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Attractive attributes of carbon nanotubes (CNTs),, which include high aspect ratio, excellent electrical and thermal conductivity, high melting point, and longer lifetime, have made this one-dimensional material a promising candidate for next-generation field emitters. In the present work, CNTs were grown directly on copper foil, copper foam, nickel foil and nickel foam through chemical vapor deposition. A comparison between CNT-based two-dimensional emitters, i.e. CNTs synthesized on copper and nickel foils, CNT-based three-dimensional (3D) emitters, i.e. CNTs developed on foams of copper and nickel, have been demonstrated. We observed that CNTs on foams exhibited better field emission compared to CNTs synthesized on foils. This is due to the multistage structure of the 3D foams which provides high surface area for CNT growth and hence enhanced field emission response. In the present work, a CNT sample prepared on nickel foam was observed to exhibit better field emission response compared to the other samples synthesized in the present study. This particular sample demonstrateda very low turn-on field of 0.93 V/µm, excellent field enhancement factor of 13,343 and good stability of electron emission.

Polaritons with a twist The ability to design and fabricate optical systems with tunable topological features makes them especially attractive for developing analogs of topological condensed matter systems, which by themselves tend to be fixed or limited in their tunability. Liu et al. now show that the combination of a two-dimensional material with a photonic crystal can be used to develop an analogous quantum spin Hall system. The strong coupling between the monolayer tungsten disulfide excitons with a nontrivial hexagonal photonic crystal gives rise to helical topological polaritons observed at up to 200 kelvin. The topological polaritons can be actively tuned by temperature and may further be manipulated with electric or magnetic fields, thereby providing a flexible platform with which to explore exotic topological phenomena and new phases of quantum matter. Science , this issue p. 600

The point of common coupling (PCC) voltage of the grid integrated solar photovoltaic (GSPV) system rises when the load becomes less and the SPV array generation at its peak, leads to the overvoltage at the PCC. It causes the system to shut down its operation to protect itself and the load from the overvoltage. The GSPV system also suffers from substandard power quality (PQ) due to the unbalanced or nonlinear load currents. This work addresses these issues and presents the control scheme for the GSPV-battery system. This work presents the variable power mode of operation to restrain the rise in PCC voltages below its maximum permissible limit as set by the IEEE std. 1547. The system operates either in unity power factor (UPF) mode or the constant power generation (CPG) mode or in voltage regulation (VR) mode, depending upon the battery status and PCC voltage amplitude. The system also provides shunt compensation at PCC to reduce the negative impact of the unbalanced-nonlinear load currents in the AC grid and maintains the PQ in the AC grid as per the IEEE std. 519. A simulation study is performed to demonstrate the efficacy of the presented control algorithm.

A key to successful Well, Reservoir and Facilities Management (WRFM) is to have an up-to-date opportunity funnel. In large mature fields, WRFM opportunity identification is heavily dependent on effective exploitation of measured & interpreted data. This paper presents a suite of data driven workflows, collectively called WRFM Opportunity Finder (WOF), that generates ranked list of opportunities across the WRFM opportunity spectrum.The WOF was developed for a mature waterflooded asset with over 500 active wells and over 30 years of production history. The first step included data collection and cleanup using python routines and its integration into an interactive visualization dashboard. The WOF used this data to generate ranked list of following opportunity types: (a) Bean-up/bean-down candidates (b) Watershut-off candidates (c) Add-perf candidates (d) PLT/ILT data gathering candidates, and (e) well stimulation candidates. The WOF algorithms, implemented using python, largely comprised of rule-based workflows with occasional use of machine learning in intermediate steps.In a large mature asset, field/reservoir/well reviews are typically conducted area by area or reservoir by reservoir and is therefore a slow process. It is challenging to have an updated holistic overview of opportunities across the field which can allow prioritization of optimal opportunities. Though the opportunity screening logic may be linked to clear physics-based rules, its maturation is often difficult as it requires processing and integration of large volumes of multi-disciplinary data through laborious manual review processes. The WOF addressed these issues by leveraging data processing algorithms that gathered data directly from databases and applied customized data processing routines. This led to reduction in data preparation and integration time by 90%. The WOF used workflows linked to petroleum engineering principles to arrive at ranked lists of opportunities with a potential to add 1-2% increment in oil production. The integrated visualization dashboard allowed quick and transparent validation of the identified opportunities and their ranking basis using a variety of independent checks. The results from WOF will inform a range of business delivery elements such as workover & data gathering plan, exception-based-surveillance and facilities debottlenecking plan.WOF exploits the best of both worlds - physics-based solutions and data driven techniques. It offers transparent logic which are scalable and replicable to a variety of settings and hence has an edge over pure machine learning approaches. The WOF accelerates identification of low capex/no-capex opportunities using existing data. It promotes maximization of returns on already made investments and hence lends resilience to business in the low oil price environment.

