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Abstract The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to over six million deaths worldwide. In human immune system, the type 1 interferon (IFN) pathway plays a crucial role in fighting viral infections. However, the ORF8 protein of the virus evade the immune system by interacting with IRF3, hindering its nuclear translocation and consequently downregulate the type I IFN signaling pathway. To block the binding of ORF8–IRF3 and inhibit viral pathogenesis a quick discovery of an inhibitor molecule is needed. Therefore, in the present study, the interface between the ORF8 and IRF3 was targeted on a high-affinity carbon nanotube by using computational tools. After analysis of 62 carbon nanotubes by multiple docking with the induced fit model, the top five compounds with high docking scores of − 7.94 kcal/mol, − 7.92 kcal/mol, − 7.28 kcal/mol, − 7.19 kcal/mol and − 7.09 kcal/mol (top hit1-5) were found to have inhibitory activity against the ORF8–IRF3 complex. Molecular dynamics analysis of the complexes revealed the high compactness of residues, stable binding, and strong hydrogen binding network among the ORF8-nanotubes complexes. Moreover, the total binding free energy for top hit1-5 was calculated to be − 43.21 ± 0.90 kcal/mol, − 41.17 ± 0.99 kcal/mol, − 48.85 ± 0.62 kcal/mol, − 43.49 ± 0.77 kcal/mol, and − 31.18 ± 0.78 kcal/mol respectively. These results strongly suggest that the identified top five nanotubes (hit1-5) possess significant potential for advancing and exploring innovative drug therapies. This underscores their suitability for subsequent in vivo and in vitro experiments, marking them as promising candidates worthy of further investigation.

Microgrids are the future version of advanced distribution networks due to the fast growth of renewable energy resources near consumers’ side. The microgrids are operated in on-grid mode (OGM) with the utility grid, and isolation mode (IM) without the utility grid. This dual operational mode causes protection and control challenges in the microgrids. This research paper suggests an advanced hardware-supported fault detection, phase identification & localization method for AC microgrids. The scheme deploys a Discrete Kalman Filter (DKF) for state estimation of voltage and current signals. Then, a Mathematical Morphology (MM) is engaged for generating a novel fault detection/classification index named segregated energy signature (SES) from estimated voltage and current signals. The system is considered to be faulty if the SES is higher than a predefined threshold setting, while phase identification is achieved by default because of the per-phase implementation of DKF&MM. Moreover, the directional features of the cumulative energy signature (CES) are also computed from MM-based non-fundamental current and voltage to localize the faulty section. The established scheme is tested on the CIGRE microgrid benchmark test bed on Matlab-Simulink software. In addition, the suggested method is also examined on the dSPACE MicroLab testing hardware setup in the Smart grid lab at USPCAS-E, NUST, Pakistan. The result illustrates that the proposed scheme successfully detects, classifies, and localizes the low impedance fault (LIF) as well as high impedance fault (HIF) in both operational modes and topological structures with 96.6% accuracy.

International tourism is highly dependent on air travel, which enhances destination countries’ economic growth and living standards while causing environmental degradation. This study investigates the effect of air travel on environmental degradation in BRICS nations. The study used feasible generalized least squares (FGLS) to evaluate the environmental Kuznets curve using data spanning 1995 to 2018. The study finds evidence of N-shaped EKC in the economies of the BRICS nations. We also find a positive relationship between tourism, urbanization, and environmental degradation (as measured by CO2 and SO2 emissions from civil aviation) and a negative relationship between economic development and environmental degradation. To achieve sustainable growth and environmental protection and promote green technology, the BRICS governments should devise policies that address the aviation industry’s short- and long-term needs.

