Satellite Power System Research Articles

Aqueous Supercapacitor with Wide-Temperature Operability and over 100,000 Cycles Enabled by Water-in-Salt Electrolyte.

Supercapacitors are crucial in renewable energy integration, satellite power systems, and rapid power delivery applications for mitigating voltage fluctuations and storing excess energy. Aqueous electrolytes offer a promising solution for low-cost and safe supercapacitors. However, they still face limitations in cycle life and wide-temperature range performance. Here, we present a symmetric supercapacitor utilizing activated carbon electrodes and a "water-in-salt" electrolyte (WiSE) based on lithium perchlorate. The WiSE electrolyte exhibits an expanded electrochemical stability window, endowing the aqueous supercapacitor with remarkable stability and long cycle life of over 100,000 cycles at 500 mA g-1 with more than 91% capacity retention. Moreover, the supercapacitor demonstrates good rate capability and wide temperature operability ranging from -20 to 80 °C. The use of high concentrations of salt in the aqueous electrolyte contributes not only to the enhancement of supercapacitor performance and cycle life but also to the temperature stability range, enabling all-season operability.

Fault diagnosis of satellite power system based on unsupervised knowledge acquisition and decision-making

Fault diagnosis (FD) is an important foundation for the maintenance of complex aerospace systems, such as satellite power systems, in which the attribute reduction has essential effect to eliminating data redundancy and improving diagnostic results. However, due to the difficulty and high cost of obtaining labels in some situations, especially early failures, FD based on unsupervised methods is of great significance but less commonly-studied. Moreover, with respect to FD preprocessing, unsupervised attribute reduction (UAR) usually applying clustering methods suffers from the need for cluster number, randomness, inability to handle non-spherical clusters, etc. Therefore, this paper proposes an unsupervised FD strategy including a knowledge acquisition method to mine the rules from the unlabeled data, a decision-making method to process the acquired knowledge, and a diagnosis decision for the fault identification. As for the preprocessing part, this paper proposes a wrapper UAR method (named DPC-UAR) based on the density peak clustering (DPC) and heuristic method, which can automatically identify the cluster centers and deal with the nonspherical data. Finally, experiments of attribute reduction performance on UCI data show that compared with other UAR methods, DPC-UAR has the greatest effect to improve performance of unsupervised learning algorithms, and plays a relatively good role in the supervised algorithm. Experiments on satellite power system fault diagnosis illustrated that the proposed FD strategy based on DPC-UAR has high accuracy, a high fault detection rate, and a low false alarm rate.

Accurate Satellite Operation Predictions Using Attention-BiLSTM Model with Telemetry Correlation

In satellite health management, anomalies are mostly resolved after an event and are rarely predicted in advance. Thus, trend prediction is critical for avoiding satellite faults, which may affect the accuracy and quality of satellite data and even greatly impact safety. However, it is difficult to predict satellite operation using a simple model because satellite systems are complex and telemetry data are copious, coupled, and intermittent. Therefore, this study proposes a model that combines an attention mechanism and bidirectional long short-term memory (attention-BiLSTM) with telemetry correlation to predict satellite behaviour. First, a high-dimensional K-nearest neighbour mutual information method is used to select the related telemetry variables from multiple variables of satellite telemetry data. Next, we propose a new BiLSTM model with an attention mechanism for telemetry prediction. The dataset used in this study was generated and transmitted from the FY3E meteorological satellite power system. The proposed method was compared with other methods using the same dataset used in the experiment to verify its superiority. The results confirmed that the proposed method outperformed the other methods owing to its prediction precision and superior accuracy, indicating its potential for application in intelligent satellite health management systems.

