New hybrid neuro-evolutionary algorithms for renewable energy and facilities management problems

Esta tesis tiene como objetivo la optimización de la explotación de recursos energéticos renovables, así como la mejora en la gestión de instalaciones en ingeniería oceánica y aeropuertos, usando métodos computacionales híbridos pertenecientes a una rama de la Inteligencia Artificial (IA), denominada aprendizaje máquina, para este fin. Hoy en día, los combustibles fósiles constituyen la fuente energética más importante del planeta, sin embargo, estas formas de energía contribuyen al Cambio Climático en gran medida, afectando los ecosistemas severamente. Por esta razón, se tiende gradualmente al uso de fuentes de energía renovables que garanticen un desarrollo sostenible. Sin embargo, se observa una lenta evolución en este sentido, y la única cuestión que cabe preguntarse es cuándo las energías renovables tendrán mayor penetración en el sistema que los actuales combustibles fósiles. Para responder a esta pregunta, una buena manera es centrarse en el principal inconveniente de este tipo de energías: la variabilidad natural inherente al recurso. Esto significa que las predicciones sobre los parámetros más importantes de los que dependen las energías renovables son necesarias para conocer la cantidad de energía que será obtenida en un momento dado. El otro tema abordado en esta tesis está relacionado con los parámetros que influyen en diferentes actividades marinas y aeropuertos, cuyo conocimiento de su comportamiento es necesario para desarrollar una correcta gestión de las instalaciones en estos entornos. Por ejemplo, la altura significativa de las olas (Hs) es un parámetro básico en la caracterización de las olas, muy importante para el desarrollo de actividades marinas como el diseño y mantenimiento de barcos, estructuras marinas, convertidores energéticos de ola, etc. Por otro lado, la escasa visibilidad en los aeropuertos, normalmente causada por la niebla, es otro aspecto fundamental para el correcto desarrollo de actividades aeroportuarias, y que puede causar retrasos en los vuelos, desvíos y cancelaciones, o accidentes en el peor de los casos. En este trabajo se ha realizado un análisis del estado del arte de los modelos de aprendizaje máquina que se utilizan actualmente, con el objetivo de resolver los problemas asociados a los temas tratados con anterioridad. Diferentes contribuciones han sido propuestas: - Uno de los pilares esenciales de este trabajo está centrado en la estimación de los parámetros más importantes en la explotación de energías renovables. Con este propósito, los algoritmos Vectores Soporte para Regresión (VSR), Redes Neuronales (RN) (Perceptrones Multicapa (MLP) y Máquinas de Aprendizaje Extremo (MAE)) y Procesos Gaussianos son utilizados en diferentes problemas prácticos. El rendimiento de estos algoritmos es analizado en cada uno de los experimentos realizados, tanto la precisión de los mismos como la especificación de las características internas. - Otro de los aspectos tratados está relacionado con problemas de selección de características. Concretamente, con el uso de algoritmos evolutivos como Algoritmos de Agrupación Genética (AAG) o los algoritmos de Optimización de Arrecife de Coral (OAC) hibridizados con otros métodos de aprendizaje máquina como clasificadores y regresores. En este sentido, el AAG o OAC analizan diferentes conjuntos de características para obtener aquel que resuelva el problema con la mayor precisión, y el regresor empleado proporciona la predicción en función de las características obtenidas por el Algoritmo Genético (AG), reduciendo el coste computacional con gran fiabilidad en los resultados. La metodología mencionada es aplicada a múltiples problemas: predicción de Hs, relevante en aplicaciones energéticas y actividades marinas, estimación de eventos puntuales como son las rampas de viento (ERV), variaciones indeseables en la potencia eléctrica producidas por un parque eólico, predicción de la radiación solar global en áreas de España y Australia, realmente importante en términos de energía solar, y la estimación de eventos de baja visibilidad en aeropuertos. Los casos prácticos citados son desarrollados con el consecuente análisis previo de la base de datos empleada, normalmente, en términos de variables meteorológicas

Towards Automated Machine Learning on Imperfect Data for Situational Awareness in Power System