Flexible capacitive energy storage applications require polymer nanocomposites with high dielectric properties, which can be accomplished by addition of inorganic nanofillers to the polymer matrix. Low-density polyethylene (LDPE), known for its good dielectric characteristics and wide use in electrical insulation have been investigated for the desired applications. However, the improvement of its breakdown strength still continues with the use of various nanomaterials employed as nanofillers. In this study, a waste-derived material known as biomass fly ash (BFA) as a nanofiller to improve the dielectric properties of LDPE has been explored. BFA exhibits versatility in its composition with various metal oxides, making it an attractive choice as a nanofiller. The BFA-LDPE sheets were prepared using a conventional solvent mixing and subsequent hot-pressing process, incorporating BFA loadings ranging from 1 % to 4 wt%. The effects of different BFA loadings were carefully examined, and the synthesized nanocomposites were extensively characterized using various characterization methods, such as XRD, SEM, FTIR, TGA and dielectric constant measurements, to investigate the crystallographic properties, morphology, chemical composition, and thermal stability. Among all the nanocomposites, 4 wt%BFA-LDPE exhibited the highest dielectric constant, with a value of 11.58, compared to simple LDPE that had a dielectric constant of 8.33. This improvement is ascribed to the synergistic effects of different inorganic metal oxides (SiO2, MgO, and Fe2O3) present in BFA. The results showed a significant enhancement in dielectric properties, indicating that the waste-derived BFA can be purposefully applied as an effective nanofiller in the LDPE-based composites with even less than 4% loading for electrical insulating applications in future studies.

In this paper, a real-time adaptive clustering-based distributed voltage regulation scheme is presented for the voltage regulation of unbalanced distribution systems. In the proposed methodology, first, adaptive clusters for the distributed control are formulated based on voltage to injected power sensitivity, and a threshold criterion is designed that limits the size of the clusters to strongly controlled nodes only. Having performed the clustering, a multi-objective optimization problem is solved for power factor optimization within clusters for voltage regulation of all the control zones of the distribution system. Voltage quality factor and voltage unbalance indices are utilized to show the improvements in voltage profile and reduction in voltage unbalance, respectively. Furthermore, for optimally finding the tap positions for multiple OLTCs, another optimization problem is designed. An IEEE 123-node test feeder with multiple wind and solar DGs of varying capacities is used for the simulations, and two test cases with different scenarios are simulated to test the proposed scheme. All the results verify that the proposed scheme effectively regulates the voltage of the unbalanced system without massive remote data requirements and active power curtailment which ensures the stability of the system’s voltage profile and prevents OLTC-hunting even in highly dynamic operational scenarios.

Abstract One of the challenging issues in the realization of medium‐voltage DC (MVDC) distribution system is high‐speed protection scheme design against quickly rising short circuit fault‐DC current. This paper proposes a high‐speed protection scheme based on the processing of energy signals through mathematical morphology (MM). The voltage and current signals measured at a distribution line section (DLS) are used to obtain the energy signal. The proposed scheme develops a modified MM‐based filter to extract the transient information in the energy signal rapidly and distinctly in the form of positive/negative polarity. The polarity detection of any disturbance in the analysed energy signal indicates the occurrence of a sudden disturbance in the system. The sudden disturbance occurrence confirmation triggers the faulty DLS identification and classification algorithms of the proposed scheme. The faulty DLS identification and classification algorithms are based on the detected polarities of the energy signal. The protection scheme requires low bandwidth communication. The proposed protection scheme is evaluated by using a ±2.5 kV radial distribution network containing 4 feeders under different fault conditions in MATLAB/SIMULINK software. Simulation results verify that the proposed scheme can isolate the faulty portion within a few milliseconds after fault inception under a variety of fault cases.

