Modernized electrical grid automated to improve the efficiency, reliability, economics, and sustainability of the production and distribution of electricity.
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1st ACM International Conference on Embedded Systems for Energy-Efficient Buildings
co-located with ACM SenSys 2014
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Optimal Power Flow (OPF) is a crucial part of the Energy Management System (EMS) as it determines individual generator outputs that minimize generation cost while satisfying transmission, generation, and system-level operating constraints. OPF relies on a core EMS routine, namely state estimation, which computes system states, principally bus voltages/phase angles at the buses. However, state estimation is vulnerable to false data injection attacks in which an adversary can alter certain measurements to corrupt the estimator's solution without being detected.
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Advanced metering infrastructure (AMI) is a critical part of a modern smart grid that performs the bidirectional data flow of sensitive power information such as smart metering data and control commands. The real-time monitoring and control of the grid are ensured through AMI. While smart meter data helps to improve the overall performance of the grid in terms of efficient energy management, it has also made the AMI an attractive target of cyberattackers with a goal of stealing energy.
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State estimation is very important for securely, reliably, and efficiently maintaining a power grid. If state estimation is not protected, an attacker can compromise meters or communication systems and introduce false measurements, which can evade existing Bad Data Detection (BDD) algorithms and lead to incorrect state estimation. This kind of attack is stealthy and widely known as an Undetected False Data Injection (UFDI) attack.
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Submitted by Anonymous on Mon, 01/28/2019 - 3:02pm
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Submitted by Anonymous on Mon, 01/28/2019 - 2:52pm
The 9th International Conference on Sustainable Energy Information Technology
The 9th International Conference on Sustainable Energy Information Technology (SEIT-2019) aims to bring together academia, industry, research scholars, business entrepreneurs, practitioners, managers, and policy makers responsible in delivering advanced sustainable energy solutions for competitive advantage and cost savings in modern industrial and business sectors.
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If you are a computer scientist/engineer with a keen interest in entrepreneurial opportunities and have solid academic or industry experience in machine learning and signal processing, you may be the ideal candidate for a unique startup-focused opportunity at NYU. We seek a candidate able to take a lead role in a new project being started at NYU's Music and Audio Research Lab (MARL) and funded by the National Science Foundation. The project involves developing and commercializing technology originating from MARL's research for monitoring the health of manufacturing machinery in real time.
