Pulakraj Aryan

Distributed generators (DGs) are recent emerging technologies which are executed in distribution system (DS) to reduce power losses (PLs). However, optimal location and sizing of DG (OLSDG) remain a challenging combinatorial optimization problem due to its large search space and load uncertainties. Numerous scholars attempted to effectively handle this optimization problem characterized by a fixed load model (constant power (CP)), which fails to represent the real-world consumer behavior. This assumption causes a research gap, as practical DS is mainly dependent on characteristics of voltage magnitude and ZIP load models better represents this behavior. To overcome these challenges, this work reports a novel metaheuristic Algorithm inspired by quantum principles (Multipartite Adaptive Quantum inspired Evolutionary Algorithm (MAQiEA)) to discover the OLSDG. The originality of the work stands out by using realistic load model (ZIP load model), utilizing multipartite adaptive variation operator and analyzing the influence of diverse DGs with CP and ZIP load models. This study investigates two different scenarios to analyze the effect of various DGs on CP and ZIP load models. In first case, a study was conducted to analyze the influence of diverse DGs with CP load which are tabulated in the statistical analysis results that demonstrate the efficacy and soundness of MAQiEA. For performance assessment, proposed algorithm is compared with some well-known other metaheuristic algorithms. Tabulated results demonstrate that, MAQiEA outperforms other metaheuristic algorithms in PL reduction. Finally, the approach is suitable in terms of overall operational framework which is an important investigation in mitigating the PLs.

An enhanced type-2 fuzzy integral–derivative plus proportional-derivative (T2FID+PD) controller is suggested for mitigating frequency deviations in a triple-area hybrid power system (HPS). Optimal controller settings are computed using the African vulture optimizer (AVO). The thermal PS & distributed renewable generation (DRG) are interconnected in Area-1. This DRG unit comprises wind-turbine & solar-photovoltaic generation. The thermal & gas and thermal & nuclear facilities are interconnected in Area-2 and Area-3, respectively. The nonlinearities, including generation rate constraints (GRC), boiler dynamics (BD), and governor dead band (GDB), are also modeled. Moreover, the performance evaluation of an AVO-based tuned T2FID+PD control is compared with its contemporaries. Also, the effect of integrating electric vehicles is studied. Additionally, the developed controller's robustness to parameter variations in power systems is ascertained. Finally, the impact of deregulation is studied by simulating nominal, unilateral, and contract violation scenarios. Amid deregulation, the impact of communication latency and changes in distribution participation are analyzed. The AVO-tuned T2FID+PD controller shows enhanced frequency regulation capabilities than its contemporaries as demonstrated through simulation studies on IEEE 39 Bus system. The suggested controller lowers settling time of frequency variation by 92.20%, 92.09%, and 92.19%; overshoot by 97.00%, 7.94%, and 96.08%; and undershoot by 89.68%, 93.37%, and 89.17% compared to several well-known studies in the literature.

Hydropower plants are well known for their nonlinear and non-minimum phase behavior, which poses significant challenges in automatic generation control (AGC) and automatic voltage regulation (AVR). Although integrated AGC-AVR methods are common for thermal power plants, there's still a need for robust, unified solutions tailored to hydropower systems. This paper proposes a simple and unified fractional-order frequency-shifted PID controller with a fractional filter (FS-FPIDF) to address this gap. A fractional-order model of a non-reheated hydropower plant is derived and then frequency-shifted to design the FS-FPIDF controller, featuring a single tuning parameter optimized via integral square error (ISE) minimization of voltage and frequency deviations. The proposed control scheme demonstrates robustness against sudden load changes, nonlinearities, model uncertainties, and stealthy cyber-threats, including false data injection (FDI) and denial of service (DoS). Additionally, the methodology is scalable and has been evaluated on a renewable-integrated IEEE 39-bus hydro-thermal system to assess its performance in maintaining frequency and voltage stability amidst the stochastic nature of renewable energy sources. Practical feasibility of this proposal is ascertained using OPAL-RT-based hardware-in-loop validation. Compared to conventional integer-order control methods, the proposed FS-FPIDF controller ensures superior regulation without increasing design complexity.

The continued growth of electric vehicle (EV) deployment has placed increasing emphasis on the development of charging infrastructure that is efficient, reliable, and compliant with safety requirements over a wide range of power levels. In EV charging systems, DC–DC converters work as a key interface for voltage adaptation, power regulation, and battery protection, making the choice of converter topology a crucial design consideration. This study provides a comparative and application-focused review of commonly employed isolated and non-isolated DC–DC converter topologies used in EV charging architectures. The comparison is carried out by examining voltage gain behavior, efficiency tendencies, switching and thermal stress, soft-switching capability, component utilization, control complexity, cost-related aspects, and practical deployment constraints. Fundamental operating principles and representative time-domain simulations are used to highlight relative performance trends of PWM-based and resonant isolated converters under typical charging conditions. Rather than introducing new converter structures or control methods, the objective of this work is to offer practical, design-oriented insights that support informed topology selection. Based on the comparative analysis, non-isolated converters are found to be well suited for low- to medium-power onboard charging applications, whereas isolated resonant converters are more appropriate for high-power and fast-charging systems when safety, scalability, efficiency trends, and system-level implementation factors are considered together.

