., the number of sensors is drastically reduced, and it runs with the incubator under operation. Since the sensors used in the new calibration process are already installed in the commercial incubator, no additional hardware is necessary; and the calibration necessity can be diagnosed in real time without the presence of technical professionals in the neonatal intensive care unit (NICU). Experimental tests involving the aforementioned calibration system are carried out in a commercial incubator in order to validate the proposal.

Identification and adaptive speed control for switched reluctance motor using DSP

In this paper, a simple adaptive speed control for switched reluctance motor (SRM) is described and implemented using Digital Signal Processor (DSP). The controller structure design is based on PWM control scheme that have been used for the SRM current controller. To realize the speed control, the speed reference is calculated as function of current machine. From experimental results it is shown that the overall switched reluctance motor speed control gives good transient and steady state responses.

Modeling and controller performance assessment for a switched reluctance motor drive based on setpoint relay.

This paper considers the implementation of a current control method for switched reluctance motors (SRMs) and presents a novel approach to the accurate on-line modeling of an SRM drive. A simple autotuning technique for the SRM drives using a PWM controller is considered. Furthermore, conventional PI control and Internal Model Control (IMC) are considered to validate this method and present corresponding robust control analysis for the process. The control structures are comparatively analyzed using standard robustness measures for stability and performance. The proposed PWM controller is simulated and a hardware prototype is then implemented using digital signal processor control to evaluate the method using a 12/8, three-phase SRM. The experimental results of the SRM drive model validates the performance of the current loop.

Fuzzy logic controller implementation on a FPGA using VHDL

Fuzzy-logic-based control (FLC) systems have emerged as one of the most promising areas for research in Applied Computational Intelligence. These systems operate with knowledge represented in a linguistic form (IF-THEN rules) that describes relations which are not precisely known, but those effects are intuitively understood by humans. This fundamental feature makes FLC a powerful tool for industrial applications, since complex systems can be controlled by easily comprehensive rules. The growth in number of fuzzy logic applications led to the need of finding efficient and economic ways to implement them. The Field Programmable Gate Arrays (FPGAs) devices, with their reconfigurable logic, practicality, portability, low consumption of energy, high operation speedy and large datastorage capacity, are a great choice for FLC embedded systems project development and prototyping. In this paper, the design and implementation of a Mamdani-type Fuzzy controller is demonstrated using VHDL programming language.