Contents of Neurocomputing 23 (1998)

Georg Thimm thimm at rotondo.idiap.ch
Wed Dec 2 08:43:25 EST 1998


Dear reader,

Please find below a compilation of the contents for Neurocomputing and Scanning
the Issue written by V. David Sanchez A.  More information on the journal are
available at the URL http://www.elsevier.nl/locate/jnlnr/05301 .

The contents of this and other journals published by Elsevier are distributed
also by the ContentsDirect service (see at the URL http://www.elsevier.nl/locate/ContentsDirect).

Please feel free to redistribute this message. My apologies if this message
is inappropriate for this mailing list; I would appreciate a feedback.


With kindest regards,

     Georg Thimm


Dr. Georg Thimm
Research scientist &                         WWW: http://www.idiap.ch/~thimm
Current Events Editor of Neurocomputing      Tel.: ++41 27 721 77 39 (Fax: 12)
IDIAP / C.P. 592 / 1920 Martigny / Suisse    E-mail: thimm at idiap.ch

======================================================================
Journal : NEUROCOMPUTING
ISSN : 0925-2312
Vol./Iss. : 23 / 1-3

A comparative study of medium-weather-dependent load
forecasting using enhanced artificial/fuzzy neural network
and statistical techniques
Elkateb , M.M.
pp.: 3-13

Prediction of iron losses of wound core distribution
transformers based on artificial neural networks
Georgilakis , P.S.
pp.: 15-29

Laboratory investigation of a digital recurrent network for
transmission line directional protection
Sanaye-Pasand , M.
pp.: 31-46

A neural network based estimator for electricity
spot-pricing with particular reference to weekend and public
holidays
Wang , A.J.
pp.: 47-57

A neural network based protection technique for combined 275
kV/400 kV double circuit transmission lines
Xuan , Q.Y.
pp.: 59-70

Artificial neural networks for short-term energy
forecasting: Accuracy and economic value
Hobbs , Benjamin F.
pp.: 71-84

Power system security boundary visualization using neural
networks
McCalley , James D.
pp.: 85-96

The use of artificial neural networks for condition
monitoring of electrical power transformers
Booth , C.
pp.: 97-109

Neural networks for power system condition monitoring and
protection
Cannas , B.
pp.: 111-123

Recurrent neural network for forecasting next 10 years loads
of nine Japanese utilities
Kermanshahi , Bahman
pp.: 125-133

Use of neural networks for customer tariff exploitation by
means of short-term load forecasting
Verona , Francesco Bini
pp.: 135-149

Topology--independent artificial neural network for overload
screening
Riquelme , Jesu's
pp.: 151-160

A neural network-based tool for preventive control of
voltage stability in multi-area power systems
Maiorano , A.
pp.: 161-176

An incipient fault detection system based on the
probabilistic radial basis function network: Application to
the diagnosis of the condenser of a coal power plant
Mun~oz , A.
pp.: 177-194

Electric utility coal quality analysis using artificial
neural network techniques
Salehfar , H.
pp.: 195-206

A class of hybrid intelligent system for fault diagnosis in
electric power systems
Jota , Patricia R.S.
pp.: 207-224

Arcing fault detection using artificial neural networks
Sidhu , T.S.
pp.: 225-241

The artificial neural-networks-based relay algorithm for the
detection of stochastic high impedance faults
Snider , L.A.
pp.: 243-254

Artificial neural network for reactive power optimization
El-Sayed , Mohamed A.H.
pp.: 255-263

Evolving artificial neural networks for short term load
forecasting
Srinivasan , Dipti
pp.: 265-276

Simple recurrent neural network: A neural network structure
for control systems
Herna'ndez , Rafael Parra
pp.: 277-289

======================================================================
      Neurocomputing 23 (1998) vii-ix Scanning the issue

A comparative study of medium-weather-dependent load forecasting using
enhanced artificial/fuzzy neural network and statistical techniques is
presented by M.M. Elkateb, K. Solaiman and Y. Al-Turki. The introduction of
a time index feature significantly enhances the performance of the ANN and
FNN techniques. On the conventional side, an AutoRegressive Integrated
Moving Average ARIMA technique is used.

P.S. Georgilakis, N.D. Hatziargyriou, N.D. Doulamis, A.D. Doulamis and
S.D. Kollias describe the Prediction of iron losses of wound core
distribution transformers based on artificial neural networks. Generation
of training and test data, selection of candidate attributes and the
generation of the neural network structure are discussed. Suitability for
prediction and classification of individual core and transformer specific
iron losses is confirmed.

In Laboratory investigation of a digital recurrent network for transmission
line directional protection M. Sanaye-Pasand and O.P. Malik describe a
recurrent neural network based technique for identifying the direction of a
fault on a transmission line. Experimental evaluation shows that the
approach is accurate, fast, and robust.

A.J. Wang and B. Ramsay present A neural network based estimator for
electricity spot-pricing with particular reference to weekend and public
holidays. The estimator consists of two parts, the front-end processor and
the neural network based predictor. The estimator is tested on a real
System Marginal Price (SMP) prediction problem.

A neural network based protection technique for combined 275 kV/400 kV
double circuit transmission lines is introduced by Q.Y. Xuan,
R.K. Aggarwal, A.T. Johns, R.W. Dunn and A. Bennett. The technique extracts
in a pre-processing step the main features from the measured signals. The
test results confirm that the adaptive protection technique works well for
double-circuit lines with different voltage levels on the two circuits.