The increasing penetration of renewable energy sources (such as solar and wind) and incoming widespread electric vehicles charging introduce new challenges in the power system. Due to the variability and uncertainty of these sources, reliable and cost-effective operations of the power system rely on high level of situational awareness. Thanks to the wide deployment of sensors (e.g., phasor measurement units (PMUs) and smart meters) and the emerging smart Internet of Things (IoT) sensing devices in the electric grid, large amounts of data are being collected, which provide golden opportunities to achieve high level of situational awareness for reliable and cost-effective grid operations.To better utilize the data, this dissertation aims to develop Machine Learning (ML) methods and provide fundamental understanding and systematic exploitation of ML for situational awareness using large amounts of imperfect data collected in power systems, in order to improve the reliability and resilience of power systems.However, building excellent ML models needs clean, accurate and sufficient training data. The data collected from the real-world power system is of low quality. For example, the data collected from wind farms contains a mixture of ramp and non-ramp as well as the mingle of heterogeneous dynamics data; the data in the transmission grid contains noisy, missing, insufficient and inaccurate timestamp data. Employing ML without considering these distinct features in real-world applications cannot build good ML models. This dissertation aims to address these challenges in two applications, wind generation forecast and power system event classification, by developing ML models in an automated way with less efforts from domain experts, as the cost of processing such large amounts of imperfect data by experts can be prohibitive in practice.First, we take heterogeneous dynamics into consideration, especially for ramp events. A Drifting Streaming Peaks-over-Threshold (DSPOT) enhanced self-evolving neural networks-based short-term wind farm generation forecast is proposed by utilizing dynamic ramp thresholds to separate the ramp and non-ramp events, based on which different neural networks are trained to learn different dynamics of wind farm generation. As the efficacy of the neural networks relies on the quality of training datasets (i.e., the classification accuracy of ramp and non-ramp events), a Bayesian optimization based approach is developed to optimize the parameters of DSPOT to enhance the quality of the training datasets and the corresponding performance of the neural networks. Experimental results show that compared with other forecast approaches, the proposed forecast approach can substantially improve the forecast accuracy, especially for ramp events. Next, we address the challenges of event classification due to the low-quality PMU measurements and event logs. A novel machine learning framework is proposed for robust event classification, which consists of three main steps: data preprocessing, fine-grained event data extraction, and feature engineering. Specifically, the data preprocessing step addresses the data quality issues of PMU measurements (e.g., bad data and missing data); in the fine-grained event data extraction step, a model-free event detection method is developed to accurately localize the events from the inaccurate event timestamps in the event logs; and the feature engineering step constructs the event features based on the patterns of different event types, in order to improve the performance and the interpretability of the event classifiers. Moreover, with the small number of good features, we need much less training data to train a good event classifier, which can address the challenge of insufficient and imbalanced training data, and the training time is negligible compared to neural network based approaches. Based on the proposed framework, we developed a workflow for event classification using the real-world PMU data streaming into the system in real time. Using the proposed framework, robust event classifiers can be efficiently trained based on many off-the-shelf lightweight machine learning models. Numerical experiments using the real-world dataset from the Western Interconnection of the U.S power transmission grid show that the event classifiers trained under the proposed framework can achieve high classification accuracy while being robust against low-quality data. Subsequently, we address the challenge of insufficient training labels. The real-world PMU data is often incomplete and noisy, which can significantly reduce the efficacy of existing machine learning techniques that require high-quality labeled training data. To obtain high-quality event logs for large amounts of PMU measurements, it requires significant efforts from domain experts to maintain the event logs and even hand-label the events, which can be prohibitively costly or impractical in practice. So we develop a weakly supervised machine learning approach that can learn a good event classifier using a few labeled PMU data. The key idea is to learn the labels from unlabeled data using a probabilistic generative model, in order to improve the training of the event classifiers. Experimental results show that even with 95\% of unlabeled data, the average accuracy of the proposed method can still achieve 78.4\%. This provides a promising way for domain experts to maintain the event logs in a less expensive and automated manner. Finally, we conclude the dissertation and discuss future directions

Artificial Intelligence Application in Machine Condition Monitoring and Fault Diagnosis

The subject of machine condition monitoring and fault diagnosis as a part of system maintenance has gained a lot of interest due to the potential benefits to be learned from reduced maintenance budgets, enhanced productivity and improved machine availability. Artificial intelligence (AI) is a successful method of machine condition monitoring and fault diagnosis since these techniques are used as tools for routine maintenance. This chapter attempts to summarize and review the recent research and developments in the field of signal analysis through artificial intelligence in machine condition monitoring and fault diagnosis. Intelligent systems such as artificial neural network (ANN), fuzzy logic system (FLS), genetic algorithms (GA) and support vector machine (SVM) have previously developed many different methods. However, the use of acoustic emission (AE) signal analysis and AI techniques for machine condition monitoring and fault diagnosis is still rare. In the future, the applications of AI in machine condition monitoring and fault diagnosis still need more encouragement and attention due to the gap in the literature

Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications

This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators

Enhancing statistical wind speed forecasting models : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, Manawatū Campus, New Zealand

In recent years, wind speed forecasting models have seen significant development and growth. In particular, hybrid models have been emerging since the last decade. Hybrid models combine two or more techniques from several categories, with each model utilizing its distinct strengths. Mainly, data-driven models that include statistical and Artificial Intelligence/Machine Learning (AI/ML) models are deployed in hybrid models for shorter forecasting time horizons (< 6hrs). Literature studies show that machine learning models have gained enormous potential owing to their accuracy and robustness. On the other hand, only a handful of studies are available on the performance enhancement of statistical models, despite the fact that hybrid models are incomplete without statistical models. To address the knowledge gap, this thesis identified the shortcomings of traditional statistical models while enhancing prediction accuracy. Three statistical models are considered for analyses: Grey Model [GM(1,1)], Markov Chain, and Holt’s Double Exponential Smoothing models. Initially, the problems that limit the forecasting models' applicability are highlighted. Such issues include negative wind speed predictions, failure of predetermined accuracy levels, non-optimal estimates, and additional computational cost with limited performance. To address these concerns, improved forecasting models are proposed considering wind speed data of Palmerston North, New Zealand. Several methodologies have been developed to improve the model performance and fulfill the necessary and sufficient conditions. These approaches include adjusting dynamic moving window, self-adaptive state categorization algorithm, a similar approach to the leave-one-out method, and mixed initialization method. Keeping in view the application of the hybrid methods, novel MODWT-ARIMA-Markov and AGO-HDES models are further proposed as secondary objectives. Also, a comprehensive analysis is presented by comparing sixteen models from three categories, each for four case studies, three rolling windows, and three forecasting horizons. Overall, the improved models showed higher accuracy than their counter traditional models. Finally, the future directions are highlighted that need subsequent research to improve forecasting performance further