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
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.
Feasibility Analysis of Hybrid Green Power Generation System for Healthcare Institutions
Section: Research Articles Keywords: Feasibility, Hybrid Green Power, Techno-economic analysis, Environmental, Healthcare Reliable and sustainable electricity supply is essential for healthcare facilities, where power interruptions can compromise critical medical services and patient safety. This study evaluates the techno-economic and environmental feasibility of a grid-connected hybrid green power generation system for healthcare institutions using the HOMER Pro optimization software. The proposed configuration comprises a 50 kW solar photovoltaic (PV) array, a 50 kW biogas generator, a 10 kW fuel cell integrated with a 10 kW electrolyzer and a 10 kg hydrogen storage tank, a 50 kW power converter, and grid connectivity. Simulation results demonstrate that the optimal hybrid system is both economically and technically viable, achieving an NPC of $168,314 and a COE of $0.156/kWh. The system generated sufficient electricity to export approximately 128,194 kWh/year of surplus power to the utility grid while purchasing only 19,239 kWh/year, indicating high energy generation capability, operational flexibility, and reduced dependence on grid electricity. Environmental assessment further revealed a renewable energy fraction of 93.2% with significantly reduced CO₂ emissions, highlighting the system's potential to mitigate environmental impacts. Sensitivity analysis confirmed that the proposed configuration remains economically attractive under a wide range of technical and financial uncertainties, demonstrating its robustness and resilience.
Copyright (c) 2026 Paul Kusi https://creativecommons.org/licenses/by/4.0
Bankability Assessment of Lithium-ion and Vanadium Redox Flow Batteries in Kenyan Hybrid Renewable Energy Systems
Section: Research Articles Keywords: Bankability gap, Hybrid Renewable Energy Systems (HRES), Lithium-ion (Li-ion), Vanadium Redox Flow Battery (VRFB) Reliable and affordable electricity remains essential for rural development; however, in Sub-Saharan Africa (SSA), particularly Kenya, the major barrier to scalable rural electrification is increasingly the bankability gap, where technically feasible Hybrid Renewable Energy Systems (HRES) fail to attract sustainable investment. This study evaluates how battery storage technology selection influences investment viability by comparing lithium-ion (Li-ion) and vanadium redox flow battery (VRFB) systems in a rural Kenyan HRES. An integrated framework combining Homer Pro optimization, project-level financial modelling, Life Cycle Assessment (LCA), and AHP-based Multi-Criteria Decision Analysis (MCDA) was applied to assess techno-economic performance, environmental sustainability, and investor attractiveness. Results show that Li-ion systems achieve stronger short-term financial bankability, with lower Levelized Cost of Storage (LCOS) of KSh 23.17/kWh compared to KSh 28.32/kWh for VRFB systems, alongside higher baseline Investor Attractiveness Index (IAI). However, Li-ion technologies exhibit higher lifecycle environmental burden and replacement risk. In contrast, VRFB systems demonstrate lower emissions, longer operational lifetime, and stronger suitability under concessional and sustainability-oriented financing structures. Sensitivity analysis reveals that battery bankability depends not only on technical performance but also on financing structure, discount rate, and investor class.
Copyright (c) 2026 Meyo Otieno Omondi George, Nderu John, Lucas Mogaka Ongondo https://creativecommons.org/licenses/by/4.0
An Improved Electromagnetism-like Algorithm for Gaussian Process Hyperparameter Optimization in Remaining Useful Life Prediction of Electrolytic Capacitors
Section: Research Articles Keywords: Gaussian Process Regression (GPR), lectromagnetism-like Algorithm, Electrolytic Capacitors, Negative Log Marginal Likelihood, Remaining Useful Life This paper introduces an innovative optimization procedure to enhance gaussian process regression (GPR) for hyperparameter tuning. The technique estimates the remaining useful life (RUL) of electrolytic capacitors under electrical overstress conditions. The traditional electromagnetism-like (EM) optimization technique suffers premature convergence and insufficient exploitation near local optimal solutions. It also suffers ineffective charge relocation within high-dimensional search spaces. To address these limitations, the adaptive chaotic opposition-based learning electromagnetism-like (ACOEM) algorithm is proposed. The new algorithm incorporates three techniques to overcome these challenges: opposition-based learning to increase initial population size and speed up the optimization process; chaotic map-based scaling of forces to strike a balance between exploration and exploitation; and lévy flights to avoid local minima. The ACOEM optimizer fine-tunes the hyperparameters of the GPR kernel by minimizing the negative log marginal likelihood (NLML) objective function. The proposed ACOEM-GPR predictor is successfully applied to National Aeronautics and Space Administration (NASA’s) electrochemical capacitor electrical overstress dataset based on three (3) stress levels: 10V, 12V and 14V. Compared to the vanilla GPR predictor, ACOEM-GPR exhibited superior performance in terms of the correlation coefficient , improving from 0.3015 to 0.7482 for 10V stress and from 0.5615 to 0.8921 for 14V stress. Additionally, the ACOEM-GPR method showed significant improvements in mean squared error (MSE) reducing the MSE from 9458 to 3410 and root mean squared error (RMSE) from 97.3 to 58.4 at 10V. ACOEM-GPR is proven to produce accurate and robust RUL predictions allowing for effective predictive maintenance of power converter capacitors.
