Tarun Varshney

This article develops an analytic hierarchy process (AHP)-assisted model-reduction approach for a higher-order airport-centric microgrid (ACM). The full-order ACM model is approximated by a computationally simpler lower-order transfer function while retaining its essential steady-state and transient characteristics. The model approximation helps in analysis and controller design. For the same, a multi-objective fitness function is formulated from selected time moments and Markov parameters of the original and reduced models. AHP is used to determine the relative importance of the individual matching objectives through pairwise comparisons and normalized priority weights. The resulting weighted optimization problem is solved using the brown-bear optimization algorithm (BBOA) to estimate coefficients of the reduced model. During optimization, exact matching of steady-state gain is imposed to eliminate steady-state mismatch, while Hurwitz stability conditions ensure that the reduced model remains stable. Effectiveness of the proposed approach is evaluated using step and impulse responses, frequency-domain characteristics, time-domain specifications, and integral error indices. The results show that the AHP–BBOA-based reduced model closely reproduces the dominant dynamics of the higher-order ACM while offering a compact representation suitable for controller design, simulation, and real-time frequency-response studies.

1. Brain Computer Interfaces (BCI) play a substantial role in the biomedical field. It is a system intended to interpret a user’s brain intentions in machine instructions (Zhang et al., 2019). It c...

This paper presents the optimal placement and sizing of Distributed Generation (DG) using the Random Drift Particle Swarm Optimization (RDPSO) algorithm. The study focuses on minimizing power losses, thus improving profiles of the voltage and maintaining stability of the system. The proposed methodology combines analysis of power flow through the Backward Forward Sweep (BFS) approach with RDPSO to ascertain optimal DG configurations. The proposed strategy is tested on the IEEE 33-bus system. The results obtained have shown a significant improvement in voltage stability and a reduction of power losses. Simulations with one, two, and three DGs highlight the scalability and effectiveness of the approach. The study underscores the potential of RDPSO in optimising DG placement, providing a workable way to enhance the dependability and efficiency of contemporary distribution networks.

The increasing global importance of sustainable and “green” approaches has transformed renewable energy integration into a priority for multiple sectors, including the industrial sector. Industrial processes and activities that are typically energy-intensive and pose both challenges and opportunities for the use of on-site Renewable Energy (RE) sources such as solar, wind, geothermal, and biomass. In this chapter, we introduce the theme of incorporating renewable energy into industrial processes by analyzing the technical, economic, and regulatory aspects. An extensive examination of the current renewable energy literature, encompassing various technological solutions, challenges to integration, and case studies on successful applications, is presented. Furthermore, the role of EMS, smart grids, clean energy systems, and energy storage in the optimal utilization of renewable energy is also discussed in this chapter.

Reinforcement learning offers efficient solutions for optimizing complex decision-making tasks through continuous state-action-reward cycle with real-time adaptability. This work presents twin delayed deep deterministic (TD3) policy gradient RL based adaptive speed controller for the DC motor model while considering the impact of various uncertainties from dynamic environment into account. Various benchmark controller techniques are also utilized for similar objective in order to perform comparative analysis. Responses of each controller are plotted for both constant and variable desired speeds to evaluate their efficacy, robustness, and adaptability to uncertainties. Values of various types of error indices, including integral of squared error (ISE), integral of time-weighted absolute error (ITAE), integral of absolute error (IAE), integral of time-weighted squared error (ITSE), and their respective time-weighted variants are calculated, and tabulated for each type of speed controller for both test cases. Error indices analysis is also utilized to compare, and evaluate each controller’s tracking precision and error minimization qualities in dynamic operating conditions for efficient speed regulation for the DC motor.

