Control barrier functions (CBFs) have been widely used for synthesizing controllers in safety-critical applications. When used as a safety filter, a CBF provides a simple and computationally efficient way to obtain sa...
Control barrier functions (CBFs) have been widely used for synthesizing controllers in safety-critical applications. When used as a safety filter, a CBF provides a simple and computationally efficient way to obtain safe controls from a possibly unsafe performance controller. Despite its conceptual simplicity, constructing a valid CBF is well known to be challenging, especially for high-relative degree systems under nonconvex constraints. Recently, work has been done to learn a valid CBF from data based on a handcrafted CBF (HCBF). Even though the HCBF gives a good initialization point, it still requires a large amount of data to train the CBF network. In this work, we propose a new method to learn more efficiently from the collected data through a novel prioritized data sampling strategy. A priority score is computed from the loss value of each data point. Then, a probability distribution based on the priority score of the data points is used to sample data and update the learned CBF. Using our proposed approach, we can learn a valid CBF that recovers a larger portion of the true safe set using a smaller amount of data. The effectiveness of our method is demonstrated in simulation on a two-link arm.
A Power systems are undergoing a rapid change in generation mix due to the growth of Inverter-Based Resources (IBRs) such as wind and solar PV. This rapid growth creates new challenges for protection engineers. The ou...
A Power systems are undergoing a rapid change in generation mix due to the growth of Inverter-Based Resources (IBRs) such as wind and solar PV. This rapid growth creates new challenges for protection engineers. The output current of an IBR facility under short circuit conditions differs significantly from that of a conventional rotating synchronous source facility, posing protection problems. The protection schemes for the transmission line, which were designed locally at the planning stage, may operate reliably for low penetration of IBRs but in the case of high penetration, fault detection and location in the transmission line will be challenged. In this paper, a traveling wave-based protection schemes with the aid of Artificial Neural Network (ANN) as an innovative method to overcome these challenges and facilitate the widespread deployment of IBRs in transmission systems will be presented. A three-bus transmission network with voltage level of 380Kv and 94% of renewables penetration is simulated using DIgSILENT Power Factory is used in this study. The results demonstrates that fault identification is a quick and reliable way to identify a variety of faults, particularly single line to ground faults, which are the most challenging fault for the protection system.
In the transition to clean, cheap, and sustainable energy, microgrid-based renewable energy resources (RES) are widely utilized in islanded or grid-connected modes. However, due to the intermittent nature of RES such ...
In the transition to clean, cheap, and sustainable energy, microgrid-based renewable energy resources (RES) are widely utilized in islanded or grid-connected modes. However, due to the intermittent nature of RES such as photovoltaic (PV) or wind energy systems, energy storage systems (ESSs) such as batteries are mandated to satisfy load demands and stabilize system operation. For instance, stabilizing the dc bus voltage in islanded microgrids is crucial to keeping the system reliable, dependable, and stable. This paper proposes an adaptive dc bus voltage control technique based on a fuzzy-PI controller. Moreover, a performance comparison between fuzzy-PI and conventional proportional-integrator (PI) controllers is performed based on a MATLAB/Simulink model. The results show that the fuzzy-PI controller has a faster response to any reference voltage variation and less overshoot compared to conventional PI controllers. Additionally, the proposed controller can efficiently stabilize the dc bus voltage during load variation portions.
This paper addresses the challenge of deploying multiple service function chains (SFCs) in 5G networks. We propose a new network model for network function virtualization (NFV) network traffic that captures the dynami...
This paper addresses the challenge of deploying multiple service function chains (SFCs) in 5G networks. We propose a new network model for network function virtualization (NFV) network traffic that captures the dynamically changing nature of service requests and the availability of network resources. The deployment of multiple SFCs is then formulated as a constrained optimal resource allocation problem for dynamical systems, and its approximate solution is obtained by using a constrained least-squares method. Specifically, the proposed approach treats each NFV node as a discrete-time nonlinear dynamical system and seeks the suboptimal deployment of virtual network functions (VNFs) while minimizing the overall cost function for all NFV nodes. The cost function considers both the cost for service providers and quality of service (QoS) for users. Examples are provided to illustrate the proposed new network model. Due to the analytical nature of the obtained constrained least-squares solution, we expect this algorithm to be more efficient in addressing the service requests in large-scale 5G networks.
A power and Energy Management System (P/EMS) is crucial to Microgrid's effective practical and reliable operation. In the Shipboard Microgrid (SMG), the load demand is dominated by the high penetration of propulsi...
A power and Energy Management System (P/EMS) is crucial to Microgrid's effective practical and reliable operation. In the Shipboard Microgrid (SMG), the load demand is dominated by the high penetration of propulsion loads, which is very dynamic. Some particular loads in naval ships draw very high power for very short periods. A hybrid application of battery and supercapacitor is proposed to cater to the energy consumed by pulse load in a naval ship and for backup purposes. The system proposed in this model is a stand-alone SMG with a Photovoltaic (PV) and Hybrid Energy Storage System (HESS). A rule-based energy supervision and power allocation technique have also been presented considering the specific fuel consumption of diesel engine generators and the characteristics of hybrid energy stored sources (battery and supercapacitor) of the entire SMG. The simulation studies using MATLAB/Simulink software indicate that the proposed P/EMS strategy enables management of power and energy contributions of hybrid resources on the ship. In addition, a reasonable allocation of power among HESS units, which can effectively smooth out the load power fluctuations of the proposed SMG has been introduced.
