arXiv subjects

Satish Vedula

Publications and source records attributed to Satish Vedula.

14 recordsLinked to original sources

arXiv2026

Functional Subspace Projection for Detection of Coordinated Stealthy Attacks in Power Systems

Modern power grids are increasingly vulnerable to coordinated cyber-attacks, particularly false data injection attacks (FDIAs) that can evade conventional residual-based detectors. While most existing detection methods rely on instantaneous measurements, coordinated dynamic attacks can remain stealthy at each time step while introducing structured temporal deviations. This paper develops a joint framework for modeling and detecting such attacks in multi-area power systems. A time-aggregated attack model is first formulated to capture temporal evolution and inter-area coordination. For detection, a kernel-embedded functional subspace detection (KEFSD) method is proposed, which models residual trajectories in a reproducing kernel Hilbert space (RKHS) and employs RKHS-constrained functional principal component analysis (PCA) to identify anomalous temporal patterns. Simulation results on a modified IEEE 14-bus system demonstrates the proposed method achieves improved detection performance compared to the conventional residual based 2-norm detector.

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arXiv2026

Disturbance-Adaptive Finite-Time Control of Three-Phase Rectifiers

Three-phase AC-DC rectifiers are fundamental components in modern power electronics systems, yet achieving rapid voltage regulation and precise current tracking under load and grid disturbances remains challenging due to nonlinear dynamics and measurement uncertainties. This paper presents a finite-time control method for three-phase AC-DC rectifiers that achieves millisecond-scale regulation of DC-link voltage and grid currents under varying conditions. The proposed design employs a transformed augmented error-state dynamics model, extending the voltage dynamics to a two-state system to construct an adaptive sliding surface that guarantees fast finite-time convergence. A nonlinear sliding-mode voltage regulator with an online disturbance estimator ensures rapid and robust voltage tracking, while a fast current controller achieves finite-time dq-axis current tracking with minimal chattering. Theoretical results establish finite-time stability and provide explicit gain selection conditions. Simulation results demonstrate up to 99.40 per cent and 87.5 per cent reductions in voltage and current convergence times, respectively, compared to conventional robust controllers. Laboratory experiments further validate the approach, showing 33.33 per cent lower voltage ripple, 33.33 per cent faster rise time, and 32.43 per cent reduced steady-state error relative to a recent method. These results confirm improvements in transient performance, convergence, and overall system stability, highlighting the method's practical applicability for high-performance rectifier control.

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arXiv2026

Predictive Energy Management for Hybrid Powertrains

Hybrid power trains (HPT) run on multiple energy sources, often involving energy storage systems/batteries (ESS). As a result, the risk of battery degradation and the reliability of energy storage elements pose a major challenge in designing an energy-efficient hybrid power train. This paper presents an energy management strategy that adaptively splits power demand between the engine and the battery pack in a hybrid power train taking into account the battery degradation. Incorporating the battery degradation model directly into the underlying optimization problem is challenging on multiple fronts: 1) Any reasonable degradation model will, due to its complexity, result in a complicated optimization problem that is impractical for real-time implementation 2) the models contain a lot of time-varying parameters that can only be determined through destructive experimental procedures. As a result, it is essential to devise heuristics that reasonably capture the degradation per usage of the batteries. One such heuristic considered in this paper is the absolute power extracted from the battery. A distributed model predictive strategy is then developed to coordinate the power split to maximize efficiency while mitigating the failure risk due to battery degradation. The designed EM strategy is demonstrated through a realistic simulation of three different hybrid power trains: hybrid road vehicles (for example: a hybrid electric vehicle (HEV)), hybrid surface vehicles (for example: dynamically positioned hybrid ships (DPS)), and hybrid aerial vehicles (for example: hybrid electric aircraft (HEA)). The results show the effectiveness of the energy management strategy in managing battery degradation.

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arXiv2025

Predictive Energy Management for Mitigating Load Altering Attacks for Islanded Microgrids Using Battery Energy Storage Systems

An increasing number of smart devices controlling loads opens a potential pathway for false data attacks which could alter the loads. The presence of energy storage with its ability to quickly respond to discrepancies in loads offers a promising solution for security by preventing further instabilities and potential blackouts. This paper proposes a control methodology for secure predictive energy management that uses batteries to mitigate the impact of load-altering attacks. To that extent, we develop a microgrid model along with the primary control for microgrid. The developed models and the optimization algorithm are validated through a real-time numerical simulation of a modified IEEE 9 bus system involving a battery as one of the energizing sources. The results show the effectiveness of the battery in countering the load alterations.

