Comparison of Artificial Intelligence and Machine LearningMethods used in Electric Power System Operation
Preprint
- 5 April 2024
- preprint
- Published by MDPI AG in Preprints.org
Abstract
The methods of Artificial Intelligence (AI) have been used in the planning and operation of power systems for more than 40 years. In recent years, due to the development of microprocessor and data storage technologies, the effectiveness of this use has greatly increased. This paper provides a systematic overview of the application of AI including the use of Machine Learning (ML) to the power system. The potential application areas are divided into four blocks and the classification matrix has been used for clustering the AI application tasks. Furthermore, the data acquisition methods for setting the parameters of AI and ML algorithms are presented and discussed in a systematic way considering the supervised and unsupervised learning methods. Based on this, three complex application examples: wind power generation forecasting, smart grid security as-sessment (using two methods), and automatic system fault detection are presented and discussed in detail. Summary and outlook conclude the paper.Keywords
All Related Versions
- Version 1, 2024-04-05, Preprints
- Published version: Energies, 17 (11), 2790.
This publication has 54 references indexed in Scilit:
- Power management strategy based on Elman neural network for grid-connected photovoltaic-wind-battery hybrid systemComputers and Electrical Engineering, 2022
- Quantum computing based hybrid deep learning for fault diagnosis in electrical power systemsApplied Energy, 2021
- A survey on multi-agent deep reinforcement learning: from the perspective of challenges and applicationsArtificial Intelligence Review, 2020
- Study of AdaBoost and Gradient Boosting Algorithms for Predictive Analytics2019
- State of the art in big data applications in microgrid: A reviewAdvanced Engineering Informatics, 2019
- A whale optimization algorithm-trained artificial neural network for smart grid cyber intrusion detectionNeural Computing and Applications, 2019
- Stacked sparse autoencoder with PCA and SVM for data-based line trip fault diagnosis in power systemsNeural Computing and Applications, 2018
- Ensemble learning: A surveyWIREs Data Mining and Knowledge Discovery, 2018
- Dezentrales vernetztes Energiemanagement2010
- Short‐term water demand forecast modeling techniques—CONVENTIONAL METHODS VERSUS AIJournal AWWA, 2002