Development of 3-phase fault detection, protection, and automation application with the present of DG in AC power system using GOOSE protocol DOI Creative Commons

Zhongsheng Shi,

Zumrat Druzhinin

Heliyon, Год журнала: 2024, Номер 10(6), С. e27482 - e27482

Опубликована: Март 1, 2024

In this research work, a protection and automation solution is developed, encompassing SEL751 SEL751A relays that communicate through the IEC-61850 Generic Object-Oriented Substation Event (GOOSE) protocol to deliver high-speed detection clearance of 3-phase (3P) fault. The study case standard IEEE 13-bus grid including main system generator (G1), power lines, loads, distributed generation (DG), bus bars, feeders, equipped with for detecting over-current faults based on time current. Two relays, B1 in B2 DG side, are integrated GOOSE communication system. These settings configured such way that, event breaker being disconnected, cannot be connected network, making it suitable setting anti-islanding mode (AIM). efficiency relay tested by subjecting 3P fault, selected Time-Overcurrent (TOC) U3 inverse curve. Throughout paper, descriptions study, grid, assumptions, calculations, analysis results systematically presented. For verification settings, performance practically accurately analyzed detail. obtained indicate presented strategy quite effective configuration, operation, coordination fast protocol.

Язык: Английский

Timely detection of skin cancer: An AI-based approach on the basis of the integration of Echo State Network and adapted Seasons Optimization Algorithm DOI

Mengdi Han,

Shuguang Zhao, Huijuan Yin

и другие.

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 94, С. 106324 - 106324

Опубликована: Апрель 22, 2024

Язык: Английский

Процитировано

34
Optimal economic model of a combined renewable energy system utilizing Modified DOI

Wang Zehao,

Chen Zile,

Simin Yang

и другие.

Sustainable Energy Technologies and Assessments, Год журнала: 2025, Номер 74, С. 104186 - 104186

Опубликована: Янв. 23, 2025

Язык: Английский

Процитировано

2
A comprehensive review of advancements in green IoT for smart grids: Paving the path to sustainability DOI Creative Commons

P. Pandiyan,

S. Saravanan,

Raju Kannadasan

и другие.

Energy Reports, Год журнала: 2024, Номер 11, С. 5504 - 5531

Опубликована: Май 22, 2024

Electricity consumption is increasing rapidly, and the limited availability of natural resources necessitates efficient energy usage. Predicting managing electricity costs challenging, leading to delays in pricing. Smart appliances Internet Things (IoT) networks offer a solution by enabling monitoring control from broadcaster side. Green IoT, also known as Things, emerges sustainable approach for communication, data management, device utilization. It leverages technologies such Wireless Sensor Networks (WSN), Cloud Computing (CC), Machine-to-Machine (M2M) Communication, Data Centres (DC), advanced metering infrastructure reduce promote environmentally friendly practices design, manufacturing, IoT optimizes processing through enhanced signal bandwidth, faster more communication. This comprehensive review explores advancements smart grids, paving path sustainability. covers energy-efficient communication protocols, intelligent renewable integration, demand response, predictive analytics, real-time monitoring. The importance edge computing fog allowing distributed intelligence emphasized. addresses challenges, opportunities presents successful case studies. Finally, concludes outlining future research avenues providing policy recommendations foster advancement IoT.

Язык: Английский

Процитировано

12
Energy demand forecasting using convolutional neural network and modified war strategy optimization algorithm DOI Creative Commons
Huanhuan Hu,

Shufen Gong,

Bahman Taheri

и другие.

Heliyon, Год журнала: 2024, Номер 10(6), С. e27353 - e27353

Опубликована: Фев. 29, 2024

Predicting the electricity demand is a key responsibility for energy industry and governments in order to provide an effective dependable supply. Traditional projection techniques frequently rely on mathematical models, which are limited their ability recognize complex patterns correlations data. Machine learning has emerged as viable tool estimating last decade. In this study, Modified War Strategy Optimization-Based Convolutional Neural Network (MWSO-CNN) been provided prediction. To increase precision of prediction, MWSO-CNN approach integrates benefits modified war strategy optimization technique convolutional neural network. improve efficiency, employed adjust hyperparameters CNN algorithm. The suggested tested real-world dataset, findings show that it outperforms many state-of-the-art machine predicting demand. general, could offer successful cost-effective consumption, will benefit both business society whole.

