Doctor of Philosophy

dissertationThree major catastrophic failures in photovoltaic (PV) arrays are ground-faults, line-to-line faults, and arc faults. Although the number of such failures is few, recent fire events on April 5, 2009, in Bakersfield, California, and April 16, 2011, in Mount Holly, North Carolina suggest the need for improvements in present fault detection and mitigation techniques, as well as amendments to existing codes and standards to avoid such accidents. A fault prediction and detection technique for PV arrays based on spread spectrum time domain reflectometry (SSTDR) has been proposed and was successfully implemented. Unlike other conventional techniques, SSTDR does not depend on the amplitude of the fault-current. Therefore, SSTDR can be used in the absence of solar irradiation as well. However, wide variation in impedance throughout different materials and interconnections makes fault locating more challenging than prediction/detection of faults. Another application of SSTDR in PV systems is the measurement of characteristic impedance of power components for condition monitoring purposes. Any characteristic variations in one component will simultaneously alter the operating conditions of other components in a closed-loop system, resulting in a shift in overall reliability profile. This interdependence makes the reliability of a converter a complex function of time and operating conditions. Details of this failure mode, mechanism, and effect analysis (FMMEA) have been developed. By knowing the present state of health and the remaining useful life (RUL) of a power converter, it is possible to reduce the maintenance cost for expensive high-power converters by facilitating a reliability centered maintenance (RCM) scheme. This research is a step forward toward power converter reliability analysis since the cumulative effect of multiple degraded components has been considered here for the first time in order to estimate reliability of a power converter

PV faults: Overview, modeling, prevention and detection techniques

pre-printRecent PV faults and subsequent fire-hazards on April 5, 2009, in Bakersfield, California, and April 16, 2011, in Mount Holly, North Carolina provide evidence of a lack of knowledge among PV system manufacturers and installers about different PV faults. The conducted survey within the scope of this paper describes various faults in a PV plant, and explains the limitations of existing detection and suppression techniques. Different fault detection techniques proposed in literatures have been discussed and it was concluded that there is no universal fault detection technique that can detect and classify all faults in a PV system. Moreover, this digest proposes a transmission line model for PV panels that can be useful for interpreting faults in PV using different refelectomery methods

Solar Photovoltaic and Thermal Energy Systems: Current Technology and Future Trends

Solar systems have become very competitive solutions for residential, commercial, and industrial applications for both standalone and grid connected operations. This paper presents an overview of the current status and future perspectives of solar energy (mainly photovoltaic) technology and the required conversion systems. The focus in the paper is put on the current technology, installations challenges, and future expectations. Various aspects related to the global solar market, the photovoltaic (PV) modules cost and technology, and the power electronics converter systems are addressed. Research trends and recommendations for each of the PV system sectors are also discussed.Junta de Andalucía P11-TIC-7070Ministerio de Ciencia e Innovación TEC2016-78430-

A comprehensive study of diagnosis faults techniques occurring in photovoltaic generators

Recently, many focuses have been done in the field of renewable energies, especially in solar photovoltaic energy. Photovoltaic generator, considered as the heart of any photovoltaic installation, exhibits sometimes malfunctions which involve degradations on the overall photovoltaic plant. Therefore, diagnosis techniques are required to ensure failures detection. They avoid dangerous risks, prevent damages, allow protection, and extend their healthy life. For these purposes, many recent studies have given focuses on this field. This paper summarizes a large number of such interesting works. It presents a survey of photovoltaic generator degradations kinds, several types of faults, and their major diagnosis techniques. Comparative studies and some critical analyses are given. Other trending diagnosis solutions are also discussed. A proposed neural networks-based technique is developed to clarify the main process of diagnosis techniques, using artificial intelligence. This method shows good results for modelling and diagnosing the healthy and faulty (shaded) photovoltaic array

Fault diagnosis for PV arrays considering dust impact based on transformed graphical feature of characteristic curves and convolutional neural network with CBAM modules

