arXiv subjects

Sourav Patel

Publications and source records attributed to Sourav Patel.

13 recordsLinked to original sources

arXiv2024

A Distributed Malicious Agent Detection Scheme for Resilient Power Apportioning in Microgrids

We consider the framework of distributed aggregation of Distributed Energy Resources (DERs) in power networks to provide ancillary services to the power grid. Existing aggregation schemes work under the assumption of trust and honest behavior of the DERs and can suffer when that is not the case. In this article, we develop a distributed detection scheme that allows the DERs to detect and isolate the maliciously behaving DERs. We propose a model for the maliciously behaving DERs and show that the proposed distributed scheme leads to the detection of the malicious DERs. Further, augmented with the distributed power apportioning algorithm the proposed scheme provides a framework for resilient distributed power apportioning for ancillary service dispatch in power networks. A controller-hardware-in-the-loop (CHIL) experimental setup is developed to evaluate the performance of the proposed resilient distributed power apportioning scheme on an 8-commercial building distribution network (Central Core) connected to a 55 bus distribution network (External Power Network) based on the University of Minnesota Campus. A diversity of DERs and loads are included in the network to generalize the applicability of the framework. The experimental results corroborate the efficacy of the proposed resilient distributed power apportioning for ancillary service dispatch in power networks.

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arXiv2024

Enhanced Convolution Neural Network with Optimized Pooling and Hyperparameter Tuning for Network Intrusion Detection

Network Intrusion Detection Systems (NIDS) are essential for protecting computer networks from malicious activities, including Denial of Service (DoS), Probing, User-to-Root (U2R), and Remote-to-Local (R2L) attacks. Without effective NIDS, networks are vulnerable to significant security breaches and data loss. Machine learning techniques provide a promising approach to enhance NIDS by automating threat detection and improving accuracy. In this research, we propose an Enhanced Convolutional Neural Network (EnCNN) for NIDS and evaluate its performance using the KDDCUP'99 dataset. Our methodology includes comprehensive data preprocessing, exploratory data analysis (EDA), and feature engineering. We compare EnCNN with various machine learning algorithms, including Logistic Regression, Decision Trees, Support Vector Machines (SVM), and ensemble methods like Random Forest, AdaBoost, and Voting Ensemble. The results show that EnCNN significantly improves detection accuracy, with a notable 10% increase over state-of-art approaches. This demonstrates the effectiveness of EnCNN in real-time network intrusion detection, offering a robust solution for identifying and mitigating security threats, and enhancing overall network resilience.

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arXiv2022

mu-synthesis-based Generalized Robust Framework for Grid-following and Grid-forming Inverters

Grid-following and grid-forming inverters are integral components of microgrids and for integration of renewable energy sources with the grid. For grid following inverters, which need to emulate controllable current sources, a significant challenge is to address the large uncertainty of the grid impedance. For grid forming inverters, which need to emulate a controllable voltage source, large uncertainty due to varying loads has to be addressed. In this article, a mu-synthesis-based robust control design methodology, where performance under quantified uncertainty is guaranteed, is developed under a unified approach for both grid-following and grid-forming inverters. The control objectives, while designing the proposed optimal controllers, are: i) reference tracking, disturbance rejection, harmonic compensation capability with sufficient LCL resonance damping under large variations of grid impedance uncertainty for grid-following inverters; ii) reference tracking, disturbance rejection, harmonic compensation capability with enhanced dynamic response under large variations of equivalent loading uncertainty for grid-forming inverters. A combined system-in-the-loop (SIL), controller hardware-in-the-loop (CHIL) and power hardware-in-the-loop (PHIL) based experimental validation on 10 kVA microgrid system with two physical inverter systems is conducted in order to evaluate the efficacy and viability of the proposed controllers.

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arXiv2021

Gradient-Consensus: Linearly Convergent Distributed Optimization Algorithm over Directed Graphs

In this article, we propose a new approach, optimize then agree for minimizing a sum $ f = \sum_{i=1}^n f_i(x)$ of convex objective functions over a directed graph. The optimize then agree approach decouples the optimization step and the consensus step in a distributed optimization framework. The key motivation for optimize then agree is to guarantee that the disagreement between the estimates of the agents during every iteration of the distributed optimization algorithm remains under any apriori specified tolerance; existing algorithms do not provide such a guarantee which is required in many practical scenarios. In this method, each agent during each iteration maintains an estimate of the optimal solution and, utilizes its locally available gradient information along with a finite-time approximate consensus protocol to move towards the optimal solution (hence the name Gradient-Consensus algorithm). We establish that the proposed algorithm has a global R-linear rate of convergence if the aggregate function $f$ is strongly convex and Lipschitz differentiable. We also show that under the relaxed assumption of $f_i$'s being convex and Lipschitz differentiable, the objective function error residual decreases at a Q-linear rate (in terms of the number of gradient computation steps) until it reaches a small value, which can be managed using the tolerance value specified on the finite-time approximate consensus protocol; no existing method in the literature has such strong convergence guarantees when $f_i$ are not necessarily strongly convex functions. The communication overhead for the improved guarantees on meeting constraints and better convergence of our algorithm is $O(k\log k)$ iterates in comparison to $O(k)$ of the traditional algorithms. Further, we numerically evaluate the performance of the proposed algorithm by solving a distributed logistic regression problem.

