The Self-Organizing Map (SOM) algorithm has attracted a great deal of interest among researches and practitioners in a wide variety of fields. The SOM has been analyzed extensively, a number of variants have been developed and, perhaps most notably, it has been applied extensively within fields ranging from engineering sciences to medicine, biology, and economics. We have collected a comprehensive list of 5384 scientific papers that use the algorithms, have benefited from them, or contain analyses of them. The list is intended to serve as a source for literature surveys. The present addendum contains 2092 new articles, mainly from the years 1998-2002. We have provided a keyword index to help finding articles of interest, and additionally a modern automatically constructed variant of a thematic index: a WEBSOM interface to the whole article collection of years 1981-2000. The SOM of SOMs is available at http://websom.hut.fi/websom/somref/search.cgi for browsing and searching the collection.

Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum

Available transfer capability (ATC) calculation is a complicated task that involves the determination of total transfer capability (TTC) and two margins: transmission reliability margin (TRM) and capacity benefit margin (CBM). Three currently used methods of TTC determination are presented and compared in this paper. Besides these methods, the transfer-based security constrained OPF (TSCOPF) method is proposed in this paper as a replacement of the conventional SCOPF method for use in the deregulation environment. Both TRM and CBM, which account for reliability of the system, are seldom mentioned in papers associated with ATC. This paper presents a probabilistic method to assess TRM and proposes rules and a procedure to allocate CBM and two methods of incorporating CBM into ATC. A modified IEEE RTS is utilized to demonstrate the proposed methods, and the results show that the values of ATC are quite different when margins are taken into account and the methods of incorporating ATC affect the ATC value significantly.

Assessment of available transfer capability and margins

Mathematical Model and Solution of Electric Network.- Load Flow Analysis.- Stochastic Security Analysis of Electrical Power Systems.- Power Flow Analysis in Market Environment.- HVDC and FACTS.- Mathematical Model of Synchronous Generator and Load.- Power System Transient Stability Analysis.- Small-Signal Stability Analysis of Power Systems.

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Modern Power Systems Analysis

Applications of computational intelligence techniques for solving the revived optimal power flow problem

This paper presents a new methodology for reliability evaluation of composite generation and transmission systems, based on nonsequential Monte Carlo simulation (MCS) and artificial neural network (ANN) concepts. ANN techniques are used to classify the operating states during the Monte Carlo sampling. A polynomial network, named group method data handling (GMDH), is used, and the states analyzed during the beginning of the simulation process are adequately selected as input data for training and test sets. Based on this procedure, a great number of success states are classified by a simple polynomial function, given by the ANN model, providing significant reductions in the computational cost. Moreover, all types of composite reliability indices (i.e., loss of load probability, frequency, duration, and energy/power not supplied) can be assessed not only for the overall system but also for areas and buses. The proposed methodology is applied to the IEEE Reliability Test System (IEEE-RTS), to the IEEE-RTS 96, and to a configuration of the Brazilian South-Southeastern System.

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Composite Reliability Assessment Based on Monte Carlo Simulation and Artificial Neural Networks

This paper proposes a neural network solution methodology for the problem of real power transfer capability calculations. Based on the optimal power flow formulation of the problem, the inputs, for the neural network are generator status, line status and load status and the output is the transfer capability. The Quickprop algorithm is used in the paper to train the neural network. A case study of the IEEE 30-bus system is presented demonstrating the feasibility of this approach. The new method will be useful for reliability assessment in the new utility environment.

Real power transfer capability calculations using multi-layer feed-forward neural networks

Power system reliability evaluation using learning vector quantization and Monte Carlo simulation

Artificial neural networks (ANN) based on the self organizing map (SOM) algorithm has received considerable attention. This paper proposes a new method for power system reliability evaluation by combining Monte Carlo simulation and self organizing map which greatly reduces the computing burden of the loss of load probability calculation compared to Monte Carlo simulation only. A case study of the IEEE RTS system is presented demonstrating the efficiency of this approach.

Power system reliability evaluation using self organizing map

This paper presents a method for classifying power system states as loss-of-load or not using Kohonen's self-organizing map (SOM). The main feature of SOM is the ability to map input data from an n-dimensional space to a lower dimensional (usually two dimensional) space while maintaining the original topological relationships. Real and reactive power at each load bus and available real power generation at each generation bus are taken as input features. OPF calculations are performed on the weights of each neuron in the map to determine whether the neuron is representative of loss-of-load or not. The loss-of-load status of a new system state can be quickly identified by the loss-of-load status of the nearest neuron. An example illustrating the approach shows that the SOM can accurately classify the loss-of-load status of power system states. This proposed method is useful for power system operation, power system reliability assessment and state screening.

Loss-of-load state identification using self-organizing map

Modern digital protective relays generate various flies and reports which contain abundant data regarding fault disturbances and protection system operation. This paper presents an expert system based application for automated analysis of digital relay data. In this application, forward chaining reasoning is used to predict expected protection operation while backward chaining reasoning is employed to validate and diagnosis of actual protection operation. An EMTP/C++ based digital relay model with capability of insertion of user-defined errors and generation of files and reports is developed. The analysis capability of this application is tested using the relay model.

X. Luo

Papers

Real power transfer capability calculations using multi-layer feed-forward neural networks

Power system reliability evaluation using learning vector quantization and Monte Carlo simulation

Power system reliability evaluation using self organizing map

Loss-of-load state identification using self-organizing map

Automated analysis of digital relay data based on expert system