Attitude Control Simulator for the Korea Pathfinder Lunar Orbiter

Jung, Dawoon; Kwon, Jae Wook; Baek, Kwangyul; Ahn, Han Woong

doi:10.1007/978-981-13-3305-7_202

Cited by 4 publications

(6 citation statements)

References 2 publications

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“…For this reason, the simulation data that can be clearly labeled with normal and abnormal status are used. The data sets are generated using LUNar Attitude and Orbit Control System (AOCS) SIMulator (LUNASIM) [21], a spacecraft attitude control simulator developed in-house at the Korea Aerospace Research Institute (KARI) to test the performance of attitude control flight software for the Korea Pathfinder Lunar Orbiter (KPLO) [22]. The training data for the learning model comprises normal data, and the test data comprises normal and anomaly data.…”

Section: Methodsmentioning

confidence: 99%

“…KPLO is KARI's first lunar mission; therefore, the operational data is not available. We used simulated attitude control performance data generated by LUNASIM [21]. Certain aspects of the KPLO mission are similar to low-Earth and geosynchronous orbit missions; however, key differences exist that prevent us from blindly applying existing Earth-orbit AD standards.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…LUNASIM [21] is being developed in-house at KARI to test the performance of attitude control flight software for the KPLO, a lunar orbiter mission currently in its critical design phase. LUNASIM is composed of attitude control flight software (FSW), equipment, environment, and dynamics models, I/O handlers, and simulation infrastructure.…”

Section: Lunasimmentioning

confidence: 99%

“…Measuring AD performance requires a healthy differential model, so we use simulation data that can be clearly labeled as normal and abnormal. The data set utilizes soft-sensor-based data generated by LUNar Attitude and Orbit Control System (AOCS) SIMulator (LUNASIM) [21], a satellite attitude control simulator developed in-house at the Korea Aerospace Research Institute (KARI) to test the performance of attitude control flight software for the Korea Pathfinder Lunar Orbiter (KPLO) [22]. A series of processes for AD using multidimensional time series spacecraft AOCS data includes the following works: (a) preprocessing of multi-channel time series data (including purification, integration, cleanup and transformation) and data feature extraction using deep neural networks; (b) selecting diagnostic methods based on generative models considering actual data conditions (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Deep Generative Models-Based Anomaly Detection for Spacecraft Control Systems

Ahn

Jung

Choi

2020

Sensors

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A spacecraft attitude control system provides mechanical and electrical control to achieve the required functions under various mission scenarios. Although generally designed to be highly reliable, mission failure can occur if anomalies occur and the attitude control system fails to properly orient and stabilize the spacecraft. Because accessing spacecraft to directly repair such problems is usually infeasible, developing a continuous condition monitoring model is necessary to detect anomalies and respond accordingly. In this study, a method for detecting anomalies and characterizing failures for spacecraft attitude control systems is proposed. Herein, features are extracted from multidimensional time-series data of a simulation of the attitude control system. Then, the artificial neural network learning algorithms based on two types of generation models are applied. A Bayesian optimization algorithm with a Gaussian process is used to optimize the hyperparameters for the neural network to improve the performance. The performance is evaluated based on the reconstruction error through the algorithm using the newly generated data not used for learning as input data. Results show that the detection performance depends on the operating characteristics of each submode in the operation scenarios and type of generation model. The diagnostic results are monitored to detect anomalies in operation modes and scenarios.

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Section: Methodsmentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

Section: Lunasimmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Generative Models-Based Anomaly Detection for Spacecraft Control Systems

Ahn

Jung

Choi

2020

Sensors

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“…The LVDC distribution system is being built on the South Korean island Geochado by Korea Electric Power Company (KEPCO). For this project, a ground fault detection system was imported from overseas [1,7,10]. In 2018, there was an analysis of IT grounding system protection devices applied to power converter systems (PCS) imported from overseas [10].…”

Section: Introductionmentioning

confidence: 99%

Ground Fault Detection Using Hybrid Method in IT System LVDC Microgrid

Lee

Park

2020

Energies

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Low voltage direct current (LVDC) microgrid systems have many advantages over low voltage alternating current (LVAC) systems. Furthermore, LVDC microgrids are growing in use because they are easy to link to distributed energy resources (DER) and energy storage systems (ESS), etc. Currently, IT system LVDC microgrids are widely used in direct current (DC) railways, hospitals, photovoltaic (PV) systems, and so on. When a ground fault occurs in an IT system LVDC microgrid, the ground fault may not be detected because the fault current is very small and there is no current path. In this paper, ground fault detection is proposed using a hybrid method that comprises pulsation signal generator injection and detailed coefficients of discrete wavelet transform (DWT). The LVDC microgrid was modeled and simulated using power systems computer-aided design (PSCAD). In addition, the proposed hybrid method was implemented using MATLAB’s wave menu, a script m-file, and the PSCAD library. The proper threshold was selected and tested by fault resistance change and load variation. In order to verify the superiority of the proposed hybrid method, a comparative study with the conventional method was performed. The results of various simulations show that the proposed hybrid detection method has normal operation and accurately and rapidly detects ground faults.

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