Novel statistical method for data drift detection in satellite telemetry

SummaryAutonomy is becoming a prime requirement for satellite mission control operations. Data‐driven methods like Machine Learning models are playing a key role in bringing in autonomy. Health keeping data from satellite telemetry is a key ingredient in these data‐driven methods. In real‐world satellite operations, the health‐keeping telemetry data gradually drifts due to adverse space weather effects and wear and tear of electronic and mechanical components. The key question that arises is how to detect and quantify the data drift which is generally a gradual phenomenon. This paper discusses a novel statistical method for detecting data drift occurring in satellite telemetry. For the purpose of experimental work in this paper, an actual telemetry data set of the BUS CURRENT sensor which is part of the Electrical Power System of a Low Earth Orbit Satellite was considered. Data drift detection test was carried out using this sensor data using the developed novel statistical method and with Kolmogorov Smirnov test which is a probabilistic method. Both results are analysed and compared. Thereafter novel statical method was used to check its efficacy using a synthetic data set with induced drift.

Fast Arc Detection Technology Based on Fractal Dimension for SADA Slip Ring in Satellite Power System

The solar array drive assembly (SADA) slip ring is a critical link that provides electrical power and electric signal transmission between solar arrays and satellite power systems, which is prone to arc faults in the space environment. If these arc faults cannot be detected and eliminated quickly enough, they will seriously threaten the safety of the satellite power system and the satellite. In this paper, a fast arc detection method based on fractal dimension is proposed that adapts to different operating modes of power systems. The detection method collects the current differential signal data flowing through the SADA slip ring, and, according to the trend of the fractal dimension change in this signal, the fault identification algorithm is designed for different operating modes of the power system to achieve real-time and rapid identification of arc faults. Finally, the effectiveness of the proposed method is demonstrated using test data under several different fault conditions.

Parameter probabilistic prediction for satellite power system based on unsupervised multi-dimensional sequence segmentation

With the increasing operational autonomy and mission complexity of satellites, the power system, as one of the most critical subsystems in a satellite platform, needs to provide high-quality and fast-response power output under complex load requirements. State monitoring is valuable to ensure the power system's stable and safe operation. Monitoring and predicting the key parameters based on the satellite power system's telemetry data are meaningful in detecting potential anomalies. However, the multi-physical high-dimensional telemetry data of the satellite power system are oriented to the operating conditions. The dynamic operating conditions of the satellite power system make it difficult to establish the proper parameter prediction model with high adaptability. To address these challenging issues, this paper proposes a parameter probabilistic prediction based on unsupervised time series segmentation. Firstly, the telemetry data under dynamic operation conditions are divided based on the unsupervised dynamic factor model and K-Means algorithm. The dynamic factor model is used to extract the common factor sequences of the multidimensional telemetry data according to the dynamic relationship between different parameters, followed by the clustering operation using K-Means to classify the multidimensional telemetry data into different sub-sequences. Secondly, a probabilistic prediction method based on the spatio-temporal fusion of multi-physical parameters is proposed. The method utilizes a Bayesian neural network to learn the spatial relationship between parameters and applies Gaussian process regression to establish the temporal dependence of parameters. Afterward, a specific strategy is utilized to fuse the prediction results of the two, thus realizing the fusion of spatio-temporal information of satellite multi-dimensional telemetry data. The experimental results based on the real meteorological satellite power system telemetry data in 2010 show that the average absolute errors of the key parameter predictions after the division of the working conditions are all less than 0.25, which is improved by more than 37.08 % compared with the pre-division. This indicates that the proposed method can well recognize the dynamic operation modes and has a positive advancing effect on the parameter prediction task. Meanwhile, the average absolute error of the proposed prediction method is improved by at least 40.4 % compared with other single prediction models. It shows that the method proposed in this paper can realize high-precision prediction of parameters, and has good application prospects in condition monitoring and anomaly detection of satellite power systems.

Univariate exploratory data analysis of satellite telemetry

SummaryLarge low Earth orbit satellite constellations require machine learning methods for enabling autonomy in health keeping of the satellites. Autonomy in health keeping entail's fault detection, isolation and reconfiguration. However, prior to model building, it becomes imperative to conduct exploratory data analysis of the data to gain an intuition of data and to decide the best model. Univariate exploratory data analysis has been carried out on a BUS CURRENT sensor of electrical power system of a low Earth orbit satellite to gain an understanding of data. Various aspects of data like presence of outliers, sampling frequency, missing values, comparison of imputation methods to fill missing values seasonality and trend analysis, stationarity test on data, rolling mean and autocorrelation and partial auto correlation plots have been made, and a detailed statistical analysis of results has been conducted.

