SOFT COMPUTING METHODS FOR CHANNEL AND NOISE ESTIMATION IN POWER-LINE COMMUNICATIONS

This work presents original developed methods, belonging to the soft computing area, that have been applied to solve complex signal processing problems arising in communication systems with “adverse” conditions. The adverse conditions are given by the fact that the considered physical channel is non-linear, non stationary, and the noise is coloured impulsive noise. Modified vector quantization algorithms have been developed for adaptive channel estimation and equalization inside a digital transmission. An unsupervised hierarchical clustering method based on fuzzy partitions has been proposed and application to identification of different noise sources is discussed. The presented soft computing techniques have been developed within the area of digital communication, nevertheless they posses general applicabilit

A sequential algorithm for training the SOM prototypes based on higher-order recursive equations

A novel training algorithm is proposed for the formation of Self-Organizing Maps (SOM). In the proposed model, the weights are updated incrementally by using a higher-order difference equation, which implements a low-pass digital filter. It is possible to improve selected features of the self-organization process with respect to the basic SOM by suitably designing the filter. Moreover, from this model, new visualization tools can be derived for cluster visualization and for monitoring the quality of the map

Clustering techniques applied to a high-speed train’s pantograph–catenary subsystem for electric arc detection and classification

Assessment of the current collection properties of a pantograph–catenary system mounted on a train is of great importance. Excessive electric arcing can lead to wear of the system’s components, and, at the same time, it can be an index of wear status. In this paper we investigate the possibility of detecting arcing events in the pantograph–catenary collection system without the need of additional equipment installed on-board the train. Data that is currently measured and recorded for modern high-speed trains (i.e. voltage and current) are analysed in order to detect and quantify electric arcs and shed light on the current collection quality of the pantograph–catenary system. This work was performed in cooperation with Trenitalia s.p.a. who provided the data it collects on-board high-speed trains in regular passenger service

A multi-objective optimization algorithm based on self-organizing maps applied to wireless power transfer systems

In this work, a new multi-objective population-based optimization algorithm is presented and tested. In this contribution, the concepts of fast non-dominating sorting and density estimation using the crowding distance are used to create a multi-objective optimization algorithm based on previous work, which is a single objective evolutionary optimization algorithm based on self-organizing maps (SOMs). The SOMs paradigm introduces a strong collaboration between neighbors solutions that improves exploitation. Furthermore, the representative power of the SOMs enhances the exploration and diversification. A state of the art benchmark approach is used to evaluate the performance of the proposed algorithm, obtaining positive results. The test problem uses an analytical model of an inductively coupled wireless power transfer system (WPT). The objective is to optimize the WPT model characteristics in order to allow simultaneous data and power transfer between the coils. The WPT design approach uses more degrees of freedom than existing techniques leading to a number of solutions where both the power signals and the data signal can coexist on the same physical channel achieving good figures of merit

An evolutionary algorithm for global optimization based on self-organizing maps

In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approach which is based on the sequential learning paradigm for self-organizing maps (SOMs). A modified version of the SOM is proposed where each cell contains an individual, which performs a search for a locally optimal solution and it is affected by the search for a global optimum. The movement of the individuals in the search space is based on a discrete-time dynamic filter, and various choices of this filter are possible to obtain different dynamics of the centroids. In this way, a general framework is defined where well-known algorithms represent a particular case. The proposed algorithm is validated through a set of problems, which include non-separable problems, and compared with state-of-the-art algorithms for global optimization

Design and Experimental Characterization of a Combined WPT - PLC System

In this contribution, the authors perform the design and show the experimental results relative to a prototype of a combined wireless power transfer (WPT)–power line communications (PLC) system, in which the WPT channel is interfaced to a PLC environment to allow data transfer when the cabled connection is no longer available. The main rationale behind this idea stays in the fact that PLC communication is now a popular choice to enable communications, for instance, in smart grids and in home automation, while WPT devices start to be available in the market (i.e. for mobile phones) and soon they will be a reality also for higher power (i.e. vehicle battery charging). In particular, theoretical insights about the requirements of the system are given; a two coils system has been implemented and a measurement campaign, together with simulations, show that the system is of great potentiality and could be used in applications where both wireless power and data transfer are needed (such as vehicles battery charging), achieving maximum power transfer and good data rate in order to transmit high-speed signals

A Scalable Predictive Maintenance Model for Detecting Wind Turbine Component Failures Based on SCADA Data

In this work, a novel predictive maintenance system is presented and applied to the main components of wind turbines. The proposed model is based on machine learning and statistical process control tools applied to SCADA (Supervisory Control And Data Acquisition) data of critical components. The test campaign was divided into two stages: a first two years long offline test, and a second one year long real-time test. The offline test used historical faults from six wind farms located in Italy and Romania, corresponding to a total of 150 wind turbines and an overall installed nominal power of 283 MW. The results demonstrate outstanding capabilities of anomaly prediction up to 2 months before device unscheduled downtime. Furthermore, the real-time 12-months test confirms the ability of the proposed system to detect several anomalies, therefore allowing the operators to identify the root causes, and to schedule maintenance actions before reaching a catastrophic stage.Comment: Paper presented at the conference IEEE PES General Meeting 2019, August 4-8 (Atlanta, USA

A Multi-Objective Method for Short-Term Load Forecasting in European Countries

In this paper we present a novel method for daily short-term load forecasting, belonging to the class of “similar shape” algorithms. In the proposed method, a number of parameters are optimally tuned via a multi-objective strategy that minimizes the error and the variance of the error, with the objective of providing a final forecast that is at the same time accurate and reliable. We extensively compare our algorithm with other state-of-the-art methods. In particular, we apply our approach upon publicly available data and show that the same algorithm accurately forecasts the load of countries characterized by different size, different weather conditions, and generally different electrical load profiles, in an unsupervised manner

Comparison and clustering analysis of the daily electrical load in eight European countries

This paper illustrates and compares the ability of several clustering algorithms to correctly associate a given aggregate daily electrical load curve with its corresponding day of the week. In particular, popular clustering algorithms like the Fuzzy c-Means, Spectral Clustering and Expectation Maximization are compared, and it is shown that the best results are obtained if the daily data are compressed with respect to a single feature, namely the so-called “Morning Slope”. Such a feature-based clustering appears to outperform the clustering results obtained upon using other classic features, and also with respect to using other conventional compression methods, such as the Principal Component Analysis, in all the examined European countries. This result is particularly interesting, as this feature provides a direct physical interpretation that can be used to obtain insights on the structure of the daily load profiles