Recently, the concept of microgrids has emerged in the world due to the integration of distributed energy resources (DERs) at the distribution end. The design of a reliable protection strategy is one of the top-most challenges associated with microgrids. This is because of the transition of microgrids between grid-tied and autonomous modes of operation. This paper presents a state-of-the-art microgrid protection scheme based on the Kalman filter (KF). The proposed scheme uses the one-end current signal of a distribution line for the detection and classification of faults. Firstly, the KF is applied to each phase of a three-phase current signal individually to generate residuals and total harmonic distortion (THDs). Next, the variations in the residuals and THDs of each phase are compared with pre-specified threshold values to detect the faulty events in the microgrid. As each phase is processed through KF individually, therefore, the proposed scheme is inherently phase segregated. Afterward, the KF is applied to extract the third harmonic component from the three-phase current and voltage signals. Then, the KF-based reactive power (KFBRP) is obtained from the extracted third harmonic components. Finally, the directional properties of the three-phase KFBRP are used to locate the faulty section in the microgrids. Extensive simulations in MATLAB/ Simulink software are performed for the grid-tied as well as the autonomous modes of operation under radial and meshed topologies. The results show that the proposed scheme is highly robust in all testing scenarios without any false tripping and blinding issues.

Remote electrification remains a critical issue for the Gilgit Batistan region of Pakistan, where most of the population lives without access to the national grid and mostly relies on traditional energy resources, which undermines the huge potential of renewable energy in the region. This paper addresses the energy access issue of 1.8 million people of Gilgit-Baltistan (GB), of which 86% reside in the rural areas. To resolve the problem of energy access in the region, this paper analyzes cost-effective solutions based on renewable energy resources and proposes suitable hybrid systems for 14 independent sites. For this purpose, available energy resources and regional demands have been assessed to present the optimized hybrid energy systems for the identified sites. Hybrid optimization model for multiple energy resources is designed to carry out the techno-economic feasibility analysis. Moreover, equivalent forest absorbing CO2, glacier mass loss, and social cost have been evaluated. Results show that the combination of wind and hydro with battery backup gives the most optimum cost of energy (COE) ranging between 0.0470–0.0968 $/kWh. Similarly, 8349 acres of forest are needed to absorb the annual CO2emissions. As 0.58 million tons of glacier, and 0.882 million $/year social cost of carbon (SCC) can be lost due to emissions of fossil fuel-based generation. To ameliorate from such adverse effects, a community-based hybrid system may provide clean and green energy to increase energy access across Gilgit-Baltistan.

COVID-19 is a severe global pandemic that has caught the whole world unprepared. In the absence of a clear timeline for this pandemic to end, it is need of the hour to investigate the effect of this pandemic on both previous and anticipated investments. Global economic unrest has hindered the ramping deployment of Renewable energy projects. The most quick actions that may be taken to mitigate the effects and to up-rise the investment portfolio policies are a very critical tool in hands of government for a very immediate effect have also been made without keeping the context of COVID-19 into account. New variants of diff rent nature are being discovered and every now and then new lock downs are happening. In this context different policies have to be evaluated under the pandemic scenario. A case study of a large scale renewable energy project for a higher education institute in Pakistan is being used to measure the difference during COVID and pre COVID times. This paper provides a framework to investigate the impact of COVID on renewable energy system projects under current net-metering, net-billing and self-consumption policies. A recent investment in a photovoltaic system is assessed based on previously projected financial benefits versus the pandemic effected ones. This research concludes that investing in photovoltaic systems are still a viable option even in an extreme pandemic situation with less than 0.5 years increase in payback period, and the government can still provide a stimulus for investing in green energy by implementing net-metering policies on a larger scale.

Owing to the economic, social, and political problems in the expansion of transmission infrastructure, novel transmission topologies are in demand to efficiently utilize the existing infrastructure. On the other hand, there is a tremendous increase in wind power generation around the globe. One of the main challenges hindering the penetration of large-scale wind power generation is the network congestion due to limited network capacity. To address these two issues, this study develops a co-optimized optimal transmission switching (OTS) and dynamic line rating (DLR) model to optimize system resources by mitigating network congestion and maximizing wind power accommodation. This new concept of exploiting the inherent flexibility in transmission network is named as flexible transmission dynamic line rating (FTDLR), which deploys OTS in coordination with DLR as a control tool to utilize existing assets. A two-stage stochastic unit commitment framework is used to deploy the proposed FTDLR model, which is used to dynamically increase the line capacity based on the meteorological parameters and at the same time optimally select candidate lines to be switched off from the network. A comprehensive analysis is performed to characterize the FTDLR performance on system operation cost, network congestion, and wind power curtailment. The proposed FTDLR model is further tested as a part of contingency analysis where both generator failure and transmission line outages are considered. Test results performed on the IEEE 24-bus network demonstrate that by using FTDLR, the service operator could substantially reduce system dispatch cost, improve wind power accommodation, and relieve network congestion. The scalability and feasibility of the FTDLR optimization problem is validated on the larger network of the IEEE 118-bus system.

