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.

This work presents the application of metaheuristic optimization techniques for tuning the PID controller gains for Zeta Power Converter (ZPC). The objective is to enhance transient performance by reducing overshoot, rise time, and settling time, while simultaneously minimizing steadystate error and control effort. To simplify controller design, a Reduced Order Model (ROM) is employed to represent the originally eighth-order ZPC system. The Jaya Optimization Algorithm (JOA), which does not require algorithm-specific control parameters, is used to determine optimal PID gains based on a selected performance index. For comparative analysis, the Grey Wolf Optimization (GWO) algorithm is also applied using the same performance criterion. Simulation results demonstrate that the optimized PID controller achieves superior dynamic performance for the ZPC system.

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.

The study provides a unique Δ-circuit technique for rapid calculations of short-circuit currents in large microgrid (MG) networks supplied by inverter-based distributed generators (IBDGs), employing virtual impedance current limiters (VICLs). The Δ-circuit approach is widely used for calculating short-circuit currents in electrical networks due to its efficiency and ease of execution. However, the normal Δ-circuit is unsuited for islanded MGs because of their unique features, such as the lack of a slack bus. This method ensures precise simulations and fast computational performance while being applicable to various fault types, including single-line, two-line, and three-line faults. This approach is validated using MATLAB Simulink on a 6-bus test network of an islanded MG. The findings enhance the understanding of fault behavior in droop-controlled islanded AC microgrids and contribute to developing effective protection measures.

The growing reliance on decentralised energy resources has fuelled interest in microgrids (MGs), which may run independently in islanded mode or in conjunction with the main power grid. Analysing load flow in MGs provides unique issues, especially in islanded mode, when typical approaches based on a slack bus are inadequate. This work presents a Modified Gauss-Seidel (MGS) approach for incorporating droop control parameters into load flow analysis while treating voltage and frequency as dynamic variables. This technique improves power sharing across Distributed Generators (DGs), maintains voltage and frequency stability, and reduces system losses. The effectiveness of the proposed approach was validated on 6-bus microgrid system comprising both voltage dependent loads and EV Loads, demonstrating its ability to improve stability, reduce power losses, and achieve balanced load distribution.

The energy policies of the $$21^{st}$$ century are increasingly focused on promoting generation solutions with minimal environmental impact. In response to strategic initiatives, the accelerating depletion of fossil fuel reserves has led to integrating renewable sources for power generation. The uncertain nature of solar and wind energy sources, along with fluctuating load demands, leads to frequency instability. This study addresses the challenge of frequency instability by designing a Bayat-tuned fractional-order proportional-integral-derivative (FOPID) controller for a decentralized microgrid $$(Dz \mu G)$$ . The proposed $$Dz \mu G$$ model consists of environmentally friendly energy sources such as a biogas turbine generator (BTG), a biodiesel engine generator (BEG), other distributed generation units (DGUs), and energy storage devices (ESDs). The mathematical modeling of $$Dz \mu G$$ components is carried out using first-order transfer functions, which are combined to derive the overall transfer function of $$Dz \mu G$$ model. This composite model is then approximated as a first-order plus time delay (FOPTD) system to simplify FOPID controller design. The parameters of the FOPID controller are optimized using the Bayat method to achieve robust performance under set-point tracking (SPT) and load disturbance rejection (LDR) scenarios. Based on this approach, three controller variants i.e., FOPID- $$Bayat_{SP1.4}$$ , FOPID- $$Bayat_{SP2.0}$$ , and FOPID- $$Bayat_{LD1.4}$$ , are developed. To validate the effectiveness of the proposed control strategy, various simulation scenarios are considered, including load disturbances and varying levels of solar and wind power penetration. The performance of the controllers is evaluated in terms of frequency deviation, error mitigation, and transient behavior under SPT and LDR conditions. A comparative analysis using error indices, time-domain metrics, control effort, and frequency plots confirms the effectiveness of the Bayat-tuned FOPID designs. Furthermore, real-time validation using the OPAL-RT simulator underscores their practical potential in maintaining frequency stability within $$Dz \mu G$$ systems. Owing to the performance analysis, it is justified that discussed FOPID–Bayat controllers consistently ensured controllability with a minimum rise time of $$4.02 \times 10^{-5}\,\text {s}$$ , a nearly constant settling time of $$\sim 49.8\,\text {s}$$ , and reduced control effort down to 0.12. Furthermore, error index evaluation confirmed that FOPID–Bayat $$_{SP2.0}$$ outperformed other configurations by achieving the lowest IAE (8.737), ITAE (223.0), ITSE (40.39), and ISE (1.706), thereby demonstrating superior efficiency and robustness.

