Abstract
This paper presents a robust and efficient fault detection and diagnosis framework for handling small faults and oscillations in synchronous generator (SG) systems. The proposed framework utilizes the Brunovsky form representation of nonlinear systems to mathematically formulate the fault detection problem. A differential flatness model of SG systems is provided to meet the conditions of the Brunovsky form representation. A combination of high-gain observer and group method of data handling neural network is employed to estimate the trajectory of the system and to learn/approximate the fault- and uncertainty-associated functions. The fault detection mechanism is developed based on the output residual generation and monitoring so that any unfavorable oscillation and/or fault occurrence can be detected rapidly. Accordingly, an average L1-norm criterion is proposed for rapid decision making in faulty situations. The performance of the proposed framework is investigated for two benchmark scenarios which are actuation fault and fault impact on system dynamics. The simulation results demonstrate the capacity and effectiveness of the proposed solution for rapid fault detection and diagnosis in SG systems in practice, and thus enhancing service maintenance, protection, and life cycle of SGs.
Highlights
Fault detection and identification (FDI) approaches for nonlinear systems have drawn attention in the last few decades, as they play a vital role in modern complex systems with a higher reliability requirement
The third class of FDI approaches is called data-driven techniques which have been employed for fault detection and protection in synchronous generator (SG) and interconnected power systems [8,12,14,15,16,18,37]
The FDI mechanism in this paper is developed based on output residual generation and monitoring so that any unfavorable oscillation and/or fault occurrence can be detected rapidly
Summary
Fault detection and identification (FDI) approaches for nonlinear systems have drawn attention in the last few decades, as they play a vital role in modern complex systems with a higher reliability requirement. The second class of FDI approaches is called model-based techniques (analytical redundancy), which is established on the mathematical model of the underlying system In this category, observer-based methods are quite popular as they can either estimate states and faults of the system directly or compare residual evaluation function with a predefined/adaptive threshold. The third class of FDI approaches is called data-driven techniques which have been employed for fault detection and protection in SG and interconnected power systems [8,12,14,15,16,18,37]. Assumptions 1 and 4 consider the reasonable aspects of the practical dynamic systems, Remi.ea.,rtkhe1.uAnbsosuunmdpetdiosnigsn1aalsndan4dctohnesiridvearrtihaetiroenasaorne anbolte aadsmpeicstssibolfet.hAespsruamctpictiaolndy2ncaomnsicidseyrssttehmess,ysi.e.,tetmheuunncbeorutanindteidess,igconvaelrsinangdatvhaeririevtyaroifatmioondealremnisomt aatdcmheisssaibnlde.vAarsisautimonpst.ioAnss2ucmopntsiiodner5s sthtaends systfoemr tuhencsemrtaalilnftaiueslt,sc,oiv.ee.r,inthgeafavualrtiestiyzeofismsomdaelllmeristmhaantcthhees uapnpdevrabroiautniodnos.f Amsosdueml upnticoenrt5aisntatinedssand for dthisetusmrbaalnl cfea.uIlnts,siu.ec.h, tahecafaseu,lthseizseyisstesmasltlaertethvanriathtieonupdpueer tboouthnedfoafumltomdealyubnecebrutariendtiuesnadnerd the disturbance In such a case, the system state variation due to the fault may be buried under the effects of model uncertainties and disturbance. Most developed FDI schemes fail to detect the fault accurately [39,40,41]
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