Copyright (c) 2026 Kofi Addo Annan, Emmanuel Assuming Frimpong, Francis Boafo Effah, Elvis Twumasi, Abdul-Majid Issah Malori, Mark Ampomah Otu https://creativecommons.org/licenses/by/4.0
Numerical Analysis and Simulation Approach of Copper Antimony Sulfide Based Thin Film Solar Cell with Dual Heterojunction
Section: Research Articles Keywords: CuSbS2, Dual heterojunction, BSF, BMO, SCAPS-1D This paper presents a SCAPS-1D numerical investigation of a Copper Antimony Sulfide (CuSbS2)-based thin-film solar cell using a dual-heterojunction n-ZnSe/p-CuSbS2/p+-BMO structure. The influence of ZnSe window-layer thickness and ferroelectric BSF materials including BFO, BTO and BMO was evaluated under AM1.5G illumination using literature-based material parameters. Under the adopted idealized simulation conditions, the optimized BMO-based structure produced Voc = 1.257 V, Jsc = 46.819 mA/cm2, FF = 83.21% and PCE = 48.96%. Because this result is unusually high for a CuSbS2-based thin-film device, it is interpreted as a numerical upper-limit case that requires further validation using complete optical, recombination and experimental device analysis.
Copyright (c) 2026 Hafizur Rahman Alve, Samia Azmol, Mrinmoy Dey https://creativecommons.org/licenses/by/4.0
Prospects and Challenges of Deploying Solar-Wind Hybrid Energy Systems for Off-Grid Electrification in Zimbabwe
Section: Research Articles Keywords: Hybrid systems, Off-grid electrification, Renewable energy, Solar energy, Wind energy, Zimbabwe Zimbabwe has a long history of electricity shortages especially in off-grid and rural regions where extension of grids is costly. Although the solar photovoltaic systems are quite common, their intermittency restricts regular power supply. The combination of wind and solar power can be more reliable due to the complementary nature of the resources. The paper evaluates these opportunities and challenges of implementing solar-wind hybrid energy systems to off-grid electrification in Zimbabwe, including the resource potential, the main strengths, and the obstacles, including the high cost, inaccessible wind data, and policy limitations.
Copyright (c) 2026 Trust Nemerai, Nyasha Kajengo https://creativecommons.org/licenses/by/4.0
Warm-starting conventional solver for ACOPF prediction using Modified Seagull Optimization Algorithm and physics-informed spatiotemporal graph neural network
Section: Research Articles Keywords: AC optimal power flow, Modified Seagull optimization algorithm, Spatiotemporal graph neural network, Warm starting The penetration of renewable energy has intensified the computational challenges associated with solving the AC Optimal Power Flow (ACOPF) problem. The Newton–Raphson (NR) solver is highly sensitive to initialization and may exhibit slow convergence under dynamic conditions. This research proposes a physics-informed spatiotemporal graph neural network (PIStGNN) framework to generate initial conditions for warm-starting the NR solver to improve computational efficiency. The model integrates graph convolutional networks to capture system topology and recurrent architectures (LSTM/GRU) to model temporal variability. A physics-informed loss function enforces power flow constraints, while a Modified Seagull Optimization Algorithm (MoSOA) enhances hyperparameter tuning. The proposed method is compared with DC and flat start warm-start strategies for IEEE 33-, 57-, and 118-bus systems. The results show that the PIStGNN method maintains high accuracy, with MAE values of 0.0126, 0.0446, and 0.0063 for IEEE 33-, 57-, and 118-bus systems respectively. The proposed PIStGNN initialization maintains the lowest runtime across all test systems. On average, the proposed method achieved 1.3–18% computational time reduction compared to conventional initialization techniques. Overall convergence is faster even with more iterations. The high-quality starting point makes the path to convergence more stable. The physics-informed learning provides efficient and scalable ACOPF solutions.