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.

Decision makers consistently face the challenge of simultaneously assessing numerous attributes, determining their respective importance, and selecting an appropriate method for calculating their weights. This article addresses the problem of automatic generation control (AGC) in a two area power system (2-APS) by proposing fuzzy analytic hierarchy process (FAHP), an multi-attribute decision-making (MADM) technique, to determine weights for sub-objective functions. The integral-time-absolute-errors (ITAE) of tie-line power fluctuation, frequency deviations and area control errors, are defined as the sub-objectives. Each of these is given a weight by the FAHP method, which then combines them into an single final objective function. This objective function is then used to design a PID controller. To improve the optimization of the objective function, the Jaya optimization algorithm (JOA) is used in conjunction with other optimization techniques such as sine cosine algorithm (SCA), Luus-Jaakola algorithm (LJA), Nelder-Mead simplex algorithm (NMSA), symbiotic organism search algorithm (SOSA) and elephant herding optimization algorithm (EHOA). Six distinct experimental cases are conducted to evaluate the controller's performance under various load conditions, with data plotted to show responses corresponding to fluctuations in frequency and tie-line exchange. Furthermore, statistical analysis is performed to gain a better understanding of the effectiveness of the JOA-based PID controller. For non-parametric evaluation, Friedman rank test is also used to validate the performance of the proposed JOA-based controller.

Multi-criteria decision-making (MCDM) presents a significant challenge in decision-making processes, aiming to ascertain optimal choice by considering multiple criteria. This paper proposes rank order centroid (ROC) method, MCDM technique, to determine weights for sub-objective functions, specifically, addressing issue of automatic generation control (AGC) within two area interconnected power system (TAIPS). The sub-objective functions include integral time absolute errors (ITAE) for frequency deviations and control errors in both areas, along with ITAE of fluctuation in tie-line power. These are integrated into an overall objective function, with ROC method systematically assigning weights to each sub-objective. Subsequently, a PID controller is designed based on this objective function. To further optimize objective function, Jaya optimization algorithm (JOA) is implemented, alongside other optimization algorithms such as teacher-learner based optimization algorithm (TLBOA), Luus-Jaakola algorithm (LJA), Nelder-Mead simplex algorithm (NMSA), elephant herding optimization algorithm (EHOA), and differential evolution algorithm (DEA). Six distinct case analyses are conducted to evaluate controller's performance under various load conditions, plotting data to illustrate responses to frequency and tie-line exchange fluctuations. Additionally, statistical analysis is performed to provide further insights into efficacy of JOA-based PID controller. Furthermore, to prove the efficacy of JOA-based proposed controller through non-parametric test, Friedman rank test is utilized.

The main causes of frequency instability or oscillations in islanded microgrids are unstable load and varying power output from distributed generating units (DGUs). An important challenge for islanded microgrid systems powered by renewable energy is maintaining frequency stability. To address this issue, a proportional integral derivative (PID) controller is designed in this article. Firstly, islanded microgrid model is constructed by incorporating various DGUs and flywheel energy storage system (FESS). Further, considering first order transfer function of FESS and DGUs, a linearized transfer function is obtained. This transfer function is further approximated into first order plus time delay (FOPTD) form to design PID control strategy, which is efficient and easy to analyze. PID parameters are evaluated using the Chien-Hrones-Reswick (CHR) method for set point tracking and load disturbance rejection for 0% and 20% overshoot. The CHR method for load disturbance rejection for 20% overshoot emerges as the preferred choice over other discussed tuning methods. The effectiveness of the discussed method is demonstrated through frequency analysis and transient responses and also validated through real time simulations. Moreover, tabulated data presenting tuning parameters, time domain specifications and comparative frequency plots, support the validity of the proposed tuning method for PID control design of the presented islanded model.

