The objective of this thesis is to study the load forecasting of distribution feeders and substations for Fong-Shan District of Taiwan Power Company. To increase the accuracy of load forecasting, the load characterization of customers served has been investigated. The typical load patterns of different customers classes and derived by performing the statistic of power consumption data retrieved. The daily load profiles and load consumptions data distribution feeders and substations have been solved by considering the typical load patterns and energy consumption of all customers served. To investigate the correlation ship of temperature and energy consumption of customer classes, the temperature sensitivity of customer energy consumption has been used to update the load composition and the contribution of load change by different customer classes. To perform the load forecasting of distribution systems, the linear, nonlinear and hybrid load forecasting modules have been proposed. The historical load data of distribution feeders and substations in Fong-Shan District have been used to derive the load forecasting modules. To analyze the accuracy of load forecasting by considering the temperature effect, the temperature change is included in the load forecasting module. With the load forecasting derived, the proper load transfers among different distribution feeders and different substations have been determined to achieve the load balancing of service areas.

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.

Fault detection in PV systems is a key factor in maintaining the integrity of any PV system. Faults in photovoltaic systems can cause irrevocable damages to the stability of the PV system and substantially decrease the power output generated from the array of PV modules. Among'st the various AC and DC faults in a PV system, the clearance of the AC side faults is achieved by conventional AC protection schemes,the DC side, however , there still exists certain faults which are difficult to detect and clear. This paper deals with the modeling, detection and classification of these types of DC faults. It is essential to be able to simulate the PV characteristics and faults through software. In this thesis a comprehensive literature survey of fault detection methods for DC side of a PV system is presented. The disparities in the techniques employed for fault detection are studied . A new method for modeling the PV systems information only from manufacturers datasheet using both the Normal Operating Cell temperature conditions (NOCT) and Standard Operating Test Conditions (STC) conditions is then proposed.The input parameters for modeling the system are Isc,Voc,Impp,Vmpp and the temperature coefficients of Isc and Voc for both STC and NOCT conditions. The model is able to analyze the variations of PV parameters such as ideality factor, Series resistance, thermal voltage and Band gap energy of the PV module with temperature. Finally a novel intelligent method based on Probabilistic Neural Network for fault detection and classification for PV farm with string inverter technology is proposed.

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Made available in DSpace on 2014-06-12T15:58:28Z (GMT). No. of bitstreams: 2 arquivo4572_1.pdf: 1149011 bytes, checksum: 92aae8f6f9b5145bfcecb94d96dbbc0b (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2004 / Instituto Federal de Educação, Ciência e Tecnologia de Pernambuco / Um nariz artificial é um sistema modular composto de duas partes principais: um sistema sensor, formado de elementos que detectam odores e um sistema de reconhecimento de padrões que classifica os odores detectados. Redes neurais artificiais têm sido utilizadas como sistema de reconhecimento de padrões para narizes artificiais e vêm apresentando resultados promissores. Desde os anos 80, pesquisas para criação de narizes artificiais, que permitam detectar e classificar odores, vapores e gases automaticamente, têm tido avanços significativos. Esses equipamentos podem ser utilizados no monitoramento ambiental para controlar a qualidade do ar, na área de saúde para realizar diagnóstico de doenças e nas indústrias de alimentos para o controle de qualidade e o monitoramento de processos de produção. Esta dissertação investiga a utilização de quatro técnicas diferentes de redes neurais para criação de sistemas de reconhecimento de padrões em narizes artificiais. O trabalho está dividido em quatro partes principais: (1) introdução aos narizes artificiais, (2) redes neurais artificiais para sistema de reconhecimento de padrões, (3) métodos para medir o desempenho de sistemas de reconhecimento de padrões e comparar os resultados e (4) estudo de caso. Os dados utilizados para o estudo de caso, foram obtidos por um protótipo de nariz artificial composto por um arranjo de oito sensores de polímeros condutores, expostos a nove tipos diferentes de aguarrás. Foram adotadas as técnicas Multi-Layer Perceptron (MLP), Radial Base Function (RBF), Probabilistic Neural Network (PNN) e Time Delay Neural Network (TDNN) para criar os sistemas de reconhecimento de padrões. A técnica PNN foi investigada em detalhes, por dois motivos principais: esta técnica é indicada para realização de tarefas de classificação e seu treinamento é feito em apenas