Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels

Cespedes, Adolfo Javier Jara; Pangestu, Bramandika Holy Bagas; Hanazawa, Akitoshi; Cho, Mengu

doi:10.3390/app12178634

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…The voltage and readings are sensitive owing to the PV characteristic of the solar cell [23]. These solar equipment problems, electrical noise [24], collection system errors, and software system problems may cause certain data outliers.…”

Section: Data Pre-processingmentioning

confidence: 99%

Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations

Erdogan,

Fekih Hassen

2023

Energies

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The growing demand for electric vehicles (EV) in the last decade and the most recent European Commission regulation to only allow EV on the road from 2035 involved the necessity to design a cost-effective and sustainable EV charging station (CS). A crucial challenge for charging stations arises from matching fluctuating power supplies and meeting peak load demand. The overall objective of this paper is to optimize the charging scheduling of a hybrid energy storage system (HESS) for EV charging stations while maximizing PV power usage and reducing grid energy costs. This goal is achieved by forecasting the PV power and the load demand using different deep learning (DL) algorithms such as the recurrent neural network (RNN) and long short-term memory (LSTM). Then, the predicted data are adopted to design a scheduling algorithm that determines the optimal charging time slots for the HESS. The findings demonstrate the efficiency of the proposed approach, showcasing a root-mean-square error (RMSE) of 5.78% for real-time PV power forecasting and 9.70% for real-time load demand forecasting. Moreover, the proposed scheduling algorithm reduces the total grid energy cost by 12.13%.

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Section: Data Pre-processingmentioning

confidence: 99%

Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations

Erdogan,

Fekih Hassen

2023

Energies

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An interpretable graph neural network for real-world satellite power system anomaly detection based on graph filtering

Di,

Wang,

Zhao

et al. 2024

Expert Systems with Applications

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Generalized Time‐Series Analysis for In Situ Spacecraft Observations: Anomaly Detection and Data Prioritization Using Principal Components Analysis and Unsupervised Clustering

Finley,

Martinez‐Ledesma,

Paterson

et al. 2024

Earth and Space Science

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In situ spacecraft observations are critical to our study and understanding of the various phenomena that couple mass, momentum, and energy throughout near‐Earth space and beyond. However, on‐orbit telemetry constraints can severely limit the capability of spacecraft to transmit high‐cadence data, and missions are often only able to telemeter a small percentage of their captured data at full rate. This presents a programmatic need to prioritize intervals with the highest probability of enabling the mission's science goals. Larger missions such as the Magnetospheric Multiscale mission (MMS) aim to solve this problem with a Scientist‐In‐The‐Loop (SITL), where a domain expert flags intervals of time with potentially interesting data for high‐cadence data downlink and subsequent study. Although suitable for some missions, the SITL solution is not always feasible, especially for low‐cost missions such as CubeSats and NanoSats. This manuscript presents a generalizable method for the detection of anomalous data points in spacecraft observations, enabling rapid data prioritization without substantial computational overhead or the need for additional infrastructure on the ground. Specifically, Principal Components Analysis and One‐Class Support Vector Machines are used to generate an alternative representation of the data and provide an indication, for each point, of the data's potential for scientific utility. The technique's performance and generalizability is demonstrated through application to intervals of observations, including magnetic field data and plasma moments, from the CASSIOPE e‐POP/Swarm‐Echo and MMS missions.

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Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels

Cited by 5 publications

References 21 publications

Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations

Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations

An interpretable graph neural network for real-world satellite power system anomaly detection based on graph filtering

Generalized Time‐Series Analysis for In Situ Spacecraft Observations: Anomaly Detection and Data Prioritization Using Principal Components Analysis and Unsupervised Clustering

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