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Advances in AI Methods for Wind Power Forecasting and Monitoring

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A3: Wind, Wave and Tidal Energy".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2893

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Special Issue Editors

Dr. Fouzi Harrou

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Guest Editor

1. Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
2. King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
Interests: fault detection and diagnosis; deep learning and machine learning; wind and solar power forecasting; renewable energy systems
Special Issues, Collections and Topics in MDPI journals

Dr. Ying Sun

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Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
Interests: environmental statistics, in particular in the areas of spatiotemporal statistics; functional data analysis; visualization; computational statistics, with an exceptionally broad array of applications

Dr. Muddu Madakyaru

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Guest Editor

Department of Chemical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
Interests: System Identification; ault detection and diagnosis and model predictive control

Dr. Ramakrishna Kini

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Guest Editor

Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
Interests: fault detection and power electronics; automotive control systems; diagnosis

Special Issue Information

Dear Colleagues,

This Special Issue aims to combine the latest research on AI methods for forecasting and monitoring wind power. Wind power has become an essential part of the global energy mix, and accurate forecasting and monitoring of wind power production are crucial for efficient energy management and grid stability. However, wind power forecasting and monitoring are challenging due to the high dimensional and intermittent nature of wind.

Artificial intelligence (AI) has shown significant promise in addressing these challenges, and recent research has explored various AI-based methods for wind power forecasting and monitoring. These methods include machine learning algorithms, deep learning techniques, fuzzy logic, and evolutionary computing. AI-based approaches can improve wind power forecasting and monitoring accuracy and reliability and provide valuable insights for energy management and decision-making.

This Special Issue aims to showcase the latest research in AI methods for wind power forecasting and monitoring. We invite researchers and practitioners to submit original research articles, reviews, and case studies that address the following topics:

(1) AI-based methods for wind power forecasting,

(2) AI-based methods for wind power monitoring,

(3) Integration of AI with traditional forecasting and monitoring methods,

(4) Machine learning algorithms for anomaly detection/identification in wind turbines,

(5) Applications of AI in wind power management and decision-making,

(6) AI-based control scheme for wind turbines,

(7) Challenges and future directions of AI in wind power forecasting and monitoring.

This Special Issue will provide a platform for researchers to share their findings and insights on AI methods for wind power control, forecasting and monitoring. It will also facilitate collaboration and knowledge-sharing between researchers and practitioners in the field, and contribute to developing more accurate and reliable wind power forecasting and monitoring methods.

Dr. Fouzi Harrou
Dr. Ying Sun
Dr. Muddu Madakyaru
Dr. Ramakrishna Kini
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

25 pages, 1107 KiB

Open AccessArticle

Short-Term Wind Power Prediction Based on Multi-Feature Domain Learning

by Yanan Xue, Jinliang Yin and Xinhao Hou

Energies 2024, 17(13), 3313; https://doi.org/10.3390/en17133313 - 5 Jul 2024

Viewed by 477

Abstract

Wind energy, as a key link in renewable energy, has seen its penetration in the power grid increase in recent years. In this context, accurate and reliable short-term wind power prediction is particularly important for the real-time scheduling and operation of power systems. However, many deep learning-based methods rely on the relationship between wind speed and wind power to build a prediction model. These methods tend to consider only the temporal features and ignore the spatial and frequency domain features of the wind power variables, resulting in poor prediction accuracy. In addition to this, existing power forecasts for wind farms are often based on the wind farm level, without considering the impact of individual turbines on the wind power forecast. Therefore, this paper proposes a wind power prediction model based on multi-feature domain learning (MFDnet). Firstly, the model captures the similarity between turbines using the latitude, longitude and wind speed of the turbines, and constructs a turbine group with similar features as input based on the nearest neighbor algorithm. On this basis, the Seq2Seq framework is utilized to achieve weighted fusion with temporal and spatial features in multi-feature domains through high-frequency feature extraction by DWT. Finally, the validity of the model is verified with data from a wind farm in the U.S. The results show that the overall performance of the model outperforms other wind farm power prediction algorithms, and reduces MAE by 25.5% and RMSE by 20.6% compared to the baseline persistence model in predicting the next hour of wind power. Full article

