Recently, the integration of renewable energy sources, specifically photovoltaic (PV) systems, into power networks has grown in significance for sustainable generation. Researchers have investigated different control algorithms maximum point tracking (MPPT) to enhance efficiency PV systems. This article presents an innovative method address problem systems amidst swiftly changing weather conditions. MPPT techniques supply load during irradiance fluctuations and ambient temperatures. A novel...

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

Abstract Economic development relies on access to electrical energy, which is crucial for society’s growth. However, power shortages are challenging due non-renewable energy depletion, unregulated use, and a lack of new sources. Ethiopia’s Debre Markos distribution network experiences over 800 h outages annually, causing financial losses resource waste diesel generators (DGs) backup use. To tackle these concerns, the present study suggests hybrid generation system, combines solar biogas...

In this paper, a comprehensive energy management framework for microgrids that incorporates price-based demand response programs (DRPs) and leverages an advanced optimization method-Greedy Rat Swarm Optimizer (GRSO) is proposed. The primary objective to minimize the generation cost environmental impact of microgrid systems by effectively scheduling distributed resources (DERs), including renewable sources (RES) such as solar wind, alongside fossil-fuel-based generators. Four distinct...

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...

Abstract This paper explores scenarios for powering rural areas in Gaita Selassie with renewable energy plants, aiming to reduce system costs by optimizing component numbers meet demands. Various scenarios, such as combining solar photovoltaic (PV) pumped hydro-energy storage (PHES), utilizing wind PHES, and integrating a hybrid of PV, wind, have been evaluated based on diverse criteria, encompassing financial aspects reliability. To achieve the results, meta-heuristics Multiobjective Gray...

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 intricacies of HVDC built upon Modular Multi-Level Converters (MMCs), especially concerning rapid rise DC fault currents. We propose a novel identification and classification lines only by employing Long Short-Term Memory (LSTM) networks integrated with...

Abstract This paper presents a comprehensive study on the implementation and analysis of PID controllers in an automated voltage regulator (AVR) system. A novel tuning technique, Virtual Time response-based iterative gain evaluation re-design (V-Tiger), is introduced to iteratively adjust gains for optimal control performance. The begins with development mathematical model AVR system initialization using Pessen Integral Rule. time-response then conducted evaluate performance, followed by...

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 complexities. Therefore, having a well-designed, high-speed, robust controller is critical ensure the effective operation these devices. In DC-DC converter, steady-state performance minimum error dynamic response relies on design. This paper presents design multi-stage PID an N-filter combined one plus proportional...

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

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 elevated currents in the grid, particularly affecting multi-modular converters (MMCs) vulnerability large current transients. This paper proposes a novel approach that combines disjoint-based Bootstrap Aggregating (Bagging) technique Bayesian optimization (BO) for detection grids....

Electric furnaces play an important role in many industrial processes where precise temperature control is essential to ensure production efficiency and product quality. Traditional proportional-integral-derivative (PID) controllers their modified versions are commonly used maintain stability by reacting quickly deviations. In this study, the real PID plus second-order derivative (RPIDD2) controller introduced for first time applications, which a novel alternative that has not yet been...

This paper proposes an advanced Load Frequency Control (LFC) strategy for two-area hydro-wind power systems, using a hybrid Long Short-Term Memory (LSTM) neural network combined with Genetic Algorithm-optimized PID (GA-PID) controller. Traditional controllers, while extensively used, often face limitations in handling the nonlinearities and uncertainties inherent interconnected leading to slower settling time higher overshoot during load disturbances. The LSTM + GA-PID controller mitigates...

Transmission lines are vital for delivering electricity over long distances, yet they face reliability challenges due to faults that can disrupt power supply and pose safety risks. This research introduces a novel approach fault detection classification by analyzing voltage current patterns across transmission line phases. Leveraging comprehensive dataset of diverse scenarios, various machine learning algorithms—including Random Forest (RF), K-Nearest Neighbors (KNN), Long Short-Term Memory...

Solar-powered EV charging stations offer a sustainable and reliable alternative to traditional infrastructure, significantly alleviating stress on legacy grid systems. However, the intermittent nature of renewable energy sources poses challenge for management in power distribution networks. To address this, optimal charge/discharge scheduling EVs becomes crucial. This paper introduces an innovative Opposition-based Competitive Swarm Optimization (OCSO) technique minimize total cost IEEE...

The critical necessity for sophisticated predictive maintenance solutions to optimize performance and extend lifespan is underscored by the widespread adoption of lithium-ion batteries across industries, including electric vehicles energy storage systems. This study introduces a comprehensive framework that incorporates real-time health diagnostics with state-of-charge (SOC) estimation, utilizing an Improved Random Forest (IRF) algorithm address current limitations in battery management...

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

This research introduces an advanced finite control set model predictive current (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 causing range issues, including leakage currents, electromagnetic interference (EMI), and accelerated system degradation. The proposed strategy employs that predicts inverter's future states, enabling selection optimal switching states from to achieve dual...

Abstract 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 (CS) algorithms adapting varying conditions, including fluctuations pressure temperature. Through meticulous simulations analyses, explores collaborative integration these techniques with boost converters enhance...

Abstract This paper presents an innovative control scheme designed to significantly enhance the power factor of AC/DC boost rectifiers by integrating adaptive neuro-fuzzy inference system (ANFIS) with predictive current control. The strategy addresses key challenges in quality and energy efficiency, demonstrating exceptional performance under diverse operating conditions. Through rigorous simulation, proposed achieves precise input shaping, resulting a remarkably low total harmonic...

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

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