Electrical Engineering and Energy
type: channel
description: Electrical Engineering and Energy (ELENE) is an international, double-blind peer-reviewed open-access journal publishing high-quality research in electrical engineering, electronics, and energy systems. This journal supports and amplifies research related to SDG 7 - Affordable & Clean Energy and SDG 9 - Industry, Innovation & Infrastructure. ELENE does not charge any article processing or submission fees. In addition, ELENE publishes accepted manuscripts as Articles in Press prior to their formal assignment to an issue. Frequency is triannual (April, August, December). View full aims & scope
publisher: ELENE Press
language: en
publication name: Electrical Engineering and Energy
issn: 3062-3979
copyright: Authors retain copyright of their work and grant the journal the right to publish it under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This allows for maximum dissemination and reuse with appropriate citation.
items: A Comparison of SMD and Through-Hole Components on Conducted Noise Emission
items: Optimal Sizing and Techno-Economic Analysis of Battery Energy Storage System for Peak Shaving and Voltage Profile Improvement on a Rural Distribution Network
items: Profit Maximization for Generating Companies Using Chaotic Whale Optimization with Inertia Weight
items: Integrated Planning and Coordinated Allocation of DG and DSTATCOM Considering Radial Distribution Network Reconfiguration
items: Automated Conversion of Unstructured Geospatial Feeder Data into Analytical Models: An 11 kV Case Study
items: Selecting and Estimating Optimum Passive Designs of Energy Efficiency in Educational Buildings in Turkey
items: IoT and LED-Based Approach in Smart Street Lighting: A Review
items: Hybrid Adaptive MPPT Algorithm with Intelligent Exploration and Dither-Based Tracking for PV Systems
items: Performance Evaluation of Deep Learning Architectures for Tile Defect Detection
items: EMG-Based Multi-Class Gesture Recognition with Normalized Muscle Power Evaluation
items: Secure Audio Steganography Using Vectorized LSB and Chaos-Based Encryption
items: Carbon Footprint Reduction and Strategic Benefits of Energy Efficiency: A Case Study in the Healthcare Sector
items: Development of Photovoltaic Devices Based on Fullerene–Graphene Hybrids
items: Assessment of the Energy Efficiency of Data Centers in Türkiye
items: Capacitive Sensor System for Measuring Moisture Content in Transformer Oil
items: Thermal, Exergy, and Environmental Performance Analysis of Modified Fin Geometries in Panel Radiators: A CFD-Based Study
items: A Comparative Study on Wavelet and EMD Methods for Remote detection of Rotational Imbalance
items: Improving Performance of Squirrel Cage Induction Motor Used in Submersible Pumps
items: Estimating Solar Power Generation with RF, GB, and SVR Algorithms Based on Meteorological Data and Orientation Angles: Adıyaman Case Study
items: Evaluation of Magnet Demagnetization Behavior in Permanent Magnet Linear Synchronous Motors
items: Comparative Analysis of Grid-Following (GFL) and Grid-Forming (GFM) Inverters: Control and Transition Capabilities
items: Investigation of the Effects of TiO₂ and Al₂O₃ Nanoparticles on Exhaust Gas Temperature in Direct Injection Gasoline Engines
items: A Comparative Analysis of Genetic Algorithm Replacement Strategies in a Real-World Optimization Problem: An Elitism and Diversity Oriented Perspective
items: A Moving Average Filter based DSTATCOM Control Approach

A Comparison of SMD and Through-Hole Components on Conducted Noise Emission
type: item
description: Section: Research Articles Keywords: Electromagnetic Compatibility (EMC), Electromagnetic Interference (EMI), EMI Filter Design, DC–DC Converters Switched-mode DC-DC converter circuits create quite challenging electromagnetic compatibility problems. Additionally, the design preferences directly affect the EMC performance of the circuits. The study focuses on the conducted noise emissions of the same Boost converter circuits constructed with two different types of assembly technologies, Through-Hole and SMD. For this purpose, two Boost converter circuits using the same integrated circuit, LT1070, were designed by using Through-hole and SMD assembly technologies. The conducted emission measurements were taken according to Class B of EN 55032 standard requiring measuring the noise emission in the frequency range between 150 KHz and 30 MHz. The results obtained from measurements show that the emission amplitude at 160 kHz for Boost converter constructed with Through-Hole components is higher than the standard requirement while the converter constructed with SMD components meets the standard at the same frequency. The conducted emission amplitudes of both Boost converter at 200 KHz and 240 KHz cannot meet the requirements of the standard, but the peak values for SMD-based circuit are higher than the emission amplitudes of Boost converter constructed with Through-Hole components at the same frequencies. Because of failing both circuits on the tests of En 55032 standard, a low-pass π type filter was designed. After adding the π filter to the Boost converter circuits, the conducted emission values have met the standard requirements in all frequency range. The efficiency of the designed filter completely suppresses the noise all frequency range of EN 55032, from 150 KHz to 30 MHz
subject: Research Articles
  topic: section
subject: Electromagnetic Compatibility (EMC)
  topic: keywords
subject: Electromagnetic Interference (EMI)
  topic: keywords
subject: EMI Filter Design
  topic: keywords
subject: DC–DC Converters
  topic: keywords
creator: Deniz Metin
creator: Ersoy Kelebekler
rights: Copyright (c) 2025 Deniz Metin, Ersoy Kelebekler https://creativecommons.org/licenses/by/4.0
date: 2026-01-29
publication date: 2026-01-29
starting page: 120
ending page: 130
doi: 10.64470/elene.2026.24