Health assessment of satellite storage battery pack based on solar array impact analysis

The space environment has grown more complex due to the high density of satellite missions, and the likelihood of collisions causing unexpected damage to solar arrays has increased. The current research on the assessment of battery pack health condition is based only on the data from battery packs themselves, without considering the impact of solar arrays' performance on battery packs. This would lead to an overestimation of battery packs' health, and if a satellite flight and control plan is developed based on this, it could result in excessive battery pack usage, potentially shortening the lifespan of the satellite. To address this issue, this paper proposed a novel framework for assessing the health condition of satellite battery packs, which expands the applicable scenarios of the assessment. Firstly, through an energy balance analysis of satellite power systems, the impact of solar array performance degradation on battery aging in different scenarios was discussed. Then, an Informer-based model was constructed to predict the output current of the solar array based on the decline mechanism of solar arrays. Next, a current supply and demand graph was created by combining the output current of the solar array with the load demand current, and an impact factor was defined to quantify the solar array's influence on the battery pack. A rule-based model of evidence reasoning and belief rule base was then improved by the impact factor to achieve evaluation. Finally, the telemetry data from a certain type of satellite were used to predict the current of the solar array. The prediction accuracy is significantly improved compared to traditional methods. Under the assumption of damage to the solar array, the health condition of the battery pack was evaluated, and the assessment result was consistent with the usage of the battery pack, demonstrating the effectiveness of the proposed method.

Health Index for Satellite System Based on Characteristics of Telemetry Data

Since satellites are non-repairable systems, it is important to detect anomalies early to prevent failures. Data-driven approaches to anomaly detection, which have been activelystudied in recent years, have problems such as low explainability and insufficient training data in initial operation. Thus, we propose a health index that is commonly available for all satellites. It is possible to share training data and examples of anomalies by monitoringhealth status with the same index across different satellites. In this study, we extended our health index defined for the satellite power system to the entire satellite system. Then,we applied the health index to the operational data of the Suzaku satellite and confirmed that the index is useful for anomaly detection.

Satellite Power System State Prediction Based on Online Learning With Parameter Association Rules

Satellite Power System State Prediction Based on Online Learning With Parameter Association Rules

Research on bus converter for SAR satellite composited bus system

The composite bus system is a new type of satellite power system, which is suitable for high-power SAR satellites. The bus converter in the compound bus system is very important. This paper analyzes the design requirements of the bus converter, selects PSM LLC as the topology of the bus converter, and verifies the rationality of the scheme through simulation.

DESIGN AND ANALYSIS OF THE CHAMELEON SCHEDULING ALGORITHM FOR RECONFIGURABLE COMPUTING

Conventional power generation is the source of widespread worry over the depletion of nonrenewable energy sources and the environmental difficulties that this would inevitably cause. As a direct consequence of this, an increasing number of people are gravitating toward renewable energy sources like as photovoltaic boards and wind generators. Wind power is being put to use for a vast array of applications, some of which include the charging of batteries, the pumping of water, the generation of electricity for homes, the heating of swimming pools, the operation of satellite power systems, and many more. However, despite the fact that they do not need any sort of upkeep and do not result in any kind of pollution, their installation costs are high in many different contexts. Wind energy is quickly becoming a more significant contributor to the total installed power capacity around the globe. In the field of wind energy, the PMSG-based wind turbine equipped with a variable-speed and variable-pitch control system are the most common type of wind power generator. This device is capable of operating either independently or while linked to an existing grid. It is necessary to have a complete understanding of the machine's modelling, control, dynamic, and steady-state analyses in order to obtain the maximum possible amount of power from the wind, researchers must make an accurate prediction of the machine's performance in either mode of operation. The complexity of the systems, the sensors required, the speed of combination, the cost, the breadth of effectiveness, the technology employed, and the popularity of the systems all vary. Multiple control computations are performed on the Wind Energy Conversion System (WECS) in order to produce maximum power in a variety of wind speed scenarios. This is done so that the system can respond appropriately. The purpose of this work is to suggest several methodologies for obtaining dynamic features from the WECS integrated grid. The first technique makes use of the Firefly Algorithm (FA) as well as the Artificial Neural Network (ANN) methods. Both are classification algorithms. By utilizing the most effective parameters for design, the design of the grid-integrated power system design was able to achieve a voltage THD of 11.47 percent.

