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Wind generation system is becoming increasingly important as renewable energy sources due to its advantages such as low maintenance requirement and mainly it does not cause environmental contamination. This paper presents the improvement procedure of the transient state and the regulation of the output frequency by adjusting the terminal capacitor. The aim is to provide frequency control of a self-excited induction generator in remote site using different strategies which are based on the adjustment of the reactive power at the outputs of a three-phase self-excited induction generator. A thyristor controlled reactor and a switched resistive load will be used to control reactive power. The proposed particle swarm optimization algorithm technique, location of the thyristor controlled reactor device, and parameter value are optimized simultaneously. The results obtained by this strategy will be compared with those provided by the use of Fuzzy Logic Controller. This study will be conducted through the analysis of the frequency in the steady state and transient case using a developed induction generator numerical model built using MATLAB/Simulink. Simulation and experimental results will be exposed and analyzed considering a resistive inductive load on a laboratory test bench.

A substantial level of significance has been placed on renewable energy systems, especially photovoltaic (PV) systems, given the urgent global apprehensions regarding climate change and the need to cut carbon emissions. One of the main concerns in the field of PV is the ability to track power effectively over a range of factors. In the context of solar power extraction, this research paper performs a thorough comparative examination of ten controllers, including both conventional maximum power point tracking (MPPT) controllers and artificial intelligence (AI) controllers. Various factors, such as voltage, current, power, weather dependence, cost, complexity, response time, periodic tuning, stability, partial shading, and accuracy, are all intended to be evaluated by the study. It is aimed to provide insight into how well each controller performs in various circumstances by carefully examining these broad parameters. The main goal is to identify and recommend the best controller based on their performance. It is notified that, conventional techniques like INC, P&O, INC-PSO, P&O-PSO, achieved accuracies of 94.3, 97.6, 98.4, 99.6 respectively while AI based techniques Fuzzy-PSO, ANN, ANFIS, ANN-PSO, PSO, and FLC achieved accuracies of 98.6, 98, 98.6, 98.8, 98.2, 98 respectively. The results of this study add significantly to our knowledge of the applicability and effectiveness of both AI and traditional MPPT controllers, which will help the solar industry make well-informed choices when implementing solar energy systems.

The main problem associated with a doubly fed induction generator (DFIG) during fault is large inrush currents induced in rotor winding, which has detrimental effects on the machine’s AC excitation converter. A simple conventional resistance inclusion (crowbar) is employed with a PI controller to protect a DFIG from transient current, but it is observed that this method is not enough to keep transient over-current to an admissible level. In this paper, an effective current limiting technique along with reactive power control is proposed in order to maintain stability, reduce transient current surge to an acceptable level, and enhance the Fault Ride Through capacity of DFIG. The proposed dynamic control technique not only limits the fault current during voltage dip to a permissible level but also controls the reactive power during fault. The behavior of a proposed technique is analyzed by introducing unsymmetrical faults in the MATLAB-based model of DFIG. An enhanced crowbar-based fault ride-through is employed for the rotor side controller to limit inrush current and control reactive power.

The present power generation corporations are working on Renewable Power Systems (RPS) for supplying electrical power to the automotive power industries. There are several categories of RPSs available in the atmosphere. Among all of the RPSs, the most general power network used for Electric Vehicles (EVs) is hydrogen fuel which is available in nature. The H 2 fuel is fed to the Proton Exchange Membrane Fuel Stack (PEMFS) for producing electricity for the EV stations. The advantages of this selected fuel system are more power conversion efficiency, environmentally friendly, low carbon emissions, more power density, less starting time, plus able to work at very low-temperature values. However, this fuel stack faces the issue of a nonlinear power density curve. Due to this nonlinear power supply from the fuel stack, the functioning point of the overall network changes from one position of the I–V curve to another position. So, the peak voltage extraction from the fuel stack is not possible. In this article, there are various metaheuristic optimization-based Maximum Power Point Tracking (MPPT) methodologies are studied along with the conventional methods for obtaining the Maximum Power Point (MPP) position of the PEMFS. From the simulative investigation, the Continuous Different Slope Value-based Cuckoo Search Method (CDSV with CSM) provides better efficiency with more output power. Also, for all the MPPT methods comprehensive analysis has been made by utilizing the simulation results.

