By Jean?Claude Trigeassou(auth.)
Tracking and analysis of electric computing device faults is a systematic and monetary factor that's prompted by way of ambitions for reliability and serviceability in electric drives.
This ebook offers a survey of the strategies used to observe the faults taking place in electric drives: electric, thermal and mechanical faults of computer, faults of the static converter and faults of the strength garage unit.
analysis of faults happening in electric drives is a necessary a part of a world tracking process used to enhance reliability and serviceability. This prognosis is played with a wide number of options: parameter estimation, country commentary, Kalman filtering, spectral research, neural networks, fuzzy common sense, man made intelligence, and so on. specific emphasis during this booklet is wear the modeling of desktop in defective situations.
electric Machines prognosis provides unique effects bought in most cases by way of French researchers in numerous domain names. it will likely be necessary as a suggestion for the perception of extra strong electric machines and certainly for engineers who've to watch and preserve electric drives. because the tracking and prognosis of electric machines remains to be an open area, this booklet can be very necessary to researchers.Content:
Chapter 1 Faults in electric Machines and their analysis (pages 1–22):
Chapter 2 Modeling Induction laptop Winding Faults for analysis (pages 23–68):
Chapter three Closed?Loop prognosis of the Induction computing device (page 0):
Chapter four Induction computer analysis utilizing Observers (pages 93–129):
Chapter five Thermal tracking of the Induction computer (pages 131–166):
Chapter 6 analysis of the inner Resistance of an automobile Lead?acid Battery by way of the Implementation of a version Invalidation?based process (pages 167–192):
Chapter 7 electric and Mechanical Faults analysis of Induction Machines utilizing sign research (pages 193–225):
Chapter eight Fault prognosis of the Induction laptop through Neural Networks (pages 227–270):
Chapter nine Faults Detection and prognosis in a Static Converter (pages 271–319):
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Extra resources for Electrical Machines Diagnosis
For an electric car, the frequent phases of rapid acceleration and deceleration may potentially be enough to excite all the diagnosis model poles, which is actually no longer true for a fixed-speed or slow changing application. From an operational point of view, the final choice will depend on the criticality of the diagnosis, meaning the time elapsed between the fault interruption, its diagnosis (detection, location, characterization of fault severity), and the decisionmaking process. In other words, is immediate shutdown required or could a control reconfiguration limit the effects of the fault while making it possible to obtain a sufficient electromagnetic torque until the next scheduled shutdown?
26 Electrical Machines Diagnosis stationary fields (in the electrotechnical sense of the term), as well as the equivalence between winding systems, will be dealt with in this first section. These are elementary yet fundamental conceptions for understanding winding fault modeling, while allowing us to introduce the notations which will appear in the chapter. The following section will go into detail on equation formulation and obtaining models in their basic form, suitable for detecting short-circuiting in inter turns.
3. 3. 4). We can deduce from this that the inductance 32 Lps also corresponds to the principal inductance of the dummy two-phase stator winding with nsdq = coils and a resistance of Rs . 3 2 ns In the same way, Lcr corresponds to the self-inductance of the equivalent two-phase rotor winding, and Mcsr = 32 n2b Msr is the maximum mutual inductance between the dummy stator and rotor two-phase windings. 4. 3. 1. 3 involves electrical angles (for example, the electrical angle pΘ referencing the rotor), and electrical pulsations; it can therefore be applied to all machines, independent of the number of pole pairs.