Thermal Impact Analysis and Electric–Thermal Coupled Modeling of Photovoltaic/Battery Space Power System with Different Surface Coatings

Thermal performance has long been recognized as a critical attribute for space systems. Thermal control surface coating is a common method in passive thermal protection. Unfortunately, limited analyzing models and data on the influence of thermal control coatings’ α/ε (absorptivity/emissivity) on the space power system have been published to date. To fill this gap, we proposed a multiphysics model that combined environmental temperature calculating and electrical performance analysis together for the satellite power system. In this paper, different coating materials are applied to the radiator surface and thermal insulation surface, respectively. Additionally, a new concept of energy storage, named energy storage voltage, is introduced. The results are analyzed and parametric fits with different formulas using ordinary least squares are conducted. Finally, the change rules are presented, which will prove particularly useful to the space industry, for example, in thermal designs and on-orbit battery studies.

Design of Power Supply System for High Power SAR Satellite

Synthetic Aperture Radar (SAR) satellite power system has the characteristics of high power, pulse power supply and fast response speed. Based on the combination of domestic and foreign SAR satellite power supply structures, a SAR satellite power supply system scheme with a composite bus architecture is proposed in this paper. By adding battery discharge regulators (BDR) between the platform bus and the SAR load bus as a grid-connected controller, the SAR load bus can supply power to the platform load, and the solar array and battery pack can be used efficiently while achieving dual bus output. Aiming at the composite bus, this paper also proposes an efficient high-power filter technology. Finally, the ground test results verify the correctness of the scheme, which can meet the different power supply requirements of the SAR satellite stable and pulse load equipment.

Simultaneous-Fault Diagnosis of Satellite Power System Based on Fuzzy Neighborhood ζ-Decision-Theoretic Rough Set

Due to the increasing complexity of the entire satellite system and the deteriorating orbital environment, multiple independent single faults may occur simultaneously in the satellite power system. However, two stumbling blocks hinder the effective diagnosis of simultaneous-fault, namely, the difficulty of obtaining the simultaneous-fault data and the extremely complicated mapping of the simultaneous-fault modes to the sensor data. To tackle the challenges, a fault diagnosis strategy based on a novel rough set model is proposed. Specifically, a novel rough set model named FNζDTRS by introducing a concise loss function matrix and fuzzy neighborhood relationship is proposed to accurately mine and characterize the relationship between fault and data. Furthermore, an attribute rule-based fault matching strategy is designed without using simultaneous-fault data as training samples. The numerical experiments demonstrate the effectiveness of the FNζDTRS model, and the diagnosis experiments performed on a satellite power system illustrate the superiority of the proposed approach.

Modeling and Analysis of Non-Linear Phenomena of Satellite Power System in Space Environment and Hazard-Risk Evaluations

With the aim to solve the problem of the non-linear phenomenon of the satellite power system under the influence of the space environment factors, which threatens the stability of the power system, in this paper, the abnormal charging and discharging of solar arrays and solar array drive assembly (SADA) induced by the space plasma environment and accidental events, such as debris impact, and the non-linear behaviors of the solar array and load during the Earth eclipse are modeled and analyzed. On this basis, the hazard risk evaluations of the above non-linear phenomena are carried out, and the weak links of the satellite power system in the space environment are identified. The results show that the unexpected energy of the solar array will increase the current stress and power loss of the power device, resulting in S3R over-regulation. An SADA arc fault will reduce the power supply capacity of the solar array, damage the shunt regulator, and affect the quality of the bus. The non-linear behavior of the solar array and load during the Earth eclipse may damage the battery charge and discharge regulator, seriously affecting the stability of the power system.

