Multiobjective design optimization of IGBT power modules considering power cycling and thermal cycling

Insulated-gate bipolar transistor (IGBT) power modules find widespread use in numerous power conversion applications where their reliability is of significant concern. Standard IGBT modules are fabricated for general-purpose applications while little has been designed for bespoke applications. However, conventional design of IGBTs can be improved by the multiobjective optimization technique. This paper proposes a novel design method to consider die-attachment solder failures induced by short power cycling and baseplate solder fatigue induced by the thermal cycling which are among major failure mechanisms of IGBTs. Thermal resistance is calculated analytically and the plastic work design is obtained with a high-fidelity finite-element model, which has been validated experimentally. The objective of minimizing the plastic work and constrain functions is formulated by the surrogate model. The nondominated sorting genetic algorithm-II is used to search for the Pareto-optimal solutions and the best design. The result of this combination generates an effective approach to optimize the physical structure of power electronic modules, taking account of historical environmental and operational conditions in the field

Integrating reliability into performance-oriented design of fault-tolerant switch-mode DC-DC converters for photovoltaic energy-conversion applications

This work bridges the disconnect between two consequential design concerns in switch-mode power converters deployed in photovoltaic energy-processing applications: steady-state performance and system reliability. A general framework for fault-tolerant design is presented in the context of a multiphase, interleaved boost converter. A unified, system-level, steady-state description for this topology is proposed. The theoretical derivations are validated against detailed numerical simulations, and their applicability over a wide range of ambient conditions is demonstrated. The steady-state characterization of the converter is then employed to specify the failure rates of circuit components and establish the effects of ambient temperature, insolation, number of phases, and device ratings on system reliability. A Markov reliability model is derived to assess the reliability of a general N-phase converter. The proposed analytical tools provide a methodical framework for design of fault-tolerant, multiphase converters employed in a wide range of photovoltaic systems

A Decision Support System to Analyze, Predict, and Evaluate Solar Energy System Performance: PVSysCO (photovoltaic System Comparison)

In 2010, the U.S. Department of Energy announced the SunShot Initiative, which aims to reduce the total installation cost of solar technologies by 75% between 2010 and 2020. This implies that solar energy is a top priority in the U.S. and many other countries. The purpose of this dissertation research is focused on creating a model to better understand the performance and reliability of photovoltaic (PV) energy systems over time. The model will be used to analyze, predict, and evaluate the performance of PV systems, taking into consideration technological and geographical location attributes. The overall research goal is to build a Solar Energy Blue Book, conceptually similar to the Kelley Blue Book, which allows consumers to estimate the value of a used car. The Solar Energy Blue Book, a solar energy system evaluation tool, will allow consumers to estimate the value of a used solar energy system, taking into consideration many factors, such as latitude (which determines the quantity of incoming sunlight) and zip code (which determines the approximate cost of electricity). The Solar Blue Book will allow potential solar energy system consumers the opportunity to understand the return on investment for new and in particular, used solar energy systems

DETAILED RELIABILITY MODELS OF INTEGRATED SOLAR POWER TECHNOLOGIES IN ELECTRIC POWER SYSTEMS

The contribution of solar power in electric power system has been growing rapidly due to the significant negative impact of carbon emissions generated by conventional power sources. Large scale photovoltaic (PV) and concentrated solar power (CSP) have been installed around the world. However, these technologies involve major concerns regarding the reliability of system generation. The output power generation from solar technologies acts quite differently from that of conventional generation. The PV and CSP are composed of major components that have different failure characteristics. The interactions of the different component topologies in various commercially available PV system configurations will significantly influence the reliability of a PV system. Moreover, the output power of PV and CSP are highly variable and depend on the solar irradiation resulting in discontinuous and variable electricity generation. All these factors have a direct impact on the overall generation system adequacy. It is, therefore, vital to incorporate these factors in the reliability modeling of PV and CSP systems. An analytical probabilistic technique is employed in this thesis to develop detailed reliability models of PV and CSP systems. This thesis investigates the impact of PV/CSP system components on the reliability performance of PV/CSP systems. Different studies were conducted on test systems in this thesis considering system load variation, growth in solar capacity, geographical location, and seasonal effects. These analyses have been expanded to quantify the comparative reliability of a generation system with large scale PV and CSP. The power output of PV is also affected by dust accumulation on PV panel surfaces. The deposition of dust on PV panels will reduce the net solar irradiation absorbed by the solar panel, and lower the solar panel efficiency. This project is extended to incorporate the cumulative dust in the reliability model of the PV system. A regression model is adopted to develop a probabilistic model of PV power reduction caused by cumulative dust. This work also investigates the impact of a dust-removal strategy on the overall system adequacy. The concept and methodology discussed in this thesis can be used effectively by system planners and electric utilities to evaluate the reliability benefit of utilizing solar power in existing generation systems

HEALTH ESTIMATION AND REMAINING USEFUL LIFE PREDICTION OF ELECTRONIC CIRCUIT WITH A PARAMETRIC FAULT

Degradation of electronic components is typically accompanied by a deviation in their electrical parameters from their initial values. Such parametric drifts in turn will cause degradation in performance of the circuit they are part of, eventually leading to function failure due to parametric faults. The existing approaches for predicting failures resulting from electronic component parametric faults emphasize identifying monotonically deviating parameters and modeling their progression over time. However, in practical applications where the components are integrated into a complex electronic circuit assembly, product or system, it is generally not feasible to monitor component-level parameters. To address this problem, a prognostics method that exploits features extracted from responses of circuit-comprising components exhibiting parametric faults is developed in this dissertation. The developed prognostic method constitutes a circuit health estimation step followed by a degradation modeling and remaining useful life (RUL) prediction step. First, the circuit health estimation method was developed using a kernel-based machine learning technique that exploits features that are extracted from responses of circuit-comprising components exhibiting parametric faults, instead of the component-level parameters. The performance of kernel learning technique depends on the automatic adaptation of hyperparameters (i.e., regularization and kernel parameters) to the learning features. Thus, to achieve high accuracy in health estimation the developed method also includes an optimization method that employs a penalized likelihood function along with a stochastic filtering technique for automatic adaptation of hyperparameters. Second, the prediction of circuit’s RUL is realized by a model-based filtering method that relies on a first principles-based model and a stochastic filtering technique. The first principles-based model describes the degradation in circuit health with progression of parametric fault in a circuit component. The stochastic filtering technique on the other hand is used to first solve a joint ‘circuit health state—parametric fault’ estimation problem, followed by prediction problem in which the estimated ‘circuit health state—parametric fault’ is propagated forward in time to predict RUL. Evaluations of the data from simulation experiments on a benchmark Sallen–Key filter circuit and a DC–DC converter system demonstrate the ability of the developed prognostic method to estimate circuit health and predict RUL without having to monitor the individual component parameters