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Power disturbance monitoring through techniques for novelty detection on wind power and photovoltaic generation
- Source :
- Sensors; Volume 23; Issue 6; Pages: 2908
- Publication Year :
- 2023
-
Abstract
- Novelty detection is a statistical method that verifies new or unknown data, determines whether these data are inliers (within the norm) or outliers (outside the norm), and can be used, for example, in developing classification strategies in machine learning systems for industrial applications. To this end, two types of energy that have evolved over time are solar photovoltaic and wind power generation. Some organizations around the world have developed energy quality standards to avoid known electric disturbances; however, their detection is still a challenge. In this work, several techniques for novelty detection are implemented to detect different electric anomalies (disturbances), which are k-nearest neighbors, Gaussian mixture models, one-class support vector machines, self-organizing maps, stacked autoencoders, and isolation forests. These techniques are applied to signals from real power quality environments of renewable energy systems such as solar photovoltaic and wind power generation. The power disturbances that will be analyzed are considered in the standard IEEE-1159, such as sag, oscillatory transient, flicker, and a condition outside the standard attributed to meteorological conditions. The contribution of the work consists of the development of a methodology based on six techniques for novelty detection of power disturbances, under known and unknown conditions, over real signals in the power quality assessment. The merit of the methodology is a set of techniques that allow to obtain the best performance of each one under different conditions, which constitutes an important contribution to the renewable energy systems.
- Subjects :
- Power quality disturbance
Energia eòlica
Informàtica::Intel·ligència artificial::Aprenentatge automàtic [Àrees temàtiques de la UPC]
Photovoltaic generation
Energies [Àrees temàtiques de la UPC]
Statistics
novelty detection
machine learning
power quality disturbance
wind generation
photovoltaic generation
Estadística
Photovoltaic power generation
Biochemistry
Atomic and Molecular Physics, and Optics
Renewable energy sources
Analytical Chemistry
Machine learning
Aprenentatge automàtic
Wind power
Novelty detection
Electrical and Electronic Engineering
Energies renovables
Instrumentation
Wind generation
Energia solar fotovoltaica
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Sensors; Volume 23; Issue 6; Pages: 2908
- Accession number :
- edsair.doi.dedup.....8184c25c93e899e2e7b1f354a42add1d