J. J. Cárdenas

Load forecasting framework of electricity consumptions for an Intelligent Energy Management System in the user-side

This work presents an electricity consumption-forecasting framework configured automatically and based on an Adaptative Neural Network Inference System (ANFIS). This framework is aimed to be implemented in industrial plants, such as automotive factories, with the objective of giving support to an Intelligent Energy Management System (IEMS). The forecasting purpose is to support the decision-making (i.e. scheduling workdays, on-off production lines, shift power loads to avoid load peaks, etc.) to optimize and improve economical, environmental and electrical key performance indicators. The base structure algorithm, the ANFIS algorithm, was configured by means of a Multi Objective Genetic Algorithm (MOGA), with the aim of getting an automatic-configuration system modelling. This system was implemented in an independent section of an automotive factory, which was selected for the high randomness of its main loads. The time resolution for forecasting was the quarter hour. Under these challenging conditions, the autonomous configuration, system learning and prognosis were tested with success.

Evolutive ANFIS training for energy load profile forecast for an IEMS in an automated factory

In this paper an evolutive algorithm is used to train an adaptative-network-based fuzzy inference system (ANFIS), particularly a genetic algorithm (GA). The GA is able to train the antecedent and consequent parameters of an ANFIS, which is used for energy load profile forecasting in an automated factory. This load forecasting is useful to support an intelligent energy management system (IEMS), which enables the user to optimize the energy consumptions by means of getting the optimal work points, scheduling the production according to these points, etc. The proposed training algorithm showed excellent results with complex plants like industrial energy consumers in the user-side, where the randomness of the loads is higher than in utility loads. Real data from an automated car factory were used to test the presented algorithms. Appropriated results were obtained.

New Model of a Converter-Based Generator Using Electrostatic Synchronous Machine Concept

In this paper, a new method for modeling converter-based power generators in ac-distributed systems is proposed. It is based on the concept of electrostatic synchronous machines. With this new concept, it is possible to establish a simple relationship between the dc and ac side and to study stability in both the small and large signals of the microgrid by considering a dc-link dynamic and high variation in the power supplied. Also, for the purpose of illustration, a mathematical and electrical simulation is presented, based on MATLAB and PSCAD software. Finally, an experimental test is performed in order to validate the new model.

STLF in the user-side for an iEMS based on evolutionary training of Adaptive Networks

It is a fact that the short-term load forecasting (STLF) in the user side is growing interest. Consequently, intelligent energy management systems (iEMSs) are including this capability in order to take autonomous decisions. In this context, this paper presents a new STLF scheme based on Adaptative Networks Fuzzy Inference Systems (ANFIS). This ANFIS has an exponential output membership functions (e-ANFIS) and has been trained by means of a novel evolutionary training algorithm (ETA). Due to the computational burden required by ETA, parallel computing was used to eliminate this problem especially for embedded applications. This new scheme has been tested with real data from an automotive factory and it shows better results in comparison with typical adaptative network structures (neural network and ANFIS).

Load forecasting in the user side using wavelet-ANFIS

At present, the intelligent energy management systems (IEMS) are used to maximiz the relation between productivity and cost using a variety of energy sources. In this work, we present a method of short-time load forecasting, using the ANFIS model and a component of preprocessing based in the discrete wavelet transform; the models was implemented in the user-side, analyzing real data of a factory in order to test the proposed algorithm.

An energy prediction method using Adaptive Neuro-Fuzzy Inference System and Genetic Algorithms

This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithms (GA). The GA has been used for the selection of the training inputs of the ANFIS in order to minimize the training result error. The presented algorithm has been installed and it is being operating in an automotive manufacturing plant. It periodically communicates with the plant to obtain new information and update the database in order to improve its training results. Finally the obtained results of the algorithm are used in order to provide a short-term load forecasting for the different modeled consumption processes.

Modeling and studying of power flow in a parking lot with plug-in vehicles and the impact in the public utility

For a massive introduction of electric vehicles to the market is required to have a reliable infrastructure. The Infrastructure needs includes enough charging stations around cities and electric parking lots. Currently the grid is able to support the electric consumption and hardly support recharging the fleet of electric vehicles. Likewise, the public utility has to keep high power quality without blackout, voltage dips, harmonics, etc. This paper studies the electric infrastructure of an electric parking lot. It is considered a high penetration of charging stations. The analysis has been performed by means of vehicle battery and charging station modeling in MATLAB Simulink. The paper analyzes the power flow into the distributed transformer using three different scenarios.

Multidimensional intelligent diagnosis system based on Support Vector Machine Classifier

Heeding the diagnostic requirements of electromechanical systems applied in automotive and aeronautical sectors, a multidimensional diagnostic system based on Support Vector Machine classifier is presented in this paper. In this context, different stationary and non-stationary speed and torque conditions are taken into account over an experimental actuator, in the same way, different single and combined failures scenarios are analyzed. In order to achieve a proper reliability in the diagnosis process, a multidimensional strategy is proposed: currents and vibrations from an electro-mechanical actuator are acquired. A great deal of features is calculated using statistical parameters from the acquired signals in time and frequency domain. Additionally, advanced time-frequency domain analysis techniques, such as Wavelet Packet Transform and Empirical Mode Decomposition, are used to achieve features which provide information in non-stationary conditions. The feature space dimensionality is analyzed by a feature reduction stage based on Partial Least Squares, which optimizes and reduces the feature set to be used for diagnosis proposes. The classification core is based on Support Vector Machine. Moreover, this work provides a performance comparison between the proposed classification algorithm and others such as Neural Network, k- Nearest Neighbor and Classification Trees. Experimental results are presented to demonstrate the feasibility and diagnostic capability of the proposed system.

Effects of the pre-processing algorithms in fault diagnosis of wind turbines

Abstract The wind sectors pends roughly 2200M€ in repair the wind turbines failures. These failures do not contribute to the goal of reducing greenhouse gases emissions. The 25–35% of the generation costs are operation and maintenance services. To reduce this amount, the wind turbine industry is backing on the Machine Learning techniques over SCADA data. This data can contain errors produced by missing entries, uncalibrated sensors or human errors. Each kind of error must be handled carefully because extreme values are not always produced by data reading errors or noise. This document evaluates the impact of removing extreme values (outliers) applying several widely used techniques like Quantile, Hampel and ESD with the recommended cut-off values. Experimental results on real data show that removing outliers systematically is not a good practice. The use of manually defined ranges (static and dynamic) could be a better filtering strategy.

Characterization of physiological networks in sleep apnea patients using artificial neural networks for Granger causality computation

Different studies have used Transfer Entropy (TE) and Granger Causality (GC) computation to quantify interconnection between physiological systems. These methods have disadvantages in parametrization and availability in analytic formulas to evaluate the significance of the results. Other inconvenience is related with the assumptions in the distribution of the models generated from the data. In this document, the authors present a way to measure the causality that connect the Central Nervous System (CNS) and the Cardiac System (CS) in people diagnosed with obstructive sleep apnea syndrome (OSA) before and during treatment with continuous positive air pressure (CPAP). For this purpose, artificial neural networks were used to obtain models for GC computation, based on time series of normalized powers calculated from electrocardiography (EKG) and electroencephalography (EEG) signals recorded in polysomnography (PSG) studies.