Copyright (c) 2026 Abdul-Majid Issah Malori, Emmanuel Asuming Frimpong, Francis Boafo Effah, Elvis Twumasi, Peter Asigri, Bernard Marfo Adjei https://creativecommons.org/licenses/by/4.0
End to end prediction of optimal power flow in sustainable power system using physics-informed spatiotemporal graph neural network
Section: Research Articles Keywords: alternating current optimal power flow, renewable energy, reconfiguration, physics-informed loss function, spatiotemporal graph neural network This paper proposes a physics-informed spatiotemporal graph neural network (PIStGNN) for end-to-end ACOPF prediction in sustainable power systems. The proposed framework embeds physical power flow constraints, grid topology, and temporal dynamics of renewable generation. The model is validated on IEEE 33-, 57-, and 118-bus systems using time-series 60,048 datasets generated in Pandapower. All experiments are implemented in Python and performance are evaluated using prediction accuracy, physical feasibility, and computational efficiency. The results show that system voltage profiles remain within acceptable operational limits of 0.96–1.04 p.u. across all scenarios. The system efficiency analysis reveals that moderate renewable penetration reduces transmission losses, while excessive penetration increases losses due to reverse power flow and congestion in larger systems. The proposed PIStGNN achieves prediction errors on the order of 10⁻² p.u. for voltage magnitude and angle while delivering speedups of up to 10,135× compared with Newton–Raphson-based solvers. Furthermore, physics-informed recurrent graph models achieve minimum uncertainty values of 1.06 × 10⁻³, 2.88 × 10⁻³, and 1.19 × 10⁻³ for the IEEE 33-, 57-, and 118-bus systems, respectively. The results demonstrate that integrating physics-informed learning with spatiotemporal graph modeling provides an efficient surrogate for ACOPF prediction in sustainable power systems.
Copyright (c) 2026 Abdul-Majid Issah Malori, Emmanuel Asuming Frimpong, Francis Effah Boafo , Elvis Twumasi, Peter Asigri, Bernard Marfo Adjei https://creativecommons.org/licenses/by/4.0
Optimal Sizing and Placement of Li-ion BESS for Virtual Inertia and Frequency Stability Enhancement in Grids with High Solar Penetration
Section: Research Articles Keywords: Frequency Stability, High PV Penetration, Li-ion BESS, Virtual Inertia Emulation (VIE) The global trend of large-scale high-capacity photovoltaic power generation has led to high instability due to inherent intermittency of solar energy and lack of mechanical inertia. Battery energy storage systems (BESS) as a solution can provide quick-response virtual inertia emulation (VIE). This study focused on the optimal combined sizing and location of lithium-ion BESS to achieve optimal virtual inertia supply and improve the frequency stability for grid networks with up to 40%-PV penetration. The optimization scheme aims to reduce the Life-Cycle Cost (LCC) and system penalties related to frequency deviations, the maximum frequency deviation and maximum allowable rate of change of frequency using the Teaching-Learning Based Optimization (TLBO). Simulations on modified IEEE 14 and 30 benchmark systems showed quantifiable benefits in optimal sizing of the assigned rating and capacities, namely the 44.4 MW and 193.9 MWh at Bus 8 on the 14-bus system, leading to significant attenuation of frequency deviation; in the 30-bus test case during a generator outage scenario, the frequency deviation was reduced from 0.503 Hz without BESS to 0.312Hz with BESS, and recovery time was reduced from 25s to 8s. In addition, the reliability indicators were improved, as shown with a decrease of the SAIDI index from 180 h/yr to 160 h/yr.
