No description available.

The technology of power electronics is used increasingly during recent years, and the electronic power facilities are used more and more in the power system. The non-linear electronic loads produce heavy harmonic currents and could significantly degrade the power quality. Nonlinear loads, including the un-interruptible power supply, motor control and converter, etc, are important equipment in a modern factory, however, these nonlinear loads could lead to power facility malfunction and capacitor damage. The harmonics would eventually cause severe unexpected capital loss. Power quality has become an important study. This thesis proposes the probabilistic neural network (PNN) for power harmonics detection from distorted waves. Originally, Fourier transform is often used to analyze distorted waves in frequency spectrum, and low-pass filter is used to eliminate the fundamental component where characteristic harmonics can be detected. The complicated process is difficult to operate in real time. PNN based processing model with physical harmonic data is used to simplify the process. Computer simulation will show a simplified model and shorter processing time for harmonic detection in the active filter. The Intranet based distributed characteristic harmonic monitoring system.LabVIEW language was used to develop the Human-Machine Interface(HMI) , and DataSocket tool was used to share the information on net.

The proposed study develops a framework that can accurately capture and model input and output variables for multidisciplinary systems to mitigate the computational cost when uncertainties are involved. The dimension of the random input variables is reduced depending on the degree of correlation calculated by relative entropy. Feature extraction methods; namely Principal Component Analysis (PCA), the Auto-Encoder (AE) algorithm are developed when the input variables are highly correlated. The Independent Features Test (IndFeaT) is implemented as the feature selection method if the correlation is low to select a critical subset of model features. Moreover, Artificial Neural Network (ANN) including Probabilistic Neural Network (PNN) is integrated into the framework to correctly capture the complex response behavior of the multidisciplinary system with low computational cost. The efficacy of the proposed method is demonstrated with electro-mechanical engineering examples including a solder joint and stretchable patch antenna examples.

Power quality has attracted considerable attentions from utilities and customers due to the popular uses of the sensitive electronic equipment. Harmonics, voltage swell, voltage sag, and power interruption could downgrade the service quality. Harmonic currents injected by non-linear loads throughout the network could degrade the quality of services to sensitive high-tech customers such as the science park of Xin-Zhu and Tai-Nan in Taiwan. In recent years, massive rapid transit system (MRT) and high speed railway (HSR) have been rapidly developed, with the applications of wide-spread semi-conductor technologies in the auto-traction system. Swell and sag could occur from thundering, capacitor switching, motor starting, nearby circuit faults, or artificial calamity, and could also attribute to the power interruption. To ensure the power quality, harmonic and voltage disturbances detection becomes important. Fourier transformation is used to analyze distorted waves in the frequency domain, with low-pass filter used to eliminate the fundamental component, and then characteristic harmonics can be detected. The complicated process is difficult to operate in real-time. The method-based processing model with physical harmonic data is needed to simplify the processing architecture. The thesis proposes to use wavelet transformation (WT) and probabilistic neural network (PNN) for power quality and characteristic harmonics detection. Wavelet-probabilistic network (WPN) is first used to extract distorted waves. PNN based processing model will then analyze the harmonic components. Computer simulation shows a simplified model to shorten the processing time in this study.

The paper proposes a model of detection for voltages and harmonics using wavelet-probabilistic network (WPN). WPN is a two-layer structure, containing the wavelet layer and probabilistic network. It uses the wavelet transformation (WT) and probabilistic neural network (PNN) to analyze distorted waves and classify tasks. In this thesis, the field programmable gate array (FPGA) is employed for the hardware realization of WPN. In the implementation process, by the use of the hardware description language, the WPN algorithm has been embedded into the FPGA chip. Firstly, we divide the mathematical formula of basic WPN algorithm into several parts in order to set up each module individually, then we integrate all modules to complete the design of basic WPN algorithm with digital circuits by the bottom-up process.

