Abstract
A better environment can be achieved through the reduced emission of pollutants, the optimization of green energy production and the optimization of maintenance interventions, which is an important contribution in getting equipment functioning as efficiently and effectively as possible and, of no less importance, to minimize the downtime caused by faults. These are the key points presented in this paper, which also emphasizes the very recent contribution of 3D models in aiding fault diagnosis and terology in general. The way to achieve the above-mentioned objectives is through on-condition maintenance in two main fields, wind farms and Diesel engines. In wind farms, maintenance is done through the control of variables, such as vibration signals and the balance of electrical currents. As for Diesel engines, on-condition variables are the emissions of PM10, NO(x), CO, HC and CO(2). However, there are problems in both situations, namely, in the first case, the distance and accessibility of the generators and, in the second case, the problems associated with the fact that the equipment is not static. Another common problem in both situations is the measuring and transmission of the values of the on-condition variables, because, in the case of wind farms, the machine is placed on top of the tower and, in the case of Diesel engines, the vehicles are in operation most of the time and most of the measurements need be made while the vehicles are running. Also, although the two situations seem different, they have many issues in common, such as those above-mentioned, for which we will propose convergent solutions that have an Integrated Modular System for Terology (SMIT - Sistema Modular Integrado de Terologia) as a base platform. In addition, to collect, transmit and manage data, we also propose low-cost hardware devices and open-source software, with time series, Hidden Markov Models and genetic algorithms incorporated into them, to enable the prediction of new maintenance interventions. Another important development that is mentioned, with the objective of achieving a more effective terology system, is the implementation of 3D models to aid fault diagnosis and maintenance interventions in general. All these subjects are treated in this paper in a cohesive and synergistic way in order to achieve more effective terology management with an environmental perspective.

Abstract
The use of open-source software in many institutions and organizations is increasing. However, a balance should be considered between the software cost and the cost of its technical support and reliability. In this article, a maintenance system for wind farms will be presented. It is connected to an information system for maintenance, called SNIFF (Terology Integrated Modular System) as a general base to manage the assets and as a support strategic line to the evolution of this system, which incorporates on-condition maintenance modules, and the support to the research and development done around this theme. The SMIT system is based on a TCP/IP network, using a Linux server running a PostgreSQL database and Apache web server with PHP, and Octave and R software for numerical analysis. Maintenance technicians, chiefs, economic and production management personnel can access SMIT database through SMIT clients for Windows. In addition, this maintenance system for wind systems uses also special low cost hardware for data acquisition on floor level. The hardware uses a distributed TCP/IP network to synchronize SMIT server master clock through Precision Time Protocol. Usually, the manufactures construct, deploy and give the means for the suppliers to perform the wind system's maintenance. This is a very competitive area, where companies tend to hide the development details and implementations. Within this scenario, the development of maintenance management models for multiple wind equipments is important, and will allow countries to be more competitive in a growing market. For on-condition monitoring, the algorithms are based on Support Vector Machines and time series analysis running under Octave and R open-source software's.

Abstract
This study illustrates and discusses an original approach to classify the electricity consumers according to their daily load patterns. This approach exploits the notion of entropy introduced by Renyi for setting up specific clustering procedures. The proposed procedures differ with respect to typical methods adopted for electricity consumer classification, based on the Euclidean distance notion. The algorithms tested include firstly a classical method based on the between-cluster entropy and its slight variation. Then, a novel procedure is presented, based on the calculation of the similarity between centroids, with successive refinement to allow effective identification of the outliers. The outcomes of the classification carried out by using the proposed procedure are compared to the results of other available techniques, using a set of clustering validity indicators for ranking the clustering methods. On the basis of these results, it emerges that the novel procedure exhibits better clustering performance with respect to both the literature approaches and the classical entropy-based method, for different numbers of clusters. The results obtained are of key relevance for assisting the electricity suppliers in identifying a reduced number of load pattern-dependent classes, to be associated with distinct consumer groups for load aggregation or tariff purposes. © 2010 © The Institution of Engineering and Technology.

Abstract
This paper presents a new control chip design, based on Field Programmable Gate Array (FPGA) technology, for multi-motor electric vehicles. The control chip builds around a reusable intellectual property (IP) core, named Propulsion Control System (PCS); which features motor control functions with field orientation methods, and energy loss minimization of induction motors. To reduce the cost, implementation issues related with the limited number of dedicated multipliers were overcome using an efficient computational block, based on resource sharing strategy. Due to the parallel processing offered by FPGAs, the resulting implementation can be effortlessly adapted to different electric vehicles topologies, like single or multi-motor drive. As proof of concept, two prototypes with single and multi-motor configurations were developed with the control chip design implemented in a low cost Xilinx Spartan 3 FPGA. Experimental verification of the energy loss minimization algorithm is provided, showing considerable energy savings (>15%) in low speed conditions and improving the electric vehicle range per charge.

Abstract
The correct estimation of the friction coefficient in automotive applications is of paramount importance in the design of effective vehicle safety systems. In this article a new parametrization for estimating the peak friction coefficient, in the tire-road interface, is presented. The proposed parametrization is based on a feedforward neural network (FFNN), trained by the Extreme Learning Machine (ELM) method. Unlike traditional learning techniques for FFNN, typically based on backpropagation and inappropriate for real time implementation, the ELM provides a learning process based on random assignment in the weights between input and the hidden layer. With this approach, the network training becomes much faster, and the unknown parameters can be identified through simple and robust regression methods, such as the Recursive Least Squares. Simulation results, obtained with the CarSim program, demonstrate a good performance of the proposed parametrization; compared with previous methods described in the literature, the proposed method reduces the estimation errors using a model with a lower number of parameters.

Abstract
This paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn. Crown Copyright