Power system dynamics and control, Artificial intelligence, Soft computing techniques, MicroGrid, Smart Grid
22
Scopus Publications
Scopus Publications
Multiphysics Analysis of Heat Sink Designs for Efficient Thermal Performance in Power Electronics Systems Praveen Kumar Singh, Nesamani Natarajan, Sariki Murali SAE Technical Papers, 2026 <div class="section abstract"><div class="htmlview paragraph">The thermal management capability of power electronic (PE) systems has a critical impact on the performance and efficiency of electric, fuel cell, or hybrid vehicles. Bus bars, high resistance sensor devices, semiconductor switches, power capacitors are the primary components, which make a major contribution in total heat generation in electrical drive unit. As PE packaging sizes are projected to become smaller, the challenge of managing increased heat dissipation becomes more critical. This paper numerically compares six different cooling strategies to determine the best possible thermal management scenario. A coupled physics co-simulation framework is used to analyze a 35W motor inverter integrated with water cooled heat sink. A multi-physics finite element model, integrating fluid, electrical, and thermal fields, is employed to analyze heat generation within the PE system and the associated cooling mechanisms. The power losses from the inverter system are dynamically computed in 1-D simulation and fed to the multi-physics finite element model as input. The Cooper-Mikic-Yovanovich (CMY) correlation is used to simulate the contact losses between busbar connections. This model considers the effect of surface roughness and topology on the electrical and thermal contact resistances. This research improves the comprehension of optimized cooling techniques, demonstrating the best design with calibrated cooling parameters. Additionally, it presents an effective numerical procedure for analyzing PE cooling phenomena.</div></div>
Anomaly-Resilient Control for Automatic Generation Control Systems: Detection and Mitigation of Data Integrity Attacks Puli Tarun, G. Jawahar Sagar, Harshitha Puli, Sariki Murali, Lokesh Mani Sai Balaji Loya, Tarkeshwar Matho 2025 IEEE 4th International Conference on Smart Technologies for Power Energy and Control Stpec 2025 Conference Report, 2025 The increasing reliance on cyber-physical infrastructure in modern power grids has exposed key components, such as Automatic Generation Control (AGC) systems, to sophisticated cyber threats particularly false data injection attacks that can compromise grid stability and economic operations. While deep reinforcement learning (DRL) has emerged as a promising tool for adaptive, attack-resilient control, it often lacks robust mechanisms for actively detecting and mitigating deceptive inputs in real time. This paper proposes an enhanced DRLbased AGC controller integrated with a novel attack-resilient control framework that combines dynamic anomaly detection and adaptive control reconfiguration. The hybrid architecture enables proactive identification of integrity violations and realtime adjustments to control policies, safeguarding frequency regulation and system reliability during adversarial events. Simulation results confirm that the framework preserves acceptable frequency bounds, minimizes disruption to market operations, and offers a substantial advancement over conventional defenses. By synergizing intelligent learning with systemic resilience, this work charts a new path toward secure and stable smart grid control.
Transportation Electrification in Crisis: A 2025 Review of Electric Vehicle Mobility and Infrastructure Resilience During Disruptive Events Puli Tarun, G. Jawahar Sagar, Harshitha Puli, Sariki Murali, Lokesh Mani Sai Balaji Loya, Tarkeshwar Matho 2025 IEEE 4th International Conference on Smart Technologies for Power Energy and Control Stpec 2025 Conference Report, 2025 Transportation electrification continues to evolve as a cornerstone strategy in global climate action. However, the increasing integration of Electric Vehicles (EVs) into urban mobility systems introduces new vulnerabilities during disruptive events such as wildfires, floods, cyberattacks, and grid failures. This 2025 review critically evaluates the state-of-theart advancements in EV mobility resilience, focusing on recent developments in charging infrastructure, disaster planning, and evacuation logistics. We extend previous findings by incorporating insights from real-world disruptions (e.g., 2023-2024 climate-related events), advancements in vehicle-to-grid (V2G) technologies, and the emergence of artificial intelligence (AI)driven evacuation planning. Particular emphasis is placed on the interplay between smart grids, decentralized charging networks, and adaptive routing during emergencies. The study identifies persistent gaps related to congestion management, mixed fleet behavior, and equity in access to emergency infrastructure. The findings aim to support policy development, infrastructure planning, and technological innovation that enhance the role of EVs as both mobility assets and energy resources during future disruptive events.