Optimal Sizing and Techno-Economic Analysis of Battery Energy Storage System for Peak Shaving and Voltage Profile Improvement on a Rural Distribution Network
type: item
description: Section: Research Articles Keywords: Battery Energy Storage System (BESS), Net Present Value (NPV), Non-Wires Alternative (NWA), System Advisor Model (SAM) Distribution networks are increasingly strained by peak load demands and voltage regulation problems. This paper benchmarks Particle Swarm Optimization, Boda-Boda Optimization, and Adaptive Boda-Boda Optimization Algorithm with Fuzzy Logic (ABBOA-Fuzzy) to size and site Battery Energy Storage System (BESS) as a Non-Wires Alternative for rural 11kV feeder support. Baseline analysis of the feeder indicated a peak load of 99.6%, and a voltage drop to 0.936 p.u. The optimization process demonstrated the superiority of the ABBOA-Fuzzy algorithm, which converged faster. The optimized solution guided the selection of a commercially available 400kW/1200kWh BESS which reduced peak demand by 15.5% and raised the minimum voltage to 0.952 p.u. A 15-year techno-economic analysis using the System Advisor Model, accounting for battery degradation confirmed the economic viability with a Net Present Value of $43,643 and an Internal Rate of Return of 15.54%. The study recommends this framework for utility BESS planning.
subject: Research Articles
  topic: section
subject: Battery Energy Storage System (BESS)
  topic: keywords
subject: Net Present Value (NPV)
  topic: keywords
subject: Non-Wires Alternative (NWA)
  topic: keywords
subject: System Advisor Model (SAM)
  topic: keywords
creator: Job Omambia Mwene
creator: Christopher Maina Muriithi
creator: Irene Ndunge Muisyo
rights: Copyright (c) 2025 J.O. Mwene, CM Muriithi, IN Muisyo https://creativecommons.org/licenses/by/4.0
date: 2026-01-23
publication date: 2026-01-23
starting page: 17
ending page: 36
doi: 10.64470/elene.2026.1019

Profit Maximization for Generating Companies Using Chaotic Whale Optimization with Inertia Weight
type: item
description: Section: Research Articles Keywords: Chaotic Whale Optimization Algorithm, Electricity Market Optimization, Inertia Weight, Metaheuristic Algorithms, Profit Maximization Optimization of power generation scheduling is critical for generating companies (GENCOs) operating in deregulated electricity markets, where maximizing profit under operational constraints is challenging. Traditional metaheuristic algorithms often suffer from premature convergence and poor exploration-exploitation balance. Here we propose an improved Whale Optimization Algorithm incorporating chaotic mapping and inertia weight (CWOA-IW) to enhance population diversity and convergence behaviour. The algorithm is applied to the profit maximization problem of GENCOs, considering market prices and system constraints. Validation on 23 benchmark functions and three power system test cases (3-unit 10-bus, IEEE 39-bus, IEEE 118-bus) demonstrates superior accuracy, stability, and faster convergence compared to standard WOA, Grey Wolf Optimizer, and Particle Swarm Optimization. The results indicate that CWOA-IW provides a robust and scalable optimization framework, offering practical benefits for GENCOs in competitive electricity markets.
subject: Research Articles
  topic: section
subject: Chaotic Whale Optimization Algorithm
  topic: keywords
subject: Electricity Market Optimization
  topic: keywords
subject: Inertia Weight
  topic: keywords
subject: Metaheuristic Algorithms
  topic: keywords
subject: Profit Maximization
  topic: keywords
creator: Kofi Afum Danso
creator: Peter Asigri
creator: Nicholas Kwesi Prah II
creator: Daniel Kwegyir
creator: Richeson Akwanfo
creator: Mawuli Kweku Afenu
creator: Nana Ama Abaka Mensah
creator: Adwoa Darkoa Addi-Oppey
creator: Daniel Opoku
rights: Copyright (c) 2025 Kofi Afum Danso, Peter Asigri, Nicholas Kwesi Prah II, Daniel Kwegyir, Richeson Akwanfo, Mawuli Kweku Afenu, Nana Ama Abaka Mensah, Adwoa Darkoa Addi-Oppey, Daniel Opoku https://creativecommons.org/licenses/by/4.0
date: 2026-01-23
publication date: 2026-01-23
starting page: 72
ending page: 99
doi: 10.64470/elene.2026.22

Integrated Planning and Coordinated Allocation of DG and DSTATCOM Considering Radial Distribution Network Reconfiguration
type: item
description: Section: Research Articles Keywords: CTLBO, Distributed Generation, DSTATCOM, Network reconfiguration, , Radial distribution systems In order to enhance the operational efficacy of distribution power networks (DPNs) across techno economic and environmental aspects within a real-time operational framework, meticulous regulation of active as well as reactive power is imperative. In the present study, a Comprehensive Teaching-Learning based Optimization (CTLBO) algorithm is employed for network reconfiguration (NR) and optimal allocation of Distributed generations (DGs) along with Distribution Static Synchronous Compensators (DSTATCOMs) for single-objective in the IEEE 33-bus radial distribution systems (RDSs). Several case studies demonstrate that simultaneous NR and DGs along with DSTATCOM allocation is the most effective solution for reduction of network active power losses ultimately reduces operational costs and emission. The results further demonstrates the superiority in terms of convergence characteristics, solution robustness and global optimality of the CTLBO algorithm under complex , multi-criteria constraints for NR and DGs along with DSTATCOM allocation in RDS against established bio-inspired metaheuristics such as the Gravitational Search Algorithm (GSA), Fireworks algorithm (FWA), Harmony Search Algorithm (HSA), Genetic Algorithm (GA) and Refined genetic algorithm (RGA).
subject: Research Articles
  topic: section
subject: CTLBO
  topic: keywords
subject: Distributed Generation
  topic: keywords
subject: DSTATCOM
  topic: keywords
subject: Network reconfiguration
  topic: keywords
subject: , Radial distribution systems
  topic: keywords
creator: Imran Ahmad Quadri
rights: Copyright (c) 2025 Imran Ahmad Quadri https://creativecommons.org/licenses/by/4.0
date: 2026-01-23
publication date: 2026-01-23
starting page: 57
ending page: 71
doi: 10.64470/elene.2026.21

