Hardware-in-the-Loop Validation of Optimal Adaptive Protection for Utility Networked Microgrid Applications
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The Bipartisan Infrastructure Law kick-started a surge in clean energy investments in the United States. Securing these systems and their supporting infrastructure from the outset is imperative, starting from the conceptual design and continuing through the entire engineering lifecycle. Cyber-Informed Engineering is an ideal approach to seamlessly integrate the necessary advanced control capabilities and adaptive protection systems, especially considering the trend towards cloud-based infrastructure and process controls. Learn more about CIE at https://www.energy.gov/ceser/articles/cyber-informed-engineering-bridge-between-cyber-and-critical-infrastructure-securing.
This paper presents a methodology for simultaneous fault detection, classification, and topology estimation for adaptive protection of distribution systems. The methodology estimates the probability of the occurrence of each one of these events by using a hybrid structure that combines three sub-systems, a convolutional neural network for topology estimation, a fault detection based on predictive residual analysis, and a standard support vector machine with probabilistic output for fault classification. The input to all these sub-systems is the local voltage and current measurements. A convolutional neural network uses these local measurements in the form of sequential data to extract features and estimate the topology conditions. The fault detector is constructed with a Bayesian stage (a multitask Gaussian process) that computes a predictive distribution (assumed to be Gaussian) of the residuals using the input. Since the distribution is known, these residuals can be transformed into a Standard distribution, whose values are then introduced into a one-class support vector machine. The structure allows using a one-class support vector machine without parameter cross-validation, so the fault detector is fully unsupervised. Finally, a support vector machine uses the input to perform the classification of the fault types. All three sub-systems can work in a parallel setup for both performance and computation efficiency. In conclusion, we test all three sub-systems included in the structure on a modified IEEE123 bus system, and we compare and evaluate the results with standard approaches.
The penetration of distributed energy resources (DERs) in distribution systems is rapidly increasing. This can lead to large variations in the operating load and the fault current seen by a relay, which can impact the reliability of overcurrent protection. Adaptive overcurrent relaying (AOCR) can address this concern by enabling the relay to adjust its pickup settings based on the system operating conditions. This paper presents a novel dynamic AOCR approach that allows the relays to locally estimate the pickup settings and dynamically modify them. The approach is based on a two-stage estimation algorithm that uses local measurements and system information to estimate the worst-case fault current and the relay pickup settings. The relay pickup settings are then dynamically modified based on the estimated fault current. The performance of the proposed approach is evaluated on multiple configurations of the EPRI J1 feeder and the IEEE 13-bus system. The results show that the proposed approach can effectively improve the reliability of overcurrent protection in distribution systems with DERs.
In microgrids, the short-circuit current magnitude is significantly limited by more than an order of magnitude due to the relatively small inverter-based resources. Commercially available protective devices for distribution cannot reliably protect a microgrid due to their dependence on the magnitude of the fault current. Moreover, overcurrent relays typically cannot function properly for a microgrid because they are incapable of detecting faults and/or performing the coordination between the relays in inverter-based microgrids operated in the islanded mode. This paper proposes a model-adaptive relay designed to adjust the relay curves based on the available generation and the network topology. The proposed method runs a real-time model of the microgrid, which gathers information from the network to calculate the available short-circuit current in the specified node. The fault current from the model is then used for the adaptive algorithm to calculate the relay settings, considering coordination with the downstream fuses and upstream reclosers. This work presents the validation of the proposed method in Hardware-in-the-Loop, in a hardware testbed as well as field deployed in a real microgrid in East-Tennessee.
This study reviewed existing conventional and nonconventional protection schemes for grid-connected and islanded mode operations in North American microgrid projects. The microgrid projects investigated in this study used different types of distributed energy resources (DERs) and integrated hydropower/diesel generators, gas/steam/wind turbines, and photovoltaic systems with energy storage. In this work, conventional protection schemes were defined as those within the IEEE Standard C37.2-2008, whereas nonconventional schemes were those not defined within this standard. The pros and cons of conventional and nonconventional protection schemes were discussed in detail. The overvoltage, undervoltage, and frequency elements were the most common conventional protection schemes applied in microgrid projects in North America. These protection elements were used to detect the islanded conditions and faults that could not be sensed by overcurrent relays because of small fault currents contributed by low-inertia DERs and power-electronic sources. Directional overcurrent elements were used to distinguish between external (grid) and internal (microgrid) faults. Adaptive protection was the most popular nonconventional protection scheme applied to the microgrid projects. In conclusion, different types of DERs and operational modes must be considered in order to address the protection and control challenges of each microgrid and to obtain the best technical and economical solution.
