Search Results (10,575)

With the shift in advanced packaging toward 3D integration and flexible electronics, it is becoming critical to produce high-quality silicon nitride films under low thermal budgets. To overcome the limitations of low-temperature deposition, this study compares two gas mixtures—SiH₄/NH₃/N₂ and SiH₄/N₂—in plasma-enhanced chemical vapor deposition of silicon nitride coatings. We systematically evaluated how the NH₃ precursor affects deposition kinetics, chemical bonds, non-uniformity, optical properties, and internal stress at different RF powers and electrode gaps. The test results show that NH₃, with its lower dissociation energy, avoids the high activation barrier associated with pure N₂ plasma, leading to a higher reactive nitrogen flux and a doubled deposition rate. In the SiH₄/NH₃/N₂ system, raising RF power from 300 W to 900 W reduced hydrogen content from 23.58% to 12.25%. This suppression of hydrogen promoted structural densification, shifting the mechanical stress from 173.3 MPa to −989.7 MPa. At a larger electrode gap of 19 mm, NH₃’s better diffusion characteristics offset the electric field sensitivity typical of N₂ systems, reducing large-area film non-uniformity by 28.7% compared to a 13 mm gap. This work offers a practical, mass-production-friendly approach for depositing robust, low-hydrogen, highly uniform silicon nitride films at low temperatures. Full article

A short-term performance test of blended biodiesel (FAME), green diesel (HVO), and diesel was experimentally assessed in a 100 kW Cummins 6BTAA5.9-G12 diesel engine under multiple load conditions. The objective was to determine the technical feasibility, operational trade-offs, and optimal blend formulations for renewable energy deployment in diesel power plants. All tested blends operated stably without engine modification, confirming the “drop-in capability” of FAME–HVO mixtures for existing diesel engines. Specific fuel consumption (SFC) increased notably at high loads, with penalties up to 15.15% for B30D20 and B35D15 relative to neat diesel, although overall efficiency improved with load. Among the ternary fuels, B30D10 and B30D20 provided the most balanced compromise between combustion reactivity and flow properties. Exhaust gas temperatures rose with load for all fuels, with FAME-rich blends exhibiting higher temperatures than neat diesel, while coolant-side analysis showed D100 and D50 as thermally favorable and B50–B100 imposing the highest cooling demand. The results emphasize the need for injection system recalibration on an energy basis for HVO-rich fuels, and for strengthened filtration and maintenance practices for FAME-rich blends to avoid filter clogging and injection instability. Considering performance, operability, and system stability up to 100 kW, B30D10 and B35D15 are identified as optimal compromise blends. The study highlights the necessity of future work on long-term durability, fuel system compatibility, supply chain robustness, and techno-economic viability to safely scale green diesel use in Indonesian stationary power generation. Full article

The large-scale integration of distributed photovoltaics (DPVs) into the distribution network exacerbates voltage fluctuations and substantially increases network losses. To improve the voltage quality and economic efficiency of distribution networks, a Volt/Var optimization (VVO) model is established. Coordinating multiple heterogeneous devices, the model aims to minimize the total voltage deviation, the total network losses, and the regulation cost of discrete equipment simultaneously. Considering multi-constraint coupling characteristics, a quantitative method is proposed to evaluate the reactive power regulation potential of DPVs under intricate operating conditions. Then, the multi-strategy integrated rime optimization algorithm (MSIRIME) is utilized for the model solution. Fuch chaotic mapping generates uniformly distributed and ergodic initial populations. A dual-branch search mechanism combining the snow ablation optimizer with the rime optimization significantly enhances global exploration capabilities. The guided learning strategy balances exploration and exploitation for high-dimensional VVO, preventing local optima. Case tests on a modified IEEE 33-bus system demonstrate that the proposed model exhibits excellent effectiveness and robustness. Moreover, MSIRIME exhibits better optimization performance than some classic and recently proposed strategies, reducing the average network losses and voltage deviation over 30 independent runs by at least 5.87% and 52.22%, respectively, relative to those of the compared methods. Full article

