Power control of an autonomous wind energy conversion system based on a permanent magnet synchronous generator with integrated pumping storage.

Wind energy plays a crucial role as a renewable source for electricity generation, especially in remote or isolated regions without access to the main power grid. The intermittent characteristics of wind energy make it essential to incorporate energy storage solutions to guarantee a consistent power supply. This study introduces the design, modeling, and control mechanisms of a self-sufficient wind energy conversion system (WECS) that utilizes a Permanent magnet synchronous generator (PMSG) in conjunction with a Water pumping storage station (WPS).

The optimum sizing of zero-emission water-cooled VCR cycle based on exergo-economic-environmental assessment criteria by triple-objective MPSO.

Renewable energies are interesting as an alternative and sustainable resource for air conditioning applications. But initial investment cost of equipment, whose employed for converting the renewable energy into usable shape and also for air conditioning duty, are significant. Therefore, determining the optimum sizing has high priority.

Rotor angle stability of a microgrid generator through polynomial approximation based on RFID data collection and deep learning.

The article proposes a novel approach to assess rotor angle stability in microgrids by enhancing the Modified Galerkin Method (MGM), which is based on the Polynomial Approximation, using real-time RFID data acquisition. Due to their reliance on assumptions, traditional rotor angle stability methodologies frequently fail in online transient stability testing. MGM successfully captures the dynamic behavior of microgrids by approximating state variables using a sequence of polynomials and coefficients.

A hybrid approach using support vector machine rule-based system: detecting cyber threats in internet of things.

While the proliferation of the Internet of Things (IoT) has revolutionized several industries, it has also created severe data security concerns. The security of these network devices and the dependability of IoT networks depend on efficient threat detection. Device heterogeneity, computing resource constraints, and the ever-changing nature of cyber threats are a few of the obstacles that make detecting cyber threats in IoT systems difficult.

A fuzzy-predictive current control with real-time hardware for PEM fuel cell systems.

This research study presents the application of the FC-PCC (Fuzzy Logic Predictive Current Control) algorithm in the context of maximum power point tracking (MPPT) for a proton exchange membrane fuel cell system employing a three-level boost converter (TLBC). The proposed approach involves the integration of an intelligent fuzzy controller with a predictive current control strategy in order to improve the performance of MPP tracking. Initially, the utilization of fuzzy logic involves the utilization of data values obtained from the PEMFC.

Experimental validation of effective zebra optimization algorithm-based MPPT under partial shading conditions in photovoltaic systems.

This study introduces a novel approach for analyzing photovoltaic (PV) systems that employ block lookup tables for speedy and efficient simulation. It introduces an innovative method for tracking the Global Maximum Power Point (GMPP) by utilizing Zebra Optimization Algorithm (ZOA). The suggested method was carefully evaluated under difficult Partial Shading Conditions (PSCs) and Dynamic Shading Conditions (DSCs) to determine its global and local search capability.

Performance analysis of DC-DC Buck converter with innovative multi-stage PIDn(1+PD) controller using GEO algorithm.

Power electronic converters are widely used in various fields of electrical equipment. Due to their fast dynamics and non-linear nature, controlling them requires dealing with various complexities. Therefore, having a well-designed, high-speed, and robust controller is critical to ensure the effective operation of these devices.

Preparation of interconnected tin oxide nanoparticles on multi-layered MXene for lithium storage anodes.

MXenes, a novel class of two-dimensional (2D) materials known for their excellent electronic conductivity and hydrophilicity, have emerged as promising candidates for lithium-ion battery anodes. This study presents a simple wet-chemical method for depositing interconnected SnO nanoparticles (NPs) onto MXene sheets. The SnO NPs act as both a high-capacity energy source and a spacer to prevent MXene sheets from restacking.

Power electronics for green hydrogen generation with focus on methods, topologies, and comparative analysis.

