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Combined Cycle Power Plants (CCPPs) generate electrical power through gas turbines and use the exhaust heat from those turbines to power steam turbines, resulting in 50% more power output compared to traditional simple cycle power plants. Predicting the full-load electrical power output ( P E ) of a CCPP is crucial for efficient operation and sustainable development. Previous studies have used machine learning models, such as the Bagging and Boosting models to predict P E . In this study, we propose employing Super Learner (SL), an ensemble machine learning algorithm, to enhance the accuracy and robustness of predictions. SL utilizes cross-validation to estimate the performance of diverse machine learning models and generates an optimal weighted average based on their respective predictions. It may provide information on the relative contributions of each base learner to the overall prediction skill. For constructing the SL, we consider six individual and ensemble machine learning models as base learners and assess their performances compared to the SL. The dataset used in this study was collected over six years from an operational CCPP. It contains one output variable and four input variables: ambient temperature, atmospheric pressure, relative humidity, and vacuum. The results show that the Boosting algorithms significantly influence the performance of the SL in comparison to the other base learners. The SL outperforms the six individual and ensemble machine learning models used as base learners. It indicates that the SL improves the generalization performance of predictions by combining the predictions of various machine learning models. [ABSTRACT FROM AUTHOR]

This study addressed the quality assessment of photovoltaic (PV) panels by analyzing their efficiency and electrical power under varying environmental conditions. A sophisticated algorithm was developed to extract and analyze PV panel voltage and current data in a complex manner, allowing the identification of key parameters such as open circuit voltage (Voc), short circuit current (Isc), and peak electrical power. These parameters were compared to the input power to accurately determine the panel efficiency. The novelty of this approach lies in its real-time implementation using a DC/DC (buck-boost) converter equipped with precise voltage and current sensors and running on a TMS320f379D board, linking theoretical knowledge with practical results. Experimental results demonstrated that temperature and irradiation significantly influence PV performance. With a 10°C increase in temperature, it resulted in a 5-10% decrease in output power, while a 100 W/m² increase in irradiation resulted in a 10-15% increase in output power. The study highlights the importance of considering both temperature and irradiation variations to optimize PV system design and operation, providing a robust method to assess PV panel quality in real-time. [ABSTRACT FROM AUTHOR]

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Here, we present the performance of a thermoelectric (TE) module consisting of n-type (La0.12Sr0.88)0.95TiO3 and p-type Ca3Co4−xO9+δ materials. The main challenge in this investigation was operating the TE module in different atmospheric conditions, since n-type has optimum TE performance at reducing conditions, while p-type has optimum at oxidizing conditions. The TE module was exposed to two different atmospheres and demonstrated higher stability in N2 atmosphere than in air. The maximum electrical power output decreased after 40 h when the hot side was exposed to N2 at 600°C, while only 1 h at 400°C in ambient air was enough to oxidize (La0.12Sr0.88)0.95TiO3 followed by a reduced electrical power output. The module generated maximum electrical power of 0.9 mW (∼ 4.7 mW/cm2) at 600°C hot side and δT ∼ 570 K in N2, and 0.15 mW (∼ 0.8 mW/cm2) at 400°C hot side and δT ∼ 370 K in air. A stability limit of Ca3Co3.93O9+δ at ∼ 700°C in N2 was determined by in situ high-temperature x-ray diffraction. [ABSTRACT FROM AUTHOR]

Perfect power system voltage stability is not possible in practice. Generally, the power grid is continually exposed to changes in its load and operating conditions. Therefore, dynamic stability analysis is one the most important and effective elements for greater security, stability and reliability of planning, design, operation and economic aspects of electric power networks. This study investigates and reports on the dynamic stability of the power system with a large‐scale photovoltaic system (L‐S PV). Two different scenarios with centralised PV power plants are considered in the medium voltage level without voltage regulation capabilities. Simulation results with these scenarios will show how the voltage instability decreases with the L‐S PV based on the bus status, disturbance location, and disturbance duration. In addition, the study discusses network terminal voltage, generator's rotor angle, generator's terminal current, generator's reactive power and electrical power output. This study is an extension of the earlier published conference paper. [ABSTRACT FROM AUTHOR]

Partial shading of a photovoltaic (PV) installation has an inconsistent impact on power production. This study investigates the effect of partial shading on PV performance. The experiments were carried out with a 90‐W PV module under both variable and constant irradiations with shaded area increased from 0 to 80% to observe the effect of variable solar radiation at certain shading points. The effect of shading under irradiation levels from 300 to 800 W/m2 was investigated. At a 600 W/m2 irradiation level, the shading impact factor was 1.25 with a 25% shading, while at 75% shading the impact factor decreased to 0.86. Results also show that for every 100 W/m2 increase in irradiation level, the electrical power output was enhanced by 3.89, 3.37, 2.27, and 2.02 W at 0, 25, 50, and 75% shading, respectively. The efficiency level was increased by 0.29, 0.27, 0.25, and 0.22% at 0, 25, 50, and 75% shading, respectively. Increasing the shaded area by 10% causes a 12.41 W drop in power output and a 2.3% drop in electrical efficiency. Partial shading not only deteriorates the PV performance, but also causes long‐term degradation of the module. [ABSTRACT FROM AUTHOR]