arXiv subjects

Narayan Bhusal

Publications and source records attributed to Narayan Bhusal.

At least 19 recordsLinked to original sources

arXiv2024

Correlating Power Outage Spread with Infrastructure Interdependencies During Hurricanes

Power outages caused by extreme weather events, such as hurricanes, can significantly disrupt essential services and delay recovery efforts, underscoring the importance of enhancing our infrastructure's resilience. This study investigates the spread of power outages during hurricanes by analyzing the correlation between the network of critical infrastructure and outage propagation. We leveraged datasets from Hurricanemapping.com, the North American Energy Resilience Model Interdependency Analysis (NAERM-IA), and historical power outage data from the Oak Ridge National Laboratory (ORNL)'s EAGLE-I system. Our analysis reveals a consistent positive correlation between the extent of critical infrastructure components accessible within a certain number of steps (k-hop distance) from initial impact areas and the occurrence of power outages in broader regions. This insight suggests that understanding the interconnectedness among critical infrastructure elements is key to identifying areas indirectly affected by extreme weather events.

cs.IR↗

arXiv2023

A Deep Reinforcement Learning-based Reserve Optimization in Active Distribution Systems for Tertiary Frequency Regulation

Federal Energy Regulatory Commission (FERC) Orders 841 and 2222 have recommended that distributed energy resources (DERs) should participate in energy and reserve markets; therefore, a mechanism needs to be developed to facilitate DERs' participation at the distribution level. Although the available reserve from a single distribution system may not be sufficient for tertiary frequency regulation, stacked and coordinated contributions from several distribution systems can enable them participate in tertiary frequency regulation at scale. This paper proposes a deep reinforcement learning (DRL)-based approach for optimization of requested aggregated reserves by system operators among the clusters of DERs. The co-optimization of cost of reserve, distribution network loss, and voltage regulation of the feeders are considered while optimizing the reserves among participating DERs. The proposed framework adopts deep deterministic policy gradient (DDPG), which is an algorithm based on an actor-critic method. The effectiveness of the proposed method for allocating reserves among DERs is demonstrated through case studies on a modified IEEE 34-node distribution system.

eess.SY↗

arXiv2023

Reserve Allocation in Active Distribution Systems for Tertiary Frequency Regulation: A Coalitional Game Theory-based Approach

This paper proposes a coalitional game theory-based approach for reserve optimization to enable DERs participate in tertiary frequency regulation. A two-stage approach is proposed to effectively and precisely allocate spinning reserve requirements from each DER in distribution systems. In the first stage, two types of characteristic functions: worthiness index (WI) and power loss reduction (PLR) of each coalition are computed. In the second stage, the equivalent Shapley values are computed based on the characteristic functions, which are used to determine distribution factors for reserve allocation among DERs.

eess.SY↗

arXiv2023

Deep Q-Learning-based Distribution Network Reconfiguration for Reliability Improvement

Distribution network reconfiguration (DNR) has proved to be an economical and effective way to improve the reliability of distribution systems. As optimal network configuration depends on system operating states (e.g., loads at each node), existing analytical and population-based approaches need to repeat the entire analysis and computation to find the optimal network configuration with a change in system operating states. Contrary to this, if properly trained, deep reinforcement learning (DRL)-based DNR can determine optimal or near-optimal configuration quickly even with changes in system states. In this paper, a Deep Q Learning-based framework is proposed for the optimal DNR to improve reliability of the system. An optimization problem is formulated with an objective function that minimizes the average curtailed power. Constraints of the optimization problem are radial topology constraint and all nodes traversing constraint. The distribution network is modeled as a graph and the optimal network configuration is determined by searching for an optimal spanning tree. The optimal spanning tree is the spanning tree with the minimum value of the average curtailed power. The effectiveness of the proposed framework is demonstrated through several case studies on 33-node and 69-node distribution test systems.

eess.SY↗

arXiv2023

Cyber-Physical Power System Layers: Classification, Characterization, and Interactions

