Decentralized Voltage Stability Monitoring and Control With Distributed Computing Coordination

Lee, Hyojong; Srivastava, Anurag K.; Krishnan, V. V. G.; Niddodi, Shwetha; Bakken, David E.

doi:10.1109/jsyst.2021.3057614

Cited by 5 publications

(5 citation statements)

References 33 publications

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“…In a complementary effort [17], the effects of the generator var restrictions are considered when obtaining Thevenin equivalent parameters. Reference [18] proposes a Thevenin-parameter-based index to trigger the required remedial action with respect to the voltage status. Obtaining the equivalent Thevenin impedance is challenging and has become even more critical for newly emerging flexible loads.…”

Section: ) Data-driven Methodsmentioning

confidence: 99%

An Adaptive Data-driven Method Based on Fuzzy Logic for Determining Power System Voltage Status

Shazdeh,

Golp顁a,

Bevrani

2024

Journal of Modern Power Systems and Clean Energy

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This paper proposes an adaptive method based on fuzzy logic that utilizes data from phasor measurement units (PMUs) to assess and classify generating-side voltage trajectories. The voltage variable and its associated derivatives are used as the input variables of a fuzzy-logic block. In addition, the voltage trajectory is compared with the pre-selected pilot-bus voltage to make a reliable decision about the voltage operational state. Different types of short-term voltage dynamics are considered in the proposed method. The fuzzy membership functions are determined using a systematic method that considers the current situation of the voltage trajectory. Finally, the voltage status is categorized into four classes to determine appropriate remedial actions. The proposed method is validated on a IEEE 73-bus power system in a MATLAB environment.

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Section: ) Data-driven Methodsmentioning

confidence: 99%

An Adaptive Data-driven Method Based on Fuzzy Logic for Determining Power System Voltage Status

Shazdeh,

Golp顁a,

Bevrani

2024

Journal of Modern Power Systems and Clean Energy

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“…Furthermore, FL can be tailored to reflect the specific behavior of local PSSA models. Additionally, Distributed computing architecture allows reacting swiftly to local instabilities leading to enhanced scalability, reliability, and efficient computations for DRL in PSSA [134]. However, network communication and information security challenges are still major concerns.…”

Section: B Future Research Directionsmentioning

confidence: 99%

“…However, network communication and information security challenges are still major concerns. For instance, a decentralized voltage stability approach has been proposed in [134]. This approach uses a distributed coordination platform to leverage large amounts of real-time data as a secondary emergency control mechanism.…”

Section: B Future Research Directionsmentioning

confidence: 99%

Navigating the Landscape of Deep Reinforcement Learning for Power System Stability Control: A Review

Massaoudi,

Abu-Rub,

Ghrayeb

2023

IEEE Access

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The widespread penetration of inverter-based resources has profoundly impacted the electrical stability of power systems (PSs). Deepening grid integration of photovoltaic and wind systems is introducing unforeseen uncertainties for the electricity sector. As a cutting-edge machine learning technology, deep reinforcement learning (DRL) breakthroughs have been in the spotlight over the last few years with potential contributions to PS stability (PSS). The ubiquitous DRL architecture, by learning from the dynamism inherent in PSs, produces near-optimal actions for PSS. This article provides a rigorous review of the latest research efforts focused on DRL to derive PSS policies while accounting for the unique properties of power grids. Furthermore, this paper highlights the theoretical advantages and the key tradeoffs of the emerging DRL techniques as powerful tools for optimal power flow. For all methods outlined, a discussion on their bottlenecks, research challenges, and potential opportunities in large-scale PSS is also presented. This review aims to support research in this area of DRL algorithms to embrace PSS against unseen faults and different PS topologies.INDEX TERMS Deep reinforcement learning, dynamic security control, electric power systems, power system stability, smart grids. Nomenclature

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“…The number of Distributed Generators equipped with Battery Energy Storage Systems (DERs) connected to Active Distribution Networks (ADNs) by a Voltage Source Converter (VSC) is rapidly increasing to meet the carbon-free target [1]. However, the randomness of the primary source (wind and sun), the undesired interactions among the DERs and the unpredictability of the load demand make the voltage control imperative in the existing distribution networks to avoid larger voltage variations and voltage instability [2][3][4][5][6][7][8][9][10][11]. Failing to mitigate over-voltage can also result in VSC tripping, leading to the uncontrolled disconnection of the affected DER unit [12].…”

Section: Introductionmentioning

confidence: 99%

Applying the Integral Controllability Property in a Multi-Loop Control for Stable Voltage Regulation in an Active Distribution Network

Fusco,

Russo

2024

Energies

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Distributed Energies Resources (DERs) can be controlled for supporting the voltage regulation at nodes of an Active Distribution Network (ADN) where they are connected. However, since the ADN is a Multi-Input Multi-Output (MIMO) system with coupled dynamics, the controller of a DER mutually interacts with all other controllers through the distribution lines. These interactions lead to operating conflicts which may drive the ADN to work close to its voltage stability boundaries. To achieve a stable voltage regulation without new investment in the existing ADNs, the present paper proposes a straightforward decentralized design of the multi-loop controllers based on the property of integral controllability. The main feature of the method is that the design problem can be expressed by a single parameter designed both for reducing the effects of the undesired coupling and for increasing the degree of robust stability in the presence of parameter uncertainty in the matrix plant. Simulation studies are developed to illustrate the design result and the performance achieved under different operating conditions. The performance is also compared with the one obtained by another method in terms of the integral absolute error.

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Decentralized Voltage Stability Monitoring and Control With Distributed Computing Coordination

Cited by 5 publications

References 33 publications

An Adaptive Data-driven Method Based on Fuzzy Logic for Determining Power System Voltage Status

An Adaptive Data-driven Method Based on Fuzzy Logic for Determining Power System Voltage Status

Navigating the Landscape of Deep Reinforcement Learning for Power System Stability Control: A Review

Applying the Integral Controllability Property in a Multi-Loop Control for Stable Voltage Regulation in an Active Distribution Network

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