Modern radar must adapt to changing environments, and changepoint detection is a method to do so. Many radar systems employ a prediction action cycle to proactively determine transmission modes while spectrum sharing....
Modern radar must adapt to changing environments, and changepoint detection is a method to do so. Many radar systems employ a prediction action cycle to proactively determine transmission modes while spectrum sharing. This method constructs and implements a model of the environment to predict unused frequencies, and subsequently transmits in predicted available bands. For these selection strategies to succeed, the assessment of the underlying environmental models must be robust to change. Changepoint detection increases this robustness. Changepoint detection is the identification of sudden changes, or changepoints, in the distribution from which data is drawn. This information allows the models to discard “garbage” data from a previous distribution, which has no relation to the current state of the environment. For spectrum sharing applications, these changepoints may represent interferers leaving and entering the spectral environment, or changing their spectrum access patterns. In this work, we demonstrate the effectiveness of the addition of changepoint detection to spectrum sharing algorithms in changing environments. Bayesian online changepoint detection (BOCD) is applied to the sense-and-predict algorithm to increase the accuracy of its models and improve its performance. The use of changepoint detection allows for dynamic and robust spectrum sharing even as interference patterns change suddenly and dramatically. BOCD is especially advantageous because it enables online changepoint detection, allowing models to be updated continuously as data are collected. This strategy can also be applied to other predictive algorithms that create and maintain models in changing environments.
In this paper, we propose a digital twin (DT)assisted resource demand prediction scheme to enhance prediction accuracy for multicast short video streaming. Particularly, we first construct user DTs (UDTs) for collecti...
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In this paper, we propose a digital twin (DT)-assisted resource demand prediction scheme to enhance prediction accuracy for multicast short video streaming. Particularly, we first construct user DTs (UDTs) for collect...
In this paper, we propose a digital twin (DT)-assisted resource demand prediction scheme to enhance prediction accuracy for multicast short video streaming. Particularly, we first construct user DTs (UDTs) for collecting real-time user status, including channel condition, location, watching duration, and preference. A reinforcement learning-empowered K-means++ algorithm is developed to cluster users based on the collected user status in UDTs. We then analyze users' watching duration and preferences in each multicast group to obtain the swiping probability distribution and recommended videos, respectively. The obtained information is utilized to predict radio and computing resource demand of each multicast group. Initial simulation results demonstrate that the proposed scheme can accurately predict resource demand.
This paper presents an optimal modulation and systematic filter design approach for a single-stage dual-active-bridge (DAB) based dc-ac microinverter to achieve improved differential-mode (DM) noise performance for el...
This paper presents an optimal modulation and systematic filter design approach for a single-stage dual-active-bridge (DAB) based dc-ac microinverter to achieve improved differential-mode (DM) noise performance for electromagnetic interference (EMI) tests. As DM filters contribute significantly to the overall converter volume, the main objective of this work is to leverage the degrees of freedom in the DAB converters to effectively attenuate the EMI noise. In addition, the DM filter design method needs to ensure near unity power factor converter operation. To achieve these targets, this paper analyzes three modulation strategies based on fixed or variable switching frequency operation where the different control modulation variables are varied to find the simulated DM noise spectrum. Based on the required DM attenuation, a constrained optimization problem is formulated to determine minimal DM filter parameters. Simulation results show that a spread spectrum approach with variable switching frequency is shown to minimize the DM EMI attenuation effort by spreading the noise profile. A fully GaN 400 W hardware prototype demonstrating the spread-sprectrum approach.
Despite the increasing prevalence of DC microgrids, they encounter instability issues due to imbalances in supply and demand, especially when dealing with specific loads such as pulse load or variable pulse load. The ...
Despite the increasing prevalence of DC microgrids, they encounter instability issues due to imbalances in supply and demand, especially when dealing with specific loads such as pulse load or variable pulse load. The system faces severe changes which reduce the effectiveness of typical PV-Battery control systems. The solution to this problem frequently involves the use of energy storage devices such as supercapacitors SCs. When exposed to transients, batteries risk having their lifespans shortened and are challenged in keeping the stability of DC bus voltage and reducing overall transients. In order to increase the stability of DC microgrids and thereby increase their reliability and resilience, it is possible to combine the advantages of batteries and supercapacitors. Integrating both batteries and supercapacitors effectively enables DC microgrids to manage both steady-state and transient conditions. The main goal of this research paper is to investigate how the hybrid energy storage system (HESS) improves the stability of DC microgrids. According to sets of results, the proposed scheme has successfully reduced DC bus voltage variations regardless of variations in solar irradiance and the different pulsed load profiles.
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