Effect of homogenization temperature on microstructure and mechanical properties of Al-Mg-Si alloy containing low-melting point elements

To evaluate the effect of homogenization conditions on the possible loss of low-melting-point Pb and Bi to the surface in the free-cutting AA6026 alloy, three homogenization regimes were applied: 480 degrees C/12 h, 530 degrees C/12 h, and 550 degrees C/6 h. The microstructural characterization by optical, scanning, and transmission electron microscopy coupled with EDS analysis, macroanalysis of chemical composition by ICP-AES as well tensile tests at room and 500 degrees C, and Charpy impact test were employed to evaluate the different homogenization regimes. It was found that the choice of homogenization temperature had no significant effect on the level of loss of low-melting point elements. The optimal homogenization regime appeared to be 550 degrees C/ 6 h as it led to almost complete beta-AlFeSi -> alpha-AlFe(Mn)Si transformation and beta-Mg2Si dissolution resulting in improved mechanical properties. The presence of the liquid phase did not lead to catastrophic failure due to the liquid metal embrittlement of the aluminum matrix, but the wetting of Fe,Mn - bearing constituents by molten Pb led to decohesion of the constituents. The morphological change to globular alpha-AlFe(Mn)Si decreased the surface area and interconnectivity of the microconstituents, which improved the hot ductility. During cooling after homogenization at T > 500 degrees C, the Q-phase precipitated, pointing up the potential quench sensitivity of the alloy. However, precipitation of the Q-phase laths in dispersoid-free zones reduced strain localization and improved room temperature ductility and impact toughness

Implementation of slow coherency based controlled islanding using DIgSILENT powerfactory and MATLAB

Intentional controlled islanding is a novel emergency control technique to mitigate wide-area instabilities by intelligently separating the power network into a set of self-sustainable islands. During the last decades, it has gained an increased attention due to the recent severe blackouts all over the world. Moreover, the increasing uncertainties in power system operation and planning put more requirements on the performance of the emergency control and stimulate the development of advanced System Integrity Protection Schemes (SIPS). As compared to the traditional SIPS, such as out-of-step protection, ICI is an adaptive online emergency control algorithm that aims to consider multiple objectives when separating the network. This chapter illustrates a basic ICI algorithm implemented in PowerFactory. It utilises the slow coherency theory and constrained graph partitioning in order to promote transient stability and create islands with a reasonable power balance. The algorithm is also capable to exclude specified network branches from the search space. The implementation is based on the coupling of Python and MATLAB program codes. It relies on the PowerFactory support of the Python scripting language (introduced in version 15.1) and the MATLAB Engine for Python (introduced in release 8.4). The chapter also provides a case study to illustrate the application of the presented ICI algorithm for wide-area instability mitigation in the PST 16 benchmark system.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Intelligent Electrical Power Grid

Smart DFT Based PMU Prototype

This paper presents recent innovation in the field of the Synchronized Measurement Technology. An advanced, low complexity algorithm for synchrophasor estimation and the processing platform running embedded Linux are used to develop a cost-efficient Phasor Measurement Unit (PMU) prototype. The Smart Discrete Fourier Transform (SDFT) is adapted for the purpose of synchrophasor estimation, being implemented by using Python programming language. The developed prototype sends synchro-measurements according to the IEEE Standard C37.118 specifications. The prototype performance characteristics are evaluated by using RTDS power system simulator as hardware-in-the-loop. The obtained results suggest further algorithm improvements to fully comply with the IEEE Standard C37.118.1a-2014 specified requirements. The developed prototype offers an affordable PMU solution for improving the grid observability.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Intelligent Electrical Power Grid

Synchronized measurement technology supported AC and HVDC online disturbance detection

