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The occurrence of faults in electrical distribution networks is a significant concern due to the potential damage to electrical equipment, system instability, and the disruption of reliable energy. Detecting faults in a timely and accurate manner is crucial, especially in complex underground distribution networks with branches, non-homogeneous cables, and various loads. The inherent complexity of such networks poses challenges in pinpointing faulty sections, necessitating specialized detection methods. This research focuses on utilizing the k-Nearest Neighbors (k-NN) algorithm for detecting faulty sections in an underground distribution network. Practical data, obtained from Tenaga Nasional Berhad Malaysia (TNB), including measurements of current swell and voltage sags for 17 network sections, was used to calculate the Euclidean Distance. Given that 70% of faults in the distribution network are attributed to Single Line to Ground Fault (SLGF), this study specifically targets this fault type. Additionally, various fault resistances were tested to observe the k-NN algorithm's performance. The results indicate that the k-NN algorithm successfully detected faulty sections, demonstrating effectiveness across different fault resistances and ranks. This research contributes valuable insights into improving fault detection mechanisms in underground distribution networks. [ABSTRACT FROM AUTHOR]

Weather station devices are used to monitor weather parameter conditions, such as wind direction, speed, rainfall, solar radiation level, temperature, and humidity. This article discusses the design of a customized weather station embedded with gas concentration readings, whereby the gas concentration measurement includes oxygen (O2), carbon dioxide (CO2), and carbon monoxide (CO). The measurements and data processing of input sensors were transmitted to an Arduino Uno microcontroller, and the input data were then remitted to Wemos D1 Mini to be uploaded to a cloud server. Furthermore, the gas sensors' characterization methods were also considered to reveal the obtained results of accuracy, precision, linearity, and hysteresis. An android-based mobile application was also designed for monitoring purposes. The system in our experiment utilized an internet connection with a field station, base station, and database server. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is a novel machine for data analysis and has advantageous characteristic of good generalization. Because of this characteristic, SVM is used in this work for fault classification and diagnosis in distribution systems. This work proposes an effective fault location method using SVM to identify the fault type, faulty section, and fault distance. The classification and regression analysis of the SVM are performed to locate a fault. The proposed method utilizes the voltage sag magnitude and angle measured at the primary substation of a distribution system. First, the fault type is identified using one- versus-one concept of support vector classification. The next step identifies the faulty section by calculating fault resistance, finding possible faulty sections and ranking the possible sections. Finally, the fault distance is identified using support vector regression analysis. The performance of the proposed method is tested using SaskPower distribution system from Canada having 20 line sections. Test cases are carried out under various fault scenarios considering the fault type and fault resistance. The results of fault distance are compared for different kernel functions, and the most accurate kernel is chosen. Test results show that the proposed method obtains reliable fault location. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Fast and effective fault location in distribution system is important to improve the power system reliability. Most of the researches rarely mention about effective fault location consisting of faulted phase, fault type, faulty section and fault distance identification. This work presents a method using support vector machine to identify the faulted phase, fault type, faulty section and distance at the same time. Support vector classification and regression analysis are performed to locate fault. The method uses the voltage sag data during fault condition measured at the primary substation. The faulted phase and the fault type are identified using three-dimensional support vector classification. The possible faulty sections are identified by matching voltage sag at fault condition to the voltage sag in database and the possible sections are ranked using shortest distance principle. The fault distance for the possible faulty sections isthen identified using support vector regression analysis. The performance of the proposed method was tested on an unbalanced distribution system from SaskPower, Canada. The results show that the accuracy of the proposed method is satisfactory. [ABSTRACT FROM AUTHOR]