Energy, Renewable Energy, Sustainability and the Environment, Electrical and Electronic Engineering, Ocean Engineering
166
Scopus Publications
Scopus Publications
Real-Time stability enhancement of DDPMSG-based tidal hybrid power systems using heuristic optimization Javed Khan Bhutto, Asit Mohanty, Pragyan P. Mohanty, Abhaya S. Satpathy, Soumya Ranjan Das Scientific Reports, 2026 In this research, we analyze the optimal configuration and stability issues associated with a tidal hybrid power system employing a Direct Drive Permanent Magnet Synchronous Generator (DDPMSG). The Tidal system has become unstable since the natural tidal pattern and wind power input have been disturbed. The use of a controller for the Unified Power Flow Controller (UPFC) makes the system stable. Stability analysis, which uses Eigen and Nyquist plots, is used to see how well the proposed controllers work. Additionally, it is evident that the regulator can be effectively calibrated when the variables significantly influence the system’s performance. So, to find the best output for the suggested controller, it is best to use heuristic optimization methods like the Differential Evolution Algorithm (DEA) and the Firefly Algorithm (FA), followed by hybrid FA. The results indicate that the hybrid FA-based system demonstrates enhanced stability, evidenced by increased performance in settling time, rising time, peak overshoot, and damping. The performance and durability assessment of the controller in question is executed using real-time data via OPAL-RT 5142, a digital simulation platform tailored for real-time applications.
Observer aided robust control for cyber physical power grids with event triggered sliding mode controller Asit Mohanty, Agileswari Ramasamy, Abhaya satpathy, S. Mohanty, Reji Kumar Rajamony, Javed Khan Bhutto, Hadi Hakami, P. Mohanty, A. Megalingam, Haiter Lenin Allasi Scientific Reports, 2026 The growing integration of renewable energy in both islanded and interconnected microgrids has rendered Cyber-physical stability and resilience a vital area of research. Conventional controllers, including PID and linear state-feedback, are susceptible to network-induced delays, denial-of-service (DoS) attacks, and false data injection, resulting in diminished reactive power support and the risk of voltage collapse. This paper proposes an Observer-Aided Robust Control Framework that integrates an Event-Triggered Sliding Mode Controller (ET-SMC) with improved anomaly detection to address these challenges. An Extended Kalman Filter (EKF) and Sliding Mode Observer (SMO) are formulated to estimate hidden state variables and identify malicious data alterations with high sensitivity, facilitating dependable control decisions in the presence of Cyber-attacks. The performance of anti-windup PID and baseline SMC is evaluated against ET-SMC with observer augmentation, demonstrating that the proposed strategy offers enhanced robustness, quicker transient response, and diminished chattering. A stability-guaranteed Event-triggered communication protocol is developed through Lyapunov analysis to reduce bandwidth consumption while maintaining voltage and reactive power regulation. The proposed framework is validated on a real-time OPAL-RT hardware-in-the-loop (HIL) microgrid testbed, demonstrating its effectiveness in scenarios involving renewable intermittency, communication noise, and coordinated Cyber-attacks. Comparative results demonstrate that ROC-based detection performance and time-domain simulations underscore the advantages of observer-aided ET-SMC in ensuring resilient, low-bandwidth, and real-time Cyber-physical control for next-generation power grids.