This research article meticulously examines advanced power electronic converters crucial for optimizing electrolyzer perfor- mance in hydrogen production systems. It conducts a thorough review of mature electrolyzer types, detailing their specifications, electric models, manufacturers, and scalability. To meet the high current and stable DC voltage demands of industrial electrolyzers, the study delves into a broad spectrum of AC-DC and DC-DC converter topologies.

A hybrid LSTM random forest model with grey wolf optimization for enhanced detection of multiple bearing faults.

Bearing degradation is the primary cause of electrical machine failures, making reliable condition monitoring essential to prevent breakdowns. This paper presents a novel hybrid model for the detection of multiple faults in bearings, combining Long Short-Term Memory (LSTM) networks with random forest (RF) classifiers, further enhanced by the Grey Wolf Optimization (GWO) algorithm. The proposed approach is structured in three stages: first, time and frequency domain features are manually extracted from vibration signals; second, these features are processed by a dual-layer LSTM network, which is specifically designed to capture complex temporal relationships within the data; finally, the GWO algorithm is employed to optimize feature selection from the LSTM outputs, feeding the most relevant features into the RF classifier for fault classification.

Enhancing HVDC transmission line fault detection using disjoint bagging and bayesian optimization with artificial neural networks and scientometric insights.

DC grid fault protection techniques have previously faced challenges such as fixed thresholds, insensitivity to high-resistance faults, and dependency on specific threshold settings. These limitations can lead to elevated fault currents in the grid, particularly affecting multi-modular converters (MMCs) vulnerability to large fault current transients. This paper proposes a novel approach that combines the disjoint-based Bootstrap Aggregating (Bagging) technique and Bayesian optimization (BO) for fault detection in DC grids.

Classification of RTV-coated porcelain insulator condition under different profiles and levels of pollution.

Due to the limited hydrophobic properties of porcelain insulators, applying anti-pollution flashover coatings is crucial to enhance their functionality. This research outlines a classification system for assessing contamination levels on 22 kV porcelain insulators, both with and without coatings. It synthesizes six classification criteria derived through both numerical simulations and experimental studies to effectively gauge contamination severity.

Efficient DC motor speed control using a novel multi-stage FOPD(1 + PI) controller optimized by the Pelican optimization algorithm.

This paper introduces a novel multi-stage FOPD(1 + PI) controller for DC motor speed control, optimized using the Pelican Optimization Algorithm (POA). Traditional PID controllers often fall short in handling the complex dynamics of DC motors, leading to suboptimal performance. Our proposed controller integrates fractional-order proportional-derivative (FOPD) and proportional-integral (PI) control actions, optimized via POA to achieve superior control performance.

Maximizing torque per volume index for SHESM based on two-dimensional method and meta-heuristic optimization algorithms.

In this paper, a permanent magnet synchronous machine (PMSM) with an auxiliary winding (AW) on the rotor is analyzed by two-dimensional approach. This PMSM with AW (AWPMSM) can be used in many applications such as propulsion system, aircraft and traction because it includes rotor flux control capability. First, the magnetic field in different parts of AWPMSM is calculated based on Maxwell equations.

Impact of atmospheric conditions on the flash-over voltage of the transmission line insulators using central composite design.

The insulators of overhead power lines play a crucial role in maintaining the reliability of transmission and distribution networks. Because they are exposed to harsh and dynamic environmental conditions, it is essential to investigate the impact of environmental parameters such as pollution, inclined angle with the cross arm, and temperature on the dielectric performance of the insulators of overhead lines. Conventionally, the effect of such parameters can be investigated through experimental measurements of the insulator flashover voltage.

Finite control set model predictive current control for three phase grid connected inverter with common mode voltage suppression.

