Adaptive RAO ensembled dichotomy technique for the accurate parameters extraction of solar PV system.

The parameter extraction process for PV models poses a complex nonlinear and multi-model optimization challenge. Accurately estimating these parameters is crucial for optimizing the efficiency of PV systems. To address this, the paper introduces the Adaptive Rao Dichotomy Method (ARDM) which leverages the adaptive characteristics of the Rao algorithm and the Dichotomy Technique. ARDM is compared with the several recent optimization techniques, including the tuna swarm optimizer, African vulture's optimizer, and teaching-learning-based optimizer. Statistical analyses and experimental results demonstrate the ARDM's superior performance in the parameter extraction for the various PV models, such as RTC France and PWP 201 polycrystalline, utilizing manufacturer-provided datasheets. Comparisons with competing techniques further underscore ARDM dominance. Simulation results highlight ARDM quick processing time, steady convergence, and consistently high accuracy in delivering optimal solutions.

Performance analysis of DFIG support microgrid using GA optimized restricted Boltzmann Machine algorithm.

Voltage and reactive power regulation in a deregulated microgrid can be achieved by strategically placing the Static Synchronous Compensator (STATCOM) in coordination with other renewable energy sources, thus ensuring high-end stability and independent control. STATCOM plays a crucial role in effectively addressing power quality issues such as voltage fluctuation and reactive power imbalances caused by the intermittent nature of wind energy conversion systems. To successfully integrate STATCOM into the existing system, it is essential that the control system employed for STATCOM coordination aligns with the Doubly-Fed Induction Generator (DFIG) controller within the microgrid. Therefore, an efficient control algorithm is required in the microgrid, capable of coordinating with the DFIG controller while maintaining system stability. The utilization of a Genetic Algorithm (GA) in calibrating the Restricted Boltzmannn Machine (RBM) can streamline the process of determining optimal hyperparameters for specific tasks, eliminating the need for computationally intensive and time-consuming grid searches or manual tuning. This approach is particularly advantageous when dealing with large datasets within short time durations. In this research, a Simulink model comprising a DFIG-based microgrid and STATCOM has been developed to demonstrate the effectiveness of the proposed control system using RBM in managing STATCOM and facilitating microgrid operations.

HARNet in deep learning approach-a systematic survey.

A comprehensive examination of human action recognition (HAR) methodologies situated at the convergence of deep learning and computer vision is the subject of this article. We examine the progression from handcrafted feature-based approaches to end-to-end learning, with a particular focus on the significance of large-scale datasets. By classifying research paradigms, such as temporal modelling and spatial features, our proposed taxonomy illuminates the merits and drawbacks of each. We specifically present HARNet, an architecture for Multi-Model Deep Learning that integrates recurrent and convolutional neural networks while utilizing attention mechanisms to improve accuracy and robustness. The VideoMAE v2 method ( https://github.com/OpenGVLab/VideoMAEv2 ) has been utilized as a case study to illustrate practical implementations and obstacles. For researchers and practitioners interested in gaining a comprehensive understanding of the most recent advancements in HAR as they relate to computer vision and deep learning, this survey is an invaluable resource.

A novel optimized neural network model for cyber attack detection using enhanced whale optimization algorithm.

Cybersecurity is critical in today's digitally linked and networked society. There is no way to overestimate the importance of cyber security as technology develops and becomes more pervasive in our daily lives. Cybersecurity is essential to people's protection. One type of cyberattack known as "credential stuffing" involves using previously acquired usernames and passwords by attackers to access user accounts on several websites without authorization. This is feasible as a lot of people use the same passwords and usernames on several different websites. Maintaining the security of online accounts requires defence against credential-stuffing attacks. The problems of credential stuffing attacks, failure detection, and prediction can be handled by the suggested EWOA-ANN model. Here, a novel optimization approach known as Enhanced Whale Optimization Algorithm (EWOA) is put on to train the neural network. The effectiveness of the suggested attack identification model has been demonstrated, and an empirical comparison will be carried out with respect to specific security analysis.

An efficient MPPT techniques for inter-harmonic reduction in grid connected hybrid wind and solar energy systems.