Short Term Injection Re-distribution (STIR) is a python based real-time WaterFlood optimization technique for brownfield assets that uses advanced data analytics. The objective of this technique is to generate recommendations for injection water re-distribution to maximize oil production at the facility level. Even though this is a data driven technique, it is tightly bounded by Petroleum Engineering principles such as material balance etc. The workflow integrates and analyse short term data (last 3-6 months) at reservoir, wells and facility level. STIR workflow is divided into three modules: Injector-producer connectivity Injector efficiency Injection water optimization First module uses four major data types to estimate the connectivity between each injector-producer pair in the reservoir: Producers data (pressure, WC, GOR, salinity) Faults presence Subsurface distance Perforation similarity – layers and kh Second module uses connectivity and watercut data to establish the injector efficiency. Higher efficiency injectors contribute most to production while poor efficiency injectors contribute to water recycling. Third module has a mathematical optimizer to maximize the oil production by re-distributing the injection water amongst injectors while honoring the constraints at each node (well, facility etc.) of the production system. The STIR workflow has been applied to 6 reservoirs across different assets and an annual increase of 3-7% in oil production is predicted. Each recommendation is verified using an independent source of data and hence, the generated recommendations align very well with the reservoir understanding. The benefits of this technique can be seen in 3-6 months of implementation in terms of increased oil production and better support (pressure increase) to low watercut producers. The inherent flexibility in the workflow allows for easy replication in any Waterflooded Reservoir and works best when the injector well count in the reservoir is relatively high. Geological features are well represented in the workflow which is one of the unique functionalities of this technique. This method also generates producers bean-up and injector stimulation candidates opportunities. This low cost (no CAPEX) technique offers the advantages of conventional petroleum engineering techniques and Data driven approach. This technique provides a great alternative for WaterFlood management in brownfield where performing a reliable conventional analysis is challenging or at times impossible. STIR can be implemented in a reservoir from scratch in 3-6 weeks timeframe.

Capacitance resistance modeling (CRM) is a data-driven analytical technique for waterflood optimization developed in the early 2000s. The popular implementation uses only production/injection data as input and makes simplifying assumptions of pressure maintenance and injection being the primary driver of production. While these assumptions make CRM a quick plug & play type of technique that can easily be replicated between assets they also lead to major pitfalls, as these assumptions are often invalid. This study explores these pitfalls and discusses workarounds and mitigations to improve the reliability of CRM. CRM was used as a waterflood optimization technique for 3 onshore oil fields, each having 100s of active wells, multiple stacked reservoirs, and over 15 years of pattern waterflood development. The CRM algorithm was implemented in Python and consists of 4 modules: 1) Connectivity solver module – where connectivity between injectors and producers is quantified using a 2 year history match period, 2) Fractional Flow solver module – where oil rates are established as a function of injection rates, 3) Verification module – which is a blind test to assess history match quality, 4) Waterflood optimizer module – which redistributes water between injectors, subject to facility constraints and estimates potential oil gain. Additionally, CRM results were interpreted and validated using an integrated visualization dashboard. The two main issues encountered while using CRM in this study are 1) poor history match (HM) and 2) very high run time in the order of tens of hours due to the large number of wells. Poor HM was attributed to significant noise in the production data, aquifer support contributing to production, well interventions such as water shut-offs, re-perforation, etc. contributing to oil production. These issues were mitigated, and HM was improved using data cleaning techniques such as smoothening, outlier removal, and the usage of pseudo aquifer injectors for material balance. However, these techniques are not foolproof due to the nature of CRM which relies only on trends between producers and injectors for waterflood optimization. Runtime however was reduced to a couple of hours by breaking up the reservoir into sectors and using parallelization.

