While solar energy holds great significance as a clean and sustainable energy source, photovoltaic panels serve as the linchpin of this energy conversion process. However, defects in these panels can adversely impact energy production, necessitating the rapid and effective detection of such faults. This study explores the potential of using infrared solar module images for the detection of photovoltaic panel defects through deep learning, which represents a crucial step toward enhancing the efficiency and sustainability of solar energy systems. A dataset comprising 20,000 images, derived from infrared solar modules, was utilized in this study, consisting of 12 classes: cell, cell-multi, cracking, diode, diode-multi, hot spot, hot spot-multi, no-anomaly, offline-module, shadowing, soiling, and vegetation. The methodology employed the exemplar Efficientb0 model. From the exemplar model, 17,000 features were selected using the NCA feature selector. Subsequently, classification was performed using an SVM classifier. The proposed method applied to a dataset consisting of 12 classes has yielded successful results in terms of accuracy, F1-score, precision, and sensitivity metrics. These results indicate average values of 93.93% accuracy, 89.82% F1-score, 91.50% precision, and 88.28% sensitivity, respectively. The proposed method in this study accurately classifies photovoltaic panel defects based on images of infrared solar modules.

Emotion recognition is one of the most important issues in human–computer interaction (HCI), neuroscience, and psychology fields. It is generally accepted that emotion recognition with neural data such as electroencephalography (EEG) signals, functional magnetic resonance imaging (fMRI), and near-infrared spectroscopy (NIRS) is better than other emotion detection methods such as speech, mimics, body language, facial expressions, etc., in terms of reliability and accuracy. In particular, EEG signals are bioelectrical signals that are frequently used because of the many advantages they offer in the field of emotion recognition. This study proposes an improved approach for EEG-based emotion recognition on a publicly available newly published dataset, VREED. Differential entropy (DE) features were extracted from four wavebands (theta 4–8 Hz, alpha 8–13 Hz, beta 13–30 Hz, and gamma 30–49 Hz) to classify two emotional states (positive/negative). Five classifiers, namely Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Naïve Bayesian (NB), Decision Tree (DT), and Logistic Regression (LR) were employed with DE features for the automated classification of two emotional states. In this work, we obtained the best average accuracy of 76.22% ± 2.06 with the SVM classifier in the classification of two states. Moreover, we observed from the results that the highest average accuracy score was produced with the gamma band, as previously reported in studies in EEG-based emotion recognition.

A sliding mode fuzzy control method which combines sliding mode and fuzzy logic control for DC-DC boost converter is designed to achieve robustness and better performance. A fuzzy sliding mode controller in which sliding surface whose reference is obtained from the output of the outer voltage loop is used to control the inductor current. A linear PI controller is used for the outer voltage loop. The control system is simulated using Matlab/Simulink. The simulation results are presented for input voltage and load variations. Simulated results are given to show the effectiveness of the control system.

DSP implementation of speed control of three phase induction motor drive is presented in this paper. A closed loop speed control has been achieved using constant V/f technique which is a simple scalar control method used to control the magnitude of the control quantities. In this study, a fourth order polynomial drived from V/f curve is constructed instead of using look up table which takes much time for determination of voltage from frequency value. A PI controller is used in speed control. dSPACE DS-1103 controller board is used for the implementation with Matlab/Simulink, which has a simple real time interface. Steady-state speed characteristics and transient responses with various reference speed commands are presented by experimental system. The simulation and experimental results provide a smooth speed response and good performance under various dynamic operations. Real time speed control has been implemented and some results are presented.

As global energy demands rise, there is an increasing need to transition from fossil fuels, which contribute to environmental harm and have limited reserves, to more sustainable and renewable energy sources. This shift is vital for both protecting the environment and ensuring long-term energy security. Renewable energy, such as wind power, plays a significant role in mitigating climate change and reducing greenhouse gas emissions while also being environmentally harmonious. Wind energy, in particular, is gaining importance as a clean, renewable source of power, with wind turbines serving as key components of this transformation. The success of wind energy projects depends largely on proper site selection. Factors such as wind potential, the topographical structure, environmental sensitivity, and legal considerations must all be carefully analyzed to ensure maximum performance and minimal environmental impact. The site selection process is crucial in optimizing energy production while promoting sustainability. Effective micro-siting strategies, which focus on the specific placement of turbines within a site, are also essential for improving energy efficiency and minimizing environmental disruption. This study highlights the importance of careful site analysis to ensure the successful and sustainable implementation of wind energy projects.

With the consuming of fossil fuels, sustainable alternative sources are needed for energy production. Renewable energy is an important source in the fight against climate change and provides economic benefits by increasing energy security. Solar energy is one of the popular renewable energy sources. The photovoltaic system is a technology that produces electrical energy from solar radiation. These systems, which are clean, renewable, and environmentally friendly, are important in terms of meeting our future energy needs. Photovoltaic system power forecasting is an important tool for both energy planning and energy management, as it increases the predictability of bulk power system. In addition, power estimates also allow improving the system efficiency and planning the maintenance date. In this study, due to its high accuracy, fast computation, and easy applicability, Extreme Learning Machine (ELM) method was used to estimate the daily average energy production of a solar power plant located in Elazig/Turkey. To improve the performance of the model, ELM’s hyperparameters were optimized using the Modified Golden Sine Algorithm (GoldSA-II). To show the power generation change that occurs in parallel with the seasonal weather change, graphs of the real power of different months from the four seasons and the power estimated by ELM are presented in the study. The radiation, temperature, wind speed, real power, and ELM predicted power values for the ten-month period in which the operating data were obtained are given in the table. By examining this table, the effect of weather conditions on production can be observed more clearly. The R and RMSE values of the ELM model, which are calculated separately for each month, are presented in the form of a radar chart. The average daily R and RMSE values calculated for the ten-month period were calculated as 0.95 and 0.0716, respectively, and the performance of the ELM model used in the study was proven by the calculated R and RMSE values.