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The high penetration level of solar photovoltaic (SPV) generation systems imposes a major challenge to the secure operation of power systems. SPV generation systems are connected to the power grid via power converters. During a fault on the grid side; overvoltage can occur at the direct current link (DCL) due to the power imbalance between the SPV and the grid sides. Subsequently; the SPV inverter is disconnected; which reduces the grid reliability. DC-link voltage control is an important task during low voltage ride-through (LVRT) for SPV generation systems. By properly controlling the power converters; we can enhance the LVRT capability of a grid-connected SPV system according to the grid code (GC) requirements. This study proposes a novel DCL voltage control scheme for a DC–DC converter to enhance the LVRT capability of the two-stage grid-connected SPV system. The control scheme includes a “control without maximum power point tracking (MPPT)” controller; which is activated when the DCL voltage exceeds its nominal value; otherwise, the MPPT control is activated. Compared to the existing LVRT schemes the proposed method is economical as it is achieved by connecting the proposed controller to the existing MPPT controller without additional hardware or changes in the software. In this approach, although the SPV system will not operate at the maximum power point and the inverter will not face any over current challenge it can still provide reactive power support in response to a grid fault. A comprehensive simulation was carried out to verify the effectiveness of the proposed control scheme for enhancing the LVRT capability and stability margin of an interconnected SPV generation system under symmetrical and asymmetrical grid faults.

Cloud and fog computing along with network function virtualization technology have significantly shifted the development of network architectures. They yield in reduced capital and operating expenditures, while achieving network flexibility and scalability to accommodate the massive growth in data traffic volumes from user terminals requesting various services and applications. Now cloud solutions here offer abundant computing and storage resources, at the detriment of high end-to-end delays, hence limiting quality of service for delay-sensitive applications. Meanwhile, fog solutions offer reduced delays, at the detriment of limited resources. Existing efforts focus on merging the two solutions and propose multi-tier hybrid fog-cloud architectures to leverage their both saliencies. However, these approaches can be inefficient when the applications are delay-sensitive and require high resources. Hence this work proposes a novel standalone heterogeneous fog architecture that is composed of high-capacity and low-capacity fog nodes, both located at the terminals proximity. Thereby, realizing a substrate network that offers reduced delays and high resources, without the need to relay to the cloud nodes. Moreover, the work here leverages and deploys a deep learning network to propose a service function chain provisioning scheme implemented on this architecture. The scheme predicts the popular network functions, and maps them on the high-capacity nodes, whereas it predicts the unpopular network functions and maps them on the low-capacity nodes. The goal is to predict the next incoming function and prefetch it on the node. Hence, when a future request demands the same function, it can be cached directly from the node, at reduced resources consumption, processing times, cost, and energy consumption. Also, this yields in higher number of satisfied requests and increased capacity. The deep learning network yields reduced loss model and high success rates.