An efficient contingency evaluation method for power system security analysis

A new unequal-weighted sampling method for efficient reliability analysis

Improvements on the line outage distribution factor for power system security analysis

The efficient bounding method for DC contingency analysis is improved using reciprocity properties. Knowing the consequences of the outage of a branch, these properties provide the consequences on that branch of various kinds of outages. This is used in order to reduce computation times and to get rid of some difficulties, such as those occurring when a branch flow is close to its limit before outage. Compensation, sparse vector, sparse inverse and bounding techniques are also used. A program has been implemented for single branch outages and tested on the actual French EHV 650 bus network. Computation times are 60% of the efficient bounding method. The relevant algorithm is described in detail in the first part of this paper. In the second part, reciprocity properties and bounding formulas are extended for multiple branch outages and for multiple generator or load outages. An algorithm is proposed in order to handle all these cases simultaneously.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Sleep Apnea Syndrome (SAS) has attracted considerable attention in recent years because it is known to cause catastrophes when people suffering from it fall asleep while driving. In addition, approximately 70% of SAS patients have other lifestyle-related diseases, which are further complicated owing to SAS; therefore, its early detection is important. Moreover, the detection of pre-SAS, which is likely to turn into SAS in the future, may help in the early detection of SAS. In this study, a method to identify SAS and pre-SAS using only sound data has been proposed. In the proposed method, peculiar breathing sound patterns in SAS are analyzed using the k-means clustering method and perform discriminant analysis using quantification theory based on the results. Because respiratory sound data is extensive and non-uniform during sleep, an efficient analysis method is required. The presented analysis method focuses on the features of SAS sounds, and performs the analysis almost automatically; therefore, the time required for analysis is reduced and the uniformity of the analysis results is guaranteed. Using this method, it is possible to distinguish between non-SAS and SAS symptoms including pre-SAS symptoms, which is difficult to perform using the Apnea-Hypopnea Index, which is a measure of SAS that employs breathing sound data only. Thus, this method, which also analyzes pre-SAS, is convenient for the early detection and treatment of SAS and can also be expected to contribute toward the early detection of preliminary lifestyle-related diseases.

An efficient method for ripple analysis of networks with synchronized switching power converters is proposed. While the focus of previous publications is on the operation of a single power converter, the proposed method extends the steady-state and ripple analysis to the area of power converter systems. A network consisting of several converters is modeled as a single piece-wise linear system. The inherent steady-state problem is solved using a new, fast, robust, and accurate steady-state algorithm based on a new set of boundary constraints. The proposed method is efficient in solving problems of practical design such as the determination of voltage ripples and analysis of current distribution in converter common nodes, evaluation of decoupling capacitors, and assessment of various synchronization schemes. Numerical examples are included.

With a great capability of dealing with epistemic uncertainty, evidence theory has been utilised to conduct reliability analysis for engineering systems recently. Unfortunately, the discontinuous nature of uncertainty quantification using evidence theory incurs a huge computational cost. This paper proposes an efficient method to improve the computational efficiency of evidence theory for reliability analysis. In this method, evidence variables are transformed into random variables. Support vector regression is used to construct the approximation model of the limit-state function. The most probable point (MPP) of the approximate reliability problem with only random variables is searched out. Based on the MPP, the most probable focal element (MPFE) of the original problem with evidence variables is identified. According to the MPFE and the monotonicity of the limit-state function, contributions of some focal elements to belief and plausibility in evidence theory can be judged directly. Hence, the number of focal elements involved in the calculation of extreme values of the limit-state function is reduced. Four numerical examples are utilised to test the performance of the proposed method. Results indicate that the proposed method can reduce the computational cost on reliability analysis under epistemic uncertainty while ensuring the high accuracy of reliability analysis results.

In residential apartment buildings, vibrations induced by a range of sources can be transferred to neighboring residential units through the walls and slabs. For accurate floor vibration analysis of a shear wall type building, it is essential to use a refined finite element model. The accuracy of finite element analysis (FEA) increases with the increasing number of nodes. On the other hand, it is impractical to model an entire building structure with a fine mesh to accurately predict floor vibrations because of the significant number of degrees of freedom (DOFs). Therefore, structural engineers generally use an isolated floor model, however such model has some limitations in accuracy and adaptability. In this study, an efficient analytical method that can accurately predict floor vibrations using the matrix condensation technique was proposed. Because only DOFs associated with translation perpendicular to walls or slabs are employed in the proposed method, more nodes can be used for floor vibration analysis resulting in more accurate analytical results. The modeling technique using super elements and substructures was introduced to reduce the computational time required for the matrix condensation. Numerical analysis of an example structure confirmed that the proposed method can provide more accurate results compared to the conventional finite element model with a considerably reduced computational time.

Efficient universal method for numerical analysis of slotted cavities used in different gyrodevices is proposed. It can be useful both for incorporation in different computer codes to simulate gyrodevice operation and for detailed investigations of different aspects concerning their basic physics.