Copyright (c) 2026 Eng. Kaveke Kiima, Dr. Irene Muisyo, Dr. Linus Aloo https://creativecommons.org/licenses/by/4.0
Impact of Oxygen Concentration on High-Load PEMFC Efficiency: A Simulink-Based Study
Section: Research Articles Keywords: Concentration Loss, High load operation, MATLAB-Simulink, Oxygen enrichment, PEMFC Energy consumption and the utilization of sustainable energy sources are of great importance for the economic independence and environmental stability of nations. Turkey, as a country heavily dependent on energy imports, faces a critical need to develop domestic and renewable energy resources. The low carbon emissions and environmentally friendly nature of hydrogen have attracted significant attention within the scope of international agreements such as the Kyoto Protocol. Proton Exchange Membrane Fuel Cells (PEMFCs) stand out among clean energy technologies due to their low operating temperatures (60–80°C), high power density, and zero-emission potential. However, concentration losses occurring on the cathode side under high load conditions represent a significant problem that considerably reduces PEMFC efficiency and has not been sufficiently addressed in the literature. This study investigates the effect of inlet air O₂ on PEMFC efficiency under different load conditions using a MATLAB-Simulink simulation model. The results demonstrate that increasing the oxygen concentration under 50% load conditions yields only a marginal efficiency improvement of approximately 3%. In contrast, under 90% load conditions, increasing the O₂ enrichment level from 21% to 100% improves efficiency by approximately 10%, and this gain can reach up to 15% with additional pressurization measures. This finding clearly demonstrates that increasing oxygen concentration provides a meaningful contribution only under high load conditions, while such intervention is unnecessary within the optimal load range. The results present a practical strategy for improving PEMFC efficiency in heavy-load applications such as intercontinental cargo ships and freight trains, and directly contribute to the literature on PEMFC systems operating under high load conditions.
Copyright (c) 2025 Cemaleddin Uğuz, Mehmet Akif Şahman https://creativecommons.org/licenses/by/4.0
Adaptive Active Power Control for Aligning Photovoltaic Output with Consumer Demand in Rural Feeders
Section: Research Articles Keywords: Adaptive Active Power Control, Photovoltaic Integration, Reverse Power Flow, Smart Inverter, Volt-VAR, Volt-Watt The increasing global integration of photovoltaic (PV) systems, vital for sustainable development, presents significant operational challenges in rural distribution networks. These feeders, particularly those in Kenya with high resistance-to-reactance (R/X) ratios, are prone to reverse power flow (RPF) and voltage instability, with generic smart inverter controls often proving ineffective due to insufficient voltage rise. This paper addresses this critical gap by developing and validating an adaptive, coordinated smart inverter control algorithm. Employing both Volt-VAR for reactive power support and Active Power Curtailment (APC), the strategy dynamically aligns PV output with real-time local load demand using quasi-static time-series simulations in OpenDSS for a representative Murang'a 11kV rural feeder in Kenya. This grid compliance was achieved at a quantified cost of approximately 1,500 kWh of curtailed renewable energy per day during the dry season. Critically, a comparative seasonal analysis was performed by repeating the simulations under rainy season (long rains) conditions, characterized by significantly reduced and variable solar irradiance. The rainy season results confirmed the absence of reverse power flow due to the naturally lower PV generation, which remained below local load demand throughout the day. This seasonal comparison validates that RPF is predominantly a dry-season phenomenon in this context and that the proposed adaptive control strategy is both critically necessary during high-irradiance periods and benignly non-intrusive during low-irradiance seasons. The study validates a practical methodology for high-penetration PV integration in challenging rural grids, emphasizing the necessity of feeder-specific, seasonally-informed control strategies to balance grid safety with renewable energy yield.
