A Reinforcement Learning based Power System Stabilizer for a Grid Connected Wind Energy Conversion System

Kosuru, Rahul; Chen, Pengcheng; Liu, Shichao

doi:10.1109/epec48502.2020.9320127

Cited by 6 publications

(6 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this research, the TD3 method and QL method were implemented by replacing the existing PI controller and PSS. This paper is an extension of [40], where a Q-learning algorithm was implemented on the rotor-side converter for a small range of changes in wind speeds. The major contributions of the current work are discussed in Section 3.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In state 1, the action chosen can be any action and is not necessarily action 1. The Q-table, which is updated as per Algorithm 1, will choose either the action with max Q or a random Q, which is updated as per Equation (40). Even though any action can be chosen, the trained agent will use the Q values from the trained model.…”

Section: Q-learning Algorithm On Rscmentioning

confidence: 99%

See 1 more Smart Citation

Deep Reinforcement Learning for Stability Enhancement of a Variable Wind Speed DFIG System

2022

Self Cite

View full text Add to dashboard Cite

Low-frequency oscillations are a primary issue for integrating a renewable source into the grid. The objective of this study was to find sensitive parameters that cause low-frequency oscillations and design a Twin Delayed Deep Deterministic Policy Gradient (TD3) agent controller to damp the oscillations without requiring an accurate system model. In this work, a Q-learning (QL)-based model-free wind speed DFIG was designed on the rotor-side converter (RSC), and a QL-based model-free DC-link voltage regulator was designed on the grid-side converter (GSC) to enhance the stability of the system. In the next step, the TD3 agent was trained to learn the system dynamics by replacing the inner current controllers of the RSC, which replaced the QL-based model. In the first stage, the conventional PSS and Proportional–Integral (PI) controllers were introduced to both the RSC and GSC. Then, the system was trained to become model-free by replacing the PSS and the PI controller with a QL algorithm under very small wind speed variations. In the second stage, the QL algorithm was replaced with the TD3 agent by introducing large variations in wind speed. The results reveal that the TD3 agent can sustain the stability of the DFIG system under large variations in wind speed without assuming a detailed control structure beforehand, while QL-based controllers can stabilize the doubly fed induction generator (DFIG)-equipped wind energy conversion system (WECS) under small variations in wind speed.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Q-learning Algorithm On Rscmentioning

confidence: 99%

Deep Reinforcement Learning for Stability Enhancement of a Variable Wind Speed DFIG System

2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Lower-frequency oscillations triggered by grid-integrated winddriven turbines intimidate the steadiness of the complete electrical power structure. Hence, a strengthening-based electrical power system stabilizer can rapidly regulate the control variables online and dampen the lower-frequency oscillation under a time-dependent wind speed setup [43].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, it accrues knowledge gradually and acquires a detailed strategy that makes the most of specifically defined objectives. Ideally, RL is a Markov decision procedure [43,242,243].…”

mentioning

confidence: 99%

Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review

Behara

Saha

2022

Energies

View full text Add to dashboard Cite

Wind-driven turbines utilizing the doubly-fed induction generators aligned with the progressed IEC 61400 series standards have engrossed specific consideration as of their benefits, such as adjustable speed, consistent frequency mode of operation, self-governing competencies for voltage and frequency control, active and reactive power controls, and maximum power point tracking approach at the place of shared connection. Such resource combinations into the existing smart grid system cause open-ended problems regarding the security and reliability of power system dynamics, which needs attention. There is a prospect of advancing the art of wind turbine-operated doubly-fed induction generator control systems. This section assesses the smart grid-integrated power system dynamics, characteristics, and causes of instabilities. These instabilities are unclear in the wind and nonlinear load predictions, leading to a provisional load-rejection response. Here, machine learning computations and transfer functions measure physical inertia and control system design’s association with power, voltage, and frequency response. The finding of the review in the paper indicates that artificial intelligence-based machine and deep learning predictive diagnosis fields have gained prominence because of their low cost, less infrastructure, reduced diagnostic time, and high level of accuracy. The machine and deep learning methodologies studied in this paper can be utilized and extended to the smart grid-integrated power context to create a framework for developing practical and accurate diagnostic tools to enhance the power system’s accuracy and stability, software requirements, and deployment strategies.

show abstract

“…In [72], the authors propose an EPS stabilizer structure based on the DL algorithm. The proposed stabilizer is designed to provide SSS in the presence of wind generation in the EPS.…”

mentioning

confidence: 99%

Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review

Senyuk,

Beryozkina,

Safaraliev

et al. 2024

Energies

View full text Add to dashboard Cite

Modern electrical power systems are characterized by a high rate of transient processes, the use of digital monitoring and control systems, and the accumulation of a large amount of technological information. The active integration of renewable energy sources contributes to reducing the inertia of power systems and changing the nature of transient processes. As a result, the effectiveness of emergency control systems decreases. Traditional emergency control systems operate based on the numerical analysis of power system dynamic models. This allows for finding the optimal set of preventive commands (solutions) in the form of disconnections of generating units, consumers, transmission lines, and other primary grid equipment. Thus, the steady-state or transient stability of a power system is provided. After the active integration of renewable sources into power systems, traditional emergency control algorithms became ineffective due to the time delay in finding the optimal set of control actions. Currently, machine learning algorithms are being developed that provide high performance and adaptability. This paper contains a meta-analysis of modern emergency control algorithms for power systems based on machine learning and synchronized phasor measurement data. It describes algorithms for determining disturbances in the power system, selecting control actions to maintain transient and steady-state stability, stability in voltage level, and limiting frequency. This study examines 53 studies piled on the development of a methodology for analyzing the stability of power systems based on ML algorithms. The analysis of the research is carried out in terms of accuracy, computational latency, and data used in training and testing. The most frequently used textual mathematical models of power systems are determined, and the most suitable ML algorithms for use in the operational control circuit of power systems in real time are determined. This paper also provides an analysis of the advantages and disadvantages of existing algorithms, as well as identifies areas for further research.

show abstract

A Reinforcement Learning based Power System Stabilizer for a Grid Connected Wind Energy Conversion System

Cited by 6 publications

References 18 publications

Deep Reinforcement Learning for Stability Enhancement of a Variable Wind Speed DFIG System

Deep Reinforcement Learning for Stability Enhancement of a Variable Wind Speed DFIG System

Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review

Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review

Contact Info

Product

Resources

About