Chaos Synchronization Based Novel Real-Time Intelligent Fault Diagnosis for Photovoltaic Systems

The traditional solar photovoltaic fault diagnosis system needs two to three sets of sensing elements to capture fault signals as fault features and many fault diagnosis methods cannot be applied with real time. The fault diagnosis method proposed in this study needs only one set of sensing elements to intercept the fault features of the system, which can be real-time-diagnosed by creating the fault data of only one set of sensors. The aforesaid two points reduce the cost and fault diagnosis time. It can improve the construction of the huge database. This study used Matlab to simulate the faults in the solar photovoltaic system. The maximum power point tracker (MPPT) is used to keep a stable power supply to the system when the system has faults. The characteristic signal of system fault voltage is captured and recorded, and the dynamic error of the fault voltage signal is extracted by chaos synchronization. Then, the extension engineering is used to implement the fault diagnosis. Finally, the overall fault diagnosis system only needs to capture the voltage signal of the solar photovoltaic system, and the fault type can be diagnosed instantly

Modeling and fault detection in DC side of Photovoltaic Arrays

Fault detection in PV systems is a key factor in maintaining the integrity of any PV system. Faults in photovoltaic systems can cause irrevocable damages to the stability of the PV system and substantially decrease the power output generated from the array of PV modules. Among\u27st the various AC and DC faults in a PV system, the clearance of the AC side faults is achieved by conventional AC protection schemes,the DC side, however , there still exists certain faults which are difficult to detect and clear. This paper deals with the modeling, detection and classification of these types of DC faults. It is essential to be able to simulate the PV characteristics and faults through software. In this thesis a comprehensive literature survey of fault detection methods for DC side of a PV system is presented. The disparities in the techniques employed for fault detection are studied . A new method for modeling the PV systems information only from manufacturers datasheet using both the Normal Operating Cell temperature conditions (NOCT) and Standard Operating Test Conditions (STC) conditions is then proposed.The input parameters for modeling the system are Isc,Voc,Impp,Vmpp and the temperature coefficients of Isc and Voc for both STC and NOCT conditions. The model is able to analyze the variations of PV parameters such as ideality factor, Series resistance, thermal voltage and Band gap energy of the PV module with temperature. Finally a novel intelligent method based on Probabilistic Neural Network for fault detection and classification for PV farm with string inverter technology is proposed

Fuzzy logic system for intermixed biogas and photovoltaics measurement and control

Abstract: This study develops a new integrated measurement and control system for intermixed biogas and photovoltaic systems to achieve safe and optimal energy usage. Literature and field studies show that existing control methods on small- to medium-scale systems fall short of comprehensive system optimization and fault diagnosis, hence the need to revisit these control methods.The control strategy developed in this study is intelligent as it is wholly based on fuzzy logic algorithms. Fuzzy logic controllers due to their superior nonlinear problem solving capabilities to classical controllers considerably simplify controller design.The mathematical models that define classical controllers are difficult or impossible to realize in biogas and photovoltaic generation process. A microcontroller centered fuzzy logic measurement and control embedded system is designed and developed on the existing hybrid biogas and photovoltaic installations. The designed system is able to accurately predict digester stability, quantify biogas output, and carry out biogas fault detection and control. Optimized battery charging and photovoltaic fault detection and control are also successfully implemented. The system is able to optimize the operation and performance of biogas and photovoltaic energy generation

A comprehensive review and performance evaluation in solar (PV) systems fault classification and fault detection techniques

The renewable energy industry is growing faster than ever before and in particular solar systems have significantly expanded. Abnormal conditions lead to a reduction in the maximum available power from solar (photovoltaic) systems. Thus, it is necessary to identification, detection, and monitoring of various faults in the PV system that they are the key factors to increase the efficiency, reliability, and lifetime of these systems. Up to now, faults on PV components and systems have been identified; some of them have physical damage on PV systems and some of them are electrical faults that occur on the DC side or AC side of the PV system. Here, the faults will be divided into groups based on their location of occurrence. This paper provides a comprehensive review of almost all PV system faults and fault detection techniques of PV system proposed in recent literature

Analysis of new indicators for fault detection in grid connected PV systems for BIPV applications

