Research on GPS Receiver Data Processing Algorithm Based on Wavelet Analysis

Ershen Wang,Tao Pang,Yongming Yang 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.11

Reliability is an essential performance in GPS navigation system. Therefore, satellite fault detection is considered as one of the most important functions in GPS receivers. For the declining problem of the ability of fault detection for the traditional GPS satellite fault detection algorithm under the condition of small fault, a new GPS satellite fault detection algorithm based on wavelet analysis is proposed. The raw pseudorange measurements and the positioning data information are transformed by wavelet analysis and the data jumping point can be detected and identified through the different wavelet scales, and the satellite fault could be detected. Two kinds of GPS satellite fault detection methods are given in detail, and the advantages and disadvantages of them are compared. Validated by the real collected raw data from the GPS receiver, the results show that the wavelet analysis method can detect smaller mutations in a sequence of parameters, and the feasibility and effectiveness of applying the wavelet analysis algorithm in satellite fault detection for GPS receiver are verified.

Comparative Study of Three Fault Diagnostic Methods for Three Phase Inverter with Induction Motor

Furqan Asghar,Muhammad Talha,Sung Ho Kim 한국지능시스템학회 2017 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.17 No.4

In recent times, inverters are considered as the basic building block in an electrical drive system used widely in many industrial drive applications. However, the reliability of these inverters is mainly affected by the failure of power electronic switches. Various faults in inverter may influence the system operation by unexpected maintenance, which increases the cost factor and reduce overall efficiency. In this paper, comparative study of three different fault detection and diagnosis systems for three phase inverter is presented. The basic purpose of these fault detection and diagnosis systems is to detect single or multiple faults efficiently. These techniques rely on the neural network for fault detection and diagnosis by using Clarke transformed two-dimensional features extraction, three-dimensional features extraction and features extraction using discrete wavelet transform (DWT) with a different number of features in each technique. Several features are extracted using different mechanisms and used in the neural network as input for fault detection and diagnosis. Furthermore, a simulation study is carried out to analyze the fault detection and diagnosis response of these techniques. Also, a comparative study has been performed by considering fault detection time and accuracy. Comparison results prove the supremacy of three-dimensional feature extraction technique over other two techniques as it can detect and diagnose single, double and triple faults in a single cycle with high accuracy as compared to other two techniques multi-cycles detection.

Satellite Fault Detection and Isolation Scheme with Modified Adaptive Fading EKF

Jun Kyu Lim,Chan Gook Park 대한전기학회 2014 Journal of Electrical Engineering & Technology Vol.9 No.4

This paper presents a modified adaptive fading EKF (AFEKF) for sensor fault detection and isolation in the satellite. Also, the fault detection and isolation (FDI) scheme is developed in three phases. In the first phase, the AFEKF is modified to increase sensor fault detection performance. The sensor fault detection and sensor selection method are proposed. In the second phase, the IMM filer with scalar penalty is designed to detect wherever actuator faults occur. In the third phase of the FDI scheme, the sub-IMM filter is designed to identify the fault type which is either the total or partial fault. An important feature of the proposed FDI scheme can decrease the number of filters for detecting sensor fault. Also, the proposed scheme can classify fault detection and isolation as well as fault type identification.

저궤도 인공위성의 센서 및 구동기 통합 고장검출 및 분리 기법

임준규(Jun Kyu Lim),이준한(Junhan Lee),박찬국(Chan Gook Park) 제어로봇시스템학회 2011 제어·로봇·시스템학회 논문지 Vol.17 No.11

An integrated fault detection and isolation method is proposed in this paper. The main objective of this paper is development fault detection, isolation and diagnosis algorithm based on the DKF (Decentralized Kalman Filter) and the bank of IMM (Interacting Multiple Model) filters using penalty scalar for both partial and total faults and the outlier detection algorithm for preventing false alarm also included. The proposed FDI (Fault Detection and Isolation) scheme is developed in four phases. In the first phase, the outlier detection filter is designed to prevent false alarm as a pre-filter. In the second phases, two local filters and master filter are designed to detect sensor faults. In the third phases, the proposed FDI scheme checks sensor residual to isolate sensor faults and 11 EKFs actuator fault models are designed to detect wherever actuator faults occur. In the last phases, four filters are designed to identify the fault type which is either the total fault or partial fault. The developed scheme can deal with not only sensor and actuator faults, but also preventing false alarm. An important feature of the proposed FDI scheme can decreases fault isolation time and figure out not only fault detection and isolation but also fault type identification. To verify the proposed FDI algorithm performance, the Simulator is also developed under the Matlab/Simulink environment.

