Fault diagnosis in photovoltaic (PV) arrays is essential in enhancing power output as well as the useful life span of a PV system. Severe faults such as Partial Shading (PS) and high impedance faults, low location mismatch, and the presence of Maximum Power Point Tracking (MPPT) make fault detection challenging in harsh environmental conditions. In this regard, there have been several attempts made by various researchers to identify PV array faults. However, most of the previous work has focused on fault detection and classification in only a few faulty scenarios. This paper presents a novel approach that utilizes deep two-dimensional (2-D) Convolutional Neural Networks (CNN) to extract features from 2-D scalograms generated from PV system data in order to effectively detect and classify PV system faults. An in-depth quantitative evaluation of the proposed approach is presented and compared with previous classification methods for PV array faults-both classical machine learning based and deep learning based. Unlike contemporary work, five different faulty cases (including faults in PS-on which no work has been done before in the machine learning domain) have been considered in our study, along with the incorporation of MPPT. We generate a consistent dataset over which to compare ours and previous approaches, to make for the first (to the best of our knowledge) comprehensive and meaningful comparative evaluation of fault diagnosis. It is observed that the proposed method involving fine-tuned pre-trained CNN outperforms existing techniques, achieving a high fault detection accuracy of 73.53%. Our study also highlights the importance of representative and discriminative features to classify faults (as opposed to the use of raw data), especially in the noisy scenario, where our method achieves the best performance of 70.45%. We believe that our work will serve to guide future research in PV system fault diagnosis. INDEX TERMS Photovoltaic array, maximum power point tracking, fault classification, convolutional neural network, scalograms, transfer learning.

Faults detection and analysis in PV system are considered critical for ensuring safety and increasing output power of PV arrays. PV faults do not only reduce output power and efficiency but also lessen the working life time of a system. Most common and chronic PV faults are line to line, line to ground, shadowing fault, and arc fault while less common and acute faults are hotspot, degradation, bypass diode, and connection faults. Event of PV fault detection failures, such as most recent in Mount Holly, USA in 2011 evinced the improvement in current fault detection and mitigation techniques to shrink such failures. There are various limitations in the existing fault detection techniques, as identified in this paper, which may cause misdetection of the faults. This paper is focused on mathematical formulation of various PV faults and lead to the latter's critical analysis in terms of efficiency, accuracy, complexity, and reliability. The presented work also helps to identify nature and causes of occurrence of a PV fault. This research work serves as a special set of references and recommendations for researchers and PV manufacturing industry to improve fault detection prospects in solar PV systems.

Wireless Power Transfer (WPT) is an innovative technology employed for enhancing the energy sustainability of wireless devices with a limited life span. The idea of integrating WPT in wireless communication leads to the idea of Simultaneous Wireless Information and Power Transfer (SWIPT) that transfers information and power to wireless devices simultaneously, thereby resulting in a drastic increase in spectral efficiency of the network. SWIPT aided Cooperative Relaying (CoR) has emerged as a new trend for Fifth Generation (5G) and Beyond 5G (B5G) systems owing to the rapidly increasing challenges faced by these networks. Cooperative relaying combined with SWIPT can be helpful in overcoming the rising demands of next generation wireless networks by providing an enhanced date rate, low latency, shorter coverage, wide spread connectivity of massive number of devices along with energy-efficiency. This article provides a comprehensive review of SWIPT technology that enables the use of CoR networks for 5G and B5G mobile networks including the significance, technologies, and protocols which can be applied. This article also examines the deployment of cooperative SWIPT involving a single relay, multiple relays and optimal relay selection, multi antenna systems and optimal beamforming .SWIPT under the influence of Hardware Impairments (HI), imperfect Channel State Information (CSI), non-linear energy harvesting models, Intelligent Reconfigurable Surface (IRS), massive MIMO, massive access for the Internet of Things (IoT) has been discussed in detail. Meanwhile, this study discusses key challenges being faced in the implementation of SWIPT for future wireless networks that need to be addressed efficiently.

Fault analysis in photovoltaic (PV) arrays is considered important for improving the safety and efficiency of a PV system. Faults do not only reduce efficiency but are also detrimental to the life span of a system. Output can be greatly affected by PV technology, configuration, and other operating conditions. Thus, it is important to consider the impact of different PV configurations and materials for thorough analysis of faults. This paper presents a detailed investigation of faults including non-uniform shading, open circuit and short circuit in different PV interconnections including Series-Parallel (SP), Honey-Comb (HC) and Total-cross-Tied (TCT). A special case of multiple faults in PV array under non-uniform irradiance is also investigated to analyze their combined impact on considered different PV interconnections. In order to be more comprehensive, we have considered monocrystalline and thin-film PV to analyze faults and their impact on power grids. Simulations are conducted in MATLAB/Simulink, and the obtained results in terms of power(P)-voltage(V) curve are compared and discussed. It is found that utilization of thin-film PV technology with appropriated PV interconnections can minimize the impact of faults on a power grid with improved performance of the system.Energies 2020, 13, 156 2 of 23 ground faults which led towards a large fire accident [6]. Another similar accident happened due to undetected faults in a 1 MW PV system of Mount Halley, North Carolina in 2011 [7]. Thus, timely diagnosis of faults in PV systems is very important for the prevention of such large fire accidents [8]. The modeling of a PV system under electrical faults [9] has been studied in the literature [10]. A review of faults in PV systems is presented in [11]. Power generation is also dependent upon the type of PV material [12]. The impact of shading on different PV technologies [13] has been largely investigated in the past [14][15][16][17][18]. The performance of crystalline PV material can be affected greatly by environmental conditions [14]. P-V curve analysis for studying the impact of shading on polycrystalline and thin-film PV modules was performed in [15]. Thin-film PV performed better as compared to polycrystalline PV under severe shading in terms of power output. The experimental analysis of PV material in Anatolia also proved that thin-film technology has less impact on shading and high temperature as compared to crystalline PV material [16]. Only shading and temperature conditions are analyzed for thin-film PV technology [17]. Further research is needed to investigate the impact of short circuit and open circuit faults on thin-film PV technology.Different methods of maximum power point tracking (MPPT) [18] have been investigated but improvements in the algorithm cannot compensate for significant power losses that occurr through fault occurrence in PV arrays. A reconfiguration technique was adopted in [19] to increase power generation but this technique requires a complex switching matrix with many sensors and prope...