A smart grid provides a bidirectional flow of electricity and information whilst ensuring wellbalanced electricity supply and demand. The key enabler for the smart grid is its robust communication infrastructure. Choosing the best communication technology for the smart grid is crucial as it involves a mixture of critical and non-critical traffic. This study provides a comprehensive review on smart grid communication and its possible solutions for a reliable two-way communication toward supporting diversified power grid applications. Existing networking methods along with their advantages and weaknesses are highlighted for future research directions. The communication network architecture in the smart grid, with details on each networking technology, switching methods and medium for data communication, is critically reviewed to identify the existing research gaps. A discussion on issues and challenges encountered in smart grid communication for current implementation is highlighted together with the recommendations for further improvement. Overall, the highlighted issues and recommendations from this study are useful to researchers, technology providers and industries to develop new communication technologies for the smart grid that will provide reliable, robust, and suitable two-way communication in the future.
In the future, as populations grow and more end-user applications become available, the current traditional electrical distribution substation will not be able to fully accommodate new applications that may arise. Consequently, there will be numerous difficulties, including network congestion, latency, jitter, and, in the worst-case scenario, network failure, among other things. Thus, the purpose of this study is to assist decision makers in selecting the most appropriate communication technologies for an electrical distribution substation through an examination of the criteria’s in-fluence on the selection process. In this study, nine technical criteria were selected and processed using machine learning (ML) software, RapidMiner, to find the most optimal technical criteria. Several ML techniques were studied, and Naïve Bayes was chosen, as it showed the highest performance among the rest. From this study, the criteria were ranked in order of importance from most important to least important based on the average value obtained from the output. Seven technical criteria were identified as being important and should be evaluated in order to determine the most appropriate communication technology solution for electrical distribution substation as a result of this study.
Resource optimisation is critical because 5G is intended to be a major enabler and a leading infrastructure provider in the information and communication technology sector by supporting a wide range of upcoming services with varying requirements. Therefore, system improvisation techniques, such as machine learning (ML) and deep learning, must be applied to make the model customisable. Moreover, improvisation allows the prediction system to generate the most accurate outcomes and valuable insights from data whilst enabling effective decisions. In this study, we first provide a literature study on the applications of ML and a summary of the hyperparameters influencing the prediction capabilities of the ML models for the communication system. We demonstrate the behaviour of four ML models: k nearest neighbour, classification and regression trees, random forest and support vector machine. Then, we observe and elaborate on the suitable hyperparameter values for each model based on the accuracy in prediction performance. Based on our observation, the optimal hyperparameter setting for ML models is essential because it directly impacts the model’s performance. Therefore, understanding how the ML models are expected to respond to the system utilised is critical.
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