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arXiv2025

Degradation-based Energy Management for Microgrids in the Presence of Energy Storage Elements

Integration of Inverter-based Resources (IBRs) such as solar-powered plants which lack the intrinsic characteristics such as the inertial response of the traditional synchronous-generator (SG) based sources presents a new challenge in the form of analyzing the grid stability under their presence. For example, solar power is available for approximately from 9 AM-5 PM. However, the result of the rise in power consumption after 6 PM and the reverting back to the non-renewable source of power generation during that period puts immense stress on the grid, testing the ramp limitations of the SGs. Failure to meet the required power demand due to SG ramp limitations leads to failure of the power grid and other catastrophes. Numerous mitigation techniques exist in order to address the ramping issues with adding the energy storage elements (ESE) to the grid being one. ESEs have higher ramping capabilities compared to the traditional SGs. Also, the ESEs can store the energy and supply it to the grid when required making them extremely responsive to high ramp situations. However, the rate of degradation of the ESEs is faster than the SGs. This raises an important issue of addressing the degradation of the ESEs while meeting the required power demand objectives and constraints. This work proposes a battery degradation-aware model predictive energy management strategy and it is tested via a numerical simulation on multiple physical systems such as Shipboard Power Systems (SPS). Moreover, the risk arising due to the fault in the IBR is also studied by means of a numerical simulation. Overall, the goal of this study is to make the existing power grid more robust, resilient, and risk-free from component degradation and eventual failures.

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arXiv2024

Distributed Model-Predictive Energy Management Strategy for Shipboard Power Systems Considering Battery Degradation

With the integration of loads such as pulse power loads, a new control challenge is presented in meeting their high ramp rate requirements. Existing onboard generators are ramp rate limited. The inability to meet the load power due to ramp rate limitation may lead to instability. The addition of energy storage elements in addition to the existing generators proves a viable solution in addressing the control challenges presented by high ramp rate loads. A distributed energy management strategy maximizing generator efficiency and minimizing energy storage degradation is developed that facilitates an optimal adaptive power split between generators and energy storage elements. The complex structure of the energy storage degradation model makes it tough for its direct integration into the optimization problem and is not practical for real-time implementation. A degradation heuristic to minimize absolute power extracted from the energy storage elements is proposed as a degradation heuristic measure. The designed strategy is tested through a numerical case study of a consolidated shipboard power system model consisting of a single generator, energy storage element, and load model. The results show the impact of the designed energy management strategy in effectively managing energy storage health.

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arXiv2024

Decentralized Droop-based Finite-Control-Set Model Predictive Control of Inverter-based Resources in Islanded AC Microgrid

This paper presents an improved droop control method to ensure effective power sharing, voltage regulation, and frequency stabilization of inverter-based resources (IBRs) connected in parallel in an islanded AC microgrid. In the contemporary droop control algorithm, the distance between connected inverters affects the effectiveness of the active power-frequency and the reactive power-voltage droop characteristics which results in poor power sharing at the primary level of the microgrid. That is, high impedance emanating from long transmission lines results in instability, poor voltage tracking, and ineffective frequency regulation. Hence, in this work, we use a finite-control-set model predictive controller (FCS-MPC) in the inner loop, which gives efficient voltage tracking, good frequency regulation, and faster performance response. FCS-MPC is easy to implement in fast switching converters and does not suffer from computational burden unlike the continuous-set MPC and is also devoid of issues of multiple-loop, parameter variation, and slow response associated with conventional droop control methods. We derived the condition for bounded stability for the FCS-MPC and the proposed method is tested via a numerical simulation on three IBRs. The results show effective power sharing, capacitor voltage tracking, and efficient frequency regulation with reduced oscillations to changes in load.

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arXiv2024

Fault-Ride-Through (FRT) Control of a grid-connected Fixed-Speed Wind Energy Conversion System using STATCOM

Wind energy conversion system (WECS) is stochastic in nature and has low inertia to grid voltage instability, poor reactive power compensation and most importantly fault susceptibility. Variable speed WECS such as a doubly-fed induction generators (DFIG) are well known to reach steady state quickly after fault occurrence without the need for an external reactive power source because of the presence of a back-to-back converter that provides independent control of the active and reactive power unlike in the fixed-speed squirrel cage induction generator (SCIG) counterpart that cant be stabilized unless ab external source of reactive power support is present. However, controlling DFIG is complicated and costly due to complete tripping unlike the fixed-speed generators which doesnt trip completely when fault occurs. Hence, in this work, a 48-pulse, 3-phase static synchronous compensator (STATCOM) is used to ensure reactive power compensation and fault-ride through (FRT) control of the SCIG against over-voltage emanating from fault occurrence in a grid-connected power system. The goal here is to guarantee voltage stability and fault-ride through control against injected faults within certain time ranges at the point of common coupling (PCC) between the AC sources, the load, and the fixed-speed WECS.

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arXiv2024

Fault-Tolerant Decentralized Control for Large-scale Inverter-based Resources for Active Power Tracking

Integration of Inverter Based Resources (IBRs) which lack the intrinsic characteristics such as the inertial response of the traditional synchronous-generator (SG) based sources presents a new challenge in the form of analyzing the grid stability under their presence. While the dynamic composition of IBRs differs from that of the SGs, the control objective remains similar in terms of tracking the desired active power. This letter presents a decentralized primal-dual-based fault-tolerant control framework for the power allocation in IBRs. Overall, a hierarchical control algorithm is developed with a lower level addressing the current control and the parameter estimation for the IBRs and the higher level acting as the reference power generator to the low level based on the desired active power profile. The decentralized network-based algorithm adaptively splits the desired power between the IBRs taking into consideration the health of the IBRs transmission lines. The proposed framework is tested through a simulation on the network of IBRs and the high-level controller performance is compared against the existing framework in the literature. The proposed algorithm shows significant performance improvement in the magnitude of power deviation and settling time to the nominal value under faulty conditions as compared to the algorithm in the literature.