This article presents an adaptive control algorithm for a multiobjective grid-tied solar photovoltaic (PV)-distribution static compensator. The multiobjective system extracts the maximum energy available across the solar PV array and delivers it to the load and the grid connected at the common point of interconnection (PIC). In addition to this, the system reduces the losses on the distribution network and improves the quality of the voltage at the PIC by maintaining the grid currents balanced with THD value less than 5%. The perturb and observe-based maximum power point tracking method is used to extract the maximum energy, while the voltage source converter (VSC) is controlled by the presented improved Widrow–Hoff algorithm. This control algorithm improves the grid current power quality by estimating the fundamental component of the load current. It uses two weight to track the desired signal as compared to the other existing similar algorithms, which improves its tracking accuracy, reduces the steady-state error, and oscillation form the desired output signal. The same algorithm is used for synchronization of the VSC with the distribution network. Hence, it eliminates the need for another algorithm for the synchronization, which further improves its performance in dynamic conditions. An additional feed-forward term corresponding to the solar PV array ( I PVFF) power is added in the VSC control to improve its dynamic response in the severe atmospheric conditions. The performance of the system is validated through simulated and test results in various dynamic conditions.

This paper presents a solar photovoltaic (SPV) array-battery energy storage-based microgrid with a two-stage converter. One is a DC-DC converter, and another one is a voltage source converter (VSC). It acts in standalone mode or grid-connected mode depends on utility grid conditions. A bidirectional DC-DC converter (BDC) is used for maintaining the DC link voltage. Maximum power point tracking (MPPT) with an incremental conductance (INC) technique is used to extract the maximum power from the SPV array. Adaptive fixed frequency third-order generalized integrator (AFFTOGI) controller is used for estimating the load fundamental and quadrature components from the distorted grid and unbalanced load. In grid-connected mode, VSC works in current control mode, and in standalone mode, it works in voltage control mode. In this paper, dynamic simulation under different operation conditions and experiments on developed laboratory prototypr are performed to evaluate the system and control algorithm performance.

Objectives/Scope The amount of time and effort required to access and integrate Subsurface data from multiple sources is significant. Using Advanced Data Analytics, mainly python, an integrated subsurface dashboard titled Hybrid Integrated Visualization Environment (HIVE) was created using Spotfire to empower the integrated Exploration, Development and Well Reservoir and Facilities Management (WRFM) subsurface teams in: Professionalizing data and knowledge management to have "one" version of the truth. Data consolidation and preparation to avoid repetitive manual work & Enhancing opportunity identification to optimize production and value Methods, procedure, process The approach of subsurface data integration can be broken down into 4 major steps, namely: Step 1: Python programming was used to pre-process, restructure and create unified data frames. Use of python significantly reduces the time required to pre-process a diverse number of subsurface data sources consisting of static, dynamic reservoir models, log data, historical production & pressure data and wells & completion data to name a few. Step 2: - Standard diagnostic industry recognized diagnostic plots were automated using advanced analytic techniques in HIVE with the help of unified data frames. Step 3: HIVE was created to link various internal corporate data stores like pressure, temperature, rate data from PI System (stores real time measured data), Energy Components (EC) and Oil Field Manager (OFM) in real time. This was done to ensure that data from various petroleum engineering disciplines could now be visualized and analyzed in a structured manner to make integrated business decisions. Step 4: One of the key objectives of pursuing this initiative was to ensure that subsurface professionals in Shell Trinidad and Tobago were trained and upskilled in the use of python as well visualization tools like Spotfire and Power BI to ensure the maintenance and improvement of HIVE going forward. Results, Observations, Conclusions The development of HIVE has made it easier and more efficient to access and visualize subsurface data, which was extremely time consuming earlier while using older conventional techniques. Standard diagnostic plots and visuals were developed and are now used to drive integrated decision making, with key focus being water and sand production management from a production management perspective. Consequently, HIVE also drives enhanced integration between disciplines (Petrophysics, Petroleum Geology, Production Technology, Reservoir Engineering and Production operations) and departments (Developments, Upstream and Exploration). Novel/Additive Information The petroleum industry has started to embrace the application of advanced data analytics in our day-to-day work. A successful application of these techniques results in transforming the ways of working by increasing efficiency, transparency and integration among teams.

