January – Smart Grid Sensor Integration and AI-Edge Data Analytics Applications for Smart Grids and Smart Cities

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Written by Stephen Ugwuanyi, Joseph Melone, Kinan Ghanem, Nishal Ramadas, and Sivapriya M. Bhagavathy

Integrating hydrogen and other unconventional gasses in emerging energy networks will play a major role in the transition to a net-zero energy system. This will also bring significant technical challenges that require an efficient, reliable, and low-cost solution to measure and classify the flow of complex gas mixtures. The inaccuracy of measurement and detection of leakage during production, storage, transportation and refuelling operations can result in serious economic implications and safety risks. These challenges are not limited to resolving the uncertainty in quality control but also encompass efficient flow metering, quality assurance, eliminating measurement errors and integration of the sensor technologies. In this article, we will discuss the challenges of integrating gas measurement and detection technologies and identify potential opportunities for innovation.

Written by Swetha Shekarappa G, Sheila Mahapatra, M. Senbagavalli, and Saurav Raj

Strong interest in the creation of low-power wireless sensors has increased as a result of the Internet of Things' (IoT) fast growth. In today's IoT systems, wireless sensors are incorporated to collect data in a dependable and useful way to monitor processes and manage mechanisms in sectors like mass transit, electricity, civil infrastructure, smart buildings, environment monitoring, universal health care, defence, production, and fabrication.

Written by Sagnik Basumallik, Anurag Srivastava, Arman Ahmed and Yinghui Wu

Motivation for Synchrophasor Data Analytics and Impact: Uncertainty of renewables, penetration of inverter-based resources, and varying load dynamics have increased the operational complexity of the grid. Monitoring the power system dynamics under such highly complex scenarios is essential, and Phasor Measurement Units (PMU) provide synchronized high-resolution measurements. PMUs enable coordinated system-wide situational awareness and faster post-event analysis by providing real-time Global Position System (GPS) time-stamped voltage and current phasor data [1]. Analyzing PMUs data comes with multiple challenges, such as data drift, data anomalies, and missing data. For data quality, efficient approaches are needed to detect, process, and remove such inconsistencies from the PMU measurements before being provided to critical PMU data analytics applications.

Written by Wei Zhang, Andrew Keong Ng, and B. Sivaneasan

Many real deployments of smart city systems do not have the luxury of unlimited resources. Energy supply is often among the critical system constraints that either set an upper bound of the system’s usability or force the system to embark on new designs and upgrades. Consider a typical scenario that data is collected by sensors with limited resources and pending data transmission from sensors to a much more resourceful host, e.g., server or the cloud, for further processing and analysis.

Past Issues

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IEEE Smart Grid Bulletin Compendium

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