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arXiv2024

Battery Degradation Heuristics for Predictive Energy Management in Shipboard Power Systems

The presence of Pulse Power Loads (PPLs) in the Notional Shipboard Power System (SPS) presents a challenge in the form of meeting their high ramp rate requirements. Considering the ramp rate limitations on the generators, this might hinder the power flow in the grid. Failure to meet the ramp rate requirements might cause instability. Aggregating generators with energy storage elements usually addresses the ramp requirements while ensuring the power demand is achieved. This paper proposes an energy management strategy that adaptively splits the power demand between the generators and the batteries while simultaneously considering the battery degradation and the generator's efficient operation. Since it is challenging to incorporate the battery degradation model directly into the optimization problem due to its complex structure and the degradation time scale which is not practical for real-time implementation, two reasonable heuristics in terms of minimizing the absolute battery power and minimizing the battery state of charge are proposed and compared to manage the battery degradation. A model predictive energy management strategy is then developed to coordinate the power split considering the generator efficiency and minimizing the battery degradation based on the two heuristic approaches. The designed strategy is tested via a simulation of a lumped notional shipboard power system. The results show the impact of the battery degradation heuristics for energy management strategy in mitigating battery degradation and its health management.

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arXiv2021

Low-bandwidth Modular Mathematical Modeling of DC Microgrid Systems for Control Development with Application to Shipboard Power Systems

In recent years, DC and AC microgrid (MG) systems have attracted a major attention due to various potential for integration of future technology into conventional systems and control. The integration of such technology requires appropriate tools for complex design, analysis and optimization. This paper presents a mathematical low-bandwidth modeling (LBM) approach that can be used for control development in DC and further be extended to AC MG systems. In this work, first a simplified mathematical model of a medium voltage DC (MVDC) shipboard MG system is presented, next, the overall system-level connection convention is presented to display the overall mathematical coupling of the individual sub-systems, then, a simplified example of the control development is presented, and last, the overall system under a test scenario is implemented in Simulink Real-time.

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arXiv2021

Degradation Aware Predictive Energy Management Strategy for Ship Power Systems

Integration of modern defence weapons into ship power systems poses a challenge in terms of meeting the high ramp rate requirements of those loads. It might be demanding for the generators to meet the ramp rates of these loads. Failure to meet so, might lead to stability issues. This is addressed by conglomeration of generators and energy storage elements to handle the required power demand posed by loads. This paper proposes an energy management strategy based on model predictive control that incorporates the uncertainty in the load prediction. The proposed controller optimally coordinates the power split between the generators and energy storage elements to guarantee that the power demand is met taking into considerations the ramp rate limitations and the load uncertainty. A low bandwidth model consisting of a single generator and a single energy storage element is used to validate the results of the proposed energy management strategy. The results demonstrate the robustness of the controller under load prediction uncertainty and demonstrate the effect of load uncertainty on battery capacity loss.

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arXiv2021

Model and Load Predictive Control for Design and Energy Management of Shipboard Power Systems

In current Medium Voltage DC (MVDC) Shipboard Power Systems (SPSs), multiple sources exist to supply power to a common dc bus. Conventionally, the power management of such systems is performed by controlling Power Generation Modules (PGMs) which include fuel operated generators and underlying converters. Moreover, energy management is performed by the emerging single or hybrid Energy Storage Systems (ESSs). This paper presents a model and load predictive control framework for power and energy management of SPSs. Here, MPC with load prediction is used for three main objectives: (1) to request power and energy from generators and energy storage elements according to their individual State of Power (SOP) and ramp-rate limitations, (2) to consider and integrate the generator cost and degradation, and (3) to reach a specific parking (final) State of Charge (SOC) for the ESSs at the end of the prediction horizon. The solution of the optimization problem is demonstrated using MATLAB and the functionality of the control framework is validated in real-time simulation environment.

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arXiv2020

Multi-Model Resilient Observer under False Data Injection Attacks

In this paper, we present the concept of boosting the resiliency of optimization-based observers for cyber-physical systems (CPS) using auxiliary sources of information. Due to the tight coupling of physics, communication and computation, a malicious agent can exploit multiple inherent vulnerabilities in order to inject stealthy signals into the measurement process. The problem setting considers the scenario in which an attacker strategically corrupts portions of the data in order to force wrong state estimates which could have catastrophic consequences. The goal of the proposed observer is to compute the true states in-spite of the adversarial corruption. In the formulation, we use a measurement prior distribution generated by the auxiliary model to refine the feasible region of a traditional compressive sensing-based regression problem. A constrained optimization-based observer is developed using l1-minimization scheme. Numerical experiments show that the solution of the resulting problem recovers the true states of the system. The developed algorithm is evaluated through a numerical simulation example of the IEEE 14-bus system.

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