Efficient bidirectional energy exchange between an alternating current (AC) grid and a direct current (DC) source has been enabled through advanced power converter topologies. In this study, a single-stage AC-DC dual active bridge (DAB) converter employing phase-shift modulation (PSM) was investigated, with a particular focus on performance within the overmodulation regime. Bidirectional switching modules were implemented on the AC side to facilitate seamless energy transfer. Two conventional modulation strategies—sinusoidal and triangular—and a novel back-calculated modulation method were examined for their performance in both linear and overmodulation operating regions. The proposed back-calculation method incorporates an off-line generated reference current waveform designed to approximate linear control characteristics while substantially minimizing current harmonic distortion under overmodulated conditions. This approach extends the linear relationship between the reference current and power transfer capability beyond the conventional modulation limits, thereby enhancing converter performance in high-demand scenarios. Simulation-based analysis demonstrated that, in the linear region, the proposed method reduced average current total harmonic distortion (THD) by at least 45% when compared to conventional sinusoidal and triangular modulation techniques. Moreover, within the overmodulation regime, the linear correlation between the reference current and power transfer was extended by approximately 16.5%. The current harmonic distortion remained below 5% and 8% at modulation ratios of 108% and 112%, respectively, underscoring the robustness of the proposed strategy. These results suggest that the proposed PSM method is highly effective in achieving improved power exchange with reduced harmonic content in both linear and overmodulated operation, thereby offering a viable solution for high-performance AC-DC power conversion in smart grids and renewable energy systems.

In recent decades, the strategic placement of capacitors for compensating inductive reactive power has been extensively investigated by network operators and researchers globally, owing to its profound impact on minimizing power losses, improving voltage regulation, and enhancing overall voltage stability. The installation of shunt capacitors has been demonstrated to significantly improve the efficiency and performance of power systems by regulating voltage levels at load points, as well as at distribution and transmission system buses. This approach not only reduces inductive reactive power but also corrects the system’s power factor, thereby optimizing energy utilization. In this study, the optimal sizing and placement of capacitor banks within a specific section of the Duhok city distribution network were systematically analyzed. The Electrical Transient Analyzer Program (ETAP) software was employed to simulate and evaluate power losses and voltage drops both before and after capacitor installation. The findings reveal a marked improvement in the voltage profile across the network, accompanied by a substantial reduction in power losses. These results underscore the critical role of capacitor banks in enhancing the operational efficiency of distribution networks, providing a robust framework for future implementations in similar systems. The methodology and outcomes presented herein offer valuable insights for network operators seeking to optimize power system performance through reactive power compensation.

Accurate monitoring of turbine speed is essential for ensuring operational stability and efficiency in power generation systems, particularly within the context of low-carbon and renewable energy integration. This study evaluates the performance of three Variable Reluctance Sensors (VRSs)—VRS1, VRS2, and VRS3—used for real-time speed monitoring of the Steam Turbine Generator (STG) 1.0 at the Tambak Lorok Combined Cycle Power Plant (CCPP). The evaluation was conducted using statistical methods, including Root Mean Square Error (RMSE), standard deviation, and two-factor Analysis of Variance (ANOVA) without replication, to assess the accuracy and consistency of the sensors under varying operational conditions. The operational conditions were simulated through a motor controlled by a Variable Speed Drive (VSD), which allows for precise control over speed variations. The results indicate that the VRSs exhibit high accuracy and reliability, with RMSE values ranging from 0.08% to 0.28%. Among the three sensors, VRS3 demonstrated the highest performance, achieving minimal variability, with a standard deviation of 0.000 at a frequency of 50.00 Hz. ANOVA revealed no significant differences in performance between the three sensors (P-value = 1.000), suggesting uniformity in their measurement capabilities. These findings substantiate the suitability of VRSs for turbine speed monitoring in power plants, ensuring operational stability and supporting the integration of renewable energy technologies. The results reinforce the potential of VRSs as a reliable tool for improving the efficiency of sustainable energy systems

A novel approach for road crack detection and segmentation was proposed, incorporating Einstein operators within an Adaptive Neuro-Fuzzy Inference System (ANFIS). This methodology leverages advanced fuzzy aggregation techniques and adaptive mechanisms, combined with dynamic Einstein sum and product operators, to enhance the identification of cracks. The model was designed to effectively manage varying crack intensities, geometries, and noise levels, thereby ensuring high sensitivity and accuracy in real-world road conditions. In the preprocessing stage, robust fuzzification was applied using Gaussian membership functions alongside Einstein operators, which significantly improved feature extraction. The segmentation framework based on ANFIS ensured precise detection and delineation of cracks. The performance of the proposed model was demonstrated through a comparative analysis, showing superior accuracy (95.2%), precision (94.1%), recall (96.4%), and F1-score (95.2%) when compared to state-of-the-art models. Statistical validation was conducted, with p-values < 0.01 for all performance metrics, confirming the reliability and statistical significance of the results. Advanced post-processing techniques, including fuzzy morphological refinement and adjacency matrix-based connectivity analysis, were employed to accurately identify even faint or disconnected cracks. The proposed method exhibits exceptional resilience to environmental variations, offering a reliable and adaptive solution for road maintenance and monitoring. This work highlights the potential of fuzzy logic, statistical validation, and adaptive mechanisms in addressing real-world challenges in road crack detection.

