Adaptive Coordination of Damping Controllers for Enhanced Power System Stability

Silva-Saravia, Horacio; Pulgar-Painemal, Héctor; Schoenwald, David A.; Ju, Wenyun

doi:10.1109/oajpe.2020.3008597

Cited by 13 publications

(7 citation statements)

References 38 publications

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“…The TA considers the entire action of the system after a disturbance, and it is used to assess the dynamic response under different system conditions. A large TA reflects large and sustained oscillations after a disturbance [13], [14]. In other terms, an increased system damping would imply a low TA.…”

Section: Background On Controllers Coordinationmentioning

confidence: 99%

“…A model-based approach has been proposed to solve the coordination problem [13], which relies on a linearized model. A concise description of this approach is presented next.…”

Section: Background On Controllers Coordinationmentioning

confidence: 99%

“…The main flaws of these approaches included the employment of functions with a lack of physical interpretation, the requirement for expert intervention to target specific oscillation modes, and the lack of adaptability to handle different faults and operational scenarios. To overcome these challenges, the novel concept of adaptive dynamic coordination of DCs was recently proposed [13]. The coordination problem was redefined as the decision-making process to activate or deactivate several IBR-DCs depending on the system state.…”

Section: Introductionmentioning

confidence: 99%

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Data-driven adaptive dynamic coordination of damping controllers

Zelaya-Arrazabal

Liu

Zhao

et al. 2022

2022 North American Power Symposium (NAPS)

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This work proposes a data-informed approach for an adaptable coordination of damping controllers. The novel concept of coordination is based on minimizing the Total Action, a single metric that measures the system's dynamic response postdisturbance. This is a performance measure based on the physics of the power system, which encapsulates the oscillation energy related to synchronous generators. Deep learning theory is used to propose a Total Action function approximator, which captures the relationship between the system wide-area measurements, the status of damping controllers, and the conditions of the disturbance. By commissioning the switching status (on/off) of damping controllers in real-time, the oscillation energy is reduced, enhancing the power system stability. The concept is tested in the Western North America Power System (wNAPS) and compared with a model-based approach for the coordination of damping controllers. The data-informed coordination outperforms the model-based approach, demonstrating exceptional adaptability and performance to handle multi-modal events. The proposed scheme shows outstanding reductions in lowfrequency oscillations even under various operating conditions, fault locations, and time delay considerations.

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Section: Background On Controllers Coordinationmentioning

confidence: 99%

“…A model-based approach has been proposed to solve the coordination problem [13], which relies on a linearized model. A concise description of this approach is presented next.…”

Section: Background On Controllers Coordinationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data-driven adaptive dynamic coordination of damping controllers

Zelaya-Arrazabal

Liu

Zhao

et al. 2022

2022 North American Power Symposium (NAPS)

Self Cite

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“…Due to the dynamic nature of the power grid, delayed measurements might be an additional source of error for timesensitive applications, like controls, since the data might not reflect the current state of the system when the measurement is processed [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Noise-Immune Machine Learning and Autonomous Grid Control

Obert

Trevizan

Chavez

2023

IEEE Open J. Power Energy

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Most recently, stochastic control methods such as deep reinforcement learning (DRL) have proven to be efficient and quick converging methods in providing localized grid voltage control. Because of the random dynamical characteristics of grid reactive loads and bus voltages, such stochastic control methods are particularly useful in accurately predicting future voltage levels and in minimizing associated cost functions. Although DRL is capable of quickly inferring future voltage levels given specific voltage control actions, it is prone to high variance when the learning rate or discount factors are set for rapid convergence in the presence of bus noise. Evolutionary learning is also capable of minimizing cost function and can be leveraged for localized grid control, but it does not infer future voltage levels given specific control inputs and instead simply selects those control actions that result in the best voltage control. For this reason, evolutionary learning is better suited than DRL for voltage control in noisy grid environments. To illustrate this, using a cyber adversary to inject random noise, we compare the use of evolutionary learning and DRL in autonomous voltage control (AVC) under noisy control conditions and show that it is possible to achieve a high mean voltage control using a genetic algorithm (GA). We show that the GA additionally can provide superior AVC to DRL with comparable computational efficiency. We illustrate that the superior noise immunity properties of evolutionary learning make it a good choice for implementing AVC in noisy environments or in the presence of random cyber-attacks.

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“…The measurements, typically taken 30 times a second, can quickly track system changes undetectable through traditional monitoring systems used in the industry [1]. This makes new energy management applications possible, including model validation [2], dynamic state estimation [3], oscillation monitoring, islanding detection and wide area monitoring and control [4].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Area Angle Monitoring Using Synchrophasors: A Practical Framework and Utility Deployment

Dobson

Martin

et al. 2021

IEEE Trans. Smart Grid

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This paper develops a practical framework of Area Angle Monitoring (AAM) to monitor in real time the stress of bulk power transfer across an area of a power transmission system. Area angle is calculated from synchrophasor measurements in real time to provide alert to system operators if the area angle exceeds pre-defined thresholds. This paper proposes a general method to identify the warning threshold of area angle and a simplified method to quickly update area angle thresholds under significant topology change. A mitigation strategy to relieve the area stress is also proposed. In order to handle the limited coverage of synchrophasor measurements, this paper proposes a method to estimate phase angles for boundary buses without synchrophasor measurements, which extends the application scenario of AAM. AAM is verified for a power transmission area in the Western Electricity Coordinating Council system with both simulated data and synchrophasor measurements recorded from real events. A utility deployment for real-time application of AAM with livestream and recorded synchrophasor data is described.

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Adaptive Coordination of Damping Controllers for Enhanced Power System Stability

Cited by 13 publications

References 38 publications

Data-driven adaptive dynamic coordination of damping controllers

Data-driven adaptive dynamic coordination of damping controllers

Noise-Immune Machine Learning and Autonomous Grid Control

Real-Time Area Angle Monitoring Using Synchrophasors: A Practical Framework and Utility Deployment

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