With the rapid growth of load demand, the distribution system is becoming more and more complicated, and the operational efficiency and service quality deteriorated. Power system protection is important for service reliability and quality assurance. Various faults may occur due to natural and artificial calamity. To reduce the outage duration and promptly restore power services, fault section estimate has to be done effectively with appeared fault alarms. The distribution system containing numerous protective facilities and switch equipment ranges over wide boundary. It becomes very complicated for dispatchers to obtain restoration plan for out-of-service areas. To cope with the problem, an effective tool is helpful for the restoration. This thesis proposes the use of Bi-directional associative memory networks (BAMN) to develop alarm processing. And use of Probabilistic Neural Network (PNN) to develop fault section detection, fault isolation, and restoration system. A distribution system is selected for computer simulation to demonstrate the effectiveness of the proposed system. The thesis proposes to use Bi-directional Associative Memory Network¡]BAMN¡^ to pre-process the signal gained from SCADA Interface, and transmit correct signal to Probabilistic Neural Network (PNN) for restoration plan . Computer simulation shows a simplified model to shorten the processing time in this study.

Power system protection is important for service reliability and quality assurance. Various faults may occur due to natural and artificial calamity. To reduce the outage duration and promptly restore power services, fault section estimate has to be done effectively with appeared fault alarms. Dispatchers could study the changed statuses of primary/back-up relays and circuit breakers to identify the fault section and fault types. It is difficult to process too many alarms under various conditions in a large power system. Single fault, multiple faults, single and multiple faults could coexist with the failed operation of relays and circuit breakers, or with the erroneous data communication. Dispatchers need more time to process the many uncertainties before identifying the fault. This thesis presents the use of artificial intelligence for fault section detection in substation with neural networks. Probabilistic Neural Networks (PNN) are proposed for fault detection system in substation. The proposed methodology will use primary/back-up information of protective relays and circuit breakers to detect the fault sections involving single fault, multiple faults, or fault with the failure operation of the relays and circuit breakers. This paper also presents a fuzzy theory-based method to identify fault types. It is derived to improve the inadequacy of making decisions by selecting a fixed threshold value and has the capability of non-deterministic decision making with a prior knowledge of uncertainties in fault location, fault resistance and the a size of loads. The proposed approach has been tested on a typical taipower system with accurate results.

The technology of power electronics is used increasingly during recent years, and the electronic power facilities are used more and more in the power system. The non-linear electronic loads produce heavy harmonic currents and could significantly degrade the power quality. Nonlinear loads, including the un-interruptible power supply, motor control and converter, etc, are important equipment in a modern factory, however, these nonlinear loads could lead to power facility malfunction and capacitor damage. The harmonics would eventually cause severe unexpected capital loss. Identification of harmonic sources location becomes an important study for power quality. An effective tool is thus helpful for the harmonic source locating. This paper proposes a method to deal with the harmonic sources and location detection in the power system by using the artificial neural network (ANN). The non-linear loading characteristics are studied by the power flow analysis, and then the proposed methodology uses the Probabilistic Neural Networks¡]PNN¡^and wavelet-probabilistic network (WPN) for harmonic source locating. An IEEE 14-bus power system is used for study to show the effectiveness of the proposed approach.

No description available.

Este trabalho apresenta um método para o reconhecimento e a detecção automática dos diferentes valores ou graus de deformação plástica em Ensaios Não Destrutivos empregando o Ruído Magnético de Barkhausen. O método é baseado no uso de uma Rede Neural Probabilística que permite o diagnóstico automático dos diferentes valores de deformação plástica, conteúdo de carbono, estas medidas são procedentes das medições das amostras de placas de aço AISI 1006, 1050 e 1070, esta base de dados foi feita pelo grupo de pesquisadores do Laboratório de Dinâmica e Instrumentação LADIN da Escola Politécnica da USP, departamento da Mecânica. Os excelentes resultados da rede neural probabilística de detectar automaticamente os valores de deformação mostram a efetividade do desempenho da rede neural probabilística que tem um desempenho superior aos métodos não destrutivos tradicionais e que realmente esta nova tecnologia é uma excelente solução para o diagnóstico. / This work presents a method for automatic detection and recognition of different levels or degrees of plastic deformation in Non-Destructive Testing using the Magnetic Barkhausen Noise. The method is based on using a Probabilistic Neural Network that allows the automatic diagnosis of the different values of plastic deformation and carbon content. The measurements corresponds to samples of steel plates AISI 1006, 1050 and 1070, this database was made by the group of researchers from the Laboratory of Dynamics and Instrumentation LADIN the Polytechnic School of USP, Department of Mechanical Engineering. The results show the effectiveness of the probabilistic neural network to automatically detect plastic deformation levels as well as carbon content level. This method has a superior performance in comparison to traditional nondestructive methods.