A low voltage grid-tied solar PV (GSPV) system suffers from poor power quality (PQ) due to the distorted currents drawn by the loads affixed at the point of common coupling (PCC). Moreover, during the peak PV (Photovoltaic) array generation and low load condition, the PCC voltages rise beyond the prescribed limit as par the IEEE std. 1547. Hence, this work presents the control and operation of the GSPV system to satisfy the IEEE-519 and IEEE-1547 standards by improving the PQ at the PCC and to limit the rise in PCC voltages as par the specified limit, respectively. The system either works at unity power factor (UPF) mode or voltage regulation (VR) mode depending upon the PCC voltage variation. It maintains the grid currents distortionfree and balanced during its operation. Here, an in-loop filter based second-order generalized integrator (ILF-SOGI) algorithm is used to control the GSPV system. The proportional-integral (PI) based perturb and observe (P&O) algorithm is used to maximize the efficiency of the GSPV system. A simulation study is carried out in MATLAB environment for performance validation of the presented GSPV system.

The distribution grid (DG) suffers from poor power quality due to the nonlinear/unbalanced loads. Hence, this paper presents the generalized maximum correntropy criteria (GMCC) adaptive filter-based control algorithm for the solar PV renewable energy system (SPV-RES) to improve power quality in the DG. The SPV-RES improves the power quality in the DG by forcing the DG currents balanced and distortion-free. During the daytime, the SPV-RES feeds the extracted energy of the PV array at the point of interconnection (POI) and improves the power quality. During the nighttime, it operates as a shunt compensator and improves the power quality only. Hence, the utilization factor of the presented system is higher as compared to the conventional system as the presented system operates all the time. Moreover, the presented SPV-RES feeds reactive power to compensate for the load reactive power demand. Hence, the DG power factor remains unity and the line losses are reduced. To validate the performance of the presented system, a simulation model is developed and its performance is investigated in the presence of the nonlinear/unbalanced load.

The harmonics currents drawn by the nonlinear loads distort the point of common coupling (PCC) voltages. It affects the performance of the solar PV system (SPVS) and other sensitive loads, connected at the same PCC. Moreover, it increases the line losses in the AC network. Hence, this paper presents an adaptive filter-based control algorithm for the SPVS to improve the power quality (PQ) in the AC network as par the IEEE standard 519 and to reduce its line losses. The presented adaptive filter algorithm effectively filters out the harmonics from the sensed voltages and currents and reduces the DC offset caused by the sensor circuitry. Moreover, in this paper, an adaptive DC-link control approach is presented to regulate the DC link voltage of the SPVS, which ensures that the modulation index remains less than 1. Hence, the harmonics currents injected by the SPVS at the PCC, are reduced. Moreover, the SPVS separates itself from the AC network to uphold the IEEE std. 1547, if any abnormality occurs in the AC network voltages. In this condition, it feeds power to the loads by changing its mode of operation to the islanded mode. Hence, the energy generated by the SPVS is effectively utilized and its payback period reduces. A Simulation study is carried out in various dynamics conditions to uphold the performance of the presented control algorithm.

The high line impedance of the low voltage AC network (LVACN), coupled with the distorted currents drawn by the nonlinear loads at the point of common coupling (PCC), creates serious power quality issues in the AC network. Furthermore, higher penetration of renewable energy sources like the solar photovoltaic (SPV) in modern LVACN escalates such power quality issues because of their fluctuating behavior. In this context, this paper presents an adaptive filtering strategy-based control for the SPV-battery energy storage (BES) system feeding nonlinear loads to improve the power quality at the PCC. The adaptive filtering scheme ensures that the distortions in the load currents are not reflected in the LVACN. Thus, the currents and voltages in LVACN remain linear. Furthermore, a power management scheme is developed to optimize the operation of the SPV-BES system. It supports the PCC voltages by limiting the supplied and drawn power from the LVACN, during peak SPV array generation or low load condition and no SPV array generation or the peak load condition, respectively. The SPV-BES system is also capable of transferring its mode of operation from a grid connected system, to a standalone system, when the supply from the LVACN is interrupted. Thus, the overall system efficiency and reliability are enhanced. Both the simulation and the experimental analyses of the SPV-BES system are presented to demonstrate the effectiveness of the power management scheme and the adaptive filtering strategy.