B.F. Hobbs, U. Helman, S. Jitprapaikulsarn, S. Konda and D. Maratukulam
describe Artificial neural networks for short-term energy forecasting:
Accuracy and economic value. Eighteen electric utilities and five gas
utilities are surveyed. The utilities report on the significant error
reduction in daily electric load forecasts when using artificial neural
networks. An average of $800K in savings per year and uility is estimated.

J.D. McCalley, G. Zhou and V. Van Acker present Power system security
boundary visualization using neural networks. The relationship between the
precontingency operating parameters and the postcontingency performance
measure is mapped using neural networks. The best set of operating
parameters is selected using genetic algorithms.

C. Booth and J.R. McDonald describe The use of artificial neural networks
for condition monitoring of electrical power transformers. Artificial
neural networks are used in this context for estimation, e.g. in the
determination of transformer winding vibration levels, and for
classification, e.g. in the automatic separation of healthy/unhealthy data.

In Neural networks for power system condition monitoring and protection
B. Cannas, G. Celli, M. Marchesi and F. Pilo propose a methodology based on
a locally recurrent, globally feed-forward network and a neural state
classifier. The accurate prediction of control variables and the fast
recognition of abnormal events is demonstrated.

In Recurrent neural network for forecasting next 10 years loads of nine
Japanese utilities B. Kermanshahi applies a Recurrent Neural Network (RNN)
and a 3-layer Backpropagation (BP) network for long-term load
forecasting. The RNNs forecast the loads one year ahead whereas the BP
networks forecast the next five to ten years.

In Use of neural networks for customer tariff exploitation by means of
short-term load forecasting F.B. Verona and M. Ceraolo apply Radial Basis
Function (RBF) networks trained with leave-one-out cross validation for
electric load forecasting. A prototype system shows good performance
allowing some load management.

J. Riquelme, A. Gómez and J.L. Martínez describe Topology-independent
artificial neural networks for overload screening. The Artificial Neural
Networks (ANN) are capable of identifying the set of harmful
contingencies. Experimental results from a real-size power network are
presented. ANNs enhanced with bus power injections can handle topological
changes in the power system.

A. Maiorano and M. Trovato present A neural network-based tool for
preventive control of voltage stability in multi-area power systems. The
power system is decomposed into a number of areas for each of which a
trained neural network outputs an area-based voltage-stability index. The
minimum among the index values characterizes the voltage stability of the
whole power system.

A. Muñoz and M.A. Sanz-Bobi describe An incipient fault detection system
based on the probabilistic radial basis function network: Application to
the diagnosis of the condenser of a coal power plant. The Probabilistic
Radial Basis Function Network (PRBFN) is introduced. The faults are
detected by comparing the actual plant behavior with its prediction. The
prediction makes use of a model of normal condition operation.

H. Salehfar and S.A. Benson describe the Electric utility coal quality
analysis using artificial neural network techniques. Impurities and ash
forming species in coal are determined using the neural network. Results
are compared to those using Computer-Controlled Scanning Electron
Microscopy (CCSEM) methods and used to predict the deposition tendency and
slagging behavior of ash under different operation conditions.

P.R.S. Jota, S.M. Islam, T. Wu and G. Ledwich present A class of hybrid
intelligent system for fault diagnosis in electric power systems. A hybrid
intelligent system based on neuro-fuzzy, neuro-expert and fuzzy-expert
algorithms is used to detect a number of faults in a range of electric
power system equipment in Australia and Brazil.

T.S. Sidhu, G. Singh and M.S. Sachdev describe a technique for Arcing fault
detection using artificial neural networks. Acoustic radiation, infrared
radiation and radio waves produced by arcing are recorded on a DSP-based
data acquisition system. Classification is done using three-layer
feedforward neural networks. Experimental results are reported.

The artificial neural-networks-based relay algorithm for the detection of
stochastic high impedance faults (HIF) is described by L.A. Snider and
Y.S. Yuen. Low-order harmonics of residual quantities are used as the
inputs to the artificial neural network. Arcs associated with high
impedance faults distort the voltage and current waveforms and are modeled
via simulation. The distortions are recognized by the algorithm.

M.A.H. El-Sayed presents an Artificial neural network for reactive power
optimization. Transmission losses are minimized using a neural network
scheme. This scheme is enhanced by a rule-based approach when the network
does not provide for a feasible solution. Numerical results from a real
power system provides confirmation of the applicability of the scheme.

D. Srinivasan describes Evolving artificial neural networks for short term
load forecasting. The Artificial Neural Networks (ANN) are generated using
a genetic algorihtm (GA) and forecast one-day ahead hourly electric
loads. The approach avoids the use of large historical data sets and
frequent retraining. When compared with statistical methods to solve the
same problem the neural network approach shows a better performance.

R. Parra Hernández, J. Álvarez Gallegos and J.A. Hernández Reyes present
(SRNN) Simple recurrent neural network: A neural network structure for
control systems. SRNNs are used to control linear and nonlinear dynamic
systems. Results show that inverse modeling of dynamic systems is feasible
using SRNNs and that only a few parameters are needed when using SRNNs to
control dynamical systems.

I appreciate the cooperation of all those who submitted their work for
inclusion in this issue.

V. David Sanchez A. 
Editor-in-Chief




More information about the Neur-sci mailing list