This paper provides a strategy to identify layers and sub-layers of cyber-physical power systems (CPPS) and characterize their inter- and intra-actions. The physical layer usually consists of the power grid and protection devices whereas the cyber layer consists of communication, and computation and control components. Combining components of the cyber layer in one layer complicates the process of modeling intra-actions because each component has different failure modes. On the other hand, dividing the cyber layers into a large number of sub-layers may unnecessarily increase the number of system states and increase the computational burden. In this paper, we classify system layers based on their common, coupled, and shared functions. Also, interactions between the classified layers are identified, characterized, and clustered based on their impact on the system. Furthermore, based on the overall function of each layer and types of its components, intra-actions within layers are characterized. The strategies developed in this paper for comprehensive classification of system layers and characterization of their inter- and intra-actions contribute toward the goal of accurate and detailed modeling of state transition and failure and attack propagation in CPPS, which can be used for various reliability assessment studies.

eess.SY↗

arXiv2022

Sensitivity Comparison of Two-photon vs Three-photon Rydberg Electrometry

We investigate the sensitivity of three-photon EIT in Rydberg atoms to radio frequency detection and compare it against conventional two-photon systems. Specifically, we model the 4-level and 5-level atomic system and compare how the transmission of the probe changes with different powers of the lasers used and strengths of the RF field. In this model, we also define a sensitivity metric to best relate to the operation of the current best experimental implementation based on shot noise limited detection. We find that the three-photon system boasts much narrower line widths compared to the conventional two-photon EIT. However, these narrow line features do not align with the regions of the best sensitivity. In addition to this, we calculate the expected sensitivity for the two-photon Rydberg sensor and find that the best achievable sensitivity is over an order of magnitude better than the current measured values of 5 uV/m/Hz. However, by accounting for the additional noise sources in the experiment and the quantum efficiency of the photo-detectors, the values are in good agreement.

physics.atom-ph↗

arXiv2022

Experimental Guesswork with Quantum Side Information using Twisted Light

The guesswork is an information-theoretic quantity which can be seen as an alternate security criterion to entropy. Recent work has established the theoretical framework for guesswork in the presence of quantum side information, which we extend both theoretically and experimentally. We consider guesswork when the side information consists of the BB84 states and their higher-dimensional generalizations. With this side information, we compute the guesswork for two different scenarios for each dimension. We then perform a proof-of-principle experiment using Laguerre-Gauss modes to experimentally compute the guesswork for higher-dimensional generalizations of the BB84 states. We find that our experimental results agree closely with our theoretical predictions. This work shows that guesswork can be a viable security criterion in cryptographic tasks, and is experimentally accessible across a number of optical setups.

quant-ph↗

arXiv2022

Noisy Coherent Population Trapping: Applications to Noise Estimation and Qubit State Preparation

Coherent population trapping is a well-known quantum phenomenon in a driven $Λ$ system, with many applications across quantum optics. However, when a stochastic bath is present in addition to vacuum noise, the observed trapping is no longer perfect. Here we derive a time-convolutionless master equation describing the equilibration of the $Λ$ system in the presence of additional temporally correlated classical noise, with an unknown decay parameter. Our simulations show a one-to-one correspondence between the decay parameter and the depth of the characteristic dip in the photoluminescence spectrum, thereby enabling the unknown parameter to be estimated from the observed spectra. We apply our analysis to the problem of qubit state initialization in a $Λ$ system via dark states and show how the stochastic bath affects the fidelity of such initialization as a function of the desired dark-state amplitudes. We show that an optimum choice of Rabi frequencies is possible.

quant-ph↗

arXiv2021

Single-mode input squeezing and tripartite entanglement in three-mode ponderomotive optomechanics simulations

