Search results

The purpose of this paper is to describe a technique for modeling transformer internal faults using transmission line modeling (TLM) method. In this technique, a model for simulating a two winding single phase transformer is modified to be suitable for simulating an internal fault in both windings.

TLM technique is mainly used for modeling transformer internal faults. This was first developed in early 1970s for modeling two‐dimensional field problems. Since, then, it has been extended to cover three dimensional problems and circuit simulations. This technique helps to solve integro‐differential equations of the analyzed circuit. TLM simulations of a single phase transformer are compared to a custom built transformer in laboratory environment.

It has been concluded from the real time studies that if an internal fault occurs on the primary or secondary winding, the primary current will increase a bit and secondary current does not change much. However, a very big circulating current flows in the shorted turns. This phenomenon requires a detailed modeling aspect in TLM simulations. Therefore, a detailed inductance calculation including leakages is included in the simulations. This is a very important point in testing and evaluating protective relays. Since, the remnant flux in the transformer core is unknown at the beginning of the TLM simulation, all TLM initial conditions are accepted as zero.

The modeling technique presented in this paper is based on a low frequency (up to a few kHz) model of the custom‐built transformer. A detailed capacitance model must be added to obtain a high‐frequency model of the transformer. A detailed arc model, aging problem of the windings will be applied to model with TLM + finite element method.

Using TLM technique for dynamical modeling of transformer internal faults is the main contribution. This is an extended version of an earlier referenced paper of the authors and includes inductance calculation, leakages calculation, and BH curve simulation while the referenced paper only includes piecewise linear inductance values. This modeling approach may help power engineers and power system experts understand the behavior of the transformer under internal faults.

This paper attempts to investigate the effect of corruption on the real and accrual earnings management of target firms in the process of mergers and acquisitions.

The sample includes target firms from the European area that participate in mergers or acquisitions announced during 2010–2020. The preliminary empirical part estimates the level of earnings management during the period two years before the deal's announcement to identify whether the sample follows the manipulation behavior that the literature suggests for target firms. The primary empirical analysis focuses on the impact of corruption on real and accrual-based earnings management proxies, employing regression models and two alternative proxies for corruption. The existing literature points out that the combination of low levels of corruption and an integrated legal system reduces earnings manipulation.

The findings provide strong evidence for systematic downwards accounting manipulation practices, whereas the findings for real earnings management are not significant. The findings of the main empirical part show that corruption is positively associated with accrual-based manipulation and negatively related to real earnings management. In essence, in economies with a high level of transparency, managers adopt the manipulation of operating activities as a less detectable practice of earnings management instead of engaging in accounting procedures.

This study contributes to the literature highlighting the diversification of these firms' manipulation strategies according to the national level's corruption status.

The purpose of this paper is to propose a distributed smartphone sensing-enabled system, which assumes an intelligent transport signaling (ITS) infrastructure that operates traffic lights in a smart city (SC). The system is able to handle priorities between groups of cyclists (crowd-cycling) and traffic when approaching traffic lights at road junctions.

The system takes into consideration normal probability density function (PDF) and analytics computed for a certain group of cyclists (i.e. crowd-cycling). An inference model is built based on real-time spatiotemporal data of the cyclists. As the system is highly distributed – both physically (i.e. location of the cyclists) and logically (i.e. different threads), the problem is treated under the umbrella of multi-agent systems (MAS) modeling. The proposed model is experimentally evaluated by incorporating a real GPS trace data set from the SC of Melbourne, Australia. The MAS model is applied to the data set according to the quantitative and qualitative criteria adopted. Cyclists’ satisfaction (CS) is defined as a function, which measures the satisfaction of the cyclists. This is the case where the cyclists wait the least amount of time at traffic lights and move as fast as they can toward their destination. ITS system satisfaction (SS) is defined as a function that measures the satisfaction of the ITS system. This is the case where the system serves the maximum number of cyclists with the fewest transitions between the lights. Smart city satisfaction (SCS) is defined as a function that measures the overall satisfaction of the cyclists and the ITS system in the SC based on CS and SS. SCS defines three SC policies (SCP), namely, CS is maximum and SS is minimum then the SC is cyclist-friendly (SCP1), CS is average and SS is average then the SC is equally cyclist and ITS system friendly (SCP2) and CS is minimum and SS is maximum then the SC is ITS system friendly (SCP3).

Results are promising toward the integration of the proposed system with contemporary SCs, as the stakeholders are able to choose between the proposed SCPs according to the SC infrastructure. More specifically, cyclist-friendly SCs can adopt SCP1, SCs that treat cyclists and ITS equally can adopt SCP2 and ITS friendly SCs can adopt SCP3.

The proposed approach uses internet connectivity available in modern smartphones, which provide users control over the data they provide to us, to obviate the installation of additional sensing infrastructure. It extends related study by assuming an ITS system, which turns traffic lights green by considering the normal PDF and the analytics computed for a certain group of cyclists. The inference model is built based on the real-time spatiotemporal data of the cyclists. As the system is highly distributed – both physically (i.e. location of the cyclists) and logically (i.e. different threads), the system is treated under the umbrella of MAS. MAS has been used in the literature to model complex systems by incorporating intelligent agents. In this study, the authors treat agents as proxy threads running in the cloud, as they require high computation power not available to smartphones.