Automatic generation control (AGC) is employed in power systems to maintain balance between generation and load by adjusting output of generators in real time. Controller continuously monitors system frequency and tie-line power flow by responding to fluctuations in electricity demand and supply and optimizes generator dispatch, reduces power imbalances, and enhances grid stability. This work proposes and solves the issues of the AGC in two-area interconnected power systems by proposing a new approach based on both Jaya algorithm and the rank exponent method. In particular, we design a proportional-integral-derivative controller with derivative filtering (PIDm), where the effect of the noise is mitigated by the use of a filter with derivative gain. We propose to build the objective function, to tune the controller’s parameters, as the linear combination of three sub-objectives, namely integral of time multiplied absolute error (ITAE) for frequency deviations, tie-line power deviation, and area-control errors (ACEs). The rank method is exploited to evaluate the weights of these sub-objectives, while the final overall objective function is minimized exploiting the Jaya algorithm. The proposed controller’s performance is assessed in six different scenarios with load disturbances, and its effectiveness is compared to state-of-art controllers tuned using salp swarm algorithm (SSA), Nelder-Mead simplex (NMS), symbiotic organisms search (SOS), elephant herding optimization (EHO), and Luus-Jaakola (LJ) optimization algorithms. To illustrate the frequency and tie-line power changes, results are also shown, and a statistical study is finally carried out to evaluate the recommended controller’s overall effectiveness. Additionally, Friedman rank test as no-parametric statistical analysis is also done in order to evaluate the significance level of optimization algorithms. Our numerical findings evidence that the proposed PIDm controller outperforms other existing optimization-based controllers in terms of performance and utility, thus proving to be very effective for handling AGC issues in two-are interconnected power systems.

Solar power has been a major force in power generation in the modern world because it's one of the cleanest forms of energy and is easy to harvest and put to use today. The sun is abundantly everywhere on the earth, which is why the future of renewable energy is going to be more solar power. Shading conditions are generally a major problem in photovoltaic systems because they significantly affect the performance of PV systems, hence the need to study different shading conditions. The present work carries out a simulation of various PV modules connected with bypass diodes under different shading conditions by creating faults, these conditions are partially compared with the voltage, current, and power visualized from the scope over time in the form of a graph. The simulation software used in the present work is MATLAB Simulink.

In a renewable energy-based islanded microgrid system, frequency control is one of the major challenges. In general, frequency oscillations occur in islanded microgrids due to the stochastic nature of load and variable output power of distributed generating units (DGUs). In the presented research proposal, frequency oscillations are suppressed by implementing the proportional integral derivative (PID) controller-based control design strategy for an islanded microgrid. The modeling of the islanded microgrid is firstly presented in the form of a linearized transfer function. Further, the derived transfer function is approximated into its equivalent first-order plus dead time (FOPDT) form. The approximated FOPDT transfer function is obtained by employing the reaction curve method to calculate the parameters of the FOPDT transfer function. Furthermore, the desired frequency regulation is achieved for the manifested FOPDT transfer function by incorporating PID control design. For PID controller tuning, different rule-based methods are implemented. Additionally, comparative analysis is also performed to ensure the applicability of the comparatively better rule-based tuning method. The Wang–Chan–Juang (WCJ) method is found effective over other rule-based tuning methods. The efficacy of the WCJ method is proved in terms of transient response and frequency deviation. The tabulated data of tuning parameters, time domain specifications, and error indices along with responses are provided in support of the presented control strategy.

Regular yoga and meditation practices are gaining recognition as tools that help in prevention of various diseases. To have vitality in life, personal well-being and emotional harmony, meditation is one of the best practices. This study focused on identification of brain state associated with Isha Shoonya meditation. Hyperparameter tuning has been used in order to achieve the best accuracy results. Four models were designed with One-Dimensional Convolutional Neural Network (ld-CNN): model A with 11 layers (without Independent Components Analysis (ICA)), model B with 13 layers, model C with 9 layers and model D with 11 layers. ICA method was not applied to data before feeding to model A and was applied to rest of the models. The results were compared among all the models on the same dataset. Accuracy of 70.16% for model A without ICA, 98.70 % for model B with 13 layers, 99.81 % for model C with 9 layers and 100 % for model D with 11 layers was attained. The model D with 11 layers has shown the best result among all the models.