Due to the increase in penetration of renewable energy sources, the control technique plays a vital role to determine the performance of Microgrid (MG). Recently, the Internet of Things (IoT) and cloud computing has gained significance in solving various industrial problems. Robust and scalable Information Communication Technology (ICT) infrastructure is critical for efficient control of MG. IoT Devices with efficient measurement and control capability can play a key role in the MG environment. In this paper three layers hierarchical control of inverter based MG was developed using cloud-based IoT infrastructure and machine learning (ML) based islanding detection scheme. MG was operated in both island and grid connected mode. In the Primary layer, a voltage frequency (V-F) droop control with virtual impedance control was applied to avoid the disturbances in island mode. Moreover, Active Reactive (P-Q) power control was used for grid connected mode. In the secondary layer voltage and frequency deviations were removed by using the decentralized averaging based method. Voltage and frequency from each distributed generator (DG) were communicated by using a lightweight IoT-based protocol through an edge device (ED). Context-aware policy (CAP) was adopted in ED to optimize traffic flow over a communication network (CN) by comparing the difference in the present and previous data values. In the tertiary layer, a cloud-based ML model was developed using an artificial neural network (ANN) for islanding detection. ANN model was trained by data produced by simulating islanding scenarios in Matlab. Phasor measurement unit (PMU) data was communicated to the cloud for island prediction. The Proposed scheme was implemented on a modified IEEE-13 bus system with four inverter-based distributed generators (DGs) in Matlab, and Microsoft cloud services were used. The successful implementation of MG hierarchical control using an IoT feedback network with less data traffic along with cloud-based islanding detection using machine learning are the main contributions in this work. The whole system achieves stability within 2 seconds of islanding according to IEEE 1547 standards.

Unanticipated faults can hinder the operation of the utility grid, consumers, and businesses. This study aims to ensure the continued power supply to the consumers during outages and peak hours from the stored energy in the batteries of electric vehicles (EVs). Considering the bi-directional charging feature, the energy capacity model of the EV-batteries in a centralized parking lot (PL) and distributed EVs is developed. Various factors that affect the energy capacity of EVs are considered and modeled. The optimal location of PL and charging/discharging time and rate are found for reliability improvement, losses reduction, and voltage regulation of the distribution system. The problem is formulated as a mixed-integer multi-objective optimization problem. The first objective function is formulated to maximize the energy output from the EVs-batteries during blackouts or to minimize the energy not supplied (ENS) to the consumers, whereas the second objective function minimizes the losses of the distribution system. Moreover, an IEEE 37 Node-Test Feeder and seven test cases are considered for this study. Simulation results show that PL of EVs could be employed as a power source during the islanded and grid-connected mode for improvement of reliability in the power systems.

Islanding detection with the rising grid supporting inverter-based distributed generation is becoming more critical protection due to its high droop gains and overall decreased system inertia leading to rapid changes in the electrical parameters. Traditional methods for islanding detection in this regard are susceptible to significant problems such as non-detection zone, false-positive detection, and inefficient mode of validation. Therefore, to attenuate these problems, this paper proposes a hybrid islanding detection technique based on unsupervised anomaly detection using autoencoders. This technique uses the rate of change of frequency as primary and phase angles of the voltage and current as secondary detection parameters, demonstrating improved performance, reliability, and robustness due to its shared advantage of both active frequency drift and autoencoder. Furthermore, a dialectic model of offline and online validation schemes is also proposed to ensure the reliability of detection. Extensive simulations and validations have been carried out on multiple networks to generate data sets used to train, test, and validate the technique and compute its statistical significance, thereby confirming its effectiveness. The optimal islanding detection time for the base cases was recorded as 20 milliseconds with an F1-score of 0.991, dependability index of 0.998, security index of 0.995, with total harmonic distortion of 4.56% and zero non-detection zones, which complies with IEC 61000-3-2 and IEEE standard 1547’s requirement of detection within two seconds after islanding.