Various faults can occur during the operation of PV arrays, and both the dust-affected operating conditions and various diode configurations make the faults more complicated. However, current methods for fault diagnosis based on I-V characteristic curves only utilize partial feature information and often rely on calibrating the field characteristic curves to standard test conditions (STC). It is difficult to apply it in practice and to accurately identify multiple complex faults with similarities in different blocking diodes configurations of PV arrays under the influence of dust. Therefore, a novel fault diagnosis method for PV arrays considering dust impact is proposed. In the preprocessing stage, the Isc-Voc normalized Gramian angular difference field (GADF) method is presented, which normalizes and transforms the resampled PV array characteristic curves from the field including I-V and P-V to obtain the transformed graphical feature matrices. Then, in the fault diagnosis stage, the model of convolutional neural network (CNN) with convolutional block attention modules (CBAM) is designed to extract fault differentiation information from the transformed graphical matrices containing full feature information and to classify faults. And different graphical feature transformation methods are compared through simulation cases, and different CNN-based classification methods are also analyzed. The results indicate that the developed method for PV arrays with different blocking diodes configurations under various operating conditions has high fault diagnosis accuracy and reliability

PV ground-fault detection using spread spectrum time domain reflectometry (SSTDR)

pre-printA PV ground-fault detection technique using spread spectrum time domain reflectometry (SSTDR) method has been introduced in this paper. SSTDR is a reflectometry method that has been commercially used for detecting aircraft wire faults. Unlike other fault detection schemes for a PV system, ground fault detection using SSTDR does not depend on the amplitude of fault-current and highly immune to noise signals. Therefore, SSTDR can be used in the absence of the solar irradiation as well. The proposed PV ground fault detection technique has been tested in a real-world PV system and it has been observed that PV ground fault can be detected confidently by comparing autocorrelation values generated using SSTDR. The difference in the autocorrelation peaks before and after a ground-fault in the PV system are significantly higher than the threshold set for ground-fault detection

A Density Peak-Based Clustering Approach for Fault Diagnosis of Photovoltaic Arrays

Fault diagnosis of photovoltaic (PV) arrays plays a significant role in safe and reliable operation of PV systems. In this paper, the distribution of the PV systems’ daily operating data under different operating conditions is analyzed. The results show that the data distribution features significant nonspherical clustering, the cluster center has a relatively large distance from any points with a higher local density, and the cluster number cannot be predetermined. Based on these features, a density peak-based clustering approach is then proposed to automatically cluster the PV data. And then, a set of labeled data with various conditions are employed to compute the minimum distance vector between each cluster and the reference data. According to the distance vector, the clusters can be identified and categorized into various conditions and/or faults. Simulation results demonstrate the feasibility of the proposed method in the diagnosis of certain faults occurring in a PV array. Moreover, a 1.8 kW grid-connected PV system with 6×3 PV array is established and experimentally tested to investigate the performance of the developed method

Towards energy transition: conjoint assessment of large-scale PV system performance and interconnection impacts in isolated microgrid

Environmental, energy security and electricity demand concerns stimulate solar-grid integration. However, intermittent, non-dispatchable PV characteristics may challenge passive grid operation. This paper contains the comprehensive planning and assessment of a 2 MWp CdTe-based PV system deployment proposed for hybrid operation in an isolated 11 kV 10-bus microgrid in Brunei. The presented approach combinedly assesses PV system performance and scenario-based interconnection impacts based on a detailed PV system model considering deployment conditions. Various interconnection points with multiple sets of feeder-specific measured load profiles are examined. Results show the PV system designed for maximum annual generation achieves performance ratio of 90.6%. While time-series power flow assessment reveals grid operation enhancement, there are concerns at times of generation-demand mismatch requiring proper genset sequencing and reactive power management. Meanwhile, faster relay operating time and reverse fault current are demonstrated in existing protection scheme. Dynamic grid stabilities are preserved in various generation intermittency and loss events, including the most challenging condition of further inertia and spinning reserve reduction reaching a frequency of 96.02%. Finally, optimal interconnection point fulfilling multi-objectives on losses, voltage profile and line reserve capacity is identified. The findings indicate a good prospect of the synergy for advancing energy transition. The analysis could facilitate RE planning and policymaking