Model Order Reduction (MOR) is a technique used to simplify the mathematical representation of complex systems, such as Pressurized Heavy Water Reactors (PHWRs), in order to make them more computationally efficient and easier to analyze. This is done by identifying and eliminating the redundant or insignificant variables in the system, resulting in a reduced order model (ROM) that captures the essential dynamics of the original system. In the context of PHWRs, MOR can be used to reduce the large number of equations and variables that describe the reactor's behavior, making it possible to perform detailed simulations and analyses that would otherwise be infeasible. MOR can be applied to different levels of the PHWR model, such as the core, the primary circuit, or the whole plant. The choice of the MOR method depends on the particular aspect of the PHWR that needs to be studied. The most common MOR methods are the balanced truncation, proper orthogonal decomposition and the reduced basis method. MOR can be used to improve the efficiency of the design and control of PHWRs, as well as to provide a better understanding of the underlying physical processes and to enhance the safety and reliability of the reactor.

Transportation is the backbone of every economy that directly affects every individual. Most of the transportation is based on conventional vehicles which work on IC engines. The emission of conventional vehicles has an adverse effect on the environment. It emits several greenhouse gases like carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and other greenhouse gases that contribute to global warming. The electric vehicles have offered a viable solution to this problem. The overall performance of electric vehicles is better than conventional vehicles in terms of energy efficiency and is cost-effective. However, it is challenging to maintain power quality, power demand, voltage regulation, harmonic contamination, frequency deviation, system adequacy, etc. Electric vehicles can be used as an energy storage element and can feedback power to the grid in case of emergencies. The main objective of the paper is to analyze the effect of increased penetration of electric vehicles on transmission lines, and transformers, and maintain the stability of the grid. The analysis has been done using MATLAB/SIMULINK.

The paper's main goal is to design a practical yet highly customizable electric bicycle. As the number of automobiles on the road worldwide rises at an alarming rate each year, the world's reliance on oil-based fuel has become almost unrestrained. Increased usage of nonrenewable fossil fuels causes environmental issues such as the “greenhouse effect,” health issues for city dwellers, and concerns about fuel supply stability. To wean ourselves off of our reliance on oil, a large amount of money is being invested in the creation of electric vehicles (EVs) that could be mass-produced. This paper examines the design of electric bicycles. The goal of this research is to figure out how to make a basic, low-cost electronic bicycle with two-way driving control. Electric cycles are the finest development in our ever congested world to provide an easy solution to daily commute woes. They are not only save a lot of fuel and keep the environment clean but also help you develop good health with little pedal exercise during your commute.

This paper shows the detailed study on optimization of fractional-order PID Controller (FOPID) for fractional-order estimated non-linearized dynamical thermal system. Initially, parameters of integer-order PID (IOPID) controller have been optimized, and then keeping those optimized values of gains the same, exponents of FOPID controller have been optimized and finally gains and exponents of FOPID controller have been optimized. The performance of both IOPDS and FOPIDs controllers are compared for most popular conventional Nelder–Mead’s, Integer point algorithms, and nature-inspired Cuckoo Search (CS) optimization algorithms. Simulation results proclaim the effectiveness and efficiency of the FOPID Controllers with CS optimization algorithm in terms of Mean Square Error (MSE).

Micro-grids have attracted much interest in the context of the vision of smart grids, which require methodical processes for their optimal assembly, the distribution of DG costs, and the increase of performance. The problem related to this issue defined in this work is a planning problem. Therefore, the general public and users of the proposed approach will be utility planners. The optimal micro-grid infrastructure must be determined through the optimization process. The micro-grid built using the proposed design, and the entire distribution system, has the reliability indicators optimized for this study. Utilities often use standard indexes to assess the reliability of distribution networks. A chain of algorithms is proposed for optimal allocationof DG taking into account system reliability. Load flow studies and reliability analysis are conducted on IEEE 14 Bus system to investigate the effect of reliability issues for optimal allocation of DG. A cost factor index is proposed.