Diagnóstico automático de eventos e distúrbios em unidades geradoras e usinas utilizando lógica fuzzy

The electric power systems are getting more complex and covering larger areas day by day. This fact has been contribuiting to the development of monitoring techniques that aim to help the analysis, control and planning of power systems. Supervisory Control and Data Acquisition (SCADA) systems, Wide Area Measurement Systems and disturbance record systems. Unlike SCADA and WAMS, disturbance record systems are mainly used for offilne analysis in occurrences where a fault resulted in tripping of and apparatus such as a transimission line, transformer, generator and so on. The device responsible for record the disturbances is called Digital Fault Recorder (DFR) and records, basically, electrical quantities as voltage and currents and also, records digital information from protection system devices. Generally, in power plants, all the DFRs data are centralized in the utility data centre and it results in an excess of data that difficults the task of analysis by the specialist engineers. This dissertation shows a new methodology for automated analysis of disturbances in power plants. A fuzzy reasoning system is proposed to deal with the data from the DFRs. The objective of the system is to help the engineer resposnible for the analysis of the DFRs’s information by means of a pre-classification of data. For that, the fuzzy system is responsible for generating unit operational state diagnosis and fault classification.

Desenvolvimento de metodologia de análise de indicadores de sustentabilidade como ferramenta para tomada de decisão utilizando lógica fuzzy

Dissertação (Mestrado em Tecnologia Nuclear)

A Fuzzy Decision Strategy for Topic Identification and Dynamic Selection of Language Models

International audience

Controle aplicado a poços com método de elevação e bombeio centrífugo submerso

The Electrical Submersible Pumping is an artificial lift method for oil wells employed in onshore and offshore areas. The economic revenue of the petroleum production in a well depends on the oil flow and the availability of lifting equipment. The fewer the failures, the lower the revenue shortfall and costs to repair it. The frequency with which failures occur depends on the operating conditions to which the pumps are submitted. In high-productivity offshore wells monitoring is done by operators with engineering support 24h/day, which is not economically viable for the land areas. In this context, the automation of onshore wells has clear economic advantages. This work proposes a system capable of automatically control the operation of electrical submersible pumps, installed in oil wells, by an adjustment at the electric motor rotation based on signals provided by sensors installed on the surface and subsurface, keeping the pump operating within the recommended range, closest to the well s potential. Techniques are developed to estimate unmeasured variables, enabling the automation of wells that do not have all the required sensors. The automatic adjustment, according to an algorithm that runs on a programmable logic controller maintains the flow and submergence within acceptable parameters avoiding undesirable operating conditions, as the gas interference and high engine temperature, without need to resort to stopping the engine, which would reduce the its useful life. The control strategy described, based on modeling of physical phenomena and operational experience reported in literature, is materialized in terms of a fuzzy controller based on rules, and all generated information can be accompanied by a supervisory system

Control and Supervision of Wind Energy Conversion Systems

This paper is about a PhD thesis and includes the study and analysis of the performance of an onshore wind energy conversion system. First, mathematical models of a variable speed wind turbine with pitch control are studied, followed by the study of different controller types such as integer-order controllers, fractional-order controllers, fuzzy logic controllers, adaptive controllers and predictive controllers and the study of a supervisor based on finite state machines is also studied. The controllers are included in the lower level of a hierarchical structure composed by two levels whose objective is to control the electric output power around the rated power. The supervisor included at the higher level is based on finite state machines whose objective is to analyze the operational states according to the wind speed. The studied mathematical models are integrated into computer simulations for the wind energy conversion system and the obtained numerical results allow for the performance assessment of the system connected to the electric grid. The wind energy conversion system is composed by a variable speed wind turbine, a mechanical transmission system described by a two mass drive train, a gearbox, a doubly fed induction generator rotor and by a two level converter.

Estimativa de bem-estar de frango de corte em função da concentração de amônia e grau de luminosidade no galpão de produção

Brazil is an important poultry meat export country, and large parts of its destination are countries with specific rearing restrictions related to broiler s welfare. One of the aerial pollutants mostly found in high concentrations in closed poultry housing environment is ammonia. There are evidences that broilers welfare may be compromised by the continuous exposition to this pollutant in rearing housing. This research aimed to estimate broilers welfare reared under specific thermal environmental attributes and bird s density, as function of the ammonia concentration and light intensity inside the housing environment using the Fuzzy Theory. Results showed that the best welfare value (0.89 in the scale: 0-1) approximately 90% of the ideal was found in the conditions that associated the ideal thermal environment, with bird s density between 13-15 birds m-2, with values of the ammonia concentration in the environment below 5 ppm, and light intensity near 1 lx. Using the predictive method it was possible to estimate broilers welfare with relation to the ammonia concentration and light intensity in the housing.