Tese de mestrado integrado em Engenharia da Energia e do Ambiente, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2014Neste trabalho apresentamos um novo procedimento para deteção automática de falhas em sistemas fotovoltaicos (PV) ligados à rede. A maioria dos métodos de diagnóstico para deteção de falhas em sistemas PV já conhecidos são consumidores de tempo e precisam de equipamento caro. De forma a evitar completamente o uso de modelação e simulação do sistema PV no processo de deteção de falhas, nós definimos dois novos indicadores de corrente e voltagem, NRc e NRv, respetivamente, no lado DC do inversor do sistema PV. Este método é baseado na avaliação desses novos indicadores. Foram definidos limites para esses indicadores de forma a ter em conta a configuração do sistema PV: número de módulos PV incluídos em série e em paralelo que formam o PV array. A definição desses limites para os indicadores de voltagem e corrente têm que ser adaptados ao sistema PV específico a supervisionar e um período de treino é recomendado de forma a garantir um diagnóstico correto e reduzir assim a probabilidade de falsas falhas detetadas. Um estudo de simulações foi feito em MatLab e usando informação monitorizada pelo inversor, piranómetros, termopares e células de referência instaladas no sistema PV conectado à rede localizado no Centro de Desenvolvimento das Energias Renováveis (CDER), Algéria. O método proposto é simples mas eficaz na deteção das falhas principais de um sistema PV e foi experimentalmente validado e demonstrou a sua eficácia na deteção de falhas presentes em aplicações conectadas à rede.In this work we present a new procedure for automatic fault detection in grid connected photovoltaic (PV) systems. Most diagnostic methods for fault detection of PV systems already known are time consuming and need expensive hardware. In order to completely avoid the use of modeling and simulation of the PV system in the fault detection procedure we defined two new current and voltage indicators, NRc and NRv respectively, in the DC side of the inverter of the PV system. This method is based on the evaluation of these indicators. Thresholds for these indicators are defined taking into account the PV system configuration: Number of PV modules included in series and parallel interconnection to form the PV array. The definition of these thresholds for the voltage and current indicators must be adapted to the specific PV system to supervise and a training period is recommended to ensure a correct diagnosis and reduce the probability of false faults detected. A simulation study was carried using MatLab and the data used was monitored by the inverter, pyranometers, thermocouples and reference cells installed in a grid connected PV system located in the Centre de Développement des Energies Renouvelables (CDER), Algeria. The proposed method is simple but effective detecting the main faults of a PV system and was experimentally validated and has demonstrated its effectiveness in the detection of main faults present in grid connected applications

Decision-Making for Utility Scale Photovoltaic Systems: Probabilistic Risk Assessment Models for Corrosion of Structural Elements and a Material Selection Approach for Polymeric Components

abstract: The solar energy sector has been growing rapidly over the past decade. Growth in renewable electricity generation using photovoltaic (PV) systems is accompanied by an increased awareness of the fault conditions developing during the operational lifetime of these systems. While the annual energy losses caused by faults in PV systems could reach up to 18.9% of their total capacity, emerging technologies and models are driving for greater efficiency to assure the reliability of a product under its actual application. The objectives of this dissertation consist of (1) reviewing the state of the art and practice of prognostics and health management for the Direct Current (DC) side of photovoltaic systems; (2) assessing the corrosion of the driven posts supporting PV structures in utility scale plants; and (3) assessing the probabilistic risk associated with the failure of polymeric materials that are used in tracker and fixed tilt systems. As photovoltaic systems age under relatively harsh and changing environmental conditions, several potential fault conditions can develop during the operational lifetime including corrosion of supporting structures and failures of polymeric materials. The ability to accurately predict the remaining useful life of photovoltaic systems is critical for plants ‘continuous operation. This research contributes to the body of knowledge of PV systems reliability by: (1) developing a meta-model of the expected service life of mounting structures; (2) creating decision frameworks and tools to support practitioners in mitigating risks; (3) and supporting material selection for fielded and future photovoltaic systems. The newly developed frameworks were validated by a global solar company.Dissertation/ThesisDoctoral Dissertation Civil and Environmental Engineering 201

Automatic supervision of Pv systems and degradation analysis of thin film PV modules