Hierarchical cognize framework for the multi-fault diagnosis of the interconnected system based on domain knowledge and data fusion

An interconnected system (ICS) is a complex industry system with multiple sensors, multiple tasks, and massive interaction. It is also of great importance for conducting the fault diagnosis technology research. Multi-fault diagnosis (MFD) is an urgent problem in engineering, while the complex mapping relationships among the system sensors, data patterns in single sensors, and fault modes in ICSs bringing severe challenges. The faults of ICS are similar to human disease in multiple dimensions. Enlightening the understanding of diseases in medicine guides us: hierarchical cognition and knowledge-data-fusion are important systematic ideas. Inspired by these, we propose a hierarchical cognize framework (HCF), which covers the cognition of sensors, data patterns in single sensors, and data climates. Subsequently, we propose a fuzzy neighbourhood three-way decision (FN3WD), experience fused self-adaptation Gaussian-mixture-model (EFSA-GMM), and coding-with-knowledge-discrimination (CWKD) to construct an HCF. To comprehensively verify the HCF, we successfully apply the HCF to the MFD of a satellite power system. Classic models of two-mainstream strategies are introduced as comparisons, specifically, MC-DCNN, MC-SVM, ML-DCNN, and ML-SVM. Compared to the comparative models, the HCF performs an increase of 12.35%, 7.72%, 6.90%, and 8.10% at least in accuracy, precision, recall, and F1-score, respectively, in 10 times cross-validation. Benefitting from the fusion of knowledge, the HCF has cognitive advantages in obtaining a high accuracy and precision diagnosis results. Meanwhile, the time consumption of the HCF is approximately 130 s, which is considerably reduced by as much as 50% compared with the deep learning models.

Improving the Satellite Power Supply Continuity using Flywheel Energy Storage System

Recently, Flywheel Energy Storage (FES) systems are gaining significant interest from National Aeronautics and Space Administration Glenn Research Center (NASA's GRC) in satellite applications due to their numerous advantages as an energy storage solution over the rest of the alternatives. Regarding FESS features such as high cycle life, long service life, high efficiency, high energy density and low environmental impact, this paper introduces a FESS management controller for use with the solar PV system in an integrated manner that ensures the continuity of the satellite power system and, thus, increases its efficiency. In addition, a prototype of the management control under various operating modes of the proposed unit is established. The satellite power supply management controller was implemented practically as a model using the Arduino controller. The practical results proved the continuity of the satellite operation in different operating modes using the FES system, which in turn would lead to improving the efficiency of the satellite feeding source compared to its performance using solar PV system only.

Graph neural network approach for anomaly detection

To ensure the stable long-time operation of satellites, evaluate the satellite status, and improve satellite maintenance efficiency, we propose an anomaly detection method based on graph neural network and dynamic threshold (GNN-DTAN). Firstly, we build the graph neural network model for telemetry data. The graph construction module in the model extracts the relationship between features, and the spatial dependency extraction module and the temporal dependency extraction module extract the spatial and temporal dependencies of the data, respectively. The trained model is then used to predict the data, and the anomaly score between the predicted and actual values is calculated. Finally, the wavelet variance is used to analyze the data period. A dynamic threshold method based on the period time window is used to detect anomalies in the data set. Experimental results of satellite power system telemetry data show that the proposed algorithm's accuracy reaches more than 98%, with good effectiveness and robustness.

A deep learning approach to fault detection in a satellite power system using Gramian angular field

In this paper, an approach based on time series-to-image mapping is proposed for fault detection in a satellite power system (SPS). This approach exploits the possibilities of encoding the SPS time series data as images using Gramian angular fields (GAF). The resulting images are analysed by a convolutional neural network (CNN) for recognising faulty and normal conditions of SPS. Validation with NASA's advanced diagnostics and prognostics testbed (ADAPT) dataset has demonstrated that the combination of CNN with GAF results in better performance when compared to other image encoding methods such as spectrogram and recurrence plot (RP). The proposed approach yields an accuracy of 85.13% with precision 84% and F1 score 0.91 suggesting that encoding multivariate time series data to images using GAF is worth considering for SPS fault diagnosis when compared to other time series-to-image encoding based approaches.