No abstract

The faults in transmission lines should be identified for attaining high quality energy in electrical power systems. Savings can be made in both time and energy if the transmission line faults are classified accurately. The present study examined phase-ground, phase-phase-ground, phase-phase, phase-phase-phase and no fault cases. Support Vector Machine (SVM), K-Nearest Neighbours Algorithm (KNN), Decision Tree (DT), Ensemble, Linear discriminant analysis (LDA) classifiers were used for classifying the transmission line faults. These algorithms were compared with regard to parameters such as accuracy, error rate, prediction speed and training time. The accuracy and minimum error of SVM and KNN classifiers were 99.7 % and 0.0011 respectively. DT classifier is faster than the other classifiers with a predicted speed of 29000 obs/sec. Whereas LDA had the shortest training time of 0.76992 sec. The results have indicated that SVM, KNN classifiers have similar performances. In addition, the classifiers SVM, KNN acquired minimum error with the highest accuracy compared with the other classifiers. While DT has the highest estimation speed, LDA has the shortest training time.

Intelligent methods have been applied to many fields for a long time. Recently, Visible Light Communication (VLC) systems widely include learning and classification models to improve their performances. The classification of L-Pulse Position Modulation (L-PPM) formats is crucial for VLC systems since the modulation order L is very effective for providing energy efficiency and increasing the transmission capacity. In this paper, therefore, it is reported for the first time, the classification of PPM schemes in VLC systems by using Decision Tree, Knearest neighbor (KNN), Support Vector Machine (SVM), and a Direct Decision-based Linear Model (LM) technique. A novel feature extraction model is derived to be able to classify the type of PPM modulation schemes. A comparison has been given to observe the performance of classification schemes by taking into account the level of Signal to Noise Ratio and the transmission distance between receiver and transmitter. The KNN method gives the best accuracy performance against other schemes at the SNR of 25dB and the distance of 2.32m and more, while the Tree model is superior to KNN at the distances of 2.25m and 2.20m. Additionally, it has been obtained the best accuracy of 97.85% by the Decision Tree Model at the distance of 2.20m.

The need for new energy sources has increased due to reasons such as the development of technology, the increase in electricity demand, the decrease of fossil resources, and environmental pollution. Renewable energy sources are self-renewing, friendly, and clean energy sources. Microgrids are small power energy networks consisting of renewable and non-renewable energy sources, batteries, inverters, and loads. They can be operated connected to the network and independently from the network. Metaheuristic methods are algorithms that can achieve optimum results in the search space. In this study, optimization of a microgrid composed of a wind turbine, solar panel, diesel generator, inverter, and loads has been investigated with multi-objective hybrid metaheuristic algorithms. Optimization is aimed at reducing emissions, increasing reliability, and optimizing energy resources. Swallow Swarm Optimization (SSO) and Hybrid Particle Swallow Swarm Optimization (HPSSO) with different iterations and populations are compared for the first time.

Intelligent methods have been applied to many fields for a long time. Recently, Visible Light Communication (VLC) systems widely include learning and classification models to improve their performances. The classification of L- Pulse Position Modulation (L-PPM) formats is crucial for VLC systems since the modulation order L is very effective for providing energy efficiency and increasing the transmission capacity. In this paper, therefore, it is reported for the first time, the classification of PPM schemes in VLC systems by using Decision Tree, K-nearest neighbor (KNN), Support Vector Machine (SVM), and a Direct Decision-based Linear Model (LM) technique. A novel feature extraction model is derived to be able to classify the type of PPM modulation schemes. A comparison has been given to observe the performance of classification schemes by taking into account the level of Signal to Noise Ratio and the transmission distance between receiver and transmitter. The KNN method gives the best accuracy performance against other schemes at the SNR of 25dB and the distance of 2.32m and more, while the Tree model is superior to KNN at the distances of 2.25m and 2.20m. Additionally, it has been obtained the best accuracy of 97.85% by the Decision Tree Model at the distance of 2.20m.