Wind power forecasting using advanced neural networks models

In this paper, an advanced model, based on recurrent high order neural networks, is developed for the prediction of the power output profile of a wind park. This model outperforms simple methods like persistence, as well as classical methods in the literature. The architecture of a forecasting model is optimised automatically by a new algorithm, that substitutes the usually applied trial-and-error method. Finally, the online implementation of the developed model into an advanced control system for the optimal operation and management of a real autonomous wind-diesel power system, is presented     

Optimal Robot Speed Trajectory by Minimization of the Actuator Motor Electromechanical Losses

This paper considers the control problem of a robotic manipulator with separately excited dc motor drives as actuators. An innovative method is proposed which achieves robot speed-control requirements, with simultaneous minimization of total electromechanical losses, while the drives follow the desired speed profiles of the robot joints under various loads and random load disturbances. If there is no demand for a specific speed profile, the optimal speed trajectory is determined by minimizing an electromechanical losses criterion. Controllable energy losses, such as armature copper losses, armature iron losses, field copper losses, stray load losses, brush load losses, friction and windage losses, can be expressed proportionally to the squares of the armature and the field (exciting) currents, the angular velocity and the magnetic field flux. The controllable energy loss term is also included in the optimal control integral quadratic performance index, defined for the whole operation period. Thus the appropriate control signals required for following the desired trajectory by simultaneous energy loss minimization for the whole operation interval are achieved. Two case studies of optimal robot control with and without minimization of actuator energy losses are presented and compared, showing the energy savings that can be achieved by the proposed methodology.     

Fault detection using parameter estimation

This paper presents a brief summary of fault-detection methods using parameter estimation techniques. An overview of the fault-detection system design methodology is first presented, followed by the principles of parameter-estimation fault-detection techniques. Applications from the field of industrial processes are given and finally a case study is described which applies the general techniques to the fault detection of D.C. motors using multiprocessor systems.     

Predictive control techniques for energy and indoor environmental quality management in buildings

The aim of the present paper is to present a model-based predictive controller, combined with a Building Energy Management System (BEMS). The overall system predicts the indoor environmental conditions of a specific building and selects the most appropriate actions so as to reach the set points and contribute to the indoor environmental quality by minimizing energy costs. The controller is tested using a BEMS installation in Hania, Crete, Greece.     

On the performance of two-hop message spreading in DTNs

In this paper, a Delay Tolerant Network environment is considered where the source is in full control of the two-hop spreading mechanism by setting key parameters such as the number of copies allowed to be spread in the network and the delay bound of the messages. The introduced analysis allows for a differentiation between the source of the message and the intermediate nodes (in terms of e.g. transmission power, speed or cooperation degree). Analytical expressions for the cumulative distribution function (cdf) of the delivery delay and the induced overhead are extracted, taking into account the fact that the source node may continue spreading copies after the message delivery. In addition, a fairly accurate approximate expression for the cdf of the delivery delay is also derived and validated through simulations.     

Classification of washing machines vibration signals using discretewavelet analysis for feature extraction

Wavelets provide a powerful tool for nonstationary signal analysis. In vibration monitoring, the occurrence of occasional transient disturbances makes the recorded signal nonstationary, especially during the start-up of an engine. Through the wavelet analysis, transients can be decomposed into a series of wavelet components, each of which is a time-domain signal that covers a specific octave frequency band. Disturbances of small extent (duration) are amplified relative to the rest of the signal when projected to similar size wavelet bases and, thus, they can be easily detected in the corresponding frequency band. This paper presents a new method for extracting features in the wavelet domain and uses them for classification of washing machines vibration transient signals. The discrete wavelet transform (DWT), in conjunction with statistical digital signal processing techniques, is used for feature extraction. The Karhunen Loeve transform (KLT) is used for feature reduction and decorrelation of the feature vectors. The Euclidean, Mahalanobis, and Bayesian distance classifiers, the learning vector quantization (LVQ) classifier, and the fuzzy gradient classifier are used for classification of the resulting feature space. Classification results are illustrated and compared for the rising part of vibration velocity signals of a variety of real washing machines with various defects     

Study of various TDMA schemes for wireless networks in the presenceof deadlines and overhead

The objective of this paper is to determine the minimum system dropping rate (or, equivalently, dropping probability) induced by time division multiple access (TDMA) schemes supporting time-constrained applications with common maximum cell delay tolerance. Expressions are derived for the induced system dropping rate for various TDMA schemes with different overhead and the maximum number of users than can be admitted in the network without violating the maximum dropping rate constraint is determined. The system dropping rate achieved by suboptimal TDMA schemes is compared against the optimal (although ideal) TDMA scheme performance. The performance limiting factors associated with the suboptimal schemes are identified, and the magnitude of their (negative) impact is evaluated. Based on this information it is possible to point to performance improving modifications which should be pursued to the extent permitted by technological constraints. Finally, based on this derivations a network designer may choose the best TDMA scheme-among realizable variations of those considered here-to use in a particular situation     

Selection of window sizes for optimizing occupational comfort and hygiene based on computational fluid dynamics and neural networks

The present paper presents a novel computational method to optimize window sizes for thermal comfort and indoor air quality in naturally ventilated buildings. The methodology is demonstrated by means of a prototype case, which corresponds to a single-sided naturally ventilated apartment. Initially, the airflow in and around the building is simulated using a Computational Fluid Dynamics model. Local prevailing weather conditions are imposed in the CFD model as inlet boundary conditions. The produced airflow patterns are utilized to predict thermal comfort indices, i.e. the PMV and its modifications for non-air-conditioned buildings, as well as indoor air quality indices, such as ventilation effectiveness based on carbon dioxide and volatile organic compounds removal. Mean values of these indices (output/objective variables) within the occupied zone are calculated for different window sizes (input/design variables), to generate a database of input–output data pairs. The database is then used to train and validate Radial Basis Function Artificial Neural Network input–output “meta-models”. The produced meta-models are used to formulate an optimization problem, which takes into account special constraints recommended by design guidelines. It is concluded that the proposed methodology determines appropriate windows architectural designs for pleasant and healthy indoor environments.