Fault-weighted quantification method of fault detection coverage through fault mode and effect analysis in digital I&C systems

Cho, J.,Lee, S.J.,Jung, W. North-Holland Pub. Co 2017 Nuclear engineering and design Vol.316 No.-

<P>The one of the most outstanding features of a digital I&C system is the use of a fault-tolerant technique. With an awareness regarding the importance of thequantification of fault detection coverage of fault-tolerant techniques, several researches related to the fault injection method were developed and employed to quantify a fault detection coverage. In the fault injection method, each injected fault has a different importance because the frequency of realization of every injected fault is different. However, there have been no previous studies addressing the importance and weighting factor of each injected fault. In this work, a new method for allocating the weighting to each injected fault using the failure mode and effect analysis data was proposed. For application, the fault-weighted quantification method has also been applied to specific digital reactor protection system to quantify the fault detection coverage. One of the major findings in an application was that we may estimate the unavailability of the specific module in digital I&C systems about 20-times smaller than real value when we use a traditional method. The other finding was that we can also classify the importance of the experimental case. Therefore, this method is expected to not only suggest an accurate quantification procedure of fault-detection coverage by weighting the injected faults, but to also contribute to an effective fault injection experiment by sorting the importance of the failure categories. (C) 2017 Elsevier B.V. All rights reserved.</P>

Fault Detection and Classification with Optimization Techniques for a Three-Phase Single-Inverter Circuit

V. Gomathy,S. Selvaperumal 전력전자학회 2016 JOURNAL OF POWER ELECTRONICS Vol.16 No.3

Fault detection and isolation are related to system monitoring, identifying when a fault has occurred, and determining the type of fault and its location. Fault detection is utilized to determine whether a problem has occurred within a certain channel or area of operation. Fault detection and diagnosis have become increasingly important for many technical processes in the development of safe and efficient advanced systems for supervision. This paper presents an integrated technique for fault diagnosis and classification for open- and short-circuit faults in three-phase inverter circuits. Discrete wavelet transform and principal component analysis are utilized to detect the discontinuity in currents caused by a fault. The features of fault diagnosis are then extracted. A fault dictionary is used to acquire details about transistor faults and the corresponding fault identification. Fault classification is performed with a fuzzy logic system and relevance vector machine (RVM). The proposed model is incorporated with a set of optimization techniques, namely, evolutionary particle swarm optimization (EPSO) and cuckoo search optimization (CSO), to improve fault detection. The combination of optimization techniques with classification techniques is analyzed. Experimental results confirm that the combination of CSO with RVM yields better results than the combinations of CSO with fuzzy logic system, EPSO with RVM, and EPSO with fuzzy logic system.

Fault Detection and Classification with Optimization Techniques for a Three-Phase Single-Inverter Circuit

Gomathy, V.,Selvaperumal, S. The Korean Institute of Power Electronics 2016 JOURNAL OF POWER ELECTRONICS Vol.16 No.3

Fault detection and isolation are related to system monitoring, identifying when a fault has occurred, and determining the type of fault and its location. Fault detection is utilized to determine whether a problem has occurred within a certain channel or area of operation. Fault detection and diagnosis have become increasingly important for many technical processes in the development of safe and efficient advanced systems for supervision. This paper presents an integrated technique for fault diagnosis and classification for open- and short-circuit faults in three-phase inverter circuits. Discrete wavelet transform and principal component analysis are utilized to detect the discontinuity in currents caused by a fault. The features of fault diagnosis are then extracted. A fault dictionary is used to acquire details about transistor faults and the corresponding fault identification. Fault classification is performed with a fuzzy logic system and relevance vector machine (RVM). The proposed model is incorporated with a set of optimization techniques, namely, evolutionary particle swarm optimization (EPSO) and cuckoo search optimization (CSO), to improve fault detection. The combination of optimization techniques with classification techniques is analyzed. Experimental results confirm that the combination of CSO with RVM yields better results than the combinations of CSO with fuzzy logic system, EPSO with RVM, and EPSO with fuzzy logic system.