Automated Conversion of Unstructured Geospatial Feeder Data into Analytical Models: An 11 kV Case Study
type: item
description: Section: Research Articles Keywords: Data-driven diagnostics, Distribution networks, KMZ metadata extraction, Power system modelling, Topological healing The modernization of power distribution grids, driven by the integration of DER, necessitates advanced modelling capabilities. A critical challenge is the semantic gap between the extensive geospatial asset data and the detailed electrical models required for engineering analysis. This data is often stored in static, unstructured KMZ formats that contain inherent topological errors. To address this, this paper presents a novel low-cost, Python framework that fully automates the conversion of this raw GIS data into solvable mathematical models for computation. This process generates executable files for industry-standard, script-based simulators such as MATPOWER and OpenDSS. The framework's core technical contributions include an XPath and Regex-based engine for metadata extraction. A graph-theory pipeline then utilizes a Minimum Spanning Tree (MST) algorithm to algorithmically heal topological disconnections. A Dijkstra-based method is then used for model abstraction. The core methodology was first validated using a bidirectional, reverse-engineering process on a 4-bus test case. This confirmed a lossless round-trip conversion to and from a data-rich KMZ format. Subsequently, it was applied to a complex, real-world 11 kV Kenyan distribution feeder. The generated model converged in a Newton-Raphson power flow. This demonstrated its utility as a powerful diagnostic instrument by enabling detailed feeder voltage profiling and loss analysis. Results were cross-validated against DIgSILENT PowerFactory, a graphic-based simulator, showing excellent numerical agreement. This study validates a scalable framework that transforms static, error-prone GIS data into dynamic, multi-platform diagnostic models. This approach provides a feasible pathway to accelerate grid modernization.
subject: Research Articles
  topic: section
subject: Data-driven diagnostics
  topic: keywords
subject: Distribution networks
  topic: keywords
subject: KMZ metadata extraction
  topic: keywords
subject: Power system modelling
  topic: keywords
subject: Topological healing
  topic: keywords
creator: Amon Okemo
creator: Christopher Maina Muriithi
creator: John Nderu
rights: Copyright (c) 2025 Okemo Amon, C M Muriithi, J N Nderu https://creativecommons.org/licenses/by/4.0
date: 2026-01-23
publication date: 2026-01-23
starting page: 37
ending page: 56
doi: 10.64470/elene.2026.20

Selecting and Estimating Optimum Passive Designs of Energy Efficiency in Educational Buildings in Turkey
type: item
description: Section: Research Articles Keywords: energy efficiency, green energy, passive designs The consumption of energy in building sector consists about 40% of energy consumptions by words. In this investigation we analysis using the passive design in educational buildings by selecting the Technology faculty building in Selçuk university at Konya city as example of Tik-tip project .To estimate the optimum case of passive designs, firstly proposal two new educational building envelopment ( cubic building with 1:1 aspect ratio and cylindrical building) at same floor area of original existing envelopment at same façades design and more compact by less outdoor walls and then analysis to use eight passive designs (orientation , double façades, window wall ratios, court yards, green-lands and environment’s area, shading device, set temperature and building tightness) in each new envelopments by simulations each ones using Autodesk Revit 2020 and analysis optically same designs by Ecotect 2011 based on actually yearly weather data selecting it in NASA weather data web site .At the result of analysis the proposed the new envelopment’s we have more energy efficient at each one about %16.7 decreasing in cooling load and %20.76 saving of heating load in cubic building with % 19.9 saving in cylindrical envelopment and then have saving in total yearly air-conditioning load about % 19.3 and % 18.75 in cubic and cylindrical envelopment respectively. A last when used the other passive designs in optimum case we have total energy saving in air conditioning load about %33.36 in cubic envelopment and %30.9 in cylindrical envelopment when compared it with existing building envelopment of technology faculty in Selçuk University.
subject: Research Articles
  topic: section
subject: energy efficiency
  topic: keywords
subject: green energy
  topic: keywords
subject: passive designs
  topic: keywords
creator: Ashraf Najmaldeen Hussein Hussein
creator: Mustafa Acaroğlu
rights: Copyright (c) 2025 Ashraf Najmaldeen Hussein Hussein Hussein, Mustafa Acaroğlu https://creativecommons.org/licenses/by/4.0
date: 2026-01-21
publication date: 2026-01-21
doi: 10.64470/elene.2026.23

IoT and LED-Based Approach in Smart Street Lighting: A Review
type: item
description: Section: Review Articles Keywords: Smart Street Lighting, LED-Based Systems, Internet of Things (IoT) Traditional street lighting systems are inadequate in performing basic functions such as monitoring, control, and centralized management. The traditional lighting technologies used in these systems do not allow for the optimization of energy consumption and maintenance activities. A significant portion of the existing infrastructure is designed with older generation lamps and analog control units, which imposes major limitations in terms of energy efficiency and sustainability. However, rapid technological advances in recent years and increased industrial activity focused on smart lighting solutions have enabled the integration of sensor-based, communication-enabled, and remotely accessible Internet of Things (IoT) and LED-based systems into street lighting systems. This study examines IoT and LED-based systems developed to increase energy efficiency and reduce operating costs in street lighting applications. The study describes current technological developments in IoT applications by bringing together smart poles equipped with LED lamp technology, smart sensors, communication networks, and monitoring unit components. Furthermore, LED-based systems used in lighting systems are evaluated in comparison with traditional high-pressure sodium (HPS) and mercury vapor (HPM) lamps. Technical parameters such as power consumption, light efficiency, color rendering index (CRI), and lifespan are analyzed. Furthermore, this study demonstrates the applicability of these systems in smart city infrastructures in line with Turkey's National Energy Efficiency Action Plan and contributes to future applications.
subject: Review Articles
  topic: section
subject: Smart Street Lighting
  topic: keywords
subject: LED-Based Systems
  topic: keywords
subject: Internet of Things (IoT)
  topic: keywords
creator: Ahmet Nur
rights: Copyright (c) 2025 Ahmet Nur https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 128
ending page: 141
doi: 10.64470/elene.2025.18