The increasing penetration of distributed generation (DG) in distribution systems poses significant operational challenges, including increased power losses, voltage profile deviations, and variations in short-circuit currents. These issues may compromise network safety, reliability, and the selectivity of protection schemes under different operating scenarios. This paper proposes a hybrid optimization methodology for the optimal placement and sizing of DG, aiming to minimize active power losses while ensuring voltage regulation and keeping short-circuit currents within permissible limits. An integrated approach is proposed that combines a mesh-to-radial network reconfiguration strategy with a modified Ant Lion Optimization algorithm, known as ALO-DG, enabling the simultaneous optimization of network topology and the allocation of distributed generators at candidate buses. The problem is formulated taking into account power balance constraints, voltage limits, distribution network capacity limits, and short-circuit current limits. The proposed methodology achieved substantial reductions in active power losses in the IEEE 33-bus and 69-bus test systems, reaching 84.42% and 91.56%, respectively. These improvements were accompanied by enhanced voltage profiles while preserving the radial operating structure of the distribution networks. Furthermore, the proposed hybrid methodology serves as a tool for the planning and operation of distribution systems with high DG penetration, particularly in scenarios where grid security and protection coordination are critical considerations. Full article

As renewable energy is increasingly integrated via high-voltage direct current (HVDC) transmission, hybrid multi-infeed receiving-end grids containing both line-commutated converters (LCC) and modular multilevel converters (MMC) have become common, and wideband resonance problems in power-electronized networks are growing more prominent. This paper proposes an active resonance analysis and suppression strategy for such systems. First, a wideband current source converter model and a wideband voltage source converter model are adopted to describe the LCC and MMC, respectively, and a positive-sequence s-domain model of the system is established. A two-stage s-domain nodal admittance matrix method is then applied to efficiently determine the wideband resonance modes and the corresponding mode shape eigenvectors. A dual criterion combining the matching degree between resonance frequencies and LCC characteristic harmonics with the modal damping ratio identifies high-risk resonance modes. On this basis, an active damping strategy that realizes a parallel virtual resistance on the AC side through MMC supplementary control is proposed, together with a quantitative design method for the virtual conductance. At the control implementation level, a modulation wave reconstruction bypass injection scheme superimposes the high-frequency damping command directly in the $\alpha \beta$ stationary reference frame, thereby bypassing the PI controller and reducing the amplitude attenuation and phase distortion caused by the high-frequency limitation of the integral path. PSCAD/EMTDC simulation results on an IEEE 9-bus test system demonstrate that the proposed strategy effectively suppresses resonance amplification and wideband power oscillations excited by LCC characteristic harmonics without affecting the fundamental power transmission. Full article

Power systems continuously generate large-scale load time series data for forecasting, consumption analysis, and equipment health monitoring. However, real-world load measurements are often contaminated by anomalies caused by sensor faults, communication errors, and abnormal consumption behaviors, which may degrade data quality and affect operational decision-making. To address this issue, this paper proposes an integrated anomaly detection framework named PEL, which combines Prophet-based seasonal-trend decomposition, ensemble empirical mode decomposition (EEMD), and a multilayer long short-term memory (LSTM) network. Prophet is first employed to decompose the original series into trend, seasonal, holiday, and residual components. Sample entropy analysis and white noise tests are then adopted to evaluate whether the residual component still contains complex structured information requiring secondary decomposition. Next, EEMD is applied to the residual component to extract multi-scale intrinsic mode functions. Finally, all decomposed components are normalized and fed into a multilayer LSTM model for anomaly detection. Experiments on a real-world power load dataset demonstrate that the proposed PEL framework achieves an accuracy of 99.92%, a precision of 97.33%, a recall of 100%, an F1-score of 98.65%, and an AUC of 0.9996, outperforming or matching several baseline and hybrid models. Full article

