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

Abstract
This paper describes an improved micro-power electric generator where energy harvested from human movements is used as an everlasting mechanical energy source to suffice smart hip implant electronics power needs. Its architecture is designed so that the mechanical energy promotes the movement of a combination of magnets and a spring embedded inside a Teflon tube, used to reduce friction. The changing magnetic field induces current in two coils so that the output of the generator is the sum of their signals. The end result is like a double generator in one casing. Produced electrical energy is stored in an energy reservoir handed over to a power management module. Experimental results shows that energy harvested from human walking can be used as an effective power source for hip prosthesis implants. (C) 2010 Published by Elsevier Ltd.

Abstract
Recent developed button heat pulse probes (BHPP) demonstrated a great potential for soil water content measurements. This new probe compared to conventional heat pulse probes (HPP), does not use needles, and measurement accuracy is significantly improved. This new design, with the possibility to assembly the probe and electronics in the same package, with low-cost, and with less power consumption compared to conventional HPP, make it suitable to be connected to wireless data acquisition systems in precision agriculture. The probe was tested in agar to demonstrate the potential advantages of the button heat pulse sensor for soil water content measurements. It was possible to have an 0.5 °C temperature rise with only 156mW of power consumption, a ten times power reduction in heat-pulse soil water content measurements. These tests showed the potential use of the button heat pulse sensor for the determination of soil water content. © 2010 IEEE.