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Summary
With the massive integration of distributed renewable energy sources (RESs) into the power system, the demand for timely and reliable network monitoring, control, and fault analysis is rapidly increasing. Following successful deployment of Phasor Measurement Units (PMUs) in transmission systems for power monitoring, a new opportunity to utilize PMU measurement data for power quality assessment in distribution grid systems is emerging. The main objective is to develop a generic and interoperable wireless sensor network infrastructure that can provide access to a large number of sensors and actuators each having different traffic characteristics. This project aims at developing a new sensor network with the objective of promoting wireless based 'Internet of Things' (IoT), or simply 'Machine to Machine' (M2M) communications.
Description
In conventional transmission systems, wired sensor technologies are largely used in power plants and substations to control and monitor critical parameters. The distribution system does not normally have the support of any infrastructure network. Despite the fact that wired technologies offer a good level of reliability and security, they are very costly. Wireless communication networks would be the most cost effective solution. Such networks must be able to carry large data measured by sensors and actuators. While some are very short and sporadic in nature with a very low duty cycle, the main traffic consists of PMU data which is in real-time and has low latency requirements. In addition, a wireless network should be capable of handling heterogeneous traffic patterns with various Quality of Service (QoS) requirements, such as bandwidth and delay. Therefore, the main objective in this investigation has been to develop a Machine-to-Machine (M2M) communication network that can support wide ranging sensory data, including high rate synchrophasor data for real-time communication. In particular, we evaluate the suitability of the emerging IEEE 802.11ah standard by exploiting its important features, such as classifying the power grid sensory data into different categories according to their traffic characteristics. For performance evaluation we use our hardware in the loop combined grid communication network testbed to access the performance of the network.
Major Accomplishments
The outcome of this research has been published in the PROCEEDINGS OF THE IEEE and the IEEE Network Magazine.