Unexpected Operating Conditions Research Articles

Analysis of Ruggedness of 4H-SiC Power MOSFETs with Various Doping Parameters

This work investigates the effect of the doping concentration of SiC power metal-oxide–semiconductor field-effect transistors (MOSFETs) under an unclamped inductive switching (UIS) condition. Switching circuits such as inverters and motor-drive circuits often face unexpected operating conditions; therefore, a UIS test is performed to assess the avalanche ruggedness of the device, and design parameters such as the doping concentration should be considered to improve the UIS characteristics. Technology computer-aided design circuit simulation results, such as the current flows during failure and electrical changes, were obtained by changing the doping concentration of each region in the SiC power MOSFET.

Damping Inter-Area Oscillations With Large-Scale PV Plant by Modified Multiple-Model Adaptive Control Strategy

Studies suggest that inter-area low-frequency oscillations can be damped with supplementary damping control of large-scale photovoltaic (PV) plants. Multiple-model adaptive control (MMAC) is applied as the damping strategy to deal with uncertain variations of the postdisturbance operating conditions. To reduce the scales of the model bank and the damper bank, K-means clustering algorithm is used taking inter-area modes as features for operating condition clustering, and a common damper is designed for each cluster. Based on the deviation between the output dynamic responses of the actual system and models, Bayesian approach is employed to calculate the probability of each model representing the actual system in real time, followed by updating weights of each damper's output. The weighted average of the individual damper's outputs is attached to the reactive power control of the PV plant. A new continuous form of probability calculation with an inertial link is proposed to smooth weight fluctuation caused by discrete calculation in conventional MMAC and ease stress of power regulation of the PV plant. The nonlinear simulation results demonstrate that the proposed control strategy is able to sufficiently damp the inter-area modes of interest in unexpected operating conditions without any prior knowledge about the postdisturbance state.

Designing petrochemical plants to permit retrofitting of any additional controls needed to meet community noise limits.

Designing petrochemical, power, or industrial plants to meet community noise limits can be expensive. To meet stringent limits, incremental costs often exceed one million dollars per decibel. There is considerable uncertainty, including: overly conservative or unrepresentative vendor noise data, limitations of ray‐tracing software, questionable accuracy beyond 1000 m, lack of TL and absorption data below 125 Hz, unreliable IL data for pipe lagging at all frequencies, unexpected operating conditions, and vendors not meeting their equipment noise limits. Atmospheric effects can cause variations of A‐weighted community noise levels of 25 dB at 100 m from a refinery, which can make meeting a not‐to‐exceed noise limit difficult and risky. Further, lack of experience with noise control of any participant (noise control engineer, project, owner, regulators, or vendors) can substantially increase uncertainty. Including sufficient noise controls to cover these uncertainties will be expensive, often overly conservative, and often unnecessarily expensive. One way to substantially lower costs and risks is designing the plant to permit delaying some but not all controls until start‐up—if needed. This paper describes the planning needed to permit a partial reliance on retrofit controls, what type of controls can be delayed until start‐up, and space allocation. Examples are included.

Fault detection in rotating machinery using kernel-based probability density estimation

In this paper, a method is presented which allows abnormal or unexpected operating conditions to be identified from measured response data. Potential applications of such a technique cover a wide range of engineering situations where a definite, early warning of an abnormal state is essential, but where classification of the particular abnormality is of lesser importance. In the technique described, unexpected operating conditions are identified by the presence of measured data which are significantly different from those known to correspond to normal operating conditions or responses. The proposed approach to the detection of such novel data is based upon probability density function (PDF) estimation using a kernel method. The theory behind this approach is presented for the multivariate case, and the need for data pre-processing in practical applications of PDF estimation is highlighted. The kernel method is demonstrated on simulated data sets. Finally, vibration response data from an electric motor with three levels of phase imbalance are used to illustrate the application of the PDF estimation method to the detection of abnormal conditions. The proposed method yields a clear indication of the existence of a fault in the AC motor, with no prior knowledge of the particular fault state.

LHCb base-line level-0 trigger 3D-Flow implementation

The LHCb Level-0 trigger implementation with the 3D-Flow system offers full programmability, allowing it to adapt to unexpected operating conditions and enabling new, unpredicted physics. The implementation is described in detail and refers to components and technology available today. The 3D-Flow Processor system is a new, technology-independent concept in very fast, real-time system architectures. Based on the replication of a single type of circuit of 100k gates, which communicates in six directions: bi-directional with North, East, West, and South neighbors, unidirectional from Top to Bottom, the system offers full programmability, modularity, ease of expansion and adaptation to the latest technology. A complete study of its applicability to the LHCb calorimeter triggers is presented. Full description of the input data handling, either in digital or mixed digital-analog form, of the data processing, and the transmission of results to the global level-0 trigger decision unit are provided. Any level-0 trigger algorithm (2×2, 3×3, 4×4, etc.) with up to 20 steps, can be implemented with zero dead-time, while sustaining input data rate (up to 32-bit per input channel, per bunch crossing) at 40 MHz. For each step, each 3D-Flow processor can execute up to 26 operations, inclusive of compare, ranging, finding local maxima, and efficient data exchange with neighboring channels. (One-to-one correspondence between input channel and trigger tower.) Populated with only two main types of components, front-end FPGAs and 3D-Flow processors, a single type of board, it is shown how the whole Level-0 calorimeter trigger can be accommodated into six crates (9U), each containing 16 identical boards. All 3D-Flow inter-chip Bottom to Top ports connection are all contained on the board (data are multiplexed 2 : 1, PCB traces are shorter than 6 cm); all 3D-flow inter-chip North, East, West, and South ports connections, between boards and crates, are multiplexed (8+2) : 1 and are shorter than 1.5 m. Full implementation of a 3D-Flow system, for the most complex trigger algorithm, requires 320 cables to north and south crates and 40 cables to east and west crates (Cable cost=$2 each). For applications requiring a simpler real-time algorithm (e.g., requiring less than 20 steps, which is equivalent to 10 layers of 3D-Flow processors), then the number of connections for the inter-boards (North and South), and inter-crates (East and West) will also be reduced to the number of layers used by the simpler algorithm, thus not requiring to install all cables (e.g., applications requiring only nine layers of 3D-Flow processors will save 32 cables to the North, 32 to the South, four to the East, and four to the West crates). Details are also given on timing and synchronization issues, ASIC design verification, real-time performance monitoring and design (software and hardware) development tools.

Applications of probability density estimation to the detection of abnormal conditions in engineering

A method is described for the identification of abnormal or unexpected conditions from measured response data. Such a technique would be useful in a wide range of engineering situations where a clear, early warning of an abnormal condition is required, but where classification of the specific abnormality is only of secondary importance. In this work, occurrences of unexpected operating conditions are indicated by measured data which exhibit a high degree of novelty with respect to that corresponding to normal conditions or responses. The proposed approach is based upon the probability density function (PDF) estimation using a kernel method, the basis of which is described. The need for data compression in practical applications of PDF estimation is highlighted and a method demonstrated which is based on the wavelet transform. The combined data compression and PDF estimation approach for novelty detection is applied to data measured from a gearbox with a progressive fault and to radar data corresponding to six military targets. In both cases, abnormal situations are clearly identified on the basis of novel data inputs.