Principal Component Analysis-Based Shading Defect Identification and Categorization in Standalone PV Systems Using I-V Curves

Photovoltaic (PV) system health monitoring and fault diagnosis are essential for optimizing power generation, enhancing reliability, and prolonging the lifespan of PV power plants. Shading, especially in PV systems, leads to unique voltage-current (I-V) characteristics, serving as indicators of system health. This paper presents a cost-effective and highly accurate method for detecting, diagnosing, and classifying shading faults based on real I-V data obtained through electrical measurements under both healthy and shaded conditions. The method leverages Principal Component Analysis (PCA) to separate classes, and a confusion matrix assesses classification accuracy. The results demonstrate a success rate exceeding 98% in various configurations, using experimental data from a 250 W PV module. Importantly, this method relies solely on existing electrical measurements, eliminating the need for additional sensors, making it both efficient and cost-effective

Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

From MDPI via Jisc Publications RouterHistory: accepted 2021-09-30, pub-electronic 2021-10-03Publication status: PublishedTo ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learning to knowledge-driven approaches, including generative models such as adversarial networks and transfer learning. Finally, this review provides insights into different works to highlight various operating conditions and different numbers and types of failures, and provides links to some publicly available datasets in the field. The clear organization of the abundant information on this subject may result in rigorous guidelines for the trends adopted in the future

Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation

An economic development model analyzes the adoption of alternative strategy capable of leveraging the economy, based essentially on RES. The combination of wind turbine, PV installation with new technology battery energy storage, DSM network and RES forecasting algorithms maximizes RES integration in isolated islands. An innovative model of power system (PS) imbalances is presented, which aims to capture various features of the stochastic behavior of imbalances and to reduce in average reserve requirements and PS risk. Deep learning techniques for medium-term wind speed and solar irradiance forecasting are presented, using for first time a specific cloud index. Scalability-replicability of the FLEXITRANSTORE technology innovations integrates hardware-software solutions in all areas of the transmission system and the wholesale markets, promoting increased RES. A deep learning and GIS approach are combined for the optimal positioning of wave energy converters. An innovative methodology to hybridize battery-based energy storage using supercapacitors for smoother power profile, a new control scheme and battery degradation mechanism and their economic viability are presented. An innovative module-level photovoltaic (PV) architecture in parallel configuration is introduced maximizing power extraction under partial shading. A new method for detecting demagnetization faults in axial flux permanent magnet synchronous wind generators is presented. The stochastic operating temperature (OT) optimization integrated with Markov Chain simulation ascertains a more accurate OT for guiding the coal gasification practice

A review of automated solar photovoltaic defect detection systems : approaches, challenges, and future orientations

The development of Photovoltaic (PV) technology has paved the path to the exponential growth of solar cell deployment worldwide. Nevertheless, the energy efficiency of solar cells is often limited by resulting defects that can reduce their performance and lifespan. Therefore, it is crucial to identify a set of defect detection approaches for predictive maintenance and condition monitoring of PV modules. This paper presents a comprehensive review of different data analysis methods for defect detection of PV systems with a high categorisation granularity in terms of types and approaches for each technique. Such approaches, introduced in the literature, were categorised into Imaging-Based Techniques (IBTs) and Electrical Testing Techniques (ETTs). Although several review papers have investigated recent solar cell defect detection techniques, they do not provide a comprehensive investigation including IBTs and ETTs with a greater granularity of the different types of each for PV defect detection systems. Types of IBTs were categorised into Infrared Thermography (IRT), Electroluminescence (EL) imaging, and Light Beam Induced Current (LBIC). On the other hand, ETTs were categorised into Current-Voltage (I-V) characteristics analysis, Earth Capacitance Measurements (ECM), Time Domain Reflectometry (TDR), Power Losses Analysis (PLA), and Voltage and Current Measurements (VCM). Approaches based on digital/signal processing and Machine Learning (ML) models for each method are included where relevant. Moreover, the paper critically analyses the advantages and disadvantages of each of the adopted techniques, which can be referred to by future studies to identify the most suitable method considering the use-case’s requirements and setting. The adoption of each of the reviewed techniques depends on several factors, including the deployment scale, the targeted defects for detection, and the required location of defect analysis in the PV system, which are expanded further in the presented analysis. From a high-level perspective, while IBTs provide a high-resolution visual representation of the module surface, allowing for the detection and diagnosis of small structural defects that may be missed by other techniques, ETTs can detect electrical faults beyond the PV module’s surface. On the IBT level, the most notable adopted techniques in the literature are IRT- and EL-based. While IRT techniques are more practical for large-scale applications than EL imaging, the latter is considered a non-intrusive technique that is highly efficient in localising defects of solar cells. The paper also discusses challenges observed in the state-of-the-art related to data availability, real-time monitoring, accurate measurements, computational efficiency, and dataset distribution, and reviews data pre-processing and augmentation approaches that can address some of these challenges. Furthermore, potential future orientations are identified, addressing the limitations of PV defect detection systems

