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Lightning-related outages are of great concern for power utility companies especially in the tropical regions. Tenaga Nasional Berhad as the system operator in Malaysia realized the importance of this issue. In this paper, statistical information on transmission line outages, together with lightning stroke data, are presented. Lightning data were obtained from TNB Lightning Detection Network which generates lightning density map and other information, such as peak current, polarity, and location of the detected strokes with 500-m location accuracy. A case study on the 500 kV A-to-B line represents statistical information of collected strokes where 18 kA and 14 kA were reported as median values of current peak for negative and positive strokes, respectively. Mean values are 22 kA for negative strokes and 17 kA for positive strokes. About 93% of 658 619 collected strokes from 2004 to 2015 are negative whereas the remaining are positive. A total of 582 (90%) negative strokes and 65 (10%) positive strokes were recorded along a selected case line where 83% of outages were reported to occur on the top phase. Lightning stroke density, terrain characteristics, tower footing resistance, and monsoon activities were highlighted as contributing factors on line performance due to lightning in this study. [ABSTRACT FROM AUTHOR]

Lightning has become a significant threat to electronics in many countries where the natural phenomenon has previously been treated only as an occasional attacker of careless living beings. Most tropical countries, several southern states of the U.S.A., Japan, and several parts of Australia, experience heavy annual lightning occurrence density [1]–[12]. These regions also record high levels of lightning related injuries and accidents. [ABSTRACT FROM PUBLISHER]

Fault detection via power consumption monitoring of industrial robots is a substantial problem considered in this article, in which the healthy measurements of power consumption and encoders data for a prespecified task are employed as a reference for comparison to diagnose the potential failures or excessive degradation in the robot joints. Since most electrical and mechanical faults directly affect the consumed energy, the proposed solution analyzes the comparison outcomes between the healthy reference data with that monitored in a real time for each individual task. To integrate the power measurements with a base station, a ZigBee-based wireless data acquisition circuit has been developed to process the joints data. This article suggests a measurement-based mathematical model called Bode equations vector fitting as a robust fitting method to estimate such power consumption patterns. The achieved estimates allow a clear distinction for the potential failures in the robot joints that affect the power rate patterns even when involving sharp fluctuations. A table-based neural network classifier is presented to indicate the faulty joint or encoder according to the time intervals that divided for the executed task. The experimental results demonstrate the performance verification and feasibility of the proposed approach in ABB-IRB-1200 robot manipulator. [ABSTRACT FROM AUTHOR]