Hybrid Deep Learning and Kalman Filtering for Reactive Power Fault Diagnosis in Smart Microgrids Mrutyunjay Senapati, Pratap K. Panigrahi, Asit Mohanty, Soumya Ranjan Das IEEE Access, 2026 The increasing utilization of renewable energy sources, bi directional power flows, and distributed intelligence has made contemporary smart microgrids significantly more complex to operate. This has rendered traditional protection and monitoring systems inadequate. Traditional detection techniques frequently struggle to precisely identify anomalies in highly dynamic operational environments and amidst measurement uncertainty. This research offers a hybrid architecture for anomaly detection and fault classification that integrates Long Short-Term Memory (LSTM) networks with an Extended Kalman Filter (EKF) for monitoring smart microgrid to overcome these challenges. The LSTM portion leverages its ability to learn over time to uncover nonlinear patterns in how reactive power works and to spot early signals of weird things like voltage sags, cyber-induced setpoint shifts, and islanding situations. The EKF, on the other hand, delivers recursive estimates of nonlinear system states in real time.This dynamically rectifies measurement inaccuracies and enhances system visibility. The proposed system employs residual based threshold logic and classification algorithms that analyze variation in voltage,frequency and reactive power to accurately distinguish between various failure states. The concept is implemented in MATLAB utilizing the IEEE 13-bus and IEEE 33-bus distribution test systems in a variety of operational situations. The simulation findings show that the proposed hybrid method works better than LSTM and EKF-based methods on their own because it finds more accurate detections, responds faster, and has less false alarms.
An Improved and Efficient High-Boost Switched Capacitor Multilevel Inverter Pratik Kar, Durgesh Prasad Bagarty, Prakash Kumar Ray, Rachita Ruchismita Sarangi, Asit Mohanty Power Electronics and Drives, 2026 This paper presents a fundamental nine-level switched-capacitor multilevel inverter (CSMLI) powered by a single DC source. The configuration employs two capacitors, nine switches, and one diode to generate nine distinct output voltage levels. It achieves an inherent voltage-boosting capability of up to four times the input voltage. The switched-CSMLI represents an enhanced topology that utilises a strategic switching scheme to guarantee adequate capacitor charging and natural voltage balancing. The circuit adopts a novel charging approach that increases the number of charging states, thereby maintaining sufficient stored charge across the capacitors. An extended version of the basic CSMLI structure is also introduced to further increase the number of discrete output voltage levels. The proposed topology is evaluated against other comparable state-of-the-art inverters to demonstrate its advantages and effectiveness. The base configuration delivers improved harmonic performance and requires relatively small passive LC (inductor-capacitor) filters for harmonic mitigation. Simulation studies are conducted using the Power simulation (PSIM) platform, and a hardware prototype is developed to experimentally verify the performance and validate the proposed design.
Intelligent protection systems for grid-connected renewables: A review of AI techniques and applications Mrutyunjay Senapati, Pratap K Panigrahi, Asit Mohanty, Reji Kumar Rajamony, Prakash K Ray, Haiter Lenin Allasi Results in Engineering, 2025 The integration of renewable energy sources (RES) like solar photovoltaics, wind turbines, and hybrid storage into contemporary power systems presents notable challenges for protection, stemming from their intermittent generation, inverter-based interfaces, and bidirectional power flows. Traditional protection schemes, initially developed for predictable and unidirectional grids, are increasingly inadequate for managing complexities and therefore there is urgent requirement for Artificial Intelligence (AI) techniques that provide adaptability, speed, and resilience in dynamic and uncertain operating conditions. This review critically examines the role of AI in enhancing grid protection, focusing on fault detection, isolation, classification, adaptive relay coordination, islanding detection, and the mitigation of cyber-physical threats. This study synthesizes several research works by categorizing AI methodologies such as machine learning, deep learning, fuzzy logic, reinforcement learning, and hybrid models, while also benchmarking their comparative performance and practical applicability. The methodology entails a systematic examination of simulation studies, pilot projects, and restricted real-world implementations, offering a comprehensive assessment of existing capabilities and constraints. Research indicates that AI-driven protection systems can reduce detection time to less than 100 ms, decrease false trips, and enhance resilience to varying grid conditions. However, challenges remain, including data scarcity, insufficient field validation, issues with interpretability, and the absence of standardized evaluation frameworks. The review concludes by outlining future research directions, highlighting field trials, cost-effective deployment strategies, explainable AI, and alignment with IEC/IEEE protection standards. This work provides a synthesis and roadmap for the advancement of intelligent, reliable, and certifiable protection systems within renewable-integrated smart grids.