Background/Objectives: Given the limited evidence on multi-distance visual function assessment in presbyopia, this study aimed to compare dynamic binocular visual function between presbyopic and non-presbyopic (NP) participants at different distances, and to further evaluate the effects of additional power (ADD) on dynamic sharpness discrimination, binocular integration, and dynamic stereopsis in presbyopic participants. Methods: A total of 54 presbyopic and 77 NP participants were tested at 0.4 m, 0.7 m, 1 m, and 3 m using a dichoptic rotating ring system with red-blue anaglyph glasses. Presbyopia was classified as low (LP, ADD < 1.5D) or high (HP, ADD ≥ 1.5D). Tests included dynamic sharpness discrimination, binocular integration, and stereopsis. To account for potential confounders, generalized linear models (GLM) were applied with sex, eye laterality, age, ADD, spherical equivalent (SE), and group as covariates, allowing comparison of visual function outcomes across different viewing distances between NP and ADD-stratified presbyopic groups. Results: There was no statistically significant difference in the passing rates of dynamic sharpness discrimination test between the presbyopic and NP groups (all p > 0.05). At 0.4 m, 0.7 m, and 1 m, the presbyopic group showed significantly lower passing rates in the binocular integration test compared with the NP group (all p < 0.05), while no significant difference was observed at 3 m (p = 0.051). Furthermore, the passing rates for binocular integration test at all distances were significantly lower in the HP group than those in both the NP and LP groups (all p < 0.05). GLM analysis indicated that both SE and age were potential confounders in the comparison of binocular integration between presbyopic and NP groups (both p < 0.05). There were no significant differences in the passing rates of binocular dynamic stereopsis test at any distance between the NP and presbyopic groups, or between the LP and HP groups (all p > 0.05). Conclusions: This novel dynamic testing method revealed ADD-dependent impairment of binocular integration at near-to-intermediate distances in patients with presbyopia. Full article

Optimal sizing of hybrid renewable energy systems for desert camps is a multi-objective problem that must account for cost, reliability, component degradation, and uncertainty. This paper introduces an improved multi-objective neural architecture search (IMONAS) framework for hybrid nanogrid sizing in the desert environment of Hafr Al-Batin, Saudi Arabia. The framework combines neural optimization, stochastic uncertainty modeling, and explicit battery degradation modeling, a combination not addressed in the reviewed studies for this application. Six test cases are examined by varying uncertainty assumptions, battery degradation, and the annual duration of uncertain operation. For each case, IMONAS provides Pareto-front solutions that specify the photovoltaic, diesel generator, battery autonomy, and inverter choices while minimizing the cost of energy (COE) and the loss of power supply probability (LPSP). IMONAS is compared with the original MONAS and five other multi-objective optimization methods. In addition to visual Pareto-front comparisons, the assessment uses Pareto-dominance indicators, namely the C-metric and an aggregated score derived from pairwise C-metric comparisons across the algorithms and cases. The results provide a validated sizing framework for remote arid-region nanogrids under uncertainty and battery degradation. Full article

The rapid development of wind and solar energy necessitates a solution to the problem of the optimal spatial placement of wind farms (WFs) and solar farms (SFs) within electric power systems. The non-stationary generation schedules of WFs and SFs place increased demands on the flexibility of conventional generation, determined by the intensity of net load fluctuations. This paper proposes a methodology for the spatial optimization of WF and SF location, in which the optimization criteria include net load indicators (rate of net load change and net load increment), the base power of the RES system, and the economic criterion of maximum electricity generation. Unlike existing approaches, in which the geographical smoothing effect on power fluctuations is treated as an incidental outcome, the proposed methodology employs it as an explicit optimization criterion for RES placement. The algorithm provides for the preliminary ranking of candidate sites based on the maximum electricity generation criterion, followed by the redistribution of generating capacities among sites with an acceptable capacity factor in accordance with the selected optimization criterion. The methodology was tested on a model comprising six potential wind farm sites and two solar farm sites with a total installed capacity of 600 MW and a maximum power system load of 3000 MW. The obtained results show that the optimal redistribution of installed capacities among sites allows a 31.5% reduction in net load variability intensity to be achieved with an 11.6% reduction in electricity generation relative to the maximum possible. The study is based on idealized daily generation and consumption profiles and is theoretical in nature, proposing a pre-screening tool for RES siting that complements rather than replaces subsequent network-constrained planning studies, including power-flow analysis and grid verification, and establishes a methodological foundation for further development using real multi-year retrospective data. Full article