Hybrid Adaptive MPPT Algorithm with Intelligent Exploration and Dither-Based Tracking for PV Systems
type: item
description: Section: Research Articles Keywords: Adaptive Control, Global Maximum Power Point, Incremental Conductance, MPPT, Partial Shading, Photovoltaic, PV Systems, P&O This paper proposes an improved maximum power point tracking (MPPT) approach by developing a hybrid Perturb and Observe (P&O) and Incremental Conductance (INC) algorithm based on adaptive control, considering both existing influential perturbations, such as temperature and insolation changes, and integrating the kinks. The proposed method leverages P&O's fast decision-making capability for dynamic response and INC's slope estimation technique for higher precision around the maximum power point (MPP). Moreover, dither-gradient estimation and an adaptive exploration strategy are included to make the model more robust against noise and local maxima in partial shading. The controller works in two coordinated modes—Track and Explore—for achieving rapid convergence as well as steady-state stability. The oscillations are suppressed, and re-explorations can be made when the environment changes a lot, with a combination of dynamic reference power, exponential moving average (EMA) filtering, and irradiance-drop detection. The MATLAB/simulation results verified that the introduced hybrid P&O–Inc algorithm provides faster MPP tracking, better steady-state performance, and better tracking ability in PSC as well as under changing irradiances than other conventional detached MPPT methods.
subject: Research Articles
  topic: section
subject: Adaptive Control
  topic: keywords
subject: Global Maximum Power Point
  topic: keywords
subject: Incremental Conductance
  topic: keywords
subject: MPPT
  topic: keywords
subject: Partial Shading
  topic: keywords
subject: Photovoltaic
  topic: keywords
subject: PV Systems
  topic: keywords
subject: P&O
  topic: keywords
creator: Hamzah Naji
rights: Copyright (c) 2025 Hamzah Abdulkhaleq Naji https://creativecommons.org/licenses/by/4.0
date: 2026-01-23
publication date: 2026-01-23
starting page: 1
ending page: 16
doi: 10.64470/elene.2025.1017

Performance Evaluation of Deep Learning Architectures for Tile Defect Detection
type: item
description: Section: Research Articles Keywords: Deep Learning, Image Processing, Tile Defect Detection, Transfer Learning In this study, an artificial intelligence-based quality control system was developed for the automatic detection and classification of defects in a tile. The dataset created to reduce human-induced errors in the production process and increase inspection accuracy consists of a total of 405 images. During the model development phase, CNN, MobileNetV2, ResNet50, and EfficientNetB0 architectures were used. The performance of the models was evaluated using the 10-fold cross-validation method for an objective comparison. The experimental results show that the EfficientNetB0 architecture achieved the highest performance with an accuracy rate of 96.73%. ResNet50 achieved 95.45%, CNN achieved 94.91%, and MobileNetV2 achieved 92.36% accuracy.
subject: Research Articles
  topic: section
subject: Deep Learning
  topic: keywords
subject: Image Processing
  topic: keywords
subject: Tile Defect Detection
  topic: keywords
subject: Transfer Learning
  topic: keywords
creator: Ayça Demir
creator: Humar Kahramanlı Örnek
rights: Copyright (c) 2025 Ayça Demir, Humar Kahramanlı Örnek https://creativecommons.org/licenses/by/4.0
date: 2025-12-24
publication date: 2025-12-24
starting page: 118
ending page: 127
doi: 10.64470/elene.2025.16

EMG-Based Multi-Class Gesture Recognition with Normalized Muscle Power Evaluation
type: item
description: Section: Research Articles Keywords: Classifier, Muscle, Power Analysis, sEMG The analysis of musculoskeletal system movements using electromyography (EMG) signals is a fundamental requirement in fields such as prosthetic control, human-machine interaction, and neuromuscular rehabilitation. This study presents a comprehensive approach that not only evaluates movement recognition accuracy but also quantitatively assesses the level of muscle force required for each movement. In the study, the muscle loading profile of each hand movement was created using EMG signal energy normalized to the Rest state. Five different classifier models were compared under 5-fold cross-validation (CV) and Leave-One-Subject-Out (LOSO) protocols. The results showed that the Extension movement had the highest normalized power value and that classification accuracy reached its highest level with SVM-RBF (86.95%). Furthermore, Out-of-Bag (OOB) error analysis revealed that the model converged stably around 600–800 trees, while accuracy differences between individuals were attributed to physiological variations. The proposed framework offers a new evaluation perspective for both ergonomic task design and clinical performance monitoring by assessing gesture recognition performance alongside muscle strength requirements.
subject: Research Articles
  topic: section
subject: Classifier
  topic: keywords
subject: Muscle
  topic: keywords
subject: Power Analysis
  topic: keywords
subject: sEMG
  topic: keywords
creator: Enes Halit Aydın
creator: Önder Aydemir
rights: Copyright (c) 2025 Enes Halit AYDIN, Önder AYDEMİR https://creativecommons.org/licenses/by/4.0
date: 2025-12-24
publication date: 2025-12-24
starting page: 87
ending page: 102
doi: 10.64470/elene.2025.13

Secure Audio Steganography Using Vectorized LSB and Chaos-Based Encryption
type: item
description: Section: Research Articles Keywords: Audio Stenography, Chaotic Algorithms,, LSB Data Hiding , Data Security, Hybrid Encryption In the era of digital transformation, the protection of sensitive multimedia data against growing cyber threats has become increasingly critical. Traditional cryptographic and steganographic techniques, while effective individually, often fall short when faced with advanced detection and attack methods, making hybrid security approaches a necessity. In this study, a hybrid security approach combining chaotic algorithms and the Least Significant Bit (LSB) embedding method is proposed. The method is enhanced through key-dependent parameter assignment, as well as additional steps such as transient periods and square matrix transformation. Furthermore, vectorization after square matrix transformation simplified indexing in embedding and extraction steps, thereby improving computational efficiency. As a result, high security and integrity were achieved for both visual and audio data. In the encryption process, seven different chaotic structures (Logistic Map, Lorenz System, Piecewise Linear Chaotic Map, Tent Map, Hénon Map, Chua Circuit, Chebyshev Map) were supported, and the method was tested on these algorithms. The original content was encrypted using XOR and then embedded into the audio signal via the LSB method. The proposed method was evaluated using the EBU SQAM audio dataset and standard test images with Peak Signal-to-Noise Ratio (PSNR), Signal-to-Noise Ratio (SNR), Mean Squared Error (MSE), Structural Similarity Index Measure (SSIM), and Bit Error Rate (BER) metrics. Experimental results demonstrated that for all tests, the extracted images achieved Visual_PSNR = ∞, Visual_SSIM = 1, and Visual_BER = 0. For audio data, PSNR values ranged between 102–107 dB and SNR values approximately 53–99 dB depending on the content type. These findings reveal that the proposed method ensures both the integrity of the content and the reliable preserva…
subject: Research Articles
  topic: section
subject: Audio Stenography
  topic: keywords
subject: Chaotic Algorithms,
  topic: keywords
subject: LSB Data Hiding
  topic: keywords
subject: Data Security
  topic: keywords
subject: Hybrid Encryption
  topic: keywords
creator: Zeynel Ümit Korkmaz
creator: Fahrettin Horasan
creator: Zeynep Çetinkaya
rights: Copyright (c) 2025 Zeynel Ümit Korkmaz, Fahrettin Horasan, Zeynep Çetinkaya https://creativecommons.org/licenses/by/4.0
date: 2025-12-24
publication date: 2025-12-24
starting page: 68
ending page: 86
doi: 10.64470/elene.2025.12

