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1

A New Efficient Method for Load-Flow Solution for Radial Distribution Networks

Singh D. K., Ghosh S.

Przegląd Elektrotechniczny

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2011

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R. 87, nr 12a

66-73

EN

A new efficient method is proposed for load-flow solution of radial distribution networks. Simple transcendental equations are used to relate the sending-end voltage, receiving-end voltage and voltage drops in each branch of the distribution system. The effect of charging capacitance of the line has been incorporated in load-flow solution. A computer algorithm is developed in such a way that there is no need to adopt any sequential node numbering scheme for the solution of the networks. The angle of the receiving-end voltage is also computed along with the magnitude of the voltage. It is an iterative method. The flat voltage (1pu) start from substation to every end-node is considered. The voltage magnitude and angle are updated after each successive iteration and the voltage drops are then computed by using the new obtained values of voltage magnitude and angle. The comparison of speed and memory requirement by the proposed method with the other methods has been verified to show its efficiency.

PL

Zapropowano nową metodę kontroli przepływu mocy w radialnej sieci rozproszonej. Użyto prostego równania transcedentalnego do opisu napięcia wysyłanego, otrzymanego i spadki napięcia w każdej z gałęzi sieci. Opracowano odpowiedni algorytm komputerowy a obliczenia są przeprowadzane iteracyjne, gdzie amplituda i kąt napięcia są sukcesywnie zmieniane.

2

Speech nonfluency detection and classification based on linear prediction coefficients and neural networks

Kobus A., Kuniszyk-Jóźkowiak W., Smołka E., Codello I.

Journal of Medical Informatics & Technologies

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2010

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Vol. 15

135--143

EN

The goal of the paper is to present a speech nonfluency detection method based on linear prediction coefficients obtained by using the covariance method. The application “Dabar” was created for research. It implements three different methods of LP with the ability to send coefficients computed by them into the input of Kohonen networks. Neural networks were used to classify utterances in categories of fluent and nonfluent. The first one was Kohonen network (SOM), used to reduce LP coefficients representation of each window, which were used as input data to SOM input layer, to a vector of winning neurons of SOM output layer. Radial Basis Function (RBF) networks, linear networks and Multi-Layer Perceptrons were used as classifiers. The research was based on 55 fluent samples and 54 samples with blockades on plosives (p, b, d, t, k, g). The examination was finished with the outcome of 76% classifying.

3

Linked S2 and S1 design with experimental and CFD verification

Waggott J., Maier W.

Zeszyty Naukowe. Cieplne Maszyny Przepływowe - Turbomachinery / Politechnika Łódzka

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1999

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nr 115

405--412

EN

A precise method of deriving advantageous meridional (S2) flowpath shapes is presented. This design method eliminates radial, or "potential surface" pressure gradients, refines secondary flow, and improves efficiency. The derivation is based on two-dimensional (S1) airfoil or vane shapes and can be applied to most types of turbomachinery. The elimination of radial pressure gradients in high specific speed machines allows the use of airfoils or vanes with less hub to shroud variation. Both CFD calculations and experimental results show that aerodynamic improvements extend far beyond flowpath regions where this design technique is applied.