Abstract
In recent years, a new generation of power grid system, referred to as the Smart Grid, with an aim of managing electricity demand in a sustainable, reliable, and economical manner has emerged. With greater knowledge of operational characteristics of individual appliances, necessary automation control strategies can be developed in the Smart Grid to operate appliances in an efficient manner. This paper provides a way of classifying different operational cycles of a household appliance by introducing an unsupervised learning algorithm called k-means clustering. An intrinsic method known as silhouette coefficient was used to measure the classification quality. An identification process is also discussed in this paper to help users identify the operation mode each types of operation cycle stands for. A case study using a typical household refrigerator is presented to validate the proposed method. Results show that the proposed the classification and identification method can partition and identify different operation cycles adequately. Classification of operation cycles for such appliances is beneficial for Smart Grid as it provides a clear and convincing understanding of the operation modes for effective power management.
Highlights
For a century, utility companies have used conventional power grid system to provide electricity.Lack of techniques to automatically monitor the grid system and transfer useful data has forced utility companies to send workers out to gather data needed for optimal management of power grid systems.For example, these utility workers perform multiple functions on-site including reading meters, looking for faulty equipment, and measuring voltage
To link each cluster to the specific operation mode, an identification process considering cluster characteristics related to operation mode is provided
To validate the proposed method, a case study was conducted by testing it on a typical household refrigerator
Summary
Lack of techniques to automatically monitor the grid system and transfer useful data has forced utility companies to send workers out to gather data needed for optimal management of power grid systems. These utility workers perform multiple functions on-site including reading meters, looking for faulty equipment, and measuring voltage. In such a conventional and, potentially outdated power grid system, there is little chance to optimally manage supply and demand effectively.
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