Accurate, uncertainty-aware estimation of instantaneous wind turbine output is a prerequisite for integrating onshore assets into low-emission energy systems, where operational monitoring, energy-performance verification, and cooperative asset management depend on auditable digital representations of turbine behaviour. This study develops a Digital Shadow-based power-curve modelling framework on fourteen years of Supervisory Control and Data Acquisition records from an operational Vestas V52 onshore turbine (850 kW, Dundalk Institute of Technology, Ireland; 457,429 ten-minute records spanning 2006–2020) and benchmarks seven methods under identical preprocessing on a strict chronological hold-out (training 2006–2017; testing 2018–2020; n = 52,388). A parallel random 75/25 split is reported only as a within-distribution diagnostic; it quantifies an optimistic R² inflation of 0.003–0.027 depending on architecture. The Artificial Neural Network attains the best chronological performance (R² = 0.9924, BCa 95% confidence interval 0.9910–0.9931, RMSE = 19.79 kW); only the ANN and a one-dimensional Convolutional Neural Network with twenty-four-step wind-speed lags (R² = 0.9921) deliver clear positive skill against the IEC-style manufacturer power curve. Split-conformal calibration of a Quantile Regression Forest raises empirical 90% prediction-interval coverage from 0.534 to 0.904 at a width inflation from 30 to 51 kW. The framework qualifies as a Digital Shadow and is positioned, through a Horizon Europe Technology Readiness Level audit and an explicit mapping to ISO 50001:2018 Plan–Do–Check–Act energy management and Renewable Energy Community governance under Directive (EU) 2018/2001, as an auditable monitoring layer for cooperative onshore wind operations. The empirical evidence base is a single turbine; multi-turbine, multi-site replication is the natural follow-on validation. Full article

Accurate electricity price forecasting (EPF) is essential for market participants to optimize trading strategies and for power systems to accommodate the increasing penetration of volatile renewable energy sources. However, electricity price series are characterized by strong nonlinearity, high volatility, and significant structural breaks, which pose substantial challenges to conventional forecasting models. Although numerous hybrid deep learning models have been proposed for EPF, most existing approaches either overlook structural breaks or treat them as outliers rather than as signals of regime shifts, often resulting in systematic forecasting degradation when market conditions change abruptly. To address this issue, this study proposes COCAL-TTL, a novel multi-stage structural break-aware forecasting framework that integrates regime-adaptive data partitioning with a functionally differentiated hybrid deep learning architecture. First, a joint detection scheme combining the Iterated Cumulative Sum of Squares (ICSS) algorithm and the Chow test is employed to partition Spanish electricity market data from 2014 to 2023 into distinct regimes. Within each regime, CEEMDAN is applied to extract multi-scale features, which are subsequently reconstructed into trend, periodic, and random components based on an independent sample t-test and Fast Fourier Transform (FFT). The CNN-SE Attention-LSTM (CAL) model, with hyperparameters optimized by the Osprey Optimization Algorithm (OOA), serves as the primary forecasting engine. In addition, a dedicated heterogeneous error correction module, namely TTL, is introduced, in which Temporal Convolutional Network, Transformer, and LSTM are designed to capture local transients, long-range dependencies, and transitional dynamics in the residual series, respectively. Empirical results demonstrate that compared with the Naive benchmark, COCAL-TTL achieves percentage MAPE improvements of 58.48% and 48.97% in low- and high-volatility regimes, respectively. These findings indicate that the proposed structural break-aware framework provides a robust data-driven solution for EPF under heterogeneous market conditions and offers technical support for stable electricity market operation in the context of renewable energy integration. Full article