Rising ambient temperatures due to climate change will increase urban populations’ exposures to extreme heat. During hot hours, a key protective adaptation is increased air conditioning and associated consumption of electricity for cooling. But during cold hours, milder temperatures have the offsetting effect of reducing consumption of electricity and other fuels for heating. We elucidate the net consequences of these opposing effects in 36 cities in different world regions. We couple reduced-form statistical models of cities’ hourly responses of electric load to temperature with temporally downscaled projections of temperatures simulated by 21 global climate models (GCMs), projecting the effects of warming on the demand for electricity circa 2050. Cities' responses, temperature exposures and impacts are heterogeneous, with changes in total annual consumption ranging from –2.7 to 5.7%, and peak power demand increasing by as much as 9.5% at the multi-GCM median. The largest increases are concentrated in more economically developed mid-latitude cities, with less developed urban areas in the tropics exhibiting relatively small changes. The results highlight the important role of the structure of electricity demand: large temperature increases in tropical cities are offset by their inelastic responses, which can be attributed to lower air-conditioning penetration.
Increasing levels of inverter-based distributed energy resources (IBDERs) impact the legacy overcurrent distribution protection systems. The fault current injections of IBDERs are limited to between 1-2 p.u. of the rated current. The combination of the limited fault current and varying system load makes it increasingly difficult to reliably set the overcurrent protection. To address this challenge, researchers are proposing adaptive overcurrent protection mechanisms that can adapt to changing system conditions. Accurate modeling of IBDERs is required to develop reliable protection schemes. This paper presents a photovoltaics-based IBDER model in OpenDSS for adaptive overcurrent protection. The paper presents the validation of the proposed IBDER model against a detailed model in electromagnetic transient programs. Protection analysis of the Electric Power Research Institute J1 feeder with the proposed IBDER model is also presented.
Stimuli-responsive materials offer new opportunities to resolve long-standing material challenges and are rapidly gaining pivotal roles in diverse applications. For example, smart protective garments that rapidly transport water vapor and autonomously block chemical threats are expected to enable an effective new paradigm of adaptive personal protection. However, the incorporation of these seemingly incompatible properties into a single responsive system remains elusive. Herein, a bistable membrane that can rapidly, selectively, and reversibly transition from a highly breathable state in a safe environment to a chemically protective state when exposed to organophosphate threats such as sarin is demonstrated. Dynamic response to chemical stimuli is achieved through the physical collapse of an ultrathin copolymer layer on the membrane surface, which efficiently gates transport through membrane pores composed of single-walled carbon nanotubes (SWNTs). The adoption of nanometer-wide SWNTs for ultrafast moisture conduction enables a simultaneous boost in size-sieving selectivity and water-vapor permeability by decreasing nanotube diameter, thereby overcoming the breathability/protection trade-off that limits conventional membrane materials. Adaptive multifunctional membranes based on this platform greatly extend the active use of a protective garment and present exciting opportunities in many other areas including separation processes, sensing, and smart delivery.
Substantial differences in fault levels between grid-tied and islanded modes is one of the primary challenges of microgrid protection. During grid-tied mode, the bulk grid provides significant short-circuit, while during islanded operation the short-circuit magnitude is small due to inverter-based resources limiting their current output close to nominal ratings. Consequently, conventional distribution protection strategies based on overcurrent cannot reliably protect microgrids when operating in islanded mode. Fuses and circuit breakers are particularly affected because of their inverse characteristics. Presently, the absence of affordable solutions for protecting microgrids in islanded mode leads to microgrids shutting down during electrical faults. The contribution of this article is two-fold. The first innovation proposes specific hardware modifications to grid-forming inverters to increase their short-circuit current during electrical faults. The second innovation introduces a novel control strategy designed to preserve control stability margins even when the grid-filter saturates, ensuring sinusoidal output currents under normal and fault conditions. Through experimental results, the inverter with the proposed modifications can provide more than three-times its nominal current during electrical faults. For the prototype testbed, this was sufficient to enable the use of traditional legacy overcurrent protection, achieving the fuse-to-relay and relay-to-relay minimum coordination time for the line-to-ground, line-to-line to ground, and three-phase electrical faults.
