Uncertainty analysis for industries investing in energy equipment and renewable energy sources

This paper studies the optimal design and operation of new energy equipment including renewable energy sources for prosumer industries. In order to augment the interest of industries in performing energy actions, the economic parameters of the investment are analysed and the risk related to it considering the uncertainty in energy markets is evaluated. A two-stage optimization approach is proposed considering the whole lifetime of the energy equipment and an uncertainty analysis performed through the evaluation of the deterministic model under Latin Hypercube Samples of uncertain parameters. A case study based on a real industry is presented, whose results expose the robustness of the optimization methodology and the acceptable risk of investing in renewable energy sources and energy equipment for prosumer purposes.Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPostprint (published version

Deep-compact-clustering based anomaly detection applied to electromechanical industrial systems

The rapid growth in the industrial sector has required the development of more productive and reliable machinery, and therefore, leads to complex systems. In this regard, the automatic detection of unknown events in machinery represents a greater challenge, since uncharacterized catastrophic faults can occur. However, the existing methods for anomaly detection present limitations when dealing with highly complex industrial systems. For that purpose, a novel fault diagnosis methodology is developed to face the anomaly detection. An unsupervised anomaly detection framework named deep-autoencoder-compact-clustering one-class support-vector machine (DAECC-OC-SVM) is presented, which aims to incorporate the advantages of automatically learnt representation by deep neural network to improved anomaly detection performance. The method combines the training of a deep-autoencoder with clustering compact model and a one-class support-vector-machine function-based outlier detection method. The addressed methodology is applied on a public rolling bearing faults experimental test bench and on multi-fault experimental test bench. The results show that the proposed methodology it is able to accurately to detect unknown defects, outperforming other state-of-the-art methods.Peer ReviewedPostprint (published version

Advances in power quality analysis techniques for electrical machines and drives: a review

The electric machines are the elements most used at an industry level, and they represent the major power consumption of the productive processes. Particularly speaking, among all electric machines, the motors and their drives play a key role since they literally allow the motion interchange in the industrial processes; it could be said that they are the medullar column for moving the rest of the mechanical parts. Hence, their proper operation must be guaranteed in order to raise, as much as possible, their efficiency, and, as consequence, bring out the economic benefits. This review presents a general overview of the reported works that address the efficiency topic in motors and drives and in the power quality of the electric grid. This study speaks about the relationship existing between the motors and drives that induces electric disturbances into the grid, affecting its power quality, and also how these power disturbances present in the electrical network adversely affect, in turn, the motors and drives. In addition, the reported techniques that tackle the detection, classification, and mitigations of power quality disturbances are discussed. Additionally, several works are reviewed in order to present the panorama that show the evolution and advances in the techniques and tendencies in both senses: motors and drives affecting the power source quality and the power quality disturbances affecting the efficiency of motors and drives. A discussion of trends in techniques and future work about power quality analysis from the motors and drives efficiency viewpoint is provided. Finally, some prompts are made about alternative methods that could help in overcome the gaps until now detected in the reported approaches referring to the detection, classification and mitigation of power disturbances with views toward the improvement of the efficiency of motors and drives.Peer ReviewedPostprint (published version

Power disturbance monitoring through techniques for novelty detection on wind power and photovoltaic generation

Novelty detection is a statistical method that verifies new or unknown data, determines whether these data are inliers (within the norm) or outliers (outside the norm), and can be used, for example, in developing classification strategies in machine learning systems for industrial applications. To this end, two types of energy that have evolved over time are solar photovoltaic and wind power generation. Some organizations around the world have developed energy quality standards to avoid known electric disturbances; however, their detection is still a challenge. In this work, several techniques for novelty detection are implemented to detect different electric anomalies (disturbances), which are k-nearest neighbors, Gaussian mixture models, one-class support vector machines, self-organizing maps, stacked autoencoders, and isolation forests. These techniques are applied to signals from real power quality environments of renewable energy systems such as solar photovoltaic and wind power generation. The power disturbances that will be analyzed are considered in the standard IEEE-1159, such as sag, oscillatory transient, flicker, and a condition outside the standard attributed to meteorological conditions. The contribution of the work consists of the development of a methodology based on six techniques for novelty detection of power disturbances, under known and unknown conditions, over real signals in the power quality assessment. The merit of the methodology is a set of techniques that allow to obtain the best performance of each one under different conditions, which constitutes an important contribution to the renewable energy systems.Postprint (published version