um passo, o que torna a etapa de criação dessas redes muito rápida. Os resultados foram comparados através dos valores dos erros médios de classificação utilizando o método estatístico de Teste de Hipóteses. As redes PNN correspondem a uma nova abordagem para criação de sistemas de reconhecimento de padrões de odor. Estas redes tiveram um erro médio de classificação de 1.1574% no conjunto de teste. Este foi o menor erro obtido entre todos os sistemas criados, entretanto mesmo com o menor erro médio de classificação, os testes de hipóteses mostraram que os classificadores criados com PNN não eram melhores do que os classificadores criados com a arquitetura RBF, que obtiveram um erro médio de classificação de 1.3889%. A grande vantagem de criar classificadores com a arquitetura PNN foi o pequeno tempo de treinamento dos mesmos, chegando a ser quase imediato. Porém a quantidade de nodos na camada escondida foi muito grande, o que pode ser um problema, caso o sistema criado deva ser utilizado em equipamentos com poucos recursos computacionais. Outra vantagem de criar classificadores com redes PNN é relativa à quantidade reduzida de parâmetros que devem ser analisados, neste caso apenas o parâmetro relativo à largura da função Gaussiana precisou ser investigado

In general humans are said to be social animals. In the huge expanded internet, it's really difficult to detect and find out useful information about a medical illness. In anticipation of more definitive studies of a causal organization between stroke risk and social network, It would be suitable to help social individuals to detect the risk of stroke. In this work, a DRFS methodology is proposed to find out the various symptoms associated with the stroke disease and preventive measures of a stroke disease from the social media content. We have defined an architecture for clustering tweets based on the content using Spectral Clustering an iterative fashion. The class label detection is furnished with the use of highest TF-IDF value words. The resultant clusters obtained as the output of spectral clustering is prearranged as input to the Probability Neural Network (PNN) to get the suitable class labels and their probabilities. Find Frequent word set using support count measure from the group of clusters for identify the risk factors of stroke. We found that the anticipated approach is able to recognize new symptoms and causes that are not listed in the World Health Organization (WHO), Mayo Clinic and National Health Survey (NHS). It is marked that they get associated with precise outcomes portray real statistics. This type of experiments will empower health organization, doctors and Government segments to keep track of stroke diseases. Experimental results shows the causes preventive measures, high and low risk factors of stroke diseases.

With the deregulation of power industry and the market competition, stable and reliable power supply is a major concern of the independent system operator (ISO). Power-quality (PQ) study has become a more and more important subject lately. Harmonics, voltage swell, voltage sag, and power interruption could downgrade the service quality. In recent years, high speed railway (HSR) and massive rapid transit (MRT) system have been rapidly developed, with the applications of widespread semiconductor technologies in the auto-traction system. The harmonic distortion level worsens due to these increased uses of electronic equipment and non-linear loads. To ensure the PQ, power-quality disturbances (PQD) detection becomes important. A detection method with classification capability will be helpful for detecting disturbance locations and types. Electric machinery fault diagnosis is another issue of considerable attentions from utilities and customers. ISO need to provide a high quality service to retain their customers. Fault diagnosis of turbine-generator has a great effect on the benefit of power plants. The generator fault not only damages the generator itself, but also causes outages and loss of profits. With high-temperature, high-pressure and factors such as thermal fatigues, many components may go wrong, which will not only lead to great economic loss, but sometimes a threat to social security. Therefore, it is necessary to detect generator faults and take immediate actions to cut the loss. Besides, induction motor plays a major role in a power system. For saving cost, it is important to run periodical inspections to detect incipient faults inside the motor. Preventive techniques for early detection can find out the incipient faults and avoid outages. This dissertation developed various soft computing (SC) algorithms for detection including power-quality disturbances (PQD), turbine-generator fault diagnosis, and induction motor fault diagnosis. The proposed SC algorithms included support vector machine (SVM), grey clustering analysis (GCA), and probabilistic neural network (PNN). Integrating the proposed diagnostic procedure and existing monitoring instruments, a well-monitored power system will be constructed without extra devices. Finally, all the methods in the dissertation give reasonable and practical estimation method. Compared with conventional method, the test results showed a high accuracy, good robustness, and a faster processing performance.