This paper investigates frequency regulation of an airport microgrid (AIM) through the application of an integral absolute error (IAE)-assisted control approach. The islanded AIM is initially captured using a linearized transfer function model to accurately reflect its dynamic characteristics. This model is then simplified using a first-order plus dead time (FOPDT) approximation derived via a reaction-curve-based method, which balances between model simplicity and accuracy. Two different proportional–integral–derivative (PID) controllers are designed to meet distinct objectives: one focuses on set-point tracking (SPT) to maintain the target frequency levels, while the other addresses load disturbance rejection (LDR) to reduce the effects of load fluctuations. A thorough comparison of these controllers demonstrates that the SPT-mode PID controller outperforms the LDR-mode controller by providing an improved transient response and notably lower error measures. The results underscore the effectiveness of combining IAE-based control with reaction curve modeling to tune PID controllers for islanded AIM systems, contributing to enhanced and reliable frequency regulation for microgrid operations.

There were typographical mistakes in the original publication

Microgrids are being considered to be very crucial in enhancing the involvement of renewable energy sources (RESs) in electrical grids and also improving their overall sustainability and resilience. Modern day control techniques are getting attention by researchers for optimal control and management of microgrids, as it is found in many articles that classical control techniques are short-falling in adaptability in different environments, data handling and data driven decision making. This article provides systematic review to follow a thorough evaluation of the present status of research on reinforcement learning (RL)-based microgrid control. The description of microgrid systems, their components, control and management challenges are also provided in this survey. Further, majorly implemented RL-based algorithms for microgrid control are discussed. Summary and critical assessment of main findings and contributions in this field are tabulated through methodical categorization of selected articles. Emerging algorithms for microgrid control are also discussed thoroughly. A comparative analysis based on performance of RL algorithms, and RL based control with other types of control frameworks is also systematically presented. Further safety and deploying challenges, along with ongoing trends in RL implementation for microgrids, are elaborated. This work will be helpful for practitioners and researchers who want to investigate the best way to design microgrid systems, especially when it comes to using RL algorithms for microgrid control systems and management.

Background: Meditation and Yoga practices are being adopted and gaining considerable interest as a tool that prevents the occurrence of numerous ailments. Meditation is well prescribed in several old religious manuscripts and has origins in past Indian practices that encourage emotional and personal well-being. Two different classification tasks were performed. One way to identify the mind state allied with Vipassana meditation and another was to identify the mind state allied with Himalayan Yoga meditation. The tasks were performed for classifying non-meditative and meditative states with varying cut-off frequencies to obtain the best results. Purpose: This study is mainly focused on how the high-pass cut-off influences the single-trial accuracy of the model. The performance of the model depends on appropriate pre-processing. The results of High-pass Filter (HPF) at different settings were methodically assessed. Although there are many factors on which the accuracy of the model depends, like the HPF, Independent Components Analysis (ICA), model building and the hyperparameter tuning. One important preprocessing step is to effectively choose the filter to improve the classification results. Methods: Inception Convolutional Gated Recurrent Neural Network (IC-RNN) and Convolutional Neural Network (CNN) models were designed and compared to examine the varying effects of HPF. Results and Conclusion: The highest accuracy of 86.19% was attained for IC-RNN, and 99.45% was achieved for CNN model with filter setting at 1 Hz for the Vipassana meditation classification task. The highest accuracy of 88.15% was attained for IC-RNN, and 100% was achieved for CNN model with the same filter setting at 1 Hz for the Himalayan Yoga meditation classification task. HPF at 1 Hz steadily produced good results. Based on the outcomes, the guidelines are suggested for filter settings to increase the performance of the model.

The development of energy systems into automated systems has been heavily driven by the rise of the Internet of Things (IoT) and Industrial Internet of Things (IIoT). Such technologies ensure real-time monitoring, predictive maintenance, and integrated use of distributed energy resources. It focuses on the significant role played by IoT and IIoT in home automation energy systems and reflects on productivity, reliability, and sustainability. The chapter presents an in-depth examination of the ways in which these technologies facilitate the evolution of traditional energy grids into smart, adaptive, self-regulating systems through case studies and analysis.