Pantograph Spark Fault Detection using YOLO

Pantograph-catenary is now the dominant form of current collection for modern electric trains because they can be used for higher voltages. Faults in pantograph-catenary systems threaten the operation and safety of railway transportation. They need to be continuously monitored and controlled to maintain safe transport. Pantograph may be damaged as a result of extreme weather conditions which can affect its normal operation, leading to failure of pantograph and overhead contact line systems. Poor contact between pantograph and overhead contact line causes thermal erosion to the wire. When the pantographs are exposed to air, they could deteriorate due to electrochemical reaction with the environment since they are made of metals. Movement of catenary lines and pantograph in high crosswinds has been found to cause the wire to be trapped in the pantograph. There is a serious issue regarding the quality of images generated by pantograph video monitoring system on high-speed railway trains which often shows inconsistencies of catenary faults. The application of traditional image processing and deep learning techniques have been unable to meet the requirements of spark detection. In this paper,  a modern deep learning algorithm is proposed to detect sparks in the pantograph. Specifically, the YOLOv3 model is used to counter this problem that traditional image processing algorithms have been unable to. The results on a very large sample of data show the efficiency and real-time performance of the proposed method, which meets the requirements of pantograph spark detection in high-speed railway. Keywords: High-speed railway pantograph; Spark detection; Deep learning; YOLOv3; DOI: 10.7176/ISDE/12-3-02 Publication date:September 30th 202

The Use of Artificial Neural Networks in the Theoretical Investigation of Faults in Transmission Lines

This paper describes the development of a fast, efficient, artificial neural network (ANN) based fault diagnostic system (FDS) for the location of fault on transmission lines. The principal functions of this diagnostic system are: detection of fault occurrence, identification of faulted sections and classification of faults into types. This has been achieved through a cascaded, multilayer ANN structure using the back-propagation (BP) learning algorithm. This paper shows that the FDS accurately identifies High Impedance Faults, which are relatively difficult to identify with other methods. Test results are simulated and generated in MATLAB using Apo 132KV transmission line in Apo transmission substation, Abuja. These results amply demonstrate the capability of the FDS in terms of accuracy and speed with respect to detection, localization, and classification of faults in transmission lines.http://dx.doi.org/10.4314/njt.v34i4.2

Real-time power system topology change detection and identification

This thesis proposes a framework for detection and identification of system topological changes in near real-time that utilizes the statistical properties of electricity generation and demand, which are assumed to be known. Instead of relying on offline models as with traditional methods, the proposed method is model-free, and exploits the high-speed synchronized measurements provided by phasor measurement units (PMUs). In this framework, a statistical quickest change algorithm is applied to the voltage phase angle measurements collected from PMUs to detect the change-point that corresponds to the system topology change instant. An advantage of this algorithm is that the operator also has full control over the tradeoff between detection delay and false alarm rate. Additionally, a full measurement set is not necessary for its implementation and good results can be achieved even for a few PMU measurements. A scheme for systematic PMU bus selection is presented along with a method to partition the power system such that the aforementioned algorithm for line outage detection can be applied in parallel to each area, allowing for even faster detection. The optimal partitioning scheme is formulated as an integer program and solved using a greedy algorithm. In the second half of the thesis, an adaptive line outage detection algorithm that accounts for the transient dynamics following a line outage is proposed. A more accurate governor power flow model of the power system is used. This new algorithm is shown to have better performance compared to existing algorithms for line outage detection. In order to lend support for the work done in this thesis, case studies are done through simulations on standard IEEE test systems

Efficient large flow detection over arbitrary windows: an exact algorithm outside an ambiguity region