The last twenty years presented increased interest for the study of cerebral processes caused by external events (stimuli). One of the most significant endogenous components of Evoked Potentials is the P600 component. The P600 component may be defined as the most positive peak in the time window between 500 and 800 msec after an eliciting stimulus. This component is thought to reflect the response selection stage of information processing. P600 component is usually less pronounced compared to other components, such as the N100 or the P300. Frequently the P600 component appears as a not-easily discernible secondary peak overlying the ascending negative-going slope of the P300 waveform. In our study we used ERP data from various groups of patients and healthy controls. Patients were recruited from the outpatient university clinic of Eginition Hospital of the University of Athens. The controls were recruited from hospital staff and local volunteer groups. The aim of the study is the implementation of classification systems for these groups, using P600 features. This is usually not achieved well using as features the ERPs amplitude and latency. So for that reason, in our study, we want to extract new features using advanced techniques for processing the original ERPs, such as the Independent Component Analysis (ICA) method. However as a precursor of ICA, is considered the Principal Component Analysis (PCA) method, which we used for comparison reasons to ICA. In the application of ICA we achieve the decomposition of the recorded signals in ICs, supposing temporally independent components and propose ICs selection techniques in order to recompose the P600 component. The next stage was the use of a classification method based on the features extracted using the original data, data extracted through PCA processing and ICA-processed data. First we applied Kolmogorov-Smirnov test to check the normality of the distribution of the features, then we used the Logistic Regression method for classification and finally we have done two implementations of classification using Probabilistic Neural Networks. The first implementation was done with the creation of 15 features from the P600 peak amplitudes from the subjects’ data and the second implementation was done with the creation of four meta-features from the subjects’ P600 amplitude data. The results show that the application of ICA, combined with the logistic regression classification technique, provides notable improvement, compared to the classification performance based on the original ERPs. The main merit of the application is that classification is based on single parameters, i.e. amplitude of the P600 component, or its latency or its termination latency, which are directly related to the brain mechanisms related to ERP generation and pathological processes. / Τα τελευταία 20 χρόνια παρουσιάζεται αυξημένο ενδιαφέρον για την μελέτη εγκεφαλικών επεξεργασιών που προκλήθηκαν από εξωτερικά γεγονότα (ερέθισμα). Ένα από τα πιο σημαντικά ενδογενή συστατικά των Προκλητών Δυναμικών είναι το συστατικό P600. Το συστατικό P600 μπορεί να οριστεί σαν η πιο θετική αιχμή στο χρονικό διάστημα μεταξύ 500 και 800 msec μετά από ένα εκλυτικό ερέθισμα. Το συστατικό αυτό θεωρείται ότι απεικονίζει το στάδιο επιλογής απόκρισης της επεξεργασίας πληροφορίας. Το συστατικό P600 είναι συνήθως λιγότερο έντονο συγκρίνοντας το με άλλα συστατικά, όπως το N100 ή το P300. Συχνά το συστατικό P600 εμφανίζεται ως μια δυσδιάκριτη δεύτερη αιχμή, επικαλύπτοντας την ανοδική αρνητική κλίση της κυματομορφής του P300. Στη μελέτη μας χρησιμοποιήσαμε δεδομένα ΠΔ από ποικίλες ομάδες ασθενών και υγιών μαρτύρων. Οι ασθενείς συλλέχθησαν από τη πανεπιστημιακή κλινική του Αιγηνήτειου Νοσοκομείου του Πανεπιστημίου Αθηνών. Οι υγιείς συλλέχθησαν από το προσωπικό του νοσοκομείου και ομάδες εθελοντών. Ο σκοπός της μελέτης είναι η εφαρμογή συστημάτων ταξινόμησης για αυτές τις ομάδες, χρησιμοποιώντας χαρακτηριστικά του P600. Αυτό συνήθως δεν επιτυγχάνεται καλά χρησιμοποιώντας σαν χαρακτηριστικά το πλάτος και τον λανθάνοντα χρόνο των ΠΔ. Για αυτό το λόγο, στην μελέτη μας, θέλουμε να εξάγουμε νέα χαρακτηριστικά χρησιμοποιώντας προηγμένες τεχνικές για επεξεργασία των αρχικών ΠΔ, όπως τη μέθοδο Ανάλυσης Ανεξαρτήτων Συνιστωσών (ICA). Εντούτοις ως πρόδρομο της ICA, θεωρείται η μέθοδος Ανάλυσης Κύριων Συνιστωσών (PCA), την οποία χρησιμοποιήσαμε για συγκριτικούς λόγους με την ICA. Στην εφαρμογή της ICA προχωρήσαμε στην αποσύνθεση των καταγραφόμενων σημάτων σε Ανεξάρτητες Συνιστώσες και διερευνήσαμε τρεις τεχνικές επιλογής ανεξαρτήτων συνιστωσών μέσω των οποίων επανασυνθέσαμε το συστατικό P600. Το επόμενο βήμα ήταν η χρήση μεθόδου ταξινόμησης βασισμένης στα χαρακτηριστικά που εξάχθηκαν χρησιμοποιώντας τα αρχικά δεδομένα, τα δεδομένα με επεξεργασία PCA και τα δεδομένα με επεξεργασία ICA. Πρώτα εφαρμόσαμε το τεστ Kolmogorov-Smirnov για τον έλεγχο της κανονικότητας της κατανομής των χαρακτηριστικών, μετά χρησιμοποιήσαμε τη μέθοδο Λογαριθμικής Παλινδρόμησης (Logistic Regression) για ταξινόμηση και τελικά πραγματοποιήσαμε δύο εφαρμογές ταξινόμησης χρησιμοποιώντας Πιθανοκρατικά Νευρωνικά Δίκτυα (Probabilistic Neural Networks). Η πρώτη εφαρμογή έγινε με την δημιουργία 15 χαρακτηριστικών από τα πλάτη των αιχμών του P600 από τα δεδομένα των ομάδων και η δεύτερη εφαρμογή έγινε με την δημιουργία τεσσάρων μετά-χαρακτηριστικών από τα δεδομένα των πλατών των ομάδων. Τα αποτελέσματα δείχνουν ότι η εφαρμογή της ICA, συνδυασμένη με την τεχνική ταξινόμησης λογαριθμικής παλινδρόμησης, παρέχει αξιοσημείωτη βελτίωση, συγκριτικά με την απόδοση ταξινόμησης βάση των αρχικών ΠΔ. Η κύρια αξία της εφαρμογής είναι ότι η ταξινόμηση πετυχαίνει ποσοστά μεγαλύτερα του 80% βασιζόμενη σε μία μόνο κάθε φορά παράμετρο, π.χ. το πλάτος του συστατικού P600, ή τον λανθάνοντα χρόνο του ή τον λανθάνοντα χρόνο τερματισμού του, οι οποίες σχετίζονται άμεσα με τους μηχανισμούς του εγκεφάλου σχετικούς με την παραγωγή ΠΔ και τις παθολογικές διαδικασίες.