CUSUM 분석을 활용한 태양광 발전설비 이상현상 사전감지 알고리즘

이재훈,정진화,채영태 한국생활환경학회 2020 한국생활환경학회지 Vol.27 No.3

This study propose fault detection algorithm of photovoltaic(PV) generation system for efficient operation and management(O&M) with cumulative sum control chart (CUSUM). The fault characteristics was analyzed to operation data between inverter and PV power output in representative day of fault. Each current of channel compared with PV power output to identify minor faults that could not be found in the inverter. Finally, fault detection algorithm tested to find optimal hyperparameter with drift and threshold in channel of PV generation system. As a result of the characteristics between operation data (current, voltage, input power, power factor, module temperature, solar irradiation) and PV power output, fault type of current, voltage, input power, and power factor are identical to PV power output at fault sections. Also, whole channel showed fault characteristics at same sections. However, some channels showed normal pattern in other fault sections, so it is limited to identify fault type at inverter and connection band. Optimal hyperparameter of the CUSUM was searched to detect fault with different fault level of the channels. In case of threshold=0.3 and drift=0.005 of CUSUM, the model demonstrated high detection performance in decreased by 50% of current. The threshold=0.25 and drift=0.01 was optimal hyperparameter in decreased by 20% of current. The results show that CUSUM not only accurately detects fault section but responded some weather changes as normal with optimal model condition.

비접지 배전계통 지락고장 검출 알고리즘 및 프로그램 개발

박소영(Park, So-Young),신창훈(Shin, Chang-Hoon) 한국산학기술학회 2009 한국산학기술학회논문지 Vol.10 No.10

비접지 배전계통에서 전체고장의 약 70%를 차지하는 지락고장 발생시에는 지락전류가 작아 검출이 어렵지 만 고장상태로 전원 공급을 지속할 경우 사고 파급 및 기기 소손을 유발할 가능성이 있기 때문에 지락고장 처리는 매우 중요하다. 본 논문에서는 GPT(접지형 계기용 변압기, Ground Potential Transformer)에서 감지하는 영상전압 신호 를 이용하여 고장선로를 검출하고, 비상시 연계선로를 이용하여 정전구역을 복구하기 위해 평상시 개폐기가 열려 있 는 상태로 배전선로 사이를 연결하는 상시개방점을 이동하며 각 구간을 차례로 분리하면서 GPT 신호의 사라짐 여부 를 감시하여 고장구간을 검출하는 방법을 제안한다. 고장구간 탐색 시 전체 정전이 없고, 고장구간 검출과 건전구간 복구가 동시에 가능하며 다양한 형태의 배전계통 구성에 적용 가능하다는 점에서 효율적이다. 본 논문에서 제안하는 고장처리 방법을 프로그램으로 개발하여 베트남 배전자동화 시범사업에 적용함으로써 알고리즘 및 프로그램의 적정 성을 검증하였다. The ground fault is occupying 70% among the total number of faults in ungrounded distribution power system. When the ground fault occurs in ungrounded system, the fault current is so small that it is hard to detect. But fault handling is very important because to continue power supply during fault conditions may cause the fault spreading and the distribution device in trouble. This paper presents the fault line detection method by using GPT signal detecting zero sequence voltage, and the fault section detection method by detecting whether GPT signal is disappeared or not during shifting normally open switch, which is connecting switch between distribution lines with open state in order to restore the outage area under emergency situation, and during isolating each section one by one which belongs to the fault line. This method is efficient because there is no whole power interruption during the fault section detection, and it is possible to perform both the fault section detection and the service restoration for the outage area at the same time, and it can apply to various distribution system configuration. Program for the fault restoration was developed applying proposed method, and it has been validated by applying to the pilot project of distribution automation system in Vietnam which has the ungrounded distribution system.

Performance Comparison of GPS Fault Detection and Isolation via Pseudorange Prediction Model based Test Statistics

Jangsik Yoo,Jongsun Ahn,Young Jae Lee,Sangkyung Sung 대한전기학회 2012 Journal of Electrical Engineering & Technology Vol.7 No.5

Fault detection and isolation (FDI) algorithms provide fault monitoring methods in GPS measurement to isolate abnormal signals from the GPS satellites or the acquired signal in receiver. In order to monitor the occurred faults, FDI generates test statistics and decides the case that is beyond a designed threshold as a fault. For such problem of fault detection and isolation, this paper presents and evaluates position domain integrity monitoring methods by formulating various pseudorange prediction methods and investigating the resulting test statistics. In particular, precise measurements like carrier phase and Doppler rate are employed under the assumption of fault free carrier signal. The presented position domain algorithm contains the following process; first a common pseudorange prediction formula is defined with the proposed variations in pseudorange differential update. Next, a threshold computation is proposed with the test statistics distribution considering the elevation angle. Then, by examining the test statistics, fault detection and isolation is done for each satellite channel. To verify the performance, simulations using the presented fault detection methods are done for an ideal and real fault case, respectively.