Quantum entanglement is a crucial resource for a wide variety of quantum technologies. However, the current state-of-art methods to generate quantum entanglement in optomechanical systems are not as efficient as all-optical methods utilizing nonlinear crystals. This article proposes a new scheme in which two single-mode squeezed light fields are injected into an optomechanical cavity. We demonstrate through our numerical simulations that the quantum entanglement can be substantially enhanced with the careful selection of squeezing strength and squeezing angle of the two quadrature squeezed light fields. Our results represent a significant improvement in output bipartite photon-photon entanglement over the previously demonstrated schemes using two coherent light fields as inputs. These simulations predict a maximum increase in bipartite optical entanglement by a factor of about 6, as well as increases in the quantum noise of the output light. A perceived loss of quantum information at certain squeezing angles is attributed to tripartite entanglement between the two optical fields and the optomechanical oscillator (OMO). At particular squeezing angles, the bipartite (or tripartite) entanglement can be increased, thus introducing a method of optically controlling the intracavity entanglement. These mechanics can benefit various optical quantum technologies utilizing optomechanical entanglement and continuous variable quantum optics.

quant-ph↗

arXiv2021

Hosting Capacity Approach Implications

This paper revisits the generation hosting capacity (HC) calculation approach to account for grid operational flexibility--the ability to reconfigure the system safely. In essence, the generation hosting capacity is determined against the set of limiting factors--voltage, thermal (conductor loading), reverse flow (at the feeder head, station transformer, or substation), and change in the voltage (due to sudden change in generation output)). Not that long ago, California Investor-Owned Utilities (IOUs) added a new criterion that does not allow reverse flow at the supervisory control and data acquisition (SCADA) points that can change the system configuration, aiming to prevent the potential transfer of reverse flow to an adjacent feeder. This new criterion intended to capture operational constraints as part of hosting capacity-known as hosting capacity with operational flexibility (OpFlex). This paper explores the shortfalls of such an approach and proposes performing actual transfer analysis when determining hosting capacity rather than implementing the OpFlex approach. Furthermore, we discuss the need for transition to determining hosting capacity profile (all intervals) rather than a flat line (one, worst performing interval) hosting capacity. A hosting capacity profile would inform the developers of interval-by-interval limits and opportunities, creating new opportunities to reach higher penetration of DERs at a lower cost. With technological and computational advancements, such an approach is neither out of implementation reach nor that computationally expensive. In return, far more DER can be interconnected once programmed not to violate certain generation profiles as part of the interconnection requirement, and utilities would be better informed of their actual operational flexibility, benefiting society overall.

eess.SY↗

arXiv2021

Smart Quantum Statistical Imaging beyond the Abbe-Rayleigh Criterion

The manifestation of the wave nature of light through diffraction imposes limits on the resolution of optical imaging. For over a century, the Abbe-Rayleigh criterion has been utilized to assess the spatial resolution limits of optical instruments. Recently, there has been an enormous impetus in overcoming the Abbe-Rayleigh resolution limit by projecting target light beams onto spatial modes. These conventional schemes for superresolution rely on a series of spatial projective measurements to pick up phase information that is used to boost the spatial resolution of optical systems. Unfortunately, these schemes require a priori information regarding the coherence properties of "unknown" light beams. Furthermore, they require stringent alignment and centering conditions that cannot be achieved in realistic scenarios. Here, we introduce a smart quantum camera for superresolving imaging. This camera exploits the self-learning features of artificial intelligence to identify the statistical fluctuations of unknown mixtures of light sources at each pixel. This is achieved through a universal quantum model that enables the design of artificial neural networks for the identification of quantum photon fluctuations. Our camera overcomes the inherent limitations of existing superresolution schemes based on spatial mode projection. Thus, our work provides a new perspective in the field of imaging with important implications for microscopy, remote sensing, and astronomy.

quant-ph↗

arXiv2021

Low-Light Shadow Imaging using Quantum-Noise Detection with a Camera

We experimentally demonstrate an imaging technique based on quantum noise modification after interaction with an opaque object. By using a homodyne-like detection scheme, we eliminate the detrimental effect of the camera's dark noise, making this approach particularly attractive for imaging scenarios that require weak illumination. Here, we reconstruct the image of an object illuminated with a squeezed vacuum using a total of 800 photons, utilizing less than one photon per frame on average.