Ambiências térmica, aérea e acústica para reprodutores suínos

The swine breeder rearing environment directly affects the animal's performance. This research had the objective of developing a thermal, aerial and acoustic environmental evaluation pattern for boar housing. The experiment was carried on a commercial swine farm in Salto County -SP, Brazil. Thermal, aerial and acoustic environment data of rearing conditions were registered. Data were statistically analyzed using as threshold the ideal housing environment that leads to animal welfare. Results showed that ambient temperature was around 70% beyond normal range, while air relative humidity, air speed and gases concentration were within threshold values. Noise level data besides being within normal range did not present large variation. In relation to the fuzzy logic analysis it was possible to build up a scenario which indicated that the best welfare indexes to male swine breeders happens when thermal comfort index are close to 80%, and noise level is lower than 40 dB. In the other hand the worst welfare index occur in the sector where the thermal comfort values are below 40% at the same time that the noise level is higher than 80 dB leading to inadequate conditions to the animal, and may directly interfere in the reproduction system performance.

Multi-objective analysis of impacts of distributed generation placement on the operational characteristics of networks for distribution system planning

Recent advances in energy technology generation and new directions in electricity regulation have made distributed generation (DG) more widespread, with consequent significant impacts on the operational characteristics of distribution networks. For this reason, new methods for identifying such impacts are needed, together with research and development of new tools and resources to maintain and facilitate continued expansion towards DG. This paper presents a study aimed at determining appropriate DG sites for distribution systems. The main considerations which determine DG sites are also presented, together with an account of the advantages gained from correct DG placement. The paper intends to define some quantitative and qualitative parameters evaluated by Digsilent (R), GARP3 (R) and DSA-GD software. A multi-objective approach based on the Bellman-Zadeh algorithm and fuzzy logic is used to determine appropriate DG sites. The study also aims to find acceptable DG locations both for distribution system feeders, as well as for nodes inside a given feeder. (C) 2010 Elsevier Ltd. All rights reserved.

Development of Control Systems for Safety Instrumented Systems

Safety Instrumented Systems (SIS) are designed to prevent and / or mitigate accidents, avoiding undesirable high potential risk scenarios, assuring protection of people`s health, protecting the environment and saving costs of industrial equipment. The design of these systems require formal methods for ensuring the safety requirements, but according material published in this area, has not identified a consolidated procedure to match the task. This sense, this article introduces a formal method for diagnosis and treatment of critical faults based on Bayesian network (BN) and Petri net (PN). This approach considers diagnosis and treatment for each safety instrumented function (SIF) including hazard and operability (HAZOP) study in the equipment or system under control. It also uses BN and Behavioral Petri net (BPN) for diagnoses and decision-making and the PN for the synthesis, modeling and control to be implemented by Safety Programmable Logic Controller (PLC). An application example considering the diagnosis and treatment of critical faults is presented and illustrates the methodology proposed.

Spatial portability of numerical models of leaf wetness duration based on empirical approaches

Leaf wetness duration (LWD) models based on empirical approaches offer practical advantages over physically based models in agricultural applications, but their spatial portability is questionable because they may be biased to the climatic conditions under which they were developed. In our study, spatial portability of three LWD models with empirical characteristics - a RH threshold model, a decision tree model with wind speed correction, and a fuzzy logic model - was evaluated using weather data collected in Brazil, Canada, Costa Rica, Italy and the USA. The fuzzy logic model was more accurate than the other models in estimating LWD measured by painted leaf wetness sensors. The fraction of correct estimates for the fuzzy logic model was greater (0.87) than for the other models (0.85-0.86) across 28 sites where painted sensors were installed, and the degree of agreement k statistic between the model and painted sensors was greater for the fuzzy logic model (0.71) than that for the other models (0.64-0.66). Values of the k statistic for the fuzzy logic model were also less variable across sites than those of the other models. When model estimates were compared with measurements from unpainted leaf wetness sensors, the fuzzy logic model had less mean absolute error (2.5 h day(-1)) than other models (2.6-2.7 h day(-1)) after the model was calibrated for the unpainted sensors. The results suggest that the fuzzy logic model has greater spatial portability than the other models evaluated and merits further validation in comparison with physical models under a wider range of climate conditions. (C) 2010 Elsevier B.V. All rights reserved.

A hybrid planning method for transmission networks in a deregulated environment

The reconstruction of power industries has brought fundamental changes to both power system operation and planning. This paper presents a new planning method using multi-objective optimization (MOOP) technique, as well as human knowledge, to expand the transmission network in open access schemes. The method starts with a candidate pool of feasible expansion plans. Consequent selection of the best candidates is carried out through a MOOP approach, of which multiple objectives are tackled simultaneously, aiming at integrating the market operation and planning as one unified process in context of deregulated system. Human knowledge has been applied in both stages to ensure the selection with practical engineering and management concerns. The expansion plan from MOOP is assessed by reliability criteria before it is finalized. The proposed method has been tested with the IEEE 14-bus system and relevant analyses and discussions have been presented.

Visualization of learning in multilayer perceptron networks using principal component analysis

This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as back-propagation and can also be used to provide insight into the learning process and the nature of the error surface.