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.

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Indoor scenes tend to be abundant with planar homogeneous texture, manifesting as regularly repeating scene elements along a plane. In this work, we propose to exploit such structure to facilitate high-level scene understanding. By robustly fitting a texture projection model to optimal dominant frequency estimates in image patches, we arrive at a projective-invariant method to localize such semantically meaningful regions in multi-planar scenes. The recovered projective parameters also allow an affine-ambiguous rectification in real-world images marred with outliers, room clutter, and photometric severities. Qualitative and quantitative results show our method outperforms existing representative work for both rectification and detection. We then explore the potential of homogeneous texture for two indoor scene understanding tasks. In scenes where vanishing points cannot be reliably detected, or the Manhattan assumption is not satisfied, homogeneous texture detected by the proposed approach provides alternative cues to obtain an indoor scene geometric layout. Second, low-level feature descriptors extracted upon affine rectification of detected texture are found to be not only class-discriminative but also complementary to features without rectification, improving recognition performance on the MIT Indoor67 benchmark.

Potential implementation of smart grid technologies has been given wide attention for modernization of electrical power systems. Existing power grid infrastructure of Pakistan is ill-suited to accommodate increased renewable energy sources and poses interoperability issues for seamless transition towards decentralization and digitalization of the power grid. Modernization of power grid through realization of smart grid technologies is much needed to meet the ever-increasing energy demand of the country. The China Pakistan Economic Corridor (CPEC) is a conglomerate of multibillion dollar infrastructural projects with a major focus on energy sector which offers a great opportunity for the country to invest appropriately in the power grid transformation. This research study is focused on devising a technical and policy framework for conversion of the existing power grid into smart grid under the umbrella of CPEC. Potential key drivers are identified for the deployment of smart technologies to upgrade the power grid of Pakistan. The presented research proposes a stage-wise implementation plan to upgrade the power infrastructure of the country. In particular, this research study presents cost benefit analysis for a case study with an aim to reduce the interruption index values. Obtained values of the cost benefit analysis suggest that the investment incurred for smart grid realization can be paid back within a period of twenty months and offers a positive net present value which establishes the financial feasibility of the project. Importantly, policies and recommendations are suggested which would enable the concerned stakeholders to ensure grid modernization as per the proposed smart grid implementation plan.