Enhanced Grid-Following (E-GFL) Inverter: A Unified Control Framework for Stiff and Weak Grids
Not Available
Askarian, Alireza↗
Engineering topics
Salapaka, Srinivasa
Publications and source records attributed to Salapaka, Srinivasa.
Isochronous Architecture-Based Voltage-Active Power Droop for Multi-Inverter Systems
This article proposes an isochronous architecture for parallel inverters with only voltage-active power droop (VP-D) control for improving active power sharing as well as plug-and-play of multi-inverter-based distributed energy resources (DERs). The isochronous framework obviates the need for an explicit regulation of the frequency while allowing for sharing of reactive power. The article shows that the detrimental effects of circulating currents between inverters can be addressed in the framework developed. The isochronous architecture is implemented by employing a GPS to disseminate clock timing signals that enable the microgrid to maintain the nominal system frequency. Small signal eigenvalue analysis of a Multi-inverter Microgrid system near the steady-state operating point is presented to evaluate the system stability. Moreover, unlike traditional droop-based methods, the isochronous strategy lends itself to analytical guarantees which are developed in the article. Furthermore, the effect of delays in receiving GPS clock signals by an inverter is experimentally characterized and an inverter isolation mechanism is proposed that disconnects inverters facing large GPS communication delays. Hardware experiments on an 1.2 kVA-prototype and controller-hardware-in-the-loop validation on the CIGRE distribution network are conducted to demonstrate the effectiveness of the proposed architecture towards active and reactive power sharing between inverters with load scenarios.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Multi-objective optimization of peel and shear strengths in ultrasonic metal welding using machine learning-based response surface methodology
Ultrasonic metal welding (UMW) is a solid-state joining technique with varied industrial applications. Despite of its numerous advantages, UMW has a relative narrow operating window and is sensitive to variations in process conditions. As such, it is imperative to quantitatively characterize the influence of welding parameters on the resulting joint quality. The quantification model can be subsequently used to optimize the parameters. Conventional response surface methodology (RSM) usually employs linear or polynomial models, which may not be able to capture the intricate, nonlinear input-output relationships in UMW. Furthermore, some UMW applications call for simultaneous optimization of multiple quality indices such as peel strength, shear strength, electrical conductivity, and thermal conductivity. To address these challenges, this paper develops a machine learning (ML)-based RSM to model the input-output relationships in UMW and jointly optimize two quality indices, namely, peel and shear strengths. The performance of various ML methods including spline regression, Gaussian process regression (GPR), support vector regression (SVR), and conventional polynomial regression models with different orders is compared. A case study using experimental data shows that GPR with radial basis function (RBF) kernel and SVR with RBF kernel achieve the best prediction accuracy. The obtained response surface models are then used to optimize a compound joint strength indicator that is defined as the average of normalized shear and peel strengths. In addition, the case study reveals different patterns in the response surfaces of shear and peel strengths, which has not been systematically studied in the literature. While developed for the UMW application, the method can be extended to other manufacturing processes.
42 ENGINEERING↗
Fouling modeling and prediction approach for heat exchangers using deep learning
In this article, we develop a generalized and scalable statistical model for accurate prediction of fouling resistance using commonly measured parameters of industrial heat exchangers. This prediction model is based on deep learning where a scalable algorithmic architecture learns non-linear functional relationships between a set of target and predictor variables from large number of training samples. Here, the efficacy of this modeling approach is demonstrated for predicting fouling in an analytically modeled cross-flow heat exchanger, designed for waste heat recovery from flue-gas using room temperature water. The performance results of the trained models demonstrate that the mean absolute prediction errors are under 10 –4 KW –1 for flue-gas side, water side and overall fouling resistances. The coefficients of determination (R 2 ), which characterize the goodness of fit between the predictions and observed data, are over 99%. Even under varying levels of measurement noise in the inputs, we demonstrate that predictions over an ensemble of multiple neural networks achieves better accuracy and robustness to noise. We find that the proposed deep-learning fouling prediction framework learns to follow heat exchanger flow and heat transfer physics, which we confirm using locally interpretable model agnostic explanations around randomly selected operating points. Overall, we provide a robust algorithmic framework for fouling prediction that can be generalized and scaled to various types of industrial heat exchangers.
42 ENGINEERING↗
Extreme Antiscaling Performance of Slippery Omniphobic Covalently Attached Liquids
Scale formation presents an enormous cost to the global economy. Classical nucleation theory dictates that to reduce the heterogeneous nucleation of scale, the surface should have low surface energy and be as smooth as possible. Past approaches have focused on lowering surface energy via the use of hydrophobic coatings and have created atomically smooth interfaces to eliminate nucleation sites, or both, via the infusion of low-surface-energy lubricants into rough superhydrophobic substrates. Although lubricant-based surfaces are promising candidates for antiscaling, lubricant drainage inhibits their utilization. Here, we develop methodologies to deposit slippery omniphobic covalently attached liquids (SOCAL) on arbitrary substrates. Similar to lubricant-based surfaces, SOCAL has ultralow roughness and surface energy, enabling low nucleation rates and eliminating the need to replenish the lubricant. To enable SOCAL coating on metals, we investigated the surface chemistry required to ensure high-quality functionalization as measured by ultralow contact angle hysteresis (<3°). Using a multilayer deposition approach, we first electrophoretically deposit (EPD) silicon dioxide (SiO 2 ) as an intermediate layer between the metallic substrate and SOCAL. The necessity of EPD SiO 2 is to smooth (<10 nm roughness) as well as to enable the proper surface chemistry for SOCAL bonding. To characterize antiscaling performance, we utilized calcium sulfate (CaSO 4 ) scale tests, showing a 20× reduction in scale deposition rate than untreated metallic substrates. Descaling tests revealed that SOCAL dramatically decreases scale adhesion, resulting in rapid removal of scale buildup. Furthermore, our work not only demonstrates a robust methodology for depositing antiscaling SOCAL coatings on metals but also develops design guidelines for the creation of antifouling coatings for alternate applications such as biofouling and high-temperature coking.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Joining Techniques for Novel Metal Polymer Hybrid Heat Exchangers
In the United States, over 50% of the unrecovered energy from industrial processes is in the form of low-grade heat (< 220°C). Materials and maintenance costs of common heat exchangers are typically too high to justify their usage. Polymers, though more affordable, are usually unsuitable for HX applications due to their low thermal conductivity (~0.2 W/mK). Here, we show that metal-polymer hybrids may be attractive from both performance and cost perspectives. The use of polymers further increases the resistance to corrosion by sulfuric and carbonic acids often present in flue gases. An ongoing work explores different configurations of layered polyimide-copper macroscale hybrids for heat exchanger applications using numerical simulations. This paper explores a manufacturing pathway for producing such layered hybrid tubes that involves directly rolling and bonding tapes made of polymer and copper foil into tubes. A critical problem in the fabrication process is the bonding of metal and polymers. We explore approaches involving adhesives (epoxy, acrylic and silicone) for metal/polymer interfaces and direct welding (ultrasonic) for metal/metal interfaces that can be integrated into the manufacturing process. We report characterizations of the thermomechanical properties of these joining processes. This work paves the way for realizing cost-effective manufacturing of heat exchangers for low grade waste heat recovery.
Kuntumalla, Gowtham↗