Electrification of the transportation sector can play a vital role in reshaping smart cities. With an increasing number of electric vehicles (EVs) on the road, deployment of well-planned and efficient charging infrastructure is highly desirable. Unlike level 1 and level 2 charging stations, level 3 chargers are super-fast in charging EVs. However, their installation at every possible site is not techno-economically justifiable because level 3 chargers may cause violation of critical system parameters due to their high power consumption. In this paper, we demonstrate an optimized combination of all three types of EV chargers for efficiently managing the EV load while minimizing installation cost, losses, and distribution transformer loading. Effects of photovoltaic (PV) generation are also incorporated in the analysis. Due to the uncertain nature of vehicle users, EV load is modeled as a stochastic process. Particle swarm optimization (PSO) is used to solve the constrained nonlinear stochastic problem. MATLAB and OpenDSS are used to simulate the model. The proposed idea is validated on the real distribution system of the National University of Sciences and Technology (NUST) Pakistan. Results show that an optimized combination of chargers placed at judicious locations can greatly reduce cost from $3.55 million to $1.99 million, daily losses from 787kWh to 286kWh and distribution transformer congestion from 58% to 22% when compared to scenario of optimized placement of level 3 chargers for 20% penetration level in commercial feeders. In residential feeder, these statistics are improved from $2.52 to $0.81 million, from 2167kWh to 398kWh and from 106% to 14%, respectively. It is also realized that the integration of PV improves voltage profile and reduces the negative impact of EV load. Our optimization model can work for commercial areas such as offices, university campuses, and industries as well as residential colonies.

Because of electricity markets, environmental concerns, transmission constraints, and variable renewable energy sources (VRES), coordinated operation of demand response (DR) and battery energy storage systems (BESS) has become critical. In turn, the optimal coordinated operation of DR and BESS by an entity can affect overall electricity market outcomes and transmission network conditions. The coordinated operation is desirable for the profit-seeking entity, but it may adversely affect the cost and revenues of other market participants or cause system congestion. Though few coordinated operation models already exist, our aim in this research is to provide a novel multi-objective optimization-based methodology for the coordinated operation of DR and BESS to boost market profit. Moreover, another goal is to simultaneously study the combined effects of such coordinated models on transmission networks and electricity markets for the first time. This paper has proposed a new method for coordinated DR and BESS utilization by a load-serving entity (LSE) to increase its profit. Moreover, it has employed agent-based modeling of the electricity systems (AMES) for testing our coordinated DR and BESS method under day-ahead market and transmission system conditions. Simulation results of case studies indicate that the operating costs of all LSEs decreased, and there was as much as 98,260 $/day in cost savings for BESS deploying LSE1. Although revenues of cheaper generation companies (GenCos) decreased, those of expensive GenCos increased or showed mixed trends. For example, GenCo 3 exhibits an 8765 $/day decrease in revenue for 25% BESS capacity, whereas a 6328 $/day increase in revenue for 37.5% BESS capacity. The variance of LMPs, widely used as a risk index, greatly decreased for the LSE utilizing the coordinated methodology, somewhat decreased for other LSEs but increased for cheaper GenCos with no LSE at the local node. Since BESS deployment decisions of an LSE can have system-wide or market-wide consequences, simulation analysis before deployment can help reduce market distortions or system congestions.