In this work, the operation of photovoltaic system, wind turbine driven doubly fed induction generator along with battery has been observed. Also, a searching space minimization-based artificial bee colony scheme is developed for tracking the maximum power in a doubly fed induction generator-based system. To track maximum power in solar systems, an improved adaptive reference voltage approach has been presented. Several conventional and optimization-based techniques are used by DFIG and photovoltaic systems to get around the non-linearity features in the output parameters. Regarding DFIG, the artificial bee colony method based on searching space minimization can be used to solve the shortcomings of the perturb and observe algorithm. Because of its weather-sensitive nature, it can withstand sudden changes in wind speed. The suggested searching space minimization based artificial bee colony strategy uses a mechanism for determining the range of optimal rotor speed in order to track the maximum power point more quickly. The maximum power point tracking performance of the adaptive reference voltage technique is superior to that of current perturb and observed-based systems. However, a huge processing memory is required in order to track the maximum possible power point. This paper proposes an enhanced maximum power point tracking technique based on adaptive reference voltage that does not require a memory unit. Additionally, despite sudden changes in irradiation conditions, improved adaptive reference voltage can drift-free and reliably monitor the maximum power point. The new adaptive reference voltage technique uses temperature and radiation sensors to identify the region nearest to the maximum power point. This helps the system respond more quickly. The proposed system with searching space minimization based artificial bee colony and improved adaptive reference voltage schemes displays lower inter-harmonic content in grid current compared to perturb and observe scheme. The proposed scheme has been implemented in MATLAB & simulink atmosphere and OPAL-RT displayed satisfactory results.

Development of renewable energy fed three-level hybrid active filter for EV charging station load using Jaya grey wolf optimization.

This work develops a hybrid active power filter (HAPF) in this article to operate in conjunction with the energy storage system (ESS), wind power generation system (WPGS), and solar energy system (SES). It employs three level shunt voltage source converters (VSC) connected to the DC-bus. Optimization of the gain values of the fractional-order proportional integral derivative controller (FOPIDC) and parameter values of the HAPF is achieved using the Jaya grey wolf hybrid algorithm (GWJA). The primary objectives of this study, aimed at enhancing power quality (PQ), include: (1) ensuring swift stabilization of DC link capacitor voltage (DCLCV); (2) reducing harmonics and improving power factor (PF); (3) maintaining satisfactory performance under different combinations of loads like EV charging load, non linear load and solar irradiation conditions. The proposed controller's performance is evaluated through three test scenarios featuring different load configurations and irradiation levels. Additionally, the HAPF is subjected to design using other optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) to assess their respective contributions to PQ improvement.

Implementation of high step-up power converter for fuel cell application with hybrid MPPT controller.

As of now, there are multiple types of renewable energy sources available in nature which are hydro, wind, tidal, and solar. Among all of that the solar energy source is used in many applications because of its features are low maitainence cost, less human power for handling, a clean source, more availability in nature, and reduced carbon emissions. However, the disadvantages of solar networks are continuously depending on the weather conditions, high complexity of the solar energy storage, and lots of installation place is required. So, in this work, the Proton Exchange Membrane Fuel Stack (PEMFS) is utilized for supplying the power to the local consumers. The merits of this fuel stack are high power density, ability to work at very less temperature values, efficient heat maintenance, and water management. Also, this fuel stack gives a quick startup response. The only demerit of PEMFS is excessive current production, plus very less output voltage. To optimize the current supply of the fuel stack, a Wide Input Operation Single Switch Boost Converter (WIOSSBC) circuit is placed across the fuel stack output to improve the load voltage profile. The advantages of the WIOSSBC are less current ripples, uniform voltage supply, plus good voltage conversion ratio. Another issue of the fuel stack is nonlinear power production. To linearize the issue of fuel stack, the Grey Wolf Algorithm Dependent Fuzzy Logic Methodology (GWADFLM) is introduced in this article for maintaining the operating point of the fuel cell near to Maximum Power Point (MPP) place. The entire system is investigated by utilizing the MATLAB software.

Evaluation and deployment of a unified MPPT controller for hybrid Luo converter in combined PV and wind energy systems.

This work emphasizes the development and examination of a Hybrid Luo Converter integrated with a unified Maximum Power Point Tracking (MPPT) for both grid and independent hybrid systems. The primary objectives of this hybrid system are to efficiently harness power from intermittent and variable renewable sources while elevating low-voltage energy inputs to utility-grade levels. Unlike previous studies employing specific MPPT algorithms for solar and wind sources, this work aims to simplify the control system by utilizing a unified MPPT controller. This research also introduces a novel approach involving dual-lift hybrid Luo converters to create hybrid systems, operating exclusively or concurrently based on the availability of renewable resources. To maximize power generation from all renewable sources, a unified MPPT algorithm is developed. The hybrid system, incorporates 500 W wind and 560 W PV systems, the innovative Luo converter, and the unified MPPT controller. A comprehensive comparative analysis is presented, comparing the hybrid system's performance with that of traditional control algorithms, such as the Perturb & Observe, and Radial Basis Function Network controllers. The successful prototype of the converter validates the practicality of the proposed approach.