(This article belongs to the Special Issue Advances in AI Methods for Wind Power Forecasting and Monitoring)

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15 pages, 2228 KiB

Open AccessArticle

Wind Power Prediction Based on EMD-KPCA-BiLSTM-ATT Model

by Zhiyan Zhang, Aobo Deng, Zhiwen Wang, Jianyong Li, Hailiang Zhao and Xiaoliang Yang

Energies 2024, 17(11), 2568; https://doi.org/10.3390/en17112568 - 26 May 2024

Viewed by 475

Abstract

In order to improve wind power utilization efficiency and reduce wind power prediction errors, a combined prediction model of EMD-KPCA-BilSTM-ATT is proposed, which includes a data processing method combining empirical mode decomposition (EMD) and kernel principal component analysis (KPCA), and a prediction model combining bidirectional long short-term memory (BiLSTM) and an attention mechanism (ATT). Firstly, the influencing factors of wind power are analyzed. The quartile method is used to identify and eliminate the original abnormal data of wind power, and the linear interpolation method is used to replace the abnormal data. Secondly, EMD is used to decompose the preprocessed wind power data into Intrinsic Mode Function (IMF) components and residual components, revealing the changes in data signals at different time scales. Subsequently, KPCA is employed to screen the key components as the input of the BiLSTM-ATT prediction model. Finally, a prediction is made taking an actual wind farm in Anhui Province as an example, and the results show that the EMD-KPCAM-BiLSTM-ATT combined model has higher prediction accuracy compared to the comparative model. Full article

(This article belongs to the Special Issue Advances in AI Methods for Wind Power Forecasting and Monitoring)

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25 pages, 1844 KiB

Open AccessFeature PaperArticle

Enhancing Wind Turbine Performance: Statistical Detection of Sensor Faults Based on Improved Dynamic Independent Component Analysis

by K. Ramakrishna Kini, Fouzi Harrou, Muddu Madakyaru and Ying Sun

Energies 2023, 16(15), 5793; https://doi.org/10.3390/en16155793 - 4 Aug 2023

Cited by 5 | Viewed by 1326

Abstract

Efficient detection of sensor faults in wind turbines is essential to ensure the reliable operation and performance of these renewable energy systems. This paper presents a novel semi-supervised data-based monitoring technique for fault detection in wind turbines using SCADA (supervisory control and data acquisition) data. Unlike supervised methods, the proposed approach does not require labeled data, making it cost-effective and practical for wind turbine monitoring. The technique builds upon the Independent Component Analysis (ICA) approach, effectively capturing non-Gaussian features. Specifically, a dynamic ICA (DICA) model is employed to account for the temporal dynamics and dependencies in the observed signals affected by sensor faults. The fault detection process integrates fault indicators based on

I^{2} d

I^{2} e

, and squared prediction error (SPE), enabling the identification of different types of sensor faults. The fault indicators are combined with a Double Exponential Weighted Moving Average (DEWMA) chart, known for its superior performance in detecting faults with small magnitudes. Additionally, the approach incorporates kernel density estimation to establish nonparametric thresholds, increasing flexibility and adaptability to different data types. This study considers various types of sensor faults, including bias sensor faults, precision degradation faults, and freezing sensor faults, for evaluation. The results demonstrate that the proposed approach outperforms PCA and traditional ICA-based methods. It achieves a high detection rate, accurately identifying faults while reducing false alarms. It could be a promising technique for proactive maintenance, optimizing the performance and reliability of wind turbine systems. Full article

(This article belongs to the Special Issue Advances in AI Methods for Wind Power Forecasting and Monitoring)

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