The microgrids operate in tie-up (TU) mode with the main grid normally, and operate in isolation (IN) mode without the main grid during faults. In a dynamic operational regime, protecting the microgrids is highly challenging. This article proposes a new microgrid protection scheme based on a state observer (SO) aided by a recurrent neural network (RNN). Initially, the particle filter (PF) serves as a SO to estimate the measured current/voltage signals from the corresponding bus. Then, a natural log of the difference between the estimated and measured current signal is taken to estimate the per-phase particle filter deviation (PFD). If the PFD of any single phase exceeds the preset threshold limit, the proposed scheme successfully detects and classifies the faults. Finally, the RNN is implemented on the SO-estimated voltage and current signals to retrieve the non-fundamental harmonic features, which are then utilized to compute RNN-based state observation energy (SOE). The directional attributes of the RNN-based SOE are employed for the localization of faults in a microgrid. The scheme is tested using Matlab® Simulink 2022b on an International Electrotechnical Commission (IEC) microgrid test bed. The results indicate the efficacy of the proposed method in the TU and IN operation regimes on radial, loop, and meshed networks. Furthermore, the scheme can detect both high-impedance (HI) and low-impedance (LI) faults with 99.6% of accuracy.

To promote online businesses and sales, e-commerce industry focuses to fulfill users' demands by giving them top set of recommendations which are ranked through different ranking measures.Deep learning based auto-encoder models have further improved the performance of recommender systems. A state-of-the-art collaborative denoising auto-encoder (CDAE) models user-item interactions as a corrupted version of users rating inputs. However, this architecture still lacks users' ratings-trend information which is an important parameter to recommend top-N items to users. In this paper, building upon CDAE characteristics, we propose a novel users rating-trend based collaborative denoising auto-encoder (UT-CDAE) which determines user-item correlations by evaluating rating-trend(High or Low) of a user towards a set of items. This inclusion of a user's rating-trend provides additional regularization flexibility which helps to predict improved top-N recommendations. The correctness of the suggested method is verified through different ranking evaluation metrics i.e., (mean reciprocal rank, mean average precision and normalized discounted gain), for various input corruption values, learning rates and regularization parameters.Experiments on standard ML-100K and ML-1M datasets show that suggested model has improved performance overstate-of-the-art denoising auto-encodermodels.

Phasor measurement unit (PMU) technology is a need of the power system due to its better resolution than conventional estimation devices used for wide-area monitoring. PMUs can provide synchronized phasor and magnitude of voltage and current measurements for state estimation of the power system to prevent blackouts. The drawbacks of a PMU are the high cost of the device and its installation. The main aspect of using PMUs in electrical networks is the property to observe the adjacent buses, thereby making it possible to observe the system with fewer PMUs than the number of buses through their optimal placement. In the last two decades, exhaustive research has been done on this issue. Considering the importance of this field, a comprehensive review of the progress achieved until now is carried out and the limitations of existing reviews in the literature are highlighted. This paper can be seen as a major attempt to provide an up-to-date review of the research work carried out in this all-important field of PMU placement and indicates that some perspectives of optimal PMU placement still need attention. Eventually, the work will open a new standpoint for the research community to fill the research gap.

Silicone rubber is a promising insulating material that has been performing well for different insulating and dielectric applications. However, in outdoor applications, environmental stresses cause structural and surface degradations that diminish its insulating properties. This effect of degradation can be reduced with the addition of a suitable filler to the polymer chains. For the investigation of structural changes and hydrophobicity four different systems were fabricated, including neat silicone rubber, a micro composite (with 15% micro-silica filler), and nanocomposites (with 2.5% and 5% nanosilica filler) by subjecting them to various hydrothermal conditions. In general, remarkable results were obtained by the addition of fillers. However, nanocomposites showed the best resistance against the applied stresses. In comparison to neat silicone rubber, the stability of the structure and hydrophobic behavior was better for micro-silica, which was further enhanced in the case of nanocomposites. The inclusion of 5% nanosilica showed the best results before and after applying aging conditions.

