As the world is pledged towards net zero carbon by 2050, the need for clean and efficient energy transitions is more critical than ever. Optimizing the power grid transfer capacity is crucial for maintaining grid stability and reliability. Ageing infrastructure, population growth, and revolutionary technological developments increase the demand for grid modernization and resilience investments. Climate change and natural disasters highlight the need for adaptive load-shedding schemes. The two possible ways to optimize the grid are an ampacity increase or a voltage increase. While increasing voltage provides the most significant rise in rating, it comes with high investment costs. Out of all the options available, dynamic line rating (DLR) is the most efficient and cost-effective solution. This paper provides a comprehensive review of the optimization of the grid transfer capacity using DLR. The review critically examines different line rating methods, the DLR system, factors that need to be considered before DLR implementation, and its advantages and disadvantages. Also, the review presents the real-world applications and case studies, standards and regulations involved, and current approaches and challenges for implementing DLR in Malaysia. Additionally, we highlight the most commonly used standards to calculate the conductor’s ampacity for the steady-state and dynamic state. Moreover, this review work presents how DLR can advance the grid’s flexibility, considering its significance for cleaner energy production in the future, challenges related to wind energy power generation, and their mitigations. This work provides a shortcut path for researchers and utilities to understand DLR and as a reference for future research to advance clean energy in response to changing energy needs and climate conditions.

This paper presents a statistical algorithm for classification of fault causes on power transmission lines. The proposed algorithm is based upon the root mean square (RMS) current duration, voltage dip, and discrete wavelet transform (DWT) measured at the sending end of a line and the decision tree method, a commonly accessible measurable method. Fault duration of RMS current signal, voltage dip, and DWT gives concealed data of a fault signature as a contribution to decision tree calculation which is utilized to classify various fault causes. The proposed method was carried out in the MATLAB/SIMULINK programming platform based upon the information made with the fault analysis of the 275 kV sample transmission line considering wide variations in the operating conditions. The classifier performance of different parameters was also compared in a confusion matrix form to obtain the best classification results of the decision tree.

An auto-restoration tool to minimize the impact of faults is one of the critical requirements in a power distribution system. A fault-monitoring system is needed for practical remote supervision to identify faults and reduce their impacts, and thus reduce economic losses. An effective fault-monitoring system is beneficial to improve the reliability of a protection system when faults evolve. Therefore, fault monitoring could play an important role in enhancing the safety standards of systems. Among the various fault occurrences, the transient fault is a prominent cause in Malaysia power systems but gains less attention due to its ability of self-clearance, although sometimes it unnecessarily triggers the operation of protection systems. However, the transient fault is an issue that must be addressed based on its effect that can lead to outages and short-circuits if prolonged. In this study, the authors summarize the guidelines and related standards of fault interaction associated with a monitoring system. The necessity of transient fault detection and location techniques and their limitations, the need for signal processing, as well as recommended practices, are also discussed in this paper. Some of the practices from local power utility are also shared, indicating the current approaches, key challenges, and the opportunities for improvement of fault-monitoring systems due to transient fault, which can be correlated with the reviews provided.

Introduction: Internet gaming is now a major concern since its overuse has had a detrimental impact on people’s well-being. This study aims to investigate the association between Internet Gaming Disorder and depression, anxiety, and stress, as well as gaming elements during the COVID-19 pandemic, among university students. Methods: The cross-sectional study involved 213 students from two different institutions who were randomly selected. The participants were required to complete three sets of online questionnaires via Google Forms. The online questionnaire consists of the Internet Gaming Disorder Scale-Short Form (IGD9-SF) and the Depression, Anxiety, and Stress Scale (DASS-21). Results: The prevalence rate of IGD among university students during the COVID-19 pandemic was 9.86%. Bivariate analysis revealed biological sex (p-value = 0.011), preferred gaming platforms (p-value = <0.001), game gameplay (p-value = 0.03), history of substance use (p-value = <0.001), and stress (p-value = <0.001) to be associated with IGD. Meanwhile, binary logistic regression demonstrated that males have a higher risk of developing IGD compared with females (adjusted odds ratio (AOR) = 3.426, p-value 0.015, CI = 1.27–9.21). Students who used consoles as their preferred gaming platform were 13 times more likely to develop IGD in comparison to another platform (AOR = 13.031, p-value = 0.010, 95% CI = 1.87–91.02). Extensive gaming duration of more than 4 h a day showed a higher risk of developing IGD (AOR = 8.929, p-value 0.011, CI = 1.659–48.050). High-stress levels significantly increased the risk of IGD (AOR = 13.729, p-value = 0.001, 95% CI = 2.81–67.1). Conclusion: The prevalence of IGD among university students was high during the COVID-19 pandemic. Thus, interventions for reducing stress among university students should be implemented to reduce the risk of IGD.

Machine learning application have been widely used in various sector as part of reducing work load and creating an automated decision making tool. This has gain the interest of power industries and utilities to apply machine learning as part of the operation. Fault identification and classification based machine learning application in power industries have gain significant accreditation due to its great capability and performance. In this paper, a machine-learning algorithm known as Support Vector Machine (SVM) for fault type classification in distribution system has been developed. Eleven different types of faults are generated with respect to actual network. A wide range of simulation condition in terms of different fault impedance value as well as fault types are considered in training and testing data. Right setting parameters are important to learning results and generalization ability of SVM. Gaussian radial basis function (RBF) kernel function has been used for training of SVM to accomplish the most optimized classifier. Initial finding from simulation result indicates that the proposed method is quick in learning and shows good accuracy values on faults type classification in distribution system. The developed algorithm is tested on IEEE 34 bus and IEEE 123 bus test distribution system.