Abstract
This paper presents a concept for building up a distributed monitoring and diagnosis system for complex industrial applications. For this purpose, a hierarchical organized model with distributed, cooperating agents was developed. The hierarchical aspect guarantees a predictable behaviour of the system with a high performance and the flexibility of the system is ensured by the federal distribution (Bongaerts 1998). By using this approach, a modular component diagnosis and monitoring (CDM) system is realized that enables the integration of legacy monitoring and diagnostic tools, specific to the application area. Universal applicable mechanisms were found to perform diagnostic processes and to improve the quality of a diagnosis by handling different diagnostic mechanisms in parallel and by applying conflict resolution algorithms. This software architecture for monitoring and diagnosis was developed by the University of Karlsruhe in cooperation with three industrial partners and one research institute within the framework of the EU Esprit Program: 'DIAMOND: DIstributed Architecture for MONitoring and Diagnosis' (DIAMOND 2002).

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Abstract
Quality prediction of load evolution at different levels of distribution network is a basic requirement for adequate operation planning of modern power systems. This paper describes the models, based on artificial neural networks, developed for active and reactive power forecasting at primary substations' transformers. The main goal consists on defining a regression process characterized by good quality estimates of those future values, based on historical data. Anticipation interval shall include from the next hour to one week in advance. The implemented forecasting tool is able to deal with noisy data, holidays and special occasions and adapts forecasts in case of power network reconfiguration whenever planned. Used techniques and implementation foundations of selected forecasting models are reported. Finally, the potential of the adopted approach is sustained by illustrative examples. © 2003 IEEE.

Abstract
In this paper, the Unit Commitment (UC) problem is presented and solved, following an innovative approach based on a metaheuristic procedure. The problem consists on deciding which electric generators must be committed, over a given planning horizon, and on defining the production levels that are required for each generator, so that load and spinning reserve requirements are verified, at minimum production costs. Due to its complexity, exact methods proved to be inefficient when real size problems were considered. Therefore, heuristic methods have for long been developed and, in recent years, metaheuristics have also been applied with some success to the problem. Methods like Simulated Annealing, Tabu Search and Evolutionary Programming can be found in several papers, presenting results that are sufficiently interesting to justify further research in the area. In this paper, a resolution framework based on GRASP - Greedy Randomized Adaptive Search Procedure - is presented. To obtain a general optimisation tool, capable of solving different problem variants and of including several objectives, the operations involved in the optimisation process do not consider any particular characteristics of the classical UC problem. Even so, when applied to instances with very particular structures, the computational results show the potential of this approach.

Abstract
The need of fast algorithms for radial distribution networks that take advantage of their particular structure has been increasing, namely due to the use of genetic algorithms and meta-heuristics for optimization in planning and operation. In this paper, a new method for power flow calculation in radial networks is presented. It uses an iterative process along the branches, in a way similar to other methods, but the main idea is very different from previous approaches, since it is based on the exact power flow solution for a single branch and also because it provides a complete solution (not only voltage magnitudes). The method is fast and robust for different types of networks and loads, including heavy loads. The paper includes the theoretical derivation of the method, an illustration example and tests with benchmarking networks. © 2003 IEEE.