Identification of Stable and Unstable Power Swings using Pattern Recognition

Bera, Pallav Kumar; Işık, Can

doi:10.1109/greentech48523.2021.00053

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…Maintaining the reliability and security of the power system network requires a basic understanding of oscillations and their dangers, namely, stable and unstable oscillations. Increasing the disturbance severity, which can be either faults, increase or decrease in generation, loss or increase in a large block of load, or changes in network confguration [5], gives rise to the risk of these oscillations leading to so-called outof-step (OOS) [6].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Oscillations during Out-of-Step Condition in Power Systems

Alnassar

Nagarajan

2023

International Transactions on Electrical Energy Systems

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Power systems interconnected by weak tie lines can be subject to low-frequency oscillations because of disturbances which excites the low-frequency modes of the system; furthermore, these oscillations can be stable or unstable. The latter, if not treated, can cause severe oscillations that divide the network into smaller groups which oscillate against each other, leading to out-of-step (OOS) condition in the network. The detection of OOS condition is a challenge for power system operators in real time as it is difficult with conventional measuring instruments, to identify the instant at which the bus voltage angle between two areas connected by tie line falls out of synchronism. Conventionally, the detection of OOS condition has been carried out with impedance-based relays along with power swing blocking. With the advent of synchrophasor-based measurement units, it is now possible to measure the bus voltage angle in real time leading to direct detection of OOS condition in power system and intentional islanding. In this article, a systematic analytical study and EMT time-domain simulation study have been performed to simulate OOS condition in the power systems, and its detection is based on the voltage angle difference with wide-area measurement systems (WAMSs). The article has been carried out on a single machine infinite bus (SMIB) to monitor generator OOS, Kundur’s two-area system to detect interarea OOS, and the IEEE39 bus system to identify OOS condition and with a new algorithm. Time-domain simulation studies carried out with OPAL-RT real-time simulator in HYPERSIM environment corroborates with analytical results.

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Section: Introductionmentioning

confidence: 99%

Analysis of Oscillations during Out-of-Step Condition in Power Systems

Alnassar

Nagarajan

2023

International Transactions on Electrical Energy Systems

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Detection of High Impedance Faults in Microgrids using Machine Learning

Bera

Kumar

Pani

et al. 2022

2022 IEEE Green Energy and Smart System Systems (IGESSC)

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EVScout2.0 : Electric Vehicle Profiling through Charging Profile

Brighente

Conti

Denis

et al. 2024

ACM Trans. Cyber-Phys. Syst.

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Electric Vehicles (EVs) represent a green alternative to traditional fuel-powered vehicles. To enforce their widespread use, both the technical development and the security of users shall be guaranteed. Users’ privacy represents a possible threat that impairs the adoption of EVs. In particular, recent works showed the feasibility of identifying EVs based on the current exchanged during the charging phase. In fact, while the resource negotiation phase runs over secure communication protocols, the signal exchanged during the actual charging contains features peculiar to each EV. In what is commonly known as profiling, a suitable feature extractor can associate such features to each EV. In this paper, we propose EVScout2.0 , an extended and improved version of our previously proposed framework to profile EVs based on their charging behavior. By exploiting the current and pilot signals exchanged during the charging phase, our scheme can extract features peculiar for each EV, hence allowing their profiling. We implemented and tested EVScout2.0 over a set of real-world measurements considering over 7500 charging sessions from a total of 137 EVs. In particular, numerical results show the superiority of EVScout2.0 with respect to the previous version. EVScout2.0 can profile EVs, attaining a maximum of 0.88 for both recall and precision scores in the case of a balanced dataset. To the best of the authors’ knowledge, these results set a new benchmark for upcoming privacy research for large datasets of EVs.

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Identification of Stable and Unstable Power Swings using Pattern Recognition

Cited by 3 publications

References 18 publications

Analysis of Oscillations during Out-of-Step Condition in Power Systems

Analysis of Oscillations during Out-of-Step Condition in Power Systems

Detection of High Impedance Faults in Microgrids using Machine Learning

EVScout2.0 : Electric Vehicle Profiling through Charging Profile

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