Carbon Footprint Reduction and Strategic Benefits of Energy Efficiency: A Case Study in the Healthcare Sector
type: item
description: Section: Research Articles Keywords: Carbon emissions, Cost reduction, Electricity consumption, , Renewable energy, Solar photovoltaic system. This study investigates the impact of a solar photovoltaic (SPV) system on electricity consumption and carbon emissions in a private hospital. In 2024, the system produced 7,122,802.80 kWh of renewable electricity, preventing approximately 3,148 metric tons of CO₂e emissions and reducing total electricity costs by 72.3%. Data were analyzed using national tariff coefficients and emission factors published by the Energy Market Regulatory Authority (EPDK) and the Turkish Electricity Transmission Corporation (TEİAŞ). The results show that SPV integration effectively lowers both energy expenditures and carbon intensity in healthcare operations. This case demonstrates the technical feasibility and economic advantage of renewable energy adoption in energy-intensive institutions.
subject: Research Articles
  topic: section
subject: Carbon emissions
  topic: keywords
subject: Cost reduction
  topic: keywords
subject: Electricity consumption
  topic: keywords
subject: , Renewable energy
  topic: keywords
subject: Solar photovoltaic system.
  topic: keywords
creator: Mehmet Yorulmaz
creator: Tuğba İmat Taş
rights: Copyright (c) 2025 Mehmet Yorulmaz, Tuğba İmat Taş https://creativecommons.org/licenses/by/4.0
date: 2025-12-24
publication date: 2025-12-24
starting page: 55
ending page: 67
doi: 10.64470/0.2025.11

Development of Photovoltaic Devices Based on Fullerene–Graphene Hybrids
type: item
description: Section: Research Articles Keywords: Fullerene, Graphene Hybrid nanomaterials, Organic solar cells, Perovskite solar cells, Photovoltaic devices, Sustainable energy The development of next-generation photovoltaic devices requires the integration of advanced nanomaterials with superior electrical, optical, and mechanical properties. Among such materials, fullerenes (C₆₀ and their derivatives such as PCBM) and graphene have attracted significant attention due to their complementary functionalities. Fullerenes act as efficient electron acceptors, facilitating charge separation in donor–acceptor systems. While graphene provides excellent electrical conductivity, high optical transparency, and mechanical flexibility, making it a promising alternative to conventional transparent electrodes such as indium tin oxide (ITO). This paper reviews recent progress in the design and fabrication of fullerene–graphene hybrid photovoltaic devices. The typical device structure includes a flexible or rigid substrate, a graphene transparent electrode, a polymer–fullerene active layer, selective charge transport layers, and a metallic back contact. Experimental studies demonstrate that hybridization of fullerenes with graphene improves charge transport pathways, reduces series resistance, and enhances power conversion efficiency (PCE). Furthermore, fullerene–graphene composites contribute to device stability under prolonged illumination and thermal stress. Overall, fullerene–graphene hybrid materials represent a promising strategy for the development of high-efficiency, cost-effective, and flexible solar cells. Their potential applications extend beyond traditional photovoltaics to include portable energy sources, building-integrated photovoltaics, and wearable electronic devices, contributing to the broader goal of sustainable energy technologies.
subject: Research Articles
  topic: section
subject: Fullerene
  topic: keywords
subject: Graphene Hybrid nanomaterials
  topic: keywords
subject: Organic solar cells
  topic: keywords
subject: Perovskite solar cells
  topic: keywords
subject: Photovoltaic devices
  topic: keywords
subject: Sustainable energy
  topic: keywords
creator: Muhiddin Atajonov
rights: Copyright (c) 2025 Mukhiddin Atajonov https://creativecommons.org/licenses/by/4.0
date: 2025-12-24
publication date: 2025-12-24
starting page: 111
ending page: 117
doi: 10.64470/elene.2025.15

Assessment of the Energy Efficiency of Data Centers in Türkiye
type: item
description: Section: Research Articles Keywords: Data Centers, Energy Efficiency Values, Total Facility Power, Energy Sources Rapid developments in mobile and cloud technologies in particular are increasing the need for data storage today. This growing demand for data storage is leading to a rapid increase in the number of data centers. Data centers not only store organizations' digital information, but also process this data, play a role in its distribution, and assist in its management. In these centers, where numerous servers are housed together, security and sustainability are of critical importance to organizations, and any disruptions that may occur here can be extremely difficult to mitigate. Therefore, ensuring that data centers are designed to operate 24/7 and implementing the necessary precautions are essential requirements. The growing number of data centers has led to a significant increase in energy demand, both globally and in Türkiye. This situation poses serious problems for countries in terms of ensuring an energy supply and creating the necessary infrastructure. In recent years, the excessive energy consumption of data centers has become an area of intense research, and great efforts have been made to solve this problem. In this context, a new field of study called “Data Center Efficiency” has emerged. This study focuses on 18 data centers operating in Türkiye. Within the scope of the study, total facility power, IT equipment power, cooling capacity and total CO2 amount data were collected from these data centers. Using this data, energy efficiency values and power utilization efficiencies of the data centers were calculated. Based on the values obtained, the efficiency of data centers was compared and classified. In addition, several recommendations are presented to improve the energy efficiency of data centers.
subject: Research Articles
  topic: section
subject: Data Centers
  topic: keywords
subject: Energy Efficiency Values
  topic: keywords
subject: Total Facility Power
  topic: keywords
subject: Energy Sources
  topic: keywords
creator: Rıdvan Çelik
creator: Mehmet Akif Şahman
creator: Abdullah Oktay Dündar
rights: Copyright (c) 2025 Rıdvan Çelik, Mehmet Akif Şahman, Abdullah Oktay Dündar https://creativecommons.org/licenses/by/4.0
date: 2025-12-24
publication date: 2025-12-24
starting page: 103
ending page: 110
doi: 10.64470/elene.2025.14