Monitoring and regular performance analysis of Grid-Connected Photovoltaic (GCPV) systems are of primal importance in order to ensure an optimal energy harvesting and reliable power production at competitive costs. Main faults in GCPV systems are caused by short-circuits or open-circuits in PV modules, inverter disconnections, PV module degradation and the presence of shadows on the PV array plane. Detecting these faults can minimize generated losses by reducing the time in which the PV system is working below its optimum point of power generation. In addition, the degradation of Tin Film PV (TFPV) modules under outdoor exposure is still not fully understood and is currently object of research. A better understanding on this topic would be important for selecting the best PV technology for the appropriate climatic condition and for improving the reliability and performance of PV systems. Simulations play a crucial role in both outdoor behaviour forecasting and automatic fault detection of GCPV systems. Two PV module/array models have been used in the present thesis in order to simulate the outputs of GCPV systems of different topologies and solar cell technologies, as well as in the fault detection procedure. Moreover, five different algorithms were used for estimating the unknown parameters of both PV models in order to see how these estimated parameters affect their accuracy in reproducing the outdoor behaviour of three GCPV systems. The obtained results show that the metaheuristic algorithms are more efficient than the Levenberg-Marquardt algorithm (LMA) especially in bad weather conditions and both PV models perform well when used in the automatic fault detection procedure. A new approach for automatic supervision and remote fault detection of GCPV systems by means of OPC technology-based monitoring is presented in this thesis. The fault detection procedure used for the diagnosis of GCPV systems is based on the analysis of the current and voltage indicators evaluated also from monitored data and expected values of current and voltage obtained from the model of the PV generator. Three GCPV systems having different sizes, topologies and cell technologies have been used for the experimental validation of the proposed fault detection method. The analysis of current and voltage indicators has demonstrated effectiveness in the detection of most probable faults occurred in the PV arrays in real time. Furthermore, obtained results show that the combination of OPC monitoring along with the proposed fault detection procedure is a robust tool which can be very useful in the field of remote supervision and diagnosis of GCPV systems. Finally, the study of degradation issues of TFPV modules corresponding to four technologies: a-Si:H, a-Si:H/µc-Si:H, CIS and CdTe, deployed under outdoor conditions for long term exposure is also addressed in the present thesis. The impact of the degradation on the output power of the PV modules is analysed, in order to determine their annual degradation rate and their stabilization period. The degradation rate is obtained through a procedure based on the evolution of the module effective peak power over time. The stabilization period is evaluated by means of two methods: the evolution of DC-output power of the PV module, and the power-irradiance technique. The obtained results show that the CIS PV module is the most stable compared to the other technologies, when deployed under Continental-Mediterranean Climate. The a-Si:H and a-Si:H/µc-Si:H PV modules also perform quite well, showing degradation rates and stabilization periods similar to the expectations. The CdTe module shows poor performances, with the highest degradation rate, and long stabilization period of 32 months. Lastly, the parameter extraction technique has been also applied to analyse the evolution of model parameters for a-Si:H and a-Si:H/µc-Si:H arrays working in outdoor conditions for long term exposure.Los fallos principales en los SFCR son causados por cortocircuitos o circuitos abiertos en módulos fotovoltaicos, desconexiones de inversores, degradación de módulos fotovoltaicos y presencia de sombras en el plano del generador fotovoltaico. La detección de estos fallos puede minimizar las pérdidas generadas al reducir el tiempo en que el sistema fotovoltaico está funcionando por debajo de su punto óptimo de generación de energía. Por otro lado, la degradación de los módulos fotovoltaicos de capa delgada (TFPV) en condiciones reales de trabajo sigue siendo actualmente objeto de investigación. Una mejor comprensión de este tema es importante para seleccionar la tecnología fotovoltaica más adecuada para cada condición climática específica y mejorar así tanto la fiabilidad como el rendimiento de los sistemas fotovoltaicos. Las simulaciones desempeñan un papel crucial tanto en el pronóstico del comportamiento real como en la detección automática de fallos en los SFCR. En la presente tesis se han utilizado dos modelos de módulos fotovoltaicos para simular las salidas de los sistemas de diferentes topologías y tecnologías de células solares, así como en el procedimiento de detección de fallos. Se han utilizado cinco algoritmos diferentes para estimar los parámetros de ambos modelos con el fin de ver cómo estos parámetros afectan a su precisión en la reproducción del comportamiento real de tres SFCR. Los resultados obtenidos muestran que los algoritmos meta-heurísticos son más eficientes que el algoritmo de Levenberg-Marquardt (LMA) especialmente en malas condiciones climáticas, aunque ambos modelos pueden ser utilizados para la supervisión y la detección automática de fallos. En esta tesis se presenta un nuevo enfoque para la supervisión automática y la detección remota de fallos en SFCR mediante la monitorización basada en la tecnología OPC. El procedimiento de detección de fallos utilizado para el diagnóstico de SFCR se basa en el análisis de los indicadores de corriente y tensión evaluados también a partir de datos monitorizados y valores esperados de corriente y tensión obtenidos a partir del modelo del generador fotovoltaico. Se han utilizado tres SFCR de diferentes tamaños, topologías y tecnologías fotovoltaicas para la validación experimental del método de detección de fallos propuesto. El análisis de los indicadores de corriente y tensión ha demostrado efectividad en la detección de los fallos más probables en generadores fotovoltaicos en tiempo real. Además, los resultados obtenidos muestran que la combinación de monitorización OPC junto con el procedimiento de detección de fallos propuesto es una herramienta robusta que puede ser muy útil en el campo de la supervisión remota y el diagnóstico de SFCR. Finalmente, en la presente tesis se aborda el estudio de los problemas de degradación de módulos fotovoltaicos de capa delgada correspondientes a cuatro tecnologías: a-Si:H, a-Si:H/µc-Si:H, CIS y CdTe, en condiciones de trabajo a la intemperie durante periodos prolongados de exposición. Se analiza el impacto de la degradación en la potencia de salida de los módulos fotovoltaicos para determinar su tasa de degradación anual y su período de estabilización. Los resultados obtenidos muestran que el módulo fotovoltaico CIS es el más estable comparado con las otras tecnologías, cuando trabajan en condiciones de clima continental mediterráneo. Los módulos fotovoltaicos a-Si:H y a-Si:H/µc-Si:H también presentan un buen comportamiento, mostrando tasas de degradación y períodos de estabilización similares a los esperados. El módulo de CdTe muestra las peores prestaciones, con una mayor tasa de degradación y un largo período de estabilización de 32 meses. Por último, se ha aplicado también la técnica de extracción de parámetros para analizar la evolución de los parámetros del modelo para generadores fotovoltaicos de módulos de a-Si: H y a-Si:H/µc-Si:H en condiciones reales de trabajo durante largos periodos de tiempo