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arXiv2021

Recovery of Power Flow to Critical Infrastructures using Mode-dependent Droop-based Inverters

Recovery of power flow to critical infrastructures, after grid failure, is a crucial need arising in scenarios that are increasingly becoming more frequent. This article proposes a power transition and recovery strategy by proposing a mode-dependent droop control-based inverters. The control strategy of inverters achieves the following objectives 1) regulate the output active and reactive power by the droop-based inverters to a desired value while operating in on-grid mode 2) seamless transition and recovery of power flow injections into the critical loads in the network by inverters operating in off-grid mode after the main grid fails; 3) require minimal information of grid/network status and conditions for the mode transition of droop control. A framework for assessing the stability of the system and to guide the choice of parameters for controllers is developed using control-oriented modeling. A comprehensive controller hardware-in-the-loop-based real-time simulation study on a test-system based on the realistic electrical network of M-Health Fairview, University of Minnesota Medical Center, corroborates the efficacy of the proposed controller strategy.

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arXiv2020

Design of H-infinity-based Robust Controller for Single-phase Grid-feeding Voltage Source Inverters

This article proposes the design of H-infinity-based robust current controller for single-phase grid-feeding voltage source inverter with an LCL filter. The main objective of the proposed controller is to have good reference tracking, disturbance rejection and sufficient LCL resonance damping under a large range of variations of grid impedance. Based on the aforementioned performance requirements, frequency dependent weighting functions are designed. Subsequently, the sub-optimal control problem is formulated and solved to determine the stabilizing controller. Computational footprint of the controller is addressed for ease-of-implementability on low-cost controller boards. Finally controller hardware-in-the-loop simulations on OPAL-RT are performed in the validation stage to obtain performance guarantees of the controller. The proposed controller exhibits fast response during transients and superior reference tracking, disturbance rejection at steady-state when compared with proportional- and resonant-based current controllers.

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arXiv2020

Convex Decreasing Algorithms: Distributed Synthesis and Finite-time Termination in Higher Dimension

We introduce a general mathematical framework for distributed algorithms, and a monotonicity property frequently satisfied in application. These properties are leveraged to provide finite-time guarantees for converging algorithms, suited for use in the absence of a central authority. A central application is to consensus algorithms in higher dimension. These pursuits motivate a new peer to peer convex hull algorithm which we demonstrate to be an instantiation of the described theory. To address the diversity of convex sets and the potential computation and communication costs of knowing such sets in high dimension, a lightweight norm based stopping criteria is developed. More explicitly, we give a distributed algorithm that terminates in finite time when applied to consensus problems in higher dimensions and guarantees the convergence of the consensus algorithm in norm, within any given tolerance. Applications to consensus least squared estimation and distributed function determination are developed. The practical utility of the algorithm is illustrated through MATLAB simulations.

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arXiv2020

Distributed Power Apportioning with Early Dispatch for Ancillary Services in Renewable Grids

This article develops a distributed framework for coordinating distributed energy resources (DERs) in a power network to provide secondary frequency response (SFR) as an ancillary service to the bulk power system. A distributed finite-time protocol-based solution is adopted that allows each DER in the network to determine power reference commands. The distributed protocol respects information exchange constraints posed by a communication network layer while being robust to delays in the communications channels. The proposed framework enables coordinated response and control of the aggregated DERs by apportioning the share of generation that each DER needs to provide towards meeting any specified global SFR command while allowing for adjustments due to variability in generation and demand in order to prioritize renewable energy sources in the network. A novel early dispatch mechanism with brown start is synthesized to achieve initial DER response to changing SFR commands that is faster than state-of-the-art distributed approaches. The proposed power apportioning protocol is validated using an end-to-end power hardware-in-the-loop configuration at a distribution system scale with 40+ physical hardware DERs, underlying 7-MW power system model, a 250-DER communication topology with physical and simulated distributed controller nodes, varied communication protocols, and an underlying real-world power system model. Experimental results demonstrate the efficacy of the proposed method toward distributed coordination of hundreds of DERs for providing fast response at SFR timescales.