Язык: Английский

Процитировано

9
Optimization of energy management in Malaysian microgrids using fuzzy logic-based EMS scheduling controller DOI Creative Commons
Mohammad Nur‐E‐Alam,

Tarek Abedin,

Nur Aini Samsudin

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 6, 2025

The microgrid (MG) faces significant security issues due to the two-way power and information flow. Integrating an Energy Management System (EMS) balance energy supply demand in Malaysian microgrids, this study designs a Fuzzy Logic Controller (FLC) that considers intermittent renewable sources fluctuating patterns. FLC offers flexible solution scheduling effectively assessed by MATLAB/Simulink simulations. consists of PV, battery, grid, load. A Maximum Power Point Tracking (MPPT) controller helps extract maximum PV output manages storage providing or absorbing excess power. analysis is performed observing State Charge (SoC)of battery for each source. grid supplies additional if fail meet load demand. Total Harmonic Distortion (THD) compares performance Proportional-Integral (PIC) FLC. results show PI design reduces THD current signal, while does not reduce when used EMS. However, better control over battery's SOC, preventing overcharging over-discharging. While THD, provides superior SOC system comprising findings demonstrate zero higher than 80% lower 20%, signifying no charging discharging takes place avoid third goal was accomplished comparing confirming current's EMS designed with both maintained below 5%, following IEEE 519 harmonic standard, using block MATLAB Simulink. This highlights FLC's potential address demand-supply mismatches variability, which crucial optimizing performance.

Язык: Английский

Процитировано

1
Integrating Hybrid Stochastic and Statistical Methods for Efficient Deployment of Electric Bicycle Charging Stations in Urban Power Distribution Networks: A Coordinated Expansion Planning Approach DOI
SeyedJalal SeyedShenava, Peyman Zare, Iraj Faraji Davoudkhani

и другие.

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106304 - 106304

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

1
Machine learning optimization for hybrid electric vehicle charging in renewable microgrids DOI Creative Commons

Marwa Hassan

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Июнь 17, 2024

Abstract Renewable microgrids enhance security, reliability, and power quality in systems by integrating solar wind sources, reducing greenhouse gas emissions. This paper proposes a machine learning approach, leveraging Gaussian Process (GP) Krill Herd Algorithm (KHA), for energy management renewable with reconfigurable structure based on remote switching of tie sectionalizing. The method utilizes modeling hybrid electric vehicle (HEV) charging demand. To counteract HEV effects, two scenarios are explored: coordinated intelligent charging. A novel optimization inspired the (KHA) is introduced complex problem, along self-adaptive modification to tailor solutions specific situations. Simulation an IEEE microgrid demonstrates efficiency both scenarios. predictive model yields remarkably low Mean Absolute Percentage Error (MAPE) 1.02381 total Results also reveal reduction operation cost scenario compared

Язык: Английский

Процитировано

7
A novel approach for early gastric cancer detection using a hybrid of alexnet, extreme learning machine, and adjusted gorilla troops optimization DOI

Daguang Fan,

Huanfang Liang,

Chongxiao Qu

и другие.

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 93, С. 106126 - 106126

Опубликована: Фев. 24, 2024

Язык: Английский

Процитировано

5
A Comprehensive Review of Artificial Intelligence Approaches for Smart Grid Integration and Optimization DOI Creative Commons
Malik Ali Judge, Vincenzo Franzitta, Domenico Curto

и другие.

Energy Conversion and Management X, Год журнала: 2024, Номер 24, С. 100724 - 100724

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

5
Modified artificial neural network based on developed snake optimization algorithm for short-term price prediction DOI Creative Commons

Baozhu Li,

Majid Khayatnezhad

Heliyon, Год журнала: 2024, Номер 10(5), С. e26335 - e26335

Опубликована: Фев. 13, 2024

Short-term prices prediction is a crucial task for participants in the electricity market, as it enables them to optimize their bidding strategies and mitigate risks. However, price signal subject various factors, including supply, demand, weather conditions, renewable energy sources, resulting high volatility nonlinearity. In this study, novel approach introduced that combines Artificial Neural Networks (ANN) with newly developed Snake Optimization Algorithm (SOA) forecast short-term signals Nord Pool market. The snake optimization algorithm utilized both structure weights of neural network, well select relevant input data based on similarity curves wind production. To evaluate effectiveness proposed technique, experiments have been conducted using from two regions namely DK-1 SE-1, across different seasons time horizons. results demonstrate technique surpasses alternative methods Particle Swarm (PSO) Genetic Algorithms-based Network (PSOGANN) Gravitational Search Algorithm-based (GSONN), exhibiting superior accuracy minimal error rates prediction. show average MAPE index region 3.1292%, which 32.5% lower than PSOGA method 47.1% GSONN method. For SE-1 region, 2.7621%, 40.4% 64.7% Consequently, holds significant potential valuable tool market enhance decision-making planning activities.

Язык: Английский

Процитировано

4