Application of DSO algorithm for estimating the parameters of triple diode model-based solar PV system.

Solar Photovoltaic (SPV) technology advancements are primarily aimed at decarbonizing and enhancing the resiliency of the energy grid. Incorporating SPV is one of the ways to achieve the goal of energy efficiency. Because of the nonlinearity, modeling of SPV is a very difficult process. Identification of variables in a lumped electric circuit model is required for accurate modeling of the SPV system. This paper presents a new state-of-the-art control technique based on human artefacts dubbed Drone Squadron Optimization for estimating 15 parameters of a three-diode equivalent model solar PV system. The suggested method simulates a nonlinear relationship between the P-V and I-V performance curves, lowering the difference between experimental and calculated data. To evaluate the adaptive performance in every climatic state, two different test cases with commercial PV cells, RTC France and photo watt-201, are used. The proposed method provides a more accurate parameter estimate. To validate the recommended approach's performance, the data are compared to the results of the most recent and powerful methodologies in the literature. For the RTC and PWP Photo Watt Cell, the DSO technique has the lowest Root Mean Square Error (RMSE) of 6.7776 × 10-4 and 0.002310324 × 10-4, respectively.

A novel on design and implementation of hybrid MPPT controllers for solar PV systems under various partial shading conditions.

At present, fossil fuel-based power generation systems are reducing drastically because of their less availability in nature. In addition, it produces hazardous gasses and high environmental pollution. So, in this work, the solar natural source is selected for generating the electricity. Due to the nonlinear behavior of PV, achieving maximum voltage from the Photovoltaic (PV) system is a more tough job. In this work, various hybrid optimization controllers are studied for tracing the working power point of the PV under different Partial Shading Conditions. The studied hybrid optimization MPPT methods are equated in terms of oscillations across MPP, output power extraction, settling time of the MPP, dependency on the PV modeling, operating duty value of the converter, error finding accuracy of MPPT, algorithm complexity, tracking speed, periodic tuning required, and the number of sensing parameters utilized. Based on the simulative comparison results, it has been observed that the modified Grey Wolf Optimization based ANFIS hybrid MPPT method provides good results when equated with the other power point tracking techniques. Here, the conventional converter helps increase the PV source voltage from one level to another level. The proposed system is investigated by using the MATLAB/Simulink tool.

Analysis and mitigation of PQ disturbances in grid connected system using fuzzy logic based IUPQC.

Renewable energy integration introduces grid instability due to variable and intermittent sources like solar and wind, impacting reliability. This paper provides a thorough discussion of recent advancements and emerging trends in grid-integrated wind energy systems (GIWES) and grid-integrated solar energy systems (GISES). More than 70 research articles have been rigorously assessed and listed the technological and economic challenges. The increase in installations of grid-Integrating systems gives rise to challenges like as grid strain, peak shaving impacts, unpredictability of renewable energy sources (RES), and power quality disturbances. A variety of custom power devices, such as dynamic voltage restorers (DVR), static synchronous compensators (STATCOM), active power filters (APF), and unified power quality conditioners (UPQC), have gained popularity in response to these challenges. Among the various challenges, power quality disturbances, including voltage sag, swell, current and harmonics pose significant issues. To address these disturbances this work present a novel approach utilizing fuzzy logic (FL) to develop multi-feeder interline unified power-quality conditioners (MF-IUPQCs). The MF-IUPQC has three legs and three levels, each of which has four diode-clamped inverters. Switching is carried out through the use of space vector pulse width/duration modulation (SVPWM). Total harmonic distortion (THD) induced by nonlinear loads is reduced by the FLC-based MF-IUPQC, which also improves dynamic performance and offers a smooth DC-link voltage. The proposed control mechanism is implemented using MATLAB/Simulink. The fuzzy-based controller is compared to the industry-standard proportional-integral (PI) controller to determine its efficacy. Among them, the MF-IUPQC based on FLC delivers the smoothest voltage profile and the lowest THD.