The integration of photovoltaic (PV) systems into global energy production is rapidly expanding. However, achieving maximum power extraction remains a significant challenge due to the nonlinear electrical characteristics of PV modules, which are highly sensitive to environmental variations such as temperature fluctuations and irradiance changes. This study presents a structured design, testing, and quasi-experimental validation methodology for robust Maximum Power Point Tracking (MPPT) control in PV systems. We propose two advanced AI-based nonlinear control strategies: an Adaptive Neuro-Fuzzy Inference System combined with Fast Terminal Synergetic Control (ANFIS-FTSC) for a boost converter and ANFIS with Backstepping (ANFIS-BS) for a Single-Ended Primary Inductor Converter (SEPIC), both of which have demonstrated tracking efficiencies exceeding 99.6%. To evaluate real-time performance, a Processor-in-the-Loop (PIL) validation is conducted using an ARM-based STM32F407VG microcontroller. The methodology adheres to a Model-Based Design (MBD) framework, ensuring systematic development, implementation, and verification of the MPPT algorithms in an embedded environment. Experimental results demonstrate that the proposed controllers achieve high efficiency, rapid convergence, and robust maximum power point tracking under varying operating conditions. The successful PIL-based validation confirms the feasibility of these intelligent control techniques for real-world deployment in PV energy systems, paving the way for more efficient and adaptive renewable energy solutions.

The present power generation government companies focus on Renewable Power Sources (RPS) because their features are zero carbon footprint, unlimited power source, fewer greenhouse pollutants, fewer output wastages, plus creatinga very healthy atmosphere. In this work, the sunlight source is utilized for the Photovoltaic (PV) standalone network. The merits of sunlight sources are very optimal human resources needed, unlimited natural sources, plus easy operation. However, the solar power resource is nonlinear fashion. As a result, the operating point of the sunlight network fluctuates concerning sunlight intensity. So, in this article, the Modified Grey Wolf Methodology with Adaptive Fuzzy Logic Controller (MGWM-AFLC) is introduced to maintain the operating point of the sunlight system at the global power point position of the PV array. This controller traces the MPP with very low fluctuations in the PV-produced voltage. The advantages of this proposed method arefewer sensing devices required, less difficulty in development, more useful for rapid changes inthe sunlight temperatures, simpler to realize operation, greater economic growth, plus highly useful for household applications. The sunlight set-up generation voltage is lowwhich is improved by introducing the new Wide Power Rating High Voltage DC-DC Boost Converter (WPRHVBC). The features of this WPRHV converter are low voltage strain on semiconductor devices, few passive elements are enough to develop the circuit, plus easy understanding.

Automated Guided Vehicles (AGVs) are vital to smart factories, enabling autonomous and efficient material transport. However, precise navigation is challenging because LiDAR provides high-dimensional, dynamic spatial data, while Inertial Measurement Unit (IMU) signals are often intermittent, leading to inconsistencies and navigation drift. This work proposes the Screened Inertial Data Fusion Method (SIDFM), a novel framework that systematically screens LiDAR data using a minimal differential function and fuses it with IMU intervals through linear regression learning. The SIDFM approach ensures that only consistent LiDAR points are integrated with IMU data, reducing mismatches and improving motion estimation. SIDFM was validated using a benchmark AGV dataset and compared against baseline LiDAR-IMU fusion methods under varying acceleration conditions. Results show that SIDFM reduces navigation errors by 12.09% at low acceleration and 11.43% at high acceleration while also significantly decreasing positioning errors. These improvements enhance the stability, precision, and safety of AGVs in dynamic manufacturing environments. The findings establish SIDFM as an effective and practical solution for robust AGV navigation, with potential applications in smart factories, warehouses, and autonomous mobility systems that demand both efficiency and reliability.