Capacitive Sensor System for Measuring Moisture Content in Transformer Oil
type: item
description: Section: Research Articles Keywords: Transformer oil, dielectric strength, capacitive sensor, microcontroller, amplifier The efficiency and long-term reliability of electrical transformers are highly dependent on the quality of their insulating oil, which serves both as a dielectric medium and a cooling agent. One of the most critical factors affecting transformer oil performance is moisture contamination, which significantly reduces dielectric strength, accelerates aging processes, and increases the probability of insulation breakdown. The purpose of this study is to develop and analyze a reliable method for monitoring the water content in transformer oil. A capacitive sensor system was designed and applied to measure variations in dielectric permittivity caused by different levels of moisture concentration. Experimental measurements were carried out using transformer oil samples with controlled humidity levels, and the obtained results demonstrated a clear correlation between the sensor output and the actual water content. The proposed method provides a simple, accurate, and real-time monitoring approach, which can be effectively used for predictive maintenance and ensuring the safe operation of power transformers.
subject: Research Articles
  topic: section
subject: Transformer oil
  topic: keywords
subject: dielectric strength
  topic: keywords
subject: capacitive sensor
  topic: keywords
subject: microcontroller
  topic: keywords
subject: amplifier
  topic: keywords
creator: Rustam Baratov Jalilovich
creator: Khurshid Sattarov
creator: Mashrab Abdullayev Xamro Ugli
creator: Almardon Mustafoqulov Mamatovich
rights: Copyright (c) 2025 Rustam Baratov Jalilovich, Khurshid Sattarov, Mashrab Abdullayev Xamro Ugli, Almardon Mustafoqulov Mamatovich https://creativecommons.org/licenses/by/4.0
date: 2025-08-31
publication date: 2025-08-31
starting page: 36
ending page: 42
doi: 10.64470/elene.2025.1009

Thermal, Exergy, and Environmental Performance Analysis of Modified Fin Geometries in Panel Radiators: A CFD-Based Study
type: item
description: Section: Research Articles Keywords: CFD Simulation, Panel Radiator, Fin Geometry, Thermal Performance, Exergy analysis, Energy efficiency In this study, alternative fin geometries were designed and evaluated to enhance heat transfer performance in panel radiators. Computational Fluid Dynamics (CFD) simulations were performed using ANSYS software for the traditional design and three newly proposed fin profiles, providing detailed data on temperature distribution and thermal efficiency. Additionally, exergy efficiencies and annual CO₂ emissions were calculated for each configuration. Two of the newly designed fins exhibited thermal efficiencies close to the conventional fin (approximately 48.3%), while the third design—an I-profile fin—achieved the highest efficiency at 61.9%. The exergy efficiency increased from 13.3% in the conventional design to 15.8% in Fin 3, indicating a 2.5% improvement. Although the enhanced heat transfer resulted in a 12.5 kg increase in annual CO₂ emissions compared to the conventional design, this trade-off is considered acceptable when evaluated alongside the significant gains in energy and exergy efficiency. Overall, the findings suggest that with optimized fin geometries, it is possible to achieve a more sustainable radiator performance through a balanced approach between thermal efficiency and environmental impact.
subject: Research Articles
  topic: section
subject: CFD Simulation
  topic: keywords
subject: Panel Radiator
  topic: keywords
subject: Fin Geometry
  topic: keywords
subject: Thermal Performance
  topic: keywords
subject: Exergy analysis
  topic: keywords
subject: Energy efficiency
  topic: keywords
creator: Ayse Bilgen Aksoy
creator: Fatma Nur Uzun
creator: Yiğit Aksoy
rights: Copyright (c) 2025 Ayse Bilgen Aksoy, Fatma Nur Uzun, Yiğit Aksoy https://creativecommons.org/licenses/by/4.0
date: 2025-08-31
publication date: 2025-08-31
starting page: 43
ending page: 54
doi: 10.64470/elene.2025.10

A Comparative Study on Wavelet and EMD Methods for Remote detection of Rotational Imbalance
type: item
description: Section: Research Articles Keywords: Empirical Mode Decomposition (EMD), Machine Learning (ML), Multi-Resolution Analysis (MRA), Radar-Based Monitoring, Rotational Imbalance Detection, Wavelet Decomposition (WD) This paper presents a radar-driven method for detecting rotational imbalance in machinery using multi-resolution signal processing and machine learning. Two feature extraction strategies—Empirical Mode Decomposition (EMD) and Wavelet Decomposition (WD)—are compared with WD evaluated using Haar, Coiflet (coif2), and Symlet (sym4) mother wavelets. Four classifiers—Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (DT)—are used to assess performance. Radar signals undergo multi-resolution analysis to derive features that capture imbalance signatures. The performances of the methods are evaluated through classification accuracy, computational cost, and real-time feasibility. Wavelet-based features consistently outperform EMD across all classifiers and wavelets; Haar delivers the best accuracy of 95.28% with RF. A notable overall trade-off is achieved by Haar features with SVM, providing high accuracy with low latency (~219 ms training, ~13.5 ms prediction), while DT provides the fastest inference (0.6 ms) for strict real-time needs. These results provide a practical framework and clear guidelines for algorithm selection in industrial radar-based condition monitoring.
subject: Research Articles
  topic: section
subject: Empirical Mode Decomposition (EMD)
  topic: keywords
subject: Machine Learning (ML)
  topic: keywords
subject: Multi-Resolution Analysis (MRA)
  topic: keywords
subject: Radar-Based Monitoring
  topic: keywords
subject: Rotational Imbalance Detection
  topic: keywords
subject: Wavelet Decomposition (WD)
  topic: keywords
creator: Yunus Emre Acar
rights: Copyright (c) 2025 Yunus Emre Acar https://creativecommons.org/licenses/by/4.0
date: 2025-08-31
publication date: 2025-08-31
starting page: 15
ending page: 27
doi: 10.64470/elene.2025.1007