The scope of silicone rubber (SiR) is confined due to the deprivation of its dielectric propertiesupon exposure to various ambient stresses. The aim of this research is to develop silicone rubber-based composites by employing inorganic oxide fillers for improved dielectric and high voltage insulation properties for widening its scope in the field of electrical appliances. This study reports the preparation of different composites of silicone rubber with varying concentrations of micro and nano-silica fillers. The dielectric propertytrends of these as-prepared neat and impregnated samples were examined via an indigenously developed weathering chamber capable of applying multiple stresses of acid rain, heat, humidity, UVA radiation, and salt fog. Dielectric constant values were measured before and after applying stresses. Upon applying stresses, a periodic decline in dielectric constant was observed. Improved dielectric properties were obtained by adding micro and nano-silica as fillers. A nano silica-incorporated silicone rubber product exhibited good potential for dual applications as dielectric and high voltage insulation.

Flexible AC Transmission Systems (FACTS) are essential devices used for the efficient performance of modern power systems and many developing countries lack these devices. Due to the non-existence of these advanced technologies, the national grid remains weak and vulnerable to power stability issues that can jeopardize system stability. This study proposes novel research to solve issues of an evolving national grid through the installation of FACTS devices. FACTS devices play a crucial role in minimizing active power losses while managing reactive power flows to keep the voltages within their respective limits. Due to the high costs of FACTS, optimization must be done to discover optimal locations as well as ratings of these devices. However, due to the nonlinearity, it is a challenging task to find the optimal locations and appropriate sizes of these devices. Shunt VARs Compensators (SVCs) and Thyristor-Controlled Series Compensators (TCSCs) are the two FACTS devices considered for the study. Optimal locations for SVCs and TCSCs are determined by Voltage Collapse Proximity Index (VCPI) and Line Stability Index (Lmn), respectively. Particle Swarm Optimization (PSO) is employed to find the ideal rating for FACTS devices to minimize the system operating cost (cost due to active power loss and capital cost of FACTS devices). This technique is applied to IEEE (14 and 30) bus systems. Moreover, reliable operation of the electricity grid through the placement of FACTS for developing countries has also been analysed; Pakistan being a developing country has been selected as a case study. The planning problem has been solved for the present as well as for the forecasted power system. Consequently, in the current national network, 6.21% and 6.71% reduction in active and reactive power losses have been observed, respectively. Moreover, voltage profiles have been improved significantly. A detailed financial analysis covering the calculation of Operation Cost (OC) of the national grid before and after the placement of FACTS devices is carried out.

Epoxy and its composites have shown remarkable properties to be used for outdoor high voltage insulation applications. However, outdoor environment that contains UV-A radiations deteriorates the organic structure of epoxy-based insulators. To analyze the surface degradation and chemical changes in structure due to UV radiations, neat epoxy (NEPOX), 15 wt.% epoxy microcomposite (EMC 15) and 5 wt.% epoxy nanocomposite (ENC 5) were prepared and subjected to a specially fabricated UV chamber under accelerated conditions. The samples were analyzed with visual inspection, STRI hydrophobicity classification, and Scanning Electron Microscopy (SEM) and Fourier Transform Infrared (FTIR) spectroscopy. The analyses confirmed the pattern of gradual degradation of surface structure due to the appearance of ester and carbonyl groups due to oxidation of hydrocarbon chains. FTIR peaks of C=O stretching at ∼1725.52 increased by 157.44%, 75.86% and 33.33% for NEPOX, EMC 15 and ENC, respectively. This shows that ENC-5 expressed better resistance against surface oxidation as compared to EMC 15 and NEPOX. Addition of fillers enhanced the chain intactness and nano filler performed even better due to greater surface area and surface energy.