quant-ph↗

arXiv2021

High-dimensional encryption in optical fibers using machine learning

The ability to engineer the spatial wavefunction of photons has enabled a variety of quantum protocols for communication, sensing, and information processing. These protocols exploit the high dimensionality of structured light enabling the encodinng of multiple bits of information in a single photon, the measurement of small physical parameters, and the achievement of unprecedented levels of security in schemes for cryptography. Unfortunately, the potential of structured light has been restrained to free-space platforms in which the spatial profile of photons is preserved. Here, we make an important step forward to using structured light for fiber optical communication. We introduce a smart high-dimensional encryption protocol in which the propagation of spatial modes in multimode fibers is used as a natural mechanism for encryption. This provides a secure communication channel for data transmission. The information encoded in spatial modes is retrieved using artificial neural networks, which are trained from the intensity distributions of experimentally detected spatial modes. Our on-fiber communication platform allows us to use spatial modes of light for high-dimensional bit-by-bit and byte-by-byte encoding. This protocol enables one to recover messages and images with almost perfect accuracy. Our smart protocol for high-dimensional optical encryption in optical fibers has key implications for quantum technologies relying on structured fields of light, particularly those that are challenged by free-space propagation.

quant-ph↗

arXiv2021

A Cooperative Game Theory-based Approach to Under-frequency Load Shedding Control

This paper proposes a cooperative game theory-based under-frequency load shedding (UFLS) approach for frequency stability and control in power systems. UFLS is a crucial factor for frequency stability and control especially in power grids with high penetration of renewable energy sources and restructured power systems. Conventional UFLS methods, most of which are off-line, usually shed fixed amounts of predetermined loads based on a predetermined schedule which can lead to over or under curtailment of load. This paper presents a co-operative game theory-based two-stage strategy to effectively and precisely determine locations and amounts of loads to be shed for UFLS control. In the first stage, the total amount of loads to be shed, also referred to as deficit in generation or the disturbance power, is computed using the initial rate of change of frequency (ROCOF) referred to the equivalent inertial center. In the second stage, the Shapley value, one of the solution concepts of cooperative game theory, is used to determine load shedding amounts and locations. The proposed method is implemented on the reduced 9-bus 3-machine Western Electricity Coordinating Council (WECC) system and simulated on Real-time Digital Simulators (RTDS). The results show that the proposed UFLS approach can effectively return the system to normal state after disturbances.

eess.SY↗

arXiv2021

Smart Quantum Technologies using Photons

The technologies utilizing quantum states of light have been in the spotlight for the last two decades. In this regard, quantum metrology, quantum imaging, quantum-optical communication are some of the important applications that exploit fascinating quantum properties like quantum superposition, quantum correlations, and nonclassical photon statistics. However, the state-of-art technologies operating at the single-photon level are not robust enough to truly realize a reliable quantum-photonic technology. In Chapter 1, I present a historical account of photon-based technologies. Furthermore, I discuss recent encouraging developments in the field of quantum-photonic technologies, and major challenges for the implementation of reliable quantum technologies, setting up a stage for unveiling our smart methodologies to cope with them. Similarly, in Chapter 2, I review the fundamental concepts of quantum optics and machine learning. In Chapter 3, I present a theoretical work on a nonlinear quantum metrology scheme, showing a sub-shot-noise limited phase estimation using the displaced-squeezed light and on/off detection. Furthermore, I discuss a camera-based squeezed-light detection that can be a smart and time-efficient alternative to balanced-homodyne detection. In Chapter 4, I discuss our efforts to incorporate artificial intelligence in a quest to improve the efficiency of discriminating thermal light from coherent light sources. In Chapter 5, I present a communication protocol in presence of random phase distortions. We utilize convolutional neural networks to perform the spatial mode correction of single photons, resulting in a near-unity fidelity of correction. Finally, I wrap up my dissertation in Chapter 6 by summarizing the historical context, challenges facing state-of-art techniques, and the importance of our efforts to introduce artificial intelligence in quantum technologies.