Recurrent Neural Network Driven Load Forecasting to Enhance Disturbance Mitigation in frequency regulation of power system V V N Yaswanth Reddy, Sariki Murali, Kiran Teeparthi 2024 4th International Conference on Emerging Frontiers in Electrical and Electronic Technologies Icefeet 2024, 2024 The increasing integration of renewable energy sources and the unpredictable nature of modern power systems pose significant challenges for maintaining effective load frequency control (LFC). Conventional LFC methods struggle to manage the fast-changing dynamics and variability in load demand, leading to frequency instability. This paper addresses these challenges by introducing a novel solution that leverages machine learning (ML) techniques, specifically recurrent neural networks (RNN), to forecast load demand in control areas. The forecasted load is then fed into a three-degree-of-freedom proportional-integral-derivative (3DOF-PID) controller, which enhances the system’s ability to regulate frequency. The proposed controller is tested on a two-area, four-machine power system under step load changes, demonstrating significant improvements in dynamic response. Additionally, real-time load data from the Delhi power grid is used to validate the controller’s effectiveness in handling realistic load variations. This work underscores the potential of ML-based LFC strategies to improve stability and efficiency in modern power systems.
Assessment of Power System Resiliency with New Intelligent Controller and Energy Storage Systems Sariki Murali, Ravi Shankar, Prateek Sharma, Shivam Singh Electric Power Components and Systems, 2024 This research investigates the role of various energy storage systems (ESS) in improving the power system resiliency. Different ESS configurations are analyzed individually and in combination to assess their impact on system performance. Additionally, a fuzzy-based controller (PIλDN + FFOPI) is proposed as a secondary controller for load frequency control, with control parameters optimized using an improved Volleyball Premier League algorithm (IVPL). This algorithm leverages the powerful synergy of opposition-based learning and the versatility of the VPL algorithm, resulting in a highly effective and innovative approach. To validate the proposed approach, the modified Kundur’s 2-area test system under deregulation is first utilized, and its effectiveness is confirmed through testing with both step and random load disturbances. Sensitivity analysis is conducted to evaluate system performance across a wide range of parameter variations. Furthermore, the modified IEEE 39 bus system is utilized to assess compatibility and scalability. Results demonstrate the effectiveness of combined ESS configurations and the fuzzy-based controller in enhancing system stability and reliability. This research contributes to power system engineering by offering insights into the benefits of energy storage systems for dynamic response enhancement. The proposed fuzzy-based control strategy, tuned by the IVPL algorithm, presents a promising approach for improving power system performance and stability.
Improved LSTM-Based Load Forecasting Embedded 3DOF (FOPI)-FOPD Controller for Proactive Frequency Regulation in Power System Sariki Murali, Priyesh Saini, Kumar Abhinav, Ravi Shankar, S.K. Parida IEEE Transactions on Industry Applications, 2024 This paper presents the design and analysis of a modified volleyball premier league-optimized 3-DOF (FOPI)-FOPD controller for a proactive LFC scheme. The proposed controller incorporates forecasted load demand as one of its inputs. This unique configuration empowers the controller to proactively eliminate the disturbances. To validate the controller performance, a truncated model of the DPS has been developed which is subjected to different load profiles and are assumed to be known. It was observed that the predicted disturbance to the system (change in load demand) is of utmost importance for this proposed configuration. Thus, improved LSTM, incorporating a multi-approach feature selection and DWT has been developed, which is used to forecast day-ahead load demand. This forecasted demand is given as an input to the controller. The dynamic performance of proposed controller is evaluated by subjecting the modelled DPS with proposed controller to standard test signals. The obtained simulation results are also validated through OPAL-RT real-time simulator. The results demonstrate the superior performance and robustness of the proposed LFC approach in maintaining the frequency of the power system within acceptable limits.
Assessment of Amelioration in Frequency Regulation by deploying Novel Intelligent based Controller with Modified HVDC Tie-Line in Deregulated Environment Sariki Murali, Ravi Shankar Smart Science, 2023 This article emphasizes the design and analysis of an optimal intelligent controller for frequency regulation application. The load frequency control (LFC) mechanism is an eminent and essential mechanism to reinstate the system frequency and scheduled tie-line power to their nominal values. Employing an appropriate controller enhances the operation of the LFC mechanism. Hence, this article put forward an adoptive control policy-based fuzzy-fractional ordered PI controller parallel with fractional PIDN controller (i.e., Fuzzy (PIλf)+PIλDN) for the LFC mechanism. Besides that, a maiden attempt of using a new opposition-learning-based volleyball premier league (OVPL) algorithm is carried out to obtain optimal control parameters. The proposed optimal intelligent controller is explored for a nonlinear multi-area interconnected power system under deregulated environment. The proposed LFC scheme<apos;>s performance has been compared with several popular strategies for step and random perturb in load. Also, the robustness of the proposed scheme has been verified for a wide range of system parameter variations. On the other hand, the modified HVDC tie-line model and the impact of the inertia emulation technique (IET) using converter capacitors on transient behavior are illustrated in this article. Finally, the efficacy of the proposed LFC scheme has been verified over published literature on its platform.
