Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Citation - WoS: 10Citation - Scopus: 13An Observer-Based Fault Diagnosis in Battery Systems of Hybrid Vehicles(IEEE Computer Society help@computer.org, 2013-11) Ablay, GünyazHybrid electric vehicles (HEVs) currently use Nickel-Metal Hydride (Ni-MH) batteries which have advantages of design flexibility, superior power, environmental acceptability and recyclability, long life, wide-range operating temperature and low cost. No matter how good a battery is, a failure can always occur in a battery leading to serious inconvenience, performance deterioration and costly replacement. Thus, it is desirable to be able to detect the underlying degradation and to predict level of unsatisfactory performance. By using current, voltage and temperature measurements of Ni-MH batteries, they can be modeled so that the internal dynamics of the batteries can be estimated and state of health of the batteries can be predicted for secure and long-life operations. An observer-based fault diagnosis approach is designed to analyze the state of health of the Ni-MH battery system of HEVs in this study. Real-world input data is used to assess the efficiency of the approach in the existence of uncertainties. The possible sensor faults and unexpected parameter deviations are diagnosed efficiently with statistical evaluation of the generated residuals. © 2013 The Chamber of Turkish Electrical Engineers-Bursa. © 2020 Elsevier B.V., All rights reserved.Article Citation - WoS: 16Citation - Scopus: 23Adaptive Fault Detection Scheme Using an Optimized Self-Healing Ensemble Machine Learning Algorithm(China Electric Power Research inst, 2021) Yavuz, Levent; Soran, Ahmet; Onen, Ahmet; Li, Xiangjun; Muyeen, S. M.This paper proposes a new cost-efficient, adaptive, and self-healing algorithm in real time that detects faults in a short period with high accuracy, even in the situations when it is difficult to detect. Rather than using traditional machine learning (ML) algorithms or hybrid signal processing techniques, a new framework based on an optimization enabled weighted ensemble method is developed that combines essential ML algorithms. In the proposed method, the system will select and compound appropriate ML algorithms based on Particle Swarm Optimization (PSO) weights. For this purpose, power system failures are simulated by using the PSCAD-Python co-simulation. One of the salient features of this study is that the proposed solution works on real-time raw data without using any pre-computational techniques or pre-stored information. Therefore, the proposed technique will be able to work on different systems, topologies, or data collections. The proposed fault detection technique is validated by using PSCAD-Python co-simulation on a modified and standard IEEE-14 and standard IEEE-39 bus considering network faults which are difficult to detect.