Impact of Inertial Response for the Variable Speed Wind Turbine

Hassan, A K Abdul; Rehman, Ateeq Ur; Shabbir, Noman; Hassan, Syed Rizwan; Sadiq, Muhammad Tariq; Arshad, Jehangir

doi:10.1109/ceet1.2019.8711826

Cited by 15 publications

(7 citation statements)

References 9 publications

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“…We can find an actor-critic framework based on the DDPG algorithm in [8], which facilitates tracking stability by adapting to uncertainty and interference. In [9][10][11], the authors have developed a DDPG algorithm to improve accuracy that performs better than PID controllers. Moreover, authors in [12] have proposed a push-pull converter and a fuzzy logic-based maximum power point tracking (MPPT) algorithm to ensure that the total amount of incident energy is extracted.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Deterministic Policy Gradient to Regulate Feedback Control Systems Using Reinforcement Learning

Al‐Bawri¹,

Islam²,

Singh³

et al. 2022

Computers, Materials &Amp; Continua

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Controlling feedback control systems in continuous action spaces has always been a challenging problem. Nevertheless, reinforcement learning is mainly an area of artificial intelligence (AI) because it has been used in process control for more than a decade. However, the existing algorithms are unable to provide satisfactory results. Therefore, this research uses a reinforcement learning (RL) algorithm to manage the control system. We propose an adaptive speed control of the motor system based on depth deterministic strategy gradient (DDPG). The actor-critic scenario using DDPG is implemented to build the RL agent. In addition, a framework has been created for traditional feedback control systems to make RL implementation easier for control systems. The RL algorithms are robust and proficient in using trial and error to search for the best strategy. Our proposed algorithm is a deep deterministic policy gradient, in which a large amount of training data trains the agent. Once the system is trained, the agent can automatically adjust the control parameters. The algorithm has been developed using Python 3.6 and the simulation results are evaluated in the MATLAB/Simulink environment. The performance of the proposed RL algorithm is compared with a proportional integral derivative (PID) controller and a linear quadratic regulator (LQR) controller. The simulation results of the proposed scheme are promising for the feedback control problems.

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Section: Literature Reviewmentioning

confidence: 99%

Deep Deterministic Policy Gradient to Regulate Feedback Control Systems Using Reinforcement Learning

Al‐Bawri¹,

Islam²,

Singh³

et al. 2022

Computers, Materials &Amp; Continua

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“…The traditional grid was developed around the 20th century [8,12]. A conventional grid is shown in Fig.…”

Section: Conventional Grid and Smart Gridmentioning

confidence: 99%

Load Sharing of Transformers for Intelligent Electric Power Management

Khan¹,

Rehman²,

Asif³

et al. 2019

Journal of Applied and Emerging Sciences

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Power management has been one of the focused areas of research from the past few decades. Power blackout is the main problem nowadays and it occurs mainly due to outdated infrastructure used for electricity in industries. In a traditional grid, the consumer load information is obtained manually which is a time-consuming and expensive process. In this paper, a prototype is developed for the real-time off-site data monitoring and control of the consumer loads in the distribution network of the power grid. The designed prototype avoids the tripping of loads by the use of load sharing.

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“…Therefore, the uses of Renewable Energy Resources (RES) like solar and wind have gained popularity due to lower carbon emissions. However, wind energy generation is variable and unstable due to variations in wind speed [1,2]. The variable nature of wind depends on geographical area, weather, time of day, and season.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Wind Energy Forecasting Using Deep Learning-Based Predictive Analytics

Shabbir¹,

K黷t²,

Jawad³

et al. 2022

Computers, Materials &Amp; Continua

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Wind energy is featured by instability due to a number of factors, such as weather, season, time of the day, climatic area and so on. Furthermore, instability in the generation of wind energy brings new challenges to electric power grids, such as reliability, flexibility, and power quality. This transition requires a plethora of advanced techniques for accurate forecasting of wind energy. In this context, wind energy forecasting is closely tied to machine learning (ML) and deep learning (DL) as emerging technologies to create an intelligent energy management paradigm. This article attempts to address the short-term wind energy forecasting problem in Estonia using a historical wind energy generation data set. Moreover, we taxonomically delve into the state-of-the-art ML and DL algorithms for wind energy forecasting and implement different trending ML and DL algorithms for the day-ahead forecast. For the selection of model parameters, a detailed exploratory data analysis is conducted. All models are trained on a real-time Estonian wind energy generation dataset for the first time with a frequency of 1 h. The main objective of the study is to foster an efficient forecasting technique for Estonia. The comparative analysis of the results indicates that Support Vector Machine (SVM), Non-linear Autoregressive Neural Networks (NAR), and Recurrent Neural Network-Long-Term Short-Term Memory (RNN-LSTM) are respectively 10%, 25%, and 32% more efficient compared to TSO's forecasting algorithm. Therefore, RNN-LSTM is the best-suited and computationally effective DL method for wind energy forecasting in Estonia and will serve as a futuristic solution.

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Impact of Inertial Response for the Variable Speed Wind Turbine

Cited by 15 publications

References 9 publications

Deep Deterministic Policy Gradient to Regulate Feedback Control Systems Using Reinforcement Learning

Deep Deterministic Policy Gradient to Regulate Feedback Control Systems Using Reinforcement Learning

Load Sharing of Transformers for Intelligent Electric Power Management

Short-Term Wind Energy Forecasting Using Deep Learning-Based Predictive Analytics

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