This research introduces an advanced finite control set model predictive current control (FCS-MPCC) specifically tailored for three-phase grid-connected inverters, with a primary focus on the suppression of common mode voltage (CMV). CMV is known for causing a range of issues, including leakage currents, electromagnetic interference (EMI), and accelerated system degradation. The proposed control strategy employs a system model that predicts the inverter's future states, enabling the selection of optimal switching states from a finite set to achieve dual objectives: precise current control and effective CMV reduction, a meticulously designed cost function evaluates the potential switching states, balancing the accuracy of current tracking against the necessity to minimize CMV.

Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources.

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation.

Fault detection and diagnosis of grid-connected photovoltaic systems using energy valley optimizer based lightweight CNN and wavelet transform.

Early fault detection and diagnosis of grid-connected photovoltaic systems (GCPS) is imperative to improve their performance and reliability. Low-cost edge devices have emerged as innovative solutions for real-time monitoring, reducing latency, and improving response times. In this work, a lightweight Convolutional Neural Network (CNN) is designed and fine-tuned using Energy Valley Optimizer (EVO) for fault diagnosis.

Bayesian-optimized LSTM-DWT approach for reliable fault detection in MMC-based HVDC systems.

As Europe integrates more renewable energy resources, notably offshore wind power, into its super meshed grid, the demand for reliable long-distance High Voltage Direct Current (HVDC) transmission systems has surged. This paper addresses the intricacies of HVDC systems built upon Modular Multi-Level Converters (MMCs), especially concerning the rapid rise of DC fault currents. We propose a novel fault identification and classification for DC transmission lines only by employing Long Short-Term Memory (LSTM) networks integrated with Discrete Wavelet Transform (DWT) for feature extraction.

Assessment of carbonized himalayan chir pine biomass as an eco-friendly adsorbent for effective removal of industrial dyes.

This study investigates the use of carbonized Himalayan Chir Pine Biomass, known as Chir Pine Activated Carbon (CPAC), as an eco-friendly and cost-effective adsorbent for efficient industrial dye removal, with a focus on environmental sustainability. By applying different additive treatments, four adsorbents (C1, C2, C3, and C4) were formulated. CPAC was synthesized through pyrolysis and characterized using various analytical techniques including FE-SEM, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and differential scanning calorimetry (DSC).

A reduced vector model predictive controller for a three-level neutral point clamped inverter with common-mode voltage suppression.

This paper presents a novel, state-of-the-art predictive control architecture that addresses the computational complexity and limitations of conventional predictive control methodologies while enhancing the performance efficacy of predictive control techniques applied to three-level voltage source converters (NPC inverters). This framework's main goal is to decrease the number of filtered voltage lifespan vectors in each sector, which will increase the overall efficiency of the control system and allow for common mode voltage reduction in three-level voltage source converters. Two particular tactics are described in order to accomplish this.

Optimizing electric vehicle powertrains peak performance with robust predictive direct torque control of induction motors: a practical approach and experimental validation.

Enhancing the efficiency of the electric vehicle's powertrain becomes a crucial focus, wherein the control system for the traction motor plays a significant role. This paper presents a novel electric vehicle traction motor control system based on a robust predictive direct torque control approach, an improved version of the conventional DTC, where the traditional switching table and the hysteresis regulators are substituted with a predictive block based on an optimization algorithm. Additionally, a robust predictive speed loop regulator is employed instead of the proportional-integral regulator, which integrates a new cost function with a finite horizon, incorporating integral action into the control law based on a Taylor series expansion.

Enhancing PEM fuel cell efficiency with flying squirrel search optimization and Cuckoo Search MPPT techniques in dynamically operating environments.

This study looks into how to make proton exchange membrane (PEM) fuel cells work more efficiently in environments that change over time using new Maximum Power Point Tracking (MPPT) methods. We evaluate the efficacy of Flying Squirrel Search Optimization (FSSO) and Cuckoo Search (CS) algorithms in adapting to varying conditions, including fluctuations in pressure and temperature. Through meticulous simulations and analyses, the study explores the collaborative integration of these techniques with boost converters to enhance reliability and productivity.