Novelty detection on power quality disturbances monitoring

Complex disturbance patterns take place over the corresponding power supply networks due to the increased complexity of electrical loads at industrial plants. Such complex patterns are the result of a combination of simpler standardized disturbances. However, their detection and identification represent a challenge to current power quality monitoring systems. The detection of disturbances and their identification would allow early and effective decision-making processes towards optimal power grid controls or maintenance and security operations of the grid. In this regard, this paper presents an evaluation of the four main techniques for novelty detection: k-Nearest Neighbor, Gaussian Mixture Models, One-Class Support Vector Machine, and Stacked Autoencoder. A set of synthetic signals have been considered to evaluate the performance and suitability of each technique as an anomaly detector applied to power quality disturbances. A set of statistical features have been considered to characterize the power line. The evaluation of the techniques is carried out throughout different scenarios considering combined and single disturbances. The obtained results show the complementary performance of the considered techniques in front of different scenarios due to their differences in the knowledge modelization.This research work has been partially supported by FOFIUAQ-2018 FIN 201812 and CONACyT doctoral scholarship number 735042. The authors would like to thank the support provided by the Catalan Agency for Management of University under the grant 2017 SGR 967.Peer ReviewedPostprint (published version

Analysis of machine learning based condition monitoring schemes applied to complex electromechanical systems

In the modern industry framework, the application of condition monitoring schemes over electromechanical systems is being subjected to demanding requirements. Currently, the massive digitalization of industrial assets allows the investigation towards multiple monitoring strategies capable of emphasize deviations over the nominal system operation. However, the most prominent techniques, such as Machine Learning, present great challenges in complex systems. In this regard, the proposed study presents the analysis of the diagnostic capabilities resulting from the classical approaches based on machine learning facing to complex electromechanical systems that implies a working environment subject to different operation condition, configurations with multiple components and the presence of faults of different nature (mechanical, electrical, electromagnetic), under isolated or combined scenarios. Discriminative feature extraction capabilities and classification accuracy will be analyzed as performance measures.Peer ReviewedPostprint (published version

Deep learning based condition monitoring approach applied to power quality

Condition monitoring applied to power quality involves several techniques and procedures for the assessment of the electrical signal. Data-driven approaches are the most common methodologies supported on data and signal processing procedures. Electrical systems in factory automation become more complex with the increase of multiple load profiles connected, and unexpected electrical events can occur causing the appearance of power quality disturbances. However, emerging technologies in the techniques related to the detection and identification of power quality disturbances are analyzed and compared according to the complexity of the current electrical system, that is, including simple and combined disturbances. These new technologies allow developing more cyber-physical systems to process the new methodologies for condition monitoring. Thus, in this study, a deep learning-based approach for the identification of power quality disturbances is implemented and their performance analyzed in front of classical disturbances defined by the International standards considered in the related literature.This research work has been partially supported by FOFIUAQ-2018 FIN 201812 and CONACyT doctoral scholarship number 735042. This work was also supported in part by the European Regional Development Fund from the European Union in the FEDER Operative Programme framework of Catalonia 2014-2020.Peer ReviewedPostprint (published version

A novel deep learning-based diagnosis method applied to power quality disturbances

Monitoring electrical power quality has become a priority in the industrial sector background: avoiding unwanted effects that affect the whole performance at industrial facilities is an aim. The lack of commercial equipment capable of detecting them is a proven fact. Studies and research related to these types of grid behaviors are still a subject for which contributions are required. Although research has been conducted for disturbance detection, most methodologies consider only a few standardized disturbance combinations. This paper proposes an innovative deep learning-based diagnosis method to be applied on power quality disturbances, and it is based on three stages. Firstly, a domain fusion approach is considered in a feature extraction stage to characterize the electrical power grid. Secondly, an adaptive pattern characterization is carried out by considering a stacked autoencoder. Finally, a neural network structure is applied to identify disturbances. The proposed approach relies on the training and validation of the diagnosis system with synthetic data: single, double and triple disturbances combinations and different noise levels, also validated with available experimental measurements provided by IEEE 1159.2 Working Group. The proposed method achieves nearly a 100% hit rate allowing a far more practical application due to its capability of pattern characterization.This research work has been partially supported by FOFIUAQ-2018 FIN 201812 and CONACyT doctoral scholarship number 735042. This work has been co-financed by the European Regional Development Fund of the European Union in the framework of the ERDF Operational Program of Catalonia 2014–2020, grant number 001-P-001643.Peer ReviewedPostprint (published version