The ability Gram-negative pathogens have at adapting and protecting themselves against antibiotics has increasingly become a public health threat. Data-driven models identifying molecular properties that correlate with outer membrane (OM) permeation and growth inhibition while avoiding efflux could guide the discovery of novel classes of antibiotics. Here we evaluate 174 molecular descriptors in 1260 antimicrobial compounds and study their correlations with antibacterial activity in Gram-negative Pseudomonas aeruginosa. The descriptors are derived from traditional approaches quantifying the compounds’ intrinsic physicochemical properties, together with, bacterium-specific from ensemble docking of compounds targeting specific MexB binding pockets, and all-atom molecular dynamics simulations in different subregions of the OM model. Using these descriptors and the measured inhibitory concentrations, we design a statistical protocol to identify predictors of OM permeation/inhibition. We find consistent rules across most of our data highlighting the role of the interaction between the compounds and the OM. An implementation of the rules uncovered in our study is shown, and it demonstrates the accuracy of our approach in a set of previously unseen compounds. Our analysis sheds new light on the key properties drug candidates need to effectively permeate/inhibit P. aeruginosa, and opens the gate to similar data-driven studies in other Gram-negative pathogens.
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Foot-and-mouth disease (FMD), caused by the FMD virus (FMDV), is a highly contagious disease of cloven-hoofed livestock that can have severe economic impacts. Control and prevention strategies, including the development of improved vaccines, are urgently needed to effectively control FMD outbreaks in endemic settings. Previously, we employed two distinct strategies (codon pair bias deoptimization (CPD) and codon bias deoptimization (CD)) to deoptimize various regions of the FMDV serotype A subtype A12 genome, which resulted in the development of an attenuated virus in vitro and in vivo, inducing varying levels of humoral responses. In the current study, we examined the versatility of the system by using CPD applied to the P1 capsid coding region of FMDV serotype A subtype, A24, and another serotype, Asia1. Viruses carrying recoded P1 (A24-P1Deopt or Asia1-P1Deopt) exhibited different degrees of attenuation (i.e., delayed viral growth kinetics and replication) in cultured cells. Studies in vivo using a mouse model of FMD demonstrated that inoculation with the A24-P1Deopt and Asia1-P1Deopt strains elicited a strong humoral immune response capable of offering protection against challenge with homologous wildtype (WT) viruses. However, different results were obtained in pigs. While clear attenuation was detected for both the A24-P1Deopt and Asia1-P1Deopt strains, only a limited induction of adaptive immunity and protection against challenge was detected, depending on the inoculated dose and serotype deoptimized. Our work demonstrates that while CPD of the P1 coding region attenuates viral strains of multiple FMDV serotypes/subtypes, a thorough assessment of virulence and induction of adaptive immunity in the natural host is required in each case in order to finely adjust the degree of deoptimization required for attenuation without affecting the induction of protective adaptive immune responses.
This report presents a novel fault detection, characterization, and fault current control algorithm for a standalone solar-photovoltaic (PV) based dc microgrids. The protection scheme is based on the current derivative algorithm. The overcurrent and current directional/differential comparison based protection schemes are incorporated for the dc microgrid fault characterization. For a low impedance fault, the fault current is controlled based on the current/voltage thresholds and current direction. Generally, the droop method is used to control the power-sharing between the converters by controlling the reference voltage. In this article, an adaptive droop scheme is also proposed to control the fault current by calculating a virtual resistance R droop , and to control the converter output reference voltage. For a high impedance fault, differential comparison method is used to characterize the fault. These algorithms effectively control the converter pulsewidth and reduce the flow of source current from a particular converter, which helps to increase the fault clearing time. Additionally, a trip signal is sent to the corresponding dc circuit breaker (DCCB), to isolate the faulted converter, feeder or a dc bus. The dc microgrid protection design procedure is detailed, and the performance of the proposed method is verified by simulation analysis.