Intelligent event trigger based sliding mode control in a marine current turbine with superconducting magnetic energy storage Asit Mohanty, A. K. Ramasamy, Renuga Verayiah, Sthitapragyan Mohanty, Reji Kumar Rajamony, Haiter Lenin Allasi, Pragyan P. Mohanty Scientific Reports, 2025 Marine current turbines (MCTs) are a burgeoning renewable energy technology that may effectively capture the kinetic energy of ocean currents to produce power. Nevertheless, the sporadic and uncertain characteristics of marine currents present substantial obstacles to the reliable functioning of grid-connected MCT systems. By incorporating Superconducting Magnetic Energy Storage (SMES) into grid-connected marine current turbines and implementing intelligent event-triggered Sliding Mode Control (ETSMC), we can significantly improve the transient voltage stability of marine renewable energy systems. The sophisticated event trigger mechanism continuously checks the circumstances of the grid and the operation of the turbine in real-time. The real-time nonlinear control technique enhances the performance of SMES by effectively regulating the flow of active and reactive power, hence ensuring grid stability during transient occurrences. This integrated system aims to improve the dependability and effectiveness of marine current turbine operations, thereby supporting the progress of sustainable marine renewable energy technologies. The resilience of the system was evaluated by its implementation in real-time on a dSPACE-DS1104 board, which was connected to an experimental laboratory bench. Additionally, a comprehensive analysis was conducted by comparing actual and simulated data in order to assess both the quantitative and qualitative aspects of the system.
Enhanced fault protection coordination in wind-solar distribution grids utilizing improved hyper spherical search based optimization Shanti Swarup Rath, Prakash K. Ray, Gayadhar Panda, Asit Mohanty, Reji Kumar Rajamony, Javed Khan Bhutto, Hadi Hakami, Haiter Lenin Allasi Scientific Reports, 2025 The incorporation of renewable energy sources (RES) into distribution networks presents considerable hurdles for protective relay coordination due to variable fault currents and swiftly altering operational conditions. This paper introduces an Improved Hyper Spherical Search Algorithm (IHSSA) designed for optimal relay coordination in renewable energy source-integrated power systems. The IHSSA improves the conventional hyperspherical search method by integrating dynamic adjustment mechanisms that adaptively balance exploration and exploitation, facilitating quicker convergence and more accuracy in addressing intricate, high-dimensional optimization challenges. The algorithm's efficacy was assessed using a realistic 220/6.6 kV substation model that services a hybrid wind-solar distribution grid with 10 feeders and 5 renewable generators. Simulation results indicate that IHSSA surpasses benchmark methods, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), delivering an 18% decrease in relay operation time and a 15% enhancement in coordination margin under extreme fault situations. The method also decreased fault clearing settling time to 120 ms, demonstrating enhanced adaptation to renewable intermittency. The IHSSA architecture was executed in a real-time hardware-in-the-loop (HIL) setting utilizing OPAL-RT OP5600 connected to IEC 61,850-compatible relays to assess practical viability. The findings validate that IHSSA provides a strong, adaptive, and scalable solution for protection coordination in contemporary, renewable-dominant distribution systems, markedly improving system dependability and operational resilience.
A contribution to modelling and study of SSSC compensator employing firefly algorithm International Journal of Innovative Technology and Exploring Engineering, 2019
Robustness and stability analysis of renewable energy based two area automatic generation control International Journal of Renewable Energy Research, 2018
Optimised fractional order PID controller in automatic generation control Prakash K. Ray, B.K. Panigrahi, P.K. Rout, Asit Mohanty, Harishchandra Dubey Computer Communication and Electrical Technology Proceedings of the International Conference on Advancement of Computer Communication and Electrical Technology Accet 2016, 2017
Transient stability analysis in wind diesel hybrid system with fuzzy-PI based FACTS controllers International Review on Modelling and Simulations, 2013
