A Neural Network Approach to Design Power System Stabilizer for Damping Power Oscillations

Sarkar, Devesh Umesh; Prakash, Tapan

doi:10.1109/npsc57038.2022.10070020

2022 22nd National Power Systems Conference (NPSC) 2022

DOI: 10.1109/npsc57038.2022.10070020

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A Neural Network Approach to Design Power System Stabilizer for Damping Power Oscillations

Devesh Umesh Sarkar

Tapan Prakash

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Cited by 5 publications

(2 citation statements)

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“…introduced into the system as a supplementary controller, thereby enhancing the stability of modern power system networks. 7,8 Conventional power system stabilizer (CPSS) have been used successfully for many years, with certain limitations. CPSS is generally designed based on simplified mathematical models and relies on linear control techniques.…”

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confidence: 99%

“…10,11 Furthermore, phase compensation techniques play a vital role in modern PSSs by enabling more precise and effective damping of ELFOs, leading to enhanced stability and improved overall performance of the power system networks. 7,12 In CPSS, phase compensation is typically achieved using fixed lead-lag filters. Phase compensation techniques involve adjusting the phase response of the stabilizer control signal to achieve effective damping.…”

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confidence: 99%

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LSTM based deep neural network model of power system stabilizer for power oscillation damping in multimachine system

Sarkar

Prakash

2023

Int J Numerical Modelling

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Power system oscillation is an unavoidable threat to the stability of an interconnected modern power system. The reliable operation of a modern power system is widely related to the dampening of electromechanical low‐frequency oscillations (ELFOs). These ELFOs must be dampened appropriately to maintain the stability and reliability of the system. However, it is relatively difficult to resolve the problem of ELFOs completely with traditional power system stabilizers (PSSs). Consequently, research should be directed towards the development of efficient damping controllers, or PSSs, for power oscillation damping. Motivated from the aforementioned fact, this article presents the design of a proportional integral derivative power system stabilizer (PID‐PSS) via a long short‐term memory neural network (LSTM) based deep neural approach for damping power system oscillations in interconnected power systems. LSTM is used to train the parameters of PID‐PSS. To evaluate the performance of the proposed LSTM based PID‐PSS, diverse test cases under different operating conditions are examined. Further, the performance of the proposed LSTM based PID‐PSS is compared with traditional PSSs through time‐domain simulations. The test cases reveal the desired efficiency achieved by the proposed LSTM based PID‐PSS under diverse loading conditions.

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confidence: 99%

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confidence: 99%

LSTM based deep neural network model of power system stabilizer for power oscillation damping in multimachine system

Sarkar

Prakash

2023

Int J Numerical Modelling

Self Cite

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Bidirectional long‐short‐term memory‐based fractional power system stabilizer: Design, simulation, and real‐time validation

Jha,

Ray,

Sarkar

et al. 2024

Int J Numerical Modelling

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Power oscillations in modern power grids are inherent phenomena that may threaten system reliability. Therefore, to ensure acceptable system reliability, effective damping of power oscillations is inevitably required. In this context, this article introduces a novel approach to designing fractional power system stabilizer (FPSS) for effective damping of power oscillations. Bidirectional long‐short‐term memory (Bi‐LSTM) approach is adopted to predict the parameters of FPSS. The conventional phase compensation technique is used to train Bi‐LSTM network. To validate the efficacy of FPSS, different test scenarios of contingent operating conditions are simulated for the system. Comparative analysis is carried out with conventional power system stabilizers (PSSs) and optimization‐based PSS techniques. Additionally, a test scenario is performed against existing deep neural network‐based PSS methods to ascertain the robustness of the proposed PSS. Furthermore, the performance of the proposed Bi‐LSTM‐based FPSS is validated in real‐time simulation using an interfaced OPAL‐RT OP5700 hardware device.

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A convolutional neural network framework to design power system stabilizer for damping oscillations in multi-machine power system

Sarkar,

Prakash

2023

Neural Comput & Applic

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A Neural Network Approach to Design Power System Stabilizer for Damping Power Oscillations

Cited by 5 publications

References 21 publications

LSTM based deep neural network model of power system stabilizer for power oscillation damping in multimachine system

LSTM based deep neural network model of power system stabilizer for power oscillation damping in multimachine system

Bidirectional long‐short‐term memory‐based fractional power system stabilizer: Design, simulation, and real‐time validation

A convolutional neural network framework to design power system stabilizer for damping oscillations in multi-machine power system

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