A stealthy cyberattack (SCA) aims to gradually drive the system toward instability. Although research is continuing in attack detection algorithms, a resilient control strategy is vital if SCAs go undetected for a considerable time. Especially for a controller involved in automatic generation control (AGC), resiliency becomes more crucial as the controller has to sometimes work under abnormal conditions considering that shutdown of operation is not an option. This paper introduces an innovative approach to support dual-area thermal power systems in the face of network-related challenges such as transference latency (TL) and SCAs. We propose a new tri-parametric fractional controller (TFC) that merges the advantages of proportional-integral and proportional-derivative operations, eliminating the need for additional control loops and enhancing system efficiency. The TFC's efficacy in mitigating random and step load disturbances is established, outperforming existing controllers. We employ the complex root boundary method to define the optimal parameter search space, with an improved equilibrium optimizer algorithm determining the best controller settings. The inclusion of renewable energy sources, including solar and wind, is considered, and the robustness of the system is evaluated using uncertainty norms and complementary sensitivity functions. Our approach is validated through hardware-in-loop real-time verification on the OPAL-RT platform, demonstrating superior performance in maintaining frequency stability under SCAs and TL challenges.

Communication delays and false data injection attacks pose significant threats to the frequency control of automatic generation systems. This paper presents a Golden Jackal Optimizer (GJO)-enhanced frequency-shifted internal model control (FIMC) scheme aimed at addressing these challenges in a dual-area thermal power system. The FIMC approach employs a pole and zero shifting variable that acts as a system robustness indicator. The paper determines an analytical search range for this variable using the Routh-Hurwitz criteria, which were later utilized by GJO. The robustness and performance of the GJO-tuned FIMC are tested against random and step load disturbances, as well as system nonlinearities. The paper models various false data injection threats, assessing the effectiveness of the GJO-tuned FIMC in neutralizing these threats under inherent communication delays. Finally, the proposed strategy is verified in real-time through hardware, employing the OPAL-RT platform. The results are compared with a recent strategy, underscoring the advanced efficacy of the proposed approach.

The uncertainties associated with multi-area power systems comprising both thermal and distributed renewable generation (DRG) sources such as solar and wind necessitate the use of an efficient load frequency control (LFC) technique. Therefore, a hybrid version of two metaheuristic algorithms (arithmetic optimization and African vulture's optimization algorithm) is developed. It is called the ‘arithmetic optimized African vulture's optimization algorithm (AOAVOA)’. This algorithm is used to tune a novel type-2 fuzzy-based proportional–derivative branched with dual degree-of-freedom proportional–integral–derivative controller for the LFC of a three-area hybrid deregulated power system. Thermal, electric vehicle (EV), and DRG sources (including a solar panel and a wind turbine system) are connected in area-1. Area-2 involves thermal and gas-generating units (GUs), while thermal and geothermal units are linked in area-3. Practical restrictions such as thermo-boiler dynamics, thermal-governor dead-band, and generation rate constraints are also considered. The proposed LFC method is compared to other controllers and optimizers to demonstrate its superiority in rejecting step and random load disturbances. By functioning as energy storage elements, EVs and DRG units can enhance dynamic responses during peak demand. As a result, the effect of the aforementioned units on dynamic reactions is also investigated. To validate its effectiveness, the closed-loop system is subjected to robust stability analysis and is compared to various existing control schemes from the literature. It is determined that the suggested AOAVOA improves fitness by 40.20% over the arithmetic optimizer (AO), while frequency regulation is improved by 4.55% over an AO-tuned type-2 fuzzy-based branched controller.

This brief introduces a new fractional-order integral derivative ( ${\mathrm {FOI}}^{\lambda }\text{D}^{1-\lambda }$ ) controller for a class of integrating systems involving time delays. The stability region is explored through the complex root boundary (CRB) analysis which explicitly provides the search space of ${\mathrm {FOI}}^{\lambda }\text{D}^{1-\lambda }$ parameters for integrating time delay systems of any order. The optimal settings are obtained through a three-step optimization algorithm based on the CRB knowledge and the desired performance measures. Comparisons with other recent sophisticated tuning methods are also provided to demonstrate the effectiveness of the suggested control scheme through numerical study. Finally, a case study on a non-linear continuously stirred reactor under model uncertainties is presented to check the efficacy of the new method.