In situ diagnostics and prognostics of wire bonding faults in IGBT modules for electric vehicle drives

This paper presents a diagnostic and prognostic condition monitoring method for insulated-gate bipolar transistor (IGBT) power modules for use primarily in electric vehicle applications. The wire-bond-related failure, one of the most commonly observed packaging failures, is investigated by analytical and experimental methods using the on-state voltage drop as a failure indicator. A sophisticated test bench is developed to generate and apply the required current/power pulses to the device under test. The proposed method is capable of detecting small changes in the failure indicators of the IGBTs and freewheeling diodes and its effectiveness is validated experimentally. The novelty of the work lies in the accurate online testing capacity for diagnostics and prognostics of the power module with a focus on the wire bonding faults, by injecting external currents into the power unit during the idle time. Test results show that the IGBT may sustain a loss of half the bond wires before the impending fault becomes catastrophic. The measurement circuitry can be embedded in the IGBT drive circuits and the measurements can be performed in situ when the electric vehicle stops in stop-and-go, red light traffic conditions, or during routine servicing

On the identifiability of steady-state induction machine models using external measurements

A common practice in induction machine parameter identification techniques is to use external measurements of voltage, current, speed, and/or torque. Using this approach, it has been shown that it is possible to obtain an infinite number of mathematical solutions representing the machine parameters. This paper examines the identifiability of two commonly used induction machine models, namely the T-model (the conventional per phase equivalent circuit) and the inverse Γ-model. A novel approach based on the alternating conditional expectation (ACE) algorithm is employed here for the first time to study the identifiability of the two induction machine models. The results obtained from the proposed ACE algorithm show that the parameters of the commonly employed T-model are unidentifiable, unlike the parameters of the inverse Γ-model which are uniquely identifiable from external measurements. The identifiability analysis results are experimentally verified using the measured operating characteristics of a 1.1-kW three-phase induction machine in conjunction with the Levenberg-Marquardt algorithm, which is developed and applied here for this purpose

Non-invasive identification of turbogenerator parameters from actual transient network data

Synchronous machines are the most widely used form of generators in electrical power systems. Identifying the parameters of these generators in a non-invasive way is very challenging because of the inherent non-linearity of power station performance. This study proposes a parameter identification method using a stochastic optimisation algorithm that is capable of identifying generator, exciter and turbine parameters using actual network data. An eighth order generator/turbine model is used in conjunction with the measured data to develop the objective function for optimisation. The effectiveness of the proposed method for the identification of turbo-generator parameters is demonstrated using data from a recorded network transient on a 178 MVA steam turbine generator connected to the UK's national grid

Application of Filippov method for the analysis of subharmonic instability in dc–dc converters

We propose a method of estimating the fast-scale stability margin of dc-dc converters based on Filippov's theory-originally developed for mechanical systems with impacts and stick-slip motion. In this method one calculates the state transition matrix over a complete clock cycle, and the eigenvalues of this matrix indicate the stability margin. Important components of this matrix are the state transition matrices across the switching events, called saltation matrices. We applied this method to estimate the stability margins of a few commonly used converter and control schemes. Finally, we show that the form of the saltation matrix suggests new control strategies to increase the stability margin, which we experimentally demonstrate using a voltage-mode-controlled buck converter

Control of period doubling bifurcations in DC-DC converters

The dc to dc buck converter operating with a voltage controller is a nonlinear, nonsmooth system that presents different circuit topologies within the same switching cycle. In this paper, we propose two new bifurcation or chaos control strategies based on the use of the saltation matrix and the location of the floquet multipliers to stabilize the period-1 orbit of the circuit regardless of the input voltage of the converter. The new methods are analytically and numerically validated and their sensitivity to parameter variations is assessed

Hybrid Model-Based Fuzzy Logic Diagnostic System for Stator Faults in Three-Phase Cage Induction Motors

The widespread use of three-phase cage induction motors in so many critical industrial, commercial and domestic applications means that there is a real need to develop online diagnostic systems to monitor the state of the machine during operation. This paper presents a hybrid diagnostic system that combines a model-based strategy with a fuzzy logic classifier to identify abnormal motor states due to single-phasing or inter-turn stator winding faults. Only voltage and current measurements are required to extract the fault symptoms, which are represented as model parameters variations in an equivalent virtual healthy motor, negating the need to use complex models of faulty machines. A trust-region method is used to estimate the machine model parameters, with the final decision on the type, location and extent of the fault being made by the fuzzy logic classifier. The proposed diagnostic system was experimentally verified using a 1.0 hp three-phase test induction motor. Results show that the proposal method can efficiently diagnose single phasing and inter-turn stator winding faults even when operating with unbalanced supply voltages and in the presence of significant levels of measurement noise

A doubly-fed induction generator test facility for grid fault ride-through analysis

This paper has described a laboratory-scale DFIG test facility commissioned to investigate the low voltage fault behavior of a DFIG wind turbine generator. In this DFIG test facility, a dc motor driven by a four-quadrant dc drive is used as a prime mover to replicate the torque input from a wind turbine's drive shaft. A mechanical model created in Simulink is executed by the dSpace controller. The IGBT power converter and dc-link capacitors are over-rated to withstand various grid faults. The fault emulator permits a flexible approach to the application of grid faults and also provides an opportunity to mimic the typical grid connection impedance of a wind turbine