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arXiv2020

Robust and Optimal Single-loop Voltage Controller for Grid-forming Voltage Source Inverters

Design and implementation of an optimal and robust single-loop voltage controller is proposed for single-phase grid forming voltage source inverter (VSI). The objective of the proposed controller is to have good reference tracking and disturbance rejection. Uncertain nature of loads can significantly alter system's behavior, especially during heavily loaded conditions, and impose an uncertainty in the dynamic model. This deteriorates the robustness of the controller severely. H-Infinity-based controller design is proposed in this article to address this issue. The required objectives of the optimal controller are formulated after modeling the loads as uncertain element of the control system. Time-domain MATLAB/SIMULINK-based simulation study substantiates the fact that the resulting controller exhibits superior robustness in performance during varying loading, conditions of VSI than that of the conventional multi-loop control architecture.

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arXiv2020

Isochronous Architecture-Based Voltage-Active Power Droop for Multi-Inverter Systems

Advanced microgrids consisting of distributed energy resources interfaced with multi-inverter systems are becoming more common. Consequently, the effectiveness of voltage and frequency regulation in microgrids using conventional droop-based methodologies is challenged by uncertainty in the sizeand schedule of loads. This article proposes an isochronous architecture of 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 energyresources (DERs). In spite of not employing explicit control for frequency regulation, this architecture allows even sharing of reactive power while maintaining reduced circulating currents between inverters. The performance is achieved even when there are mismatches between commanded reference and power demanded from the actual load in the network. The isochronous architecture is implemented by employing a global positioning system (GPS) to disseminate timing signals that enable the microgrid to maintain nominal system frequency in the entire network. This enables direct control of active power through voltage source inverter (VSI) output voltage regulation, even in the presence of system disturbances. A small signal eigenvalue analysis of a multi-inverter system near the steady-state operating point is presented to evaluate the stability of the multi-inverter system with the proposed VP-D control. Simulation studies and hardware experiments on an 1.2 kVA prototype are conducted. The effectiveness of the proposed architecture towards active and reactive power sharing between inverters with load scenarios are demonstrated. Results of the hardware experiments corroborate the viability of the proposed VP-D control architecture.

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arXiv2019

Distributed finite-time termination for consensus algorithm in switching topologies

In this article, we present a finite time stopping criterion for consensus algorithms in networks with dynamic communication topology. Recent results provide asymptotic convergence to the consensus algorithm. However, the asymptotic convergence of these algorithms pose a challenge in the practical settings where the response from agents is required in finite time. To this end, we propose a Maximum-Minimum protocol which propagates the global maximum and minimum values of agent states (while running consensus algorithm) in the network. We establish that global maximum and minimum values are strictly monotonic even for a dynamic topology and can be utilized to distributively ascertain the closeness to convergence in finite time. We show that each node can have access to the global maximum and minimum by running the proposed Maximum-Minimum protocol and use it as a finite time stopping criterion for the otherwise asymptotic consensus algorithm. The practical utility of the algorithm is illustrated through experiments where each agent is instantiated by a NodeJS socket.io server.

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arXiv2017

Distributed Apportioning in a Power Network for providing Demand Response Services

Greater penetration of Distributed Energy Resources (DERs) in power networks requires coordination strategies that allow for self-adjustment of contributions in a network of DERs, owing to variability in generation and demand. In this article, a distributed scheme is proposed that enables a DER in a network to arrive at viable power reference commands that satisfies the DERs local constraints on its generation and loads it has to service, while, the aggregated behavior of multiple DERs in the network and their respective loads meet the ancillary services demanded by the grid. The Net-load Management system for a single unit is referred to as the Local Inverter System (LIS) in this article . A distinguishing feature of the proposed consensus based solution is the distributed finite time termination of the algorithm that allows each LIS unit in the network to determine power reference commands in the presence of communication delays in a distributed manner. The proposed scheme allows prioritization of Renewable Energy Sources (RES) in the network and also enables auto-adjustment of contributions from LIS units with lower priority resources (non-RES). The methods are validated using hardware-in-the-loop simulations with Raspberry PI devices as distributed control units, implementing the proposed distributed algorithm and responsible for determining and dispatching realtime power reference commands to simulated power electronics interface emulating LIS units for demand response.

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arXiv2017

Distributed Finite Time Termination of Ratio Consensus for Averaging in the presence of Delays

Distributed averaging of agent initial conditions is a well-studied problem in context of networked systems where coordination amongst the agents is of paramount importance. The asymptotic nature of convergence of distributed averaging protocols and presence of communication delays, however, makes it challenging to implement in practical settings. It is important that agents develop the ability to detect on their own when average of the initial conditions of the agents is achieved within some pre-specified tolerance and stop further computations to avoid overhead expenses in the presence of delays. This article presents a distributed finite time stopping criterion for distributed averaging using ratio consensus on a fixed interconnection topology. The practical utility of the algorithm has been illustrated through simulations.

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