Being able to exactly detect large network flows under an arbitrary time win- dow model is expected in many current and future applications like Denial- of-Service (DoS) flow detection, bandwidth guarantee, etc. However, to the best of our knowledge, there is no existing work that can achieve exact large flow detection without per-flow status. Maintaining per-flow status requires a large amount of expensive line-speed storage, thus it is not practical in real systems. Therefore, we proposed a novel model of an arbitrary time window with exactness outside an ambiguity region, which trades the level of exactness for scalability. Although some existing work also uses some techniques like sampling, multistage filters, etc. to make the system scal- able, most of them do not support the arbitrary time window model and they usually introduce a lot of false positives for legitimate flows. Inspired by a frequent item finding algorithm, we proposed Exact-outside-Ambiguity- Region Detector (EARDet), an arbitrary-window-based, efficient, simple, and no-per-flow-status large flow detector, which is exact outside an ambi- guity window defined by a high-bandwidth threshold and a low-bandwidth threshold. EARDet is able to catch all large flows violating the high- bandwidth threshold; meanwhile it protects all legitimate flows complying with the low-bandwidth threshold. Because EARDet focuses on flow clas- sification but not flow size estimation, it demonstrates amazing scalability such that we can fit the storage into on-chip Static Random-Access Memory (SRAM) to achieve line-speed detection. To evaluate EARDet, we not only theoretically proved properties of EARDet above, but also evaluated them with real traffic, and the result perfectly supports our analysis

Multi-point and multi-objective optimization of a centrifugal compressor impeller based on genetic algorithm

The design of high efficiency, high pressure ratio, and wide flow range centrifugal impellers is a challenging task. The paper describes the application of a multiobjective, multipoint optimization methodology to the redesign of a transonic compressor impeller for this purpose. The aerodynamic optimization method integrates an improved nondominated sorting genetic algorithm II (NSGA-II), blade geometry parameterization based on NURBS, a 3D RANS solver, a self-organization map (SOM) based data mining technique, and a time series based surge detection method. The optimization results indicate a considerable improvement to the total pressure ratio and isentropic efficiency of the compressor over the whole design speed line and by 5.3% and 1.9% at design point, respectively. Meanwhile, surge margin and choke mass flow increase by 6.8% and 1.4%, respectively. The mechanism behind the performance improvement is further extracted by combining the geometry changes with detailed flow analysis

RFI detection by automated feature extraction and statistical analysis

In this paper we present an interference detection toolbox consisting of a high dynamic range Digital Fast-Fourier-Transform spectrometer (DFFT, based on FPGA-technology) and data analysis software for automated radio frequency interference (RFI) detection. The DFFT spectrometer allows high speed data storage of spectra on time scales of less than a second. The high dynamic range of the device assures constant calibration even during extremely powerful RFI events. The software uses an algorithm which performs a two-dimensional baseline fit in the time-frequency domain, searching automatically for RFI signals superposed on the spectral data. We demonstrate, that the software operates successfully on computer-generated RFI data as well as on real DFFT data recorded at the Effelsberg 100-m telescope. At 21-cm wavelength RFI signals can be identified down to the 4-sigma level. A statistical analysis of all RFI events detected in our observational data revealed that: (1) mean signal strength is comparable to the astronomical line emission of the Milky Way, (2) interferences are polarised, (3) electronic devices in the neighbourhood of the telescope contribute significantly to the RFI radiation. We also show that the radiometer equation is no longer fulfilled in presence of RFI signals.Comment: 12 pages, 16 figures, accepted for publication in Astron. Note

Advanced Algorithms for Automatic Wind Turbine Condition Monitoring

Reliable and efficient condition monitoring (CM) techniques play a crucial role in minimising wind turbine (WT) operations and maintenance (O&M) costs for a competitive development of wind energy, especially offshore. Although all new turbines are now fitted with some form of condition monitoring system (CMS), very few operators make use of the available monitoring information for maintenance purposes because of the volume and the complexity of the data. This Thesis is concerned with the development of advanced automatic fault detection techniques so that high on-line diagnostic accuracy for important WT drive train mechanical and electrical CM signals is achieved. Experimental work on small scale WT test rigs is described. Seeded fault tests were performed to investigate gear tooth damage, rotor electrical asymmetry and generator bearing failures. Test rig data were processed by using commercial WT CMSs. Based on the experimental evidence, three algorithms were proposed to aid in the automatic damage detection and diagnosis during WT non-stationary load and speed operating conditions. Uncertainty involved in analysing CM signals with field fitted equipment was reduced, and enhanced detection sensitivity was achieved, by identifying and collating characteristic fault frequencies in CM signals which could be tracked as the WT speed varies. The performance of the gearbox algorithm was validated against datasets of a full-size WT gearbox, that had sustained gear damage, from the National Renewable Energy Laboratory (NREL) WT Gearbox Condition Monitoring Round Robin project. The fault detection sensitivity of the proposed algorithms was assessed and quantified leading to conclusions about their applicability to operating WTs