Refining efficiency in standalone proton exchange membrane fuel cell systems through gross hopper optimization-based maximum power point tracking control K. Nethra, K. Jyotheeswara Reddy, Ritesh Dash, Prasanta Kumar Parida, Sarat Chandra Swain, et al. Science and Technology for Energy Transition Stet, 2025 This study introduces a novel Maximum Power Point Tracking (MPPT) technique for Proton Exchange Membrane Fuel Cell (PEMFC) systems, leveraging the Gross Hopper Optimization (GHO) algorithm to achieve enhanced performance. The proposed method is applied to a stand-alone PEMFC system with a power capacity of 1.2 kW. The primary problem addressed is the challenge of achieving efficient and reliable MPPT in dynamic operating conditions, which is critical for optimizing PEMFC performance and extending its lifespan. Unlike conventional optimization techniques, the GHO algorithm is parameter-independent, making it highly adaptive and suitable for diverse and fluctuating operational scenarios. To further improve prediction accuracy, the GHO algorithm incorporates a natural cubic-spline prediction model within its iterative mechanism, which enhances power generation predictions under dynamic conditions such as abrupt changes in fuel cell temperature and reactant partial pressure. The performance of the system is evaluated through extensive simulations under steady-state and transient conditions. The key findings reveal that the proposed method achieves a tracking efficiency of more than 98.3% under standard operating conditions and maintains an efficiency greater than 96.5% during dynamic changes, outperforming the controllers based on the adaptive Neural Network (NN) and the Adaptive Neuro-Fuzzy Inference System (ANFIS). Furthermore, the GHO-based controller demonstrates faster response times with a 30% improvement in settle time and greater robustness to parameter variations compared to the benchmarks.
Design and Analysis of Modular Neural Network-Based PI-Controller Ensemble With Karush-Kuhn-Tucker Conditions for Grid-Connected Photovoltaic Systems Under Ground Fault Conditions Burla Sridhar, Soorya Prakash Shukla, Ritesh Dash, Arpan Dwivedi, G. C. Biswal, et al. IEEE Access, 2025 The increasing penetration of grid-connected solar PV system necessitates roboust and adaptive control strategies to mitigate the adverse effects of fluctuating grid voltages, variable solar irradiance, and ground faults. Traditional PI controllers through their widely used simplicity lack adaptability under non-linear and time varying conditions, leading to poor dynamic performance, increased total harmonic distortion, and compromised grid stability. To address these challenges, this paper proposes a Modular Neural Network (MNN)-based PI controller, which dynamically tunes proportional and integral gains in real time, ensuring optimal system performance. This work bridges the gap between fixed-gain PI controllers and heuristic optimization techniques by introducing a data-driven, real-time adaptive control methodology. Experimental validation through hardware implementation demonstrates a 45% reduction in steady-state error, a 30% reduction in THD, and improved reactive power compensation and grid synchronization. The findings establish the MNN-PI controller as a superior alternative for enhancing the reliability, efficiency, and grid compliance of SPV systems, particularly under dynamic operating conditions and fault scenarios.
Design and development of PI controller for DFIG grid integration using neural tuning method ensembled with dense plexus terminals R. R. Hete, Tarun Shrivastava, Ritesh Dash, L. Anupallavi, Misba Fathima, et al. Scientific Reports, 2024 In a DFIG grid interconnected system, the control of real and reactive power relies on various factors. This paper presents an approach to regulate the flow of real and reactive power using a Neural Tuning Machine (NTM) based on a recurrent neural network. The focus is on controlling the flow of reactive power from the rotor-side converter, which is proportional to the grid-side controller through a coupling voltage. The proposed NTM method leverages neural networks to fine-tune the parameters of the PI controller, optimizing performance for DFIG grid integration. By integrating dense plexus terminals, also known as dense connections, within the neural network, the control system achieves enhanced adaptability, robustness, and nonlinear dynamics, addressing the challenges of the grid. Grid control actions are based on the voltage profile at different bus locations, thereby regulating voltage. This article meticulously examines the analysis in terms of DFIG configuration and highlights the advantages of the neural tuning machine in controlling inner current loop parameters compared to conventional PI controllers. To demonstrate the robustness of the control algorithm, a MATLAB Simulink model is designed, and validation is conducted with three different benchmarking models. All calculations and results presented in the article strictly adhere to IEEE and IEC standards. This research contributes to advancing control methodologies for DFIG grid integration and lays the groundwork for further exploration of neural tuning methods in power system control.