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section

Design of a hysteresis predictive control strategy with engineering application cases

Aplicat embargament des de la data de defensa fins al 31 de juliol de 2022This doctoral thesis exposes the development of a redesigned Predictive Control strategy that uses hysteresis to improve the performance of the controlled systems in different fields of application. The approach may use one of the three hysteresis models presented in this thesis. Moreover, the hysteresis may be used as a modulation stage or as a reference trajectory generator. The first step in the methodology of this research will be to validate the hysteresis dynamic model that will be used within the control scheme. Due to the three exposed hysteresis models have the same constitution , it is assumed that the test of one is enough to guarantee the validation of the other two hysteresis systems. This validation consists on implementing the hysteresis model in an experimental platform to confirm that the model is indeed feasible. Later, it will be seen that this application is within the scope of renewable energies. Once the hysteresis model is validated, the proposed strategy is developed. This is an Adaptive-Predictive control scheme with a modulation stage for the control signal. This stage employs hysteresis to improve the functioning of the adaptive phase and in general the entire closed-loop performance. lt will be shown how the use of this modulation scenario salves the parametric drift problem commonly present in some adaptive based controlled systems. Additionally, a fault detection system within the Adaptive-Predictive control scheme is also proposed and validated through a numerical simulation. Furthermore, it will be seen how the hysteresis also can be used as a model to generate the reference trajectory needed to accomplish the control objective. Finally, the proposed strategy is implemented in a varied set of control systems to validate it. These control systems are: a nonlinear Van der Poi oscillator, a nonlinear base-isolated system, a DC-DC buck converter, and a single-phase inverter.Esta tesis doctoral expone el desarrollo de una estrategia de Control Predictivo rediseñada que utiliza histéresis para mejorar el rendimiento de los sistemas controlados en diferentes campos de aplicación. Este esquema de control puede utilizar uno de los tres sistemas de histéresis presentados en esta tesis. Además, la histéresis se puede utilizar como etapa de modulación o como generador de trayectorias de referencia. El primer paso en la metodología de esta investigación será validar el modelo dinámico de histéresis que se utilizará dentro del esquema de control. Debido a que los tres modelos de histéresis expuestos tienen la misma constitución, se asume que la prueba de uno es suficiente para garantizar la validación de los otros dos modelos de histéresis. Esta validación consiste en implementar el modelo de histéresis en una plataforma experimental para confirmar que este es realmente factible. Posteriormente, se verá que esta aplicación está dentro del ámbito de las energias renovables. Una vez validado el modelo de histéresis, se desarrolla la estrategia propuesta. Es decir, un esquema de control Adaptativo-Predictivo con una etapa de modulación para la señal de control. Esta etapa emplea histéresis para mejorar el funcionamiento de la fase adaptativa y, en general, de todo el rendimiento del sistema en lazo cerrado. Se mostrará cómo el uso de este etapa de modulación resuelve el problema de la deriva paramétrica comúnmente presente en algunos sistemas basados en control adaptativo. Adicionalmente, también se propone y valida un sistema de detección de fallos dentro del esquema de control Adaptativo-Predictivo mediante una simulación numérica. Además, se verá cómo la histéresis también se puede utilitzar como modelo para generar la trayectoria de referencia necesaria para lograr el objetivo de control. Finalmente, la estrategia propuesta se implementa en un conjunto variado de sistemas de control para validarla. Estos sistemes de control son: un oscilador Van der Poi no lineal, un sistema no lineal de base aisladora, un convertidor Buck DC-DC y un inversor monofásico.Postprint (published version

Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids

This reprint presents advances in operation and maintenance in solar plants, wind farms and microgrids. This compendium of scientific articles will help clarify the current advances in this subject, so it is expected that it will please the reader

Solar cell degradation : the role of moisture ingress

Moisture ingress is one of the key fault mechanisms responsible for photovoltaic (PV) devices degradation. Moisture and moisture induced degradation (MID) products can attack the solar cell and the PV module components which can lead to solar cell degradation (e.g., microcracks), corrosion, optical degradation, potential induced degradation (PID), etc. These MID mechanisms have dire implications for the performance reliability of PV modules. Understanding the influence of moisture ingress on solar PV device’s degradation will boost the interest in investing in solar PV power installations globally, especially in the Nordics. In this thesis, the effect of moisture ingress on 20-years old field-aged multicrystalline silicon (mc-Si) PV modules is investigated. The defective areas in the PV modules were identified using visual inspection, electroluminescence (EL), ultraviolet fluorescence (UV-F), and infrared thermal (IR-T) techniques. Scanning electron microscopy and energy dispersive Xray spectroscopy (SEM-EDS) analyses were used to elucidate the role of moisture on the observed degradation mechanisms. In addition, temperature coefficient profiling is used as a diagnostic tool to characterize different moisture induced defects. The ethylene vinyl acetate (EVA) front encapsulation was found to undergo optical degradation and the extracted cells show dark discolored Tedlar®/Polyester/Tedlar® (TPT) backsheets. Corrosion at the solder joint was dominant and is attributed to the dissolution of lead and tin (main components of solder) and the Ag grids in moisture and acetic acid due to galvanic corrosion. Degradation of the EVA encapsulation produces acetic acid, carbon dioxide, phosphorus, sulfur, fluorine, and chlorine. It was observed that under the influence of moisture ingress, leached metal ions e.g., Na, Ag, Pb, Sn, Cu, Zn, and Al migrate to the surface of the solar cells. This led to the formation of oxides, hydroxides, sulfides, phosphates, acetates, and carbonates of silver, lead, tin, copper, zinc, and aluminum. Also, other competing reactions led to the formation of stannates of copper, silver, sodium, and zinc. Similarly, migration of silver and aluminum to the surfaces of the TiO2 antireflection coating (ARC) nanoparticles (NPs) lead to the formation of titania-alumina and silver-titania complexes. Formation of these titania-metal complexes affects the opto-electrical efficiency of the TiO2 ARC in the PV module. Additionally, in the presence of moisture and acetic acid, Pb is preferentially corroded (to form lead acetate complexes) instead of the expected sacrificial Sn in the solder. In the EL and UV-F images, these degradation species appear as dark spots, and as hot spots in IR-T images. More importantly, these MID defects and fault modes lead to parasitic resistance and mismatch losses, and hence, degradation in the current-voltage (I-V) characteristics, temperature coefficients, and maximum power (Pmax) of the field-aged PV modules. The observed temperature sensitivities are characteristic of different moisture-induced defects. Taken together, this work has expounded on the understanding and detection of MID phenomenon in field-deployed solar PV modules.publishedVersio

Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems

Fault detection, control, and forecasting have a vital role in renewable energy systems (Photovoltaics (PV) and wind turbines (WTs)) to improve their productivity, ef?ciency, and safety, and to avoid expensive maintenance. For instance, the main crucial and challenging issue in solar and wind energy production is the volatility of intermittent power generation due mainly to weather conditions. This fact usually limits the integration of PV systems and WTs into the power grid. Hence, accurately forecasting power generation in PV and WTs is of great importance for daily/hourly efficient management of power grid production, delivery, and storage, as well as for decision-making on the energy market. Also, accurate and prompt fault detection and diagnosis strategies are required to improve efficiencies of renewable energy systems, avoid the high cost of maintenance, and reduce risks of fire hazards, which could affect both personnel and installed equipment. This book intends to provide the reader with advanced statistical modeling, forecasting, and fault detection techniques in renewable energy systems