Improving Performance of Squirrel Cage Induction Motor Used in Submersible Pumps
type: item
description: Section: Research Articles Keywords: Ansys, Design, Induction Motor, Submersible Pump This study examines the design of a three-phase induction motor used in submersible pumps. The motor considered is a 380V, 50Hz, 75Hp, 2-pole, 8" three-phase induction motor. The targeted induction motor performance is high efficiency at the rated load. Furthermore, the operating characteristics of the three-phase induction motor, such as starting current, starting torque, breakdown current, breakdown torque, rated current, rated torque, and magnetic flux distribution, were also investigated. The design process began with determining the general geometry of the motor and continued with the calculation of the stator, rotor, and equivalent circuit parameters. Geometric and magnetic constraints were considered during the design process, and priority was given to ensuring that the motor achieves the desired high performance. The motor design simulation was first performed in the Ansys RMxprt module, and then the motor design was simulated in the Ansys Maxwell 2D module and compared with motors from various manufacturers. The resulting motor design geometry has a 4% higher efficiency. Other performance outputs of the motor are also found to be sufficient.
subject: Research Articles
  topic: section
subject: Ansys
  topic: keywords
subject: Design
  topic: keywords
subject: Induction Motor
  topic: keywords
subject: Submersible Pump
  topic: keywords
creator: Mümtaz Mutluer
rights: Copyright (c) 2025 Mümtaz MUTLUER https://creativecommons.org/licenses/by/4.0
date: 2025-08-31
publication date: 2025-08-31
starting page: 28
ending page: 35
doi: 10.64470/elene.2025.1008

Estimating Solar Power Generation with RF, GB, and SVR Algorithms Based on Meteorological Data and Orientation Angles: Adıyaman Case Study
type: item
description: Section: Research Articles Keywords: Solar Energy, Machine Learning, Air Pollution, Orientation Angle, Energy Generation This study attempts to develop precise machine learning algorithms for estimating solar power generation using meteorological data, including air pollution (PM2.5, PM10) for different orientation angles in the dual-axis solar panel tracking system. Our study focused on maximizing energy generation for multivariable input factors in Adıyaman, rather than optimizing environmental parameters for fixed panels as previously studied. Three machine learning algorithms—Random Forest (RF), Gradient Boosting (GB), and Support Vector Regression (SVR)—were studied. The experimental setup was designed to measure power, temperature, humidity, wind speed/direction, pressure, air pollution data, and to adjust orientation angles (tilt and azimuth angles). Algorithm performances were assessed using R², MSE, and RMSE metrics. RF yielded the best results (R²: 0.83, MSE: 5.32, RMSE: 2.26). Including cumulative air pollution data improved prediction accuracy, implying particulate matter indirectly affects solar radiation and energy output.
subject: Research Articles
  topic: section
subject: Solar Energy
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Air Pollution
  topic: keywords
subject: Orientation Angle
  topic: keywords
subject: Energy Generation
  topic: keywords
creator: Abdurrahman Özbeyaz
rights: Copyright (c) 2025 Abdurrahman Özbeyaz https://creativecommons.org/licenses/by/4.0
date: 2025-08-31
publication date: 2025-08-31
starting page: 1
ending page: 14
doi: 10.64470/elene.2025.1006

Evaluation of Magnet Demagnetization Behavior in Permanent Magnet Linear Synchronous Motors
type: item
description: Section: Research Articles Keywords: Demagnetization, Permanent Magnet Linear Synchronous Motor, Magnetic Coercivity, Safe Operating This paper focus on investigating the demagnetization behavior of permanent magnets in Permanent Magnet Linear Synchronous Motors (PMLSMs) under varying thermal and electrical operation conditions. For this, two complementary Finite Element Method-based approaches are used. The first method utilized magnetostatic analysis to determine the B-H operating points of the magnets under worst-case conditions, allowing the identification of critical regions that tend to move away from the irreversible magnetic field. The second method employed time-domain transient analysis to monitor changes in the induced voltage under varying temperatures and stator current levels. The results revealed that increasing current and temperature significantly reduced the permanent flux density, indicating partial or complete demagnetization. Furthermore, the demagnetization thresholds were shown through safe operating area curves
subject: Research Articles
  topic: section
subject: Demagnetization
  topic: keywords
subject: Permanent Magnet Linear Synchronous Motor
  topic: keywords
subject: Magnetic Coercivity
  topic: keywords
subject: Safe Operating
  topic: keywords
creator: Enes Yucel
creator: Mehmet Cunkas
creator: Łukasz Knypiński
rights: Copyright (c) 2025 Enes Yucel, Mehmet Cunkas, Łukasz Knypiński (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-04-30
publication date: 2025-04-30
starting page: 52
ending page: 63
doi: 10.64470/elene.2025.1005

