This research provides an adaptive control design in a photovoltaic system (PV) for maximum power point tracking (MPPT). In the PV system, MPPT strategies are used to deliver available load under solar radiation and atmospheric temperature changes. article presents new framework enhance performance of MPPT, which will minimize complexity efficiently manage uncertainties disruptions environment system. Here, algorithm is decoupled with model reference (MRAC) techniques, gains overall...

Modern electrical transportation systems require eco-friendly refueling stations worldwide. This has attracted the interest of researchers toward a feasible optimal solution for electric vehicle (EV) charging stations. EV can be simply classified as Slow (domestic use), Fast and Ultrafast (commercial use). study highlights recent advancements in commercial DC charging. The battery voltage varies widely from 36V to 900V according EVs. focuses on non-isolated unidirectional converters...

Abstract This study extensively examined the impact of aluminium oxide (Al 2 O 3 ) and titanium dioxide (TiO nanoparticles addition in biodiesel fuel derived from Guizotia abyssinica (L.) oil. The assessment blends, which were created by combining was conducted using energy, exergy, sustainability indices. highest recorded power output 2.81 kW observed for GAB20A engine operating at 1800 rpm. experimental results revealed that exhibited lowest consumption, with a value 203 g/kWh, when...

The increasing prevalence of network connections is driving a continuous surge in the requirement for security and safeguarding against cyberattacks. This has triggered need to develop implement intrusion detection systems (IDS), one key components perimeter aimed at thwarting alleviating issues presented by invaders. Over time, have been instrumental identifying breaches deviations. Several researchers recommended implementation machine learning approaches IDSs counteract menace posed...

This paper proposes a cascaded Proportional Integral-Fractional Order Proportional-Integral-Derivative (PI-FOPID) controller to improve the frequency response of hybrid microgrid system. The optimum gains proposed are fine-tuned using Gorilla Troops Optimizer (GTO) which is recent metaheuristic optimization algorithm. case study two-area system that contains diesel generators, various renewable energy sources such as photovoltaic and wind generation systems, well different storage devices....

This paper presents a fully optimized fuzzy proportional-integral-derivative with filter (FPIDF) load frequency controller (LFC) for enhancing the performance of hybrid microgrid system. The Marine Predator Algorithm (MPA), recent optimization algorithm, is used to optimize gains as well input scaling factors and membership functions proposed PIDF controller. tested on two-area system containing various renewable energy sources storage devices. effectiveness MPA based FPIDF compared...

The increasing penetration of renewable energy sources (RES) into modern power systems may reflect the problem frequency stabilization. Therefore, this paper proposes a new combined Fuzzy Fractional-Order Proportional-Integral (FOPI) and Tilt-Integral-Derivative (TID) controller to improve response hybrid system. proposed combines advantages intelligent FOPI conventional TID controllers, resulting in more effective robust load control. In study, parameters + combination are optimized using...

Several advantages make wind-driven permanent magnet synchronous generators (PMSGs) very promising in the wind energy market, especially their fault ride-through capabilities. With high penetration levels of today, both grid and power (WP) systems are being affected by each other. Due to faults, DC-bus PMSG typically experiences overvoltage, which can negatively affect generator parameters trip system. However, advancements electronics, control systems, limiters, FACTS, storage technology it...

This paper discusses the efficient implementation of a new hybrid approach to forecasting short-term PV power production for four different plants in Algeria. The developed model incorporates time-varying filter-empirical mode decomposition (TVF-EMD) and an extreme learning machine (ELM) as essence regression. TVF-EMD technique is used deal with fluctuation data by splitting it into series more stable constant subseries. specified set features (intrinsic functions (IMFs)) utilized training...

The novelty of this research lies in the development a new battery management system (BMS) for electric vehicles, which utilizes an artificial neural network (ANN) and fuzzy logic-based adaptive droop control theory. This innovative approach offers several advantages over traditional BMS systems, such as decentralized architecture, communication-free capability, improved reliability. proposed incorporates virtual admittance, adjusts value admittance based on current state charge (SOC) each...

Electric Vehicle (EV) technology and migration are hindered by battery sizing, short driving ranges, optimal operations. This article focuses on developing a strategy for scheduling EV charging in specific region, addressing waiting time, uneven due to unevenly distributed stations (CS). The proposed approach optimizes CS using separate queues different levels, reducing time costs during peak hours. Which considers trade-offs between time-aware fairness overall factors like reachability,...

One of the significant issues in field microgrids is their islanding, where many cases, lack awareness microgrid islanding can lead to interference protective and control functions microgrid. Therefore, accurate detection utmost importance. In this article, a method based on deep neural networks presented. The proposed approach utilizes terminal parameters resources, such as sequence current components, voltage, other parameters, detect islanding. Various operational states are simulated...

In recent years, the financial and energy crises have made economical safe operation of distribution microgrids one main challenges for operators. this regard, optimal microgrid, considering favorable indicators Generation consumption, significantly improves quality development economic electricity grids. On other hand, many problems complications due to presence DG sources uncertainties caused by them in such as inability solve load using common calculation methods. Therefore, grid...

An efficient power control technique for inverter-based distributed generation (DG) in an islanded microgrid is investigated this work. The objective to raise the caliber of electricity pumped from network-connected DGs. characteristics that are taken into consideration include voltage and frequency control, dynamic response, steady-state particularly when operating island mode or there a load change. method consists internal current loop external based on synchronous reference frame...

This paper proposes the use of two recent optimization techniques, called Local Unimodal Sampling (LUS) and Spotted Hyena Optimizer (SHO), to optimally tune cascaded PID controller designed while controlling speed reducing torque ripples switched reluctance motor. The proposed implementation these algorithms has advantage better convergence lower computational efforts when compared other techniques. To demonstrate effectiveness using LUS SHO, they are designs present in previous literature,...

Fault location is one of the main challenges in distribution network due to its expanse and complexity. Today, with advent phasor measurement units (PMU), various techniques for fault using these devices have been proposed. In this research, defined as an optimization problem, determined by solving it. This done combining PMU data before after power system status estimation (PSSE) problem. Two new objective functions are designed identify faulty section based on calculating voltage...

The advanced development of large-scale solar power plants (LSSPs) has made it necessary to improve accurate forecasting models for the output energy. Solar energy is still hampered by lack predictability in its output, which remains a major hurdle industry. This paper focuses on triple deep learning (DL) techniques such as Artificial Neural Network (ANN), Recurrent (RNN) and Convolutional Network- Long-Short Term Memory CNN-LSTM address this problem. These are utilized variables (SEVs)...

Abstract Permanent magnet synchronous motors (PMSM) have become prevalent in industry and play an essential role managing industrial processes, automation systems, renewable energy sources due to their superior efficiency, torque, power density. However, because it operates like a non‐linear system with quick dynamics, variable parameters during operation, unknown disturbances, PMSM presents challenges for machine control. Non‐linear controls are required account the non‐linearities of...