Two area multi-unit thermal hydro (TAMTH) system integrated with solar and dish-Stirling solar thermal system (DSTS) is investigated to regulate the frequency disturbance. An adaptive controller with a combination of Fuzzy Logic Control (FLC), Fractional Order proportional integral derivative (FOPID) and 2 Degree of Freedom PID (2 DOFPID) is designed to achieve frequency stability. The decisive parameters of the proposed Fuzzy based FOPID-2DOFPID (FFOPID-2DOFPID) controller are optimized by Crow Search Algorithm (CSA) and craziness factor of crow in CSA (CCSA). The proposed FFOPID-2DOFPID controller is enforced in each area for both thermal and hydro units to contribute a fine-tuned stable power system. The conformation of superiority of projected controller is presented by a comparative analysis with different kind of controllers along with some newly published research works. The comparative simulation performance analysis is performed by considering undershoot, overshoot and settling time of deviations to show the supremacy of the FFOPID-2DOFPID controller.

The basic contemplate of this work is to enhance the power and frequency variances of power system. The integration of wind and solar energy along with pumped hydrogen energy storage (PHES) may enhance the challenge to maintain the stability of the system. The design of smart and knowledgeable controller is immensely obligatory for stability of hybrid power system. In this work, intelligent fuzzy fractional order proportional integral derivative cascaded with 1 + fractional order proportional integral (FFOPID (1 + FOPI)) is designed for frequency regulation of power system. The immensely influential parameters of proposed FFOPID (1 + FOPI) controller are decided by forensic-based investigation (FBI) and quasi oppositional-based FBI (QOFBI) algorithms. The integration of QOFBI and FFOPID (1 + FOPI) is tested in four different power system environments over other controllers. The supremacy of proposed QOFBI based FFOPID (1 + FOPI) controller is confirmed through simulation result analysis by considering some statistical errors such as undershoot, overshoot, settling time and integral of time-weighted absolute error. The improvement of proposed controller over other controllers is quietly detectable in terms of frequency and tie-line power deviations. These results demonstrate that intelligent optimization in conjunction with fuzzy logic based fractional-order control can effectively increase system robustness in transient scenarios. The results of this research demonstrate that PHES is an appropriate choice for preserving frequency stability throughout the development of smart and renewable-dominated power grids when combined with RESs.

Computer vision is becoming increasingly important for industrial unmanned aerial vehicles (UAVs) to do real-time object and obstacle recognition while they are engaged in autonomous navigation. Variable texture features of objects, on the other hand, frequently lead to feature disappearance, which in turn reduces the accuracy of detection. This work proposed a novel Texture-variant Obstacle Object Classification Model (TOOCM) for feature extraction and dynamic texture representation. By capturing small texture fluctuations in real-time situations, the primary goal is to enhance the accuracy of both obstacle detection and object classification. The TOOCM model incorporates an N-layer ResNet architecture designed to address the difficulties of disappearing features in an adaptable Manner through dynamic layer augmentation based on variations in texture and size. Reconstruction of each convolutional layer in the ResNet is performed on an as-needed basis, taking into consideration the amount of texture concentration in incoming picture regions. The emergence of identifiable texture regions will result in the identification of new objects, which will then trigger adaptive categorization and layer restructuring activities. TOOCM, in contrast to traditional fixed-layer deep models, offers layer-wise learning updates during the navigation of UAV, which guarantees constant performance in complicated settings. The results of the experiments show that TOOCM achieves a greater detection accuracy of 14.09%, enhanced precision of 14.53%, and a loss reduction of 14.17%, particularly in high-density obstacle settings. These results demonstrate that the suggested adaptive feature learning approach is effective.