Power restoring time in power distribution systems (PDS) can be minimized by using efficient fault localization techniques. This paper proposes a novel, robust and scalable cloud based internet of things (IoT) solution for identification and localization of faults in PDS. For this purpose, a new algorithm is developed that can detect single and multiple simultaneous faults in the presence of single and multiple device or sensor failures. The algorithm has utilized a zone based approach that divides a PDS into different zones. A current sensing device (CSD) was deployed at the boundary of a zone. The function of CSD is to provide time synchronized current measurements and communicate with a cloud server through an edge device (ED). Another contribution of this research work is the unique implementation of context aware policy (CAP) in ED. Due to CAP, only those measurements are transmitted to cloud server that differ from the previously transmitted measurements. The cloud server performed calculations at regular intervals to detect faults in PDS. A relational database model was utilized to log various fault events that occur in PDS. An IEEE 37 node test feeder was selected as PDS to observe the performance of our solution. Two test cases were designed to simulate individual and multiple simultaneous faults in PDS. A third test case was implemented to demonstrate the robustness and scalability of proposed solution to detect multiple simultaneous faults in PDS when single and multiple sensor failures were encountered. It was observed that the new algorithm successfully localized the faults for all the three cases. Consequently, significant reductions were noticed in the amount of data that was sent to the cloud server. In the end, a comparison study of a proposed solution was performed with existing methods to further highlight the benefits of our technique.

Flexible AC Transmission Systems (FACTS) play an important role in minimizing power losses and voltage deviations while increasing the real power transfer capacity of transmission lines. The extent to which these devices can provide benefits to the transmission network depend on their optimal location and sizing. However, finding appropriate locations and sizes of these devices in an electrical network is difficult since it is a nonlinear problem. This paper proposes a technique for the optimal placement and sizing of FACTS, namely the Thyristor-Controlled Series Compensators (TCSCs), Shunt VARs Compensators (SVCs), and Unified Power Flows Controllers (UPFCs). To find the optimal locations of these devices in a network, weak buses and lines are determined by constructing PV curves of load buses, and through the line stability index. Then, the whale optimization algorithm (WOA) is employed not only to find an ideal ratings for these devices but also the optimal coordination of SVC, TCSC, and UPFC with the reactive power sources already present in the network (tap settings of transformers and reactive power from generators). The objective here is the minimization of the operating cost of the system that consists of active power losses and FACTS devices cost. The proposed method is applied to the IEEE 14 and 30 bus systems. The presented technique is also compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The findings showed that total system operating costs and transmission line losses were considerably reduced by WOA as compared to existing metaheuristic optimization techniques.

Epoxy decays its dielectric characteristics and exhibits degradation of main hydrocarbon on exposure to multiple environmental stresses. Inorganic oxides-based epoxy composites have been performingwell in many applications and short-term testing; therefore, evaluation of their dielectric and structural characteristics under extreme weathering conditions may also unleash enhancement in these characteristics. To explore dielectric properties and degradation of main hydrocarbon group, neat epoxy and silica-based epoxy microcomposite (15% micro-silica loading) and nanocomposites (5% nano-silica loading)have been prepared and subjected to acid rain, heat, ultra-violet radiation, salt fog, and humidity in a chamber that was specially fabricated in view ofservice conditions. Interesting results were obtained before and after aging. Enhanced intactness of methylene group was observed in nanocomposite followed by micro composite. Similarly, for epoxy nanocomposites not only higher dielectric constant, lower energy dissipation and conductivity was recorded before application of stresses, but also nanocomposite showed superior sustainability in these properties after aging. In all analyses, microcomposites performed better than neat epoxy but in conductivity the results of both samples were found comparable.

The return on investment can be divided in capital gain and dividend payouts. It is a difficult task for management to allocate a sufficient amount in both segments, especially to prevent from agency problems. The firms pay dividends only when enough amounts have after meeting their requirements and short term needs. The purpose of the present study is to empirically investigate the factors determine the dividend payout decisions in the case of Pakistan’s engineeringsector by using the data of thirty-six firms listed on Karachi Stock Exchange from the period 1996 to 2008. By employing various panel data techniques like fixed and random effects, the results suggest that the previous dividend per share, earnings per share, profitability, cash flow, sales growth, and size of the firm are the most critical factors determining dividend policy in the engineering sector of Pakistan.