Predictive maintenance in underground mining faces challenges due to severe conditions such as confined environments, high humidity, presence of silica dust, and restricted access. This study develops a predictive framework based on oil analysis and machine learning for multiple compartments of mining equipment (engine, hydraulic system, transmission, differential). Samples were processed under ASTM standards, integrating wear metal concentrations (Fe, Cu, Cr, Pb, Al), physicochemical properties (viscosity, TBN, soot), and contaminants (Si, Na). Based on tribology, interpretable ratios were constructed. Three algorithms (Random Forest, Gradient Boosting, and XGBoost) were evaluated using cross-validation. XGBoost achieved the best balance (F1 = 0.852, AUC = 0.975), with a recall of 94.5% for the critical class and only 3 false negatives out of 199 test samples, while Random Forest presented the highest global discrimination power (AUC = 0.978). SHAP revealed that viscosity at 100 °C is the most important predictor (SHAP ~0.9), surpassing iron. No temporal wear trend was found (R² = 0.000). Threshold optimization to 0.25 reduced false negatives by 67% (from 9 to 3). The framework provides interpretable predictions with uncertainty quantification for underground environments. Full article

Designing low-voltage (LV) power distribution systems for mass-sensitive electric vehicles involves several unresolved technical challenges, including parasitic $I^{2}R$ losses, excessive mass of commercial off-the-shelf distribution units, and difficulties in isolating thermal phenomena during vehicle operation. In dynamic racing conditions, temperature measurements of LV components are strongly influenced by external heat sources such as traction batteries, motors, and inverters, complicating accurate assessment of conductor self-heating and distribution losses. This work presents a load-driven methodology for the specification, implementation, and validation of LV architectures, demonstrated using a Formula Student electric race car. The proposed approach combines harness current mapping, resistive loss modeling, and component-level topology optimization to support the development of lightweight and electrically robust systems. Within this framework, a mass-optimized programmable solid-state power distribution unit (PDU), an auxiliary battery system with a battery management system (BMS), and an optimized LV wiring harness were developed and experimentally validated through controlled subsystem tests and in-vehicle operation. The proposed methodology enabled reduction in PDU mass by 40–80% relative to commercially available solutions while maintaining programmable protection, integrated current sensing, and stable thermal operation under representative racing loads. This reduction was achieved through load-driven conductor sizing, application-specific protection threshold optimization, and elimination of redundant protection and interconnection hardware. The developed PDU achieved a mass of 155 g with measured channel resistances of 40–70 m$\mathsf{\Omega }$. The auxiliary battery pack exhibited an average internal resistance of 64.2 m$\mathsf{\Omega }$ at a total mass of 2190 g, while the optimized harness demonstrated resistivity in the range of 14.72–33.98 m$\mathsf{\Omega }$/m. Experimental validation confirmed stable operation below critical thermal limits under both nominal and off-nominal load conditions. The obtained results demonstrate that the proposed methodology enables measurable reductions in both system mass and resistive power losses through application-specific optimization of the LV architecture. However, the presented approach is primarily suited for motorsport and other highly mass-constrained applications, where reduced packaging volume, efficiency, and weight justify the increased design complexity and lower universality compared to commercial off-the-shelf solutions. Full article