Comparative Analysis of Grid-Following (GFL) and Grid-Forming (GFM) Inverters: Control and Transition Capabilities
type: item
description: Section: Research Articles Keywords: Grid-Following Inverter, Grid-Forming Inverter In recent years, interest in integration of renewable energy (RE) sources (particularly Wind Energy Systems - WES) into the electrical grid for sustainable, innovative solutions and a greener future continues to grow exponentially. To date, most RE sources have been connected to the grid through traditional grid-following inverters (GFL). Although this control method promises grid compatibility and lower complexity, the unpredictable variability of these eco-friendly RE sources leads to undesirable effects when taken into account. With the increasing interaction of renewable energy sources in today's grid, new challenges arise—such as voltage/frequency regulation and the reduction of overall system inertia—that cannot be resolved with conventional grid-following inverters (GFL). Therefore, the concept of grid-forming inverters (GFM) has been adopted. The aim of this study is to examine the comparison between grid-forming inverters and traditional grid-following inverters, particularly focusing on droop control, and to regulate the simulated test results with appropriate control systems. For this purpose, the study first compares GFM—based on different control techniques (droop, virtual synchronous generator, and synchronverter control) in the literature—with the conventional GFL technique. The circuit tested in this study involves a 40 kVA-rated inverter supplying relevant loads and the grid, with GFL and GFM control modes analyzed separately. Finally, the potential effects of inverters on load variation and grid outages will be considered.
subject: Research Articles
  topic: section
subject: Grid-Following Inverter
  topic: keywords
subject: Grid-Forming Inverter
  topic: keywords
creator: Fatih Burak
creator: Hulusi Karaca
rights: Copyright (c) 2025 Fatih Burak, Hulusi Karaca (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-04-30
publication date: 2025-04-30
starting page: 40
ending page: 51
doi: 10.64470/elene.2025.1004

Investigation of the Effects of TiO₂ and Al₂O₃ Nanoparticles on Exhaust Gas Temperature in Direct Injection Gasoline Engines
type: item
description: Section: Research Articles Keywords: Exhaust gas temperature, Nanoparticles, Gasoline engine In this study, the effects of Al₂O₃ and TiO₂ nanoparticles on exhaust gas temperature (EGT) in a direct injection gasoline engine were experimentally investigated. The Al₂O₃ and TiO₂ nanoparticles were added to fuel mixtures in concentrations of 3.5 ppm and 7 ppm, and tested at engine speeds of 1500, 2500, and 3500 rpm. The results demonstrate that nanoparticles enhance heat transfer within the combustion chamber, leading to higher combustion temperatures. This translates to greater energy production in terms of combustion efficiency and engine performance. In conclusion, the use of Al₂O₃ and TiO₂ nanoparticles as fuel additives shows potential for improving engine performance and offers a novel approach for controlling exhaust gas temperature.
subject: Research Articles
  topic: section
subject: Exhaust gas temperature
  topic: keywords
subject: Nanoparticles
  topic: keywords
subject: Gasoline engine
  topic: keywords
creator: Mehmet Selman Gokmen
creator: Hasan Aydogan
rights: Copyright (c) 2025 Mehmet Selman Gokmen, Hasan Aydogan (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-04-30
publication date: 2025-04-30
starting page: 33
ending page: 39
doi: 10.64470/elene.2025.1003

A Comparative Analysis of Genetic Algorithm Replacement Strategies in a Real-World Optimization Problem: An Elitism and Diversity Oriented Perspective
type: item
description: Section: Research Articles Keywords: Genetic Algorithm, Replacement Strategy, Elitism, Diversity, Selective Harmonic Elimination, Multilevel Inverter In this study, generation replacement strategies used in genetic algorithms are comparatively analysed in the context of a real-world optimization problem, with a focus on the balance between elitism and diversity. The problem under consideration involves solving the equations of the Selective Harmonic Elimination (SHE) method, which is widely used for controlling Multilevel Inverters (MLIs). Six algorithms, each employing a different replacement mechanism, were tested under four distinct scenarios composed of various population sizes and iteration counts. The results were evaluated using fundamental statistical metrics, boxplots, and convergence curves. The findings reveal that elitist strategies perform better in large-population, long-duration scenarios, whereas approaches prioritizing diversity yield more effective results under limited resource conditions. This study systematically demonstrates the impact of different replacement strategies on optimization performance and offers valuable insights for strategy selection in real-world optimization problems
subject: Research Articles
  topic: section
subject: Genetic Algorithm
  topic: keywords
subject: Replacement Strategy
  topic: keywords
subject: Elitism
  topic: keywords
subject: Diversity
  topic: keywords
subject: Selective Harmonic Elimination
  topic: keywords
subject: Multilevel Inverter
  topic: keywords
creator: Hüseyin Doğan
rights: Copyright (c) 2025 Hüseyin Doğan (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-04-30
publication date: 2025-04-30
starting page: 11
ending page: 32
doi: 10.64470/elene.2025.1002

A Moving Average Filter based DSTATCOM Control Approach
type: item
description: Section: Research Articles Keywords: Distribution Static Compensator (DSTATCOM), Moving Average Filter (MAF), Self-Excited Induction Generator (SEIG), Wind Energy Self-excited induction generators (SEIGs) are commonly preferred in stand-alone renewable energy systems due to their low cost and robust construction. However, the voltage and frequency stability at the SEIG output may be adversely affected by varying reactive and active power demands depending on the load profile. In such systems, DSTATCOM is used to provide voltage and frequency regulation, and its performance largely depends on the applied control algorithm. In this study, a moving average filter (MAF) based dq control algorithm is proposed for DSTATCOM supported SEIG systems. The MAF structure contributes to more accurate calculation of reference source currents by reducing the effect of harmonic content. The proposed control method is tested under nonlinear load and unbalanced-nonlinear load conditions. The results show that the system maintains voltage and frequency stability and successfully suppresses disturbances.
subject: Research Articles
  topic: section
subject: Distribution Static Compensator (DSTATCOM)
  topic: keywords
subject: Moving Average Filter (MAF)
  topic: keywords
subject: Self-Excited Induction Generator (SEIG)
  topic: keywords
subject: Wind Energy
  topic: keywords
creator: Ali Sait Ozer
creator: Fehmi Sevilmiş
creator: Hulusi Karaca
rights: Copyright (c) 2025 Ali Sait Ozer, Fehmi Sevilmiş, Hulusi Karaca (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-04-30
publication date: 2025-04-30
starting page: 1
ending page: 10
doi: 10.64470/elene.2025.1001