Copyright (c) 2025 Kamunya N.M, Muriithi, C. M., D.K Njoroge https://creativecommons.org/licenses/by/4.0
HPPC Derived Feature Augmented DNN for SOC Estimation in Electric Vehicles
Section: Research Articles Accurate State of Charge (SOC) estimation is a critical function for ensuring the safety, performance, and longevity of lithium-ion batteries used in electric vehicles. Traditional model-based estimation methods—such as coulomb counting and Kalman filtering—often suffer from cumulative errors, parameter drift, and poor adaptability under varying temperature and dynamic load conditions. In contrast, data-driven approaches provide a promising alternative by learning complex nonlinear relationships directly from experimental data without relying on predefined equivalent circuit or electrochemical models. This study introduces an HPPC-derived feature-augmented deep neural network (DNN) framework that leverages the strengths of data-driven learning for accurate and robust SOC estimation. The proposed DNN is trained exclusively on Hybrid Pulse Power Characterization (HPPC) data and enhanced through feature engineering that captures voltage recovery dynamics, internal resistance, and temperature effects. Unlike conventional HPPC-based approaches, the model demonstrates strong generalization by accurately predicting SOC during EPA Urban Dynamometer Driving Schedule (UDDS) cycles—despite no exposure to driving-cycle data during training. Experimental validation on a 14-cell lithium-ion battery pack, managed by an L9963E-based battery management system, shows that the proposed model achieves a root mean square error (RMSE) reduction of 0.72% compared to traditional estimation techniques. These results confirm that data-driven DNN models trained on HPPC-derived features can offer high-accuracy, scalable, and generalizable SOC estimation solutions suitable for real-time deployment in next-generation automotive battery management system (BMS) platforms.
Copyright (c) 2025 Abdulkadir Mühendis, Prof. Dr. Ramazan AKKAYA https://creativecommons.org/licenses/by/4.0
Optimization of the Hybrid Energy System Dedicated to Internet of Things Devices
Section: Research Articles Keywords: Piezoelectric Energy Harvester, Electromagnetic Energy Harvester, Hybrid microgenerator , Electromagnetic energy harvester, Energy autonomy, Resonance frequency The energy autonomy of electronic devices used in embedded systems and by the Internet of Things determines the efficiency of these systems. This article highlights the design of a hybrid power source combining a piezoelectric energy harvester and an electromagnetic energy harvester to improve this autonomy. Simulations performed using COMSOL Multiphysics 6.2 showed the possibility of producing an output power of 4.35 mW at a resonant frequency of 46.5 Hz. This represents a significant improvement of 74% compared to the piezoelectric-only system (2.50 mW) and 135% compared to the electromagnetic-only system (1.85 mW). The hybrid system demonstrates a bandwidth of 9 Hz and a quality factor of 5.2. Parallel coupling was found to be significantly more effective than series coupling in maximizing vibrational energy recovery, demonstrating its superiority for this application. Although the hybrid system presents a trade-off between power output and bandwidth, it offers the highest energy efficiency for stable-frequency applications. According to these analyses, this optimized hybrid energy harvesting system offers several advantages over electrochemical batteries for powering low-consumption electronic devices, making them autonomous.
Copyright (c) 2025 Tojoniaina Marina Andriamananjara, Hasina Arivony Ramasombohitra , Edmond Fils Herinantenaina https://creativecommons.org/licenses/by/4.0
Assessment of Performance of 80W Monocrystalline Photovoltaic (PV) Module in Abuja FCT
Section: Research Articles Keywords: Solar PV module, STC, Outdoor condition, Maximum Power, Module Temperature Solar PV modules are rated by manufacturers under Standard Test Conditions (STCs), which often differ from real outdoor operating conditions. Therefore, evaluating field performance is essential for accurate module selection and power prediction. In this study, a one-year performance assessment of an 80 W solar PV module manufactured by NSENI Solar Energy Limited was conducted in Abuja-FCT, Nigeria, using real-time measurements and simulation. Key electrical parameters (Pmax, VMP, and IMP), solar radiation, and module temperature were continuously monitored using calibrated instruments. In parallel, empirically based PV models incorporating manufacturer datasheet values and local climatic data were used for simulation. The results show significant deviations between STC ratings and outdoor performance. At certain periods, the module voltage and efficiency dropped below 12 V and 11%, compared to the rated 17.35 V and 18%. While VMP and efficiency decreased at high solar radiation, Pmax, IMP, and module temperature increased, with opposite trends observed under lower radiation levels. A strong agreement between measured and simulated maximum power outputs was obtained, with a correlation coefficient of 0.98 and an average error of about 5%. These findings confirm the reliability of the applied PV models and demonstrate that the developed testbed can be used as a practical PV module performance validation facility suitable for replication across Nigeria.