The objective of this paper is to develop a simple method for analyzing the parameter uncertainty of the Japanese life cycle inventory database (LCI DB), termed the inventory database for environmental analysis (IDEA). The IDEA has a weakness of poor data quality because over 60% of datasets in IDEA were compiled based on secondary data (non-site-specific data sources). Three different approaches were used to estimate the uncertainty of the brown rice production dataset, including the stochastic modeling approach, the semi-quantitative DQI (Data Quality Indicator) approach, and a modification of the semi-quantitative DQI approach (including two alternative approaches for modification). The stochastic modeling approach provided the best estimate of the true mean of the sample space and its results were used as the reference for comparison with the other approaches. A simple method for the parameter uncertainty analysis of the agriculture industry DB was proposed by modifying the beta distribution parameters (endpoint range, shape parameter) in the semi-quantitative DQI approach using the results from the stochastic modeling approach. The effect of changing the beta distribution parameters in the semi-quantitative DQI approach indicated that the proposed method is an efficient method for the quantitative parameter uncertainty analysis of the brown rice production dataset in the IDEA.

In a competitive electricity market, available transfer capability in the transmission network has become essential quantity to be declared well in advance for its commercial use. Its fast computation using DC load flow based approach is used worldwide for on line implementation. Many authors have proposed the ATC calculation based on DC load flow approach, however, the procedure for multi-transaction cases occurring simultaneously remains unattended using DC load flow approach. In this paper, a DC load flow based approach has been proposed for multi-transaction case using power transfer sensitivity and succeptance matrices. The method can be implemented for any number of transactions simultaneously. The results have been determined for intact and line contingency cases taking multi-transaction as well single transaction cases. The proposed method have been applied for IEEE 24 bus RTS.

Power grid analysis is a challenging problem for modern integrated circuits. For 3-D systems fabricated using stacked tiers with TSVs, traditional power grid analysis methods for planar (2-D) circuits do not demonstrate the same performance. An efficient IR drop analysis method for 3-D large-scale circuits, called 3-D voltage propagation method, is proposed in this paper. This method is compared with another widely used power grid analysis method, with preconditioned conjugated gradients. Simulation results demonstrate that the proposed method is more efficient for the IR drop analysis of large size 3-D power grids. Speedups between 10× to 20× over the preconditioned conjugated gradients method are shown.

Zones without reactants inside a catalyst pellet, the so‐called dead zones (DZs), have been reported in the case of catalysis and biocatalysis, two processes of fundamental importance. The presence of the DZ results from diffusional transport limitations when an apparent reaction order is in the range: −1 to 1. The formation of a DZ reduces the effectiveness of a catalyst and affects packed bed reactor productivity. For a simple reaction kinetic model, the width of a DZ inside a pellet can be predicted analytically by solving the appropriate differential mass balance model. However, for more complex kinetic models, the analytical solution is unknown, and a DZ position can be established only by applying a numerical method. The problem with the DZ appearance belongs to problems with moving boundaries. Its solution requires the application of a particular numerical procedure and a relatively long CPU time. In this work, a simple, high‐speed numerical method for the calculation of a DZ position inside a pellet is proposed. The proposed method, combined with the orthogonal collocation on finite elements can be applied to analyze a packed bed reactor operation.

The move from conceptual design, through fabrication to observation and measurement on the resulting physical structure is fraught with uncertainty. This, together with the necessary simplifications inherent when using the finite element technique, makes the development of a predictive model for the physical structure sufficiently approximate that the use of random structural models is often to be preferred. In this paper, the random uncertainties of the mass, damping and stiffness matrices in a finite element model are replaced by random matrices, and a highly efficient pseudo excitation method for the dynamic response analysis of non-parametric probability systems subjected to stationary random loads is developed. A numerical example shows that the dynamic responses calculated using a conventional (mean) finite element model may be quite different from those based on a random matrix model. For precise fabrication, the uncertainties of models cannot be ignored and the proposed method should be useful in the analysis of such problems.

We consider the problem of estimating parameter sensitivity for Markovian models of reaction networks. Sensitivity values measure the responsiveness of an output with respect to the model parameters. They help in analysing the network, understanding its robustness properties and identifying the important reactions for a specific output. Sensitivity values are commonly estimated using methods that perform finite-difference computations along with Monte Carlo simulations of the reaction dynamics. These methods are computationally efficient and easy to implement, but they produce a biased estimate which can be unreliable for certain applications. Moreover, the size of the bias is generally unknown and hence the accuracy of these methods cannot be easily determined. There also exist unbiased schemes for sensitivity estimation but these schemes can be computationally infeasible, even for very simple networks. Our goal in this paper is to present a new method for sensitivity estimation, which combines the computational efficiency of finite-difference methods with the accuracy of unbiased schemes. Our method is easy to implement and it relies on an exact representation of parameter sensitivity that we recently proved elsewhere. Through examples, we demonstrate that the proposed method can outperform the existing methods, both biased and unbiased, in many situations.

First Results on Regularized Time-Varying Operational Modal Analysis