In electric power system, disturbance detection has become an important part of grid operation and refers to the detection of a voltage and current excursion caused by the wide variety of electromagnetic phenomena. This paper proposes a computationally efficient and robust algorithm for synchronized measurement technology (SMT) supported online disturbance detection, suitable for AC and HVDC grids. The proposed algorithm is based on the robust median absolute deviation sample dispersion measure to locate dataset outliers. The algorithm is capable of identifying the disturbance occurrence and clearance measurement sample based on the dynamic criteria, driven by present power system conditions. The effectiveness of the proposed algorithm is verified by real-time simulations using a cyber-physical simulation platform, as a co-simulation between the SMT supported electric power system model and underlying ICT infrastructure. The presented results demonstrate effectiveness of the proposed algorithm, making it suitable for an AC and HVDC online disturbance detection application or as a pre-step of backup protection schemes.Intelligent Electrical Power Grid

Implementation of slow coherency based controlled islanding using DIgSILENT powerfactory and MATLAB

Intentional controlled islanding is a novel emergency control technique to mitigate wide-area instabilities by intelligently separating the power network into a set of self-sustainable islands. During the last decades, it has gained an increased attention due to the recent severe blackouts all over the world. Moreover, the increasing uncertainties in power system operation and planning put more requirements on the performance of the emergency control and stimulate the development of advanced System Integrity Protection Schemes (SIPS). As compared to the traditional SIPS, such as out-of-step protection, ICI is an adaptive online emergency control algorithm that aims to consider multiple objectives when separating the network. This chapter illustrates a basic ICI algorithm implemented in PowerFactory. It utilises the slow coherency theory and constrained graph partitioning in order to promote transient stability and create islands with a reasonable power balance. The algorithm is also capable to exclude specified network branches from the search space. The implementation is based on the coupling of Python and MATLAB program codes. It relies on the PowerFactory support of the Python scripting language (introduced in version 15.1) and the MATLAB Engine for Python (introduced in release 8.4). The chapter also provides a case study to illustrate the application of the presented ICI algorithm for wide-area instability mitigation in the PST 16 benchmark system.</p

Synchronized measurement technology supported AC and HVDConline disturbance detection

In electric power system, disturbance detection has become an important part of grid operation and refers to the detection of a voltage and current excursion caused by the wide variety of electromagnetic phenomena. This paper proposes a computationally efficient and robust algorithm for synchronized measurement technology (SMT) supported online disturbance detection, suitable for AC and HVDC grids. The proposed algorithm is based on the robust median absolute deviation sample dispersion measure to locate dataset outliers. The algorithm is capable of identifying the disturbance occurrence and clearance measurement sample based on the dynamic criteria, driven by present power system conditions. The effectiveness of the proposed algorithm is verified by real-time simulations using a cyber-physical simulation platform, as a co-simulation between the SMT supported electric power system model and underlying ICT infrastructure. The presented results demonstrate effectiveness of the proposed algorithm, making it suitable for an AC and HVDC online disturbance detection application or as a pre-step of backup protection schemes.Intelligent Electrical Power Grid

Synchrophasor-based Applications to Enhance Electrical System Performance in the Netherlands

This paper deals with the essentials of synchrophasor applications for future power systems aimed at increasing system reliability and resilience. In this work, several applications are presented, covering real-time disturbance detection and blackout prevention. Firstly, an advanced big-data management platform built in real-time digital simulation (RTDS) environment to support measurement data collection, processing and sharing among stakeholders is described. With this platform, a network splitting methodology to avoid cascading failures is presented and demonstrated, which upon the occurrence of a disturbance successfully isolates the affected part to avoid catastrophic cascade system outage. Online generator coherency identification is another synchrophasor application implemented on the platform, whose use is demonstrated in the context of controlled network splitting. By using synchrophasors, data-analytics techniques can also be used for identifying and classifying different disturbances in real-time with the least human intervention. Therefore, a novel centralized artificial intelligence (AI) based expert system to detect and classify critical events is outlined. Finally, the paper elaborates on the development of advanced system resilience metrics for real-time vulnerability assessment, with a focus on increasingly relevant dynamic interactions between distribution and transmission systems