Copyright (c) 2025 Samson Yusuf Dauda, Ndukan Ogu Arinze, Ibrahim Umar https://creativecommons.org/licenses/by/4.0
A Comparison of SMD and Through-Hole Components on Conducted Noise Emission
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
Copyright (c) 2025 Deniz Metin, Ersoy Kelebekler https://creativecommons.org/licenses/by/4.0
Optimal Sizing and Techno-Economic Analysis of Battery Energy Storage System for Peak Shaving and Voltage Profile Improvement on a Rural Distribution Network
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.
Copyright (c) 2025 J.O. Mwene, CM Muriithi, IN Muisyo https://creativecommons.org/licenses/by/4.0
Profit Maximization for Generating Companies Using Chaotic Whale Optimization with Inertia Weight
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.
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
Integrated Planning and Coordinated Allocation of DG and DSTATCOM Considering Radial Distribution Network Reconfiguration
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).
Copyright (c) 2025 Imran Ahmad Quadri https://creativecommons.org/licenses/by/4.0
Automated Conversion of Unstructured Geospatial Feeder Data into Analytical Models: An 11 kV Case Study
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.
Copyright (c) 2025 Okemo Amon, C M Muriithi, J N Nderu https://creativecommons.org/licenses/by/4.0
Selecting and Estimating Optimum Passive Designs of Energy Efficiency in Educational Buildings in Turkey
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.
Copyright (c) 2025 Ashraf Najmaldeen Hussein Hussein Hussein, Mustafa Acaroğlu https://creativecommons.org/licenses/by/4.0
IoT and LED-Based Approach in Smart Street Lighting: A Review
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.
Copyright (c) 2025 Ahmet Nur https://creativecommons.org/licenses/by/4.0
Hybrid Adaptive MPPT Algorithm with Intelligent Exploration and Dither-Based Tracking for PV Systems
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.
Copyright (c) 2025 Hamzah Abdulkhaleq Naji https://creativecommons.org/licenses/by/4.0
Performance Evaluation of Deep Learning Architectures for Tile Defect Detection
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.
Copyright (c) 2025 Ayça Demir, Humar Kahramanlı Örnek https://creativecommons.org/licenses/by/4.0
EMG-Based Multi-Class Gesture Recognition with Normalized Muscle Power Evaluation
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.
Copyright (c) 2025 Enes Halit AYDIN, Önder AYDEMİR https://creativecommons.org/licenses/by/4.0
Secure Audio Steganography Using Vectorized LSB and Chaos-Based Encryption
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 preservation of the hidden data.
Copyright (c) 2025 Zeynel Ümit Korkmaz, Fahrettin Horasan, Zeynep Çetinkaya https://creativecommons.org/licenses/by/4.0
Carbon Footprint Reduction and Strategic Benefits of Energy Efficiency: A Case Study in the Healthcare Sector
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.
Copyright (c) 2025 Mehmet Yorulmaz, Tuğba İmat Taş https://creativecommons.org/licenses/by/4.0
Development of Photovoltaic Devices Based on Fullerene–Graphene Hybrids
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.
Copyright (c) 2025 Mukhiddin Atajonov https://creativecommons.org/licenses/by/4.0
Assessment of the Energy Efficiency of Data Centers in Türkiye
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.
Copyright (c) 2025 Rıdvan Çelik, Mehmet Akif Şahman, Abdullah Oktay Dündar https://creativecommons.org/licenses/by/4.0
Capacitive Sensor System for Measuring Moisture Content in Transformer Oil
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.
Copyright (c) 2025 Rustam Baratov Jalilovich, Khurshid Sattarov, Mashrab Abdullayev Xamro Ugli, Almardon Mustafoqulov Mamatovich https://creativecommons.org/licenses/by/4.0
Thermal, Exergy, and Environmental Performance Analysis of Modified Fin Geometries in Panel Radiators: A CFD-Based Study
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.
Copyright (c) 2025 Ayse Bilgen Aksoy, Fatma Nur Uzun, Yiğit Aksoy https://creativecommons.org/licenses/by/4.0
A Comparative Study on Wavelet and EMD Methods for Remote detection of Rotational Imbalance
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.
Copyright (c) 2025 Yunus Emre Acar https://creativecommons.org/licenses/by/4.0