Traditional transmission line ratings are limited by a set of fixed conservative weather assumptions that are also known as static thermal rating (STR). Owing to STR, new line corridors are continuously required to address increasing electricity demands while minimizing the curtailment of renewable energy sources (RES). However, the expansion of an electricity network is expansive, long, and limited due to the scarcity in land and space. To overcome this issue, researchers have proposed a dynamic thermal rating (DTR) system that can increase the capacity of existing transmission lines. Research has shown that actual line ratings are higher than STR most of the time. The potential of using the DTR system to increase the reliability of power systems is therefore significant. Almost every country has begun the process of increasing the integration of RES, and consequently, the DTR system has become increasingly important. Exploring and reviewing critical studies on the DTR system are thus beneficial for researchers who are interested in the developments of DTR technology. This review paper begins by comparing the two main DTR system standards. Then, monitoring technologies of the DTR system are reviewed. Notable research on the reliability impacts of the DTR system on electrical networks are surveyed. Interactions with wind power and other smart grid technologies are also examined, and the concept of power system reliability is briefly discussed.

Partial discharge (PD) signal classification analysis on cross-linked polyethylene (XLPE) cables is complex, requiring a comprehensive understanding of the characteristics of PD patterns. In the realm of high-voltage electrical insulation, PD pattern characteristics, such as PD charge and inception voltage, are essential as assessment criteria in diagnostics systems using PD classifiers. This paper provides a review of the various PD patterns and classifiers used by previous researchers, specifically for XLPE cables. In addition, the differences of the studies on various sensor developments based on PD detection in the past 27 years are also discussed. The repeatability, recognition accuracy, recognition speed, and effect of feature sizes on each PD classification method are reviewed and explained. This review indicates that the pattern recognition for PD signal using artificial neural network (ANN) exhibits better performance than the other methods in terms of accuracy and repeatability, and the reduction of feature size does not affect the accuracy of ANN.

Dissolved gas-in-oil analysis (DGA) is a sensitive and reliable technique for the detection of incipient fault condition within oil-immersed transformers. The presence of certain key gases is monitored and quantified. There are a number of methods developed for analyzing these gases and interpreting their significance: Key Gas, Rogers Ratio, Doernenburg, Logarithmic Nomograph, IEC Ratio and Duval Triangle. This paper investigates the accuracy and consistency of these methods in interpreting the transformer condition. The evaluation is carried out on DGA data obtained from the local power utilities and from published papers. The data consists of 92 different cases. The key gases considered are hydrogen, methane, ethane, ethylene and acetylene. A MATLAB program was developed to automate the evaluation of the methods.