Indonesia, a significant exporter of coffee, faces persistent challenges in accurately identifying and classifying coffee varieties based on aromatic characteristics, primarily due to the subjective variability of human sensory evaluation. To address these limitations, an electronic nose (e-nose) system was developed for the classification of coffee varieties through the analysis of aromatic profiles. The system integrates a DHT-22 sensor and four gas sensors (MQ-5, MQ-4, MQ-3, and MQ-135) to measure humidity, temperature, and gas concentrations from coffee vapor. Data acquisition was facilitated by the Arduino Uno platform, while classification was conducted using the Naive Bayes Classifier (NBC) algorithm. The e-nose achieved a classification accuracy of 82.2%, as validated through a confusion matrix and performance metrics, including precision, recall, and F1-score. Among the gas sensors employed, the MQ-4 sensor, which detects methane, demonstrated the highest response sensitivity, whereas the MQ-3 sensor, designed to detect alcohol, exhibited the lowest. This system significantly mitigates the inherent subjectivity associated with traditional aroma assessment methods and offers considerable potential for enhancing quality control protocols in coffee production processes. Future work will focus on integrating advanced machine-learning algorithms, optimizing sensor array performance, and expanding the dataset to include a broader diversity of coffee samples. These advancements are expected to further refine the system's classification capabilities and contribute to more robust quality assurance in the coffee industry.

The environmental conditions in large-scale, intensive poultry farming systems require high precision, and accurate prediction of environmental factors is critical for effective control. Existing control methods generally focus on the prediction and control of individual environmental factors without considering the interdependencies among these factors, leading to low prediction and control accuracy. To address the complex nature of the environmental system in poultry houses, characterised by multi-factor dependencies, an adaptive environmental control system based on Multi-feature Long Short-Term Memory (Multif-LSTM) was proposed. The Multif-LSTM model within the system calculates the dependencies between environmental factors using correlation coefficients and establishes a multi-input, multi-output neural network architecture. External climate factors are also incorporated during the input phase. Experimental comparisons conducted in a duck house environment, with Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models, show that the Multif-LSTM model outperforms others in terms of prediction accuracy. For NH₃ concentration, the Root Mean Square Error (RMSE), the Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R²) values are 1.34, 8.3, and 0.55, respectively; for temperature, they are 0.29, 2.83, and 0.98; and for relative humidity, they are 1.73, 2.46, and 0.95, respectively. Compared to the average performance of the RNN and LSTM models, the RMSE is reduced by 2.5, MAPE by 4.6, and R² increased by 0.32. The results demonstrate that the Multif-LSTM model achieves higher prediction accuracy and is suitable for high-precision adaptive environmental control in poultry houses.

Electricity remains one of the most vital resources for industrial, domestic, and agricultural applications. However, electricity theft has emerged as a significant challenge, contributing to substantial power losses and severe economic repercussions for utility companies. This study examines the role of smart meters (SMs) in minimizing electricity theft and reducing energy losses by transitioning from traditional analogue meters to advanced SMs equipped with automated billing and metering systems. Data collected from the SM system in the Akre energy distribution network reveal that, following the implementation of SMs, overall electrical power losses were reduced by 17.1%, while theft incidents decreased by 96.4%. These results demonstrate that the deployment of SMs significantly contributes to lowering total power losses and yields considerable financial benefits for both utility providers (UPs) and consumers. Moreover, the system enhances the ability to remotely monitor and control customer meters, allowing continuous oversight of meter readings without requiring physical visits. This remote functionality strengthens theft prevention measures, improves grid reliability, and reduces operational costs. The findings highlight the potential of the SM system in advancing power efficiency and promoting a more secure and cost-effective energy distribution network.

The recognition of abnormal events in surveillance video streams plays a crucial role in modern security systems, yet conventional techniques such as Support Vector Machines (SVMs) and decision trees (DTs) exhibit limitations in terms of accuracy and efficiency. These traditional models are often hindered by high false alarm rates and struggle to adapt to dynamic environments with variable conditions, thus reducing their practical applicability. In response to these challenges, an innovative approach, termed Adaptive Regression for Event Recognition (ARER), has been developed, leveraging advanced regression algorithms tailored for video data analysis. The ARER model integrates deep learning techniques, allowing for more precise temporal and contextual analysis of video footage. This methodology is structured through a multi-layered architecture that progresses from basic motion detection to complex anomaly identification. Trained on an extensive dataset covering a range of environmental and situational variables, ARER demonstrates enhanced robustness and adaptability. Evaluation results indicate that the ARER model achieves a 0.35% improvement in detection accuracy and a 0.40% reduction in false positives when compared to SVMs. Additionally, system throughput is increased by 0.25%, and detection latency is reduced by 0.30% in comparison to DTs. These advancements highlight the ARER approach as a superior alternative for real-time monitoring, offering significant improvements in both reliability and performance for surveillance applications.