quant-ph↗

arXiv2021

Observation of the Modification of Quantum Statistics of Plasmonic Systems

For almost two decades, it has been believed that the quantum statistical properties of bosons are preserved in plasmonic systems. This idea has been stimulated by experimental work reporting the possibility of preserving nonclassical correlations in light-matter interactions mediated by scattering among photons and plasmons. Furthermore, it has been assumed that similar dynamics underlies the conservation of the quantum fluctuations that define the nature of light sources. Here, we demonstrate that quantum statistics are not always preserved in plasmonic systems and report the first observation of their modification. Moreover, we show that multiparticle scattering effects induced by confined optical near fields can lead to the modification of the excitation mode of plasmonic systems. These observations are validated through the quantum theory of optical coherence for single- and multi-mode plasmonic systems. Our findings constitute a new paradigm in the understanding of the quantum properties of plasmonic systems and unveil new paths to perform exquisite control of quantum multiparticle systems.

quant-ph↗

arXiv2021

Third Generation Gamma Camera SPECT System

Single Photon Emission Computed Tomography (SPECT) is a non-invasive imaging modality, frequently used in myocardial perfusion imaging. The biggest challenges facing the majority of clinical SPECT systems are low sensitivity, poor resolution, and the relatively high radiation dose to the patient. New generation systems (GE Discovery, DSPECT) dedicated to cardiac imaging improve sensitivity by a factor of 5-8. The purpose of this work is to investigate a new gamma camera design with 21 hemi-ellipsoid detectors each with a pinhole collimator for Cardiac SPECT for further improvement in sensitivity, resolution, imaging time, and radiation dose. To evaluate the resolution of our hemi-ellipsoid system, GATE Monte-Carlo simulations were performed on point-sources, rod-sources, and NCAT phantoms. The purpose of point-source simulation is to obtain operating pinhole diameter by comparing the average FWHM (Full width half-maximum) of flat-detector system with curved hemi-ellipsoid detector system. The operating pinhole diameter for the curved hemi-ellipsoid detector was found to be 8.68mm. System resolution is evaluated using reconstructed rod-sources equally spaced within the region of interest. The results were compared with results of GE discovery system available in the literature. The system performance was also evaluated using the mathematical anthropomorphic NCAT (NURBSbased Cardiac Torso) phantom with a full (clinical) dose acquisition (25mCi) for 2 mins and an ultra-low-dose acquisition of 3mCi for 5.44mins.

physics.med-ph↗

arXiv2021

Deep Ensemble Learning-based Approach to Real-time Power System State Estimation

Power system state estimation (PSSE) is commonly formulated as weighted least-square (WLS) algorithm and solved using iterative methods such as Gauss-Newton methods. However, iterative methods have become more sensitive to system operating conditions than ever before due to the deployment of intermittent renewable energy sources, low carbon technologies (e.g., electric vehicles), and demand response programs. Appropriate PSSE approaches are required to avoid pitfalls of the WLS-based PSSE computations for accurate prediction of operating conditions. This paper proposes a data-driven real-time PSSE using a deep ensemble learning algorithm. In the proposed approach, the ensemble learning setup is formulated with dense residual neural networks as base-learners and multivariate-linear regressor as meta-learner. Historical measurements and states are utilised to train and test the model. The trained model can be used in real-time to estimate power system states (voltage magnitudes and phase angles) using real-time measurements. Most of current data-driven PSSE methods assume the availability of a complete set of measurements, which may not be the case in real power system data-acquisition. This paper adopts multivariate linear regression to forecast system states for instants of missing measurements to assist the proposed PSSE technique. Case studies are performed on various IEEE standard benchmark systems to validate the proposed approach. The results show that the proposed approach outperforms existing data-driven PSSE methods techniques.

eess.SY↗