Feature selection represents a critical step in developing accurate and interpretable models for early breast cancer detection. Despite extensive research in the field of mammographic image analysis, no consensus has yet been reached on the optimal feature subsets that distinguish normal from malignant tissues. To address this gap, the present study aims to identify the most discriminative and significant features through a comprehensive multi-criterion selection framework. The aim is to integrate, as new frameworks, different combinations of t-test, ANOVA, Mutual Information (MI), and Equal Grouping Methods (EGM) to rank 19 linear and nonlinear features extracted from mammographic images. The objective is to maximize feature relevance while minimizing redundancy and enhancing diagnostic and healthcare systems. Linear features were assessed alongside nonlinear descriptors. A framework combining t-test, ANOVA, and EGM, guided by MI relevance, was employed to balance feature contributions across categories. The experimental results demonstrated that hybrid feature selection significantly enhanced diagnostic accuracy using optimal linear and nonlinear attributes. The optimization results suggested using a hybrid of six linear and eight nonlinear features. Linear features were highly accurate for detecting cancer. Haralick entropy obtained the highest average accuracy and performance, 94.14% and 93.45%; followed by kurtosis, 93.49% and 92.59%; perimeter irregularity, 93.43% and 92.65%; skewness, 93.01% and 92.25%; and volume/area, 92.82% and 91.92%. Despite the reliable discriminative power of linear descriptors, their overall effectiveness in representing intricate tissue characteristics was limited. The comparison of statistical characteristics shows a distinct performance benefit of nonlinear descriptors over linear ones for detecting breast cancer. Nonlinear descriptors, however, showcased higher accuracy and performance, with an average accuracy of 97.81% in contrast to 94.43% for linear approaches. Local phase congruency achieved the top average accuracy and performance, 97.81% and 96.61%, respectively; succeeded by wavelet entropy, 97.62% and 96.42%; Laplacian spectrum features, 97.52% and 96.32%; nonlinear diffusion, 97.10% and 95.90%; and clustering coefficient, 96.70% and 95.50%; then Shannon, Tsallis, and Rényi entropies. The results indicate that statistically validated nonlinear characteristics significantly outperform linear ones across accuracy and performance measures. Their combination significantly improves the strength and discriminative power of computer-assisted breast cancer diagnostic systems, affirming their suitability for integration into sophisticated machine learning and deep learning models. The results also show that the new multi-criterion framework’s early detection performance surpassed that of the statistical and deep learning models explored, with an average of 98.6% accuracy, 98% sensitivity, 98.9% precision, and 98.4% F1 score of early detection of breast cancer. The incorporation of statistically validated nonlinear descriptors, particularly local phase congruency and wavelet entropy, improves the discriminative ability, robustness, and clinical understanding of breast cancer computer-assisted diagnostic systems. Overall, the proposed framework confirms that integrating hybrid features substantially enhances robustness and plays a pivotal role in computer-assisted breast cancer detection. These selected features may be fed to more advanced algorithms in the future, potentially yielding improved performance. Full article

Accurate assessment of winding condition for power transformers is critical for ensuring the stable operation of modern power systems. Vibration signal has been regarded as an effective and promising evaluator for winding diagnosis. While on-line vibration monitoring offers the continuous, non-invasive and in-service assessment for winding condition, establishing precise correlations between the variable vibration patterns and specific winding condition remains challenging. To this end, an off-line vibration response analysis (VRA) technique was presented in the paper. Specifically, vibration frequency response (VFR) curves, indicating the winding response, were first obtained when the transformer was excited by the developed vibration response testing system, consisting of constant current variable-frequency power supply, intermediate transformer, accelerometers, data acquisition, control and analysis system. The VFR curves were then quantitatively and comprehensively described through four kinds of correlation indices. Finally, hierarchical integration strategy was proposed to aggregate those indices into quantitative criterion for condition assessment. The proposed method was validated on a real transformer under both normal and fault conditions, demonstrating superior performance. Notably, a 10% decrease in the evaluation criterion indicates an incipient winding looseness, while a reduction of 25% or more suggests severe looseness, prompting timely maintenance recommendations. Full article