This work presents the design and control of PV-battery system feeding power to the AC network with diesel generator (DG) set backup at unity power factor (UPF) and the loads connected at the point of common coupling (PCC). The system improves the power factor of the AC network and the DG set to unity. Furthermore, it reduces the distortion in the currents and voltages of the AC network and DG set as par the IEEE std. 519, if any nonlinear load exists at the PCC. Moreover, it mitigates the intermittency of the PV array generation and the load demand at the PCC during the daytime operation by supplying constant power to the AC network. Hence, the variation in the PCC voltages are reduced. The PV -battery system energizes the load by operating as a standalone system if the AC grid'supply is interrupted. Furthermore, it operates as an uninterrupted power supply (UPS) if the generation of PV array reduces to zero. If the battery bank also discharges, then the DG set is connected at the PCC to energize the load. Hence, the load remains energized in all conditions. Here, the DG set operates at an optimal fuel economy zone to minimize diesel consumption and operational cost. However, it is disconnected as soon as the grid supply recovers or the battery bank charges. Therefore, diesel consumption reduces to the lowest. An extensive simulation study is performed to exhibit the performance and the seamless operation of the presented system when its working mode is sudden changes.

The commercial PV systems (PVS) lose their generation and do not exploit the clean energy generated by the PV array during the grid outage condition. This work presents the islanded operation of the PVS during the grid outage without using any energy storage. When the grid is available, the system feeds the power to the load and the grid. When the grid is not available, it feeds power to the load until the generation of the PVS remains more than the load demand. Hence, the clean energy generated by the PV array is efficiently exploited. The system also acts as an active filter and provides the shunt compensation features at the point of common coupling (PCC). Hence, it reduces the effects of the nonlinear-unbalanced loads on the AC utility and upholds the IEEE std. 519 at the PCC. The performance of the presented system is validated by the MATLAB/Simulink analysis.

The diesel generator (DG) set is used to energize the load in remote areas. A battery energy storage (BES) integrated solar PV (Photovoltaic) system (SPVS) is presented in this work, to reduce the dependence on the DG set for power generation. The DG set operates only when the solar PV power and BES are not capable to supply power to loads. In a daytime, the power to loads is supplied by the solar PV array and BES. In the nighttime, the BES supplies power to loads until it discharges to its lower limit. The DG set is synchronized at the point of common coupling (PCC) only when both PV array and the BES cannot able to cope load demand and it always operates at its fuel economy zone (FCZ) to reduce the operational cost. An energy management strategy is developed to reduce the operating hours of the DG set and to reduce its unwanted switching. The SPVS also improves the power factor and power quality (as par IEEE std. 519) in a DG set. The seamless operation is incorporated in the control so that IEEE std. 1547 upholds when system changes its operating mode. Performance of this system is exhibited and validated through simulated results in a developed Simulink model.

Topological photonics has become an active subfield of photonics analogous to the electronic counterpart, and the bulk-edge correspondence leads to robust topologically protected interfacial states. However, a single-topological interface mode with fixed energy cannot be easily manipulated, hindering its applications in optical devices. Here, we study coupled-waveguide arrays mapped to a one-dimensional Su-Schrieffer-Heeger system with two coupled topological interfaces. This configuration greatly increases device versatility and tunability while keeping the confinement of coupled-interface modes inherited from the topological properties nearly intact. Theoretically predicted oscillations between coupled interfaces is experimentally observed. The spatial and energetic isolation of the coupled interface states from the bulk modes is experimentally observed and theoretically confirmed by calculating the degree of localization of the eigenstates, which is found to be comparable to a single-interface state. Finally, a proof-of-principle, all-optical logic circuit is fabricated based on coupled interfaces, demonstrating its potential in assembling on-chip topological optical devices.