Optimal CC-2DOF(PI)-PDF controller for LFC of restructured multi-area power system with IES-based modified HVDC tie-line and electric vehicles Murali Sariki, Ravi Shankar Engineering Science and Technology an International Journal, 2022 This article is introducing an optimal cascaded two-degree of freedom proportional-integral (2DOF(PI)) and proportional-derivative with filter (PDF) controller, i.e., CC-2DOF (PI)-PDF for load frequency control (LFC) mechanism. The designed controller increases the degree of freedom and rejects disturbances faster with fewer dynamics. Further, the application of an opposition-based volleyball premier league algorithm (OVPLA) has been explored to optimize the designed controller's parameters. The proposed LFC mechanism has been studied on a two-area thermal-hydro-gas restructured power system. The thermal system present in each area is equipped with generation rate constraints (GRC) for more realistic analysis. The performance of the suggested OVPLA optimized 2DOF(PI)-PDF controller has been verified over several classical controllers and algorithms separately for step and random load disturbances. Moreover, accurate modeling of the HVDC tie-line and inertia emulation strategy (IES) is discussed, and the power system’s inertia enhancement is verified. This article also highlights the ancillary services of electric vehicles (EVs) and distributed generations (DGs) to tackle any contract violation in the power system. Next, the proposed LFC mechanism's efficacy has been validated on extending a three-area power system. Finally, several case studies and comparative performance analysis with published literature demonstrate the effectiveness and efficiency of investigated LFC in handling any load disturbances.
Exploration of novel optimal fuzzy-based controller for enhancement of frequency regulation of deregulated hybrid power system with modified HVDC tie-line Sariki Murali, Ravi Shankar International Journal of Intelligent Systems, 2022 Recent advancements in the power system put forth various challenges to frequency regulation studies. These challenges show an immense impact on the power system's inertia and load pattern. The traditional load frequency control (LFC) mechanism may not be efficient to sustain the frequency regulation against these new challenges. Hence an intelligent, effective, and efficient controller is indispensable for various operating conditions of the power system. With this motive, a new intelligence‐based controller (i.e., fuzzy (FOPIλ) + PIDN) is proposed and explored for the LFC of the modern power system. Besides, an opposition‐based volleyball premier league algorithm is deployed to obtain optimal control parameters for the most promising results. The justification of the controller is evaluated on a nonlinear interconnected hybrid deregulated power system for several case studies. The effectiveness of the proposed controller has been verified by comparing it with several popular strategies for the step and random perturbation in load. Also, the firmness in the dynamic response against a wide range of system parameter variations has been analyzed. Moreover, the participation of a modified high voltage direct current model with inertia emulation technique in the system's inertia build‐up has been demonstrated. Finally, the efficacy of the proposed LFC scheme has been verified over previously published literature on their platform.
Exploration of EV Fleet role in Frequency Regulation using an Aggregate Model including Communication Delay Sariki Murali, Kumar Abhinav, Ravi Shankar 2022 IEEE Global Conference on Computing Power and Communication Technologies Globconpt 2022, 2022 The world is more focusing on the mission of zero carbon emission and coming up with suitable propagandas. Utilization of renewable energy sources (RES) and electric vehicle (EV) in generation and automobile sectors are major stepping stones towards this mission. But, the drastic rise in EVs enables more number of vehicles charging on the electric grid. This shows considerable impact on the power system dynamics. The EVs can also provide ancillary services with appropriate control technique to increase the robustness of the power system. Hence, the study of the dynamic stability analysis of the power system in necessary including EVs. This work evaluates the participation of EVs improving the frequency regulation of the power system using an aggregate model. A single area system and P. Kundur's two-area four generator model has been taken as test systems for analysis purposes. The frequency regulation of the considered test systems is studied using volleyball premier league algorithm based PID controller. The comparative studies show the ancillary support of EVs in transient response of the system for a sudden perturb in load demand. Also the dynamic behavior of the system has been verified for the presence of communication delay in the LFC mechanism.
